From 7dc933ba7071c6f0edb14a524591fe6993cde1e6 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 23 Sep 2024 16:38:05 +0200 Subject: [PATCH 01/27] - added an algorithm notebook explaining how that works - add new tutorial to read me and other lists --- README.md | 7 +- docs/source/examples_basic.rst | 1 + docs/source/img/Assume_run_learning_loop.png | Bin 0 -> 121410 bytes .../img/Assume_run_learning_loop.png.license | 3 + docs/source/img/assume_logo.png copy.license | 3 + ...forcement_learning_algorithm_example.ipynb | 943 ++++++++++++++++++ ...t_learning_algorithm_example.ipynb.license | 3 + 7 files changed, 958 insertions(+), 2 deletions(-) create mode 100644 docs/source/img/Assume_run_learning_loop.png create mode 100644 docs/source/img/Assume_run_learning_loop.png.license create mode 100644 docs/source/img/assume_logo.png copy.license create mode 100644 examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb create mode 100644 examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb.license diff --git a/README.md b/README.md index 48ac990e..380cf6ab 100644 --- a/README.md +++ b/README.md @@ -97,12 +97,15 @@ To ease your way into ASSUME we provided some examples and tutorials. The former The tutorials work completly detached from your own machine on google colab. They provide code snippets and task that show you, how you can work with the software package one your own. We have two tutorials prepared, one for introducing a new unit and one for getting reinforcement learning ready on ASSUME. How to configure a new unit in ASSUME? -**Coming Soon** +[![Open Learning Tutorial in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/assume-framework/assume/blob/main/examples/notebooks/03_custom_unit_example.ipynb) -How to introduce reinforcement learning to ASSUME? +How to use reinforcement learning for new market participants in ASSUME? [![Open Learning Tutorial in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/assume-framework/assume/blob/main/examples/notebooks/04_reinforcement_learning_example.ipynb) +How to change and adapt reinforcement learning algorithms in ASSUME? + +[![Open Learning Tutorial in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/assume-framework/assume/blob/main/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb) ### The Examples diff --git a/docs/source/examples_basic.rst b/docs/source/examples_basic.rst index 2a547b75..7c87374b 100644 --- a/docs/source/examples_basic.rst +++ b/docs/source/examples_basic.rst @@ -18,6 +18,7 @@ Here you can find several tutorials on how to use ASSUME framework to get you st examples/02_automated_run_example.nblink examples/03_custom_unit_example.nblink examples/04_reinforcement_learning_example.nblink + examples/04_reinforcement_learning_algorithm_example.nblink examples/05_market_comparison.nblink examples/06_advanced_orders_example.nblink examples/07_interoperability_example.nblink diff --git a/docs/source/img/Assume_run_learning_loop.png b/docs/source/img/Assume_run_learning_loop.png new file mode 100644 index 0000000000000000000000000000000000000000..6c69c7267875cc120868963c298d79ad7b9e62cc GIT binary patch literal 121410 zcmeFYRX`o#(j}VU?iSqL-Q9u(cMWax8QEU-QC^cHpw~vy)*CkY32bo zyLb2Qs#_#kLi2~p?(ks?1((Pi9iwgBMHO3tp&bG0Dnen`6aB2A6rkpiA5s|0|%DT zYQZ>st&!i)Qk+0phN>3>{(|}}ByCGFkMVvADL7qcgua&O`_=#VQRx8#GuwD--?v z^Al?Yb1WzRIvbZZlv<6u=cl{7vpGEqVO_+y`v?~gEe8j&ig}D=o8F)GI1v{}_ zUB=qSV4JTUHyf~*a@2Ogr7PDksNUwWW1>KNJxTCaF&5siz1!*sUNk=%S!(N}f(lD|2;x-A+P0?)xco)TYC&AcG34av&ABCTTE`9z#8!*p}OFqd-1 z?-8Me!KSYxT#U4)pYQHl>cpNLF2THwwEW>~%oss+w|EYujdUi5n1oqWdX;^wC%c7g z*GCtLZ^gC^1wXF^L)MF(oGG@tXiYalKD5#`<6_NXHd{|N@cm*V52s7|NO0aqf->9@ 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+SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb new file mode 100644 index 00000000..0573a330 --- /dev/null +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -0,0 +1,943 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "4JeBorbE6FYr" + }, + "source": [ + "# 4. RL Algorithm tutorial\n", + "\n", + "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement leanring (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL alogorithm. It ,therefore, start one level deeper, than the RL_application example and the knowledge from this tutorial is not required, if the already perconfigured algorithm in Assume should be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", + "\n", + "The outline of this tutorial is as follows. We will start with an introduction to the changed simualtion flow when we use reinforcement learning (1. From one simulation year to learning episodes). If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we dive into the tasks and reason behind a learning role (2. What role has a learning role) and then dive into the characteristics of the algorithm (3. The MATD3).\n", + "\n", + "**Please Note:** The tutorial does not cover coding tasks. It simply provides an overview and explanation of the implementation of reinforcement learning and the flow for those who would like to modify the underlying learning algorithm.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0. Install Assume\n", + "\n", + "Frist we need to install Assume in this Colab. Here we just install the ASSUME core package via pip. In general the instructions for an installation can be found here: https://assume.readthedocs.io/en/latest/installation.html. All the required steps are executed here and since we are working in colab the generation of a venv is not necessary. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "m0DaRwFA7VgW", + "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b", + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "!pip install assume-framework" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IIw_QIE3pY34" + }, + "source": [ + "And easy like this we have ASSUME installed. Now we can let it run. Please note though that we cannot use the functionalities tied to docker and, hence, cannot access the predefined dashboards in colab. For this please install docker and ASSUME on your personal machine.\n", + "\n", + "Further we would like to access the predefined scenarios in ASSUME which are stored on the git repository. Hence, we clone the repository." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_5hB0uDisSsg", + "outputId": "1241881f-e090-4f26-9b02-560adfcb3a3e", + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "!git clone https://github.com/assume-framework/assume.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fg7DyNjLuvSb" + }, + "source": [ + "**Let the magic happen.** Now you can run your first ever simulation in ASSUME. The following code navigates to the respective assume folder and starts the simulation example example_01b using the local database here in colab.\n", + "\n", + "When running locally, you can also just run `assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"` in a shell" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3eVM60Qx8SC0", + "outputId": "20434515-6e65-4d34-d44d-8c4529a46ece", + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "!cd assume && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bj2C4ElILNNv" + }, + "source": [ + "## 1. From one simulation year to learning episodes\n", + "\n", + "In a normal simulation wihtout reinforcement learning, we only run the time horizon of the simulation once. For RL the agents need to learn their strategy based on interactions. For that to work an RL agent has to see a situation, aka a simulation hour, multiple times, and hence we need to run the entire silumation hoirzon multiple times as well. \n", + "\n", + "To enable this we define a run learning function that will be called if the simulation is started and we defined in our config that we want to activate learning. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zMyZhaNM7NRP" + }, + "source": [ + "**But first some imports:**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "xUsbeZdPJ_2Q" + }, + "outputs": [], + "source": [ + "import logging\n", + "from collections import defaultdict\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import torch as th\n", + "import yaml\n", + "from torch.nn import functional as F\n", + "from tqdm import tqdm\n", + "\n", + "from assume.common.exceptions import AssumeException\n", + "from assume.reinforcement_learning.algorithms.base_algorithm import RLAlgorithm\n", + "from assume.reinforcement_learning.algorithms.matd3 import TD3\n", + "from assume.reinforcement_learning.buffer import ReplayBuffer\n", + "from assume.reinforcement_learning.learning_role import Learning\n", + "from assume.reinforcement_learning.learning_utils import polyak_update\n", + "from assume.scenario.loader_csv import (\n", + " load_config_and_create_forecaster,\n", + " load_scenario_folder,\n", + " setup_world,\n", + ")\n", + "from assume.world import World\n", + "\n", + "logger = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This flowchart provides an overview of the key stages involved in the run_learning function, which trains Deep Reinforcement Learning (DRL) agents within a simulated market environment. The process is divided into five main steps:\n", + "\n", + "**Initialization of the Learning Process**: The function begins by setting up the environment, initializing policies, and configuring necessary settings such as logging and buffer allocation. It ensures that no existing policies are overwritten without confirmation.\n", + "\n", + "**Training Loop**: This is the outer loop where multiple training episodes are executed. For each episode, the world simulation is completely re-initialized and reset after execution, meaning the simulation environment is essentially killed after each episode. Crucially, all necessary information that must persist across episodes—such as collected experience stored in the buffer—is maintained in the inter-episodic data. This data is key to ensuring the continuity of the learning process as it allows the DRL agents to build knowledge over time.\n", + "\n", + "**Evaluation Loop**: Nested within the training loop, the evaluation loop periodically assesses the performance of the learned policies. Based on average rewards, the best-performing policies are saved, and the function determines if further training is necessary.\n", + "\n", + "**Terminate Learning and Save Policies**: At the end of the training phase, the function saves the final version of the learned policies, ensuring that the results are stored for future use.\n", + "\n", + "**Final Evaluation Run**: A final evaluation run is conducted using the best policies from the training phase, providing a benchmark for overall performance.\n", + "\n", + "The flowchart visually represents the interaction between the training and evaluation loops, highlighting the progression through these key stages.\n", + "\n", + "![Learning Process Flowchart](../../docs/source/img/Assume_run_learning_loop.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "UXYSesx4Ifp5" + }, + "outputs": [], + "source": [ + "def run_learning(\n", + " world: World,\n", + " inputs_path: str,\n", + " scenario: str,\n", + " study_case: str,\n", + " verbose: bool = False,\n", + ") -> None:\n", + " \"\"\"\n", + " Train Deep Reinforcement Learning (DRL) agents to act in a simulated market environment.\n", + "\n", + " This function runs multiple episodes of simulation to train DRL agents, performs evaluation, and saves the best runs. It maintains the buffer and learned agents in memory to avoid resetting them with each new run.\n", + "\n", + " Args:\n", + " world (World): An instance of the World class representing the simulation environment.\n", + " inputs_path (str): The path to the folder containing input files necessary for the simulation.\n", + " scenario (str): The name of the scenario for the simulation.\n", + " study_case (str): The specific study case for the simulation.\n", + "\n", + " Note:\n", + " - The function uses a ReplayBuffer to store experiences for training the DRL agents.\n", + " - It iterates through training episodes, updating the agents and evaluating their performance at regular intervals.\n", + " - Initial exploration is active at the beginning and is disabled after a certain number of episodes to improve the performance of DRL algorithms.\n", + " - Upon completion of training, the function performs an evaluation run using the best policy learned during training.\n", + " - The best policies are chosen based on the average reward obtained during the evaluation runs, and they are saved for future use.\n", + " \"\"\"\n", + "\n", + " # -----------------------------------------------------------\n", + " # 1 - Initialisation of the learning process\n", + "\n", + " if not verbose:\n", + " logger.setLevel(logging.WARNING)\n", + "\n", + " # remove csv path so that nothing is written while learning\n", + " temp_csv_path = world.export_csv_path\n", + " world.export_csv_path = \"\"\n", + "\n", + " # initialize policies already here to set the obs_dim and act_dim in the learning role\n", + " actors_and_critics = None\n", + " world.learning_role.initialize_policy(actors_and_critics=actors_and_critics)\n", + " world.output_role.del_similar_runs()\n", + "\n", + " # check if we already stored policies for this simualtion\n", + " save_path = world.learning_config[\"trained_policies_save_path\"]\n", + "\n", + " if Path(save_path).is_dir():\n", + " # we are in learning mode and about to train new policies, which might overwrite existing ones\n", + " accept = input(\n", + " f\"{save_path=} exists - should we overwrite current learnings? (y/N) \"\n", + " )\n", + " if not accept.lower().startswith(\"y\"):\n", + " # stop here - do not start learning or save anything\n", + " raise AssumeException(\"don't overwrite existing strategies\")\n", + "\n", + " # Load scenario data to reuse across episodes\n", + " scenario_data = load_config_and_create_forecaster(inputs_path, scenario, study_case)\n", + "\n", + " # Information that needs to be stored across episodes, aka one simulation run\n", + " inter_episodic_data = {\n", + " \"buffer\": ReplayBuffer(\n", + " buffer_size=int(world.learning_config.get(\"replay_buffer_size\", 5e5)),\n", + " obs_dim=world.learning_role.rl_algorithm.obs_dim,\n", + " act_dim=world.learning_role.rl_algorithm.act_dim,\n", + " n_rl_units=len(world.learning_role.rl_strats),\n", + " device=world.learning_role.device,\n", + " float_type=world.learning_role.float_type,\n", + " ),\n", + " \"actors_and_critics\": None,\n", + " \"max_eval\": defaultdict(lambda: -1e9),\n", + " \"all_eval\": defaultdict(list),\n", + " \"avg_all_eval\": [],\n", + " \"episodes_done\": 0,\n", + " \"eval_episodes_done\": 0,\n", + " \"noise_scale\": world.learning_config.get(\"noise_scale\", 1.0),\n", + " }\n", + "\n", + " validation_interval = min(\n", + " world.learning_role.training_episodes,\n", + " world.learning_config.get(\"validation_episodes_interval\", 5),\n", + " )\n", + "\n", + " # -----------------------------------------\n", + " # 2 - Training loop\n", + "\n", + " eval_episode = 1\n", + "\n", + " for episode in tqdm(\n", + " range(1, world.learning_role.training_episodes + 1),\n", + " desc=\"Training Episodes\",\n", + " ):\n", + " # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\n", + " if episode != 1:\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " episode=episode,\n", + " )\n", + "\n", + " # Give the newly initliazed learning role the needed information across episodes\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " # Store updated information across episodes\n", + " inter_episodic_data = world.learning_role.get_inter_episodic_data()\n", + " inter_episodic_data[\"episodes_done\"] = episode\n", + "\n", + " # -----------------------------------------\n", + " # 3 - Evaluation loop\n", + "\n", + " if (\n", + " episode % validation_interval == 0\n", + " and episode\n", + " >= world.learning_role.episodes_collecting_initial_experience\n", + " + validation_interval\n", + " ):\n", + " world.reset()\n", + "\n", + " # load evaluation run\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " perform_evaluation=True,\n", + " eval_episode=eval_episode,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " total_rewards = world.output_role.get_sum_reward()\n", + " avg_reward = np.mean(total_rewards)\n", + " # check reward improvement in evaluation run\n", + " # and store best run in eval folder\n", + " terminate = world.learning_role.compare_and_save_policies(\n", + " {\"avg_reward\": avg_reward}\n", + " )\n", + "\n", + " inter_episodic_data[\"eval_episodes_done\"] = eval_episode\n", + "\n", + " # if we have not improved in the last x evaluations, we stop loop\n", + " if terminate:\n", + " break\n", + "\n", + " eval_episode += 1\n", + "\n", + " world.reset()\n", + "\n", + " # -----------------------------------------\n", + " # 4 - Terminate Learning and Save policies\n", + "\n", + " # if at end of simulation save last policies\n", + " if episode == (world.learning_role.training_episodes):\n", + " world.learning_role.rl_algorithm.save_params(\n", + " directory=f\"{world.learning_role.trained_policies_save_path}/last_policies\"\n", + " )\n", + "\n", + " # container shutdown implicitly with new initialisation\n", + " logger.info(\"################\")\n", + " logger.info(\"Training finished, Start evaluation run\")\n", + " world.export_csv_path = temp_csv_path\n", + "\n", + " world.reset()\n", + "\n", + " # ----------------------------------\n", + " # 5 - Final Evaluation run\n", + "\n", + " # load scenario for evaluation\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " terminate_learning=True,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8UM1QPZrIdqK" + }, + "source": [ + "## 2. What role has a learning role\n", + "\n", + "The LearningRole class in learning_role.py is a central component of the reinforcement learning framework. It manages configurations, device settings, early stopping of the learning process, and initializes various RL strategies the algorithm and buffers. This class ensures that the RL agent can be trained or evaluated effectively, leveraging the available hardware and adhering to the specified configurations. The parameters of the learning process are also described in the read-the-docs under learning_algorithms.\n", + "\n", + "### 2.1 Learning Data Management\n", + "\n", + "One key feature of the LearningRole class is its ability to load and manage the inter episodic data. This involves storing experiences and the training progress and retrieving this data to train the RL agent. By efficiently handling episodic data, the LearningRole class enables the agent to learn from past experiences and improve its performance over time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class Learning(Learning):\n", + " \"\"\"\n", + " This class manages the learning process of reinforcement learning agents, including initializing key components such as\n", + " neural networks, replay buffer, and learning hyperparameters. It handles both training and evaluation modes based on\n", + " the provided learning configuration.\n", + "\n", + " Args:\n", + " simulation_start (datetime.datetime): The start of the simulation.\n", + " simulation_end (datetime.datetime): The end of the simulation.\n", + " learning_config (LearningConfig): The configuration for the learning process.\n", + "\n", + " \"\"\"\n", + "\n", + " def load_inter_episodic_data(self, inter_episodic_data):\n", + " \"\"\"\n", + " Load the inter-episodic data from the dict stored across simulation runs.\n", + "\n", + " Args:\n", + " inter_episodic_data (dict): The inter-episodic data to be loaded.\n", + "\n", + " \"\"\"\n", + " self.episodes_done = inter_episodic_data[\"episodes_done\"]\n", + " self.eval_episodes_done = inter_episodic_data[\"eval_episodes_done\"]\n", + " self.max_eval = inter_episodic_data[\"max_eval\"]\n", + " self.rl_eval = inter_episodic_data[\"all_eval\"]\n", + " self.avg_rewards = inter_episodic_data[\"avg_all_eval\"]\n", + " self.buffer = inter_episodic_data[\"buffer\"]\n", + "\n", + " # if enough initial experience was collected according to specifications in learning config\n", + " # turn off initial exploration and go into full learning mode\n", + " if self.episodes_done > self.episodes_collecting_initial_experience:\n", + " self.turn_off_initial_exploration()\n", + "\n", + " self.set_noise_scale(inter_episodic_data[\"noise_scale\"])\n", + "\n", + " self.initialize_policy(inter_episodic_data[\"actors_and_critics\"])\n", + "\n", + " def get_inter_episodic_data(self):\n", + " \"\"\"\n", + " Dump the inter-episodic data to a dict for storing across simulation runs.\n", + "\n", + " Returns:\n", + " dict: The inter-episodic data to be stored.\n", + " \"\"\"\n", + "\n", + " return {\n", + " \"episodes_done\": self.episodes_done,\n", + " \"eval_episodes_done\": self.eval_episodes_done,\n", + " \"max_eval\": self.max_eval,\n", + " \"all_eval\": self.rl_eval,\n", + " \"avg_all_eval\": self.avg_rewards,\n", + " \"buffer\": self.buffer,\n", + " \"actors_and_critics\": self.rl_algorithm.extract_policy(),\n", + " \"noise_scale\": self.get_noise_scale(),\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The metrics in `inter_episodic_data` are stored for the following reasons:\n", + "\n", + "- `episodes_done` and `eval_episodes_done`: **Monitoring Progress** \n", + " Keeping track of the number of episodes completed.\n", + "\n", + "- `max_eval`, `all_eval`, `avg_all_eval`: **Evaluating Performance** \n", + " Storing evaluation scores and average rewards to assess the agent's performance across episodes.\n", + "\n", + "- `buffer`: **Experience Replay** \n", + " Using a replay buffer to learn from past experiences and improve data efficiency.\n", + "\n", + "- `noise_scale`: **Policy Exploration** \n", + " The noise is used to include exploration in the policy. It is decreased across episode numbers, and we store the current noise value to continue the decrease across future episodes.\n", + "\n", + "- `actors_and_critics`: **Policy Initialization** \n", + " Initializing the policy with actors and critics (`self.initialize_policy()`) ensures that the agent starts with the pre-defined strategy from the previous episode and can improve upon it through learning.\n", + "\n", + "\n", + "### 2.2 Learning Algorithm\n", + "\n", + "If learning is used, then the learning role initializes a learning algorithm which is the heart of the learning progress. Currently, only the MATD3 is implemented, but we are working on different PPO implementations as well. If you would like to add an algoithm it woulb be integrated here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0ww-L9fABnw3" + }, + "outputs": [], + "source": [ + "class Learning(Learning):\n", + " def create_learning_algorithm(self, algorithm: RLAlgorithm):\n", + " \"\"\"\n", + " Create and initialize the reinforcement learning algorithm.\n", + "\n", + " This method creates and initializes the reinforcement learning algorithm based on the specified algorithm name. The algorithm\n", + " is associated with the learning role and configured with relevant hyperparameters.\n", + "\n", + " Args:\n", + " algorithm (RLAlgorithm): The name of the reinforcement learning algorithm.\n", + " \"\"\"\n", + " if algorithm == \"matd3\":\n", + " self.rl_algorithm = TD3(\n", + " learning_role=self,\n", + " learning_rate=self.learning_rate,\n", + " episodes_collecting_initial_experience=self.episodes_collecting_initial_experience,\n", + " gradient_steps=self.gradient_steps,\n", + " batch_size=self.batch_size,\n", + " gamma=self.gamma,\n", + " actor_architecture=self.actor_architecture,\n", + " )\n", + " else:\n", + " logger.error(f\"Learning algorithm {algorithm} not implemented!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 Learning Algorithm Flow in Assume\n", + "\n", + "The following graph illustrates the structure and flow of the learning algorithm within the reinforcement learning framework.\n", + "\n", + "\"Learning\n", + "\n", + "Within the algorithm, we distinguish three different steps that are translated into ASSUME in the following way:\n", + "\n", + "1. **Initialization**: This is the first step where all necessary components such as the actors, critics, and buffer are set up.\n", + "\n", + "2. **Experience Collection**: The second step, represented in the flowchart above within the loop, involves the collection of experience. This includes choosing an action, observing a reward, and storing the transition tuple in the buffer.\n", + "\n", + "3. **Policy Update**: The third step is the actual policy update, which is also performed within the loop, allowing the agent to improve its performance over time.\n", + "\n", + "\n", + "### 3.1 Initialization\n", + "\n", + "The initialization of the actors, critics, and the buffer is handled via the `learning_role` and the `inter_episodic_data`, as described earlier. The `create_learning_algorithm` function triggers their initialization in `initialize_policy`. At the beginning of the training process, they are initialized with new random settings. In subsequent episodes, they are initialized with pre-learned data, ensuring that previous learning is retained and built upon.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class TD3(TD3):\n", + " def initialize_policy(self, actors_and_critics: dict = None) -> None:\n", + " \"\"\"\n", + " Create actor and critic networks for reinforcement learning.\n", + "\n", + " If `actors_and_critics` is None, this method creates new actor and critic networks.\n", + " If `actors_and_critics` is provided, it assigns existing networks to the respective attributes.\n", + "\n", + " Args:\n", + " actors_and_critics (dict): The actor and critic networks to be assigned.\n", + "\n", + " \"\"\"\n", + " if actors_and_critics is None:\n", + " self.create_actors()\n", + " self.create_critics()\n", + "\n", + " else:\n", + " self.learning_role.critics = actors_and_critics[\"critics\"]\n", + " self.learning_role.target_critics = actors_and_critics[\"target_critics\"]\n", + " for u_id, unit_strategy in self.learning_role.rl_strats.items():\n", + " unit_strategy.actor = actors_and_critics[\"actors\"][u_id]\n", + " unit_strategy.actor_target = actors_and_critics[\"actor_targets\"][u_id]\n", + "\n", + " self.obs_dim = actors_and_critics[\"obs_dim\"]\n", + " self.act_dim = actors_and_critics[\"act_dim\"]\n", + " self.unique_obs_dim = actors_and_critics[\"unique_obs_dim\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please also note that we make a distinction in the handling of the critics and target critics compared to the actors and target actors. You can observe this in the `initialize_policy` function. For the critics, they are assigned to the `learning_role` as there are centralized critics used for all the different actors. In contrast, the actors are assigned to specific unit strategies. Each learning unit, such as a power plant, has one learning strategy and therefore an individual actor, while the critics remain centralized.\n", + "\n", + "This distinction leads to the case where, even if learning is not active, we still need the actors to perform the entire simulation using pre-trained policies. This is essential, for example, when running simulations with previously learned policies.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Experience Collection\n", + "\n", + "Within the loop, the selection of an action with exploration noise, as well as the observation of a new reward and state, and the storing of this tuple in the buffer, are all handled within the bidding strategy. \n", + "\n", + "This specific process is covered in more detail in another tutorial. For more details, refer to [tutorial 04](04_reinforcement_learning_example.ipynb).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Policy Update \n", + "\n", + "The core of the algorithm, which comprises all other steps is embodied by the `assume.reinforcement_learning.algorithms.matd3.TD3.update_policy` function in the learning algorithms. Here, the critic and the actor are updated according to the algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class TD3(TD3):\n", + " def update_policy(self):\n", + " \"\"\"\n", + " Update the policy of the reinforcement learning agent using the Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm.\n", + "\n", + " Notes:\n", + " This function performs the policy update step, which involves updating the actor (policy) and critic (Q-function) networks\n", + " using TD3 algorithm. It iterates over the specified number of gradient steps and performs the following steps for each\n", + " learning strategy:\n", + "\n", + " 1. Sample a batch of transitions from the replay buffer.\n", + " 2. Calculate the next actions with added noise using the actor target network.\n", + " 3. Compute the target Q-values based on the next states, rewards, and the target critic network.\n", + " 4. Compute the critic loss as the mean squared error between current Q-values and target Q-values.\n", + " 5. Optimize the critic network by performing a gradient descent step.\n", + " 6. Optionally, update the actor network if the specified policy delay is reached.\n", + " 7. Apply Polyak averaging to update target networks.\n", + "\n", + " This function implements the TD3 algorithm's key step for policy improvement and exploration.\n", + " \"\"\"\n", + "\n", + " logger.debug(\"Updating Policy\")\n", + " n_rl_agents = len(self.learning_role.rl_strats.keys())\n", + " for _ in range(self.gradient_steps):\n", + " self.n_updates += 1\n", + " i = 0\n", + "\n", + " for u_id in self.learning_role.rl_strats.keys():\n", + " critic_target = self.learning_role.target_critics[u_id]\n", + " critic = self.learning_role.critics[u_id]\n", + " actor = self.learning_role.rl_strats[u_id].actor\n", + " actor_target = self.learning_role.rl_strats[u_id].actor_target\n", + "\n", + " if i % 100 == 0:\n", + " # only update target netwroks every 100 steps, to have delayed network update\n", + " transitions = self.learning_role.buffer.sample(self.batch_size)\n", + " states = transitions.observations\n", + " actions = transitions.actions\n", + " next_states = transitions.next_observations\n", + " rewards = transitions.rewards\n", + "\n", + " with th.no_grad():\n", + " # Select action according to policy and add clipped noise\n", + " noise = actions.clone().data.normal_(\n", + " 0, self.target_policy_noise\n", + " )\n", + " noise = noise.clamp(\n", + " -self.target_noise_clip, self.target_noise_clip\n", + " )\n", + " next_actions = [\n", + " (actor_target(next_states[:, i, :]) + noise[:, i, :]).clamp(\n", + " -1, 1\n", + " )\n", + " for i in range(n_rl_agents)\n", + " ]\n", + " next_actions = th.stack(next_actions)\n", + "\n", + " next_actions = next_actions.transpose(0, 1).contiguous()\n", + " next_actions = next_actions.view(-1, n_rl_agents * self.act_dim)\n", + "\n", + " all_actions = actions.view(self.batch_size, -1)\n", + "\n", + " # this takes the unique observations from all other agents assuming that\n", + " # the unique observations are at the end of the observation vector\n", + " temp = th.cat(\n", + " (\n", + " states[:, :i, self.obs_dim - self.unique_obs_dim :].reshape(\n", + " self.batch_size, -1\n", + " ),\n", + " states[\n", + " :, i + 1 :, self.obs_dim - self.unique_obs_dim :\n", + " ].reshape(self.batch_size, -1),\n", + " ),\n", + " axis=1,\n", + " )\n", + "\n", + " # the final all_states vector now contains the current agent's observation\n", + " # and the unique observations from all other agents\n", + " all_states = th.cat(\n", + " (states[:, i, :].reshape(self.batch_size, -1), temp), axis=1\n", + " ).view(self.batch_size, -1)\n", + " # all_states = states[:, i, :].reshape(self.batch_size, -1)\n", + "\n", + " # this is the same as above but for the next states\n", + " temp = th.cat(\n", + " (\n", + " next_states[\n", + " :, :i, self.obs_dim - self.unique_obs_dim :\n", + " ].reshape(self.batch_size, -1),\n", + " next_states[\n", + " :, i + 1 :, self.obs_dim - self.unique_obs_dim :\n", + " ].reshape(self.batch_size, -1),\n", + " ),\n", + " axis=1,\n", + " )\n", + "\n", + " # the final all_next_states vector now contains the current agent's observation\n", + " # and the unique observations from all other agents\n", + " all_next_states = th.cat(\n", + " (next_states[:, i, :].reshape(self.batch_size, -1), temp), axis=1\n", + " ).view(self.batch_size, -1)\n", + " # all_next_states = next_states[:, i, :].reshape(self.batch_size, -1)\n", + "\n", + " with th.no_grad():\n", + " # Compute the next Q-values: min over all critics targets\n", + " next_q_values = th.cat(\n", + " critic_target(all_next_states, next_actions), dim=1\n", + " )\n", + " next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)\n", + " target_Q_values = (\n", + " rewards[:, i].unsqueeze(1) + self.gamma * next_q_values\n", + " )\n", + "\n", + " # Get current Q-values estimates for each critic network\n", + " current_Q_values = critic(all_states, all_actions)\n", + "\n", + " # Compute critic loss\n", + " critic_loss = sum(\n", + " F.mse_loss(current_q, target_Q_values)\n", + " for current_q in current_Q_values\n", + " )\n", + "\n", + " # Optimize the critics\n", + " critic.optimizer.zero_grad()\n", + " critic_loss.backward()\n", + " critic.optimizer.step()\n", + "\n", + " # Delayed policy updates\n", + " if self.n_updates % self.policy_delay == 0:\n", + " # Compute actor loss\n", + " state_i = states[:, i, :]\n", + " action_i = actor(state_i)\n", + "\n", + " all_actions_clone = actions.clone()\n", + " all_actions_clone[:, i, :] = action_i\n", + " all_actions_clone = all_actions_clone.view(self.batch_size, -1)\n", + "\n", + " actor_loss = -critic.q1_forward(\n", + " all_states, all_actions_clone\n", + " ).mean()\n", + "\n", + " actor.optimizer.zero_grad()\n", + " actor_loss.backward()\n", + " actor.optimizer.step()\n", + "\n", + " polyak_update(\n", + " critic.parameters(), critic_target.parameters(), self.tau\n", + " )\n", + " polyak_update(\n", + " actor.parameters(), actor_target.parameters(), self.tau\n", + " )\n", + " i += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The other functions within the reinforcement learning algorithm are primarily there to store, update, and save the new policies. These functions either write the updated policies to a designated location or save them into the `inter_episodic_data`.\n", + "\n", + "If you would like to make a change to this algorithm, the most likely modification would be to the `update_policy` function, as it plays a central role in the learning process. The other functions would only need adjustments if the different algorithm features vary likethe target critics or critic architectures.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L3flH5iY4x7Z" + }, + "source": [ + "### 3.5 Start the simulation\n", + "\n", + "We are almost done with all the changes to actually be able to make ASSUME learn here in google colab. If you would rather like to load our pretrained strategies, we need a function for loading parameters, which can be found below. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cTlqMouufKyo" + }, + "source": [ + "To control the learning process, the config file determines the parameters of the learning algorithm. As we want to temper with these values in the notebook we will overwrite the learning config in the next cell and then load it into our world. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "moZ_UD7FfkOh" + }, + "outputs": [], + "source": [ + "learning_config = {\n", + " \"observation_dimension\": 50,\n", + " \"action_dimension\": 2,\n", + " \"continue_learning\": False,\n", + " \"trained_policies_save_path\": \"None\",\n", + " \"max_bid_price\": 100,\n", + " \"algorithm\": \"matd3\",\n", + " \"learning_rate\": 0.001,\n", + " \"training_episodes\": 100,\n", + " \"episodes_collecting_initial_experience\": 5,\n", + " \"train_freq\": 24,\n", + " \"gradient_steps\": -1,\n", + " \"batch_size\": 256,\n", + " \"gamma\": 0.99,\n", + " \"device\": \"cpu\",\n", + " \"noise_sigma\": 0.1,\n", + " \"noise_scale\": 1,\n", + " \"noise_dt\": 1,\n", + " \"validation_episodes_interval\": 5,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iPz8v4N5hpfr" + }, + "outputs": [], + "source": [ + "# Read the YAML file\n", + "with open(\"assume/examples/inputs/example_02a/config.yaml\") as file:\n", + " data = yaml.safe_load(file)\n", + "\n", + "# store our modifications to the config file\n", + "data[\"base\"][\"learning_mode\"] = True\n", + "data[\"base\"][\"learning_config\"] = learning_config\n", + "\n", + "# Write the modified data back to the file\n", + "with open(\"assume/examples/inputs/example_02a/config.yaml\", \"w\") as file:\n", + " yaml.safe_dump(data, file)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZlRnTgCy5d9W" + }, + "source": [ + "In order to let the simulation run with the integrated learning we need to touch up the main file that runs it in the following way." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ZlWxXxZr54WV", + "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import RLStrategy\n", + "\n", + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"./outputs\"\n", + "os.makedirs(\"./local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " \"\"\"\n", + " Available examples:\n", + " - local_db: without database and grafana\n", + " - timescale: with database and grafana (note: you need docker installed)\n", + " \"\"\"\n", + " data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + "\n", + " if data_format == \"local_db\":\n", + " db_uri = \"sqlite:///./local_db/assume_db.db\"\n", + " elif data_format == \"timescale\":\n", + " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + "\n", + " input_path = \"assume/examples/inputs\"\n", + " scenario = \"example_02a\"\n", + " study_case = \"base\"\n", + "\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", + " # hence we define this strategey to be one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=input_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=input_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equasl a test run\n", + " world.run()" + ] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "assume-framework", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "nbsphinx": { + "execute": "never" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb.license b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later From 78d99616b5b2e6b5c4add72559e55ce6b894c291 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 23 Sep 2024 16:57:29 +0200 Subject: [PATCH 02/27] - config fixes --- ...forcement_learning_algorithm_example.ipynb | 649 +++++++++++++++++- 1 file changed, 627 insertions(+), 22 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 0573a330..fc8ecc8a 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -37,7 +37,36 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: assume-framework in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (0.3.7)\n", + "Requirement already satisfied: argcomplete>=3.1.4 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (3.4.0)\n", + "Requirement already satisfied: nest-asyncio>=1.5.6 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.6.0)\n", + "Requirement already satisfied: mango-agents-assume>=1.1.1-8 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.1.4.post2)\n", + "Requirement already satisfied: numpy<2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.26.4)\n", + "Requirement already satisfied: tqdm>=4.64.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (4.66.4)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.9.0)\n", + "Requirement already satisfied: sqlalchemy>=2.0.9 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.0.30)\n", + "Requirement already satisfied: pandas>=2.0.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.2.2)\n", + "Requirement already satisfied: psycopg2-binary>=2.9.5 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.9.9)\n", + "Requirement already satisfied: pyyaml>=6.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (6.0.1)\n", + "Requirement already satisfied: pyyaml-include>=1.3.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.4.1)\n", + "Requirement already satisfied: paho-mqtt>=1.5.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (2.1.0)\n", + "Requirement already satisfied: dill>=0.3.6 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (0.3.8)\n", + "Requirement already satisfied: msgspec>=0.14.2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (0.18.6)\n", + "Requirement already satisfied: protobuf>=3.20.3 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (5.27.1)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from pandas>=2.0.0->assume-framework) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from pandas>=2.0.0->assume-framework) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from python-dateutil>=2.8.2->assume-framework) (1.16.0)\n", + "Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from sqlalchemy>=2.0.9->assume-framework) (4.12.2)\n", + "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from sqlalchemy>=2.0.9->assume-framework) (3.0.3)\n", + "Requirement already satisfied: colorama in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from tqdm>=4.64.1->assume-framework) (0.4.6)\n" + ] + } + ], "source": [ "!pip install assume-framework" ] @@ -55,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -66,7 +95,15 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cloning into 'assume'...\n" + ] + } + ], "source": [ "!git clone https://github.com/assume-framework/assume.git" ] @@ -84,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -95,7 +132,411 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_01b/ from examples/inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + " 0%| | 0/2678400 [00:00" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "metadata": { "id": "UXYSesx4Ifp5" }, @@ -381,7 +822,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -471,7 +912,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "id": "0ww-L9fABnw3" }, @@ -528,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -592,7 +1033,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -782,15 +1223,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "id": "moZ_UD7FfkOh" }, "outputs": [], "source": [ "learning_config = {\n", - " \"observation_dimension\": 50,\n", - " \"action_dimension\": 2,\n", " \"continue_learning\": False,\n", " \"trained_policies_save_path\": \"None\",\n", " \"max_bid_price\": 100,\n", @@ -798,7 +1237,7 @@ " \"learning_rate\": 0.001,\n", " \"training_episodes\": 100,\n", " \"episodes_collecting_initial_experience\": 5,\n", - " \"train_freq\": 24,\n", + " \"train_freq\": \"24h\",\n", " \"gradient_steps\": -1,\n", " \"batch_size\": 256,\n", " \"gamma\": 0.99,\n", @@ -812,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "id": "iPz8v4N5hpfr" }, @@ -842,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -851,11 +1290,170 @@ "id": "ZlWxXxZr54WV", "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_02a/base from assume/examples/inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_1 2019-03-31 00:00:00: : 2592001.0it [00:27, 94420.77it/s] \n", + "Training Episodes: 1%| | 1/100 [00:27<45:21, 27.49s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_2 2019-03-31 00:00:00: : 2592001.0it [00:27, 94242.68it/s] \n", + "Training Episodes: 2%|▏ | 2/100 [00:55<44:56, 27.52s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_3 2019-03-31 00:00:00: : 2592001.0it [00:27, 93778.01it/s]\n", + "Training Episodes: 3%|▎ | 3/100 [01:22<44:36, 27.59s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_4 2019-03-31 00:00:00: : 2592001.0it [00:27, 93996.50it/s] \n", + "Training Episodes: 4%|▍ | 4/100 [01:50<44:09, 27.60s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Episodes: 5%|▌ | 5/100 [02:16<42:57, 27.13s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Training Episodes: 5%|▌ | 5/100 [02:16<43:20, 27.37s/it]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Time must be > 1553886000.0 but is 1551398399.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[27], line 47\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 47\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 48\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 49\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equasl a test run\u001b[39;00m\n\u001b[0;32m 55\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", + "Cell \u001b[1;32mIn[17], line 102\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 100\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 102\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# Store updated information across episodes\u001b[39;00m\n\u001b[0;32m 105\u001b[0m inter_episodic_data \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mget_inter_episodic_data()\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime must be > \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# set time\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time \u001b[38;5;241m=\u001b[39m t\n", + "\u001b[1;31mValueError\u001b[0m: Time must be > 1553886000.0 but is 1551398399." + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:assume.markets.base_market:1553889600.0 Market result [(datetime.datetime(2019, 3, 29, 20, 0), datetime.datetime(2019, 3, 29, 21, 0), None)] for market EOM are empty!\n" + ] + } + ], "source": [ "import os\n", "\n", - "import RLStrategy\n", + "from assume.strategies.learning_strategies import RLStrategy\n", "\n", "log = logging.getLogger(__name__)\n", "\n", @@ -909,6 +1507,13 @@ " # after the learning is done we make a normal run of the simulation, which equasl a test run\n", " world.run()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -932,7 +1537,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.9" }, "nbsphinx": { "execute": "never" From 2f899369e6f3c10babcd945f8e4cfc38e3203b89 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 23 Sep 2024 17:31:01 +0200 Subject: [PATCH 03/27] - added results plots and comparison --- ...forcement_learning_algorithm_example.ipynb | 7 + .../04_reinforcement_learning_example.ipynb | 257 +++++++++++++++++- 2 files changed, 249 insertions(+), 15 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index fc8ecc8a..5631a93f 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -1448,6 +1448,13 @@ "text": [ "WARNING:assume.markets.base_market:1553889600.0 Market result [(datetime.datetime(2019, 3, 29, 20, 0), datetime.datetime(2019, 3, 29, 21, 0), None)] for market EOM are empty!\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_6 2019-03-31 00:00:00: : 1556474401.0it [00:29, 53550828.33it/s] \n" + ] } ], "source": [ diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index 6e122494..b66a3b41 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -1280,12 +1280,17 @@ "id": "ZlRnTgCy5d9W" }, "source": [ - "In order to let the simulation run with the integrated learning we need to touch up the main file that runs it in the following way." + "In order to let the simulation run with the integrated learning we need to touch up the main file that runs it in the following way.\n", + "\n", + "In the following cell, we let the example run in case 1 of [1], where we have one big reinforcement learning power plan exists that technically can exert my power.\n", + "\n", + "[1] Harder, N.; Qussous, R.; Weidlich, A. Fit for purpose: Modeling wholesale electricity markets realistically with multi-agent deep reinforcement learning. *Energy and AI* **2023**. 14. 100295. https://doi.org/10.1016/j.egyai.2023.100295.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1294,6 +1299,73 @@ "id": "ZlWxXxZr54WV", "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'logging' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m log \u001b[38;5;241m=\u001b[39m \u001b[43mlogging\u001b[49m\u001b[38;5;241m.\u001b[39mgetLogger(\u001b[38;5;18m__name__\u001b[39m)\n\u001b[0;32m 3\u001b[0m csv_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 4\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocal_db\u001b[39m\u001b[38;5;124m\"\u001b[39m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'logging' is not defined" + ] + } + ], + "source": [ + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"outputs\"\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + " scenario = \"example_02a\"\n", + " study_case = \"base\"\n", + "\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", + " # hence we define this strategey to be one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", + " world.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In comparison, the following cell executes example case 2 of [1] where the same capacity of the reinforcement power plant in case 1 is divided into five reinforcement learning power plants, which hence cannot exert market power anymore." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "log = logging.getLogger(__name__)\n", @@ -1302,19 +1374,64 @@ "os.makedirs(\"local_db\", exist_ok=True)\n", "\n", "if __name__ == \"__main__\":\n", - " \"\"\"\n", - " Available examples:\n", - " - local_db: without database and grafana\n", - " - timescale: with database and grafana (note: you need docker installed)\n", - " \"\"\"\n", - " data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", "\n", - " if data_format == \"local_db\":\n", - " db_uri = \"sqlite:///local_db/assume_db.db\"\n", - " elif data_format == \"timescale\":\n", - " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + " scenario = \"example_02b\"\n", + " study_case = \"base\"\n", "\n", - " scenario = \"example_02a\"\n", + " # create world\n", + " world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + " # we import our defined bidding strategey class including the learning into the world bidding strategies\n", + " # in the example files we provided the name of the learning bidding strategeis in the input csv is \"pp_learning\"\n", + " # hence we define this strategey to be one of the learning class\n", + " world.bidding_strategies[\"pp_learning\"] = RLStrategy\n", + "\n", + " # then we load the scenario specified above from the respective input files\n", + " load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # run learning if learning mode is enabled\n", + " # needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + " if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=scenario,\n", + " study_case=study_case,\n", + " )\n", + "\n", + " # after the learning is done we make a normal run of the simulation, which equals a test run\n", + " world.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following simulation represents case 3, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "log = logging.getLogger(__name__)\n", + "\n", + "csv_path = \"outputs\"\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "if __name__ == \"__main__\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", + " scenario = \"example_02c\"\n", " study_case = \"base\"\n", "\n", " # create world\n", @@ -1348,12 +1465,122 @@ " world.run()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Result Plotting " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from functools import partial\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sqlalchemy import create_engine\n", + "\n", + "os.makedirs(\"outputs\", exist_ok=True)\n", + "\n", + "engine = create_engine(db_uri)\n", + "\n", + "\n", + "sql = \"\"\"\n", + "SELECT ident, simulation,\n", + "sum(round(CAST(value AS numeric), 2)) FILTER (WHERE variable = 'total_cost') as total_cost,\n", + "sum(round(CAST(value AS numeric), 2)*1000) FILTER (WHERE variable = 'total_volume') as total_volume,\n", + "sum(round(CAST(value AS numeric), 2)) FILTER (WHERE variable = 'avg_price') as average_cost\n", + "FROM kpis\n", + "where variable in ('total_cost', 'total_volume', 'avg_price')\n", + "and simulation in ('example_02a', 'example_02b', 'example_02c')\n", + "group by simulation, ident ORDER BY simulation\n", + "\"\"\"\n", + "kpis = pd.read_sql(sql, engine)\n", + "\n", + "# sort the dataframe to have sho, bo and lo case in the right order\n", + "\n", + "# sort kpis in the order sho, bo, lo\n", + "kpis = kpis.sort_values(\n", + " by=\"simulation\",\n", + " key=lambda x: x.map({\"example_02a\": 1, \"example_02b\": 2, \"example_02c\": 3}),\n", + ")\n", + "\n", + "kpis[\"total_volume\"] /= 1e9\n", + "kpis[\"total_cost\"] /= 1e6\n", + "savefig = partial(plt.savefig, transparent=False, bbox_inches=\"tight\")\n", + "\n", + "xticks = kpis[\"simulation\"].unique()\n", + "plt.style.use(\"seaborn-v0_8\")\n", + "\n", + "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n", + "\n", + "ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.\n", + "\n", + "width = 0.4\n", + "\n", + "kpis.total_volume.plot(kind=\"bar\", ax=ax, width=width, position=1, color=\"royalblue\")\n", + "kpis.total_cost.plot(kind=\"bar\", ax=ax2, width=width, position=0, color=\"green\")\n", + "\n", + "# set x-achxis limits\n", + "ax.set_xlim(-0.6, len(kpis[\"simulation\"]) - 0.4)\n", + "\n", + "# set y-achxis limits\n", + "ax.set_ylim(0, max(kpis.total_volume) * 1.1 + 0.1)\n", + "ax2.set_ylim(0, max(kpis.total_cost) * 1.1 + 0.1)\n", + "\n", + "ax.set_ylabel(\"Total Volume (GWh)\")\n", + "ax2.set_ylabel(\"Total Cost (M€)\")\n", + "\n", + "ax.set_xticklabels(xticks, rotation=45)\n", + "ax.set_xlabel(\"Simulation\")\n", + "\n", + "ax.legend([\"Total Volume\"], loc=\"upper left\")\n", + "ax2.legend([\"Total Cost\"], loc=\"upper right\")\n", + "\n", + "plt.title(\"Total Volume and Total Cost for each Simulation\")\n", + "\n", + "savefig(\"outputs/tutorial_6_overview.png\")\n", + "plt.show()" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import seaborn as sns\n", + "\n", + "sql = \"\"\"\n", + "SELECT\n", + " product_start AS \"time\",\n", + " price AS \"Price\",\n", + " node\n", + "FROM market_meta\n", + "WHERE simulation in ('example_02a', 'example_02b', 'example_02c') AND market_id in ('EOM') \n", + "GROUP BY market_id, simulation, product_start, price, node\n", + "ORDER BY product_start, node\n", + "\n", + "\"\"\"\n", + "\n", + "df = pd.read_sql(sql, engine)\n", + "\n", + "# Convert the 'time' column to datetime\n", + "df[\"time\"] = pd.to_datetime(df[\"time\"])\n", + "\n", + "# Plot the data\n", + "plt.figure(figsize=(14, 7))\n", + "sns.lineplot(data=df, x=\"time\", y=\"Price\", hue=\"simulation\")\n", + "plt.title(\"Price over Time for Different Simulations\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Price\")\n", + "plt.legend(title=\"Simulation\")\n", + "plt.show()" + ] } ], "metadata": { @@ -1377,7 +1604,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.7" + "version": "3.11.9" }, "nbsphinx": { "execute": "never" From ed3e3e9e477f165cf6609e2ac02556cfb3e7fd17 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 24 Sep 2024 09:56:19 +0200 Subject: [PATCH 04/27] - add result plots to RL tutorial --- .../04_reinforcement_learning_example.ipynb | 923 ++++++++++++++++-- 1 file changed, 826 insertions(+), 97 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index b66a3b41..224bc5ba 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -133,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -144,7 +144,15 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR: Invalid requirement: \"'assume-framework[learning]'\"\n" + ] + } + ], "source": [ "!pip install 'assume-framework[learning]'" ] @@ -169,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -180,7 +188,15 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Cloning into 'assume-repo'...\n" + ] + } + ], "source": [ "!git clone https://github.com/assume-framework/assume.git assume-repo" ] @@ -198,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -209,7 +225,15 @@ "languageId": "shellscript" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "^C\n" + ] + } + ], "source": [ "!cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" ] @@ -225,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "xUsbeZdPJ_2Q" }, @@ -273,7 +297,6 @@ "import numpy as np\n", "import pandas as pd\n", "import torch as th\n", - "import yaml\n", "\n", "from assume import World\n", "from assume.common.base import LearningStrategy, SupportsMinMax\n", @@ -285,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "id": "UXYSesx4Ifp5" }, @@ -305,11 +328,6 @@ " self.max_bid_price = kwargs.get(\"max_bid_price\", 100)\n", " self.max_demand = kwargs.get(\"max_demand\", 10e3)\n", "\n", - " # defines the observation space\n", - " self.obs_dim = 50\n", - " # defines the action space\n", - " self.act_dim = 2\n", - "\n", " # tells us whether we are training the agents or just executing per-learnind stategies\n", " self.learning_mode = kwargs.get(\"learning_mode\", False)\n", " self.perform_evaluation = kwargs.get(\"perform_evaluation\", False)\n", @@ -382,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "id": "iApbQsg5x_u2" }, @@ -437,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "id": "_4cJ8Y8uvMgV" }, @@ -498,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "id": "0ww-L9fABnw3" }, @@ -531,17 +549,17 @@ " # 1.1 Get the Observations, which are the basis of the action decision\n", " # =============================================================================\n", " # residual load forecast\n", - " scaling_factor_res_load = None # TODO\n", + " # residual load forecast\n", + " scaling_factor_res_load = self.max_demand\n", "\n", " # price forecast\n", - " scaling_factor_price = None # TODO\n", + " scaling_factor_price = self.max_bid_price\n", "\n", - " # total capacity and marginal cost\n", - " scaling_factor_total_capacity = None # TODO\n", + " # total capacity\n", + " scaling_factor_total_capacity = unit.max_power\n", "\n", " # marginal cost\n", - " # Obs[2*foresight+1:2*foresight+2]\n", - " scaling_factor_marginal_cost = None # TODO\n", + " scaling_factor_marginal_cost = self.max_bid_price\n", "\n", " # checks if we are at end of simulation horizon, since we need to change the forecast then\n", " # for residual load and price forecast and scale them\n", @@ -638,7 +656,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -647,7 +665,18 @@ "id": "PYoI3ncSKJSX", "outputId": "4b4341d7-5a21-49c4-ee25-b8c55f693cd1" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n#scaling factors for all observations\\n#residual load forecast\\nscaling_factor_res_load = self.max_demand\\n\\n# price forecast\\nscaling_factor_price = self.max_bid_price\\n\\n# total capacity\\nscaling_factor_total_capacity = unit.max_power\\n\\n# marginal cost\\nscaling_factor_marginal_cost = self.max_bid_price\\n'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "#scaling factors for all observations\n", @@ -699,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "id": "8ehlm5Z9CbRw" }, @@ -735,7 +764,7 @@ " # 2.1 Get Actions and handle exploration\n", " # =============================================================================\n", " # ==> YOUR CODE HERE\n", - " base_bid = None # TODO\n", + " base_bid = next_observation[-1] # = marginal_costs\n", "\n", " # add niose to the last dimension of the observation\n", " # needs to be adjusted if observation space is changed, because only makes sense\n", @@ -776,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -785,7 +814,18 @@ "id": "rfXJBGOKxbk7", "outputId": "06f76c52-e215-4998-8f61-f7492b880e4d" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n#base_bid = marginal costs\\nbase_bid = next_observation[-1] # = marginal_costs\\n'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "#base_bid = marginal costs\n", @@ -812,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "id": "Y81HzlkjNHJ0" }, @@ -871,15 +911,17 @@ " # we can use our domain knowledge to guide the bid formulation\n", " bid_prices = actions * self.max_bid_price\n", "\n", - " # 3.1 formulate the bids for Pmin\n", - " # Pmin, the minium run capacity is the inflexible part of the bid, which should always be accepted\n", - " bid_quantity_inflex = None # TODO\n", - " bid_price_inflex = None # TODO\n", + " # calculate actual bids\n", + " # rescale actions to actual prices\n", + " bid_prices = actions * self.max_bid_price\n", + "\n", + " # calculate inflexible part of the bid\n", + " bid_quantity_inflex = min_power\n", + " bid_price_inflex = min(bid_prices)\n", "\n", - " # 3.1 formulate the bids for Pmax - Pmin\n", - " # Pmin, the minium run capacity is the inflexible part of the bid, which should always be accepted\n", - " bid_quantity_flex = None # TODO\n", - " bid_price_flex = None # TODO\n", + " # calculate flexible part of the bid\n", + " bid_quantity_flex = max_power - bid_quantity_inflex\n", + " bid_price_flex = max(bid_prices)\n", "\n", " # actually formulate bids in orderbook format\n", " bids = [\n", @@ -927,7 +969,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -936,7 +978,18 @@ "id": "wB7X-pFkCje3", "outputId": "ff905a9d-e3f2-4487-9e8a-9dbf4e855ab7" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n#calculate actual bids\\n#rescale actions to actual prices\\nbid_prices = actions * self.max_bid_price\\n\\n#calculate inflexible part of the bid\\nbid_quantity_inflex = min_power\\nbid_price_inflex = min(bid_prices)\\n\\n#calculate flexible part of the bid\\nbid_quantity_flex = max_power - bid_quantity_inflex\\nbid_price_flex = max(bid_prices)\\n'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "#calculate actual bids\n", @@ -1002,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "id": "U9HX41mODuBU" }, @@ -1099,9 +1152,9 @@ " # in the learning process, so we add a regret term to the reward, which is the opportunity cost\n", " # define the reward and scale it\n", "\n", - " scaling = None # TODO\n", - " regret_scale = None # TODO\n", - " reward = None # TODO\n", + " scaling = 0.1 / unit.max_power\n", + " regret_scale = 0.2\n", + " reward = float(profit - regret_scale * opportunity_cost) * scaling\n", "\n", " # store results in unit outputs which are written to database by unit operator\n", " unit.outputs[\"profit\"].loc[start:end_excl] += profit\n", @@ -1140,7 +1193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1149,7 +1202,18 @@ "id": "e1XdVXPSCo_k", "outputId": "585d94a5-7475-4e96-d0a1-5e82b711c6a5" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nscaling = 0.1 / unit.max_power\\nregret_scale = 0.2\\nreward = float(profit - regret_scale * opportunity_cost) * scaling\\n'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "scaling = 0.1 / unit.max_power\n", @@ -1172,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "id": "ZwVtpK3B5gR6" }, @@ -1227,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": { "id": "moZ_UD7FfkOh" }, @@ -1235,12 +1299,12 @@ "source": [ "learning_config = {\n", " \"continue_learning\": False,\n", - " \"trained_policies_save_path\": \"None\",\n", + " \"trained_policies_save_path\": \"null\",\n", " \"max_bid_price\": 100,\n", " \"algorithm\": \"matd3\",\n", " \"learning_rate\": 0.001,\n", - " \"training_episodes\": 100,\n", - " \"episodes_collecting_initial_experience\": 5,\n", + " \"training_episodes\": 10,\n", + " \"episodes_collecting_initial_experience\": 3,\n", " \"train_freq\": \"24h\",\n", " \"gradient_steps\": -1,\n", " \"batch_size\": 256,\n", @@ -1253,27 +1317,6 @@ "}" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iPz8v4N5hpfr" - }, - "outputs": [], - "source": [ - "# Read the YAML file\n", - "with open(f\"{inputs_path}/example_02a/config.yaml\") as file:\n", - " data = yaml.safe_load(file)\n", - "\n", - "# store our modifications to the config file\n", - "data[\"base\"][\"learning_mode\"] = True\n", - "data[\"base\"][\"learning_config\"] = learning_config\n", - "\n", - "# Write the modified data back to the file\n", - "with open(f\"{inputs_path}/example_02a/config.yaml\", \"w\") as file:\n", - " yaml.safe_dump(data, file)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -1290,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1301,14 +1344,44 @@ }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'logging' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m log \u001b[38;5;241m=\u001b[39m \u001b[43mlogging\u001b[49m\u001b[38;5;241m.\u001b[39mgetLogger(\u001b[38;5;18m__name__\u001b[39m)\n\u001b[0;32m 3\u001b[0m csv_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 4\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocal_db\u001b[39m\u001b[38;5;124m\"\u001b[39m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'logging' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_02a/base from ../inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "example_02a_base_1 2019-03-31 00:00:00: : 2592001.0it [00:29, 88492.68it/s]\n", + "example_02a_base_2 2019-03-31 00:00:00: : 2592001.0it [00:31, 83508.91it/s] \n", + "example_02a_base_3 2019-03-31 00:00:00: : 2592001.0it [00:30, 83653.36it/s] \n", + "example_02a_base_4 2019-03-31 00:00:00: : 2592001.0it [00:30, 84041.85it/s] \n", + "example_02a_base_5 2019-03-31 00:00:00: : 2592001.0it [00:30, 85265.01it/s] \n", + "Training Episodes: 100%|██████████| 5/5 [02:32<00:00, 30.56s/it]\n", + "example_02a_base 2019-03-31 00:00:00: : 2592001.0it [00:35, 73343.42it/s] \n" ] } ], @@ -1364,9 +1437,261 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Episodes: 0%| | 0/5 [00:00 wait_for=>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", + " future.result()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", + " result = coro.throw(exc)\n", + " ^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot reuse already awaited coroutine\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", + " raise KeyError(key) from err\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", + " raise KeyError(key) from err\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "Training Episodes: 20%|██ | 1/5 [00:32<02:11, 32.77s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:mango.agent.core:Agent eom_de: Caught the following exception in _check_inbox: \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Episodes: 20%|██ | 1/5 [00:32<02:11, 32.81s/it]\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mKeyError\u001b[0m: 'energy_cashflow'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4485\u001b[0m, in \u001b[0;36mDataFrame._set_item_mgr\u001b[1;34m(self, key, value, refs)\u001b[0m\n\u001b[0;32m 4484\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 4485\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4486\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 4487\u001b[0m \u001b[38;5;66;03m# This item wasn't present, just insert at end\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n", + "\u001b[1;31mKeyError\u001b[0m: 'energy_cashflow'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[34], line 32\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 32\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equals a test run\u001b[39;00m\n\u001b[0;32m 40\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:959\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 953\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m episode \u001b[38;5;129;01min\u001b[39;00m tqdm(\n\u001b[0;32m 954\u001b[0m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mtraining_episodes \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m),\n\u001b[0;32m 955\u001b[0m desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining Episodes\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 956\u001b[0m ):\n\u001b[0;32m 957\u001b[0m \u001b[38;5;66;03m# TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\u001b[39;00m\n\u001b[0;32m 958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m episode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 959\u001b[0m \u001b[43msetup_world\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mepisode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepisode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 968\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:706\u001b[0m, in \u001b[0;36msetup_world\u001b[1;34m(world, scenario_data, study_case, perform_evaluation, terminate_learning, episode, eval_episode)\u001b[0m\n\u001b[0;32m 697\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msetup_world\u001b[39m(\n\u001b[0;32m 698\u001b[0m world: World,\n\u001b[0;32m 699\u001b[0m scenario_data: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mobject\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 704\u001b[0m eval_episode: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m 705\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 707\u001b[0m \u001b[43m \u001b[49m\u001b[43masync_setup_world\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 708\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 709\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 710\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 711\u001b[0m \u001b[43m \u001b[49m\u001b[43mperform_evaluation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mperform_evaluation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 712\u001b[0m \u001b[43m \u001b[49m\u001b[43mterminate_learning\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mterminate_learning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 713\u001b[0m \u001b[43m \u001b[49m\u001b[43mepisode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepisode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 714\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_episode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_episode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:92\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 90\u001b[0m f\u001b[38;5;241m.\u001b[39m_log_destroy_pending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopping:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:133\u001b[0m, in \u001b[0;36m_patch_loop.._run_once\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 130\u001b[0m curr_task \u001b[38;5;241m=\u001b[39m curr_tasks\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 133\u001b[0m \u001b[43mhandle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# restore the current task\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m curr_task \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py:84\u001b[0m, in \u001b[0;36mHandle._run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m):\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py:434\u001b[0m, in \u001b[0;36mAgent.raise_exceptions\u001b[1;34m(self, fut)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fut\u001b[38;5;241m.\u001b[39mexception() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 431\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\n\u001b[0;32m 432\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAgent \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: Caught the following exception in _check_inbox: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfut\u001b[38;5;241m.\u001b[39mexception()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 433\u001b[0m )\n\u001b[1;32m--> 434\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m fut\u001b[38;5;241m.\u001b[39mexception()\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:92\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 90\u001b[0m f\u001b[38;5;241m.\u001b[39m_log_destroy_pending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopping:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:133\u001b[0m, in \u001b[0;36m_patch_loop.._run_once\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 130\u001b[0m curr_task \u001b[38;5;241m=\u001b[39m curr_tasks\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 133\u001b[0m \u001b[43mhandle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# restore the current task\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m curr_task \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py:84\u001b[0m, in \u001b[0;36mHandle._run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m):\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:360\u001b[0m, in \u001b[0;36mTask.__wakeup\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__step(exc)\n\u001b[0;32m 353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 354\u001b[0m \u001b[38;5;66;03m# Don't pass the value of `future.result()` explicitly,\u001b[39;00m\n\u001b[0;32m 355\u001b[0m \u001b[38;5;66;03m# as `Future.__iter__` and `Future.__await__` don't need it.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 358\u001b[0m \u001b[38;5;66;03m# instead of `__next__()`, which is slower for futures\u001b[39;00m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;66;03m# that return non-generator iterators from their `__iter__`.\u001b[39;00m\n\u001b[1;32m--> 360\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__step\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 361\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py:450\u001b[0m, in \u001b[0;36mAgent._check_inbox\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 447\u001b[0m priority, content, meta \u001b[38;5;241m=\u001b[39m message\n\u001b[0;32m 448\u001b[0m meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpriority\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m priority\n\u001b[1;32m--> 450\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 452\u001b[0m \u001b[38;5;66;03m# signal to the Queue that the message is handled\u001b[39;00m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minbox\u001b[38;5;241m.\u001b[39mtask_done()\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:525\u001b[0m, in \u001b[0;36mRoleAgent.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 524\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhandle_message\u001b[39m(\u001b[38;5;28mself\u001b[39m, content, meta: Dict[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[1;32m--> 525\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_role_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:401\u001b[0m, in \u001b[0;36mRoleContext.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 392\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhandle_message\u001b[39m(\u001b[38;5;28mself\u001b[39m, content, meta: Dict[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[0;32m 393\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Handle an incoming message, delegating it to all applicable subscribers\u001b[39;00m\n\u001b[0;32m 394\u001b[0m \u001b[38;5;124;03m for role, message_condition, method, _ in self._message_subs:\u001b[39;00m\n\u001b[0;32m 395\u001b[0m \u001b[38;5;124;03m if self._is_role_active(role) and message_condition(content, meta):\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[38;5;124;03m :param meta: meta\u001b[39;00m\n\u001b[0;32m 400\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 401\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_role_handler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:239\u001b[0m, in \u001b[0;36mRoleHandler.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 237\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m role, message_condition, method, _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_message_subs:\n\u001b[0;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_role_active(role) \u001b[38;5;129;01mand\u001b[39;00m message_condition(content, meta):\n\u001b[1;32m--> 239\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:211\u001b[0m, in \u001b[0;36mUnitsOperator.handle_market_feedback\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[marketconfig\u001b[38;5;241m.\u001b[39mproduct_type]\u001b[38;5;241m.\u001b[39mextend(orderbook)\n\u001b[0;32m 210\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_unit_dispatch(orderbook, marketconfig)\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_actual_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarketconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproduct_type\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:348\u001b[0m, in \u001b[0;36mUnitsOperator.write_actual_dispatch\u001b[1;34m(self, product_type)\u001b[0m\n\u001b[0;32m 345\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 346\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_sent_dispatch[product_type] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mcurrent_timestamp\n\u001b[1;32m--> 348\u001b[0m market_dispatch, unit_dispatch_dfs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_actual_dispatch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mproduct_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlast\u001b[49m\n\u001b[0;32m 350\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 352\u001b[0m now \u001b[38;5;241m=\u001b[39m timestamp2datetime(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mcurrent_timestamp)\n\u001b[0;32m 353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[product_type] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[0;32m 354\u001b[0m \u001b[38;5;28mfilter\u001b[39m(\n\u001b[0;32m 355\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m now,\n\u001b[0;32m 356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[product_type],\n\u001b[0;32m 357\u001b[0m )\n\u001b[0;32m 358\u001b[0m )\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:327\u001b[0m, in \u001b[0;36mUnitsOperator.get_actual_dispatch\u001b[1;34m(self, product_type, last)\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m valid_outputs:\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m key:\n\u001b[1;32m--> 327\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m unit\u001b[38;5;241m.\u001b[39moutputs[key][start:end]\n\u001b[0;32m 329\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m unit_id\n\u001b[0;32m 330\u001b[0m unit_dispatch_dfs\u001b[38;5;241m.\u001b[39mappend(data)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4311\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4308\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m 4309\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 4310\u001b[0m \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[1;32m-> 4311\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4538\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4535\u001b[0m value \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtile(value, (\u001b[38;5;28mlen\u001b[39m(existing_piece\u001b[38;5;241m.\u001b[39mcolumns), \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m 4536\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 4538\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4488\u001b[0m, in \u001b[0;36mDataFrame._set_item_mgr\u001b[1;34m(self, key, value, refs)\u001b[0m\n\u001b[0;32m 4485\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m 4486\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 4487\u001b[0m \u001b[38;5;66;03m# This item wasn't present, just insert at end\u001b[39;00m\n\u001b[1;32m-> 4488\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4489\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 4490\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iset_item_mgr(loc, value, refs\u001b[38;5;241m=\u001b[39mrefs)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1385\u001b[0m, in \u001b[0;36mBlockManager.insert\u001b[1;34m(self, loc, item, value, refs)\u001b[0m\n\u001b[0;32m 1383\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1384\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_insert_update_mgr_locs(loc)\n\u001b[1;32m-> 1385\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_insert_update_blklocs_and_blknos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m new_axis\n\u001b[0;32m 1388\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (block,)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1422\u001b[0m, in \u001b[0;36mBlockManager._insert_update_blklocs_and_blknos\u001b[1;34m(self, loc)\u001b[0m\n\u001b[0;32m 1419\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loc \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblklocs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m 1420\u001b[0m \u001b[38;5;66;03m# np.append is a lot faster, let's use it if we can.\u001b[39;00m\n\u001b[0;32m 1421\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m-> 1422\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blknos \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_blknos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblocks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1423\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m loc \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1424\u001b[0m \u001b[38;5;66;03m# np.append is a lot faster, let's use it if we can.\u001b[39;00m\n\u001b[0;32m 1425\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m0\u001b[39m)[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\numpy\\lib\\function_base.py:5618\u001b[0m, in \u001b[0;36mappend\u001b[1;34m(arr, values, axis)\u001b[0m\n\u001b[0;32m 5616\u001b[0m values \u001b[38;5;241m=\u001b[39m ravel(values)\n\u001b[0;32m 5617\u001b[0m axis \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mndim\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 5618\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", + "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", + "\u001b[1;31mClick here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "log = logging.getLogger(__name__)\n", "\n", @@ -1419,9 +1744,112 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario example_02c/base from ../inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Training Episodes: 1%| | 1/100 [00:22<36:39, 22.22s/it]\n", + "Training Episodes: 1%| | 1/100 [00:22<37:32, 22.75s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for=>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pygments\\formatters\\terminal256.py:180: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", + " self.xterm_colors.append((r, g, b))\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pygments\\formatters\\terminal256.py:180: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", + " self.xterm_colors.append((r, g, b))\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Time must be > 1552158000.0 but is 1551398399.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[36], line 32\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 32\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equals a test run\u001b[39;00m\n\u001b[0;32m 40\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:970\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 968\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 970\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 972\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 973\u001b[0m \u001b[38;5;66;03m# Store updated information across episodes\u001b[39;00m\n\u001b[0;32m 974\u001b[0m inter_episodic_data \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mget_inter_episodic_data()\n", + "File 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688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m 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"name": "stderr", + "output_type": "stream", + "text": [] + } + ], "source": [ "log = logging.getLogger(__name__)\n", "\n", @@ -1474,9 +1902,123 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting matplotlib\n", + " Downloading matplotlib-3.9.2-cp311-cp311-win_amd64.whl.metadata (11 kB)\n", + "Collecting contourpy>=1.0.1 (from matplotlib)\n", + " Downloading contourpy-1.3.0-cp311-cp311-win_amd64.whl.metadata (5.4 kB)\n", + "Collecting cycler>=0.10 (from matplotlib)\n", + " Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n", + "Collecting fonttools>=4.22.0 (from matplotlib)\n", + " Downloading fonttools-4.54.0-cp311-cp311-win_amd64.whl.metadata (166 kB)\n", + " ---------------------------------------- 0.0/166.9 kB ? eta -:--:--\n", + " -- ------------------------------------- 10.2/166.9 kB ? eta -:--:--\n", + " 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identsimulationtotal_costtotal_volumeaverage_cost
0EOMexample_02a_base1.444376e+084.263112e+0933.33
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" + ], + "text/plain": [ + " ident simulation total_cost total_volume average_cost\n", + "0 EOM example_02a_base 1.444376e+08 4.263112e+09 33.33" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from functools import partial\n", @@ -1486,6 +2028,8 @@ "\n", "os.makedirs(\"outputs\", exist_ok=True)\n", "\n", + "db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "\n", "engine = create_engine(db_uri)\n", "\n", "\n", @@ -1496,19 +2040,43 @@ "sum(round(CAST(value AS numeric), 2)) FILTER (WHERE variable = 'avg_price') as average_cost\n", "FROM kpis\n", "where variable in ('total_cost', 'total_volume', 'avg_price')\n", - "and simulation in ('example_02a', 'example_02b', 'example_02c')\n", + "and simulation in ('example_02a_base', 'example_02b_base', 'example_02c_base')\n", "group by simulation, ident ORDER BY simulation\n", "\"\"\"\n", + "\n", + "\n", "kpis = pd.read_sql(sql, engine)\n", "\n", + "kpis" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# sort the dataframe to have sho, bo and lo case in the right order\n", "\n", "# sort kpis in the order sho, bo, lo\n", + "\n", "kpis = kpis.sort_values(\n", " by=\"simulation\",\n", - " key=lambda x: x.map({\"example_02a\": 1, \"example_02b\": 2, \"example_02c\": 3}),\n", + " # key=lambda x: x.map({\"example_02a\": 1, \"example_02b\": 2, \"example_02c\": 3}),\n", ")\n", "\n", + "\n", "kpis[\"total_volume\"] /= 1e9\n", "kpis[\"total_cost\"] /= 1e6\n", "savefig = partial(plt.savefig, transparent=False, bbox_inches=\"tight\")\n", @@ -1543,25 +2111,155 @@ "\n", "plt.title(\"Total Volume and Total Cost for each Simulation\")\n", "\n", - "savefig(\"outputs/tutorial_6_overview.png\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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timePricesimulationnode
02019-03-01 01:00:00.00000031.146341example_02a_baseNone
12019-03-01 02:00:00.00000031.146341example_02a_baseNone
22019-03-01 03:00:00.00000031.146341example_02a_baseNone
32019-03-01 04:00:00.00000031.146341example_02a_baseNone
42019-03-01 05:00:00.00000031.146341example_02a_baseNone
...............
7152019-03-30 20:00:00.00000031.146341example_02a_baseNone
7162019-03-30 21:00:00.00000031.146341example_02a_baseNone
7172019-03-30 22:00:00.00000031.146341example_02a_baseNone
7182019-03-30 23:00:00.00000012.500000example_02a_baseNone
7192019-03-31 00:00:00.00000012.500000example_02a_baseNone
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720 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " time Price simulation node\n", + "0 2019-03-01 01:00:00.000000 31.146341 example_02a_base None\n", + "1 2019-03-01 02:00:00.000000 31.146341 example_02a_base None\n", + "2 2019-03-01 03:00:00.000000 31.146341 example_02a_base None\n", + "3 2019-03-01 04:00:00.000000 31.146341 example_02a_base None\n", + "4 2019-03-01 05:00:00.000000 31.146341 example_02a_base None\n", + ".. ... ... ... ...\n", + "715 2019-03-30 20:00:00.000000 31.146341 example_02a_base None\n", + "716 2019-03-30 21:00:00.000000 31.146341 example_02a_base None\n", + "717 2019-03-30 22:00:00.000000 31.146341 example_02a_base None\n", + "718 2019-03-30 23:00:00.000000 12.500000 example_02a_base None\n", + "719 2019-03-31 00:00:00.000000 12.500000 example_02a_base None\n", + "\n", + "[720 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import seaborn as sns\n", - "\n", "sql = \"\"\"\n", "SELECT\n", " product_start AS \"time\",\n", " price AS \"Price\",\n", + " simulation AS \"simulation\",\n", " node\n", "FROM market_meta\n", - "WHERE simulation in ('example_02a', 'example_02b', 'example_02c') AND market_id in ('EOM') \n", + "WHERE simulation in ('example_02a_base', 'example_02b_base', 'example_02c_base') AND market_id in ('EOM') \n", "GROUP BY market_id, simulation, product_start, price, node\n", "ORDER BY product_start, node\n", "\n", @@ -1569,18 +2267,49 @@ "\n", "df = pd.read_sql(sql, engine)\n", "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7AwA+/fRTnH/++bj77rtx6KGHxt4P03qRlccffxzXXXcdZsyYgSFDhmCTTTap+v1pLFiwILYeua6Ljo4O/7P+/fsHjlHvbVtbG37zm9/g1ltvxVNPPYUHHngAra2tOPjgg3HhhRcmGqwJIYQQ29CARAghZKWmra0NY8eOzeVaAwcODCSk9njhhRew5pprYuDAgRgwYAB+9atfRZ6/zjrrRH4+ePBgzJo1q+pz77OhQ4cCKIfHjBgxAk899RRGjBiBlpYWf9ertrY2OI6DY489FgcccEDVtcKT3Hoi630LM2TIEACIDCmbNWuWfx/zYvLkyZg1a1YgNNCUgQMHYty4cYHQOpUkA8PAgQOx00474bjjjqv6LipUshZ6enqw995745BDDsEZZ5wR+G6dddbBhRdeiEMOOQSTJ0+uyaAWV7ZKkufUK6+8gnPPPRdHHXUUTjjhBIwaNQoAcPXVV0fmqopj8ODBsfUIKLfJL7/8Uutao0eP9pOWv/nmm/j973+P++67D2uvvXZkrjVCCCGkKBjCRgghhOTEtttuizfeeCNgRJozZw6++93v4plnnsG4ceOwZMkSuK6LsWPH+n8ffPABbr755kAuJpXtttsOr732GqZNmxb4/PHHH8eIESN8A0pjYyO+/vWv4+9//zuefvpp7LXXXr4HR3t7OzbddFN89NFHgbI32GAD3HTTTXjxxReF7krtZL1vYb7yla9gxIgReOKJJwKff/7553j99ddjPZyycuONN6K1tRXf+MY3Ml9j3Lhx+Pjjj/GVr3wl8Nt///vf46GHHvJzWcWdO3nyZGyyySb+eWPGjME999yDv/zlL0ZyNDQkDxkbGxsxcuRIPPzww4Fd0jy8pPR5G4/a29sxc+bMwGdJOZJee+01lEolnH766b7xqKenxw/pK5VKANJ/73bbbYe///3vAU+jnp4ePPnkkxg7dqy259DTTz+NHXbYAbNmzUJjYyO22morXHzxxRg0aJC/AyQhhBBSL9ADiRBCCMmJY489Fo899hi++93v4qSTTkJzczNuvfVWrLrqqvj617+OgQMHYrvttsMpp5yCU045Beuttx7efPNN3Hjjjdh1112rwoo8jjvuODz++OM49thjcdppp2HIkCF47LHH8O9//xtXXHFFYLJ78MEH46677kJDQ0NVqNpZZ52FE088EWeffTYOOuggf6exN954A6eccorovamF8ePHZ7pvYRoaGnDWWWdhwoQJ/j2YN28eJk6ciMGDB0d66ujwxRdf+Nuud3d3Y+bMmXj00Ufx3HPP4dJLL60pl82xxx6L3//+9zj22GNx/PHHY+jQofjjH/+I3/3ud5gwYULiuaeccgqOPPJInHTSSfjWt76FlpYWPPDAA/jrX/9qvJPZoEGD8M477+Cll17C5ptvHhnydeGFF+Koo47CoYceiqOPPhqbbLIJSqUSXn75Zdxzzz048sgjsf766xuVm8buu++OJ598EltssQXWWWcdPPLII4GdCMN4ubYuvfRSHHbYYViwYAF+85vf4L333gNQ9l5qb2/HoEGDMHv2bDzzzDOR4ZGnnXYann32WRx99NE48cQT0dzcjHvvvReff/457rjjDm35t956a5RKJZx66qk48cQT0dbWhqeeegqLFi3yvQcJIYSQeoEGJEIIISQnVlttNfz2t7/FNddcg/POOw/9+vXD9ttvj+uvvx6DBw8GANx+++244YYbcNttt2HOnDkYNWoUjjvuOH8HrChGjBiB++67D9deey1+/OMfo6urCxtvvDFuueUWfPWrXw0cu/HGG2PDDTfEvHnzsOOOOwa+22WXXXDnnXdi4sSJOOOMM9Dc3IzNNtsMd999d+LuWkXT0NCQ6b5Fceihh6KtrQ233XYbTj31VLS3t2PXXXfFWWedFUh+bsJDDz3k77rV0NCAIUOGYIsttsDdd99d9QxMGTVqFO6//35ce+21uPjii7F8+XKsu+66uPzyy3H44YcnnrvxxhvjN7/5Da6//nr88Ic/hOu62HDDDXHzzTdX1Zs0jj/+eFxxxRU44YQTcPfdd/s7mKmMGTMGjz32GG677Tbce++9vlfN+uuvj/PPPz9V3ixMmDAB3d3d+MlPfoKmpibsv//+OPvss3HhhRdGHr/99tvjRz/6Ee6++248/fTTGD58OLbffntMnDgRp556KiZNmoTx48fj0EMPxTPPPINTTz0VZ5xxhr/7n8cGG2yA3/72t7juuuswYcIEOI6DzTffHL/61a8i700cI0eOxB133IEbbrgBF1xwAZYuXep7Be6www413RtCCCEkbxw3a4ZEQgghhBBCCCGEELJSwBxIhBBCCCGEEEIIISQRGpAIIYQQQgghhBBCSCI0IBFCCCGEEEIIIYSQRGhAIoQQQgghhBBCCCGJ0IBECCGEEEIIIYQQQhKhAYkQQgghhBBCCCGEJEIDEiGEEEIIIYQQQghJpKloASSZNWtR0SJkpqHBwbBhbZg7dzFKJbdocUiBsC4QFdYHosL6QFRYH4gH6wJRYX0gKqwP9UFez2HEiIE5SpUOPZDqlIYGB47joKHBKVoUUjCsC0SF9YGosD4QFdYH4sG6QFRYH4gK60N90FefAw1IhBBCCCGEEEIIISQRGpAIIYQQQgghhBBCSCI0IBFCCCGEEEIIIYSQRGhAIoQQQgghhBBCCCGJ0IBECCGEEEIIIYQQQhKhAYkQQgghhBBCCCGEJEIDEiGEEEIIIYQQQghJhAYkQgghhBBCCCGEEJIIDUiEEEIIIYQQQgghJBEakAghhBBCCCGEEEJIIjQgEUIIIYQQQgghhJBEaEAihBBCCCGEEEIIIYnQgEQIIYQQQgghhBBCEqEBiRBCCCGEEEIIIYQkQgMSIYQQQgghhBBCCEmEBiRCCCGEEEIIIYQQkggNSIQQQgghhBBCCCEkERqQCCGEEEIIIYQQQkgiNCARQgghhBBCCCGEkERoQCKEEEIIIYQQQgghidCARAghhBBCCCGEEEISaSpaAGLO8q4eXPWbV/HZF4tErr/F+sNx+mFj4TiO1vHvfToPt/7+LXQs6Uo9dtigVvzPNzfH3X98F5/MCMq/xog2TPjONujfUnu1nDxtASY+8h8sWtyJdVYdiAnf2RrNTY1Vx/3u75Pxl5c/h+M4+MZuX8HXtl/HqJypX3bg+gffwPxFy2uWOY51Vh2Ia/5nt8jvHvrHFPzppc9QKrli5QPABmsNwf9+a0s0NkTbnN+cMge/+MPbWLKsW0yGtHr54jsz8as/vYdly3tkBHCAncasihMO2LTqq5Lr4roHXse7n8yTKRtA+4BmfP/gMRiz3ipax7uui5se/g/emDy7pnL7NTfiW3ttgN22WL2m63jc+thbmDqrA+d+e2sMGtAv8phps8rtat7C5VhteBsmfGdrtLU2G5Vzz1Pv4Z9vTgeEmka/fo04ep+NsOOYVau+mzFnMa574HXMXSjXLwBA/5YmnHTwZthz+7aq7z6avhA3PfwmFi7uFJWhfUAzTjlkDDZae2jVd29/Mhe3/f5tLF6arhtqYXB7P/zP4VtgnVUHVn33yntf4u6n3sOy5XJ9EwCsMrgV5xy5JUYOHaB1/PLOHlx57yR8/mVH5jLXXW0gzvt2tG4z5Z1P5uLOJ9/Fftuvjb23XSv2uF/96X08+/p0uG65YW28zlCcfeSWaNAcL0yZvgATH/6PSL1samrAYbuNxj7j1o495t4/v49/vFaRX4qx662C/zl880h9dfvjb+PFd2eK9U0AAAfYcbNVMeG47au+cl0X1z/4Bt7+aK5Y8W39m3HywZth03WHJR739Iuf4dF/foTu7lLuMgxq74czDtscX1ltkNF5f5s0FQ/+fTK6ImTacoPhOO3Q4DhE7e9XXWUAzvv21hgYo9sk8cYhi5d247zvbI2WZrN+4ee/fwsvv/elSL1cfUQbJnx7GwxojR7ff/rFItzw0BtY0CGnr76y+iCc+99bo7kp3neiu6eEn/z2VXw0baGIDJuuOxRnHpHcXy5d3o0r752EabMWi8iw7cYj8f1DxiQeM79jOX7ym1fx5byluZY9oLUJJx60GcaO1hvHRnH/3z7E3yZNRUODg0N3G419E/p7oDwOuevJd/G17dfGXgm6jZhBD6Q+yEfTFuDTLxbBBUT+Xp8822ji8/J7X2LRki6ta89ZuAyP/fNjfDyjWv6psxbj/c/nZ7wrQSa9/yUWLu6EC+CTLxbho+nRyuAfr01DT8lFd08Jz74+3bic1z6chXmLlos9C0/+92MME8+8XpZfsnwXwAefz09UZi++MxOLl3WLyvD65NmYl2Coe+HtL7B0eY+cDC7w/H++wPKuagPVzLlL8M4n80R//6IlXeXBnSZzFy7H65Nn11zu8q4ePPfmDO1yk+hYWv4NM+YswTufxE9evD7IBTB99mK892l0/Y+ju6eEZ9+YDteVex7LO3vw3H+i78vrk2djzkLZfsEFsGR5N/711heRMkx6/0ss6O0DpevlSzH18qV3ZqJjqZ5uqOVvfkcnXv1gVqQM5X5Btm9yAcxesAxvTJkTKUMUH06bj8++7KipzI9nLMLHM/JZSHrp3ZmYt2g5/vlGfFsvuS6eeW0aSm5F57z76TzMmLNEu5xJ788Sq5dd3SU8m9BXua6Lv4fkl/p7c8oczF6wrEqGxcu68O93Zor2TS4A1wX+9dYXWLKs2ng7a/5SvPXRXNHyO5Z24aV3Z8Y+C49/vjkdXd0lERkWdHTilff1dabHc2/OQGeMTK99WD0OeWPyHL+/nzFnCd411Fd54Y1DPp25CJOnLTA6t/y8vhSrl9NmLcb7n8ffl0kffIn5HbL66qPpC/HJF8mGoU+/WIQp0xaKyfD2J/Mwc25yf/nB5/MxddZiMRnKc7ZkQ93bH8/FzHlLcy978bJuvPhOer+QxD965z1d3eVxXhovvdOr23Iax5Iy9EDqg3Qr3iYH7LhObisd02cv9htjT0l/Nai7p3zs4LZ++NoO68Qe97v/m4yS6wYm4F/bYW00Nzbg8ec/KZfb48acbUZ36DrdMR466nE9Gbx4vPP7NTXg0PHrGZ+fxKIlnXjyhU/L5cQ8D6/8zddbJXWlLwuzFyzFX1+ZCiD5/nj1ZZVBrdh7u3wt/NNmdfgdf5IMXj1cfXhbbt4yHh/PWOgrvShvL7Ue7bn1GtpeCLo89e9PsWBxp1m7VI7dbYvVsfrwai+VNF54+wt8+sUio3ITZeqpXCeprYfbr2nbVJ/RdhuPxHprDDY6P43n3pyOqbMWx8rlyd/U6ODw3dfPtWyPv036HLPmL4t9Np4Mba1N+PrOXxGRwa+XMc/Sk2HowJbUVcKsPPbPj7Cssyf2WXifjxo2AHtstYaIDA/834dwXTP9pdbxA3daB+399fX4go7leOrFzwAAPT15tc2yPEltvdS7WAEAa49qx2czO4xl8PqAvOvlpPe/xIdTFyTK4rrlPwDYZsMR2GCtIbmV7zFjzmI887o3jqquD2od2XGzVSO95mrl0y8W4oW3y/oq3JeGP9t9qzWw6rB89dWfXvoM8xYt12oPXn3YcM3B2HqjkbnJ8PhzH2PJ8u5MY0pPd45efRDGbTIKQPI4pDtU5/Iax5oSGM8ayqD+pp3GrIq1R+VTL5cu78bvn/s4VSZP9v4tjTh4l9G5lO0xv2M5nvb7y+T7oj7L/catjSEDW3KRYea8Jfj7q9PKMqSMZ9TneNDO62KAofd1HJ/PXITnexec0mWo3Idv7r4eGhtr9zf5y8ufYc7C5ZnmWio9hvO2im4rpl2uqNCA1AdRG8Eum6+GUTlNVv/z0RzFgKTf0LzJ2uC2ftgnwXjwyDNT0NntBlyVdxm7Glr7NfkGpFJObuXhSX5ciJf6eZbOxZO3pV9j4m/PwpfzlvgGpLTJ0YZrDcm9fKC8YqNnQCp/N2xQS+5yvDlltj9wSwrV874bNbR/7jL8660ZvgEp6j6oco3bZBQ2zHli8vx/ZvQakMzbJQBsu9EIjMngMvzZzEW9BqT822XSswyXZ1q+evyYrwzDrjkbFCdPW4Cpsxan9ivNTfn3Cx6vfzir14CULENba7OYDM+9Wa6Xsfeht38c0p5/v+Dxp5c+w7LOnlgZvPszckj+/YLHQ/+Ygu6ekpH+UuXdbYvVMXxwf+1zp89eXDEg5awzddvlmiMqBqQsv7utf771cvb8pWUDkqb8m647FHtsvWZu5Xu8/fHcZAOS8tmWGwzHdhvnZzTxePGdmb4BKcogGDCwbzQCm+S8+PTvt78oG5A06oUny7qrDcq1Pvz1lc+xZHl3pvB+75y1R7b7MiWNQ8Lvi5qo6urXtHO3XH84ts2pXi7oWO4bkJL6Ca/8AS1NuffT02Z1VAxIKXVSvQ87jVkVa45sz0WG9z+b5xuQ0p6Nep/Gb7kGhuZkxJr0/pe+ASlVBuX7vbdbC005GJBeee/LfAxIhvXcu580IOULQ9j6IK7SCHTzDuigXsuknXmN02lIlsX7XvWMaGhw0KCcl1cun7CiSpvgRJ2jVU7vdfN8Dh6B5xEjv5fHQaJ8AFBTHukYb8Tvg8YApAgZ1M8kyzdxBFKfV1rbzLPcJAID3IRnGc5PYtovqOc3ZPztSXiXjPsNlbqYe9EVGRqSn41uv1ybDMGyqmTw7oPgSKM+nkWwLB1KNejxoM40OjVent77p9PHAghMKIz6pd5L5N1PevckKbeR+tuk2oV6WTeiPgT6JqE6mTamKkn3j37fpDO56z1HqD7UMq5T60jSGCDtvS1qGc8Gxgs5PgtHc3zv33OJ8ZMiQ1SbDMihfJ1nH+FojmOBkG7IUQadOYX/vfJ1Xm3T+ylpzyCJLG3N+621lEuqoQGpD6I2mMY8OxeDTjYoj54sjb2dUHe3G/hMPU/MAyll0g9k61y8a0gOwoB0DyqJ8oGg4kgamHtfFXUfAGUgKixDVD2RUvjha5okflXFbMw4AMhSbhLBAW7CcRrtN7mcymuJ59GYMkHy5M2zfw6T9mxsyNCYMknzZRAycAPpk1VXsI/2SLsPUdSixxsEdWaSMUita82qASmDB1Le9bJiMEgvG5Crk2nPRlpXAOGJYvX31gxIGtVCagzTaCBDmKh+K2kcUjcGpFo8kITmFbrje0l9ZdJfWrkPKQZ3G3M8EyNWXl1lLUZdD93oksAxGosjxBwakPogPYGGnad1OroMXXnSrNRe56HG1jY0OIFyc/NA0uhk8nA71v3tWVA7+8iwKbeSj8LGSqZOaICMt0f9y9ATmBTkXrx/zawhbFnviXdeXq6/uq7H4fJM+4XA8xD1CEs27Ip6/zjJz6bSN4mJoH0fJI03/n2IGRxK9gthGbJ6IJnWEwmd6d2npEG2WteamrJ5Dkt5GqS1B0DecBK+bqSuEPZWLcuglBcxWy26f4ySJW+9WZHB3EUvqt9KGgPUqq/yIqBfa/BAyvNZ6I7fJHWFrgzh7/MUxSTSomRBBt370OA4ufXVeYwnMxmQeo9hCFu+0IDUB5EaBGVd1XT9jibl+r3fqwYkJ9Q5iYWwRVy2ehXJvBy396eIGA1S7ov1lUyN1SNpQ1pStazIkLsIqW3DFZ4U+IPhjB4OWWVKCw8yRa3Gyd5ktbVN6bbhTfjTPG+kJojqtWM9bwSN2x7+fUgzpFnwQIrzILXxLBzL7dMkFEEXTxwdL08gHMJm/rtzNxhohBHWEjaoLUdauHMd6G1XsamIhvhq1AupMHy/TWYI8fS9mWM8kMK31A2VUZQBSR2H1OKBJJUaQ2/8JmtASqsPdlKE6BuQ8nUSUGVIPtYV6KezjGPDZBkb+t619EDKFRqQ+iCqsirKvVFFd4U3ygOpsUEohC10mbQ8AFnLrgyG829KRkYLoYGortutVFgCoF8vRVewGpIVr/SkIC1kKoo8PZAkPAMTcyDVOCCXNuilhUd4bcVK+Fia4URSBiflPvR+biOULz4HkrwM3rXNPASrzzctD8hTZ6av0sbmQMpgOMs9hK23LibnQFKOt+CBFO7HbMkQ1NvJYx/Z/lHDO0Con8oSVurLFOWBlNDmal3wyIv8PJCE5hWJfUv+ZUfJkBaOb2WBPs0DqQ7SlEi0yzzGk+F5iJaXY+9vYQ6kfKEBqQ8i5mKZcVVTt6Pxvu/qCXbQEkm0dVyKs7hCxpUj4vWiut1GbD0q7YYOZEmAmL8M2i7Q1lawIp6F8IC8sppqbtgFsq8i5bFipKKboyEcjlRTCJugd2DaDmhWvH+KDN1K9UAqycug+SzseEHpn1PLJF63XzbBu3+JBpiAASnjeEGobeiER9j2/knbAc2G3k4PuRb00DQJYcu9PkBbhjBRYXV9IYRN7VNq2bk0V8+bQDhlgs4X1JlZQrfC59Usg3Ipk53gxGQwCGHLr/wcDEihe6ez06NnOGIIW77QgNQHEbOQSxuQ/CTaSg4kx8ytUpewpTk6iXbofYbCZRP/VZcTKFt1Qxdzx0+WIfydzOqRUo7G5ED8WUQaI9Vj7Xu8RJGHF07uHkiBJNp6E720Y9PKkTQopoVNyeZACpZVLYN33Ioug1dHo7+XCpkKyhAsS4daco5Ibjyh088DNXgglWTaho6x20oIm4m3qlCzSA1/d2VlMFl4kPI8qWXxIyqsLmkMUD9JtCuvTUVQjxebVyR5HQv207rj2PD3+RrSTLx/lPOEZNANo8uz/FoS23uE256OV5F3CO1H+UIDUh9EahCU5vacJk/WJNrqaRLJeuOum8eqkaSngfo84pJoRx2brwx6eS5EjTeahk3JxMWNaQNy4RXdiiFH38Uhj5W0LKFzSajX0fUUyFK+erxkWGVaAusiw6asJLDu7R9SE3kXGsIm/yyyJAetpX2aJIXVRSfRaFwIW5bfnfcuaDp9VcBTVGj0m+ZRHdTbMkKkjh2E9ZWJ3ijSIy2OqH4raRwSfl+Up4Oufo1CyvOmnOe0/DqxbQrqCpO5jY1QPhMvqDybRZYwulzrQg7jSZ25Xdw5RXkGrqjQgNQHkfJACrjFG7QzbQOS54GkhrD1JtHOw7UxIJPGilCVJRvm25VLehqkJRcPJNqTGgybeiAJux/Xqwz2VnT1z8ljJc3JUG4SAQ+kpIlqjSu6wbYh55GWnkQ796IrMujugCYqQ7CsKhls3IeUUBWrCc0LSKKdl7ODv9VxUq47pbDmJnVxQb8c7xJ5TxR1kiZLJchVSdOZdvR2wQseBuM5Ke/lWsaUUX1GYg6kDF4REujq17Rzczfm6XgHCvbTJhv1SHn2Z0minecOaOYy9J4jEEJXi4deeI5mkmeNSbTzhQakPoiU8s/qFq+7whuV1NCT388hkXNCUI8ohR5VVtaJqnjy6IJWMushAaJuvSwykXfQ40UuobqtJL3h82SSaCccV2MOJPXwvD0dgHSvF38HtCLzDwmG14ZliOu3bdyHxpTJiR1PLPN24t0bxzHPz2Symq1LxYCU7iUABHMgmehtG0mT4+Sx47VroreFjFgGCx6y/WPycZL3otI3mZ8blXg/aRxSPyFsqn7NvuiS97PQGUdIjt9M5jZSdVJ3QxpADeeTeQ5lGfTug8Q9qEVnVRlrXY3E6PRAEoEGpD6IVAibSYK1gDy9h6at9EcNkisGJPNyE2XKEMIWdZ5uOVJ5VryrprqhC82NdCcqogkQdUPYvJVtYRnSwgllPJCqy0kjDy8c7zSdRIU66CbRrjUkoOgVdu9+iXq9pEzSJEM6wzLURQhbmgw2noVJKFcNdUQ3L5wJWjmQcgxhkwpZAuKNBj059Impcph4/4jlYaq8jtqAw1bIdVq9kBzD1BLCFpV4v0+EsNXigSQUNgXohS5F3fO8MArdCrSN/GRwDLx/opK454FJnlsJD+a03JE6RI1FdT26aEDKFxqQ+iBeG5DaBhcwa+C6luqoztArU3K3p/J1I45JSUytVY5ntBBeSSxsIFoHCRCDOw7FHyfpaZC6NbN4yJS54s3DqJWl3CTUy5gl0TYtR7ZtaIePSRpO0mSog9AtGzKk7VAotZqrkimErYY6YhKKoC+P8jrOo0z5uNYk2rkbDJTrpXmjATKeN4CZt2pRelttKiITds0FD0m9mXVM6bpupJdc0jgk3PX0SQ8kodQYgPosksqXKVstP02G8veqIS3HBfos+YdybpsmaUokDHppnts6RM+FUs7pLc+tsWwShAakPoi3NWzeij+rW7zuVvZhA1OUe7BEqEzcdaM+y+rpIO0BFLUdcI+gwo+6rl4CxPy7FHWgr+UFJbhte7mciGch6P4NZA1hU2XKdlNqWcWNQr2ObqhM2rFp58vuilegASkthE0wJMAjrd+2cR98t/iYZ1EPXlBR1HJvdBPTmqDTNoMhbHobLISRqhM64xfJSbKuHNb1doQHkqrDJPVVWr0I6M0CkqpHoXYjgTFqwjgkjw1Z8kBXv0ZhI4QtOUF/SaRsAEYb9UiNHbIk8pZ6DoDGfRAIYctjPKk7l4v7nl5I+UEDUh+kYqnP97omWzyq+HkuNJNoe6gGMCfjalEcOitCafkJ9MqRnZwkraJJu6Gr5ZdliD9OMlluMDQhXoiorXclZEjzZhP1eMmcpNdeuUnoutiHi6sln4OkV1z81vG9ZRfp/SMUKqSSlrjYyn1I8ZKz+yz0z6lVrrzbpqvRNgNJtJUcSEYGJOGkyUnySCXIjZUjxcvZht6OMqy6wjLo1k1X0JiW1cAapzeTxiHhvsfUkz0v6jeJdvl/0vhNsp822ajHz02Xsyw6HpLh73N3EjCQQSJ/YRY9GUY3miTunLzy7BIakPokXsOWSjoIZJuopnU04e+jPZC0i9WSyX8faSxKPy+1HOHV7YaE+6LKL1W+7qqJrfAxrV08CpDBFX4WSfUgjjwMjGoi0jwUrzrQ1nmW/nvjVWRZg56uB1Kh3j/+QoMFGQq8D2mDUpteUFk8ceomwb2b3jYDOZCaMoawCSV311losO2BFBnubEEGk0Teov1jaqhO5bVkUnUT4vRm4i5sdZJEWy3WtFuQNGzqeKTZGkunJlwW0ptmeZjK/0X7SN0wOgEPpFpSImRZ+A96IGUumoSgAakPIpaE0iDBWqQ8aQakkLyqZ0CWEAAdmTx03R6zhspIbwkcFTZlI5eCetnkBNaC3j+a7seyibwrr9NCH+vFAykPV+y8c62sKCFs+vmHci/ax9sCvNjQrfL/1BA2Cx5IsWFLJfln4V07kydORrl0EtOaoNM245JoZwphKyAEX9ozEQh5/6SMMaTqpJMyWe0Rvg+6dVNSb2YdU8bJlDQOqccQNtMwIRvPInH8JqwzdTfq8cbakh6SaZuSiIX5aqaDUL/P1wsrvR6kETUONUnWX1SC+xWRQg1In376KU444QRstdVW2H333XHHHXf43/34xz/GRhttFPi79957C5S2fpBzb1QHHSbyVJ8fef1QZxilnHNzxw8rdM3M/Vk9HeR2U4kfBFkZiKq5NjSSHot7/ySuYFUfb0sG6YlJphwrOawyB393pksE0PFyKJdV24qufEhhbzkpE1XRHdDqwIilm0TbERxppOkO6TBj9dpZkkln3yGx9nCAKHmSrqn+vmASbZNyyv9Fxy9x8ltYdAnskFdYEu3K6/RdQ4tb8JAMw68liXaUTEljgHrxQFLlMvUYlgwn1HkWEiFTpjKUvw8en1v5Wbx/cr4VJlEm/ng61yTaemUnETXe0b2ftZZNgjQVVXCpVMKJJ56IsWPH4tFHH8Wnn36Ks846C6NGjcLXv/51TJkyBWeffTa+8Y1v+Oe0t7cXJW5dIeUC3mjQuQTk0bTYVxmQIpSzhDt+1Pu4srK6O0uFaCQakBRZsyZJ1qGxwUF3j5t4bzzxRBJyanrBSHo7NKbIIB4SoBm/r5JHWF3WPiEOXVfi8OC3Fnd8ySSxLsr3JfzM/T5a0PMmLUxEekCuXjvNiCUawpaaA8meDEZJ7muUSycxrQlqm9Px4DFJChu4ho3xS0pdACyFj6XoCqk6qY4HIkOuhT00dVMSSIYaZwn7BoK6JjBGTRgDVOdAKt6AVFMOJKHQqaiQznD50u1SNweSaAhbyqOxsdGAbn4yiRC2WtpH2q6SUQS8a2lAyo3CPJBmz56NTTbZBBdffDHWXXddjB8/HjvuuCMmTZoEAJgyZQo23XRTjBgxwv/r379/UeLWFXKdS3UZevL0nm+YRDvogdQ7GM6pcae5GMd9Zuzu7K+wy66apMkqu8Kf3un7q+nSCax1wugK9EByHJl74GRYucnDJV03hFGXksYkNeq7kuEsINg2iqsPosablBXVSkiAvAxxz1I6xBcwCWEr7llEUeu98apWXklBtULYQsaPLCEJlbaRRcp41H5XJwRPLuxb31tVbtyglBe5C5usAUm3PUiG0mUNl4kNYUvo8+slhE1Xv0YR/N25idR7vfTxvbSu0N2oR3dXaVOMwseEdLdJmhKJXY3z2PghOiQ3eXxYS3J5Ek9hBqSRI0fiZz/7Gdrb2+G6LiZNmoSXX34Z48aNQ0dHB2bOnIl11123KPHqGgnXQiA08BFIihn+OsoDqZbkakGZgu+jVj6iBt5ZPR3EPJCSdmET9nrx0MlnIJlnRCs0QXhV10mRQTIHFJAtIWgeYXV550BS9XzyjizhAblhOa7cYLh8TcUdPcG70U67jP7eihHL67djnqUVL6jU3ejK/yUMu74MGfSXW6Mez99rt/I6Ngm1UtecBsfvV0yMWFJ9ZVroWPhzaa9hIN1zWGyyrOaDShnniOx6lZIbrSKHpAdS+b+pgTU2iXbCGKD6ffEGpLySh+eBXhLt3mPF00EkHye6EGm4E5zsTpXJx0r003ls/KC7IZJKYPMWGpByo7AQNpU999wT06dPxx577IF9990Xb731FhzHwc9//nM8++yzGDJkCI477rhAOJsODQ2O6MBVksbe/AKNjdUzPxcVg426E0qtNDQG75XutX0DUlOyPOHvVPm9jsU1KDeJ8EDehVt93YiO0XHMynddmWfh0dj7TEo9blVdUCdE/ZobRcoH9J6NVweamxpylyPchqOu391TCnyftwwt/RpTZQDk6oGXb6RUchP7hgDKbevX3JhpAt3cXPndjpPDb1NXxhF/H8Nj38j2q1mORNtoVq7nRDxzrzY2CtTFsAx+GG2oPnhdYFOjnAzedUtu9LPssSFD7+92I2Rw3UrobXOznAx+Px1zH6Lw9XhjtnbVENMva/cPIQKeMXFtPdSuGhocoMc10tteOXn30/3UviquH1b6QMn64KD8XBA5nqjI0NJPRm+3KPeiVCrF1gUpfdXs6Ss3ud92As8j33vRpClDlUzKeEMdzySNQ8JGqqi+qAiqxtxJfYOgzqyMIeOfhXcPJcZvQRn0no1E22hscMoegSnzDFc5Pk8ZAuNYJ2WeKaC7s7bJABnmbaoTZqxuKJCsOrto6sKAdOONN2L27Nm4+OKLceWVV2KzzTaD4zgYPXo0vvOd7+Dll1/GRRddhPb2duy9997a1x02rE101dEGgwZVh+019ys/tqamBgwd2pZreY5T7jhaWvsZX3tA/+RzWlqC1U2V35uoNjU35vObQo+9uV9T1XUHfLm46rT2gf2NyvcGG62tzbk/C6AySexx3aq6MGD2Ev/1kCEDRMoHKvkUWlrif6M392hra8ldDnVwFnefl3f1+K/bBWRo7Nfsv+4/oLqet7SUv29sdESew4AB/XpfOX49iOobVFpbyzI1OMCwYdnyxw1qb628HtQfQwe1JhydTmv/fv7rpP4rHNrR1FTdfpMY0L/Ffz1saFvNcodpD9yXAWjr3xz43ptstLaYyW1C24Dyb/QMJOH64Km+/v1l+iag3OcD5QFvZBm9srVFtJm86O/de6e67amhAhL9gkdrb/tvaNTXyRU9nk3nNTWVdWa/CN0GpPcPYdQpcPug1shrDhigtqsBvbqhhBYT/ddbMfunjBdMGTSwos/b2+PkX+C/HjqkDUOHDsitfJWGBgc9JRctLdW/Ue0Dhw1tQ2tL/sPwlv5d/utSqXrs0OLphgYpfVWuJ64b0y/0snB5RW8PjqlzWenv3eeIfiGJ5YpnxqCBFZmSxiENIRffxrzGsYZ44xAAaG6O1z1RfUN/tV4Oa0Nrv/zqpTe+T5LJ7xeExtKe0SCuv/Tw+2WDvlyXhkYH6EkeSwPleRBQvm95ytCpjJNb+/dLHEs2+vcrPxkGtPWOF1L6heRrdFR9NjBt3qa03dRjC8RUZxdNXRiQxo4dCwBYvnw5zjnnHLz66qvYY489MGTIEADAxhtvjE8++QT33XefkQFp7tzFfdoDadCg/li4cCl6eoK+hsuWVgYH8+ZVG0FqocFx0OO6WLx4ufa1u7vL8nV1diee09Md8pl0K/J7ynnZsq5cflN3qKylS6uvu3Dh0qrz5s9fgnmtjVWfx9HV2yH3dPfk/iwA+KP6Usmtqguq/B0dyzBvnr7cJnhNaMmSztjf6OWoWb48n+dXLYODkuuiI6ZeLl3e7b9etixezqwsWVa5/qJFy6quv3hJJ4DyiqrE7+/s/X1dPSUsXLg0tm9Q6Vi8HEB5kpBVpqVLl/uv585bDPT0JBydTkfHMv/18uXx/UVXV7CcZYb1aqFSzqJFS2uWO8yyZZ3+67nzFqNzWdCA1CndL6Dc1oCKkSRcH/x+uUtOhs7Ocr3s6SlFluHJ1pmiG2rBqyvdEfda9UxcnpNuiaKnuyyDye9c6ulx180kl9OrM5csDfZ3SWOHJNRj581bgv6N1WOnhYsqOmfRwqW+bpAYL5iyZEmlr5q/YAna+1Wv5i5cFOwXmh2ZcAbPgLR4SfV9UfvAhQuXYqnAari6oNJTqu4bFnu6QUpfdXp9U3S/4DF/fmURbIlBHdKhq6vcN3V3J8uQJNPSUNuKG4d0hvSV1DgoDU/nA8DSiP4uqW/o6Kicu2jhUizN0xtCY3zf3duHdnXJ6AqvqYefaZilSyu6Pf/5Vfl/VL+gsmxZuf24GXVDHGquoI6OZYljyeW9Y85ST34yeOPYnhquqeogj/nzo/t7j27FBWne/MUwmOJZIavODmPbMFaYAWn27Nl4/fXXsddee/mfrb/++ujq6kJHRweGDRsWOH706NH497//bVRGqeT2+XjHnp5SlTGkq7eCOU61oaRWvIFPV3eP9rX9WGs3WZ7wcFSV3+n9trvHzeU3hZPUdUfdx67qcjq79H93uJy8nwVQ8SIoldyqutCpyO+W8rlvSTJ0RdxDD/8+pNSBrDQ0AKWe8rWjrh8wOAjIoCZx7oqQoav3fYPjiD2Hshyur2Ci+gaV7hxkUj3zTdtGFF3K+XHPEigPMFSi2m9iOUp9KAm0DXU1urOzB93NwRGJKr9YfQglhgzXB69NOkJtUpWhJ6bf9mRwXcH+qfc2RMmgTqQlZfDoMahr3TXqcc+7Ok5npvUPYdSxUldMW1fbb6nkKjLol6VOYvJ8HmrYemdnjPyWdKaXNyRJVwBlvdLdjdxRE2eXSqWquuDrhgahvsFrkyn3OPA88u6nfBnM2oFqDArXkbhxSHjS190t39dE0R3SO/H6NaVe9rjoTtoyzRBv7J/UT9gaSyeNY8tlV3L/5D6/8vrLhGcDVJ6jg3xlUMctXd2lxLFkj8Q801sQr0EXZ5m3qfnAOrvM+gObmOrsoiks4G7q1Kk47bTTMHPmTP+zt956C8OGDcOvf/1rHHvssYHj33vvPYwePdqylPWJN1CS2CK6kpBU/xw/+V2Kt1c4nDCYRNu7Vl4JQcNJeJOTWSZ9lliOcJLYxF3YhBMF+9dO2y7cdf25rFgCxLREuWpSUMHkh3Ey+FueCj0H/xlkSKJdy04/OgnMTQgkM0y4nk77TUI6UW1afZBOqg6EkvUmJPIW3aExpV7aSOSdtEOhjV23gGwJrWutI3kkJI2Sp/w65phQot0sW6WXhHSFWsfiuknJrcqDsvTKUVAS7bQddaX7J9OExRKyZE0yn7RLXtw4JO29LdxAG67hd+dcLbQ2YrFUJ9M2OvDkkNghUXcnOMmdKr1flX4fyv/zfB55bPygO5eLOyevjZpIgQaksWPHYrPNNsP555+PyZMn45lnnsE111yDk08+GXvssQdefvll3Hnnnfjss8/w29/+Fo899hiOP/74osStKyS3js/SwHs0JwnhXU9UA5j3OrfBsMa2qrqfJSG99WjS85DcNSMgQ8qzsTEoT9uqO48t63XKB6Lvg24byFy+4ommiydTLYbmtN9tirojUClhdlHrtsjSbaMx5b74ia0t7IAGRLeLHgsypBkxSjnUQV0ZUo3skvchg/6qtY44OetM9f6lPU+g3M9mMWJJGRXT2oNatkT5AVmSFn48w64jtzNg2pbh8vpKr170CD6PrAZWVaZwvxU3DqlVX+WFThtOO7fBcXKvl40J7SFcvvTuiEkySMuRpKtUJPWmfx9SjC4SfYR3KRfZjaxR5+neT51jiT6FhbA1NjbilltuwWWXXYYjjjgC/fv3x1FHHYWjjz4ajuPghhtuwI033ogbbrgBa6yxBq699lpstdVWRYlbV0huEZ1loupqrhyEremqASyLh0USOitCaSvWOuj+9qxoG5BsrK5rbO1c1IBUemKQujWz9IA8k4dD8Nxs5SrXy0HxqtdI9EAKD8gNiw54pEl7ICVNEi1MUuNkkO6b1GvHeeV5n9q4D6keSKJeUPY9BBsSPK9MUb1Ik64ZNsj544UMvzvvx5HmFaiWHT4+b5J0pg3vRMdx/A1RipBBdzwXbJ/5ylDx9jA7TxU53GfEjUPCZRTlgRTUrxnHsgJuBb73T4JIUlvX+zLoev8IepNXvKCSj/NlEBrL9pTS07tI3IfwODq887cOkWOdhPtZUsYh3nuSD4Um0R41ahQmTpwY+d1ee+0VyI9EKnjtR9JCbtLIdC3VVTtVRBmQcvNACr2PuGy0K6RhOYLPAkheLVCfkRUvg5h7Y0OOhjQZpD2Qel1/3RgZpAfkWTwc3BwGIY0akzITAgPchN8S3ha5Fnd8iTqZGj7myq0geqi/K8n7RjZUx6uX1d/Z6p+SQhPUj2z0kSarm77uqIMQtnAV1vHgaQyEsJn8bpkV/jSvQLVswFK7SDBqStZH7/rdPdETRVcz7UAtZXuUXDdWL6r9fN66M2v7SKojceOQcN9TlAeSrn6NPFfYaKGWEV1+77EFh1VKGrK0jViSMqSMpT0k7kPVQmyGZNamqUeqjb00IOVFYSFsJDtSK3iAXqxyQBaDPDzh71VF4bt859S401yM4z7L6vYrlWckyfMmKVZfRoZozSsZO+/LkBYqExj02ZdB2gPJe74uqo0rcfTk0E/kHcKmXiKprecZwiZRJ9PCZWzk/knzQJIOr1WvXXLdaqOfLW8PjXAhWzKYVNNa64juZEQH3fYW9uzLorel2ob6eHVD8KTQ0duSOluVITGETSyErvI6qe+WDGHzLmc6pkwKh48bA9RLCJvaF5iG6kjWiaS6WFV+wR5IXn2RSYXQW0bKsxGVwTC8NN8QttoXJHXnch5J4xJSGzQg9UFErdOGg1KTlf4qZay81U1wp0u400hLZpn0WRLSq4lJIYU2knECaq6N6O+tJAX17oNGaIXUs0hKgCi9ehZe0dUhj37CyUHhR8kEJLf1qpCAjKupUnlG0pKLS9cHIGi0jp6o9spgKQ9TuHrYCG1Vr53mZSqVbwaoPGcT/VVriECeXrvhth1npA4bZrPobSlvzTSvwPLnldeiXnEJOtNG3wAkLwZWvE1ky44rPywHIFcfTMeUSWF1ceOQ6vdGReaGWq6pui46NYZk6BhQeZapoVt1ML+S9YIq/9cPYctPhqCXaLZrRN26ZA+k8HsakPKCBqQ+iKSracXtV1MWAy+YOHdgINsKrq5c5etWXziyIzIUQDrPSGKC2FL1cZIy6OSVkJJDN1kvIP8sikianJYUNYo8BgB5KHyV4E5PSUo/vf3qlCMdUgnEGKcteCA1KrObnoiHYyUHUsKk3ZaBWyffDGAnjM4ohM2vI9mGYTqr+qayeKRtVuAl2s22O2T5v1TS5CR5bHjLqrIUGsKW5AXl1lb3dMtWy4pCcgyTNa9mUlhd3BigXnZhU73EzXVm+b9k2FSS97R0DqRGTeONlfuQarzpPV6gk9JNUyLRT+fhgRQdkht/rSrvQNqPcoMGpD6I7EqB2aqmySQh0YCUYQCeJFP4KtIhbFITJJ1VRMnygfSVGxuJatN2HLIRzpd0Hyo769gwWOidk4cRI/cQNuUaSW29aoCecRXZRohG8i5HIsVXyRBl3JMOCQjLkBQ2bOM+pG40IClDhoTWtYaY6iSm1UV3AhxOtGtqxHJdV84DKSWkE1BCzoU8Ez20vH9k7UeJYyrJNAhq2WpZUUjq7awG1qSwurhxiK4BVhpVD5jKYCPvTqEhbJoem6LzK815juS90E1TItFPp3lN62Aawlal22hByg0akPogkh5IprvJ1BbCpnogwahcXZn8zxJWAtOOSyxLOFGtbg4k2QlaigeSW31s7jKk1MukrXdzkyExhE3WYJDNA6n63FrKzTvXSpJHU60rutJ5RtLCZWzugAZUeyCZ5KbLS4akVXkrHkhFGtkLSHKf66KLpsE2vGCSFKoVhdpUcs+BpOEt6d1zae+fpPGMvRxI6TJIj1vK5ccfJ9lP+WGlrn7ewGqZohc90yaleaViMCWoXzOOZQWqhE7olvRGJGmLkL4cgsbVutgJrsBE3kGv6WxthEm06wcakPogkhZy091kTIwHOh5IeXs5mH5WT6s2QPLzkExAqZI2UVEnr9L3Ia5+SE5MwtdNWtGVC5lSytI27pafSy0ypSWLNiVrCFvWVWQxY2LKCrsV75+k8DEDw74NGURD+RImBrb7SIndS2PLzDGJdrUBKea4kNHL9HdLPo9GDSO7jcTygJ7eFjdieTL0JOgrC/2jrqdpY86rLzo5sdJlCt4f3RC2vDaDMUU1lGX12hXd3Tl2/Ob6YzjxsbSm8UZy99Yi8zDpzvEk+gj155gYdVV053L+d+G2SQ+k3KABqQ8i2dGbegKZTBLCXwc8kPI0INXgbWTu6VD+Lxcqk+71Ill+QIa48DEDI2LNMmgk0S5iFw/p1bMsoWRe3awlVCMQJpXDoFgNvzNR+qb5l0rCg9E0zyzp+gAk1wkbIZ0mMsgmE+/1NEDyjiv15oFUax3Juk15tCzh9zH9bNgDSTMxbdR1c/c4UcN8U/SEpDERULxfEkPY7BiQkvonKx6amv28ZH0w0R/qseH7EzcGqJdEvboLNJHn+uGd+deJutiIRTf/kKCHoG6uVxuJvNNSIVT6iBzLziElQtImNpHH14l34IoIDUh9EK/+i3Rwmp2sh8mKYtxqDqCf4E6HpJ1Pgp9Vf6ibXwYI5XMoIG7b9RW+8A5DKbk2XIM6UKsMcQOQpK1385Yhqo5YDQkwnKzl5YGUh+Lt0Rzghp+z6WpVreFBaTSmDIQ8+W15/1QnilRW0QX7hmDi4uB3thJYJyVPtuGZqMqQJZl01nvjPVYJr934xYJguzLV25JeaVq7sFnyQEoy3rhCScSrZEhMol3+b2PTh6S+W7I+ZNm5NHxsuO+MG4fUyy5sgRC2jOkYRD2QYttl9bF5o2288e6DZALrNCOWoAy6XqMS9SEQdm/LA4khbGLQgNQHqTX5ZhJ+4kHNRhbcsSLl2qEDVAOYbmI3HbQ9kGoMYQtMToTGgpWwqWqrhfeMbA2G08ICyscKyZCSENNGPqgkGaRDI7LkIqoYtWooN+8QNo0cDaphNmvZkn1k+bppBiS51VwdGWyFj+nKIOoFlSCDrRA2J2ZimUStdSTrLlNRhPV92kSvKoQtkwdSzgYk5XKxesKWB1JSCFsd6G1xfaXoHJ3EyeVz8pVF/WlGnoEJMsWNAar7vhy2LM2Ajn5NO1cmiXawjLiypcpXr6sbuiXj/RMsI00GSScB7UTeeYaw5eGBxBC2uoEGpD5IPcToepgMAKqTaFd/l0fbtpVE28YKe5LCkfZ6qciQsnpkxf24/D82NMGKB1JvWZFJk71j5D1e9POT1T4AyDuJdmCAG3O9qI+zuuOL7QqY4HljwzMRSJ4w2wgrBYLGj+qVPjsyJIWq2AvzLf83GZzWmng/zxC2sHdhmqHe+726iWnD5wPCHkix8suUXSVLwnjGut5O8F62YmDXSJwcPsemDMkyha4ZMw7RzSEmTdCAZHiuoGHTZAwp1U3rpueQ9FLU9/6BnAya98EV6KfyCWGL+kzPSJ12LDGDBqQ+iJUOTmBFMdwRBULYNK3iOqjXaGpsiL2u95l3DGDWqanXlM4lkLgVr/BANG2iYiNhb9qqiZUcSL2jxyKSJjsZFG8eMqWFapmiPicdjzavbWZeTRUajSYZ9NTxiWzoVny/ZcvzJnAfElb6REP5EpIn20om7j0Lo92eSrUtBOWZRDvNo8L/PLR4pZuYNuq6eT8PnTDfsAFMisSdAYX7Jg+dRN5FhfhGfScZwmYyrkzqO6PGISXXhffO11cFTVLVn5nZa7cArxcbukJ3btMjuPhj6v1TpJOAiAeS5u6MSUTN2xIT9Ye+Kio/2YoIDUh9EMnVbVNPIJMBYbUHklP1Oo88K+ogvrkp/rreR94x5c/MJwCAha3jE+SXHoim5doIrh4J3weNFSxxb7ACkiZnyeeQx0TFyUHhq6iyx7X1UkT7zZrPQS4nVXVZUe8lJ6pOws581kLYFBnCzzOQG03wPiTtUBgIo5OUIaWPjKLWPiMtL5wJSXU48LkXWuEn0TbT25JeaVo5kAS9twOyJNSHWj3PdEnyDpPuH3UXPGzVB5NxpU4IWyBZdSlCXxU0SQ3oV2Ov3fJ/UQ8knQVA4RyW6eFjweNzlUHT6O/mMHarVQZ/fpHjfchjQdKr17rztnDbL8o7cEWEBqQ+SD3E6IZlAdK9cJJzIPVeL+cwmaQVIa8jyuqBFMj/JL1qkhCCZ201VSMBovgOaBpeUEUMQKSfRZYk2nmE1eXhcqwSDGFLPyazB5KwcTWQJDbBe8NW7p/wVt0muenykiHRkGbpPhQlg7oTnKmBN2sdScsLZ4JuEu3w6nwtC055LzborG7byE0GpOhtazmQguXZlEHX00Ayd2FWb4ckY0bUOETtawv3QAp5RpngCho2KxuxRMtkZSMWbcOJ3FhOeyc419MNcjLoJvLO8z7kkRLBk1udtyXdT4awyUEDUh9E0gXadDcZkxUkayFsEQo9yY07q1Xc5s5f4QkiYG81tZ5C2NJyWwByk/bGBCOWdDhh0ONF75y6DGFTrqETjpg9hK1cIaTrI1D9OwLu+NZ2QIuXQdYDSe8+2JIhyRBibSc4zbrq65/MHkjl/6aeBlHo7iLlhnSmbkiGh6qb834eOs9AMjxFRWexwZbejhw71GMIm6AHUtQmJFoyxYSwxemxrPoqL3T0axz1EsJWvAeS5AK9Xn9ZDyFsEvch4DWdsY2Yh7DpLY4Qc2hA6oNIuh+b7oZmFsIWfu9Uvc7fAyl+hVTtICuGM4NyVONZAasmkjs16MoQ/lxc+cetLKsTEyEZ/LaRtKJrwWBhmp+stiTa1derBVX0tDAZILn9JpbTO18QC6lMMN7Y8EwEkr3S7CWPpgzha+sadNwa+ww/J1suOjP8Prmv935vUlhvFEFPA0MhU9DZdStsAJOi3vW2dB4m0yTajlOMR1q0TNHXAKLHIWrbqeirggxIMaF1JufKhk0llw3YGEsnHyd6HzTnGUU+C18GAQNSllQMYaIWzhO9HDXDs4k5NCD1QSRjlRsNDTm1JNFW35vmUkiWqfLas1JH50CqdNKO4e8Gkleq8iLJ+8daPoeUXBtW74OG0UFehojyhZ9FkqdHHHmsdEuGsMXnQKq8zh7CZu95JHlvFOX9Y8vzJpBnJPSIXAuhrUDyoNRGeG342vq7JFafa1SmY1Zesix6q7RhLwXzHEhy/bTjONqLHbYSWCflLpT0TgRSvKA8GQpe8JDsp/PIgRSWK2ocEu0xayZrXsSF1pmcW4TXiw2dqbtrpZX7oJsDSUAG7fvgtU2pJNoZ9Zar5KjS+S264dnEHBqQ+iCSSRiNd2FTjkuL19VJop13PofmhBC2KA8ko22YA263mURNJem+SOySYCoDEAxLkL4PekkYZWVIrEs2cu6Y5lipQaRAnp2cQ2XStgoHlPabcTAslag2aSBkwx0f0A/dEs3DlBjCVplFSd6HpIS91p5FoD7onVNr/53WJ5qQ5LkVPC5YtnEIm/Dz8PP+pBjAxHMgFRjuHJYhKnxL2gtK7XcTw0sE9aauDGGSEu9HPVfd8aYNdPRr7LmCbcP3VNTQ+dJjKF0DkkTbSLsPHpJjex2vUdd1/QWhPO9DHgakHsOFfxqQ5KABqQ9iI0bXNI8DoBHCVuWBVP1dHm07sIKUkNTQXwF2nEo+CZOBhgW3WydhQOwKeqKppOXasJEAMW0nOBv5VrzLRnqzWQyZ0vdwqL2fyLqKG0d4++MootqvadlWk8QmhP/IhrBFlwmEVnRtGU6K8sRKCFWxFk6YsBNcHLUaOXVXs01kiXvvfx5avDINPZeeKKZ6IPW2VWmvXSdhPCOZpFclyXNYWgbdBQ/ZyXp835REUlhd1DgkED7v6auCwmSC+tXs3Fo9IpNI34hF7adzLx6A/kY9VkLYUr1/gsfniU6aEqnQ78A4tsYk2g0NTuqCQdR3tB/lBw1IfRBR67S3omiYx0FHnvCgLSqELZfVVEWm5sb466qJdrN4QNlYNUnyjLKXEDR5Vc2G8aYxpV7amLQnrbZLJj0Esq3c5B3Clrd3oOtGD7R7ItpvPSUEBdLCpix5vdRBCFtSslxbhrTGhPtQD4m846jV+yJPnRnuV9MmeuEQNtO8bOq5eZLmESXpva2SqLf9eygrROVeVFuQxPWVpt6oNZF8Elk3gEjSm1HjkKAHUjZ9lRfqozYO+xbceMJoI5YCxtJRsojcB0MvKJEQNo25jtT4Iagns11D1ZtaIWyhr0wS6pNkaEDqg3iTLtlYZb3jTSYJySFsvdfLO4l2U4IHkuI1kmU1N7DyJGw4KNYVvre8OkiiHZ83x54MUfdBcgtcIFvywXySaMd7d2QhbDCKvJea7TcJ8ZDChN1EgpNkkeLL1070/rFjxEpKXGwjrDR87XD/YE+GGtpnRsGybPwQR9V9SzPA+Em0zWSQzkmVGupcBzqz0jeJipB4L1zp/lE7iXbv8RKeFjV6IEXdm6hxSNR407TMvHBjDFs6qB75eWO0EYv0WDrVAyl4fJ4kbcQSlEGuj/DrcKJnYPXxeZYNZG8f6iKAzjPV3WGUmEMDUh/EToZ+vVbWYzBRCXdE6qS40dtRJmcPpKQkvGoCxyy7wNnIM5JktLCVEDQ1AaKibKRXNItMoq31LIQsBkl5XuIoKckGa0E36aIO1Z4y1cdEtd84b6U4vEOt7IqXYLyx5v3TE76v9r1/kjyxbCXyLkqGLN4OtSYQdlKMJSaE71tafjJPZtO+Qbpt6O7WaUtnRi141Lr7ni5JG0/kEd6sUzaQvBgpeS90ZQiTZGSMGgNE6Sv1OjbRCRGPQzQ1RsrCtBUDUsoipC+LpPePptG/0s/mP57U8RoVC2FLWHDSJTBv0zDIhXVZHqkYSBkakPogfiJIkY6+/N90klo+NwcPpJzDZJqTciD5iiJbOICNBK1aiZuFW7HJ6pFYDqAUt1v14yKSMFbCSkWKDipeXeNuTqvMWXYojENnt6eeiPZrWr50CFsguXhS6Jal5NHhEKRg6JaYCNqJvOshlE8ycXItIabZk2iblZdE1X2LM8B4Y4+wB5Lhb1bPzZP0xQ5LBiRPX0WF6FrygvJ+YnIYnYwMugseknoz6w6iSXozahwSq68KmKgGDFuG5VeSE+cqUu81K2OIyLD1gsfSUbKIbFKkKYOkl2JS3+QhNX7II4m22ofr3M+wwaio8NIVERqQ+iCSiSBrSoqZZkAK1TZ1kOG9dlF7AsJgEt74lQ91NbIyENYvRxVT3PMmwYOqeA8kC6tHKc/HhqeBVjihcPw+oK8A8wqrMzUqJ5EU5hR1jNd+y58blCPcNhzHgXflpPAf0R3QAoOx4M0xyU2XmwxJYXS27kNBMiQZ8+KoNfF+Wl44E5ISwauE8+7pJqaNuq5EtUwNt7YVwlbnelsyTKd8XbWsBE8HSa+XzCFsvedHeSBFjEPU2xvQVwWEsMUl99ZBcuv4wGYcEWK5wqGt6nXThjGuYPv0rpnkBeO6LrxvZZwE0uc6Uh5IWVIxhFET71e8yuKPr14coQEpL2hA6oNIdnC1uKSn9XXJIWz5KV5VdD0PJCWbf8YcSEV4vUgm+1NJc7u1kgsqJW5bemICJN8H6bCETB4OOYXVJYVCmFI1UY3MJ1V5ndUDyUbbiBuQBiYUgpPEekhgnZQUM+AVaEmG8EAy8CzqKITNdd2aQ9h0QzJ00MlNppZVFcJmmPdJPTdPUpP1JhgH8iRpsmprJ7ikeyFpLCiXreeJY2PHqzQZwiTp8qhxiNr+gh5I+rLmRdADKdu50gakpJBKQHAsrdlX2bgPujl7inISkNpZOUsqhjDeaY2OXuqRpEUlUhs0IPVBJN2PdbZ4VAl6fiRXp+QQtto7Fg/V4uwn4U0MO3L8SXbmEDZhw0lSCJuklwOQnmsjGJYgI4P27jqOIxaqktQ2xEOmAgMwvXPyCg8wDVNJQifXSlT7NS2/EmpjKqE+cbscWTPeBMLogt+Z5KbLT4bwsy1FHicrQ8KzsOaJlX682gyy1pEsGz/EkRT6F/g85LliunupdNtI11VyO02pVPrM6ll8OAxQWobEELYV2gOp8trESy9Jb0aNQ2L1VR14IJl484vu7qw+ixSdL7cIV/6fNo6Q3eU6WEYU8mG+1eWEkQopzNMDSV34T7yfGmkTSDZoQOpjSO8qYxzCZuKBVGVAUl4nrGSbEkhqmGAYUhPtprm+R5YjFCeskjQQk3ZDD8ugY0CSy4HUW1ZqaIJI8akyiIcEZFi5ySusLtccSCHZo7wnotqvafnSSWIBta+MLls9RqT8wE5wYcOJepwl7x83/Gzty1D1LCx4JgKhflqjfeahx02NN7ryAPG/wc+/2CuzqReUdNtI3WzBms70DEhRMsjrKiC535aWQdfTQPJ5BD0Tzcd10SFsvcfEJdHW9LySokq/Gojgb7ohGLpVLifNAyn34svX1Vwcz2vzkUgZdDxmbO1Uqem1k+subAkLTrqoO9Tp3c+wbstULImABqQ+hvQKnnEImzpAT5EnLK/6Po+OJUqmpqbeAS7ivR8alFhak7JtrG7rhKnYcoWPW8VTPy8iFxQg6wrv0ZhQR6Rza9QWwlabTEm/2xQdD6So9mtavtUQtirPG0seSHUWwlbtgWTLkBZ/H9TVZNEk2oYG3jzqiJ9Pw803byCQZIAJtitTI5Z020gbv4QNYFIk7oBmW2/32NdXujn7JD3CkvqmJJIWXqL6/Dz0VV6EizT63UWGsFnQV41Oct8QlqWIJP+AvN7UmeNJPQ/192RVWZV5W4PejnKa+f2IOTQg9TGkO9q4VfU41M4ubRCQaEBSV9NzHAwnbauqGh085WK0UmUhQWvSSp5qiZckbZVZKl46IEPKSoONnW2SEiBK7poB1OiBVKNQOokKddFJaBjXfrO0Tdmwpd6yEo03YsUnGt1trOiGr52cwLoYGfJKJJ8qg6Frfh6LD6Zl6soT9T78uVe2bmLayvmV15KeDroGMCmchDARe0m0g+XZlEE3gXWtieTzkCFMkiElqn4FPJBUfVVACFstCYNFwwlTxi82DEi6G/WIGtI0jFjSelMnTYn6VZ59RFajroo61tYyIDEHkhg0IPUxrLmAC7ikhwdtQQNSjoNh5fzmBIWurgT6nWqdJdFuTBgEVRS+bDPWDQtQj82b1OSogqtGHkn3QXpikmUw7N2qWmUyDWtNotoIGnFMTPs1GW9IuqF7xHkZ2NoBzXEc35MiKQQpLTddLSR6SFrwTNSVwVbS5CgZogjUkaweSEJh31HvK+XEGZD0GmcevzuJtB2GpJNHe+h4IMnXyfjwfWkvqLRdt3w5BPVmY8b2kaQ3o8Yh6uvmhAVLG9SS78V/FoJeL+Vy4suWKr9ahmQDc/j4vGVIeizSC9ONGuO54PghTwNSdBkmqEnuTX9L2rHEDBqQ+hhSDdtDJymZikkyUFtJtIMuxZUqHpco1HGyhelIPwtAL0RE3AMpJUzBRqhKeghb8DhJGYoIYTPd5Uk9rtbVXdM+IYk4I27cZ8H2qz8LkE4SC8Sv5NkKYQPiVzRteSA5Cf22jdxoQLLusFEPqmTQGKDmUUdy3bm06tnFHBfyXInzwotDum3o6qqi2mVABku5CyP1lUUPJJ2EwSKeFlk9kHorf7YQNkVfFTBRDXvpZvFAcgSeRWoOJIte7HEyVMkhIIaW94/weFov7EvmPmT1ClRR+3A9b6r48RmpDRqQ+hhSroXha+q635oYD8JfixmQAkkN41eh1NVp09C98PWkdkJLui/SW8eHZYjLtWHD+8dJeT42VnWT6oj4gFzpqU13PKr1luTpgaSz21Ns+zUo3kboUtx9sZVEG4g3aprkpsujfCB59dteEu1oGawakDQqah56XD2tdp0ZfB+722XoftbmsWwqZTpee48bv1gLH0voM23r7egJe/m/2LhF09NA8nlkCfsuH1v+H2X0jhqHqK+bMpaZF7q7KUYh+SySFhrKZUcfmyc6HpvSetM3uGt6zMiG+cYfI+UFFfCazhrCptRTnd8SbhMmqRBIMjQg9TF6Ah1c/tc3TSZtku8j7KWjvs/iYRGH7raqqhu36UpquJwict9YW01NWem2ESaS5iFmIx9UZbJUPfqQfhZZVlO9FdCaQ9gMJ4lJhC+RtEIPhNpvhrYpGd4ZVydteCZ66CTyLix8zFvRhp3QUiA+B4itkCVA04CUw/PJYzXXo2qQnRKu7N1P3cS0/vniHtQpesKWQTFBDtt6O0kGKyHXCXVD8l5kHVMm3ZuoPj8PfZUXtYSwSdaJQFL1iL5F9S62USd1vOllQvniw0o9iu4jw9/l3TZrXZBU5206kSNhg1ERnoErKjQg9TGkc1uYNm4Ta3m4I1INYIFk0TU2cPV03STaWSbJ6iBbKs9I4hbV1pJxJq/cSLqh+zKkrNxI53Qoy+DVkeDnruv6dU46hK9cvl4dzSvfR64GJA0Xe/X+JrXfxHJsGBT9pOqhsi14JoZlSE6iLd8mymUGvwsbG+zIEJ5A2ZHBSZkghckjvE93pysdkgyQUZ9791M3MW1UOZKJk9Py9dky3kTqS2FdUS1D9b1whccOSfnZVCQNeuojNhlTJnmvRo1D4pJo27YfqeOQLDJI5gdT72WUB4grvHV9WIa4+iCeo01joVqVTcQLKiHBf5QMebdN052+w6gegt7tSfIqCuvjIgy7Kyo0IPUxggla879+Y8LAJwqvo3Gc9AFhuENWjS5pSfZMiE1qmDC5ME0GGj5WzAMpMcdI+b+t7YCBZA8kG1umxymKvPL9JBGfNLnyWjJ+37uyfhLtfAaEOokKddExIKl9XFL71SnHRuhUUgibVEJQ//oxgzEbdRJIDqMqIoF1lTHPQt8Uvr6O7swjWWvwd9e66JLeLtVyvHptmofJWpLaNA8ka2HfUQZyOzIUncg7qXwPf+FF0NMCyLj4ECFT1DjEjRtvWp6oJj1nvfPL/8XDCRPGkFLl68hQ/jz6+LxlcDVlEPFA0khTImnQ08lblISq03XmbTobt5Bs0IDUx5BOQunHeOu6pBsM0KuTaEd/V+tqqq5LsZrMMouXhY1kuUm5BGwYTcrXj5dB/cxGmEpaCJvkRDFua2ZbSZPTdhgKk1ey1loVvopOQsM8QgJseOfp5B+Sn6j2lplwX6Vzk8V5QRWRwDruWQg/BmNvB5PQ7/gy81t00c2d4i8YNUTIYBi6V8T4xfdULTCEzVad9O5FT0+1cdD7xIaHZlEhbIF+wWDxIanfinquuikTpIkaF2T63dLPIkXnS3my6/RV0npTx6hpS4bkELZKZcrdAylDvlkVNeJB57dUeUbTgpQbNCD1MaQt9aaeOCbGg+Rd2Cqf17qaGrfKGZ+EOltcrvWdI2JWiSVDx9JkUD+zsdOS7vbSNmWwteOVSX6yPMPqsiSYj6OqDUaGeCgD8oyhrXbqQ29ZBdUHoGLcq5okWhiQh68fXtF0S5b6p4TQBFtGdvMk2rXr8WDuJ4N9ylPkAdInN+Ek2uVzdMqpvJYdv8SVHzSASZFkULTmmRfXP1kycJvskCQ9WTcZUyaNaaPGAPGbttg2IFWXZ/S7BcO+U3dhs+GBpNFHS7cNVYY4DyD1mUk+i0SvHbWfzjsHksE4NoroJNoJv0VzcYSYQwNSH0O6gzN2SfcG6DoeSKFj1Pc67qW6uP6kIfn3VAZyDVqrZWFsKD0d+e2GZ0StqFYflze+668bExZgweMkLjzC2m5TGq7HHnmGMNUas66SZGzxP4v1QDIpp/y/iLBKmx5IcWEiRewEF9u/WgrjS5LBVjJzwJ4nTp4eSLpu/uH7aZrIO5gDyVTKdNLCpgqpkzGhnbb0dtzCGSDbP+l4GkjmLkzKH5lEUlhd1DgkYEAq0gMpJTQsDdkcSKrhJL5sqfIBvbmNK6w3tXaCE26fOmlKJGXwLqe703cYdXEwLbWFerz/3nK7XJGhAamPIW8hjy4rDn/nBh0PJE0DUs0hbKqFOuG6ldXUbJNkGxNFJ0F+2zvKqGVGymHBeAJEKwAbO9sUvW27ycpNnmF1/kp2DgakuDaoEjAgZU2ibcHzxA/dSgofE/cyiK4Tld9vz/smtn8VvgdJW0Rby3ljuACSR5hC1hwvUVR7qUTPLsL3U/X+MumXGhxHNol2igeVtPHGSXg2Re8EZyu8VSu8ROmnci8/Y1qEpH4rahySh77Kg7TQMN3zpQ1I6SFs8mNprfAxiV2uDXeCKypNSXBzpHzLr9UDKZB6JGbsoVLLzoQkGRqQ+hjSLuA6nWyUPDodXfiQYAhbfh5IAQt10i5B6mA2xfU9shzhZxG+bmwOJEuTVCD6/ljZ8Spl5cZPiC6Z7yVm5caeB1J1eXHkGUaVJcF8FNG7xCSvmjY1ZusXbOQZicubZtX7J2YwaMvToh5kSDKkWJPBMIQtDw/BpPx4plT1aTGXCxtmTfV20g5XeeCk9JGSXhYqOjsD2jLsxm2+oR4jU375f5KngXQ4Xy2e5Uk5kMrXDP4HQvrKdghbzUm05frK9DQI0cfKyRB9jHyKkOiy7MpQGc/FtU3JMW2tu/q6Sp+RFrIcVQ49kPKDBqQ+htoYRHYxMVy1cQ0GAGF5Y0PYah4MV3cwUddVBy+ebCZWcelnEb5uXPJmWwlBy2VWW29seBqkhdGZ1MNaZUhKOCu5sq2zq02UTDV7INW4YuQRJXaSRxsQXNE1Wk21ECYSa0Cy6YGUIoO0p0VAhpiVPhuTda+E2I0S6tkDKaNsjQKLLnHvK+X0lu17IJnpbenn4ffRMffDVmL3OH3luq71sMqwN5n0Tnjh8nU8kKTkyLL4kSRT1HMNLngo+sryRDUqjEf3dwdyJgqGTQHJOt/G+AlI2IxFeCyn01/aTFMSVz0C9yHnvjJLvlkVVYfoGKPogSQHDUh9DFvWaUAvRlUNA0sjvOKmnmO6gpuE7xXlOIkheUFPJe/cbAYkqfFo0sqNDaNJtQzV39tY5VcvnRzCJiZCbA4iG6tngNluaHn2E7UqfI+0cDX/M+WjwLbIdZLPwSMuubit+gDET9JseSeqMlR75vV+b8MLKmYl0pYMpuEyeSfRzjuELW7yG/bsy5o8XHqr7ricGCYe0zXJEaMz88xNpytDyQ3qrMC4RVJfGkzu5OoDUmUIkxRWFzUOUa8d0Fe15bU3Jlq/6p0rPa8I3LcEQ5dsyHnldWz/EGgb9j2xwjLIeIMpZel4QdWZB1LU/C5pbKi7wygxhwakPoa08jdNPGiyyhy26Dcqs/1Gw3J1ZGpscAJlVOeuqZTtGwcyuPxK5hlJCt2yNhhWjYpR3j+2PZAKSqJdtMeJiSEnz5U0nUSFOugm+QzI3phc99LKslEnk+qDuJdBbPhYb/kWjDdpibyL9IKyZWTXWdVVyaOOOBqTEV2qEi3HGmCC9zPrphtFeZzYCLdW5QCC91Y6QW6cDOrtCHogyU0B9HZI6j1W2CPNaGEwod+Kqu8rQhJtdWwpEtmQNn6z4IGk4yWqdnsSujPNE6tKhqK8oAQN3bXu6qvOOXVy14Z3+qX9KD9oQOpj9AhPTrKupOpM3MMdkWoAM829pCdTsIzYHEJKsm0Tt2M7Sq/yOj4JuPRANF6GgBwWjCdxMlh5Fr2XdhEcgKh1pohd4KLINYRNI1GhDmnhah6BJIkabudR2GgbcatfNjwTfRm8FfaYRN7Sk1RAuQ9FekGlJvIWLt8wH1EedSQ4Gcl2DY+w3ktbHffqlXnORFlPg7S+qoik6qoswQTWoiLEyiCZIDeq/KR6Id1PZ8qBlFBHosYh6r1tynEca0qkd7imDNKLYPobseRedKQMOiFsMpsUaRhvLHpBxSfyriiUvNtmrbv6ql6wOu07bmxEaocGpD6G3W0m9QfCekm0ndj3Om6VuqgTl+RklkpHlKFTs+L1oly7KnTK1uQoNQGihfuQ4gUVTu4qLUMgr0Vg0CFWvJEhJ7CCVGsIW0x4kClpyTM91ES7WZLrB/I5FBBSaMMz0cOvEz0hDxJLnhZA/K4utrw9gEq7C/cNtpJoOxqDcpU8PFHyDGEL37dUA0xkEu30cqQ9TioTirjyLRmQlMvHhY/Jh1VWXpcKkEHH00A+qXp2D6S0JNpem/H+O8i3TZqiu0ATea5yfySqRF2MIXXCx6RD+bS8oGQNvDqeWKrXTt59pc4ucEmoXrCVzY/SjdTh80nt0IDUx5DegtU8p4G+LOFD1BVUnQR3uqhb9SYmoVZclbOsVHnlSK6wJ62ahAfzUugmQCzUA8mrh7ZcoONWlQXLN9kpMLCClFMIW17J7VXSkrLruHxXlWMpTKQxxqBnM4F1XIiGTQ+kuMTFtrZMB+KNq7aehWkS7TxC2PLcubT6vlUfU3JdeEdFhbBpGc6En0dqCJs1r90Y75+CQtgC+spW/+jrjXj3uEo/JTMVybI5SlLfGemB5MboK9shbFH6VVMG8ciGlH7Cihe7xlhCeiyn01/afBZa9yHvJNoxXtO6BFOUpLdvJtGWgwakPob0AERnm8mAPL2DA52OznGCHkFxr/PKtdIQKi8uz0NwO0iDlSqvI7O1ahK3ui48EE3LtWFiRMxK2gTNpB5mliHmPtjatt0ohE05pPYk2t418zcghePT1XIaHCdU93TLqby2MSCtTqpuZ5IKVIyKYUOcLU8LtYxwv+1amqyrMsQNFm14gnkl2EpynzW8M02eqPdAtOdKwNPG4HdLVcskY7cbYQCTIlZXWFpsqJIhLoRNcsEjxRsMsFcfovRMHEmJ96PGIWpfm2cqBlOi9WuGRRfxhen48q15IGmFsAnLEPNo3Ih+VkyGuPvgyt2HmkPYIuZ3SZeKmzeR2qEBqY8hPTkyT4pZ/q87QFdljntdawN3FTf5pKTgqgEmzfU9ChteL0nPw1aS2rRcG/YTIEbIYMGIFXcfbCVN9g05GorXzXGSUKvC94h+bvET1TQPwvhyLD2PuBA2m94/qeFjBRpvLBm4k2Uo/y8ykXcUeejxuBClLFTvoBdtgPHw7mdWzyuxpMkJIUu2DP1AvNeuelut6m03WgYrHkiJSbRl60OWHUSTEu9HjUPUcWChHkg1JNHOc7wQRTChe/xCkr2xdPQxNlOEpG1UED4+L3Tug+QYqtZdfUsR87skQyk9kOSgAamPUQ9J3gLyGBoxVGt2nAGp5hA21UKdcF01gWOm3TosTBSTVrSKcMdPDGETFCOtXtoOo4vzQLKRg0mnjgZdsWsst8aYdY+khNmB45REu1lySthKYu2HTcWFj9k03sQYAOwk0fa8oGLug0UZqvvIkj0ZDAyteYQI6ExGdNHzQKou21Rvq7pZAm/TjLR8MNLGmzi9bTUHUlwYnSUZGhKehS+LcH3wZJAIYfN+lzoOM92EJk+i2qyuDOKpMXRD2CzkLATi66R0CJt6SZ0QNgkZdPL1BcdQORuQaliQdF030gMpMYQtZt5EaocGpD6GdII1411VfGWrd/24xNl55nNQJ09xq7ThRLtJA8/YciwkiU1aZTb1/souQ/KzseFpkLbLUY+FZxG3o5+1kCkDQ06eq+06iQp1iMrHkDRRTWq/SdjyNIg13rjB7yXxiqgKYVP6NnkZUu7DSidDej0NrHRn9UDKM4RNY5DdE5hU9MpgqLfVsHEJkla3bfXTqhxlWVQZ7Cw2VMkQa0CSL7+veSAljeuixiFRu0IBQI3q0pjo3Wn1zhVPjZGixytjyNyL1pYh/Ln4JkWFyVB5HesFJWjEqmVXX/WWlT2Qyq91+hj/PUPYcoMGpD6G9OpRY8zAJ1YewxUkdeUvzgPJJF49Taa4VdqqjijLQMPCCnuj0tsHdv5SLPFWE8QW5P3TmDJRkZ6YAKFnEeOBVETIVBR5rrZXvPNqukzkYCVyRz2lXtfqgWQjJ1a4PiaFQOQuQ2+djDMA2AzdCocm2GiTHnEepEXkgjLRm+p5xuXlueii44GUEsKmo7elFxuSwm2ld5qKkiMsiy1dAYQnq1BeqzLITQF09JV0iKlOiEuYpD4jahxSq77Ki6j2FxUuFoV0WGPqRix+vyBXH3XC4VWDm3gC65hnI7kDGqCXpsS7PQ7yn2dWxgvm54b1po5XfvhRm6QpIcnQgNTHkB6AmCpA0zAFddUtLol2Xqup4W3A1euG3USz5HmxEUIW5/IaNoBJEpAhyk3aRgLElHppZRcPdQWrkJCA3pUbnVwjOYbV1bJipJLkuq4St6Kru6OMrSSxcSEaPYr80lQ8kKINJ9JeDkB8mIjN++DE9N82PBM94p5FFMGQkWzlZckPFkdcHY47ppJEW9Wv6SNz6VCVJKOF9O5GATm0kmiLihDyQCoprxXdIOrxka43pPspX2dm8iyvlilqHOL/BsUjwrTMPEja0TQN6TFMYDOMFJ0vhWkSbYndlXVksLnTdloIm2Q+rCztI6w3dbx+6YEkBw1IfQyrHZxJUsyak2gr18xpNTXsgRS/c1aNrs6CWi8QUhiziig9OUrLtWHD0yB9JzgbxryYVeWI0A7J8vU8kJTzanwuXrl551kpfxZxnDKpyJLnJbCaaqE+1IfXS/QgSXqSCiTcB5ueWE6wTA+bXlBxnlhR5GHkzLJDoY48QLJnIFCROW1zg+pyes+XyoGUsCuPzSTaseHOFmUIjqnU13YXPJKag7ThIG/P8qhxSFROFtMy8yCtzieeK2zYTPO8sZLD0jh8TECGgndAA8JzimQZJAy7tSTRDhs6tQxITKItBg1IfQzpVTTTvAp+HiFNWeJC2CRWU5NcisMD+EwrVRbCRIL3pWIVkE60p1IPCRAbU2QoWdjFI3AfFKWkvrZRF0wMu3nIVMuKUZxMSZ959zPcfvUTglbaiZVdhmK8f+wm0Y6WwUYIW+p9sChDoYm8DbxY81jxz1Nnhr37kgy7gOKBZCiDrRC2VA8q4foQp6/qQW9bC/HV0BtqXy8pQxbP8iiZop5rj3K84zj+IpLtiWqSYSb1XLVOSKTGSAth8+6hJS92neTREuGdemF0su2zUcOI1SM4fqhlV9/wIobJTo8etj0DV2RoQOpj2EywJhHCph6myp/mYWJCwKVYY+cs1VPJJD7WvteLUrawm2usDAleJLaMN1Gx/laeRYzyl94C17+2wcA0z35CJ1GhDrrbDKteI3HJaJPLqby2satLnIt0kTug2WgPHk7MKqBNGeK85IqQQWeAmksOpBwNSOH7pmuACYT1mngsC4084zzR1LLLx9nTmapHWmEyxC2eFeChqWIvqbr+Od6xUd4XUeMQN+RVV+s25VnRXaCJPFc8iXayHvc9XkTHT8V7IMVFFMTJIOIBpKE3XMF+uhYDa1gHORrztrAu080LRtKhAamPEch9Ixwfq+WKbzhAj/M6ynMw7J3e6Dghi79yTMjK709AjFydy/9tbN0OBBVLHrv46BJ3D8Ny2diBTC0vIIN1F2ilbEvPQkdZeuS52q6TqFCHyLqTECrjlWu6YmXLoNcYM1Hw+5+VzPsn/HiKSOQdt1NlkYm8o1DvVdb7Y2q80ZUn7npR/ZypF5SfcFg4541uCJ4UsYsNbvQxEsQlyy1ZkkGn3y4yqXqsTAn9VtQ4pFZ9lRdpieMTzxVuG2pzj5az/N+GRxwQn/Bf2ktRK4G1sAwmoXySO9FlCmEL6U0dnRue0zEHUn7QgNTHkHaBNg0ZMZ0kxCXObnAceO/yCmGr9mCIWYVryBbCJunm6REXM23TFT41/5AfsiMnQ1q9tOICrfMsJMP4DDwc1JCUWp+LSe6lJLRD2EIhYI7him6wbRiLqU18+Fh5dGrT+yc+gbVF403IQlhEKF+RnlhmOcpq7zOyhHfGEZeEPe6YqCTaJr9bzOOkTkLY4pL/F6W3iwij0/E0EE+qnsHbISnxflSbC3vhx3mmSlOLB5L0GMZR8tUUlQZBJ3m0+PxKuWRaGJ0DeSeBtPsgaUSr2QNJMwdS3NiI1A4NSH0M8WR3GZNo6/ZzcYmz1e9qbd8lZQAQF5IXDnPJsppb2a47u6xpxCUXtxWmU5YheZJgxfsn5flUXKDFRAjKEAgJUI+RX0HT8ZLL0wsnNwOSZghbONFu3G5nOuXYCdEIlW/BM9GXIebemOamq0mGuPvQ+14yLCFVBoueWI6B/soj8X6eCXt1Eo0GPVe8/1nHC1IGg3hjc3E6U5XBTt9ULYM6dshvcUGn/Njtyl230k8Je6SZtI+ksLqocUgpZHDKEjaXB7qbVEQhvXV8+bq9ZSWFrYvWx8prvRA2WeNN3KYgJWHdHZwTRR8j6oGUUxJtR5m3mSXRNi6WxEADUh9DOn49s0u6rgdSTNia+l5nO+AkKtbzhthV2nCi3SwJSe1sHR8tv61EmOHr10MCxCRXbdkk1pXuMjaflgUXbJPcZHnIZBKak0SaV4N/XI0hAUUnibXhmegR92yKSB5d5H1ITSZuwZhnUk/VFfesxpRgSEa+bTMpNxlQuZ+mXlC2kia7bvVEVXoDkig5gPBiQ/F625YnVlp7sKE3a/EsjzQgRdzTsJE6KZG7JFHFZdp4QtiYl+iBZKE+Agl1UtoDSaO/9J6FdF4woNpruPK53PPIkm/WI7xhjd/fI8EoWAq/pwdSXtCA1MeobK8os4oXSPJmkGtFP4m2E/lafV+zB5KymhGfRDtYrqOhXKrKsRAeEberR9gSL4m+909xBiQbLtCxWzNbGpD7LuAGucnU87JiknspiSi5k/NZld+brlhJJ6H0iPf+CcoviV8neqINAHZCt4JlVssgLkLsSqQNz8Q0GaLIo47kGcIW5cGWZICpJNE280CylTQ5Sp6AV6ZwnYzTmXl4nunLEDP2EV6EDF9bZ2Inn0Rbv30k9Z2RBqTQ8d4htpP1Ri286o9lK6+LeBY28mjqbNQj7aWol39IrvxqGaKPCXuB51q+QSqGMEHP+vR5QdTnzIGUHzQg9TGkJwamoVzGSbS9vCaontz5Lq41DoZdRRk5Sm4l9bpVHVGGcABbOT6iFG9w9U60+KDiTQhtsLYbXZILtCVvsPgd/cSKN/JAyjOsLs5AYEpUu9bZUc80B5KNiQlQkS8uSaOVHEgxHkg28pJ5xCUudi31j6oM8UYsG4Y0/Xqah1xZdihMkyfpsyjDrE5i2uA1yv+tTI7CK882k2jH6EybMqi3uAgZKrszRn9vQ28mJVWPIi2sLjKJdmjsUVQOpKj2p2vEsvIsEkLhbfTTpsaGolKESO6AViVDinegyD1ICGVMI7wwqrPpk27qBGIODUh9DOmdbQJhOiau+IYhbNHx5dkt00ky+a6zCckss4Sw2cqvEeUKXpQrfJQXiY1QlbTnY8MDSSckwEZCdZPQUqD2+un1CbUOiJNc11XCfZxpSEC9hLBZ2fkrps+0mfsn9j5YWFUOyxCuo/UQRhdFXnUkrx2fdBLwRrUrnYSsKtJtI0lP2ExgHbsDWlF6u8AQtrRkwZJymI4p0/RmVP2qlxA2Xf0ahZVnEWPkV8uXXvDxrq5lQJL2QIoNYZMdy5rsBNcgYEHyrpmlfYT7cB0v3FqSy5NkaEDqY/hJKMUUrlKWVlJM7zxdA5L3P8KAlKBgTKhyKY4ywCRYsnX7Fn811VI+hST5bZQPxHmRyBtvnJTnY+NZxK4qWwon9EOmDJLVls/LyQOp1kmq6sEQ8Zl/nNfHhdqvjodD+JpFhGjY8kwE4o0WNkICfBnS7kOBMtjwTPRlMNBfedURU++8WHl6T1el0fHg0UlMG3UNqccR8LpJ8KCSbptxIfGFJdFWboW1/jHF08CGHKYbs6R5r0aNQ8JedbUkCa4FXf0aea5NA1KU17Etb/6U+iCdIiQ4hkyWQeo56KTrkOyna/Foj9uFrfxdzDneM425DskODUh9DK/eSyUF1bGQq7iGnV1U8s1w2bUn6+0tK+SBFBhEhVYCTX+3KqethJxx8tsyYIVlqHwmP0lLTeRtQQadAbkNLyxTD6RaZdJJVKiDquCbmnq9miLdi8v/w14O9TQYBtTnEfzcD4Eo0OvFVt8UlCH4uS0DOxDfNmyE13oYhZjmdG/y9kDy2iUQbxAEKvdTZzVbRdqglySPjZ2m0uSwFV4bvr4bI4Osh2Z8H1+WQ15vmo4p0/RmkgdSeLypu+CRF6rsvn7N4HklNbdI2owjrPOlSKsP0npTy/tHeCzb6GjIIOi5a6Inw6jiVkWOpPyWJN1GskEDUh9D2gXceFeVrCFsCfHleQ2GnfCKUMxKoOPouULGlWMrB1JwFzmLK5kpYQqVOiAoQ5oByX8WdmQoIiTAxB0/ONmrsdwMxtUoAgPcxvgBbim0C4lXr7R3lLGUk8qJ6a+s7oAWI4NVL6hYD6SSfRnq4FmYtM+avQNjErmb4g+yGyudeFxYZLncstxp+fHCiI9fEnSVjZ2m0uSoB71tz0OzumwVOyFs1WUlkZbkPDmJtldmbz9g2wOpFKFf62jRJelZSEdWVGRI7qOl9WbAY7MwGdLnOpIy1OKhFw5hi9vYJuocVbfRAykfaEDqY7ghZZU3psmkTV0dK0ad+LJr3e0pnAAuSnGFVwJVebQTD/qGquyy6hCVCDJsiZem8ASIyqWLi6GPlkG9JbK7wFV7osWRZ1idTqJCHYID3KT6FCzXdMDhWhgMq9cuuW5whd9m4uYY45rXv0nv0FiWIc6I1fu91WTiwc+L2AlOp5rmJVde4TLe+V67jLpmsJ/r/W9oXBbfBCQh3LpkUWcG+0wor+30TWEZ4kKuRRd9UhYEbehNU2+HNONa1BggbPyoxcOiFqL1q+a5iqhSOiNuwaVcvp1+Om2jHmlDltZOcMJ6M1CHU0LYJO5DLe0jOfVI9PXcSN1mXDSJgAakPoZNDyStAaGhPOHEuFHf5bWaGi4rKZllFg8kb4XJVjLMpCTg0hSeADHl+fjPvIBdPFR5JOfKjSkDchW1ruQVwgbUlhg0coU00aMt24DcVttQ76vaLCTdv8NUJmnBmYLN5NFx9dJmGF1c4lqbeZhMkufmVUfiEpiborNKG9WudBLTqkg/jyQPpKLCvmM9kCzKEKevbBnYowh4hAmH62TRHVFtM+q59oTGHnl50psSFRKo/7srz0I6fKuojViAdA+k8LPMvXwD7x/psE61rDCSz6OWeV64jutsfhSl22wnuF9RoQGpj2FzAKbl6WC4oui7vkfIH7cltSlV24BHdFhZLNlhbCVo9UQrKhknkBwqYSNcJun5uK4L7xNJF+i0Fd0GxxFOoq2vePP0wtFJVKiD+tyaE1zsq0ICcl5FzotYjzRLnolA/CStkptOXgYnJrl7ZTVXXgbfy7TARN4m+iscZp2VvD2QmhMMSHE7VJkkKg57F+ZNME9dgvyWPB2AJL0tLUP0vbCXRLt4DyTTMWWa3owah4TzgJpsdpEnattKyjcUhY38YFGe9JXy5ceQqgzx+YeCx+VevmJwj/WCEvT+AZL7yMrnvccKPI/Kxg/m54ZDTOPG5IFzej9O0m0kGzQg9TGkXcBNt7M3HaBHJd8Ml117Eu2gTGkeSFXJ2AwnqtJKrzEiGaWNBJRBGRI8kFx5OZJDE+wMiOMS9pkmks+KyWQxz3AN00S5cQQ8kPwk2tXHhQ2zjYaTZHtJYmMMioV43gQ/r4Txyqv4uL6hiETecX2DpGeih4m3Q173Jq+Evd59S0o0qt5b9X6aJCq2uroeEseqB1LMgodNvR03pvGeo9ROU+Hy45qDjVDjNBnCpBm1osYhOuNNG6jJl+tx0SXRi93WGCrl2dgYy6UZ3KX1ps6CoOTiuL/YU2MIW9XmRwZJtGtJxUAq0IDUx5BeUVV1h4kBSbezS06iXf6flzt+OIdKwJU8tBKos7VlbDnig9Hy/7g8BlY8HZLi131PAzuT9aTQBMlBeVwdsZFEvHx9/UFhnsladRIV6qC2OS8ePTkpu9NbfvbBsKhHWFySWoteL2khbBZEiA0LKCKRd0/IsOu9lU7OCpi1z9ySaBsk7k6i4uYfrwfjQp8yJQ8Xy+8RL39ATwjXScdxoj2HS3b6JiDY9qPGPtLtMinnTVgmKd1p2j7SwuoiQ9hC97OoEDZ1HGKayLvoZ2FvLK1XJyXFqIQdR1tvpGXQCaOTlCEplDGN8CKASUigqtsYwpYPNCD1MaRdwAMDHwNPB93BkB+WEmlAil5FNqXKpdi/rnJMuCMyDN0DKtZ7a263ca7wNsIzErxAbCRATHo+trZGjl1VtjQgN3GNz3OiliW8Mwq1zXnuxFFtPXw/K55XeuXY8jQIrEYXlEQ7bpJm1XiT4v1jU4ZCw3yNPATzuTd5hct4ty2QAyncz8YYZtMS00ZdQyqsMWmXI9s6M6o+2JQhLmGvLQN3mqeBFa8XwzFlqgdSxDjEHwdmXPDICzUMLClcLAqbzyLKbiI9r9GRofy5fNtIG89Ih/PpbIoi6RFmWjdVklOPJJ8T1G00IOUBDUh9DBsJWk1ccDN7ICWs7uQ1GK5KwpswkMsSwmYtdCnC5TXsyilNJfFd9XeVOiDbncQl3yskhE25D7aSJpusbOZZN7O0jSjUU5O2GQ4PyE2TLtpqG3HhMmH5JVFDNNTBoK2+CYgfENu8D5F9pKVQxmoZdPRm8JzsZZpt1x0rT9QgO6GfVe9nFsOZWKLeBGN3wDhgVWeq447q76XLL8tQ+Tw8PpIuPy20RD02b0zHlDphdeHnGjYGNxoueOSF2rYkfnet+IaDCJls5RNNrZMWFj3SDCjS4Xw6KQlsGJCy6Kyq3bMjQkqrztHQbSQbTUULQOJZtrwbH06dj56eSmWf37EcgI3QDBez5i/D5GkLEo/t7k2+odvP6BiQFizu9Mvt19SAtUa2J/7eru4efDazw0+k3NnVo/yOynXnLVruX3fa7MWVckOW7M9mLkJXOKlIBMs6u2N/S554sgXkn7W46ntRGXp/49yF1XWi4u0gK4Pj18ulARmWLe9W5JQrX73PX8xd4sswZ+EyRT7B8ntvcFdPCe99MhcLFy0N9A0qs+Yv7T0nh3KV3/XxjIWYu6jcB606bADa+zennr9kWTemz1mMmfOW+J819roTL1rSVVWf/HbleyCVP1+o9AtJzJhTKceWV9yUaQvQ1nsvlobkl0Ttez74fIE/OF/eVar6XlqGru5S4Pn4usGiEWt5Z7cvQ1e3/C5PARl6y1iyrDu1ns5fXG5DNXsg9Z4+X9ENbf2bMWTIgMTzlnf14PMvO/z3Pb3Pqllx8//0i0VYovStX6jtSjUg9b6esyB9vNDp1UsLSbQ//WIRlnX2+O9nzLarMz1PFHUcNXOu2jfZ0RdASF8tWGq1/O4eN7JeqPVJSnd6+m95V4+W7pjde2/Uc8OExyHe+MM73vvdi5ZU9FVzYwPWGtWufc87lnbhC6Wu6DBnQWUc4pUzvyOoMxsbHQxasBwNbglD21sU3az+btlnsSBCjy8PjdmlcCLmAirzesc2sh5I5f+zY/rLBYs7RWVQb/HMuUsix5KLl3b1yipgQPJCGUP9wloj2tHSr9F/X3JdfD6zIzAXmza7I3Ad9RZ9OnMRlnVV+nsP73epuu2TXt2w9qh28YXvFRkakOqU7p4STrnm/zBL6dhVJOu818n+47Vp+Mdr07TOMU2iHXW49907n8zDO59M8j/fa5s18d97bxh5Pdd1cdkvX8FUxaBSkcm7bvn/65Nn4/XJsyPLVV3q737qPZ2fopxvdLgx3r19Y/JsvBEhv5X8Hr1F/Pudmfj3OzMjj5GWo6EBQA/w99em4e8x9VJ25ajy+o///hR//Penoe/tGBI7u0r435v+aXROLajP9eZH3/Jf92tuwNUn74RBbf1iz126vBvn/vxfWLxMMfIpA9zJ0xbgil9Pijw3bGx+//P5scfGym5hJREAbnjozYjvxYqulKH8vMt/9UrE9/b6huVdPZHPx2b42PyOzkgZ7OSJK/+fMWeJdj2ttY54dfC1D2fjtQ8ruuGIvTbEATusHXlOT6mEH935ImbNX1b1XaOySvuLJ96JL9dRX5ffPP/WF3j+rS+M5M4btb3f+eS71ssPlNEry7NvTMezb0yv+l66Tqrt7ql/f4qnwvrKUvndPaXU9iC56xVQXqgw1R1x/VbcOCQ8tv1walC37br5ajhu/01Sy124pBPn3vqCb1QxRU3J8OaUOXhzypzI475/yBj8+k/vo6PXWOCfLxw69e6n8/BurM4XKVqRofz/1Q9m4dUPZiUcJz9ueO4/M/Dcf2ZYl8FLU+K6wJ9e+hx/eulzqzL4YaVAoH0MG9SCq07a0fcU+tXT70f2m/51Qh5ISf09ENJtfyjrtrGjV8GZ/7WF8W8gZWh6q2OSJj8brDlErNwNDa/tAFhv9cF6116rfO0o+TdYK/oaU6bHrxx1dpUijUcOgPXXGNx73eqyPIYPbsWQgf2w+iptaGvNZk9df029356VjRLkX2VQC4YNbBEtH0i+h0BZKejWgayk1cvGBgdfWW2QWPlNjQ1Yd9WBsd9vIFwP1l9jMEzVeR79xDqjBqJfU7WqKLe9jogzKsyctyRgPCrLNDi1PgHABl77zfgb1hzRjlZlRStvvrLqwEBixjCSfbTH6NUHJ4Z/eH2gJEn10nGA9daQa5MeSW2vscHBaMF+wZdBo05XnVNjHYnrE9//dF7sOQsXd0UajxwHGL/l6ujfktxm1hjRhtaWiq40/d0OgPWE6uWaI9owoCVZj6+2ygAMyKjrTdgwoU6uv+ZgcY9Vx0kem0j3T+utMVjLSNbW2oTVV2kTkSHr2KylXyPWGtke+V1cm/PuZ1x7mDJ9oVbZ077syGw8KssxOHYsrfLCW19UGY8kxzAbavQT0vpKRwZA+D5otru6kEGjHpkyeo1BkYapuQuX+xE2QPK8b+SQ/hjU1ow1hren9vdARbeFx4MfJZRB0nHcFXg/u1mzFhUtQmaamhowoL0V/3l/ZlWYSr+mBqwxok1sAFIqufj8yw7tTPVD2vth2KBW7evPW7QcQ9r7Vcnvui6mz17sh1889txHeOujuVh31YH40bHbRV5rWWc3TrnuWQDAgTutgy3XHwEAGNzWD6sMbvWvO3XW4kBIg8eaI9rQr7ncqSxd3h0If9GhfUAzRg7pb3SOKY2NDhYuL2H2nI6qurDGiDa0NMtNkj1KroupX3agOyZkatigFgxplzVk9ZRK+PzLjtgEiMOHtGLQgHhvmDzo7inLEO41GxscIxf1rMxduAyLlnZh0KD+WLgwPoTNl2lkey6rux1Lu/BlrzfkrPlLcdvjbwMAzj5yS2y27rDY8z6esRCX/bLsGXPMfhthnVUHYq2RZbfh6bMXB0JMVNR25boups9ZguUxx0bhOMBaI9sDce8SLFrSGTkZb+/fhJFDk8OI8qJjWRe6XKeqPgxqa8bwwbJ9k8fchcswv6Oz6vOhA1sw1IKBGwBmz1+KhUu6qj5fZXArBid4yeXJzHlLsHhpd/qByEePh3XbA//3IT6cugBbbDAcZx+xJbojdN68Rctx9s3PAwAOGz8am6xTbr/es1qyrDs2fMZxyobZZsWg7IUa6I4XVN0sgan8UsSNo2z1TeXCgPlLu7FgQbBvyFM3JDFv0XI/LCiO1VYZgP4ak8CszF6wFAsXV/cLSaw6rD8GtEaHZ0eNQ9T+3nVdzJizxNdtf/z3p3j1g1lYbZUBuPx7O6SW/c4nc/HT+18HAJx40KYYmRKOquKNQxyU0xx0htp/Y6ODq3/7GpYu78aW6w/3PfKP3m8jrDNqoGi9DI/vwwwa0IzhwmPppLmAR1Ojk5o2oxZ05lctzQ1YfbjcHM+rw47jxI4l+7c0YjUhw67aL3zw+Xz87u+TAQBXn7yjXwcuuuNFTJu9GFttMBwH7Lhu4Hx13pbU33tUdFsXvpi7FM++MQ3PvjEDA1qaMPHM3XL+deY0NTVg6NA2zJu3OFJn6zJiRPwCtwQMYatjWpobsd4ag2uqUFloaHCwToKnRa3ETSgcx8EaIyqrPgN7c4okDUvVifyIwf0xevXqlWbHcWJXk1T6tzRFnl80juNg3dUGYXBro/W64NHgOFh7lN3OKUxjQwPWXbXY59PU2CDq5ZTGsEGtGDlsQK+y6WetPrT3b/bzHbU0KwNMg+WHNYa3B57f6sP1BieO42ANzWNtM3BAPwwUNlqmMaS9xXp9CDNsUKvRIoIEw4f0F5+ApDFq6ABgqL3ywrqtrXfSm7QsqK4Zjho6oErnDWg104MNjux4wRRT+aWQHkfp0NTYgA3WGop57cX0DTYNyHEMH9w/V0N62jjEcZyAbhs0ID1PoIradNca0R4YE5uwZsSYt6mpwfeaVZMYr75Km/i4Jjy+LwLduYAk9dAveHW4Yriw2z+o/cKCxRUDs1r3vdcDB/RL7M9N+vsBrc0YvXoz/vPRnKryiDkMYSN1TK/1XbeVW8hzQchKj7oNbErjDExk2T4JIYSsTHg5X3THsQGdKaE0q+WxkSOOkCgcqOPJCt5Ch0TdjC6RmEIDEqlbHN9+FN/IA0qQM1RCxAm0shT9q7Zdtk5CZPF1ZqIHUvXxhBAZvCambz+S1ZmVPkIthx0BKQi16kUoLpGaqaEnSTo0IJG6xe84Ehu5ogSpAwkRR21nqfqXHkiEWCdx0YWNkhB7+BYkzdmqsIE3yoDEboAURcB+pL723gg0AlOjLomGBiRSv2hEsLEDIKQ40sbEwakqR6mESOLohMvQA4kQa5h7IMni6eES7UekDnBiLEi+/UiyUE4ga4IGJFK3OBGx2mHojk+IXZw4jR8F2ych1tBpYvQ/IsQejs5KqEJwTCuXAIYeSKQ+iMlI5NVPwRxIaTk8STI0IJH6RS+GTTmcWpAQaVJC1gNQQRNikajJYYjAN1SZhMiikcsziHAOJK8U5g8ldUAgJYJSKSU9kBxTt0ASCQ1IpG7RaeNcRSHEMgY5kOghSIg9tNKtMHkuIdYwToEk7CIYmUSb3QApiNiq5zsgyVVO2o9qgwYkUv8khbDZk4IQArNd2Agh9QU9kAipX2ypVKpuUm+oxlMb3uvcha02aEAidYvpqgjHwoTUL/R2IEQYuhIQUl/U0CRlWnNvblGRaxNiSFolF/HCo57MAxqQSB2joeiYEZQQuyjKN22VKCkXCyEkXyrhMnpuu1SZhMiisxlMgMCYVmALc4awkTpCXVhUq75gDu24tN3EEBqQSN0SpejCcJtwQuySNYSNg1RCZNHZnVh4fkoIUai0MT1l6TKJNlmZSNuVRbARcH2zNmhAInWLVr/BVRRCrBLQ9ynHUkETUgBJDkjSWXoJIVVoq0JpD0HH84iicibFEzeerHggCXjhUe/lAg1IpH5x0l1/qQIJsUxg29XkQ4PeDlTahEjitTHdBKRskoTI4hh6O0gnuffkKXGHVFIHxHu0l99I1k3aUGuDBiRSt/iutgnHBLcJpxYkRBrHxAfJlXXHJ4RU0Nky3JX2cCCE+Jh6O0iHlmnlSSPEFoGcmoh8LVWkjZ3eVmRoQCL1i85oOOJwQogcTkYPJDZQQoQxzIHENkmIMBq5PIMIp2WI8Ozn4ispimAKJFd53fu9hBeeX0j+116ZoAGJ1C16HkjsAQipV5hthRB7VAbGei5IzAVBiCw641gV6SFtlAcSewFSFGkGIhEd5Yd6k1oo1ID06aef4oQTTsBWW22F3XffHXfccYf/3eeff45jjz0WW265Jfbff38899xzBUpKisDRWU5Vj6cWJEQcJ8blOBJu+USIRdIHxvRAIsQiphYk9VQBnRmZk4n9AKkD1DrpVrJo5w6rez4UZkAqlUo48cQTMXToUDz66KO45JJLcOutt+IPf/gDXNfFqaeeiuHDh+Phhx/GwQcfjNNOOw3Tp08vSlxSBBpxqi5Hw4QURpoHoPSWxISQCloJe5kDiRBrOBpGXRVprwhPnhI9kEgdEGck9WqnzE6ESjmMYslMU1EFz549G5tssgkuvvhitLe3Y91118WOO+6ISZMmYfjw4fj8889x//33Y8CAAVhvvfXwwgsv4OGHH8bpp59elMikIJJ3YWPjJ8QmRouibJ6E2Edz51JOHAmxhOZElRNasrISmM9ZagYuqAezUpgH0siRI/Gzn/0M7e3tcF0XkyZNwssvv4xx48bhjTfewKabbooBAwb4x2+zzTZ4/fXXixKXFIBpo2aEDCH1C9snIbKwiRFSX9Si92SSaJf/uQwvJ30BhrDVLYV5IKnsueeemD59OvbYYw/su+++uOKKKzBy5MjAMaussgq++OILo+s2NDhoaOibVaWxsSHwf2WkobHy7Jqaou+Den8aG53Y4/oyrAtEpej6oLaxhobkNhduwyti+yyaousDqR+88Y4LN7Y+NCptspFtcoWGfUPxVNpk/Dg26nggf53Z2NgQmZKJunnlpB76h+B4sroeNkZ8Vivq721qbCjcTlAPzyELdWFAuvHGGzF79mxcfPHFuPLKK7F06VL069cvcEy/fv3Q2dlpdN1hw9r6/PaUgwb1L1qEwmhtbQZQVqhDh7ZFHrO8VHk9sL019rgVgZW5LpBqCqsPTY3+ywEDWhLbXFtbh/968OABK3T7LBr2D6SlpawzXTe+Psxb0u2/HjSwP9vkSgD7huLo3788l3Gc+HGsSltbi/96yJABGDqwNVd5vClRgzI3GjyI/cDKTJH9w6D5y/3XAwdW5nDe3L1//+bc62bbgIp9YciQAXVjuOlr/XRdGJDGjh0LAFi+fDnOOeccHHbYYVi6dGngmM7OTrS2mnWkc+cuLtyymJXGxgYMGtQfCxcuRU9PKf2EFZDly8sD3Z4eF/PmLY48ZsGCJf7rxYuXxx7Xl2FdICpF14eFiyuG/I6UNtfRsaxy3sKlGNDUN/vjeqbo+kDqh87OinEorj4sWFjRmR0dy1ZInUnKsG8onmXLugAApVL8OFalo6MyoV64YCnQ3ZObLOWJclkHdyv1YdGipZjXrz4m0cQe9dA/LOqozPUXLlyKefPKxp2eUlmeZcu6ctdRS5ZWxrBz5y1GU8EGpLyeg20jcKFJtF9//XXstdde/mfrr78+urq6MGLECHz00UdVx4fD2tIolVyUSn07IV1PTwnd3Sun4nd7n50LN/YeqJ/3lOKPWxFYmesCqaao+qAOPNNkCLRP1l9ReH+Jl4DXdd3Y+tDTXRkTsc6sHPA5F4c/jnX1xqfqBLJb4Ll5jkfq3KinZ8UeO5NkiuwfSj2VeqjWdy9Hl267McFV6n53d6luNnvpa/10YWa3qVOn4rTTTsPMmTP9z9566y0MGzYM22yzDd5++20sW1ZZvZ40aRK22GKLIkQlBaGzJTF3lCHELoF2lqJ42T4JsYevMxOOUXe66esh/oTUO6ZNTFpnVvoIpR8QKIcQLdTKF6m4JLJoV67JTQ+zU5gBaezYsdhss81w/vnnY/LkyXjmmWdwzTXX4OSTT8a4ceOw2mqrYcKECfjwww9x++23480338Thhx9elLikABydjkNp/BwLEyKPOul0jSxIbKCEyNKbsDdp0YUDZkKso93uAmNauW3YXK7ukDpAneepVdLzphXciDCiVGJCYQakxsZG3HLLLejfvz+OOOIIXHDBBTjqqKNw9NFH+9/NmjULhx56KB5//HHcfPPNWH311YsSlxSB74EU38CD31ALEmKTtEExVzkJsUdlvqk3KKZNlxBZPCOQvv1IdkLrh7C51M2keJwUDyRBG2q5SNqPMlNoEu1Ro0Zh4sSJkd+ts846uPfeey1LROqJqO1Gw6jGJQ6GCZHHqJ2pq6m5S0IIUfF1pqYHkpaXLyEkM9H+FfG4wl71kX0EB8+kDlCNp5KGndSoOaIF0+6T+kXHghRxOCFEjoDyNYhgYwMlRBgnIjwlhEurLiH20MjlmXJqrvgeUfRAInWAE+MN5Prf5187A9ekBSkzNCCRuifZA8maGIQQAME11eQGyPZJSBHo7TzBiSMhdtAOYbOkM6mbSX1jp4JKh4yuyNCAROoWY/d6joYJqVsYLkOILGxhhNQXtbVJOy2a/QYpirRxIetm/UIDEqlbHI2EDsGNJNjVECJNWtLDIMxRRog1NMJlGFZKiEU8xafp6OAK60xfHJeuiKS+UOuk/1IwD1igHGIMDUik7kls31SChBRGmu6lcibEHlppA5lEmxBrVNqktgVJFK/Nl9gPkDogzkgqaD/iXDEnaEAidYtjuJrKPoEQebKuikokQySEVPAngppJtNkkCZHF0bLqVhDfHC3CA4n9AKkHAnO9Shbt3MtRDaZc5MwODUikjtHoOOiARIhVgso3JYm2tDCEkAq+/Sgh7JuNkhDrZGl2Ep5BOguzhNhCXVgM2o/K72R2IlTfsSFkhQYkUrdUUiDp5UDiMgohFjDYAZWrnITYQyNtYPB4tklCRPEmyLptUjo3UdS4mt7BpCiCthyl7ve+lK6aNB9lhwYkUrfodBwBJSgoCyGkTNbFG7ZPQoTRyNcb1JlslYRIEu1fEY94WganOgcSIYURsyApWT3VuSU98bJDAxKpe7iaSkj94Bh5IMWcSAjJnUoOJI6KCakLTJuk6oBkzQMp/3II0SFuU1/X90CSzYFEskMDEqlbfNffhGM4TibENvrLN4GEvVLiEEIA6O0Y7gpPUAkhFUwnq8G2aycHErsBUhixK5JyOZCCQ1hOIrNCAxKpW0xdfxnHTYg8Jh5I3CaREPvo7lxKCLGDvgeSrGeQZ9ByaUkmdUDQA6lSJ/3qKeiFVy6TZIUGJFL3JA+G2fwJKYq0QTFbJyEFkKg0mTyXENvojlVt6UzqZlJ3FFEp2RAyQwMSqV84tiWk7sg652RzJkQW07bJNkmILHVno42Qp95EJCsPae1DJF+RiRc9iYUGJFK3+Mn+Eo6hFy4hdUygfbKBEiKJH56ScAwHzITYw9d6mg1PekwbeUmqZlIHRCXRloBJtPOBBiRSv+hkBFUPZ6dAiDhqO0tLQMgQU0IsorHjU+ArqkxCZNHYDCb2VJEk2tXXZDdAikKtj6re8saOIkbUQBIkjlGzQgMSqVsqHkjxDTy4TbioOIQQwMj9lx6ChNhDa+MJ7r5EiDVM25hbwPZo9A4mRRGseW7VS+maSfNRdmhAInWLoxHDxm3CCbFLoJ0ZaF+2T0KE0fJAolWXEGsYbhkuvSbKJk/qikD7UF7738t64dEBKTs0IJG6J7F90wOJEKsElG/KsS7jZQixhk4OJHogEWIP4y3DhfMGRoawsSMgBZHWPkSMqALXXBmhAYnULZ6i083nwBxIhFjGIAcSB6mEyKLntRtxPCFEhIDBRsOCVIRDBLsBUhgR7cNmGKeOVyCJhgYkUrcY53OgFiTECjo7JOodQAjJG+28gYQQa2htKtHbQKWGs9HjZA6eSTEEPZDc3v/R3+dWJqt7LtCAROoX03wOhBA7aLRNgN4OhNjE0WqXqlcgGyUhkgQcLEw8kISaZpSnPrsBUhSROTWFwzgD7gmcQmaGBiRSt+iEpHGXJ0LskyVclCGmhEijEfbNHEiEWMN8FzbvPJnWyXEyqSsiIjylHQOCUXO0IGWFBiRS99BCTEh94Xs6pChfxpcTUgQJIWzqG04mCbEG1SEh8Xjtw2o7YZvMDA1IZIWBHg6E1DFsnoSIYtrE2CQJESajy49NTyF6JZGiSJu3SdRN450RSSQ0IJG6RcfLgR4OhBSISQ4kUUEIIVo5kKgyCbFGUO+lNz6b4Tv+Z9TOpCCcyCRIyvcihUpcdOWDBiRSt/htXFOfchWFEDtUjLspMEcZIdZJth+xURJiDcMk2l7zlGqakcYidgOkDogMYRNoCGob4HpKdmhAIvVLb8eROBhm6yekALxkvSk5kCLOIYTI4Oi4IDGJNiHWMA2XUfZIzF2WuMuyHyBFEb3LmnArCDRKTiKzQgMSqVt0Oo6goZpqkBAbaDc1RTmzeRIii9fEdCPY2CQJkcUJbvmUircoI+eBFPEZOwJSB/i7sAkrKeZAygcakEj9EnD9jWnmrvh6DSEkhD9RNciBRAgRRscBiRYkQgrBJL+RVNOMXmhlR0CKwYmY50mrKIcWpFygAYnULTptnFsSE1IAmjmQ1MkqVzkJkUUvs4O66MJGSYgkptEy/jFiFqSIj9gNkIKIrHqBcaOICSmqKGIIDUikbtFx/eViKiH2cXRcHeLOIYTI4OUN1PRA4sSREGEM21jFfiTTONnkSV2hKCE/ibbFnQi5k3d2aEAifYLYDkU4Wz8hJAJtDyRm7CXEFjo5kAgh9gjs+KSVRdvbhk1InohxMofOpCiCkSa9IWzCw0ZW93ygAYnULUErcfQxrnS2fkJIFX5bM8iBxPZJiCyGm7Bx4wlChAk2sXQLknQEWxT0DiaFkZarRNiCRAek7NCARPo2bPyEWMfR9EBi+ySkABJGxXTZJ6QYtFoemydZSYnchc1SmcQcGpBI3WJqeOZiKiG2MG9s9HYgpL5gkySkPpFqm9yEjdQTaVVPwjvOoQtSLtCAROqXiORqYdj0CSmONG8Gtk9C7OEZadnuCKkPdFIxqEgnZYiakNN+RIoierMkpRUwCVLdQgMSqVuCbTxa8wZ3lGGvQIgNtFtabwNlyyREHj+Jtu4ubKLSEEJMPShcYZ0ZNUzm2JnUA34SbeFy0tIuET1oQCL1i9bKDZNoE2IbnWS9gNI62TgJkUcju31gR1NOHAmRJeOW4WyaZGUgykPPFVZRhnntSQw0IJG6RcdKHNDHVLiEWCVN91Z2JGbjJEQar50lzlPpgUSINUy9HaRTsoQn5OwDSJEUkQMpkB4l/6uvNNCAROqW6NjYILQfEWKfStvUU79cTSXEHpr2IypNQqQxtCB5h0iFlVVdl30AKZTqXLeucJx1oEkyiXZmaEAifQJXy4JETUiITdJD2KicCbFFxa6blARJOV5UGkKI6kGhpw3t6kx6B5MiCfoJVOdAEsmhzSqfCzQgkbolaCWOPsZlDiRCrKOtgL0QNjZOQsTR8QsM6Ew2TEJEcXQGshGHSDXNKgckdgGkXnBD/wHxiR0dkLJDAxKpX3Q6Dq6mEmIdHUcHgCnuCbGKk54DiQNmQorBpOmJ7cJGXUzqiKhMJUH7Uf71lQsn+UADEqlbnIjY2DDM50BIAXgT1ZQhsb8lMdsmIeKYNjO2S0JkUSerOsZb/xgxF6TQW/YBpEAiDUSu6iUrWz7TLGSHBiTSR4hu5FxNJcQ+GruFE0IKJC45qHB+UkJIDXBCS1ZWPJ1lswVwDpkdGpBI3WJqeaZrLiGWMG6bhBBxjBsaWyYhkmRtYXIhbLZKIkSDAqofve7ygQYk0ieINxLbc3UkhJTRSdYLqO74gsIQQgDo7RjuUmcSUghaW4YL68xw/hf2AaRIonRWwEtWoILqpEch6dCAROqWQHK1uBxIbPyEFIdmEm16BxIijxOVkTQMdSYh1jCd/0qvuYSvS81MisRJWfUQqZ+s9LlAAxKpW0wnncysT4gdHM0k2pU9iYUFIoQEiGubtB8RYhNDbwdPZVpKok3dTIpFaR/ef2HPgKDNihoxKzQgkfol4IHEwTAh9UaanmcEGyH20PHajTueEJI/QafA9EYpPaENL8zSO5gUSdADqbrui+gonVhvkgoNSKRu0crnYHG7R0JIGe225q+miolCCOlFp5kFdCYnj4SIEmhhOimQhHVm1XXZBZA6ISoHkkQFdSK8nog5NCCR+sXQSkw9SIgd/CTamh5IbJ2EWMBJD5cRHpsTQlQ00pKlnJYvTuJbQqwS5TUrvdFDWt4logcNSKRu0bESBwbJdHMgxBJ6OZA8bwe2TELk0crtoO5wIyoNIcTU26EyppVpnVUOSOwESIFEesFa1FHMgZQdGpBI3ZIWGwuELNXC8hBCyvhtU1P3cpBKiAU0Vla55kKIRTTGsSremFYuhI0xbKSOiMh1K+0la5orkERDAxLpE8S28cBo2IIghBCfNN1L5UyIPbQ8810qTUJsYRwtY1lnsgcgRRLVPoIaSjYHEskODUikT6CVz4EQYgVdDyS2T0IKgh5IhNQXOkm05aUgpD6JzqItWyRXOTNDAxKpW6pdbVOOF5KDEBLEdAXHtC0TQsxhOyOkvsjaJG3twsYugxRJqs5i/axbaEAidYtWv6EmW6MmJMQOvU0tPQEhV3cIKYK4tukGdKYlYQhZaTHcMtz1zpJKos1GT+qT6BC2/GEOpHygAYn0CeLcDJlBn5Di0M2BxIkqIfIY5uslhAgTnKzqJ9GWsvNUeyBROZNi8Wug1z64yNEnoAGJ1C06HQdXUwmxT0XhGx5PCJFDS2eqO5eyZRIiiWkL81onWyZZafA92oP/A1/mWZxj6BVIIqEBidQxSiPXaOVUuIRYolcBa+/CRusuIeI4GjpTeotkQoiCabiMsM4MexxRNZOi8fSW74CkLnII1M/AJemqmxkakEjdEnD91cjnwNEwIXaocjmOwWu3bJmEyKOjMwPhAbLiELLS4xjmQJL2QKoKYRMqhxBdkoxEIvUzoCdJVmhAInVL0EocfYw6SOZKCiF2cEIux7HQH5+QYtDwQGL+E0KEMU1M1nuMtabJPoDUCd58LtBMhD2QaEDKDg1IpH7RsRKz9RNSGGnjYdqPCLGHo6UzqTQJsYXpZFW6dVaFsAmXR0gafpWszqEtk6dPS1GSNGhAInWLjutvcDVVVBxCSC9OWOPH4e/CxsZJiDTBHEhxO5d6xxJCpNHxpA8cIqwzq67KjoAUTiinZmB3JKnSeouiBSkzNCCR+sXQ9Zc7yhBiB998lGo/onImxBomO5dSXRIij/GOT8J5A5kDidQZfkqEXuUkHMEWdDbgEDUzNCCRPkGsBxLd8Qmxj2YOJDZPQoohre1xwYUQu+iMV6kzyUqPxTbA5pYdGpBI3WI8vOV4mBArmDY1RrARIo9JM2ObJESezM1MqH2GL8vwclI0STVQunbSYJsdGpBI3aLjZijt6kgIiUJ3G7bA0YQQSXRC2LjmSog1TO0z0hGmNBiRusMPYev9H/gu//rqMIYtF2hAInWMRuy4mmuNepEQq6RNRiurO2ychEijk0Sb42VCikHL20FYZ1Z7IIkUQ4g2nt7yqr6qu0RyIAlcc2WEBiRStwR2WkzZUab3DElxCCG96G/C5gaPJ4SIobM7se/hwDZJiDimHj/iOpNJtEm9kbAri0g7CMwtBa6/kkADEqlbtPoN1VJNTUiIFTTtR/R2IKQo4hyQuA0bIYWgFT7ae4i9EDb2A6RYwuNJaaMOA9jygQYkUr9oWInZ+AkpgFDMehz0diDEHo7BluFsk4TI4xh6O0hHfTOEjdQbSR7tIjm7dFx1SSo0IJG6xdHIgaQqZCpCQuzg6Pog+aupbJyESBNYWY0L+xb2cCCEVDDVfa60zmTDJ3VHKAeSsFUn6IFEC1JWaEAi9UtwNKxxODUjIVbQ3ITNlfbHJ4QYwbz2hFjEON+KrM4Mj5O58EqKppICye39r3wn4YCkvqH9KDM0IJG6RSdONbDKSkVIiBX8pqYbwiYoCyGkjFa4TO8XXHAhRB5TbwdpnRmekLMfIEWTZCQSqZ+MYMsFGpBI3aJjJVY/phokxA6OpgcSkyARYhONsO/qQwkhUphm7PUckGy1T/YDpE7wFj1c4YldID0KLUiZoQGJ9Ali2zgbPyEF0BuznqJ92TwJKYiUnSc4byTELgb2I0JWOmzlQIoulZhCAxKpXwyXYESy9RNCqjBtamyZhMhj0i6pLgmRJ3sIjkwDDY+T2Q2Qokmau0nUT9OdEUk0NCCRukVrRxk7ohBCFLSVupdvhaNUQsTRyhsYeTQhRALHMITNFdaZVZelciZ1gxv4B4Bqqo6hAYnULVp6zU8ISgixhpcDSTOJNiHEAhqTVZc6k5BCMAnNsZdEm5BicULjyaD9KP8aymiVfKABifQJUtI5UAsSUgBpw2Gv3VJhEyJPIDkozbeEFI5puIx/jCWVSdVMiqaqCqqbawvXT2rJ7NCAROoWnUmnP0GlBYkQazi6LkigtwMh1tCYrFaMuvLiELKyYzo2rdiP7ORAIqRwHG9TlvJb6cWPoFGXJqSs0IBE+gRpHQp1IiEW8exHKYfZXk0lZGVGK+pbXApCSBRak9XKqqgIVSFsHDyTgvFqoDfPc4U9kFjj84EGJFK36Lj+0k2fEPv4TVMzBxIVNiHyBHRm7FFekl62SkKkMW1mtnUmewFSOBULUsRXEhYkJdSbU8jM0IBE6hatjoPu+IRYJ0HfB2GSMkIsog6MY3Yu5YCZkELQanuyDkjVhmOqZlIw4fGkdFhZcK8JKsSs0IBE6hctD6SIgwkhsvgx68nK1/W9HcQlImSlx6SdsU0SIo9qsDGwH4k10PBV2Q2QovHbSJQHkkQF1ditlKRDAxKpW3SsxP6WxNSChFhDu7kJr6YSQioEdGZaEm1xaQghgXam4Vnhj2llxIm4MHsCUh9E5UCSgPajfKABifQN2MoJqRv8BSPNHEiEELvEtT2Xcd+EFAL1ISHxVHZhs1mozcJWLGhAIvWLiTu+nBSEkFphAyVEHupMQuqLjA1Nyr4bzi1KOzIpmqQ6KLHZQzCslBakrBRqQJo5cybOOOMMjBs3DrvuuiuuvPJKLF++HADw4x//GBtttFHg79577y1SXGIZVdHFrqZymydCrKOr1CvhMmyghEjjmCQOZJMkRBzTcBnx8B3m0CZ1RlUdVBqBdP3kphLZaSqqYNd1ccYZZ2DQoEH4zW9+gwULFuD8889HQ0MDzj33XEyZMgVnn302vvGNb/jntLe3FyUuKQBHQ/NWxsJUg4TYJn23DCZcIcQaqv0o5hDajwixiKPRKCNPs5NEmx0BKR5vU5byO7f6q3xLY53PhcI8kD766CO8/vrruPLKK7HBBhtg2223xRlnnIEnnngCADBlyhRsuummGDFihP/Xv3//osQlBRPrZsiMoIQUhqb5iM2TEAsEE/bGHMQVV0KsYbpluPQW5uHZMxdfV1y6u7tx55234ZvfPBh77LEjDj30ANx003VYsmQxAOCQQw7AI488knu5d955G0477UStY7u6uvDllH8BKLeP0047EY8//Cv/e+lN2Eh2CvNAGjFiBO644w4MHz488HlHRwc6Ojowc+ZMrLvuusUIR+oCh974hNQl2is4zNdLiDV0wmW8SayUhwMhpIKOJz0iDhHLgRS+LruBFZZbb70RL7/8Is499wKsscaamDZtKm644af4/PPPcfXV1+Puu+/F6qsPx9KlPYXJ+Ne//glT3/oz1tlja8AFrrjiGnw4rQMf/P798gEiDUFJj8IFlcwUZkAaNGgQdt11V/99qVTCvffeix122AFTpkyB4zj4+c9/jmeffRZDhgzBcccdFwhn06GhwUFDQ9/sHRsbGwL/V0aalN/e2Oigqan6XjT0di6Og8jvVwRYF4hKPdSHBkWpJ7a73sMcJ7r9ktqph/pA6oNGpY01NES3Oc9w5GDF1ZmkDPuG4lHbWEPMOFbFU60NAjqzsbGhyl4kUQ6pD5566glccMH/ww477AAAWGutNdHaegFOPvkEzJ8/B6NGjUJrays6O5fmWm5Dg6M95lPtQ06Dg2HDhmLAvJL/WVNTQ+71s7k5XU/apK/20zUbkDo7O9GvX7+aBbnmmmvwzjvv4KGHHsLbb78Nx3EwevRofOc738HLL7+Miy66CO3t7dh77721rzlsWFufX2UbNGjlDdubu7jLf93e3oqhQ9uqjmltbQZQVoJR369IrMx1gVRTZH1obm4EADQ1Nya2u37NZRXT3JR8HKkd9g9k0JzKRKCtrSWyzbW09OrMxga2yZUE9g3FsaS74uLQ1hY9jlVpairr1uYU3ZqZ0JSoqYn9wIpKQ0MD3nrrdRx00P5oaCgbJ3bbbUc8+eSTWGedNbDvvvvitNNOw6GHHoqjjjoKu+++O5577jlMmjQJG264Ia699lrccccdeOKJJzBixAj8+Mc/xrhx4/Diiy/i6KOPxvvvv++Xdd555wEArrrqKvTv3y9Qfx988EHceeedmDp1Ktra2rD//vvjwgsvxCuvvIIf//hiAMAHT/wQu256M84442SsNXozAJsAAP79/F/x8AO/xrRp07D++utjwoQJ2G677QAAe+65J0444QT8/ve/x7vvvovRo0fj8ssvx5gxYxLvS8uyytxywIB+dVP/+1o/ndmAdN999+EXv/gFvvjiC/zpT3/CHXfcgVGjRuGUU04xvtY111yDX/7yl7j++uux4YYbYoMNNsAee+yBIUOGAAA23nhjfPLJJ7jvvvuMDEhz5y7u0x5Igwb1x8KFS9HTU0o/YQVk0aJlgdfz5i2uOmbJ0k4AZbffqO9XBFgXiEo91Ifu7nK5XZ09ie1ueWd3+fie0grbPoumHuoDqQ86OhSd2bEM8+ZVD/GWLevVmSWXbXIFh31D8SxcWDHqdnREj2NVurrK4UTd3fnrzLIHUnBO1EPdvMLyX//1Ldx++63485//gp122gXbbbc9dthhR6yyymro6OhEqVTuExYuXIqurh5MnHgzLrjgRzj99LNw7rln47DDDsM3v3kk7rrr17j11om45JJLce+9D/hzM7XeLF/e7X+2dGknurrKY8NXX52Eyy77MS6++DJstNEmeO+9d3DxxRdi7NitsMsuu+HMM8/BzbfdgTV3Oh3NrYPR1dWD5Z1lA8+Cz1/BTX/9A374wwnYbLMxeOKJx/G9730PDzzwKEaOHImenhJuvPFGTJhwEb7yldG48srLcPHFl+IXv7g78b4s6x2XAsDixcsLr/959dO2DWGZDEh/+MMfcO211+KYY47BHXfcAQBYb7318NOf/hStra04/vjjta912WWX4b777sM111yDfffdF0DZxdozHnmMHj0a//73v43kLJVclEp9O8Cxp6fkT9ZWNtSG1N0dfR+85+sAK/x9WpnrAqmmyPrg5VEpuW6iDJX+N/k4UjvsH4iqM+PqQ48yJmJ9WTlg31AcOuNYFU9nukI6Myoog3VjxeToo0/AqquujkcffRC///0jePTRhzBgQBv+53/OxgEHHOTn/+npKcF1Xey00y4YP/6rAIBddhmPv/3tzzjuuBPhOA4OPPAQnH/+OejuLvl1Wq03XvL37u4SSiUXbu/YsF+/Vpx33oXYddc9AAAjR66KDTbYCFOmTMGuu+6B/v3b4DgNaGodCBcOXNdFr10L8z9+DvsfeBj22Wd/AMBJJ52GV1+dhN/97n6cfPJpAICvfe3r2Hnn8QCAI474Ni688NzU+tyjeAX2lOpnbNrX+ulMBqS77roLF1xwAb7xjW/grrvuAgAcffTRGDBgAH7xi19oG5AmTpyI+++/H9dddx32228///MbbrgBr732Gu655x7/s/feew+jR4/OIi7po6jhh7EJQfu2fZCQPom/ipnSACtJ7vumJyghfQlHZ8twJrYnxBqGObSt60x2Ays2++zzNeyzz9ewYMF8vPjiv/Hwww/gqqsuw3rrbVB17Oqrr+G/bmlpwaqrrubrlJaWFnR1dVWdk8bGG2+ClpYW3Hnnbfj44ymYMmUypk79HOPG7VB9sBt80dnxJTbccJPAIWPGjMWnn37sv19zzbX81wMGtKG7uxupBDZo4iQyK5kyNn388cfYdtttqz7ffvvtMWPGDK1rTJkyBbfccgu+973vYZtttsGsWbP8vz322AMvv/wy7rzzTnz22Wf47W9/i8cee8zIs4msYMQ0cjZ9QuzjTT5T2x+VMyGFkGI/IoTYRsuCxBZKamfy5A9x003X++8HDx6CffbZDxMn3o4RI0bi1VdfrjqnsbEx8D4uh3DU5z090Tu5vfjiCzjhhKMwZ85s7LDDTvjxj6/G2LFbRB4bsh/BaWyOKKcU8Oprbq4+xgS2tuxkMiANHz4cH3/8cdXnr732GkaOHKl1jb/97W/o6enBrbfeil122SXwt/nmm+OGG27A73//exx44IH49a9/jWuvvRZbbbVVFnFJH8VkZaSvJ0snZIWGzZOQuoIqkxALZGxoUu2zaqzMfmCFpKenBw888Bt88MF7gc+bm5vR2tqKIUOGZr62Z7RZsqSSO2j69GmRx/7hD4/igAMOwg9/eAEOPPAQrLPOupg2bar/fdLcrV/bCHzwwTuBz95++z9Ye+11MssOhKo8LUiZyRTCdsQRR+DSSy/FhAkTAAAfffQRnnvuOfzsZz/DMccco3WNE088ESeeeGLs93vttRf22muvLOKRFQUNb3y2fkLs4zXNtMXSijs+IUSaQARbWggbWyUh4gRD2NLHq9Ij2nCrZz+wYrLRRhtjp512wXnnnY2TTz4dY8dujjlz5uDpp59AZ2cndt99T9x11+2Zrv2Vr4xGS0sLfvnLu3DwwYfiH//4Gz744H2stdbaVccOGjQYb731BqZMmQzHcXDvvfdgzpzZ6Owsb+bQ2tqK7s4l6OyYhVLPKACVNjB09K546olHsNnGG2LTTcfgyScfx5QpH+LCCy/JJLeHTqQ3SSeTAel73/seFi1ahLPOOgvLly/HSSedhKamJhx55JE4+eST85aRrKToxI57g2SuphJiD22PP3+ySgiRxtHQmi4bJSHWMPV2cIWbZ5XqZj+wwnLppVfhl7+8E3fddTu+/PILtLb2x7hxO2DixF9gwIDsO3a1tbXj3HMvxG233YyHHrof48fvicMO+y/Mnz+v6tjjjz8JV1xxMU466Vi0tbVjxx13xiGHHI4PP3wfALDNNtuh/8AR+PTZ6zF79I8C5w5cfQvsv+1w3HHHzzF37hysv/6GuO66iVhnnXUzy15GZ6WFpJHJgAQAZ511Fr7//e9j8uTJcF0Xo0ePRnt7e56ykZUdAzMxdSAh9klLQOh/SwsvIfJojIs5XCbEIhm9HeTSMjgJ78iKRGtrK0466VScdNKpkd8/9tiTGDq0DfPmLcbEiUFvpBNOOCnwfuutt8Vzz73iv/eSc0ehnjt8+HBcd93EWBkHDRqMbQ86D9NnL8bw1VbHORNvx2sfzsJND/8HAHDgQYfjuKOPijz3oYf+kChjHByO5kOmHEjLli3DhAkT8Ktf/Qpjx47F5ptvjoMOOggXXXSR75ZGSK3ouP76g2T2CITUHZ6Bia2TEHm02hkdkAixhuoVqOPsIL0rFFMgkXqjar9tN+o7Gbigkp1MBqSrrroKr7zySiCp9YQJE/Diiy/i+uuvTziTEH0cjRg2z7BEJUiIPfxd2DS1L+27hFjAxAOJjZIQcRzTGLbI8/KjOoSN/QApmNB40o34LtfiGMGWC5kMSH/9619x9dVXY9y4cf5ne++9Ny6//HI8+eSTuQlHiEdsG2eWXkIKI033UjkTYo+At0PsqgsXXQgpAj0PJGkpGMJG6gt/Uxbvf8ADKf8aysTx+ZDJgLR48WIMGjSo6vNhw4ZhwYIFNQtFCBCMAU9bTWV3QIg9HEMXJLl8DoQQDz2v3YhjCSEimOq+SvuUaaBVIWzsB0jheONJ731FeYnUz4AHElc5s5LJgLTlllvijjvuQKlU8j9zXRe//OUvMXbs2NyEIys3wX4jeTTMCSoh9givGMVB5UxIMaTtXEoIsYtW0xP2EORImdQb/npkbwuR1lE6O3yTdDLtwnbmmWfimGOOwYsvvogxY8YAAN5++23Mnz8fd911V64CkpUYrXwObP6EWCe8YBQDvR0IsYdebofeCSobJSHiOIbeDpUcZSLiVF2X/QApGr8GRjQPifoZuCankJnJ5IG0+eab4/HHH8cBBxyAzs5OlEolHHjggXjqqaewxRZb5C0jIVxNJaSOSFL4hJB6IHnnUk4bCak/qFLJyoob+m+zTGJOJg8kAFhrrbVw9tln5ykLIQFMBrhcRCHEIoYNjs2TEHmMkoOyURIiTtZmJueAxIZP6owiqyS9EDKjbUCaMGECLrjgArS3t2PChAmJx1555ZU1C0YIDNwMqRIJsUclB1Jyw3QZw0ZIIXDjCULqAI3NYAII60wm0Sb1hmfU9NqHGuopVT8dlJsazUfZ0TYgTZ061U+aPXXqVDGBCPEIJjpLdsfncJgQezgVC1IiXrtl6yREHq3BNnUmIdbQGcci4hhbSbTZCxAJ7rzzNrz22iRMnHh7+sEJORG8r373u9/it7/9NRYvXow999wLZ575Q7S2tgIAZs36Ejfc8FNMmvQKWlpa8NWv7o0TTzwVLS0tyWW66Ubdww//Oo4//kTsv//X03/HSoa2AenXv/61//p//ud/sPnmm6Nfv34iQhEChBRbShJtrqIQYp/U4TDdHQgphLiEvdx4ghCLmG755O8sLCFMxIU5eCYFE16PDKgux8E//vE33HXX7bjoosswbNgwXH75Jbjllhtw1lnnwnVdXHjhuRg4cCBuvvkXWLRoIa688lI0NDTi1FP/J6FMh7qwRjIl0T799NPx4Ycf5i0LIUF0ItjY/gkpjLTVm4r9iINUQqRRd5dJ05mcNxIiT0b7kRhV9iPh8ghJI+zRrhp2HAAPPng/vvnNb2HnnXfFJptshv/93/Px5JOPY9myZfjss0/x9tv/wfnn/z+MHr0etthiK5xwwkn4y1+e1iuTZCaTAWnYsGFYtGhR3rIQEkCddKbmc2BnQIg1HO0YNnoIEmILPa/diGMJISKYbhnu75IolQMp9QPSF5g58wuce+6Z+OpXd8bhh38dd911O3p6enDZZRfhv//7MHR3dwMAnnzycey773jMnPkFAOAPf3gM//3fh2H33XfAvvvuiUsuuQQ9PT0AgMsvvxi33HIjfvSjCfjqV3fGd77zTXzwwXu4/fZbsN9+u+Mb39gf//d/fwUAzJgxHbvssi3+/OenccghX8N+++2On/3sp365Yd544zWccMJR2HPPnXH00UfgH//4m/Jtbw4k763STnpKPXj33Xew5ZZb+59tttkYdHd3Y/LkDzBs2Cq49tqbMGzYKoHyFi/u8F+rv/mAA76Ka6/9CVy31FtUeqP86KMpOO64/8aee+6Es846DV988YX/3Ztvvo7vf/8EfPWrO2OvvXbBOeecgdmzZwMAuru78ZOf/BgHHPBV7L33rjj33DMxa9aX/rnPPPN3fOc738T48Tvh8MMPx6uvTkqVpZ7ItAvbbrvthpNOOgnjx4/HOuusUxVneNppp+UiHFm5cQxGw/RwIMQevvlI0wOJEGIBjbmqSwsSIYWgFzIjnDcwZJji2LmaJcu6MWPuYqtlrjasDQNa9abkruviggt+iPXX3wB33/0bzJ49G9dccwUaGhpw+uln49vfPgwPPng/9ttvf9x88w045ZT/wahRq+K11ybhZz+7Bj/60WXYcMON8eGH7+Hiiy/E2LFbYddd9wAAPPjgfTjjjLNx4omn4IorLsEZZ3wfe+zxVdx22z148MH7cc01V2D33ff0Zbn77ttxySVXoqenG5dd9iP0798fJ510akDeOXNm44c//AFOPPEUbL/9Tnj77f/g8ssvwdChw7DFFlv5VdILu1ZbyZLFHejsXI7hw0f4nzU1NWHQoMH48ssvMWbM5th++x3970qlEh555HfYZpvtAKDqN7/33ju47LIfYeQW/TFg1BitQepjjz2Ec8+9EOuttz5uuOFa/PjHP8LEibejo6MDP/zhD3DEEd/GRRdditmzZ+GKKy7FvffejR/84H/x8MMP4LXXXsV1192M1tZW/PSnV+LGG6/DZZddhQ8//ACXX34xzjlnAsaOHYPXX38ZZ555On75y/uw5ppradWDoslkQPrTn/6EVVZZBW+99RbeeuutwHeO49CARHIn3gPJtyARQmzhKfyUw1w2T0KsEQiXibXuehNUtkpCpAk4IBl4IEk1z6ok2uwGAixZ1o0f3vovLFke7UkjxYCWJlz9/Z20jEiTJr2ML76YgdtvvwcNDQ1Ye+11ceqpP8AVV1yCY4/9Lk477UzccMNP8dprr2CDDTbCwQcfCgDo338AzjvvIowfXzYArbXWmvjd736Ljz/+yDcgbbTRJvjGNw4HAOy993648cZr8YMfnIOWllYcfvgReOyxhzB37lxfllNOOQNbbLElAOC73z0Zt956E0488ZSAvI888iC23XYcDjvsCADAmmuuhQ8+eB+/+91vywak8A9U2knn8uUAgObm5sAhzc3N6OrqrLo3t9xyI95//33ccccvI3/zaqutjvvv/w2+XDQTA0aN0TLpfuMb38Tee+8HADjvvIvwzW8ehE8//QTt7e045pjv4sgjvw3HcbD66mtg9933xLvvvg0AmDFjBlpaWrDaaqth0KDBuOCCi7FgwQIAwP33/xpf//oh2Gef/dDU1IDNN98Ezz//Ah599CGcfvqZGlIVj5EB6YsvvsBf/vIX3/to1VVXlZKLEC0XXi6mEmKfhE0zoo/nKJUQeQx0JpUmIfKYNjPpMS1Vcd/n008/xsKFC7DvvuP9z0qlEpYvX44FC+bja187EH/84x/w4osv4L77HvGP2XjjTdDS0oI777wNH388BR99NAWff/4ZttlmnH/M6quv4b9uaWnB0KHD0NLS6r8HEDDcjB27pXL9TTF//jzMnz+/St7nn/8n9t57V/+z7u5urLXW2uU3vgdS739lYNncu1lXV1dX4JpdXV3+Lmwet9xyIx588D5ccskVGD16/cjfPGXKZEyd+jmGb7hqb5npg9hNNtnMf73aaqtj0KDB+OSTjzF+/B742tcOxAMP/AYffvgBPvnkY0ye/AHGjt0CAHDQQd/AX//6Jxx00L7YaqttsNtue2D//Q8EAHzyySf46KO/4vHHy8/HcRx0dnZi3LgdqwWoU7QNSK+88gq++93vYtmyZQCAAQMG4MYbb8Quu+wiJhwhHqlJtKkVCbGIF7OerHx1lDMhJB+CHkgxB9ErkBCLpOfyDCBu4Q2HsBGVAa1lT6B6DmHr6enB2muvi6uuurbqu7a2dixZsgTTpk0FUM495BmFXnzxBUyYcA72229/7LDDTvjud0/C9ddfHTi/sbEx8L6hITlVclNTReZSqaf3nGCt6unpwT77fA1HH3185LlOKAeS2k4GDx6Mfv1aMGfObKyzzroAysanhQsXYJVVhvvHXX/91XjssYdx0UWXYvfdv+p/Hv7Nxx13Iq699irMWJ74swI0NgbvQalUQnNzM2bN+hLf/e5R2GijTbDtttvjoIO+gX/96zm8/fZ/AACjR6+Hhx76A/71r+fwr3/9E7fdNhF/+cvTuPnmX6Cnpwff/vYx2G+/A9DY6GDw4AFYsGAJmpr6zu722gakG264ATvuuCMuueQSNDY24tJLL8VVV12FJ554QlI+QgAkbUlMCLGNbg5tQkgxpNiPuOZCiGV0ciBxa/HiGdDahPVWH1y0GLGstdY6mDnzCwwZMhTt7e0AgJdf/jf++McncOGFl+AXv7gVgwcPxlFHHYeJE6/HDjvsjKFDh+IPf3gUBxxwEM4++9zeK5Xw2WefBRJUm/Lhh+9jq622AQC89967GD58BAYPHlIl71tvvRnI7XPfffeiq6szaFSKmOc1NDRgk002xZtvvo6tt94WAPD22/9BY2MT1l9/QwDAXXfdjsceexgXX3w59thjr8D54d/c3d2NadOmoqE3p5KOUXfKlMl+CNznn3+Gjo5FWHvtdfDss3/HwIGDcfXVP/OPfeihB/zXTz31BPr164evfnUf7LnnXnjrrf/g5JOPw7x5c7H22utgxoxpWHPNtdDU1IChQ9tw6aWXY80118bXv35IulB1gPYubO+88w7OPvtsjBw5EqussgrOP/98TJkyBR0dHeknE5IBkwEux8KE2MO0vXGySog8Zu2MjZIQabLqPimdGb4uw8v7HuPG7YBVV10Vl156EaZMmYw33ngNV199BVpbW/Hhh+/jkUd+h7POOhcHH3woVlttDdx003UAgEGDBuOtt97AlCmT8dFHU3DZZf8Ps2bNQmdnV0qJ8dxww7V477138PLLL+KOO36OQw/9ZtUxhx76Tbz33ru4/fZb8Pnnn+HPf34at99+M1ZddbXyAQlV0AHwjW8cjvvu+zWeffYfePfdt/HTn16Jgw46BK2trfjkk4/xy1/eie9851hsvvmWmDNntv8X9ZuvuOISzJkzG26pnONKx1z7wAO/wTPP/B8+/PADXHHFJdh5512x5pprYdCgwZg58wu88spLmDZtKu699x4888z/obOzHOK3eHEHbrjhWrzyykuYPn0a/vKXpzBy5CgMHjwE//Vf/42//vXPePDB+zF16ue45557cP/9v6mE9fUBtD2QlixZgiFDhvjvR40ahebmZixYsMC3gBKSJ1pqjduEE2IfwyTahBDbxHjtUmcSUgwGIWx2AthIX6SxsRFXXXUdfvaza3Diicegf/8B2GOPvfD975+GU0/9HvbZ52t+Hp6zzvohTj75eOy33wE4/viTcMUVF+Okk45FW1s7dtppF3zrW9/CBx+8l1mWr351b/zv//4ArlvCIYccju9859iqY1ZddTX85CfX4dZbb8J99/0aw4ePxGmn/QD77PM1AMquvt7/QDtxsNde+2LGjBm45por0NXVifHj98T3v38GAOCf/3wGPT09+OUv78Qvf3lnoNznnnul6jfvuOPOOOSQw/HXf70ZVVgkRx75HfziF7di+vTp2GGHnfDDH14AANhzz73xxhuv4cILz4XjONhkk01x2mk/wJ133obOzk4ceuh/4csvv8Rll/0IixYtxEYbbYKrrroWjY2NGDNmLC666FLcddftuOWWG7D22mvj0kuvqMkbzDbaBiTXdass1Y2NjSiVSrkLRQiAwAg3fhc2Qoht/Jj1lAZYCZfhsJUQaRwNnekfKywLISS0C5vG8dIpkKo9kGTKIbKsscaauOaaG6o+v/vu3wbeb7rpGDz77Ev+++uum+i/9kKn5s1bjO7uEi644OLAufvv/3Xsv//X/ferrbY6nnvuFQDAjBnTAQB77bUvjjrquCo5TjjhpMD77bbbHtttt33kbwlvyqKGcXr186ijjsVRRx1bdW7c5x7Dhw8P/GaPKdc9g+WdPalt8qGH/gAAOO6471V919jYiHPOmYBzzpkQ+Py//uu//dennHIGTjnljMhr77XXvthrr32rnkNfQTuEjRDbBBKCxq6m9h5LLUiIPXS3YfO8HUSFIYQAeu1MeptwQkgFx7Ch+WNa+iCRlQUnmETbhmeA6U7CpBptDyQAuOuuu9C/f3//fXd3N371q19h8OBgsrHTTjstH+nIyk3QghQDJ6iEFEVqCJv3gg2UEHlUbwd67RJSPIE2WXwMG3MgkXpiwoRz8K8XXkDJdfHRnxz8/tYG9PS46Oope+L83+bn44D9D8y9XMcBpr38S1z/58m4qTHal+Z///d8P8yOVKNtQFp99dXx1FNPBT4bMWIE/va3vwU+cxyHBiSSCzr2I66mEmIfbQek8PGEEDF0vHYrXoFslYRIo7UOGnGMmP9R2IAkVA5ZsVHD2Wrh7LPPQ/ODr+Cj6Qux6bpDcfS+G+Pld2fi4Wc/AgDstNNuNZcRjYNRY7+BPbccib22XSvyiGHDhgmVvWKgbUD6v//7P0k5CKnC0Qge5wSVEPt4TTN1QMw9wwmxh4HOpNIkRB4nowVJTmXSgkTqh+HDh6N98Ej0W9AP7YOHY80118KU2Q3o17YQADCgrU2kXAdAU+sgDFllVay5ZrQBiSTDHEikTxC/muq9oBYkxB5eEu3kEbHLEFNCrGEQ9c02SYgVlMT2Gkcr6YMFZKEHEqk/wmGUajux5YlHzKEBidQtgcXUWPsRtyQmxDba7U18NZUQ4hEwIMUoTXrtEmKPoAdSugnJa7dSOrPqslTOpE7wdZbSTKSrp1ZaMhIJDUikbtHqN7iaSoh1vPaWpnypmwmxiIYidF1adQkpAhN9KBfBFrwyewFSNOGUCMF2IuWJF9r5jRhDAxLpE3BHGULqCE3ly9UdQoohre1x4kiIXXT0IXUmWemx2gjY4LJCAxKpYwyGuBwNE2IN0+bGrYIJkcdkZzU2SULkydzOhBpo+KrsB0jRJFVBhrDVLzQgkboluKFMTD4HP4SNWpAQa+jGsDGJNiHW0NiEjQNmQixiOjaVzlFGgxGpOzyP9uoUSNJFkhqgAYn0DbilDCF1g28/SjnOlR4NE0KiSbEU0SuQEAsENoMxiGETS6IdyoHEfoAUTGU8Wa77ajORTibPBZXs0IBE6hat1VTvWGlhCCE+/iBUM4k22ych8qiTwXgPJI6YCbFFYBM2jePFdaaT+JYQ+ySsSIpFl/h5PKkPs0IDEqlbtDoObihDSGGkKl//azZQQqzCjScIKRxHZyVURTgtQ9VYmaqZFIxX1/1d2AIuSFJlklqhAYnULxquv5VP2R0QYg3PASnVftSbA4nNkxBxTOaqbJOE2EXPfmQ5hE2mGEK0cULxZGo7EffE44pKZmhAInWLjuuvZ1jiYJgQe2g3N6YoI8QaAZ0Zt+jCNkmIVfy2phE+Kt4+qy7MnoDUB27VC7kcXbQf1Q4NSKRucXQsSN6xopIQQgJoeyAFjyeECKIx2HYZ902IXfRSBkaekzdVEWzsBkjBhBNa29mFLUujJCo0IJE6RichaNWhhBBhHM31m8pqKhsoIdIEPZBiDqIHEiFW8XO8GG3CZicHEvsBUjRVXkZeZImFsplEOzs0IJG6xdEYDVfsR1SDhNhCf/GGs1VCrGGwcynbJCF2MPPykU7LQAsSqU/ccA4kwbrpj2FpP8oMDUikTxDbxtn6CSkM7RA2Qohd4hqnv7rLmSMhNtHxdqDOJCs7nNb1DWhAIisGHAsTYg3T1VE2T0LkMWlnzH1CyMpHdQgbOwJSLHG6SLJuhvMuEXNoQCJ1S2BL4rjFVO9YcWkIIR7aip35egmxhppLIjWEjRBihco25enHusI6k5uwkXolPM+THTf25iWjRswMDUikbtGapDLFCiH28ePHU5Joh08ghNghOYKNLZIQa3iTVZMzpJJoB6/LfoAUjVcnvfZhw6hjYtQl0dCAROqXgAdSchJtujgQYg9d3eu1WzZPQuQJeO2mtM6qnW8IISI4mgsugWOkmmc4hI3dACkYvwp6dd+i5zrtR9mhAYnULYFN2GKOcS1u90gICaGpfdk+CZFHY+NSrUksISQ/TPSfdFqG6utSO5OCCe3qa8NznYbT2qEBidQtTnA5NeVYWVkIIQohhR+HP1dlAyXEAmxnhNQdJluGC1uQqkLY2GWQggl7tNvwXHd0B7EkFhqQSJ8g3gPJqhiEEKjKVy8HEseohMijtfEEE9sTYhXHIAdSRWfaaaDsBkjhONHGHAs5tJlEuwZoQCJ1TVVsbNxxHA0TYo0YfV8NM/YSUghxA2MOlwmxjMme4cLeF1XX5diZFEzFA6lc910LK4/MoV07NCCR+iZlosp8DoQUR1rzowcSIfYIzAVT3Ha56EKIHUwmq9Ij2rBnE3sBUjThHdGseOFpr4KSOGhAInWN7/ob547vHUctSIg1tNub74DEBkqINGo7i7Uf+ccSQmxgsmW4eIhplQeSUDmEGOI3Dwue6/RAqh0akAghhBjiGXbTciBRPRNSBGmLLpw4EmIXakNC4nFDHkhWCyXG0IBE6hrdVRiOhQmxh3F7YwMlRB6DdsYmSYgtsrQ2mRZKByRSb8SFU0vWTUaw1Q4NSKRPEJsQlDFshFhHt7kxhzYh9gi2M8Z9E1IPVHJop09X/S3MpWQJXZjh5aRoqgKvLe4USgek7NCAROqatNhxz7BEFUiIfbSVLxsoIeKoA+74EDbqTEJskmUiLLYLW7jlsyMgReN5A1WFsMlVTsckMRmJhAYkUuf05lqJ+5oZQQmxjqd801RvxQOJDZQQeTTamcXVXUJIBZ0FF3GPCNqPSJ0RNuVIe+EFyqT9KDM0IJG6xo9TjWnlVrZ7JIQESN/rKfg9J6uEyBPwQIo5huNlQuyiu+CiHhOXF6Z2WcIfiBRDiAHRCYlEx42s9zVDAxKpa1LbuMsJKiFFkbZ6w8kqIcUQu+jCRklIQeibkMRyIIWuzMVXUjSVhNblum9DRzGArXZoQCL1TSg2NgwbPyEFoLmDhctwGUKsodfOvEUXNkpCbOCkjGNVKhvDSAkTestugBSMXwVDOZBkdRS3YasVGpBIXeOk5EDiLk+E2Ces8NOPZwslRBq1ncUuulBnEmIVk7Zm2X5ESPFU2XLkrTphrydiDg1IpL7RzHTG1VRCLOLndEhuly5nq4TYI5ADKTlvINskIZbw9KVZEiQhUUIhbOwHSMH4jgKeB5IFz3XTRVBSDQ1I5P+3d/dRUlRn4sefmhmBAUQU0Z+JKy5G8A0B8RAjYtSjkviyajTGI3p8WWPMxphNXKOJ0Y2LOcbFl41rfIue1ZVV40tE3d2cuOZoPJoorq/HN2RQolkjogEE5HX6/v6Arq7b3XWrpod67p2u7+ccdKa76S5m7q1766nnPjdoWetU0+o8AChO3sGXTRIBPVY/y6iiTZ8EdNTmsdnzVVNwDaR6ZAfDu5QJZaEtkxVs/UYACQMDvRwIRpR38KXfAl6kx48I6wJe9CUDCSiZatPXzAsgB6F1BJAQtLwpjKThAgGjfwKF68tSbsZMQEkLfa2o/tnwvpwH4FlqEyxwkIoy17cgCwEkBC5fEW0AeqKcNR1quQ7MUgFNqX2TJWyAqr5cqhY9pa0fizkPwLeoroOoLONkCVu/EUBC0Go1tN0FQSmiDehJ7PXkfqGpbhle5NEAqMrqmxTRBnRFudd8JwsIF1VEu/6BQj4G6IO6RAGFVdYU0e4/AkgIWuYYahQi1QBs1flwzgwkAEoy+mZtY0RGTUBTvi3DC57TRvXfch6AX3F8ddPgpFGlrw8xXaQggIQBIXUyXP2CMRBQk3fSqbEdK4Ca7L7JGjZAU5Tzhov1mqJqINV/z3kAnjU0wYKz8JKfyk7erSOAhKDFtVZSnmc/GcCf/GMvPRTQkHWxynQZ0NXK6FdUZhDlHhCcupqa+TL1NstHoh8IIGGAyJgNczYA1GUN9IYlpoAXqX2TBCRAWb5NJ/K+ZnNi6gzf6ovMa2Su0+z7jwASgpZ9N5ULVEBb/a4ZuV8PoFBZfZN7LoCuWr2V7AEzntMWtYSt4X05EcCz2m5JTR8u8jNZwdY6AkgIWuYJhBorgLq88SMGZ0BXdelL6rLvoousAGgufw3twnpn/dI4zgLwLS0DqcjWmTVOIhsBJIQtcqf+0vkBD3JGbGvZDkxTARU576zSJQEdfdnxqXbtrFNFm/MAfIuDmnU1kBRqaHOXsx8IIGFASEv9pe8D+pLjunsXCzoo4Id7zOS6EVDGcAikMrUIkuJnolUEkBC0vJNcMhwARX3sbvROQEfuvsaYCahoZUe14pawAYHx0CjjjySC1DICSAhbZrEVimgD2qwMJMfrKLcCKMvceMJ6GYCi5SyibWXzFlZEO3J+D2irX02msdFDH/eBQRMEkBC0rE7OBSqgz5p0OkZguiegKzPbgTVsgKrcm040+Tub/VjqayAV9DlAbvWNMB6iiowgVevrEkJqFQEkBC3KqD7IBSrgl/OuKnuGA6riITNlYsyYCSjLW0U7mYCkNWZyIoBn8Y5odUW0C40fFffWpUEACQNC6kWqwnaPAGxWApIzfsQSU8CHjFXfxd7dBRDLn4FUfDZEwxK2wj8RcKs1SZP4b7Fts3ajpcAPaXMEkBC0rE6ust0jgNZUL1bpn4CKKONqVeMiFUBNrU9m1UBq8nc297E0PMDgjDCY+i9om0EjgISgZZ4+KOcAqLOKaOdYwQZAR7wcIOV56gYCfvRlPCyse1IDCYGpTxTQyUCi5fcXASQEzl4bW48VbIAH1uCbPi2O6/UyWAM6Mmog1b0MQMGiyD2PrbKeL2jMrH9Xhmb41rCc2uitLKGIdusIICFotdqDKQVBqecAqMubgSTUQAJUZfU1MpAAXbWulr+KdnG7sNHxEZi6mx4aIZ28de2RjgASBobsiqAAtCSLaDtexsUqoCwz26Ea1KVTAipyFuy1M5AKOxoLASX4Vl+2TyNznVbffwSQMCCk1nNQPQoAInWDL50QCE7WmMl1I6Arf/4RUEINVbQVPpJO1zICSAha3kkuc2FAT1/vDJHtAOigpwFhaaVPFreETedzgNxSGmGhbbOaqUvYtmUEkBC0+MIz43Yqd1MBP1wDMHd3AF1ZW4YbxkxAWc6CK4nni1q+0/C+nAfgWf3OoRqlD+K3Zo7aMgJICFtWEe36FwIoXN4i2tV+y8UqoCtrXkxWIKAjazOYKo1siIZd2DgPwLP6mx4qpTMpot1vBJAQtPriavWM4naPADbJ29+ocQ+oijK2lzGGTgloypvtkLwZU1j3pN8jUI0lkIosop2zsj1SEUBC2HLutciYCOhJ3rV0ZyBV/wI9FNCQedOl7nUACtZKtkNBHbQ+44ihGb5FdbEcjcz1nJeWcCCAhKBlZyBVX8goCGixu1v2EEzvBJRkLZchggSoimu89CkDqagaSHXfF/IpQF/UtUKSZAcErwGkxYsXy3nnnSdTp06V6dOnyxVXXCFr164VEZH33ntPTj/9dJk0aZIcccQR8tRTT/k8VHiSWRC0+jqVowFQL2t5qYjQQQElWctluOMKKMtdsTdR1VNrzOTmKzyrXebZNZA0imizgq113gJIxhg577zzZPXq1fIf//Efcu2118rjjz8u//Iv/yLGGPnWt74l2267rTzwwANyzDHHyLnnnivvv/++r8OFZ6l9nHoOgFdpA3DyYbonoCOKtydOUa0bSK8EVGRl0kvO5zeH+l3YOAvAt/oYZu2yrsAaSARO+63L1we//fbb8tJLL8nTTz8t2267rYiInHfeeXLllVfKgQceKO+9957cc889MnToUNlll13kD3/4gzzwwAPy7W9/29chw4N4Mkw2PhCMXGOvwpbEAJrLHDPpkoCKKGcEyUraLawGUtYDgB+19q+XGGBIQWqZtwyk0aNHy6233hoHj6pWrlwpL7/8suyxxx4ydOjQ+PEpU6bISy+9pHyU8C1Rqrf5CxQi1QBsdhHttOWlDMyAtqwxk/kyoC0jK7Dp39CJIDFzhm/1WbMaiQHcQOk/bxlII0aMkOnTp8ffVyoVmTNnjuy3336yZMkS2W677azXjxo1Sj744IM+fUZHRyQdHQOzlXR2dlj/L61Nv74oiqSrq8nPovp8hzR/vg3QFpAUQnvo7IwSX3c07XtRxX59u/ZP30JoDwhIVPuiab/c9HxnB32y3XFuCEO1z0Xinqcmx9WOAsbMzs4OWd9rh7GK+BwMDKGcH2rX6Ua6ujqkY1OHSb3u2wySWfG+238ov4e+8hZAqjd79mx5/fXX5f7775fbb79dBg0aZD0/aNAgWbduXZ/ec5tthg34pRMjRnT7PgSvujZ1qEGDumTrrYc1PF898QwZskXT59tJ2dsCbD7bw7DhQ+KvR44cKlsNH9zwmg29tQjS0KGD275/+sb5ASIiHR0bx8zBg5uPmdUIU3f3IPpkSXBu8GuLrk4RSZ/HVnVsUbskGz6smDFz7dLV1vfDGJtLz/f5obt74/V+FEWy9dbDZItN/aCrq6Owtjlo0MY+2dnVGUz79/176KsgAkizZ8+WO+64Q6699loZN26cDB48WJYtW2a9Zt26dTJkyJDmb5DiL39ZNaAzkEaM6JZPPlktvYkLsbKpVDbeLVm7doMsXbqq4fneTXdT1qU83w5oC0gKoT18umpt/PWyZZ9KZf2GhtckA0irV69r2/7pWwjtAeGoLilNGzMrm55fs2Y9fbLNcW4IQ3UsXLvOPU/9ZFXtJvmqTzf/mNnZ2dGwdIexubxCOT+sWbOx3VcqRpYuXSVr160XEZHe3kphbXP9+l4REdmwvtd7+99cvwftQJj3ANKsWbPk7rvvltmzZ8uMGTNERGT77beXnp4e63UfffRRw7K2LJWKiQMQA1Vvb0U2bCjvwF+t11CpmKY/h+pkuWKaP99Oyt4WYPPZHpLn1fUbmh/H+sRjaf0Xmw/nB4jUVrBVKintobpVcgnGTGzEucG3TfPUjHFwgzVmFvM7izrsZTKMzfB9fjCb5pNGNvaB5PyysOOqXlsGNA76/j30ldcFd9dff73cc889cs0118iRRx4ZPz5x4kR57bXXZM2aNfFjzz//vEycONHHYcKj6t2StIK8Gts9ArBZdzFTq/LWHqd3ArpSe+XAvqcGDDhRziLayee15rSMzfCudqFnP1zoZzb9SPSBtwDSwoUL5YYbbpCvf/3rMmXKFFmyZEn8Z+rUqbLDDjvID37wA1mwYIHccsst8sorr8gJJ5zg63DhSXwCSe3lG58Y4KWugAEr14Uq/RNQEaVMxqtM/esAFCuuEZxxuZp4vqju2dDvOQ/As1oJ7Wp2bN0TBX4mEaTWeVvC9tvf/lZ6e3vlxhtvlBtvvNF6bv78+XLDDTfIxRdfLF/5yldkzJgx8vOf/1w+85nPeDpaeJMRJabvA/qSk9C0OTHxI0BfbTKexlivA1Cs7D6Z7/nNob7fcx6Ab1FdB6nFj4qMIFWzArmKbJW3ANLZZ58tZ599durzY8aMkTlz5igeEUIUp/6mXaRWl7AxCgJhSfRZsh0AJdWbLimDJkvYAF15sx2MxphJBAmBipt/rTZJYWj2/ee1BhKQKefISw0kQI9dAinlQpU7O4C6rJEw7pUMmYCOONuhD3+lmCNpmCtzGoBv1WCpachAKvIzxf4w9BkBJAQtq48bhUg1gL6z7qb6OwygXCJ31q4wZAKqaiWQ3FerWc9vlmNpKIHEmQB+JQoiWP/TaJnEj1pHAAlBy4oSa0SqAdiSc85cc146KKAi86aLsO4b0NRKV6N7ojTiZdeb/h8/XlwnqM96Qt8RQAIA9BGzW2AgowcDAMqNCFKrCCAhcBlrx0lBAtRZGUgpvdNewkYHBTTUsnbdabtkOAC6srId7OeL6aD1S9Y4DcC3+vlhdSlnoTWQ4s8q8EPaHAEkBC2KUxvTCvVueh3DIKDG6m2Z0V0uVgFtGSWQACjJW2fIKIyZDe/L2IxA1F/mFTpvrF5bFvgR7Y4AEoKWef6oRqoZBAE9VgZScwzMgL74YjUtAcnUvQ6Aiswi2QobTzTGjzgPwK/akLWxA2hkBeXc4BsOBJAQtrriavXo+4C+qHHfjAbWEjbmqICK7N2Ji18eAKAmI6YbU1jB1jAYMzbDt7RgTrE3OarlUbiKbBUBJAQtyqiBVLubqnM8AMSe3Oa4XcRdTkBJ1rJv6gYCqvJ2tWSPLWrMpNsjOJF9nZeZqbf5PpIshH4ggISw5a50xrAIaLHiRymvMUYhHx+AJW9XI6gLKMm7ZbjCmFl/s5Wbr/CtvgnG9zgKbJvEj/qPABKCltXJNar1A3DIsbyU/gnoiDIuVtl1BtBVm8e6O5+PMZNAMrxL1tRMDFCFtkyafb8RQELQ8hY64y4KoCfZ33JswsZgDShLu1il5gOgLG+6g0LdwIa6MozN8Kw+o90opCDF5VEYDltGAAlhq1sbW4/OD/iQKKKdVmvFejWzVEBDZm0H6gYCqrJqeVbZz+vUQOI0AN+iujuS1ZscOhlIXES2igASglYrgeS+SGVLYkBPru6WTEWmewKqMuJHXDgCSmpB3YwlbBpjJhEkBMxsjCBtRA2koBFAAgD0CXNOYIAjqgsAKDMiSC0jgISgZc9xKaINqLOKHjZ/CeMyoC+riHb8OoVjAVDTlzGxsASkukk1y8vhW1Q3n6wlIBVYA2nTWzNPbR0BJAwImTvKMAYCapIDe576ZCwxBXS4eqZR2CYcgC3nCjb7+aKKaNd/z3kAnjU0weru2gpFkNLKoyAbASQELevCk/gR4IG1bUb2AEz/BJRU76w26ZZsjAjoy3sDRWPjCQJGCE6iUSYzkJQ+Ei0igIQBITVKrLDdIwBb/barzXBnB9DnvPAkKxDwJnNMVMkQrFvCxnkAntkt0MTdoMiWmTcrEOkIICFoWetUVbZ7BGCx+luOAZg5KqAjcmYgMVsGtOUd/zQyBOuPhaEZ3qXU1Cw0uFmtFVjcJ7Q9AkgIWs4a2lygApqSKccpL7EvYOmggKZmwSJ7cq54MACysx0UEpDo9ghNfUa7RvZ6/JmkILWMABLClrGjDF0f0GeXQGreC5MXsFysAjrivsYubEAQopzZDvY9l6KqaNcvYSvmY4C8rEwjrZsctPt+I4CEoNXmwikXqXEGEmcDIChs+AR4kH6xys1WQF/ebIfkzRi9DCRGZ4TDJGogFSnnfRY4EEBC2DJ7OTWQAG3WDaM82YF0UECFu24ga9gAdRm1PJv+FZ0EJE4D8K5+U9/a3kjFNc6olU4JCwEkBC1y3E0VSVy8MggCQdHYkhiAzZXtoLLJEwBL3mwHlcyL+iVsxX8k4FbfCI1CYkAcPyKC1CoCSACAPmHJKDDA0YUBACXGsu7WEUBC0GoFQd29nAwHwI/UOzikOwDeNK2BlPiaLgkoca8rTfkrSj2UEwE8S16/JZewFdk2afb9RwAJA0K+rcIBaOhrDSQGa0BH5LpYNU1eB6BQWZvBSM7nN5fI+przAPyyh6JaL1BZwsY1ZMsIICFoWQlI1VMNc2FAT65JJ/V6AXWui1XqPQD6apn07tcZrTEzSvka8MzE/5FCO0GUuzIZ0hBAQtAy75Ju6vuMgYCivu7CRg8FdDjurLKqFPCnT7uwFdhDk+/NeQC+1We0x4kBCp9J+Kh1BJAwIJiUq9TaWlmGQUCLte1q2obhiT5L9wR05O5q9ElARfVGaNZyGaO07js5HjM2w7eGYGk1MUChbbKErXUEkBC0vCcQxkBAkR1B6tPLARTHdbFqZyDRKwENiRLBztclb8bo9U7OA/DMykDSWWhN4LT/CCBhQGg+GSYfH/AhyjEl1rqbCqBR82k4WYGAurwFe8lAQgnV3480cQaSzjJOtIYAEoIW301t8hzXp0AAcu3CRg8FNLgK9pKtD+jLW65Xa8ykBhKC0pDRXnwNpFpQl1GxVQSQELT4BNI0BSnxOm6jAGqsooepESQyBAF9jpsuzJUBD3JW7GUXNpRQfUZ7PE4VmYWX+Dy0hgASwuYYd9mSGPAvzy5szFEBHVHO5TLccwF01HZ8yl8DqUh2/IgTAfyyxqJEDSSNDCQuI1tHAAkA0CdcfAIDGxeOAIAyI37UOgJICJorSGy00n0BWHJdfLKCDVDnKm/PxhOAvlopBvfr1Oa0LGFDoBIlkKTIxhlJzlRdpCKAhLBF+daOMwYCfqQVIbQeJcIL6HDMi1lWCniQswSS/Vcooo1ySNawNaa2lLPIaWPOS0s4EEBC0GoZSM3upiZfyDAIaMnT3ZKBJXonoMN54UkECVBX7ZNZyQ5aO0Ilx29OA/CtoQ2alMcLQAJS6wggIWiuLYmTDzIIAn7kGYCJ7wI6XEW0tbYJB5CQdw1b8q9odU8GZwTEJIpoF7oLG82+3wggYUDI3JKYkwGgxko5TnkNd3YAf5ru6JTMCmTMBFTk3fBJa8wkAwkhqR+LTJyBVHwhMK2sv3ZEAAlBq16oUs8BCIfV33IMwBFXq4AKV9YuU2VAnzuTvsauyqBUA4mhGd7ZNZA01rDR7PuPABKC5sxzsHas4HQAqEl0t9QMJC5XAQ823XRp8oxhzAQ8SO+TFq26gXR9BCSy5pO1mWOh+UdswtZvBJAQNmc9B3o+4FueJWxcqwI6mBgDYan1SXentHcuLexwrLcmkAzf7Ix2qSUg0TSDRgAJQXNl/loXqBoHA0BEmgz4ma+nhwIaXFm77IwI6Mvb19TKMnBljpDUZbRrjFJxeZTCPqH9EUBC4Kq3bvK9DIACq4h2885JkXvAAzKQgLA4anla7CJIxR2OzscAuUR1tz2qNzqKbJvZ28AgCwEkAECfMOcEBjYuHAEAZcaNltYRQELQ4rXjTdPxE6/jkhZQY118pgzARiURGUCSayw0WkVWAMRybsKmNmYmx2/mzvDOmk/qFpInftQ6AkgIWnwCadrLEycaxkDAi9QBmCVsgDpXwV7DmAnoiyNIGZerPjae4DwAz+rvR8bdRGMZJxGklhFAQtgcUWL6PeBHcueWtDmxHT9ilgpoyrjnQo8ElOTPQNKRHL85D8C3+oz2OH6k8KHs5t06AkgIWvXCs9lFKtuEA37Y3S07gkT/BHREjqtVVrAB+qKc62XsOa1OEW3OA/CvrqR1tSNoFNEmftQyAkgIW85eToYDoCi57WqOGkgAdMQ3XZo8R1Yg4IGjlqdNt/7Lxi85D8CvyJpPGpUMpChfTBcOBJAQNFfqrzHk4wM+5KihTYYg4IOjBpIwZgLqcpdAUqobmHxrxmb41tAE4wSk4hsnu7C1jgASgpY3HZ8xEFBk3TLK8XJ6KKDC1dMYMwEPcnY2rf5Z5PI4oM/qMtqNKCxhc11cIhcCSAhcejo+s2HADzsDqfkA3DQDAkCxonxjJteQgA5XLU+LNafV6aCcB+Bb8gZjche2QoOoBb53WRBAQtDcWxInXsfpAPAjTwYS3RNQ4VouQ0gX0Fcb/9w90CjVQLKWsDF3hm/WHUmlZdbxtWWBn9HmCCAhaM7zR6Lnc4EK6OlrDSQAOur2s7EYrSIrABpkDolaJcro+ghI/XyyloFU5E6EdIL+IoAEAOgbxl5gQOOmCwCgzLjR2ToCSAhblL52nH4P+GGtWU/piHY5B65WARU5U/PpkYCOKGeftJ4udBe22pszNMM3qwma+D8qbTOthieyEUBC0Fx18u1twhkFAS1R44jfyOjUcwBQ40rNZwUboC/vchm7/EuBy3fo+whJZC+81gjpsAlb/xFAQthcFUGbvAyArjwZSHRQQIcr24GNJwAPHJvB2PTrenLzFb7ZNbRNrQZSoVl4mz6vuI9oewSQEDR3BhJdH/Ahz6STZAfAn6ap+Vo73ACI5b1Y1ZrSJsdvTgPwLW06WehNDgKn/UYACUGLTyBZ9Rw4FwBepHZNuwiSwpEAcKXmE9QFPGgh3aHIzKAo9RvAL2MSyQEKGUgbP5NkhFYQQELY4vhRsy2Jm7wQQOGSc9u0wTfZZ+mdgJZNG080e4oEJEBd5OqTCWqXsVHTLwEv6oOl1X5QaNtMzmGL/Jw2RgAJQXOVQDIe1osDyDmwW0XuizoSAEm1GkhNbro0eyGAQkWuiWxCss9q1H8p/IOAPjJiVCJIefaBgRsBJITNdQLhbirgR3LXjDxFtAGocA6Z7IwIeNOXMbHY7AtqICEcdkZ7Mn5U5E6EtPz+IoCEoMWpvxn1HBgFAT+aFuuVunq9DNaADscubAD0uXZGTLKeV6qBxNAM3xoCRZs6glbbTJvDwo0AEoJWO4G40/HZkhjQky/9l2wHQJtrLDQsKwU8yNfZtOoG0vcRlJQMpEI/su4z0XcEkAAAfcMEFAAAACgdAkgYEJrvKEMRJMAHawvUlNfY6fgFHgyAmKuItv06OiWgoVZDO2sNW5O/VDDOA/DNnk+aeO6oVUieDKTWEEBC0Fxrx7k+BfywJp05imjTPwFdzboltR4AfXkvhLXGzIgi2ghJ6h3JIiNIeW6DwoUAEoLmrG1EPQfAu9SLUqWCoABq4ovDZjddGDMBbzIzHZQ2nohSvwH0Ja/zjNQy9QoNoia+JgOpNQSQEDZHOr5p9kIAhctTgFCrICiAmni5TObr6JWAhmowKDt+pHQlGzX9EvAiLRmo0Jsc2Un0yEAACUFzTYaTQSXupgJ6cnU3SpQB+lw3XVhXCqiz8yvSaWUI2hlInAgQjmQNpCLl20kYLgSQELact1MZAgFNiSlxjhVsdFBAh6urkRUIeOCo5ZnxVwpBDSSEpD6j3cSPF7iMM0qGdYkgtYIAEoJWTbNvnoFkvRCAEruGdvPB194kkQ4KqKgul2leRTvxOpWjAUov77JS4+GuC6cB+NY4Pyy+BpL1acSPWkIACUGLL1QzaiBxgQp4kjr4ssQU0OZc9m29jk4JqHAUtk8ySmOm9d6cBhAQYxJL2LSWcaIlBJAwIDQdd+0UBwAepM2JuasD6HPddCFTH9BXC+pmRpDUEUiGb/XBUoX4EdeMmwEBJAQtvnGTMRfmXADo6evd0SLXsgNo1DwDiaxAQFuUcw2bNactNAMpUQOJ8wACYowkiyAV9jnJwCk3O1tDAAkA0CfctQQAAMDARgSpFQSQEDjHhSor2AA/rF0zUopoKx0KgJrIUW9Fa5twAI36MiYWeZOGro+Q2Dui1TJli92JsPY1c9XWEEBC0GrlHNxFtJkNA3qSvS29BhLLZQBteXd8IosQ0BG5dkZMMFp1PRPvzfJy+Ga1QGPiwUurabKErTUEkBA01wnEukBVOBYAG0V5IkjJ1xd2JAAscd3AJjddmCkD6uz8inRadT0JHiModdlAGqMUcdP+I4CEASFr3svJANCUPSW2+iwdFFCRt6fRJQEljs1gLEpLTKOo+deAD/X3I02cgaRVRJsbK60ggISgxam/TZ6jzwN+2BlIaTWQyBAE1DmWyzBmAvryZvzY3VNn1GRshncNBYmKr4FUn/WEviOAhKDlTf1lHTfgR2rP1MrHBxCr1UBy1w1kzAR0ZWcg6dQNtN6b8wA8szOQTK0bFNkHkt8QQWoJASSEzZH622yCDKB41g2jlG5I/AjQF9UiSI2oGwioq22MmL8GUrGiJl8BfqTV1Cy0VhcZSP1GAAlByzkX5iYKoCjXwG71TzoooMOx7LvxZQAKFne1zF3YEn9HLQOpuM8B+ipZA6lI1hyWtd0tIYCEsNVu3bhfxigI6LHG3uwaSAB0OHcuTb6u8CMBICK1umR9+SsF9lDiRwhJ8gajMbW5o1oQFS0hgISgOes5MBsGvMuzCxuDNaAjHjObr/sGoCzv8Gf1WaUxk+xg+Ga3QBOPU1otk2GxNQSQAAB9wpQTGOC4cAQAlBwr2FoTRABp3bp1ctRRR8mzzz4bP3b55ZfL+PHjrT9z5szxeJTwwVUQlG3CAT+ihm1XM15f3KEASHKs+mbMBDzIseRbRG+JKVlHCEpdOaK4HxTYTukD/dfl+wDWrl0r559/vixYsMB6fOHChXL++efLcccdFz82fPhw7cNDIJoOuSxhA7zLs4SNDgroiNwRpNrr6JKAivpNplK7ntLGE1YNJM4D8CxlE7Zig6jJzyQFqSVeM5B6enrkxBNPlHfffbfhuYULF8oee+who0ePjv90d3d7OEr4VB1Es8o5UEQb0FXbWSa7iDaTVECHc+dSzQMBICL5M3bV+mfU9EvAj7T+QeMMmtcMpHnz5snnP/95+e53vyuTJk2KH1+5cqUsXrxYdt555369f0dHJB0dA7MFdnZ2WP8vq86O2nS4q6sj5TmRrq6o4fl2QVtAUjDtIRIRIxJ1NO97HVb/7Gjb/ulbMO0BQYj7nWkcM+mT5cK5IQydnbV+19kVSWdH899H9eFIpJC+WW0HHVHyPNDJeaCkQjk/bJH4/ChxKJ2dxV3XdSbet6PT71gYyu+hr7wGkE4++eSmjy9cuFCiKJKbbrpJnnzySRk5cqScccYZ1nK2PLbZZtiAX+c4YkS5s66GDh0Uf7311sOs54YvWxN/PWJEd8Pz7absbQE23+0hiiIxxkh396CmfW/o0MHx11tvPUy6B3tfMd3WfLcHhGHwkC02fhFFjWPm8JXx1yO3Gtr2YyY24tzgV3IsHDlymHSlXCh2d2+c70ZR43x3c+rq6oy/3nrroTK0es5AKfk+P6wztev0YcNqfWXY0MGF9YMth9c+Z6sR3bL11kML+Zy+8P176KsgZ/Rvv/22RFEkY8eOlVNOOUWee+45ueSSS2T48OFy2GGH5X6fv/xl1YDOQBoxols++WS19PZWfB+ON6tXrxeRjatkli5dZT33ySe1ANKKFWsanm8XtAUkhdIeqmfWTz9d17TvrVq1Nv56+bJPZc2gzobXoP9CaQ8Iw7q1G0REpFIxDf1yxYramPnJJ6ulu2tgzo+QD+eGMKxevS7+eunSVakBpE8/rb4uKmQ+W20Pvb298WPLln0qa7m5U0qhnB8+Wb46/jo5Rq1e3XxuuTkk56fLln8qW0T+Fnhvrt+D9g2hIM8axx57rBx88MEycuRIERHZbbfdZNGiRXL33Xf3KYBUqRipVAb2qv/e3ops2FDegb/6+zNGGn4OyY5W6TVt/3Mqe1uALZT20FtpfhzJ/rmhtyKdG7hYLVIo7QF+VQuCGmkcMzck+iTtpTz4XfuVvA5Zv76SWuyot3fjE1HU2HeLQtuA7zZQbfciIhsSX1cqxV3XVZKfuSGMPuD799BXQS64i6IoDh5VjR07VhYvXuzngOBNdQWiaTLiUjkf8CdyVesVaiECPtT6ZdbOE/RKQIPd09Lnrc3mucWIEl9xHoBfVg3txLhVaMvMV9ceDkEGkH72s5/J6aefbj325ptvytixY/0cELyp7fSU8TrGQEDZph0S055my3DAm+a7sClNzgHUWBfIjtdteq7o8dJ6f04ECEjF6EwcCZz2X5ABpIMPPliee+45ue222+Tdd9+Vu+66S+bOnStnnnmm70ODtij9IpUEJMC/tExA+1EGa0BDVEvbbcSYCahLjn454kdS9HhJ/AghSW52ZcWPCv3QxNdcTLYkyADS3nvvLT/72c/koYcekqOOOkruvPNOufrqq2Xy5Mm+Dw0AIGQVAQMZ3RcAEBQPsRzCR60Jpoj2/Pnzre8PPfRQOfTQQz0dDULhmuTa5RyYDgOaMntcci073RNQUStN1qRuYLMXAiiUNT91XK1Ws3kLHy8T78/YjJBY41aBbdN6ayJILQkyAwmIpRRX2/RAs5cB0FBdKZOjiDYAJY5+aS8PYNQEtOUplF18/IhFbAiHXUQ78bjWZxb4Oe2MABKC5lo7zt1UwJ/qJDRt8FWqhQggwd3VyAoEtOUttxI/p1hEm/MAfEs2wYqVuV5oCCn+ih29W0MACUFzpf6yTTjgkWu78IaX0kMBDdUxMysDCYCSnMNfLX7EeIkSSSmirfSRaBEBJAwYDam/dhEk1WMByq5Wa6U5o7adBoB6WUtlqBsI6Igk5wVy9UkykFAidoaeTmkSNmHrPwJICFra2lgRe4LMGAjoijIiSGQIAvpc/ZJ5MqDPDtKk90KlFWzWJ5DtBO/SrvOUIkiMi60hgISgOc8f1FgBPHLXQLL7Jx0U0JHeLw07IwJeOS9WqwlIihlIxI/gW3oGUnGNM0qNWiEvAkgIm2NtLF0e8KdWAql5T6R/AvqyMgPj1xV+JABE3Jn0SVo59cSPEJLkDcaKVmIAGUj9RgAJQbPPH3Y3N2Q4AN5kdrlNHZSeCeipxY8ap8X28gB6JqAhbyaF0RozrRpInAcQDq0d0Wj1/UcACWFz3rmhBhLgW9p4Hz9M5wT0bOpvTXdh414roM+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Convert the 'time' column to datetime\n", "df[\"time\"] = pd.to_datetime(df[\"time\"])\n", "\n", "# Plot the data\n", "plt.figure(figsize=(14, 7))\n", - "sns.lineplot(data=df, x=\"time\", y=\"Price\", hue=\"simulation\")\n", + "# Loop through each simulation and plot\n", + "for simulation in df[\"simulation\"].unique():\n", + " subset = df[df[\"simulation\"] == simulation]\n", + " plt.plot(subset[\"time\"], subset[\"Price\"], label=simulation)\n", + "\n", "plt.title(\"Price over Time for Different Simulations\")\n", "plt.xlabel(\"Time\")\n", "plt.ylabel(\"Price\")\n", "plt.legend(title=\"Simulation\")\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From e474668293e627ef9bde6691f72aaf60d0eb9b8d Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Thu, 26 Sep 2024 17:15:37 +0200 Subject: [PATCH 05/27] -add a draft tutorial on market zone coupling --- .../notebooks/08_market_zone_coupling.ipynb | 1444 +++++++++++++++++ .../08_market_zone_coupling.ipynb.license | 3 + 2 files changed, 1447 insertions(+) create mode 100644 examples/notebooks/08_market_zone_coupling.ipynb create mode 100644 examples/notebooks/08_market_zone_coupling.ipynb.license diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb new file mode 100644 index 00000000..15220fd2 --- /dev/null +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -0,0 +1,1444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "21658e52", + "metadata": {}, + "source": [ + "# Market Zone Coupling in the ASSUME Framework\n", + "\n", + "Welcome to the **Market Zone Coupling** tutorial for the ASSUME framework. In this workshop, we will guide you through understanding how market zone coupling is implemented within the ASSUME simulation environment. By the end of this tutorial, you will gain insights into the internal mechanisms of the framework, including how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted.\n", + "\n", + "**We will cover the following topics:**\n", + "\n", + "1. **Introduction to Market Zone Coupling**\n", + "2. **Setting Up the ASSUME Framework for Market Zone Coupling**\n", + "3. **Understanding the Market Clearing Optimization**\n", + "4. **Creating Exemplary Input Files for Market Zone Coupling**\n", + " - 4.1. Defining Buses and Zones\n", + " - 4.2. Configuring Transmission Lines\n", + " - 4.3. Setting Up Power Plant and Demand Units\n", + " - 4.4. Preparing Demand Forecast Data\n", + "5. **Mimicking the Market Clearing Process**\n", + " - 5.1. Calculating the Incidence Matrix\n", + " - 5.2. Implementing the Simplified Market Clearing Function\n", + " - 5.3. Running the Market Clearing Simulation\n", + "6. **Integrating with ASSUME**\n", + "7. **Analyzing the Results**\n", + "\n", + "Let's get started!" + ] + }, + { + "cell_type": "markdown", + "id": "ffdfa0a7", + "metadata": {}, + "source": [ + "## 1. Introduction to Market Zone Coupling\n", + "\n", + "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. By coupling market zones, we can simulate more realistic and complex market dynamics, considering factors like transmission constraints, regional demand and supply variations, and cross-zone trading.\n", + "\n", + "In the ASSUME framework, market zone coupling involves:\n", + "\n", + "- **Defining Multiple Market Zones:** Segmenting the market into distinct zones based on geographical or operational criteria.\n", + "- **Establishing Connections Between Zones:** Setting up transmission lines that allow energy flow between different market zones.\n", + "- **Configuring the Market Clearing Process:** Adjusting the market clearing algorithm to account for interactions and constraints across zones.\n", + "\n", + "This tutorial will walk you through each of these steps, providing code examples and configuration guidelines to help you set up market zone coupling effectively." + ] + }, + { + "cell_type": "markdown", + "id": "39361ba8", + "metadata": {}, + "source": [ + "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", + "\n", + "Before diving into market zone coupling, ensure that you have the ASSUME framework installed and set up correctly. If you haven't done so already, follow the steps below to install the ASSUME core package and clone the repository containing predefined scenarios.\n", + "\n", + "**Note:** If you already have the ASSUME framework installed and the repository cloned, you can skip executing the following code cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f5b70bb", + "metadata": {}, + "outputs": [], + "source": [ + "# Install the ASSUME framework\n", + "# !pip install assume-framework\n", + "\n", + "# Install Plotly if not already installed\n", + "# !pip install plotly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91a4bbf7", + "metadata": {}, + "outputs": [], + "source": [ + "# Clone the ASSUME repository containing predefined scenarios\n", + "# !git clone https://github.com/assume-framework/assume.git assume-repo" + ] + }, + { + "cell_type": "markdown", + "id": "4a049632", + "metadata": {}, + "source": [ + "Let's also import some basic libraries that we will use throughout the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfa74a9d", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "# import plotly for visualization\n", + "import plotly.graph_objects as go\n", + "import seaborn as sns\n", + "\n", + "# Function to display DataFrame in Jupyter\n", + "from IPython.display import display" + ] + }, + { + "cell_type": "markdown", + "id": "95e7d6fc", + "metadata": {}, + "source": [ + "**Select Input Files Path:**\n", + "\n", + "Depending on whether you're using Google Colab or a local environment, the input file paths may vary. The following code snippet helps differentiate between these environments and sets the appropriate input paths." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdda1af9", + "metadata": {}, + "outputs": [], + "source": [ + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "colab_inputs_path = \"assume-repo/examples/inputs\"\n", + "local_inputs_path = \"../inputs\"\n", + "\n", + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path\n", + "\n", + "print(f\"Using inputs path: {inputs_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ae63334", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "104d2285", + "metadata": {}, + "source": [ + "## 3. Understanding the Market Clearing Optimization\n", + "\n", + "Market clearing is a crucial component of electricity market simulations. It involves determining the optimal dispatch of supply and demand bids to maximize social welfare while respecting network constraints.\n", + "\n", + "In the context of market zone coupling, the market clearing process must account for:\n", + "\n", + "- **Connection Between Zones:** Transmission lines that allow energy flow between different market zones.\n", + "- **Constraints:** Limits on transmission capacities and ensuring energy balance within and across zones.\n", + "- **Bid Assignment:** Properly assigning bids to their respective zones and considering cross-zone trading.\n", + "- **Price Extraction:** Determining market prices for each zone based on the cleared bids and network constraints.\n", + "\n", + "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21f17651", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "# Display a simplified version of the market clearing optimization function\n", + "import pyomo.environ as pyo\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "\n", + "def simplified_market_clearing_opt(orders, market_products, nodes, incidence_matrix):\n", + " \"\"\"\n", + " Simplified market clearing optimization focusing on zone coupling.\n", + "\n", + " Args:\n", + " orders (dict): Dictionary of orders with bid_id as keys.\n", + " market_products (list): List of MarketProduct tuples.\n", + " nodes (list): List of market zones.\n", + " incidence_matrix (dict): Transmission capacity between zones.\n", + "\n", + " Returns:\n", + " model (ConcreteModel): The solved Pyomo model.\n", + " results (SolverResults): The solver results.\n", + " \"\"\"\n", + "\n", + " model = pyo.ConcreteModel()\n", + " # define duals suffix\n", + " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", + "\n", + " # Define the set of time periods\n", + " model.T = pyo.Set(initialize=[mp[0] for mp in market_products], doc=\"timesteps\")\n", + " # Define the set of zones (nodes)\n", + " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", + "\n", + " # Decision variables for bid acceptance ratios (0 to 1)\n", + " model.x = pyo.Var(\n", + " orders.keys(),\n", + " domain=pyo.NonNegativeReals,\n", + " bounds=(0, 1),\n", + " doc=\"bid_acceptance_ratio\",\n", + " )\n", + "\n", + " # Decision variables for power flows between zones at each time period\n", + " model.flows = pyo.Var(\n", + " model.T, model.nodes, model.nodes, domain=pyo.Reals, doc=\"power_flows\"\n", + " )\n", + "\n", + " # Energy balance constraints for each zone and time period\n", + " def energy_balance_rule(model, node, t):\n", + " \"\"\"\n", + " Ensures that for each zone and time period, the total supply minus demand plus imports minus exports equals zero.\n", + " \"\"\"\n", + " # Sum of accepted bid volumes in the zone at time t\n", + " supply = sum(\n", + " orders[o][\"volume\"] * model.x[o]\n", + " for o in orders\n", + " if orders[o][\"node\"] == node and orders[o][\"time\"] == t\n", + " )\n", + " # Sum of power flows into the zone\n", + " imports = sum(\n", + " model.flows[t, other_node, node]\n", + " for other_node in nodes\n", + " if other_node != node\n", + " )\n", + " # Sum of power flows out of the zone\n", + " exports = sum(\n", + " model.flows[t, node, other_node]\n", + " for other_node in nodes\n", + " if other_node != node\n", + " )\n", + " # Energy balance: supply + imports - exports = 0\n", + " return supply + imports - exports == 0\n", + "\n", + " # Apply the energy balance rule to all zones and time periods\n", + " model.energy_balance = pyo.Constraint(\n", + " model.nodes, model.T, rule=energy_balance_rule\n", + " )\n", + "\n", + " # Transmission constraints based on the incidence matrix\n", + " if incidence_matrix is not None:\n", + "\n", + " def transmission_rule(model, t, node1, node2):\n", + " \"\"\"\n", + " Limits the power flow between two zones based on transmission capacity.\n", + " \"\"\"\n", + " capacity = incidence_matrix[node1].get(node2, 0)\n", + " return (-capacity, model.flows[t, node1, node2], capacity)\n", + "\n", + " # Apply the transmission constraints to all possible flows\n", + " model.transmission_constraints = pyo.Constraint(\n", + " model.T, model.nodes, model.nodes, rule=transmission_rule\n", + " )\n", + "\n", + " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", + " model.objective = pyo.Objective(\n", + " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", + " sense=pyo.minimize,\n", + " doc=\"Total Cost Minimization\",\n", + " )\n", + "\n", + " # Choose the solver (GLPK is used here for simplicity)\n", + " solver = SolverFactory(\"glpk\")\n", + " results = solver.solve(model)\n", + "\n", + " market_clearing_prices = {}\n", + " for node in nodes:\n", + " market_clearing_prices[node] = {\n", + " t: pyo.value(model.dual[model.energy_balance[node, t]]) for t in model.T\n", + " }\n", + " # Check if the solver found an optimal solution\n", + " if results.solver.termination_condition != TerminationCondition.optimal:\n", + " raise Exception(\"Solver did not find an optimal solution.\")\n", + "\n", + " return model, results" + ] + }, + { + "cell_type": "markdown", + "id": "28be160a", + "metadata": {}, + "source": [ + "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." + ] + }, + { + "cell_type": "markdown", + "id": "9afe9795", + "metadata": {}, + "source": [ + "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", + "\n", + "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand forecasts." + ] + }, + { + "cell_type": "markdown", + "id": "dfe79e27", + "metadata": {}, + "source": [ + "### 4.1. Defining Buses and Zones\n", + "\n", + "**Buses** represent nodes in the network where energy can be injected or withdrawn. Each bus is assigned to a **zone**, which groups buses into market areas. This zoning facilitates market coupling by managing interactions between different market regions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f0a4666", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the buses DataFrame with three nodes and two zones\n", + "buses = pd.DataFrame(\n", + " {\n", + " \"name\": [\"north_1\", \"north_2\", \"south\"],\n", + " \"v_nom\": [380.0, 380.0, 380.0],\n", + " \"zone_id\": [\"DE_1\", \"DE_1\", \"DE_2\"],\n", + " \"x\": [10.0, 9.5, 11.6],\n", + " \"y\": [54.0, 53.5, 48.1],\n", + " }\n", + ")\n", + "\n", + "# Display the buses DataFrame\n", + "print(\"Buses DataFrame:\")\n", + "print(buses)" + ] + }, + { + "cell_type": "markdown", + "id": "7a8930e0", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **name:** Identifier for each bus (`north_1`, `north_2`, and `south`).\n", + "- **v_nom:** Nominal voltage level (in kV) for all buses.\n", + "- **zone_id:** Identifier for the market zone to which the bus belongs (`DE_1` for north buses and `DE_2` for the south bus).\n", + "- **x, y:** Geographical coordinates (optional, can be used for mapping or spatial analyses)." + ] + }, + { + "cell_type": "markdown", + "id": "ecf1d30b", + "metadata": {}, + "source": [ + "### 4.2. Configuring Transmission Lines\n", + "\n", + "**Transmission Lines** connect buses, allowing energy to flow between them. Each line has a specified capacity and electrical parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28ab461f", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the transmission lines DataFrame with three lines\n", + "lines = pd.DataFrame(\n", + " {\n", + " \"name\": [\"Line_N1_S\", \"Line_N2_S\", \"Line_N1_N2\"],\n", + " \"bus0\": [\"north_1\", \"north_2\", \"north_1\"],\n", + " \"bus1\": [\"south\", \"south\", \"north_2\"],\n", + " \"s_nom\": [10000.0, 10000.0, 5000.0], # Increased capacities for clarity\n", + " \"x\": [0.01, 0.01, 0.01],\n", + " \"r\": [0.001, 0.001, 0.001],\n", + " }\n", + ")\n", + "\n", + "# Display the lines DataFrame\n", + "print(\"Transmission Lines DataFrame:\")\n", + "print(lines)" + ] + }, + { + "cell_type": "markdown", + "id": "c59e3a73", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **name:** Identifier for each transmission line (`Line_N1_S`, `Line_N2_S`, and `Line_N1_N2`).\n", + "- **bus0, bus1:** The two buses that the line connects.\n", + "- **s_nom:** Nominal apparent power capacity of the line (in MVA).\n", + "- **x:** Reactance of the line (in per unit).\n", + "- **r:** Resistance of the line (in per unit)." + ] + }, + { + "cell_type": "markdown", + "id": "a81f34d1", + "metadata": {}, + "source": [ + "### 4.3. Setting Up Power Plant and Demand Units\n", + "\n", + "**Power Plant Units** represent energy generation sources, while **Demand Units** represent consumption. Each unit is associated with a specific bus (node) and has operational parameters that define its behavior in the market." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24594d95", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the total number of units\n", + "num_units = 30 # Reduced for simplicity\n", + "\n", + "# Generate the 'name' column: Unit 1 to Unit 30\n", + "names = [f\"Unit {i}\" for i in range(1, num_units + 1)]\n", + "\n", + "# All other columns with constant values\n", + "technology = [\"nuclear\"] * num_units\n", + "bidding_nodal = [\"naive_eom\"] * num_units\n", + "fuel_type = [\"uranium\"] * num_units\n", + "emission_factor = [0.0] * num_units\n", + "max_power = [1000.0] * num_units\n", + "min_power = [0.0] * num_units\n", + "efficiency = [0.3] * num_units\n", + "\n", + "# Generate 'additional_cost':\n", + "# - North units (1-15): 5 to 19\n", + "# - South units (16-30): 20 to 34\n", + "additional_cost = list(range(5, 5 + num_units))\n", + "\n", + "# Initialize 'node' and 'unit_operator' lists\n", + "node = []\n", + "unit_operator = []\n", + "\n", + "for i in range(1, num_units + 1):\n", + " if 1 <= i <= 15:\n", + " node.append(\"north_1\") # All north units connected to 'north_1'\n", + " unit_operator.append(\"Operator North\")\n", + " else:\n", + " node.append(\"south\") # All south units connected to 'south'\n", + " unit_operator.append(\"Operator South\")\n", + "\n", + "# Create the DataFrame\n", + "powerplant_units = pd.DataFrame(\n", + " {\n", + " \"name\": names,\n", + " \"technology\": technology,\n", + " \"bidding_nodal\": bidding_nodal,\n", + " \"fuel_type\": fuel_type,\n", + " \"emission_factor\": emission_factor,\n", + " \"max_power\": max_power,\n", + " \"min_power\": min_power,\n", + " \"efficiency\": efficiency,\n", + " \"additional_cost\": additional_cost,\n", + " \"node\": node,\n", + " \"unit_operator\": unit_operator,\n", + " }\n", + ")\n", + "\n", + "# Display the powerplant_units DataFrame\n", + "print(\"Power Plant Units DataFrame:\")\n", + "print(powerplant_units)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3dd42e8", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the demand units DataFrame\n", + "demand_units = pd.DataFrame(\n", + " {\n", + " \"name\": [\"demand_north_1\", \"demand_north_2\", \"demand_south\"],\n", + " \"technology\": [\"inflex_demand\"] * 3,\n", + " \"bidding_zonal\": [\"naive_eom\"] * 3,\n", + " \"max_power\": [100000, 100000, 100000],\n", + " \"min_power\": [0, 0, 0],\n", + " \"unit_operator\": [\"eom_de\"] * 3,\n", + " \"node\": [\"north_1\", \"north_2\", \"south\"],\n", + " }\n", + ")\n", + "\n", + "# Display the demand_units DataFrame\n", + "print(\"Demand Units DataFrame:\")\n", + "print(demand_units)" + ] + }, + { + "cell_type": "markdown", + "id": "c840bfbf", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Power Plant Units:**\n", + " - **name:** Identifier for each power plant unit (`Unit 1` to `Unit 30`).\n", + " - **technology:** Type of technology (`nuclear` for all units).\n", + " - **bidding_nodal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **fuel_type:** Type of fuel used (`uranium` for all units).\n", + " - **emission_factor:** Emissions per unit of energy produced (`0.0` for all units).\n", + " - **max_power, min_power:** Operational power limits (`1000.0` MW max, `0.0` MW min for all units).\n", + " - **efficiency:** Conversion efficiency (`0.3` for all units).\n", + " - **additional_cost:** Additional operational costs (`5` to `34`, with southern units being more expensive).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1` for units `1-15`, `south` for units `16-30`).\n", + " - **unit_operator:** Operator responsible for the unit (`Operator North` for northern units, `Operator South` for southern units).\n", + "\n", + "- **Demand Units:**\n", + " - **name:** Identifier for each demand unit (`demand_north_1`, `demand_north_2`, and `demand_south`).\n", + " - **technology:** Type of demand (`inflex_demand` for all units).\n", + " - **bidding_zonal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **max_power, min_power:** Operational power limits (`100000` MW max, `0` MW min for all units).\n", + " - **unit_operator:** Operator responsible for the unit (`eom_de` for all units).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1`, `north_2`, and `south`)." + ] + }, + { + "cell_type": "markdown", + "id": "585c96ee", + "metadata": {}, + "source": [ + "### 4.4. Preparing Demand Forecast Data\n", + "\n", + "**Demand Forecast Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efbb0909", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the demand forecast DataFrame\n", + "demand_df = pd.DataFrame(\n", + " {\n", + " \"datetime\": [\n", + " \"2019-01-01 00:00:00\",\n", + " \"2019-01-01 01:00:00\",\n", + " \"2019-01-01 02:00:00\",\n", + " \"2019-01-01 03:00:00\",\n", + " \"2019-01-01 04:00:00\",\n", + " \"2019-01-01 05:00:00\",\n", + " \"2019-01-01 06:00:00\",\n", + " \"2019-01-01 07:00:00\",\n", + " \"2019-01-01 08:00:00\",\n", + " \"2019-01-01 09:00:00\",\n", + " \"2019-01-01 10:00:00\",\n", + " \"2019-01-01 11:00:00\",\n", + " \"2019-01-01 12:00:00\",\n", + " \"2019-01-01 13:00:00\",\n", + " \"2019-01-01 14:00:00\",\n", + " \"2019-01-01 15:00:00\",\n", + " \"2019-01-01 16:00:00\",\n", + " \"2019-01-01 17:00:00\",\n", + " \"2019-01-01 18:00:00\",\n", + " \"2019-01-01 19:00:00\",\n", + " \"2019-01-01 20:00:00\",\n", + " \"2019-01-01 21:00:00\",\n", + " \"2019-01-01 22:00:00\",\n", + " \"2019-01-01 23:00:00\",\n", + " ],\n", + " \"demand_north_1\": [\n", + " 2400.0,\n", + " 2800.0,\n", + " 3200.0,\n", + " 3600.0,\n", + " 4000.0,\n", + " 4400.0,\n", + " 4800.0,\n", + " 5200.0,\n", + " 5600.0,\n", + " 6000.0,\n", + " 6400.0,\n", + " 6800.0,\n", + " 7200.0,\n", + " 7600.0,\n", + " 8000.0,\n", + " 8400.0,\n", + " 8800.0,\n", + " 9200.0,\n", + " 9600.0,\n", + " 10000.0,\n", + " 10400.0,\n", + " 10800.0,\n", + " 11200.0,\n", + " 11600.0,\n", + " ],\n", + " \"demand_north_2\": [\n", + " 2400.0,\n", + " 2800.0,\n", + " 3200.0,\n", + " 3600.0,\n", + " 4000.0,\n", + " 4400.0,\n", + " 4800.0,\n", + " 5200.0,\n", + " 5600.0,\n", + " 6000.0,\n", + " 6400.0,\n", + " 6800.0,\n", + " 7200.0,\n", + " 7600.0,\n", + " 8000.0,\n", + " 8400.0,\n", + " 8800.0,\n", + " 9200.0,\n", + " 9600.0,\n", + " 10000.0,\n", + " 10400.0,\n", + " 10800.0,\n", + " 11200.0,\n", + " 11600.0,\n", + " ],\n", + " \"demand_south\": [\n", + " 17400.0,\n", + " 16800.0,\n", + " 16200.0,\n", + " 15600.0,\n", + " 15000.0,\n", + " 14400.0,\n", + " 13800.0,\n", + " 13200.0,\n", + " 12600.0,\n", + " 12000.0,\n", + " 11400.0,\n", + " 10800.0,\n", + " 10200.0,\n", + " 9600.0,\n", + " 9000.0,\n", + " 8400.0,\n", + " 7800.0,\n", + " 7200.0,\n", + " 6600.0,\n", + " 6000.0,\n", + " 5400.0,\n", + " 4800.0,\n", + " 4200.0,\n", + " 3600.0,\n", + " ],\n", + " }\n", + ")\n", + "\n", + "# Convert the 'datetime' column to datetime objects and set as index\n", + "demand_df[\"datetime\"] = pd.to_datetime(demand_df[\"datetime\"])\n", + "demand_df.set_index(\"datetime\", inplace=True)\n", + "\n", + "# Display the demand_df DataFrame\n", + "print(\"Demand Forecast DataFrame:\")\n", + "print(demand_df.head())" + ] + }, + { + "cell_type": "markdown", + "id": "7809ba43", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **datetime:** Timestamp for each demand forecast.\n", + "- **demand_north_1, demand_north_2, demand_south:** Forecasted demand values for each respective demand unit.\n", + "\n", + "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." + ] + }, + { + "cell_type": "markdown", + "id": "47cf5886", + "metadata": {}, + "source": [ + "## 5. Mimicking the Market Clearing Process\n", + "\n", + "With the input files prepared, we can now mimic the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." + ] + }, + { + "cell_type": "markdown", + "id": "5cf58974", + "metadata": {}, + "source": [ + "### 5.1. Calculating the Incidence Matrix\n", + "\n", + "The **Incidence Matrix** represents the transmission capacities between different market zones. It is calculated as the sum of the capacities of transmission lines connecting each pair of zones. This matrix is crucial for enforcing transmission constraints during the market clearing process.\n", + "\n", + "**Note:** The method of calculating the incidence matrix by simply summing line capacities is a simplified approach. In real-world scenarios, more sophisticated methods are used to accurately represent the network's behavior and constraints. This approach will be extended in future implementations to better reflect real-world complexities." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ead6cb27", + "metadata": {}, + "outputs": [], + "source": [ + "# Define market products (time periods)\n", + "market_products = []\n", + "for timestamp in demand_df.index:\n", + " market_products.append(\n", + " (\n", + " timestamp, # Start time\n", + " timestamp + pd.Timedelta(hours=1), # End time\n", + " 1, # Only_hours flag (for simplicity)\n", + " )\n", + " )\n", + "\n", + "# Define nodes (zones)\n", + "nodes = buses[\"zone_id\"].unique().tolist()\n", + "\n", + "# Calculate the incidence matrix by summing the capacities of transmission lines between zones\n", + "incidence_matrix = {zone: {} for zone in nodes}\n", + "\n", + "for _, line in lines.iterrows():\n", + " # Get zones for each end of the transmission line\n", + " zone0 = buses.loc[buses[\"name\"] == line[\"bus0\"], \"zone_id\"].values[0]\n", + " zone1 = buses.loc[buses[\"name\"] == line[\"bus1\"], \"zone_id\"].values[0]\n", + "\n", + " if zone0 != zone1:\n", + " # Add capacity to zone0 -> zone1\n", + " if zone1 not in incidence_matrix[zone0]:\n", + " incidence_matrix[zone0][zone1] = 0\n", + " incidence_matrix[zone0][zone1] += line[\"s_nom\"]\n", + "\n", + " # Add capacity to zone1 -> zone0 (assuming bidirectional)\n", + " if zone0 not in incidence_matrix[zone1]:\n", + " incidence_matrix[zone1][zone0] = 0\n", + " incidence_matrix[zone1][zone0] += line[\"s_nom\"]\n", + "\n", + "# Convert lower triangle values to negative to indicate opposite direction\n", + "for i, zone0 in enumerate(nodes):\n", + " for j, zone1 in enumerate(nodes):\n", + " if i > j and zone1 in incidence_matrix[zone0]:\n", + " incidence_matrix[zone0][zone1] = -incidence_matrix[zone0][zone1]\n", + "\n", + "# Display the calculated incidence matrix\n", + "print(\"Calculated Incidence Matrix between Zones:\")\n", + "print(pd.DataFrame(incidence_matrix))" + ] + }, + { + "cell_type": "markdown", + "id": "bce38f51", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Nodes (Zones):** Extracted from the `buses` DataFrame (`DE_1` and `DE_2`).\n", + "- **Transmission Lines:** Iterated over to sum their capacities between different zones.\n", + "- **Bidirectional Flow Assumption:** Transmission capacities are added in both directions (`DE_1 -> DE_2` and `DE_2 -> DE_1`).\n", + "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values.\n", + "\n", + "**Sample Output:**\n", + "\n", + "```\n", + "Calculated Incidence Matrix between Zones:\n", + " DE_1 DE_2\n", + "DE_1 0 20000\n", + "DE_2 -20000 0\n", + "```\n", + "\n", + "This output indicates that there is a total transmission capacity of 20,000 MVA from `DE_1` to `DE_2` and vice versa, based on the sum of the capacities of the transmission lines connecting these zones." + ] + }, + { + "cell_type": "markdown", + "id": "88a22af8", + "metadata": {}, + "source": [ + "### 5.2. Implementing the Simplified Market Clearing Function\n", + "\n", + "We will use the `simplified_market_clearing_opt` function defined earlier to perform the market clearing. This function takes in the orders, market products, zones (nodes), and the incidence matrix to determine the optimal bid acceptances and power flows between zones." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de425eef", + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare the orders dictionary based on powerplant_units and demand_units\n", + "\n", + "# Initialize orders dictionary\n", + "orders = {}\n", + "\n", + "# Add power plant bids\n", + "for _, row in powerplant_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": row[\"additional_cost\"], # Assuming additional_cost as bid price\n", + " \"volume\": row[\"max_power\"], # Assuming max_power as bid volume\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Add demand bids\n", + "for _, row in demand_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": 100, # Demand bids with high price\n", + " \"volume\": -demand_df.loc[\n", + " timestamp, row[\"name\"]\n", + " ], # Negative volume for demand\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Display a sample order\n", + "print(\"\\nSample Order:\")\n", + "print(orders[\"Unit 1_2019-01-01 00:00:00\"])" + ] + }, + { + "cell_type": "markdown", + "id": "7e30920e", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume. Units in the north (`DE_1`) are cheaper (`additional_cost` ranging from 5 to 19) compared to southern units (`DE_2`) which are more expensive (`additional_cost` ranging from 20 to 34).\n", + "- **Demand Bids:** Each demand unit submits a bid for each time period with zero price and negative volume representing the demand." + ] + }, + { + "cell_type": "markdown", + "id": "c13aa212", + "metadata": {}, + "source": [ + "### 5.3. Running the Market Clearing Simulation\n", + "\n", + "We will conduct two simulations:\n", + "\n", + "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", + "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **present**." + ] + }, + { + "cell_type": "markdown", + "id": "403e85e2", + "metadata": {}, + "source": [ + "#### Simulation 1: Zero Transmission Capacity Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38f619a6", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"### Simulation 1: Zero Transmission Capacity Between Zones\")\n", + "\n", + "# Define nodes (zones)\n", + "nodes_sim1 = nodes.copy()\n", + "\n", + "# Define the incidence matrix as a dictionary with zero transmission capacity\n", + "incidence_matrix_sim1 = {\n", + " \"DE_1\": {\"DE_2\": 0.0}, # Zero capacity from DE_1 to DE_2\n", + " \"DE_2\": {\"DE_1\": 0.0}, # Zero capacity from DE_2 to DE_1\n", + "}\n", + "\n", + "# Display the incidence matrix for Simulation 1\n", + "print(\"Incidence Matrix for Simulation 1 (Zero Transmission Capacity):\")\n", + "display(pd.DataFrame(incidence_matrix_sim1))" + ] + }, + { + "cell_type": "markdown", + "id": "a774230e", + "metadata": {}, + "source": [ + "The orders before being sent to the market clearing are first preprocessed. During this preprocessing the node_id from the bids is matched with the zone_id from the buses DataFrame. The bids are then sent to the market clearing function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38142d9a", + "metadata": {}, + "outputs": [], + "source": [ + "# create a mapping from node_id to zone_id\n", + "node_mapping = buses.set_index(\"name\")[\"zone_id\"].to_dict()\n", + "\n", + "# Create a new dictionary with mapped zone IDs\n", + "orders_mapped = {}\n", + "for bid_id, bid in orders.items():\n", + " original_node = bid[\"node\"]\n", + " mapped_zone = node_mapping.get(\n", + " original_node, original_node\n", + " ) # Default to original_node if not found\n", + " orders_mapped[bid_id] = {\n", + " \"price\": bid[\"price\"],\n", + " \"volume\": bid[\"volume\"],\n", + " \"node\": mapped_zone, # Replace bus with zone ID\n", + " \"time\": bid[\"time\"],\n", + " }\n", + "\n", + "display(pd.DataFrame(orders_mapped).T.head())" + ] + }, + { + "cell_type": "markdown", + "id": "0a78a876", + "metadata": {}, + "source": [ + "Now we can run the market clearing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ffce232", + "metadata": {}, + "outputs": [], + "source": [ + "# Run the simplified market clearing for Simulation 1\n", + "model_sim1, results_sim1 = simplified_market_clearing_opt(\n", + " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "410ff142", + "metadata": {}, + "source": [ + "#### Simulation 2: Transmission Capacity Present Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70c06d1c", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "print(\"### Simulation 2: Transmission Capacity Present Between Zones\")\n", + "\n", + "# Define the incidence matrix as a dictionary with non-zero transmission capacity\n", + "incidence_matrix_sim2 = {\n", + " \"DE_1\": {\"DE_2\": 20000.0}, # Transmission capacity from DE_1 to DE_2 in MVA\n", + " \"DE_2\": {\"DE_1\": 20000.0}, # Transmission capacity from DE_2 to DE_1 in MVA\n", + "}\n", + "\n", + "# Display the incidence matrix for Simulation 2\n", + "print(\"Incidence Matrix for Simulation 2 (With Transmission Capacity):\")\n", + "display(pd.DataFrame(incidence_matrix_sim2))\n", + "\n", + "# since the orders are already mapped to zones, we can directly use the orders_mapped dictionary\n", + "\n", + "# Run the simplified market clearing for Simulation 2\n", + "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", + " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim2\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "b9631826", + "metadata": {}, + "source": [ + "### 5.4. Extracting and Interpreting the Results\n", + "\n", + "After running both simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9b70d5bf", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to extract accepted bids and calculate clearing prices\n", + "# Function to extract accepted bids, power flows, and market clearing prices using dual variables\n", + "def extract_results(model, orders, nodes):\n", + " # Extract accepted bid ratios\n", + " accepted_bids = {}\n", + " for o in model.x:\n", + " acceptance_ratio = pyo.value(model.x[o])\n", + " if acceptance_ratio > 0:\n", + " accepted_bids[o] = acceptance_ratio\n", + "\n", + " # Extract power flows between zones for each time period\n", + " power_flows = []\n", + " for t in model.T:\n", + " for node1 in nodes:\n", + " for node2 in nodes:\n", + " if node1 != node2:\n", + " flow = pyo.value(model.flows[t, node1, node2])\n", + " if flow != 0:\n", + " power_flows.append(\n", + " {\n", + " \"time\": t,\n", + " \"from_zone\": node1,\n", + " \"to_zone\": node2,\n", + " \"flow_MW\": flow,\n", + " }\n", + " )\n", + "\n", + " # Convert to DataFrame\n", + " power_flows_df = pd.DataFrame(power_flows)\n", + "\n", + " # Extract market clearing prices from dual variables of energy balance constraints\n", + " market_clearing_prices = {}\n", + " for node in nodes:\n", + " market_clearing_prices[node] = {\n", + " t: pyo.value(model.dual[model.energy_balance[node, t]]) for t in model.T\n", + " }\n", + "\n", + " # Convert clearing prices to DataFrame\n", + " clearing_prices = []\n", + " for node in market_clearing_prices:\n", + " for t in market_clearing_prices[node]:\n", + " clearing_prices.append(\n", + " {\n", + " \"zone\": node,\n", + " \"time\": t,\n", + " \"clearing_price\": market_clearing_prices[node][t],\n", + " }\n", + " )\n", + "\n", + " clearing_prices_df = pd.DataFrame(clearing_prices)\n", + "\n", + " return accepted_bids, power_flows_df, clearing_prices_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58cd1b3a", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract results for Simulation 1\n", + "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", + " model_sim1, orders, nodes_sim1\n", + ")\n", + "\n", + "print(\"Simulation 1: Power Flows Between Zones\")\n", + "display(power_flows_df_sim1.head())\n", + "\n", + "print(\"Simulation 1: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_2\"].head())\n", + "\n", + "# Extract results for Simulation 2\n", + "accepted_bids_sim2, power_flows_df_sim2, clearing_prices_df_sim2 = extract_results(\n", + " model_sim2, orders, nodes_sim1\n", + ")\n", + "\n", + "print(\"Simulation 2: Power Flows Between Zones\")\n", + "display(power_flows_df_sim2.head())\n", + "\n", + "print(\"Simulation 2: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_2\"].head())" + ] + }, + { + "cell_type": "markdown", + "id": "ca006274", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Accepted Bids:** Shows which bids were accepted in each simulation and the ratio at which they were accepted.\n", + "- **Power Flows:** Indicates the amount of energy transmitted between zones. In Simulation 1, with zero transmission capacity, there should be no power flows between `DE_1` and `DE_2`. In Simulation 2, with transmission capacity present, power flows can occur between zones.\n", + "- **Clearing Prices:** Represents the average bid price in each zone at each time period. Comparing prices across simulations can reveal the impact of transmission capacity on market prices." + ] + }, + { + "cell_type": "markdown", + "id": "a08b136c", + "metadata": {}, + "source": [ + "### 5.5. Comparing Simulations\n", + "\n", + "To better understand the impact of transmission capacity, let's compare the key results from both simulations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95b3ed34", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices for both simulations\n", + "for zone in nodes_sim1:\n", + " # Filter data for the current zone and Simulation 1\n", + " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 2\n", + " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", + "\n", + " # Add trace for Simulation 1\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim1[\"time\"],\n", + " y=zone_prices_sim1[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim1\",\n", + " line=dict(dash=\"dash\"), # Dashed line for Simulation 1\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 2\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim2[\"time\"],\n", + " y=zone_prices_sim2[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim2\",\n", + " line=dict(dash=\"solid\"), # Solid line for Simulation 2\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Simulation 1 vs Simulation 2\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price\",\n", + " legend_title=\"Simulation\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", + " ),\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", + ")\n", + "\n", + "# Display the interactive plot\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1d3e1c1c", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare power flows\n", + "plt.figure(figsize=(14, 8))\n", + "sns.heatmap(\n", + " power_flows_df_sim2.pivot_table(\n", + " index=\"time\", columns=[\"from_zone\", \"to_zone\"], values=\"flow_MW\", fill_value=0\n", + " ),\n", + " annot=True,\n", + " fmt=\".1f\",\n", + " cmap=\"coolwarm\",\n", + ")\n", + "plt.xlabel(\"Transmission Lines (From Zone -> To Zone)\")\n", + "plt.ylabel(\"Time\")\n", + "plt.title(\"Power Flows Between Zones Over Time: Simulation 2\")\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "723d53b1", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Number of Accepted Bids:** Comparing the total number of accepted bids in both simulations can show how transmission capacity affects the ability to dispatch more units.\n", + "- **Clearing Prices Plot:** By plotting the clearing prices for each zone across both simulations, we can observe how the presence of transmission capacity influences price convergence or divergence between zones.\n", + "- **Power Flows Heatmap:** Visualizing power flows in Simulation 2 demonstrates how energy is transmitted between zones when transmission capacity is available." + ] + }, + { + "cell_type": "markdown", + "id": "f5b14240", + "metadata": {}, + "source": [ + "## 6. Integrating with ASSUME\n", + "\n", + "In a real-world scenario, the ASSUME framework would handle the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we've mimicked the market clearing process using DataFrames and a simplified function to illustrate the inner workings of market zone coupling.\n", + "\n", + "To integrate this process within ASSUME:\n", + "\n", + "- **Input Files:** Prepare CSV files for buses, lines, powerplant_units, demand_units, and demand forecasts as demonstrated in section 4.\n", + "- **Configuration File:** Define the market configuration, including zone identifiers and transmission capacities.\n", + "- **Running the Simulation:** Use ASSUME's built-in functions to load the CSV files, apply the configurations, and execute the simulation.\n", + "\n", + "Refer to the ASSUME documentation for detailed instructions on configuring and running simulations with CSV input files and configuration settings." + ] + }, + { + "cell_type": "markdown", + "id": "7c0ab370", + "metadata": {}, + "source": [ + "## 7. Analyzing the Results\n", + "\n", + "After running the market clearing simulation, it's essential to analyze the results to understand how market zone coupling influenced market dynamics. Below are key metrics and methods to analyze the simulation outcomes." + ] + }, + { + "cell_type": "markdown", + "id": "d0edde5d", + "metadata": {}, + "source": [ + "### 7.1. Accepted Bids\n", + "\n", + "**Accepted Bids** indicate which power plant and demand bids were successfully dispatched in the market. Analyzing accepted bids helps in understanding supply and demand distribution across different zones." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc8dbc99", + "metadata": {}, + "outputs": [], + "source": [ + "# Display accepted bids for Simulation 1\n", + "print(\"Simulation 1: Accepted Bids DataFrame:\")\n", + "accepted_bids_df_sim1 = pd.DataFrame.from_dict(\n", + " accepted_bids_sim1, orient=\"index\", columns=[\"Acceptance_Ratio\"]\n", + ")\n", + "accepted_bids_df_sim1.reset_index(inplace=True)\n", + "accepted_bids_df_sim1.rename(columns={\"index\": \"Bid_ID\"}, inplace=True)\n", + "display(accepted_bids_df_sim1.head())\n", + "\n", + "# Display accepted bids for Simulation 2\n", + "print(\"Simulation 2: Accepted Bids DataFrame:\")\n", + "accepted_bids_df_sim2 = pd.DataFrame.from_dict(\n", + " accepted_bids_sim2, orient=\"index\", columns=[\"Acceptance_Ratio\"]\n", + ")\n", + "accepted_bids_df_sim2.reset_index(inplace=True)\n", + "accepted_bids_df_sim2.rename(columns={\"index\": \"Bid_ID\"}, inplace=True)\n", + "display(accepted_bids_df_sim2.head())" + ] + }, + { + "cell_type": "markdown", + "id": "58b1c1bd", + "metadata": {}, + "source": [ + "### 7.2. Power Flows Between Zones\n", + "\n", + "**Power Flows** show the amount of energy transmitted between different zones. This helps in verifying that transmission constraints are respected and understanding cross-zone energy trading." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f7140ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Display power flows for Simulation 1\n", + "print(\"Simulation 1: Power Flows Between Zones:\")\n", + "display(power_flows_df_sim1.head())\n", + "\n", + "# Display power flows for Simulation 2\n", + "print(\"Simulation 2: Power Flows Between Zones:\")\n", + "display(power_flows_df_sim2.head())" + ] + }, + { + "cell_type": "markdown", + "id": "b0c781d7", + "metadata": {}, + "source": [ + "### 7.3. Clearing Prices\n", + "\n", + "**Clearing Prices** represent the market price in each zone at each time period. Comparing prices across zones can reveal how zone coupling affects market equilibrium and price convergence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9f196ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Display clearing prices for Simulation 1\n", + "print(\"Simulation 1: Clearing Prices per Zone and Time:\")\n", + "display(clearing_prices_df_sim1.head())\n", + "\n", + "# Display clearing prices for Simulation 2\n", + "print(\"Simulation 2: Clearing Prices per Zone and Time:\")\n", + "display(clearing_prices_df_sim2.head())" + ] + }, + { + "cell_type": "markdown", + "id": "de908c5c", + "metadata": {}, + "source": [ + "### 7.4. Visualization (Optional)\n", + "\n", + "Visualizing the results can provide a clearer understanding of the market dynamics. Below are examples of how to plot clearing prices and power flows." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5f0b36d", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot Clearing Prices for Each Zone Over Time\n", + "plt.figure(figsize=(12, 6))\n", + "for zone in nodes_sim1:\n", + " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", + " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", + " plt.plot(\n", + " zone_prices_sim1[\"time\"],\n", + " zone_prices_sim1[\"clearing_price\"],\n", + " label=f\"{zone} - Sim1\",\n", + " linestyle=\"--\",\n", + " )\n", + " plt.plot(\n", + " zone_prices_sim2[\"time\"],\n", + " zone_prices_sim2[\"clearing_price\"],\n", + " label=f\"{zone} - Sim2\",\n", + " linestyle=\"-\",\n", + " )\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Clearing Price\")\n", + "plt.title(\"Clearing Prices per Zone Over Time: Simulation 1 vs Simulation 2\")\n", + "plt.legend()\n", + "plt.xticks(rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "17c08c41", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across both simulations. The dashed lines represent Simulation 1 (no transmission capacity), and the solid lines represent Simulation 2 (with transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9caf342a", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot Power Flows Between Zones Over Time for Simulation 2\n", + "plt.figure(figsize=(14, 8))\n", + "sns.heatmap(\n", + " power_flows_df_sim2.pivot_table(\n", + " index=\"time\", columns=[\"from_zone\", \"to_zone\"], values=\"flow_MW\", fill_value=0\n", + " ),\n", + " annot=True,\n", + " fmt=\".1f\",\n", + " cmap=\"coolwarm\",\n", + ")\n", + "plt.xlabel(\"Transmission Lines (From Zone -> To Zone)\")\n", + "plt.ylabel(\"Time\")\n", + "plt.title(\"Power Flows Between Zones Over Time: Simulation 2\")\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "be77e1f9", + "metadata": {}, + "source": [ + "**Explanation:**\n", + "\n", + "- **Power Flows Heatmap (Simulation 2):** Visualizes the amount of energy transmitted between zones over time when transmission capacity is present. The heatmap highlights periods of high or low cross-zone trading, demonstrating the impact of transmission capacity on energy distribution." + ] + }, + { + "cell_type": "markdown", + "id": "605935df", + "metadata": {}, + "source": [ + "## Conclusion\n", + "\n", + "In this tutorial, we explored how to mimic and understand market zone coupling within the ASSUME framework. We covered the following key steps:\n", + "\n", + "1. **Introduction to Market Zone Coupling:** Understanding the concept and its significance in electricity market simulations.\n", + "2. **Setting Up the ASSUME Framework:** Installing the framework and preparing the environment for zone coupling.\n", + "3. **Understanding Market Clearing Optimization:** Grasping the fundamentals of the market clearing process with a focus on zone interactions.\n", + "4. **Creating Exemplary Input Files for Market Zone Coupling:** Defining buses, transmission lines, power plant units, demand units, and preparing demand forecasts.\n", + "5. **Mimicking the Market Clearing Process:** \n", + " - **Calculating the Incidence Matrix:** Summing transmission line capacities between zones to create the incidence matrix.\n", + " - **Implementing the Simplified Market Clearing Function:** Using a simplified Pyomo model to perform market clearing.\n", + " - **Running the Market Clearing Simulation:** Executing two simulations to observe the impact of transmission capacity.\n", + "6. **Integrating with ASSUME:** Outlining the steps to integrate the process within the ASSUME framework using CSV input files and configuration settings.\n", + "7. **Analyzing the Results:** Extracting and visualizing key metrics to assess the impact of market zone coupling.\n", + "\n", + "**Key Takeaways:**\n", + "\n", + "- **Incidence Matrix:** Represents the transmission capacities between different market zones. Calculated by summing the capacities of transmission lines connecting each pair of zones. While this method is simplified, it provides a foundational understanding of how zones interact within the market.\n", + "\n", + "- **Simplified Market Clearing:** Demonstrates the core principles of market clearing, including bid acceptance and power flow management between zones.\n", + "\n", + "- **Impact of Transmission Capacity:** The presence of transmission capacity allows for energy trading between zones, potentially reducing overall costs and balancing demand more effectively. Without transmission capacity, each zone must rely solely on its local generation, which may lead to higher costs if local generation is more expensive.\n", + "\n", + "- **Limitations and Future Work:** The current method of calculating the incidence matrix by summing line capacities is limited and does not capture the full complexity of real-world transmission networks. Future implementations will enhance this approach to include more detailed network modeling, accounting for factors like line impedance, reactive power flows, and dynamic constraints.\n", + "\n", + "By following this guide, you have successfully set up a simplified multi-zone electricity market simulation, enabling a deeper understanding of market dynamics and the role of zone coupling in balancing supply and demand. You can now extend this setup to include more zones, varying transmission capacities, and diverse bidding strategies to explore a wide range of market scenarios.\n", + "\n", + "Thank you for participating in this workshop!" + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/08_market_zone_coupling.ipynb.license b/examples/notebooks/08_market_zone_coupling.ipynb.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/notebooks/08_market_zone_coupling.ipynb.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later From 30db12860273bc984d56510d49375e9af3e4bcfc Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Fri, 27 Sep 2024 12:10:18 +0200 Subject: [PATCH 06/27] -finilize the market zone coupling tutorial --- .../notebooks/08_market_zone_coupling.ipynb | 1004 ++++++++++------- 1 file changed, 623 insertions(+), 381 deletions(-) diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index 15220fd2..257bab47 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "21658e52", + "id": "d6968cbf", "metadata": {}, "source": [ "# Market Zone Coupling in the ASSUME Framework\n", @@ -23,6 +23,8 @@ " - 5.1. Calculating the Incidence Matrix\n", " - 5.2. Implementing the Simplified Market Clearing Function\n", " - 5.3. Running the Market Clearing Simulation\n", + " - 5.4. Extracting and Interpreting the Results\n", + " - 5.5. Comparing Simulations\n", "6. **Integrating with ASSUME**\n", "7. **Analyzing the Results**\n", "\n", @@ -31,25 +33,27 @@ }, { "cell_type": "markdown", - "id": "ffdfa0a7", + "id": "b7f48d5e", "metadata": {}, "source": [ "## 1. Introduction to Market Zone Coupling\n", "\n", - "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. By coupling market zones, we can simulate more realistic and complex market dynamics, considering factors like transmission constraints, regional demand and supply variations, and cross-zone trading.\n", + "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. In the ASSUME framework, implementing market zone coupling is straightforward: by properly defining the input data and configuration files, the framework automatically manages the interactions between zones, including transmission constraints and cross-zone trading.\n", "\n", - "In the ASSUME framework, market zone coupling involves:\n", + "This tutorial aims to provide a deeper understanding of how market zone coupling operates within ASSUME. While the framework handles much of the complexity internally, we will explore the underlying processes, such as the calculation of transmission capacities and the market clearing optimization. This detailed walkthrough is designed to enhance your comprehension of the framework's capabilities and the dynamics of multi-zone electricity markets.\n", "\n", - "- **Defining Multiple Market Zones:** Segmenting the market into distinct zones based on geographical or operational criteria.\n", - "- **Establishing Connections Between Zones:** Setting up transmission lines that allow energy flow between different market zones.\n", - "- **Configuring the Market Clearing Process:** Adjusting the market clearing algorithm to account for interactions and constraints across zones.\n", + "Throughout this tutorial, you will:\n", "\n", - "This tutorial will walk you through each of these steps, providing code examples and configuration guidelines to help you set up market zone coupling effectively." + "- **Define Multiple Market Zones:** Segment the market into distinct zones based on geographical or operational criteria.\n", + "- **Configure Transmission Lines:** Establish connections that allow energy flow between different market zones.\n", + "- **Understand the Market Clearing Process:** Examine how the market clearing algorithm accounts for interactions and constraints across zones.\n", + "\n", + "By the end of this workshop, you will not only know how to set up market zone coupling in ASSUME but also gain insights into the internal mechanisms that drive market interactions and price formations across different zones." ] }, { "cell_type": "markdown", - "id": "39361ba8", + "id": "ad384717", "metadata": {}, "source": [ "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", @@ -62,7 +66,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5f5b70bb", + "id": "d501b239", "metadata": {}, "outputs": [], "source": [ @@ -70,23 +74,15 @@ "# !pip install assume-framework\n", "\n", "# Install Plotly if not already installed\n", - "# !pip install plotly" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91a4bbf7", - "metadata": {}, - "outputs": [], - "source": [ + "# !pip install plotly\n", + "\n", "# Clone the ASSUME repository containing predefined scenarios\n", "# !git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { "cell_type": "markdown", - "id": "4a049632", + "id": "7935b350", "metadata": {}, "source": [ "Let's also import some basic libraries that we will use throughout the tutorial." @@ -95,16 +91,17 @@ { "cell_type": "code", "execution_count": null, - "id": "cfa74a9d", + "id": "05df5da6", "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "# import plotly for visualization\n", "import plotly.graph_objects as go\n", - "import seaborn as sns\n", + "\n", + "# import yaml for reading and writing YAML files\n", + "import yaml\n", "\n", "# Function to display DataFrame in Jupyter\n", "from IPython.display import display" @@ -112,7 +109,7 @@ }, { "cell_type": "markdown", - "id": "95e7d6fc", + "id": "bf42fa2d", "metadata": {}, "source": [ "**Select Input Files Path:**\n", @@ -123,7 +120,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fdda1af9", + "id": "11eb5fb7", "metadata": {}, "outputs": [], "source": [ @@ -140,17 +137,9 @@ "print(f\"Using inputs path: {inputs_path}\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ae63334", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "104d2285", + "id": "3491bed0", "metadata": {}, "source": [ "## 3. Understanding the Market Clearing Optimization\n", @@ -170,10 +159,8 @@ { "cell_type": "code", "execution_count": null, - "id": "21f17651", - "metadata": { - "lines_to_next_cell": 1 - }, + "id": "72cdba6b", + "metadata": {}, "outputs": [], "source": [ "# Display a simplified version of the market clearing optimization function\n", @@ -264,6 +251,22 @@ " model.T, model.nodes, model.nodes, rule=transmission_rule\n", " )\n", "\n", + " def symmetry_rule(model, t, node1, node2):\n", + " \"\"\"\n", + " Ensures that the flow from node1 to node2 is the negative of the flow from node2 to node1.\n", + " \"\"\"\n", + " if node1 != node2:\n", + " return model.flows[t, node2, node1] == -model.flows[t, node1, node2]\n", + " else:\n", + " return (\n", + " pyo.Constraint.Skip\n", + " ) # No constraint needed for flows within the same node\n", + "\n", + " # Apply the antisymmetry rule to all node pairs and time periods\n", + " model.symmetry_constraints = pyo.Constraint(\n", + " model.T, model.nodes, model.nodes, rule=symmetry_rule\n", + " )\n", + "\n", " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", " model.objective = pyo.Objective(\n", " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", @@ -289,7 +292,7 @@ }, { "cell_type": "markdown", - "id": "28be160a", + "id": "28637869", "metadata": {}, "source": [ "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." @@ -297,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "9afe9795", + "id": "26e3cfa8", "metadata": {}, "source": [ "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", @@ -307,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "dfe79e27", + "id": "01726994", "metadata": {}, "source": [ "### 4.1. Defining Buses and Zones\n", @@ -318,7 +321,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3f0a4666", + "id": "ae9695a3", "metadata": {}, "outputs": [], "source": [ @@ -335,12 +338,12 @@ "\n", "# Display the buses DataFrame\n", "print(\"Buses DataFrame:\")\n", - "print(buses)" + "display(buses)" ] }, { "cell_type": "markdown", - "id": "7a8930e0", + "id": "02d32661", "metadata": {}, "source": [ "**Explanation:**\n", @@ -353,7 +356,7 @@ }, { "cell_type": "markdown", - "id": "ecf1d30b", + "id": "01f22792", "metadata": {}, "source": [ "### 4.2. Configuring Transmission Lines\n", @@ -364,7 +367,7 @@ { "cell_type": "code", "execution_count": null, - "id": "28ab461f", + "id": "4a531a25", "metadata": {}, "outputs": [], "source": [ @@ -382,12 +385,12 @@ "\n", "# Display the lines DataFrame\n", "print(\"Transmission Lines DataFrame:\")\n", - "print(lines)" + "display(lines)" ] }, { "cell_type": "markdown", - "id": "c59e3a73", + "id": "13cb21b0", "metadata": {}, "source": [ "**Explanation:**\n", @@ -401,7 +404,7 @@ }, { "cell_type": "markdown", - "id": "a81f34d1", + "id": "8874c322", "metadata": {}, "source": [ "### 4.3. Setting Up Power Plant and Demand Units\n", @@ -412,7 +415,7 @@ { "cell_type": "code", "execution_count": null, - "id": "24594d95", + "id": "e71b7ce9", "metadata": {}, "outputs": [], "source": [ @@ -424,7 +427,7 @@ "\n", "# All other columns with constant values\n", "technology = [\"nuclear\"] * num_units\n", - "bidding_nodal = [\"naive_eom\"] * num_units\n", + "bidding_zonal = [\"naive_eom\"] * num_units\n", "fuel_type = [\"uranium\"] * num_units\n", "emission_factor = [0.0] * num_units\n", "max_power = [1000.0] * num_units\n", @@ -453,7 +456,7 @@ " {\n", " \"name\": names,\n", " \"technology\": technology,\n", - " \"bidding_nodal\": bidding_nodal,\n", + " \"bidding_zonal\": bidding_zonal, # bidding strategy used to bid on the zonal market. Should be same name as in config file\n", " \"fuel_type\": fuel_type,\n", " \"emission_factor\": emission_factor,\n", " \"max_power\": max_power,\n", @@ -467,13 +470,13 @@ "\n", "# Display the powerplant_units DataFrame\n", "print(\"Power Plant Units DataFrame:\")\n", - "print(powerplant_units)" + "display(powerplant_units)" ] }, { "cell_type": "code", "execution_count": null, - "id": "e3dd42e8", + "id": "365aa96b", "metadata": {}, "outputs": [], "source": [ @@ -492,12 +495,12 @@ "\n", "# Display the demand_units DataFrame\n", "print(\"Demand Units DataFrame:\")\n", - "print(demand_units)" + "display(demand_units)" ] }, { "cell_type": "markdown", - "id": "c840bfbf", + "id": "8ce96a59", "metadata": {}, "source": [ "**Explanation:**\n", @@ -525,7 +528,7 @@ }, { "cell_type": "markdown", - "id": "585c96ee", + "id": "e14dc7f7", "metadata": {}, "source": [ "### 4.4. Preparing Demand Forecast Data\n", @@ -536,145 +539,44 @@ { "cell_type": "code", "execution_count": null, - "id": "efbb0909", + "id": "c817bc49", "metadata": {}, "outputs": [], "source": [ - "# Define the demand forecast DataFrame\n", + "# Define the demand forecast DataFrame with a simplified datetime generation\n", "demand_df = pd.DataFrame(\n", " {\n", - " \"datetime\": [\n", - " \"2019-01-01 00:00:00\",\n", - " \"2019-01-01 01:00:00\",\n", - " \"2019-01-01 02:00:00\",\n", - " \"2019-01-01 03:00:00\",\n", - " \"2019-01-01 04:00:00\",\n", - " \"2019-01-01 05:00:00\",\n", - " \"2019-01-01 06:00:00\",\n", - " \"2019-01-01 07:00:00\",\n", - " \"2019-01-01 08:00:00\",\n", - " \"2019-01-01 09:00:00\",\n", - " \"2019-01-01 10:00:00\",\n", - " \"2019-01-01 11:00:00\",\n", - " \"2019-01-01 12:00:00\",\n", - " \"2019-01-01 13:00:00\",\n", - " \"2019-01-01 14:00:00\",\n", - " \"2019-01-01 15:00:00\",\n", - " \"2019-01-01 16:00:00\",\n", - " \"2019-01-01 17:00:00\",\n", - " \"2019-01-01 18:00:00\",\n", - " \"2019-01-01 19:00:00\",\n", - " \"2019-01-01 20:00:00\",\n", - " \"2019-01-01 21:00:00\",\n", - " \"2019-01-01 22:00:00\",\n", - " \"2019-01-01 23:00:00\",\n", - " ],\n", - " \"demand_north_1\": [\n", - " 2400.0,\n", - " 2800.0,\n", - " 3200.0,\n", - " 3600.0,\n", - " 4000.0,\n", - " 4400.0,\n", - " 4800.0,\n", - " 5200.0,\n", - " 5600.0,\n", - " 6000.0,\n", - " 6400.0,\n", - " 6800.0,\n", - " 7200.0,\n", - " 7600.0,\n", - " 8000.0,\n", - " 8400.0,\n", - " 8800.0,\n", - " 9200.0,\n", - " 9600.0,\n", - " 10000.0,\n", - " 10400.0,\n", - " 10800.0,\n", - " 11200.0,\n", - " 11600.0,\n", - " ],\n", - " \"demand_north_2\": [\n", - " 2400.0,\n", - " 2800.0,\n", - " 3200.0,\n", - " 3600.0,\n", - " 4000.0,\n", - " 4400.0,\n", - " 4800.0,\n", - " 5200.0,\n", - " 5600.0,\n", - " 6000.0,\n", - " 6400.0,\n", - " 6800.0,\n", - " 7200.0,\n", - " 7600.0,\n", - " 8000.0,\n", - " 8400.0,\n", - " 8800.0,\n", - " 9200.0,\n", - " 9600.0,\n", - " 10000.0,\n", - " 10400.0,\n", - " 10800.0,\n", - " 11200.0,\n", - " 11600.0,\n", - " ],\n", - " \"demand_south\": [\n", - " 17400.0,\n", - " 16800.0,\n", - " 16200.0,\n", - " 15600.0,\n", - " 15000.0,\n", - " 14400.0,\n", - " 13800.0,\n", - " 13200.0,\n", - " 12600.0,\n", - " 12000.0,\n", - " 11400.0,\n", - " 10800.0,\n", - " 10200.0,\n", - " 9600.0,\n", - " 9000.0,\n", - " 8400.0,\n", - " 7800.0,\n", - " 7200.0,\n", - " 6600.0,\n", - " 6000.0,\n", - " 5400.0,\n", - " 4800.0,\n", - " 4200.0,\n", - " 3600.0,\n", - " ],\n", + " \"datetime\": pd.date_range(start=\"2019-01-01\", periods=24, freq=\"h\"),\n", + " \"demand_north_1\": [2400 + i * 400 for i in range(24)],\n", + " \"demand_north_2\": [2400 + i * 400 for i in range(24)],\n", + " \"demand_south\": [17400 - i * 600 for i in range(24)],\n", " }\n", ")\n", "\n", "# Convert the 'datetime' column to datetime objects and set as index\n", - "demand_df[\"datetime\"] = pd.to_datetime(demand_df[\"datetime\"])\n", "demand_df.set_index(\"datetime\", inplace=True)\n", "\n", "# Display the demand_df DataFrame\n", "print(\"Demand Forecast DataFrame:\")\n", - "print(demand_df.head())" + "display(demand_df.head())" ] }, { "cell_type": "markdown", - "id": "7809ba43", + "id": "99e7bbbc", "metadata": {}, "source": [ "**Explanation:**\n", "\n", "- **datetime:** Timestamp for each demand forecast.\n", - "- **demand_north_1, demand_north_2, demand_south:** Forecasted demand values for each respective demand unit.\n", + "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", "\n", "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." ] }, { "cell_type": "markdown", - "id": "47cf5886", + "id": "42422a16", "metadata": {}, "source": [ "## 5. Mimicking the Market Clearing Process\n", @@ -684,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "5cf58974", + "id": "6b68ad0f", "metadata": {}, "source": [ "### 5.1. Calculating the Incidence Matrix\n", @@ -697,7 +599,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ead6cb27", + "id": "70688bfd", "metadata": {}, "outputs": [], "source": [ @@ -742,12 +644,12 @@ "\n", "# Display the calculated incidence matrix\n", "print(\"Calculated Incidence Matrix between Zones:\")\n", - "print(pd.DataFrame(incidence_matrix))" + "display(pd.DataFrame(incidence_matrix).fillna(0))" ] }, { "cell_type": "markdown", - "id": "bce38f51", + "id": "cc0dd7b2", "metadata": {}, "source": [ "**Explanation:**\n", @@ -771,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "88a22af8", + "id": "7245c636", "metadata": {}, "source": [ "### 5.2. Implementing the Simplified Market Clearing Function\n", @@ -782,7 +684,7 @@ { "cell_type": "code", "execution_count": null, - "id": "de425eef", + "id": "03f654fc", "metadata": {}, "outputs": [], "source": [ @@ -816,13 +718,16 @@ " }\n", "\n", "# Display a sample order\n", - "print(\"\\nSample Order:\")\n", - "print(orders[\"Unit 1_2019-01-01 00:00:00\"])" + "print(\"\\nSample Supply Order:\")\n", + "print(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", + "\n", + "print(\"\\nSample Demand Order:\")\n", + "print(orders[\"demand_north_1_2019-01-01 00:00:00\"])" ] }, { "cell_type": "markdown", - "id": "7e30920e", + "id": "d9c9e27c", "metadata": {}, "source": [ "**Explanation:**\n", @@ -833,20 +738,21 @@ }, { "cell_type": "markdown", - "id": "c13aa212", + "id": "40f3fd52", "metadata": {}, "source": [ "### 5.3. Running the Market Clearing Simulation\n", "\n", - "We will conduct two simulations:\n", + "We will conduct three simulations:\n", "\n", "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", - "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **present**." + "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", + "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." ] }, { "cell_type": "markdown", - "id": "403e85e2", + "id": "551990cf", "metadata": {}, "source": [ "#### Simulation 1: Zero Transmission Capacity Between Zones" @@ -855,7 +761,7 @@ { "cell_type": "code", "execution_count": null, - "id": "38f619a6", + "id": "123b4e9b", "metadata": {}, "outputs": [], "source": [ @@ -877,16 +783,16 @@ }, { "cell_type": "markdown", - "id": "a774230e", + "id": "80204af5", "metadata": {}, "source": [ - "The orders before being sent to the market clearing are first preprocessed. During this preprocessing the node_id from the bids is matched with the zone_id from the buses DataFrame. The bids are then sent to the market clearing function." + "Right now the orders contain the information about their node and not the zone. In ASSUME, the market clearing function will assign the bids to the zones based on the nodes they are connected to. This is done automatically by the framework, but for this simplified example, we will manually assign the bids to the zones." ] }, { "cell_type": "code", "execution_count": null, - "id": "38142d9a", + "id": "0514d562", "metadata": {}, "outputs": [], "source": [ @@ -910,18 +816,10 @@ "display(pd.DataFrame(orders_mapped).T.head())" ] }, - { - "cell_type": "markdown", - "id": "0a78a876", - "metadata": {}, - "source": [ - "Now we can run the market clearing" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "8ffce232", + "id": "543d3534", "metadata": {}, "outputs": [], "source": [ @@ -933,35 +831,31 @@ }, { "cell_type": "markdown", - "id": "410ff142", + "id": "11b4a94d", "metadata": {}, "source": [ - "#### Simulation 2: Transmission Capacity Present Between Zones" + "#### Simulation 2: Medium Transmission Capacity Between Zones" ] }, { "cell_type": "code", "execution_count": null, - "id": "70c06d1c", - "metadata": { - "lines_to_next_cell": 1 - }, + "id": "b7d246fa", + "metadata": {}, "outputs": [], "source": [ - "print(\"### Simulation 2: Transmission Capacity Present Between Zones\")\n", + "print(\"### Simulation 2: Medium Transmission Capacity Between Zones\")\n", "\n", - "# Define the incidence matrix as a dictionary with non-zero transmission capacity\n", + "# Define the incidence matrix as a dictionary with medium transmission capacity\n", "incidence_matrix_sim2 = {\n", - " \"DE_1\": {\"DE_2\": 20000.0}, # Transmission capacity from DE_1 to DE_2 in MVA\n", - " \"DE_2\": {\"DE_1\": 20000.0}, # Transmission capacity from DE_2 to DE_1 in MVA\n", + " \"DE_1\": {\"DE_2\": 3000.0}, # Medium transmission capacity from DE_1 to DE_2\n", + " \"DE_2\": {\"DE_1\": 3000.0}, # Medium transmission capacity from DE_2 to DE_1\n", "}\n", "\n", "# Display the incidence matrix for Simulation 2\n", - "print(\"Incidence Matrix for Simulation 2 (With Transmission Capacity):\")\n", + "print(\"Incidence Matrix for Simulation 2 (Medium Transmission Capacity):\")\n", "display(pd.DataFrame(incidence_matrix_sim2))\n", "\n", - "# since the orders are already mapped to zones, we can directly use the orders_mapped dictionary\n", - "\n", "# Run the simplified market clearing for Simulation 2\n", "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim2\n", @@ -970,18 +864,66 @@ }, { "cell_type": "markdown", - "id": "b9631826", + "id": "8b6ab5ae", + "metadata": {}, + "source": [ + "#### Simulation 3: High Transmission Capacity Between Zones" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "951c9a1b", + "metadata": { + "lines_to_end_of_cell_marker": 0, + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "print(\"### Simulation 3: High Transmission Capacity Between Zones\")\n", + "\n", + "# Define the incidence matrix as a dictionary with high transmission capacity\n", + "incidence_matrix_sim3 = {\n", + " \"DE_1\": {\"DE_2\": 10000.0}, # High transmission capacity from DE_1 to DE_2\n", + " \"DE_2\": {\"DE_1\": 10000.0}, # High transmission capacity from DE_2 to DE_1\n", + "}\n", + "\n", + "# Display the incidence matrix for Simulation 3\n", + "print(\"Incidence Matrix for Simulation 3 (High Transmission Capacity):\")\n", + "display(pd.DataFrame(incidence_matrix_sim3))\n", + "\n", + "# Run the simplified market clearing for Simulation 3\n", + "model_sim3, results_sim3 = simplified_market_clearing_opt(\n", + " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim3\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2874cc5b", "metadata": {}, "source": [ "### 5.4. Extracting and Interpreting the Results\n", "\n", - "After running both simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones." + "After running all three simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones.\n", + "\n", + "#### Extracting Clearing Prices\n", + "\n", + "The **clearing prices** for each market zone and time period are extracted using the dual variables associated with the energy balance constraints in the optimization model. Specifically, the dual variable of the energy balance constraint for a given zone and time period represents the marginal price of electricity in that zone at that time.\n", + "\n", + "In the `extract_results` function, the following steps are performed to obtain the clearing prices:\n", + "\n", + "1. **Energy Balance Constraints:** For each zone and time period, the energy balance equation ensures that the total supply plus imports minus exports equals the demand.\n", + "2. **Dual Variables:** The dual variable (`model.dual[model.energy_balance[node, t]]`) associated with each energy balance constraint captures the sensitivity of the objective function (total cost) to a marginal increase in demand or supply.\n", + "3. **Clearing Price Interpretation:** The value of the dual variable corresponds to the clearing price in the respective zone and time period, reflecting the cost of supplying an additional unit of electricity.\n", + "\n", + "This method leverages the duality in optimization to efficiently extract market prices resulting from the optimal dispatch of bids under the given constraints." ] }, { "cell_type": "code", "execution_count": null, - "id": "9b70d5bf", + "id": "508d8c51", "metadata": {}, "outputs": [], "source": [ @@ -1042,73 +984,159 @@ { "cell_type": "code", "execution_count": null, - "id": "58cd1b3a", + "id": "ced11135", "metadata": {}, "outputs": [], "source": [ "# Extract results for Simulation 1\n", "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", " model_sim1, orders, nodes_sim1\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51373042", + "metadata": {}, + "outputs": [], + "source": [ "print(\"Simulation 1: Power Flows Between Zones\")\n", - "display(power_flows_df_sim1.head())\n", - "\n", + "display(power_flows_df_sim1.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bc8fe28", + "metadata": {}, + "outputs": [], + "source": [ "print(\"Simulation 1: Clearing Prices per Zone and Time\")\n", "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_1\"].head())\n", - "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_2\"].head())\n", - "\n", + "display(clearing_prices_df_sim1.loc[clearing_prices_df_sim1[\"zone\"] == \"DE_2\"].head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7bddc51", + "metadata": {}, + "outputs": [], + "source": [ "# Extract results for Simulation 2\n", "accepted_bids_sim2, power_flows_df_sim2, clearing_prices_df_sim2 = extract_results(\n", " model_sim2, orders, nodes_sim1\n", - ")\n", - "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8b84057", + "metadata": {}, + "outputs": [], + "source": [ "print(\"Simulation 2: Power Flows Between Zones\")\n", - "display(power_flows_df_sim2.head())\n", - "\n", + "display(power_flows_df_sim2.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b9abaaf1", + "metadata": {}, + "outputs": [], + "source": [ "print(\"Simulation 2: Clearing Prices per Zone and Time\")\n", "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_1\"].head())\n", "display(clearing_prices_df_sim2.loc[clearing_prices_df_sim2[\"zone\"] == \"DE_2\"].head())" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "c62148ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Extract results for Simulation 3\n", + "accepted_bids_sim3, power_flows_df_sim3, clearing_prices_df_sim3 = extract_results(\n", + " model_sim3, orders, nodes_sim1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f94d50ce", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Simulation 3: Power Flows Between Zones\")\n", + "display(power_flows_df_sim3.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f3881f6", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Simulation 3: Clearing Prices per Zone and Time\")\n", + "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_1\"].head())\n", + "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_2\"].head())" + ] + }, { "cell_type": "markdown", - "id": "ca006274", + "id": "461c85b5", + "metadata": {}, + "source": [ + "-" + ] + }, + { + "cell_type": "markdown", + "id": "b397c6e9", "metadata": {}, "source": [ "**Explanation:**\n", "\n", "- **Accepted Bids:** Shows which bids were accepted in each simulation and the ratio at which they were accepted.\n", - "- **Power Flows:** Indicates the amount of energy transmitted between zones. In Simulation 1, with zero transmission capacity, there should be no power flows between `DE_1` and `DE_2`. In Simulation 2, with transmission capacity present, power flows can occur between zones.\n", + "- **Power Flows:** Indicates the amount of energy transmitted between zones. In Simulation 1, with zero transmission capacity, there should be no power flows between `DE_1` and `DE_2`. In Simulation 2 and 3, with medium and high transmission capacities, power flows can occur between zones.\n", "- **Clearing Prices:** Represents the average bid price in each zone at each time period. Comparing prices across simulations can reveal the impact of transmission capacity on market prices." ] }, { "cell_type": "markdown", - "id": "a08b136c", + "id": "83b13999", "metadata": {}, "source": [ "### 5.5. Comparing Simulations\n", "\n", - "To better understand the impact of transmission capacity, let's compare the key results from both simulations." + "To better understand the impact of transmission capacity, let's compare the key results from all three simulations." ] }, { "cell_type": "code", "execution_count": null, - "id": "95b3ed34", + "id": "69e86f18", "metadata": {}, "outputs": [], "source": [ "# Initialize the Plotly figure\n", "fig = go.Figure()\n", "\n", - "# Iterate over each zone to plot clearing prices for both simulations\n", + "# Iterate over each zone to plot clearing prices for all three simulations\n", "for zone in nodes_sim1:\n", " # Filter data for the current zone and Simulation 1\n", " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", " # Filter data for the current zone and Simulation 2\n", " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 3\n", + " zone_prices_sim3 = clearing_prices_df_sim3[clearing_prices_df_sim3[\"zone\"] == zone]\n", "\n", " # Add trace for Simulation 1\n", " fig.add_trace(\n", @@ -1116,7 +1144,7 @@ " x=zone_prices_sim1[\"time\"],\n", " y=zone_prices_sim1[\"clearing_price\"],\n", " mode=\"lines\",\n", - " name=f\"{zone} - Sim1\",\n", + " name=f\"{zone} - Sim1 (Zero Capacity)\",\n", " line=dict(dash=\"dash\"), # Dashed line for Simulation 1\n", " )\n", " )\n", @@ -1127,17 +1155,28 @@ " x=zone_prices_sim2[\"time\"],\n", " y=zone_prices_sim2[\"clearing_price\"],\n", " mode=\"lines\",\n", - " name=f\"{zone} - Sim2\",\n", - " line=dict(dash=\"solid\"), # Solid line for Simulation 2\n", + " name=f\"{zone} - Sim2 (Medium Capacity)\",\n", + " line=dict(dash=\"dot\"), # Dotted line for Simulation 2\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 3\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim3[\"time\"],\n", + " y=zone_prices_sim3[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim3 (High Capacity)\",\n", + " line=dict(dash=\"solid\"), # Solid line for Simulation 3\n", " )\n", " )\n", "\n", "# Update layout for better aesthetics and interactivity\n", "fig.update_layout(\n", - " title=\"Clearing Prices per Zone Over Time: Simulation 1 vs Simulation 2\",\n", + " title=\"Clearing Prices per Zone Over Time: Sim1, Sim2, & Sim3\",\n", " xaxis_title=\"Time\",\n", " yaxis_title=\"Clearing Price\",\n", - " legend_title=\"Simulation\",\n", + " legend_title=\"Simulations\",\n", " xaxis=dict(\n", " tickangle=45,\n", " type=\"date\", # Ensure the x-axis is treated as dates\n", @@ -1153,279 +1192,482 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "1d3e1c1c", + "cell_type": "markdown", + "id": "a7024e65", "metadata": {}, - "outputs": [], "source": [ - "# Compare power flows\n", - "plt.figure(figsize=(14, 8))\n", - "sns.heatmap(\n", - " power_flows_df_sim2.pivot_table(\n", - " index=\"time\", columns=[\"from_zone\", \"to_zone\"], values=\"flow_MW\", fill_value=0\n", - " ),\n", - " annot=True,\n", - " fmt=\".1f\",\n", - " cmap=\"coolwarm\",\n", - ")\n", - "plt.xlabel(\"Transmission Lines (From Zone -> To Zone)\")\n", - "plt.ylabel(\"Time\")\n", - "plt.title(\"Power Flows Between Zones Over Time: Simulation 2\")\n", - "plt.xticks(rotation=45, ha=\"right\")\n", - "plt.tight_layout()\n", - "plt.show()" + "**Explanation:**\n", + "\n", + "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across all three simulations. The dashed lines represent Simulation 1 (no transmission capacity), dotted lines represent Simulation 2 (medium transmission capacity), and solid lines represent Simulation 3 (high transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." ] }, { "cell_type": "markdown", - "id": "723d53b1", + "id": "73397059", "metadata": {}, "source": [ - "**Explanation:**\n", + "## 6. Integrating with ASSUME\n", "\n", - "- **Number of Accepted Bids:** Comparing the total number of accepted bids in both simulations can show how transmission capacity affects the ability to dispatch more units.\n", - "- **Clearing Prices Plot:** By plotting the clearing prices for each zone across both simulations, we can observe how the presence of transmission capacity influences price convergence or divergence between zones.\n", - "- **Power Flows Heatmap:** Visualizing power flows in Simulation 2 demonstrates how energy is transmitted between zones when transmission capacity is available." + "In a real-world scenario, the ASSUME framework handles the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we'll integrate our prepared data and configuration into ASSUME to execute the simulation seamlessly.\n", + "\n", + "### Step 1: Saving Input Files\n", + "\n", + "We will save the generated input DataFrames to the `inputs/tutorial_08` folder. The required files are:\n", + "- `demand_units.csv`\n", + "- `demand_df.csv`\n", + "- `powerplant_units.csv`\n", + "- `buses.csv`\n", + "- `lines.csv`\n", + "\n", + "Additionally, we'll create a new file `fuel_prices.csv`.\n", + "\n", + "#### Create the Inputs Directory and Save CSV Files" ] }, { - "cell_type": "markdown", - "id": "f5b14240", + "cell_type": "code", + "execution_count": null, + "id": "eb54851c", "metadata": {}, + "outputs": [], "source": [ - "## 6. Integrating with ASSUME\n", + "import os\n", "\n", - "In a real-world scenario, the ASSUME framework would handle the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we've mimicked the market clearing process using DataFrames and a simplified function to illustrate the inner workings of market zone coupling.\n", + "# Define the input directory\n", + "input_dir = \"inputs/tutorial_08\"\n", "\n", - "To integrate this process within ASSUME:\n", + "# Create the directory if it doesn't exist\n", + "os.makedirs(input_dir, exist_ok=True)\n", "\n", - "- **Input Files:** Prepare CSV files for buses, lines, powerplant_units, demand_units, and demand forecasts as demonstrated in section 4.\n", - "- **Configuration File:** Define the market configuration, including zone identifiers and transmission capacities.\n", - "- **Running the Simulation:** Use ASSUME's built-in functions to load the CSV files, apply the configurations, and execute the simulation.\n", + "# extend demand_df for another day with the same demand profile\n", + "demand_df = pd.concat([demand_df, demand_df])\n", + "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", "\n", - "Refer to the ASSUME documentation for detailed instructions on configuring and running simulations with CSV input files and configuration settings." + "# Save the DataFrames to CSV files\n", + "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=False)\n", + "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=False)\n", + "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", + "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", + "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", + "\n", + "print(\"Input CSV files have been saved to 'inputs/tutorial_08'.\")" ] }, { "cell_type": "markdown", - "id": "7c0ab370", + "id": "bc37c1e1", "metadata": {}, "source": [ - "## 7. Analyzing the Results\n", + "#### Create `fuel_prices.csv`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e74a43e", + "metadata": {}, + "outputs": [], + "source": [ + "# Define fuel prices\n", + "fuel_prices = {\n", + " \"fuel\": [\"uranium\", \"co2\"],\n", + " \"price\": [5, 25],\n", + "}\n", "\n", - "After running the market clearing simulation, it's essential to analyze the results to understand how market zone coupling influenced market dynamics. Below are key metrics and methods to analyze the simulation outcomes." + "# Convert to DataFrame and save as CSV\n", + "fuel_prices_df = pd.DataFrame(fuel_prices).T\n", + "fuel_prices_df.to_csv(\n", + " os.path.join(input_dir, \"fuel_prices_df.csv\"), index=True, header=False\n", + ")\n", + "\n", + "print(\"Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\")" ] }, { "cell_type": "markdown", - "id": "d0edde5d", + "id": "f223540f", "metadata": {}, "source": [ - "### 7.1. Accepted Bids\n", + "### Step 2: Creating the Configuration YAML File\n", "\n", - "**Accepted Bids** indicate which power plant and demand bids were successfully dispatched in the market. Analyzing accepted bids helps in understanding supply and demand distribution across different zones." + "The configuration file defines the simulation parameters, including market settings and network configurations. Below is the YAML configuration tailored for our tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "0711551b", + "metadata": {}, + "source": [ + "#### Create `config.yaml`" ] }, { "cell_type": "code", "execution_count": null, - "id": "cc8dbc99", + "id": "a1a7164b", "metadata": {}, "outputs": [], "source": [ - "# Display accepted bids for Simulation 1\n", - "print(\"Simulation 1: Accepted Bids DataFrame:\")\n", - "accepted_bids_df_sim1 = pd.DataFrame.from_dict(\n", - " accepted_bids_sim1, orient=\"index\", columns=[\"Acceptance_Ratio\"]\n", - ")\n", - "accepted_bids_df_sim1.reset_index(inplace=True)\n", - "accepted_bids_df_sim1.rename(columns={\"index\": \"Bid_ID\"}, inplace=True)\n", - "display(accepted_bids_df_sim1.head())\n", - "\n", - "# Display accepted bids for Simulation 2\n", - "print(\"Simulation 2: Accepted Bids DataFrame:\")\n", - "accepted_bids_df_sim2 = pd.DataFrame.from_dict(\n", - " accepted_bids_sim2, orient=\"index\", columns=[\"Acceptance_Ratio\"]\n", - ")\n", - "accepted_bids_df_sim2.reset_index(inplace=True)\n", - "accepted_bids_df_sim2.rename(columns={\"index\": \"Bid_ID\"}, inplace=True)\n", - "display(accepted_bids_df_sim2.head())" + "config = {\n", + " \"zonal_case\": {\n", + " \"start_date\": \"2019-01-01 00:00\",\n", + " \"end_date\": \"2019-01-01 23:00\",\n", + " \"time_step\": \"1h\",\n", + " \"save_frequency_hours\": 24,\n", + " \"markets_config\": {\n", + " \"zonal\": {\n", + " \"operator\": \"EOM_operator\",\n", + " \"product_type\": \"energy\",\n", + " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", + " \"opening_frequency\": \"1h\",\n", + " \"opening_duration\": \"1h\",\n", + " \"volume_unit\": \"MWh\",\n", + " \"maximum_bid_volume\": 100000,\n", + " \"maximum_bid_price\": 3000,\n", + " \"minimum_bid_price\": -500,\n", + " \"price_unit\": \"EUR/MWh\",\n", + " \"market_mechanism\": \"pay_as_clear_complex\",\n", + " \"additional_fields\": [\"bid_type\", \"node\"],\n", + " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", + " }\n", + " },\n", + " }\n", + "}\n", + "\n", + "# Define the path for the config file\n", + "config_path = os.path.join(input_dir, \"config.yaml\")\n", + "\n", + "# Save the configuration to a YAML file\n", + "with open(config_path, \"w\") as file:\n", + " yaml.dump(config, file, sort_keys=False)\n", + "\n", + "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" ] }, { "cell_type": "markdown", - "id": "58b1c1bd", + "id": "129df923", "metadata": {}, "source": [ - "### 7.2. Power Flows Between Zones\n", + "### Detailed Configuration Explanation\n", + "\n", + "The `config.yaml` file plays a key role in defining the simulation parameters. Below is a detailed explanation of each configuration parameter:\n", + "\n", + "- **zonal_case:**\n", + " - **start_date:** The start date and time for the simulation (`2019-01-01 00:00`).\n", + " - **end_date:** The end date and time for the simulation (`2019-01-02 00:00`).\n", + " - **time_step:** The simulation time step (`1h`), indicating hourly intervals.\n", + " - **save_frequency_hours:** How frequently the simulation results are saved (`24` hours).\n", + "\n", + "- **markets_config:**\n", + " - **zonal:** The name of the market. Remember, that our power plant units had a column named bidding_zonal. This is how a particluar bidding strategy is assigned to a particluar market.\n", + " - **operator:** The market operator (`EOM_operator`).\n", + " - **product_type:** Type of market product (`energy`).\n", + " - **products:** List defining the market products:\n", + " - **duration:** Duration of the product (`1h`).\n", + " - **count:** Number of products (`1`).\n", + " - **first_delivery:** When the first delivery occurs (`1h`).\n", + " - **opening_frequency:** Frequency of market openings (`1h`).\n", + " - **opening_duration:** Duration of market openings (`1h`).\n", + " - **volume_unit:** Unit of volume measurement (`MWh`).\n", + " - **maximum_bid_volume:** Maximum volume allowed per bid (`100000` MWh).\n", + " - **maximum_bid_price:** Maximum price allowed per bid (`3000` EUR/MWh).\n", + " - **minimum_bid_price:** Minimum price allowed per bid (`-500` EUR/MWh).\n", + " - **price_unit:** Unit of price measurement (`EUR/MWh`).\n", + " - **market_mechanism:** The market clearing mechanism (`pay_as_clear_complex`).\n", + " - **additional_fields:** Additional fields required for bids:\n", + " - **bid_type:** Type of bid (e.g., supply or demand).\n", + " - **node:** The market zone associated with the bid.\n", + " - **param_dict:**\n", + " - **network_path:** Path to the network files (`.` indicates current directory).\n", + " - **zones_identifier:** Identifier used for market zones (`zone_id`).\n", + "\n", + "This configuration ensures that the simulation accurately represents the zonal market dynamics, including bid restrictions and market operations." + ] + }, + { + "cell_type": "markdown", + "id": "97bf3b93", + "metadata": {}, + "source": [ + "### Step 3: Running the Simulation\n", "\n", - "**Power Flows** show the amount of energy transmitted between different zones. This helps in verifying that transmission constraints are respected and understanding cross-zone energy trading." + "With the input files and configuration in place, we can now run the simulation using ASSUME's built-in functions." + ] + }, + { + "cell_type": "markdown", + "id": "3d9088f8", + "metadata": {}, + "source": [ + "#### Example Simulation Code" ] }, { "cell_type": "code", "execution_count": null, - "id": "3f7140ed", + "id": "2fa30762", "metadata": {}, "outputs": [], "source": [ - "# Display power flows for Simulation 1\n", - "print(\"Simulation 1: Power Flows Between Zones:\")\n", - "display(power_flows_df_sim1.head())\n", + "# import the main World class and the load_scenario_folder functions from assume\n", + "from assume import World\n", + "from assume.scenario.loader_csv import load_scenario_folder\n", "\n", - "# Display power flows for Simulation 2\n", - "print(\"Simulation 2: Power Flows Between Zones:\")\n", - "display(power_flows_df_sim2.head())" + "# Define paths for input and output data\n", + "csv_path = \"outputs\"\n", + "\n", + "# Define the data format and database URI\n", + "# Use \"local_db\" for SQLite database or \"timescale\" for TimescaleDB in Docker\n", + "\n", + "# Create directories if they don't exist\n", + "os.makedirs(csv_path, exist_ok=True)\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + "\n", + "if data_format == \"local_db\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "elif data_format == \"timescale\":\n", + " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + "\n", + "# Create the World instance\n", + "world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + "# Load the scenario by providing the world instance\n", + "# The path to the inputs folder and the scenario name (subfolder in inputs)\n", + "# and the study case name (which config to use for the simulation)\n", + "load_scenario_folder(\n", + " world,\n", + " inputs_path=\"inputs\",\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + ")\n", + "\n", + "# Run the simulation\n", + "world.run()" + ] + }, + { + "cell_type": "markdown", + "id": "2e281f8c", + "metadata": {}, + "source": [ + "## 7. Analyzing the Results\n", + "\n", + "After running the simulation, you can analyze the results using the methods demonstrated in section 5. This integration with ASSUME allows for more extensive and scalable simulations, leveraging the framework's capabilities for handling complex market scenarios.\n", + "\n", + "In this section, we will:\n", + "\n", + "1. **Locate the Simulation Output Files:** Understand where the simulation results are saved.\n", + "2. **Load and Inspect the Output Data:** Read the output CSV files and examine their structure.\n", + "3. **Plot Clearing Prices:** Visualize the market clearing prices to compare with our previous manual simulations." ] }, { "cell_type": "markdown", - "id": "b0c781d7", + "id": "6472a8de", "metadata": {}, "source": [ - "### 7.3. Clearing Prices\n", + "### 7.1. Locating the Simulation Output Files\n", "\n", - "**Clearing Prices** represent the market price in each zone at each time period. Comparing prices across zones can reveal how zone coupling affects market equilibrium and price convergence." + "The simulation outputs are saved in the `outputs/tutorial_08_zonal_case` directory. Specifically, the key output file we'll work with is `market_meta.csv`, which contains detailed information about the market outcomes for each zone and time period." + ] + }, + { + "cell_type": "markdown", + "id": "4d2aca20", + "metadata": {}, + "source": [ + "### 7.2. Loading and Inspecting the Output Data" ] }, { "cell_type": "code", "execution_count": null, - "id": "d9f196ae", + "id": "80fdd812", "metadata": {}, "outputs": [], "source": [ - "# Display clearing prices for Simulation 1\n", - "print(\"Simulation 1: Clearing Prices per Zone and Time:\")\n", - "display(clearing_prices_df_sim1.head())\n", + "# Define the path to the simulation output\n", + "output_dir = \"outputs/tutorial_08_zonal_case\"\n", + "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", + "\n", + "# Load the market_meta.csv file\n", + "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", + "# drop the first column\n", + "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", "\n", - "# Display clearing prices for Simulation 2\n", - "print(\"Simulation 2: Clearing Prices per Zone and Time:\")\n", - "display(clearing_prices_df_sim2.head())" + "# Display a sample of the data\n", + "print(\"Sample of market_meta.csv:\")\n", + "display(market_meta.head())" ] }, { "cell_type": "markdown", - "id": "de908c5c", + "id": "538d2a28", "metadata": {}, "source": [ - "### 7.4. Visualization (Optional)\n", + "**Explanation:**\n", "\n", - "Visualizing the results can provide a clearer understanding of the market dynamics. Below are examples of how to plot clearing prices and power flows." + "- **market_meta.csv:** This file contains the market outcomes for each zone and time period, including supply and demand volumes, clearing prices, and other relevant metrics.\n", + "- **Columns:**\n", + " - `supply_volume`: Total volume supplied in the zone.\n", + " - `demand_volume`: Total volume demanded in the zone.\n", + " - `demand_volume_energy`: Energy demand volume (same as `demand_volume` for energy markets).\n", + " - `supply_volume_energy`: Energy supply volume (same as `supply_volume` for energy markets).\n", + " - `price`: Clearing price in the zone for the time period.\n", + " - `max_price`: Maximum bid price accepted.\n", + " - `min_price`: Minimum bid price accepted.\n", + " - `node`: Identifier for the market zone (`DE_1` or `DE_2`).\n", + " - `product_start`: Start time of the market product.\n", + " - `product_end`: End time of the market product.\n", + " - `only_hours`: Indicator flag (not used in this context).\n", + " - `market_id`: Identifier for the market (`zonal`).\n", + " - `time`: Timestamp for the market product.\n", + " - `simulation`: Identifier for the simulation case (`tutorial_08_zonal_case`)." + ] + }, + { + "cell_type": "markdown", + "id": "76b537d2", + "metadata": {}, + "source": [ + "### 7.3. Plotting Clearing Prices\n", + "\n", + "To verify that the simulation results align with our previous manual demonstrations, we'll plot the clearing prices for each zone over time. This will help us observe how transmission capacities influence price convergence or divergence between zones." + ] + }, + { + "cell_type": "markdown", + "id": "46af3ed7", + "metadata": {}, + "source": [ + "#### Processing the Market Meta Data" ] }, { "cell_type": "code", "execution_count": null, - "id": "e5f0b36d", + "id": "f00529e4", "metadata": {}, "outputs": [], "source": [ - "# Plot Clearing Prices for Each Zone Over Time\n", - "plt.figure(figsize=(12, 6))\n", - "for zone in nodes_sim1:\n", - " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", - " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", - " plt.plot(\n", - " zone_prices_sim1[\"time\"],\n", - " zone_prices_sim1[\"clearing_price\"],\n", - " label=f\"{zone} - Sim1\",\n", - " linestyle=\"--\",\n", - " )\n", - " plt.plot(\n", - " zone_prices_sim2[\"time\"],\n", - " zone_prices_sim2[\"clearing_price\"],\n", - " label=f\"{zone} - Sim2\",\n", - " linestyle=\"-\",\n", + "# Extract unique zones\n", + "zones = market_meta[\"node\"].unique()\n", + "\n", + "# Initialize an empty DataFrame to store clearing prices per zone and time\n", + "clearing_prices_df = pd.DataFrame()\n", + "\n", + "# Populate the DataFrame with clearing prices for each zone\n", + "for zone in zones:\n", + " zone_data = market_meta[market_meta[\"node\"] == zone][[\"price\"]]\n", + " zone_data = zone_data.rename(columns={\"price\": f\"{zone}_price\"})\n", + " clearing_prices_df = (\n", + " pd.merge(\n", + " clearing_prices_df,\n", + " zone_data,\n", + " left_index=True,\n", + " right_index=True,\n", + " how=\"outer\",\n", + " )\n", + " if not clearing_prices_df.empty\n", + " else zone_data\n", " )\n", - "plt.xlabel(\"Time\")\n", - "plt.ylabel(\"Clearing Price\")\n", - "plt.title(\"Clearing Prices per Zone Over Time: Simulation 1 vs Simulation 2\")\n", - "plt.legend()\n", - "plt.xticks(rotation=45)\n", - "plt.tight_layout()\n", - "plt.show()" + "\n", + "# Sort the DataFrame by time\n", + "clearing_prices_df = clearing_prices_df.sort_index()\n", + "\n", + "# Display a sample of the processed clearing prices\n", + "print(\"Sample of Processed Clearing Prices:\")\n", + "display(clearing_prices_df.head())" ] }, { "cell_type": "markdown", - "id": "17c08c41", + "id": "35d82a8f", "metadata": {}, "source": [ - "**Explanation:**\n", - "\n", - "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across both simulations. The dashed lines represent Simulation 1 (no transmission capacity), and the solid lines represent Simulation 2 (with transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." + "#### Plotting the market clearing prices" ] }, { "cell_type": "code", "execution_count": null, - "id": "9caf342a", + "id": "a3d56ac7", "metadata": {}, "outputs": [], "source": [ - "# Plot Power Flows Between Zones Over Time for Simulation 2\n", - "plt.figure(figsize=(14, 8))\n", - "sns.heatmap(\n", - " power_flows_df_sim2.pivot_table(\n", - " index=\"time\", columns=[\"from_zone\", \"to_zone\"], values=\"flow_MW\", fill_value=0\n", + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices\n", + "for zone in zones:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=clearing_prices_df.index,\n", + " y=clearing_prices_df[f\"{zone}_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Simulation\",\n", + " line=dict(width=2),\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Simulation Results\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price (EUR/MWh)\",\n", + " legend_title=\"Market Zones\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", " ),\n", - " annot=True,\n", - " fmt=\".1f\",\n", - " cmap=\"coolwarm\",\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", ")\n", - "plt.xlabel(\"Transmission Lines (From Zone -> To Zone)\")\n", - "plt.ylabel(\"Time\")\n", - "plt.title(\"Power Flows Between Zones Over Time: Simulation 2\")\n", - "plt.xticks(rotation=45, ha=\"right\")\n", - "plt.tight_layout()\n", - "plt.show()" + "\n", + "# Display the interactive plot\n", + "fig.show()" ] }, { "cell_type": "markdown", - "id": "be77e1f9", + "id": "9ed0f0a4", "metadata": {}, "source": [ "**Explanation:**\n", "\n", - "- **Power Flows Heatmap (Simulation 2):** Visualizes the amount of energy transmitted between zones over time when transmission capacity is present. The heatmap highlights periods of high or low cross-zone trading, demonstrating the impact of transmission capacity on energy distribution." + "- **Plot Details:**\n", + " - **Lines:** Each zone's clearing price over time is represented by a distinct line.\n", + " - **Interactivity:** The Plotly plot allows for interactive exploration of the data, such as zooming and hovering for specific values.\n", + " - **Aesthetics:** The clean white template and clear labels enhance readability.\n", + "\n", + "- **Interpretation:**\n", + " - **Price Trends:** Observing how clearing prices fluctuate over time within each zone.\n", + " - **Impact of Transmission Capacity:** Comparing price levels between zones can reveal the effects of transmission capacities on market equilibrium. For instance, higher transmission capacity might lead to more price convergence between zones, while zero capacity could result in divergent price levels due to isolated supply and demand dynamics." ] }, { "cell_type": "markdown", - "id": "605935df", + "id": "02eb3518", "metadata": {}, "source": [ "## Conclusion\n", "\n", - "In this tutorial, we explored how to mimic and understand market zone coupling within the ASSUME framework. We covered the following key steps:\n", - "\n", - "1. **Introduction to Market Zone Coupling:** Understanding the concept and its significance in electricity market simulations.\n", - "2. **Setting Up the ASSUME Framework:** Installing the framework and preparing the environment for zone coupling.\n", - "3. **Understanding Market Clearing Optimization:** Grasping the fundamentals of the market clearing process with a focus on zone interactions.\n", - "4. **Creating Exemplary Input Files for Market Zone Coupling:** Defining buses, transmission lines, power plant units, demand units, and preparing demand forecasts.\n", - "5. **Mimicking the Market Clearing Process:** \n", - " - **Calculating the Incidence Matrix:** Summing transmission line capacities between zones to create the incidence matrix.\n", - " - **Implementing the Simplified Market Clearing Function:** Using a simplified Pyomo model to perform market clearing.\n", - " - **Running the Market Clearing Simulation:** Executing two simulations to observe the impact of transmission capacity.\n", - "6. **Integrating with ASSUME:** Outlining the steps to integrate the process within the ASSUME framework using CSV input files and configuration settings.\n", - "7. **Analyzing the Results:** Extracting and visualizing key metrics to assess the impact of market zone coupling.\n", + "In this tutorial, we successfully integrated our custom market zone coupling simulation with the ASSUME framework. By preparing the necessary input files and configuration, running the simulation, and analyzing the results, we demonstrated how transmission capacities influence market dynamics and clearing prices across different zones.\n", "\n", "**Key Takeaways:**\n", "\n", - "- **Incidence Matrix:** Represents the transmission capacities between different market zones. Calculated by summing the capacities of transmission lines connecting each pair of zones. While this method is simplified, it provides a foundational understanding of how zones interact within the market.\n", - "\n", - "- **Simplified Market Clearing:** Demonstrates the core principles of market clearing, including bid acceptance and power flow management between zones.\n", - "\n", - "- **Impact of Transmission Capacity:** The presence of transmission capacity allows for energy trading between zones, potentially reducing overall costs and balancing demand more effectively. Without transmission capacity, each zone must rely solely on its local generation, which may lead to higher costs if local generation is more expensive.\n", - "\n", - "- **Limitations and Future Work:** The current method of calculating the incidence matrix by summing line capacities is limited and does not capture the full complexity of real-world transmission networks. Future implementations will enhance this approach to include more detailed network modeling, accounting for factors like line impedance, reactive power flows, and dynamic constraints.\n", + "- **Seamless Integration:** ASSUME allows for straightforward integration of custom market models through well-defined input and configuration files.\n", + "- **Accurate Simulation:** Properly setting up transmission constraints ensures realistic modeling of energy flows and price formations.\n", + "- **Effective Analysis:** Utilizing visualization tools like Plotly and Matplotlib facilitates comprehensive analysis of simulation outcomes.\n", "\n", - "By following this guide, you have successfully set up a simplified multi-zone electricity market simulation, enabling a deeper understanding of market dynamics and the role of zone coupling in balancing supply and demand. You can now extend this setup to include more zones, varying transmission capacities, and diverse bidding strategies to explore a wide range of market scenarios.\n", + "This setup serves as a foundation for more complex and scalable market simulations, enabling deeper insights into the interplay between supply, demand, and transmission infrastructure in multi-zone electricity markets.\n", "\n", - "Thank you for participating in this workshop!" + "Thank you for following along with this tutorial!" ] } ], From d1941b9ad8626b48dac982d1b9a7cd59d3b06b93 Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Fri, 27 Sep 2024 15:33:24 +0200 Subject: [PATCH 07/27] adjustments to the market coupling part --- .../notebooks/08_market_zone_coupling.ipynb | 4623 ++++++++++++++++- 1 file changed, 4402 insertions(+), 221 deletions(-) diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index 257bab47..6b8d1f95 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -3,7 +3,9 @@ { "cell_type": "markdown", "id": "d6968cbf", - "metadata": {}, + "metadata": { + "id": "d6968cbf" + }, "source": [ "# Market Zone Coupling in the ASSUME Framework\n", "\n", @@ -18,8 +20,8 @@ " - 4.1. Defining Buses and Zones\n", " - 4.2. Configuring Transmission Lines\n", " - 4.3. Setting Up Power Plant and Demand Units\n", - " - 4.4. Preparing Demand Forecast Data\n", - "5. **Mimicking the Market Clearing Process**\n", + " - 4.4. Preparing Demand Data\n", + "5. **Understanding the Market Clearing with Zone Coupling**\n", " - 5.1. Calculating the Incidence Matrix\n", " - 5.2. Implementing the Simplified Market Clearing Function\n", " - 5.3. Running the Market Clearing Simulation\n", @@ -34,7 +36,9 @@ { "cell_type": "markdown", "id": "b7f48d5e", - "metadata": {}, + "metadata": { + "id": "b7f48d5e" + }, "source": [ "## 1. Introduction to Market Zone Coupling\n", "\n", @@ -54,7 +58,9 @@ { "cell_type": "markdown", "id": "ad384717", - "metadata": {}, + "metadata": { + "id": "ad384717" + }, "source": [ "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", "\n", @@ -65,9 +71,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "d501b239", - "metadata": {}, + "metadata": { + "id": "d501b239", + "vscode": { + "languageId": "shellscript" + } + }, "outputs": [], "source": [ "# Install the ASSUME framework\n", @@ -83,17 +94,37 @@ { "cell_type": "markdown", "id": "7935b350", - "metadata": {}, + "metadata": { + "id": "7935b350" + }, "source": [ "Let's also import some basic libraries that we will use throughout the tutorial." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "05df5da6", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "05df5da6", + "outputId": "06e07c72-7a33-43e2-c5d6-da952b29edda" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/ck/0m6299c93kd95nnbzxsb67v00000gn/T/ipykernel_71588/808789752.py:1: DeprecationWarning: \n", + "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", + "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", + "but was not found to be installed on your system.\n", + "If this would cause problems for you,\n", + "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", + " \n", + " import pandas as pd\n" + ] + } + ], "source": [ "import pandas as pd\n", "\n", @@ -107,40 +138,12 @@ "from IPython.display import display" ] }, - { - "cell_type": "markdown", - "id": "bf42fa2d", - "metadata": {}, - "source": [ - "**Select Input Files Path:**\n", - "\n", - "Depending on whether you're using Google Colab or a local environment, the input file paths may vary. The following code snippet helps differentiate between these environments and sets the appropriate input paths." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "11eb5fb7", - "metadata": {}, - "outputs": [], - "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", - "colab_inputs_path = \"assume-repo/examples/inputs\"\n", - "local_inputs_path = \"../inputs\"\n", - "\n", - "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path\n", - "\n", - "print(f\"Using inputs path: {inputs_path}\")" - ] - }, { "cell_type": "markdown", "id": "3491bed0", - "metadata": {}, + "metadata": { + "id": "3491bed0" + }, "source": [ "## 3. Understanding the Market Clearing Optimization\n", "\n", @@ -158,9 +161,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "72cdba6b", - "metadata": {}, + "metadata": { + "id": "72cdba6b" + }, "outputs": [], "source": [ "# Display a simplified version of the market clearing optimization function\n", @@ -236,36 +241,33 @@ " model.nodes, model.T, rule=energy_balance_rule\n", " )\n", "\n", - " # Transmission constraints based on the incidence matrix\n", - " if incidence_matrix is not None:\n", - "\n", - " def transmission_rule(model, t, node1, node2):\n", - " \"\"\"\n", - " Limits the power flow between two zones based on transmission capacity.\n", - " \"\"\"\n", - " capacity = incidence_matrix[node1].get(node2, 0)\n", - " return (-capacity, model.flows[t, node1, node2], capacity)\n", + " def transmission_rule(model, t, node1, node2):\n", + " \"\"\"\n", + " Limits the power flow between two zones based on transmission capacity.\n", + " \"\"\"\n", + " capacity = incidence_matrix[node1].get(node2, 0)\n", + " return (-capacity, model.flows[t, node1, node2], capacity)\n", "\n", - " # Apply the transmission constraints to all possible flows\n", - " model.transmission_constraints = pyo.Constraint(\n", - " model.T, model.nodes, model.nodes, rule=transmission_rule\n", - " )\n", + " # Apply the transmission constraints to all possible flows\n", + " model.transmission_constraints = pyo.Constraint(\n", + " model.T, model.nodes, model.nodes, rule=transmission_rule\n", + " )\n", "\n", - " def symmetry_rule(model, t, node1, node2):\n", - " \"\"\"\n", - " Ensures that the flow from node1 to node2 is the negative of the flow from node2 to node1.\n", - " \"\"\"\n", - " if node1 != node2:\n", - " return model.flows[t, node2, node1] == -model.flows[t, node1, node2]\n", - " else:\n", - " return (\n", - " pyo.Constraint.Skip\n", - " ) # No constraint needed for flows within the same node\n", - "\n", - " # Apply the antisymmetry rule to all node pairs and time periods\n", - " model.symmetry_constraints = pyo.Constraint(\n", - " model.T, model.nodes, model.nodes, rule=symmetry_rule\n", - " )\n", + " def symmetry_rule(model, t, node1, node2):\n", + " \"\"\"\n", + " Ensures that the flow from node1 to node2 is the negative of the flow from node2 to node1.\n", + " \"\"\"\n", + " if node1 != node2:\n", + " return model.flows[t, node2, node1] == -model.flows[t, node1, node2]\n", + " else:\n", + " return (\n", + " pyo.Constraint.Skip\n", + " ) # No constraint needed for flows within the same node\n", + "\n", + " # Apply the antisymmetry rule to all node pairs and time periods\n", + " model.symmetry_constraints = pyo.Constraint(\n", + " model.T, model.nodes, model.nodes, rule=symmetry_rule\n", + " )\n", "\n", " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", " model.objective = pyo.Objective(\n", @@ -278,11 +280,6 @@ " solver = SolverFactory(\"glpk\")\n", " results = solver.solve(model)\n", "\n", - " market_clearing_prices = {}\n", - " for node in nodes:\n", - " market_clearing_prices[node] = {\n", - " t: pyo.value(model.dual[model.energy_balance[node, t]]) for t in model.T\n", - " }\n", " # Check if the solver found an optimal solution\n", " if results.solver.termination_condition != TerminationCondition.optimal:\n", " raise Exception(\"Solver did not find an optimal solution.\")\n", @@ -293,7 +290,9 @@ { "cell_type": "markdown", "id": "28637869", - "metadata": {}, + "metadata": { + "id": "28637869" + }, "source": [ "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." ] @@ -301,17 +300,21 @@ { "cell_type": "markdown", "id": "26e3cfa8", - "metadata": {}, + "metadata": { + "id": "26e3cfa8" + }, "source": [ "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", "\n", - "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand forecasts." + "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand profiles." ] }, { "cell_type": "markdown", "id": "01726994", - "metadata": {}, + "metadata": { + "id": "01726994" + }, "source": [ "### 4.1. Defining Buses and Zones\n", "\n", @@ -320,12 +323,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ae9695a3", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "ae9695a3", + "jupyter": { + "source_hidden": true + }, + "outputId": "3be943e1-af78-4833-c6ba-00a1ff4956ce" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Buses DataFrame:\n" + ] + }, + { + "data": { + "text/html": [ + "
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namev_nomzone_idxy
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1north_2380.0DE_19.553.5
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" + ], + "text/plain": [ + " name v_nom zone_id x y\n", + "0 north_1 380.0 DE_1 10.0 54.0\n", + "1 north_2 380.0 DE_1 9.5 53.5\n", + "2 south 380.0 DE_2 11.6 48.1" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Define the buses DataFrame with three nodes and two zones\n", + "# @title Define the buses DataFrame with three nodes and two zones\n", "buses = pd.DataFrame(\n", " {\n", " \"name\": [\"north_1\", \"north_2\", \"south\"],\n", @@ -344,7 +429,9 @@ { "cell_type": "markdown", "id": "02d32661", - "metadata": {}, + "metadata": { + "id": "02d32661" + }, "source": [ "**Explanation:**\n", "\n", @@ -357,7 +444,9 @@ { "cell_type": "markdown", "id": "01f22792", - "metadata": {}, + "metadata": { + "id": "01f22792" + }, "source": [ "### 4.2. Configuring Transmission Lines\n", "\n", @@ -366,24 +455,109 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "4a531a25", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "4a531a25", + "jupyter": { + "source_hidden": true + }, + "outputId": "61f797fe-3fa8-463e-d46c-6c7ec080ccf6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transmission Lines DataFrame:\n" + ] + }, + { + "data": { + "text/html": [ + "
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namebus0bus1s_nomxr
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" + ], + "text/plain": [ + " name bus0 bus1 s_nom x r\n", + "0 Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "1 Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "2 Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Define the transmission lines DataFrame with three lines\n", + "# @title Define three transmission lines\n", "lines = pd.DataFrame(\n", " {\n", " \"name\": [\"Line_N1_S\", \"Line_N2_S\", \"Line_N1_N2\"],\n", " \"bus0\": [\"north_1\", \"north_2\", \"north_1\"],\n", " \"bus1\": [\"south\", \"south\", \"north_2\"],\n", - " \"s_nom\": [10000.0, 10000.0, 5000.0], # Increased capacities for clarity\n", + " \"s_nom\": [5000.0, 5000.0, 5000.0],\n", " \"x\": [0.01, 0.01, 0.01],\n", " \"r\": [0.001, 0.001, 0.001],\n", " }\n", ")\n", "\n", - "# Display the lines DataFrame\n", "print(\"Transmission Lines DataFrame:\")\n", "display(lines)" ] @@ -391,7 +565,9 @@ { "cell_type": "markdown", "id": "13cb21b0", - "metadata": {}, + "metadata": { + "id": "13cb21b0" + }, "source": [ "**Explanation:**\n", "\n", @@ -405,7 +581,9 @@ { "cell_type": "markdown", "id": "8874c322", - "metadata": {}, + "metadata": { + "id": "8874c322" + }, "source": [ "### 4.3. Setting Up Power Plant and Demand Units\n", "\n", @@ -414,11 +592,156 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "e71b7ce9", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "e71b7ce9", + "jupyter": { + "source_hidden": true + }, + "outputId": "d34609a8-0758-4aa7-9911-72ffc942a69b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Power Plant Units DataFrame:\n" + ] + }, + { + "data": { + "text/html": [ + "
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nametechnologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
0Unit 1nuclearnaive_eomuranium0.01000.00.00.35north_1Operator North
1Unit 2nuclearnaive_eomuranium0.01000.00.00.36north_1Operator North
2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
3Unit 4nuclearnaive_eomuranium0.01000.00.00.38north_1Operator North
4Unit 5nuclearnaive_eomuranium0.01000.00.00.39north_1Operator North
\n", + "
" + ], + "text/plain": [ + " name technology bidding_zonal fuel_type emission_factor max_power \\\n", + "0 Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", + "1 Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", + "2 Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", + "3 Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", + "4 Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", + "\n", + " min_power efficiency additional_cost node unit_operator \n", + "0 0.0 0.3 5 north_1 Operator North \n", + "1 0.0 0.3 6 north_1 Operator North \n", + "2 0.0 0.3 7 north_1 Operator North \n", + "3 0.0 0.3 8 north_1 Operator North \n", + "4 0.0 0.3 9 north_1 Operator North " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# @title Create the power plant units DataFrame\n", + "\n", "# Define the total number of units\n", "num_units = 30 # Reduced for simplicity\n", "\n", @@ -444,9 +767,12 @@ "unit_operator = []\n", "\n", "for i in range(1, num_units + 1):\n", - " if 1 <= i <= 15:\n", + " if 1 <= i <= 8:\n", " node.append(\"north_1\") # All north units connected to 'north_1'\n", " unit_operator.append(\"Operator North\")\n", + " elif 9 <= i <= 15:\n", + " node.append(\"north_2\")\n", + " unit_operator.append(\"Operator North\")\n", " else:\n", " node.append(\"south\") # All south units connected to 'south'\n", " unit_operator.append(\"Operator South\")\n", @@ -468,19 +794,113 @@ " }\n", ")\n", "\n", - "# Display the powerplant_units DataFrame\n", "print(\"Power Plant Units DataFrame:\")\n", - "display(powerplant_units)" + "display(powerplant_units.head())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "365aa96b", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "365aa96b", + "jupyter": { + "source_hidden": true + }, + "outputId": "e743edad-038f-43f2-e307-b993fea1d21d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Demand Units DataFrame:\n" + ] + }, + { + "data": { + "text/html": [ + "
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nametechnologybidding_zonalmax_powermin_powerunit_operatornode
0demand_north_1inflex_demandnaive_eom1000000eom_denorth_1
1demand_north_2inflex_demandnaive_eom1000000eom_denorth_2
2demand_southinflex_demandnaive_eom1000000eom_desouth
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" + ], + "text/plain": [ + " name technology bidding_zonal max_power min_power \\\n", + "0 demand_north_1 inflex_demand naive_eom 100000 0 \n", + "1 demand_north_2 inflex_demand naive_eom 100000 0 \n", + "2 demand_south inflex_demand naive_eom 100000 0 \n", + "\n", + " unit_operator node \n", + "0 eom_de north_1 \n", + "1 eom_de north_2 \n", + "2 eom_de south " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Define the demand units DataFrame\n", + "# @title Define the demand units\n", "demand_units = pd.DataFrame(\n", " {\n", " \"name\": [\"demand_north_1\", \"demand_north_2\", \"demand_south\"],\n", @@ -501,7 +921,9 @@ { "cell_type": "markdown", "id": "8ce96a59", - "metadata": {}, + "metadata": { + "id": "8ce96a59" + }, "source": [ "**Explanation:**\n", "\n", @@ -529,21 +951,121 @@ { "cell_type": "markdown", "id": "e14dc7f7", - "metadata": {}, + "metadata": { + "id": "e14dc7f7" + }, "source": [ - "### 4.4. Preparing Demand Forecast Data\n", + "### 4.4. Preparing Demand Data\n", "\n", - "**Demand Forecast Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." + "**Demand Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "c817bc49", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "c817bc49", + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Demand DataFrame:\n" + ] + }, + { + "data": { + "text/html": [ + "
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datetime
2019-01-01 00:00:002400240017400
2019-01-01 01:00:002800280016800
2019-01-01 02:00:003200320016200
2019-01-01 03:00:003600360015600
2019-01-01 04:00:004000400015000
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" + ], + "text/plain": [ + " demand_north_1 demand_north_2 demand_south\n", + "datetime \n", + "2019-01-01 00:00:00 2400 2400 17400\n", + "2019-01-01 01:00:00 2800 2800 16800\n", + "2019-01-01 02:00:00 3200 3200 16200\n", + "2019-01-01 03:00:00 3600 3600 15600\n", + "2019-01-01 04:00:00 4000 4000 15000" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# Define the demand forecast DataFrame with a simplified datetime generation\n", + "# @title Define the demand DataFrame\n", + "\n", + "# the demand for the north_1 and north_2 zones increases by 400 MW per hour\n", + "# while the demand for the south zone decreases by 600 MW per hour\n", + "# the demand starts at 2400 MW for the north zones and 17400 MW for the south zone\n", "demand_df = pd.DataFrame(\n", " {\n", " \"datetime\": pd.date_range(start=\"2019-01-01\", periods=24, freq=\"h\"),\n", @@ -557,18 +1079,20 @@ "demand_df.set_index(\"datetime\", inplace=True)\n", "\n", "# Display the demand_df DataFrame\n", - "print(\"Demand Forecast DataFrame:\")\n", + "print(\"Demand DataFrame:\")\n", "display(demand_df.head())" ] }, { "cell_type": "markdown", "id": "99e7bbbc", - "metadata": {}, + "metadata": { + "id": "99e7bbbc" + }, "source": [ "**Explanation:**\n", "\n", - "- **datetime:** Timestamp for each demand forecast.\n", + "- **datetime:** Timestamp for each demand value.\n", "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", "\n", "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." @@ -577,7 +1101,9 @@ { "cell_type": "markdown", "id": "42422a16", - "metadata": {}, + "metadata": { + "id": "42422a16" + }, "source": [ "## 5. Mimicking the Market Clearing Process\n", "\n", @@ -587,7 +1113,9 @@ { "cell_type": "markdown", "id": "6b68ad0f", - "metadata": {}, + "metadata": { + "id": "6b68ad0f" + }, "source": [ "### 5.1. Calculating the Incidence Matrix\n", "\n", @@ -598,10 +1126,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "70688bfd", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "70688bfd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculated Incidence Matrix between Zones:\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " DE_1 DE_2\n", + "DE_2 10000.0 0.0\n", + "DE_1 0.0 -10000.0" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Define market products (time periods)\n", "market_products = []\n", @@ -650,7 +1237,9 @@ { "cell_type": "markdown", "id": "cc0dd7b2", - "metadata": {}, + "metadata": { + "id": "cc0dd7b2" + }, "source": [ "**Explanation:**\n", "\n", @@ -664,8 +1253,8 @@ "```\n", "Calculated Incidence Matrix between Zones:\n", " DE_1 DE_2\n", - "DE_1 0 20000\n", - "DE_2 -20000 0\n", + "DE_1 0 10000\n", + "DE_2 -10000 0\n", "```\n", "\n", "This output indicates that there is a total transmission capacity of 20,000 MVA from `DE_1` to `DE_2` and vice versa, based on the sum of the capacities of the transmission lines connecting these zones." @@ -674,7 +1263,9 @@ { "cell_type": "markdown", "id": "7245c636", - "metadata": {}, + "metadata": { + "id": "7245c636" + }, "source": [ "### 5.2. Implementing the Simplified Market Clearing Function\n", "\n", @@ -683,12 +1274,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "03f654fc", - "metadata": {}, - "outputs": [], + "metadata": { + "cellView": "form", + "id": "03f654fc", + "jupyter": { + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sample Supply Order:\n", + "{'price': 5, 'volume': 1000.0, 'node': 'north_1', 'time': Timestamp('2019-01-01 00:00:00')}\n", + "\n", + "Sample Demand Order:\n", + "{'price': 100, 'volume': -2400, 'node': 'north_1', 'time': Timestamp('2019-01-01 00:00:00')}\n" + ] + } + ], "source": [ - "# Prepare the orders dictionary based on powerplant_units and demand_units\n", + "# @title Prepare the orders dictionary based on powerplant_units and demand_units\n", "\n", "# Initialize orders dictionary\n", "orders = {}\n", @@ -728,18 +1338,22 @@ { "cell_type": "markdown", "id": "d9c9e27c", - "metadata": {}, + "metadata": { + "id": "d9c9e27c" + }, "source": [ "**Explanation:**\n", "\n", - "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume. Units in the north (`DE_1`) are cheaper (`additional_cost` ranging from 5 to 19) compared to southern units (`DE_2`) which are more expensive (`additional_cost` ranging from 20 to 34).\n", + "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume. Units in the north (`DE_1`) are cheaper (`additional_cost` ranging from 5 to 19) compared to southern units (`DE_2`) which are more expensive (`additional_cost` ranging from 20 to 34). This is a simplified representation of the bidding strategy. In actual ASSUME simulations, marginal costs and other factors are considered for more realistic bidding.\n", "- **Demand Bids:** Each demand unit submits a bid for each time period with zero price and negative volume representing the demand." ] }, { "cell_type": "markdown", "id": "40f3fd52", - "metadata": {}, + "metadata": { + "id": "40f3fd52" + }, "source": [ "### 5.3. Running the Market Clearing Simulation\n", "\n", @@ -753,17 +1367,79 @@ { "cell_type": "markdown", "id": "551990cf", - "metadata": {}, + "metadata": { + "id": "551990cf" + }, "source": [ "#### Simulation 1: Zero Transmission Capacity Between Zones" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "123b4e9b", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "123b4e9b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 1: Zero Transmission Capacity Between Zones\n", + "Incidence Matrix for Simulation 1 (Zero Transmission Capacity):\n" + ] + }, + { + "data": { + "text/html": [ + "
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pricevolumenodetime
Unit 1_2019-01-01 00:00:0051000.0DE_12019-01-01 00:00:00
Unit 1_2019-01-01 01:00:0051000.0DE_12019-01-01 01:00:00
Unit 1_2019-01-01 02:00:0051000.0DE_12019-01-01 02:00:00
Unit 1_2019-01-01 03:00:0051000.0DE_12019-01-01 03:00:00
Unit 1_2019-01-01 04:00:0051000.0DE_12019-01-01 04:00:00
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" + ], + "text/plain": [ + " price volume node time\n", + "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", + "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", + "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", + "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", + "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "# @title title\n", "# create a mapping from node_id to zone_id\n", "node_mapping = buses.set_index(\"name\")[\"zone_id\"].to_dict()\n", "\n", @@ -818,9 +1580,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "543d3534", - "metadata": {}, + "metadata": { + "id": "543d3534" + }, "outputs": [], "source": [ "# Run the simplified market clearing for Simulation 1\n", @@ -832,17 +1596,79 @@ { "cell_type": "markdown", "id": "11b4a94d", - "metadata": {}, + "metadata": { + "id": "11b4a94d" + }, "source": [ "#### Simulation 2: Medium Transmission Capacity Between Zones" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "b7d246fa", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "b7d246fa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 2: Medium Transmission Capacity Between Zones\n", + "Incidence Matrix for Simulation 2 (Medium Transmission Capacity):\n" + ] + }, + { + "data": { + "text/html": [ + "
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zonetimeclearing_price
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zonetimeclearing_price
24DE_22019-01-01 00:00:00100.0
25DE_22019-01-01 01:00:00100.0
26DE_22019-01-01 02:00:00100.0
27DE_22019-01-01 03:00:00100.0
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timefrom_zoneto_zoneflow_MW
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zonetimeclearing_price
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zonetimeclearing_price
24DE_22019-01-01 00:00:0031.0
25DE_22019-01-01 01:00:0030.0
26DE_22019-01-01 02:00:0030.0
27DE_22019-01-01 03:00:0029.0
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timefrom_zoneto_zoneflow_MW
02019-01-01 00:00:00DE_1DE_25100.0
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22019-01-01 01:00:00DE_1DE_24700.0
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Initialize the Plotly figure\n", "fig = go.Figure()\n", "\n", @@ -1194,7 +4009,9 @@ { "cell_type": "markdown", "id": "a7024e65", - "metadata": {}, + "metadata": { + "id": "a7024e65" + }, "source": [ "**Explanation:**\n", "\n", @@ -1204,7 +4021,9 @@ { "cell_type": "markdown", "id": "73397059", - "metadata": {}, + "metadata": { + "id": "73397059" + }, "source": [ "## 6. Integrating with ASSUME\n", "\n", @@ -1226,10 +4045,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "eb54851c", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "eb54851c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input CSV files have been saved to 'inputs/tutorial_08'.\n" + ] + } + ], "source": [ "import os\n", "\n", @@ -1256,17 +4085,29 @@ { "cell_type": "markdown", "id": "bc37c1e1", - "metadata": {}, + "metadata": { + "id": "bc37c1e1" + }, "source": [ "#### Create `fuel_prices.csv`" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "3e74a43e", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "3e74a43e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" + ] + } + ], "source": [ "# Define fuel prices\n", "fuel_prices = {\n", @@ -1286,7 +4127,9 @@ { "cell_type": "markdown", "id": "f223540f", - "metadata": {}, + "metadata": { + "id": "f223540f" + }, "source": [ "### Step 2: Creating the Configuration YAML File\n", "\n", @@ -1296,17 +4139,29 @@ { "cell_type": "markdown", "id": "0711551b", - "metadata": {}, + "metadata": { + "id": "0711551b" + }, "source": [ "#### Create `config.yaml`" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "a1a7164b", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "a1a7164b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuration YAML file has been saved to 'inputs/tutorial_08/config.yaml'.\n" + ] + } + ], "source": [ "config = {\n", " \"zonal_case\": {\n", @@ -1347,7 +4202,9 @@ { "cell_type": "markdown", "id": "129df923", - "metadata": {}, + "metadata": { + "id": "129df923" + }, "source": [ "### Detailed Configuration Explanation\n", "\n", @@ -1388,7 +4245,9 @@ { "cell_type": "markdown", "id": "97bf3b93", - "metadata": {}, + "metadata": { + "id": "97bf3b93" + }, "source": [ "### Step 3: Running the Simulation\n", "\n", @@ -1398,17 +4257,64 @@ { "cell_type": "markdown", "id": "3d9088f8", - "metadata": {}, + "metadata": { + "id": "3d9088f8" + }, "source": [ "#### Example Simulation Code" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "2fa30762", - "metadata": {}, - "outputs": [], + "metadata": { + "id": "2fa30762" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/82800 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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Plotly figure\n", "fig = go.Figure()\n", @@ -1636,7 +5796,9 @@ { "cell_type": "markdown", "id": "9ed0f0a4", - "metadata": {}, + "metadata": { + "id": "9ed0f0a4" + }, "source": [ "**Explanation:**\n", "\n", @@ -1653,7 +5815,9 @@ { "cell_type": "markdown", "id": "02eb3518", - "metadata": {}, + "metadata": { + "id": "02eb3518" + }, "source": [ "## Conclusion\n", "\n", @@ -1672,13 +5836,30 @@ } ], "metadata": { + "colab": { + "provenance": [] + }, "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" } }, "nbformat": 4, From b71a87dcf5d442ec352ffd252e373b4615dc23db Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Sat, 28 Sep 2024 15:22:41 +0200 Subject: [PATCH 08/27] - proof read original RL tutorial --- .../04_reinforcement_learning_example.ipynb | 1039 ++++------------- 1 file changed, 213 insertions(+), 826 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_example.ipynb b/examples/notebooks/04_reinforcement_learning_example.ipynb index 224bc5ba..751214ee 100644 --- a/examples/notebooks/04_reinforcement_learning_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_example.ipynb @@ -2,22 +2,23 @@ "cells": [ { "cell_type": "markdown", + "id": "9f627ddd", "metadata": { "id": "4JeBorbE6FYr" }, "source": [ "# 4. Reinforcement learning tutorial\n", "\n", - "This tutorial will introduce users into ASSUME and its ways of using reinforcement leanring (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions.\n", - "Our emphasis lies in the bidding strategy class, with less emphasis on the algorithm and role. The latter are usable as a plug and play solution in the framework. The following coding tasks will highlight the key aspects to be adjusted, as already outlined in the learning_strategies.py file.\n", + "This tutorial will introduce users into ASSUME and its ways of using reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions.\n", + "Our emphasis lies in the bidding strategy, with less focus on the algorithm and role. The latter are usable as a plug-and-play solution in the framework. The following coding tasks will highlight the key aspects to be adjusted, as already outlined in the learning_strategies.py file.\n", "\n", - "The outline of this tutorial is as follows. We will start with a basic summary of the implementation of reinforcement learning (RL) in ASSUME and its architectrue (1. ASSUME & Learning Basics) . If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we install ASSUME in this Google Colab (2. Get ASSUME running) and then we dive into the learning_strategies.py file and explain how we need to adjust conventional bidding strategies to incorporate RL (3. Make ASSUME learn).\n", + "The outline of this tutorial is as follows. We will start with a basic summary of the implementation of reinforcement learning (RL) in ASSUME and its architecture (1. ASSUME & Learning Basics) . If you need a refresher on RL in general, please visit our readthedocs (https://ASSUME.readthedocs.io/en/latest/). Afterwards, we install ASSUME in this Google Colab (2. Get ASSUME running) and then we dive into the learning_strategies.py file and explain how we need to adjust conventional bidding strategies. to incorporate RL (3. Make ASSUME learn).\n", "\n", "**As a whole, this tutorial covers the following coding tasks:**\n", "\n", - "1. How to define a step function in the assume framework?\n", + "1. How to define a step function in the ASSUME framework.\n", "\n", - "2. How do we get observations from the simulation framework?\n", + "2. How do we get observations from the simulation framework.\n", "\n", "3. How do we define actions based on the output of the actor neural network considering necessary exploration?\n", "\n", @@ -26,6 +27,7 @@ }, { "cell_type": "markdown", + "id": "762568ca", "metadata": { "id": "bj2C4ElILNNv" }, @@ -38,11 +40,12 @@ "\n", "\n", "\n", - "![architecture.svg](data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8"?>
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Yh9fzX/J6iNennz/gm0/faP5i8e/ly1I/nHMefX8VgKC0768PGOgD/DyUu3/k6Dwi+LuBn4eZK4DgK7y53o3N//RgzIjg11X0gX5/NAM8cnreiwEerv5+7dTjpkv9j+X+H03uR4t+j8iNvpd+f5Aa9CzvQZA+rfP/8vQZWiD63qfvJaxWOvyQ/h/NDvCjlOF/lx/e4vk3VP7M7a2b+1QHbp/c3Vj4m/7tv3gazB1avurlveyz1Pe/BuP+/As+z+T+3An+po39KZ8se7U0RkB/0wkmyVcNIf/OJ8tQCv9mv14/j0N/7RNnxPNWi3d1mpHvyA/uE7cBf6YlODb2D+24fxXM9Z59macoL7O6TfFd7F50bRBG7rX4un51wefGAbtG6QxElbm/cPNSCr2UgKZWffoLunm6RIQGWAI2NZmTNkE7Ma43689RN5IVT6x/4eCaP7AbZ/3NFqZfUesfB1UsOBVmZBU6xMZWHi9l0V/UzUaXStOcAsegcqsqrnrOM/VG1fPkiPEbPWca6ayq1/W1Ry2P2YSRM2mXOTdix7qzb/Y7sZPixlJPN8kpBY3cwozGmxdhGhDcgHbEvh0j8W7aGG/9j6RonFrW1pZiIfsbHUyMeqIMLbVlLVC5W7KxU0RcnxbCSt7tGhRi5A0jWPHmYnEbLHT35XGrYSpr1ZE34gF8jPnMc9fnL3pnr78AC9FhOoVK5U/Cpk6F08q4jLlQt836m9kw4U2zZHbaxHpVLq1qpDvHMV2pSGJmMxZX+XByCQOrVBxNwyV16EINJjaWpV1yU04Mz8Hs9dwkwXxod1uXI8UsZ3110zJiQsfVHqFuKxcxEpTukCRTk7Q9Y9naJzUMlj3dNDHd+Vl6meCVhowAFxqbcHmKSiCvWlj/KYGyjHnjd2IkJ6w77Ioszs2sqcp95J0uCtcIsYBleBKWqjHLtRrN3HRRHLXG1V43UhZlMBg7uYtzIDZcr58UHt+IK/X5jS/7N4UT7JlIUlbizoIvhTGkqjy5LeUgZaKFmpRM4C7eLB8zwSPVtYvxNKdEFS+owh86WrcnqjipxSGJyG7P4V7trM80QmkLMiQc4iClDHnjsB7fzhsSP9lCwjbXWTPL/sbyXq9rlthR+LwXKMcDMmwzVLcwm+2sd72+Xu9vydqgu/f8XO/Zg7le8AxjVjdJ4WiHxde5m9fHTow3ssIinZPqvOoZxq8SFVUSSj2wuK1sq1EUWJvfVWAE6WakQS/N1Jf69mgOWdaMi++wxch2wj4qRd9Jb6G12diNMEXLvDG1Q7THKH3fZ2UCRTJGMUa83TObzW7Dse7SONxeYuo9Mt6HH7LRyKqSL23cS7wymdAL/qKmrHbJZibEoI0GGO+QKn0cba/+uLoQjMWp/TpSYWt5hYleVWkFOIWEShuDipf4mEhMFOqIniUHJeR7IbttViZisAvE7eZILKdrNyhr9YiClD7AHLm0N4I0Vzdd8L0+XakdnyTJV+M6Rq6A/cbYrmJsVDqGqKfFl9bGktC+ofIyJQGzCXkLU09xgcpxVaarlmR2h1iG1BGKNZ3UI2oSvDiHbRuwKMpcWxUxfYb2t3QX7+mDwkeuUJlUehO2wr6H9utTejSMh/V3lrh61t8kPC+4G0vyIZDQqszbVTkTJLs7Hv21AM/9EEhxM41FxSnn9U/yAsRZjTS8n1ACmZlLxpCCRXIasjlfxaO6IYJE7uZjO2GwRI37OrzBnp3vA8c6T7jBRxOH0twZdt2bvWzFPgjn1QIKtzFe+8ccTzkpdpsyJdw9cykujK2W6112PxHWlMAIb6BBpSFaviGiAAx7bAj+ppBeuiG8YG0AZ2xqg23hm7/lJmLrX2lWBSqGRhMaTPmt8o7lxK8l20PMVRm6KbBcwhOoHxkltQzpQJ5VytmcjzEXRhl3vG3AazZcrG8MTRKwfCOthE4XjKvtmChiuYm3ac431mZrxdVuEq2Ju1JM6G82UCwq8ZFbmbTmO2zjYgwcKBwaA7Xb8yTFFKsYyaxmpdK23B0slkFSTmHV7U1VWdVPDVPPOTXnZz7lS7Xs402jmU6C+4nVMN3KxzNzTg92viFzbtgJNLsbc4Oqk7aNrTZO3Lo8aUle8xPMYTQvu6xDMDrMCsR+DylmLXj1tjMCpcYjVURWI7jqFSmYNmyZne7joueecSxGt9LSXH3iCTHrnemYprMSk18fFw5wxyjdtJCpJt9ECBXIqyRz1Eb3U6A4mQug6J6yZUqACWXFGQxnUyxBbYE5EGtGOrBbhz9POy/nF0nmp0JglB3L9VIyFRVmkq3kTRlf54TGXtMTfdvvfK3H4iU1rrLoJOgs7EUO4SFfkgUpTFUyz/wm7dVEuLA7XRukEIor3QxkxVUVvW92c96d9FzYtUbDNlGxuxgtWRhYbZsm0Y76peMJRNpe9ZqKKao63/xRRvxEwQ9dCTotl6nqiWkoKOkuKI3wcoYLodtZfkPfkqG4WOb5smo3oRBFi6GKpaD9jiwOVBfcVhgkmEfB6E0zaZ3Zzt24N+iy8fOuyPra2zVmq0dW1hTSvj1cazhQF1iKDI6oIVMNzCFtlMDB28RqaaKwzQJp8qPaw83Q8IO+gw+dxblNBHTzcJaJxmv3W+u0XpULzI5GRnSRwSxaLeaYa2gNLuY9HJSDD23TUg92ELxZjLXwCh9vZWi4S4l52iLGvdiESLxfHeDGs2rINvpBTl3E7AqrG2Crs4khsuCtp50HPWwKJInMG2lM0bkPtATo1HO7P/p4vT2ecfogTfP2sKoATYc8I+tiAuvQqy4AZrE9/JpaREDGaHBGG2g0WHq7R8wwhonVdgvaHJkjLSpIA2zbCK1whenQLFBd4mzpJ1LOcUnUgglWN/CVs6TDhRn6AxJPIiet4GqChSTpD8ucIWKYAwOhFYVIQPOp3BouNC10xlvijpqVsqKqHX680PJChGOzj4Ch4KfTmWdO8s10z0GjKE3hlqzrLedwOGzxRFuYpBStS7fI3RFIN3JcdOVwwTu7BNewJPTTfi/1cFFCVndDh7186Is5RywOX/jOTkt4hWdmFgAd5rpXuFN8i7iNLcEFouhcjllPb8s0hVsCab2LOSQhnZ8G+7TwdDIMp97RtKw4GDXWlp2xt3szJC50bSPH6BJyWX7CJvS2ZerQril422B4ePIamOaCrUkF/mq8GeRE4xNUGbsAz3r45MHIUNnlqfAxe6a5rXaALj3hzJk42qd5NQ4MS3i2SJBQMWU2E+LHDsh3YB9RnhzN62gRJXmtrms/UESuD6500eeqUtQ7WE7wRZFwPFI0iLotk3Li8AIPaRMjba+lK4dARaoPrlt6HLywO1IB2aJhwdFQtSDh/kxYYwqMhjhQFZoDLeNtKXpMaFxrJmouyRKAW3obQUB39xENlJVCleej00C7abNjN1DNIvEB3swH9jYdIqbJxYvqmYxibK+Tp7AcZqpxLLNwfvFXn+J2cm9rx+g6JJIekr2pOLNYv99NMyEgmLuf8D6FpExNj4ZG3w6DVFsaZ/JxmiDw6dowg2BDxSnNDr7uEHK6K9lwPnf84sBj5m7FM1Z529EZ0QymwggMjTnlog7Jt3pFmoChzt3K//IGatt8uZ61m3fS94RdN1Oii8VRbp2yOcHSdo6tk7Vcrxdy6dZqp/18vAXO+RIjmC+vVk9wSpzHaMVoRr601Sm7sKuP75Ck2ldpoQiikVQNHJY0erygW9sXlglgi6QJ9zSY5hHrgNNxOJAObIs9tIzL0bqMZgnQk2VeInVn1WulXdbnIaqA8Bl+qysZwmOykK+2ietV0YRGyJe95Dv+kNermpWrUCL5s1D3WFMVinW+5naCFSjOKjjotOSx5DnO0Js3C90tJfQO2oVTPXIcfNAbhgia84ocXHy4nlBUpneBoBoWcGd8pvJFH1oW/XKy6BK+LWW3Q5Y8qJoeH6vyAF9Ou6DyRs63o0NNoXuANs5k5EdEC4a02i4Wrq0yxfwTyZIF4PuIyBszhSyBpl18b9YUIlO9xQAhwqVZX5we4FqP3ZGFQxSJa45NsbOjZga6OYiKFcgJQDOyt0y0hxuNWB7cMDYr+L59KkiqCdpFswQXbe0Ema8VdowMVC67oJMJhDZOF32o8uBygXM7jAhKsY4iNaHdMZua8w2Qh3eFCDKDxtgWed+hIpp7HULeEFgnEZtZtdpFsSbN80rqiBZNdDELc6gRrLM5LweaQEZgsKaxKklhtkbjCLQvMCSnbrkKp2VA5xBvW9sduwEf+u2Ac2adtWEVHFBSKTyIAdujSOuKwtfKKEIMuF63M1ldaQX4+sKiHlMGr0+HmLS2+w1ublsn2LfFlmgjm16C/GirSJNpyHaT09CwrBD3jLUHRav84JAVhg8vxN15FTbnYj5I5/i2KoWY8PkGbU84jK84HDfGE32giqrrIn7Y6xF3HkhgmRf1ej4Ny9E94ySqbMkxDsOtpPmekOTDFRC48+xjf7o7fgz59CJGQMCMeMQqmgyxqj0as8WqFY/DjLXduMI6e9MSwRUkXDLRtRBxCTmVU456NOVGxCokwFdH+uW6OsdksPcWEvACCfQodiZlgBmMLa7Sx23i0E503KJW4DXoqot2BJKQguJ19hLC6+PUGcBY6hKGibFe1meqDUXghgzR1aNIIRkNYPVN4F+fQpqc1DHKfPMOfWdgO809AUfbsdvfxwZw/3UF9kxVRKmJncaCJLWQVJbOH73VbNexE43UgNMRADA3FWhvrqvXCoeMQKI8w2ggMSvGAG0ACN9SiI1fgStjI6SCx+iJzdKUK2/2xB/MTM02GGefJjFuVtce6DZ/uWUZ6K5BW7xSXkzH20TU/nYgdv11I6jskw8eAX7sYpSCho1yGRLKH9sdhWxrpt7F3LyNN8EBcTGuQc3LKdi5+pb1KdZCdsCBCcMqRuczXkan7WpWrrF5NlfXGjg5cR+66OywCJmZ02azkZrC4ovybECbHYjcbOINs+HdzcYAFwz4PyXmvc+vtZivN5vD87W0/guazUb9dP98bwf8sPfC+y1583z/pCfxZgsuePDcNo3XV/KfnsPbeDN9dq1OT3+aR043ctDE2UHlFXviiyvS4PgsAd0g9zcWk74y/PqHJa81hdRgdFPKQeBpE1waU0ggi4cPp/I4ejqeXEQ6veg45tnPneVm7FQlgy/CRbD6rqEI9151IEIOSh1LG53SIMC1Z5mQo1OrExMT6zNTIPa0VCRHg2U4b+2ZxDnXA4tNUobtJHYTK1s5v2SNrvHOpzb9UisVXa6DrTadUmoM0ADdV/6yL+nb5UbNp3OO75fNbb9It7291k/hJbRwyLHjYa2fvbT9Wfv8xT5mflms/SlGL2VuF9EhHEseA1ulpVT69PzLfy99udhJc8mg1N1qkM/V4x7B134IuYfIxf4+V3QRikW5X/h1XFSqrP78Z+1QT/Ub1bWTwhMK3rXgwquOjYdgd7FxMIV9auFUME0omr21AvJ1Fq8+svb0s5HcRyOamGs5hCEW2MWaHU0shjutLfqqZPntkE3jl29MXr2RTT8bJzc/v9lcLrbMeauqX1uDlALMlQlq1R6qUivwvr/VtI+Fn69jvreoft4aN42AaqdcK3zkeHNtBrr36Wk2qD36icqg1trLkGtW3mKGi60lStzs8CvuATbbigV/hu+Cwb7IyUYCcdLNPYiaxM/XR6Cn+hiqV/lYRYGRNV4wws22t1V7s89ts6qpExHiotpqWmv03Sat412CsEa748VN6jgr0cWtKhFAWbHrFR/sjpJ9lcNrNy5AFVIUQJMO7iMXFKXGgp+FCR/w8Uxhd50WBmWAXlINbmXJCpmTq+LTNePVC37jLCPdZZ2fMCYiaaKfCdGRzwWS4PjT3LGtggPbLJQndyw72gW6VyExONCh0nCsVbWvFh+tUPAQbqMcjGe31D3llmQddhgweIIgRgqcnojxuI0iol7mJ9t0tI1BLkSBiEpu66MnDEtHPfDcS6tnFWSxwoW4NYdDuVkKN5sUMyJI+eTsxCCUCz5lDzuE1/NUhUYNvPuczQA5umORoaFel7hQF8eQTWDrcJDdu/VmuU2QeDaV75WtNpin0B2HM1jxEMD9RB/a1Cdo39h3JjIn6WX1xHeuXYEAFuHrywCsFRivnXOgFpNcimQnAB1/bg5NS5tMzW3jk+yEoU1rHXEoIsf1BFcWXdvO6Q3AVy1Yahgp8MIqm5C0y7piR5yGQ1fMl1o/2HkkE4yMmwR8t42kDV4atdui19EVyBWpmWzhcgfpByQfGYK50DbSkMDGDNH6h0q53JG6zoJTbZfrTWiPgpt7Ba3GVHt/yoMW8nDTGU2Yz1TKizf6EG4J9krukkoDsfRIFq0zer1uhskctjiJhIo9qj1SC7koQxc5y0CX0AD29jbopDACyNohJXJoFjg09yYYHFZLM5ddeu+pZ1CD5szecavBQ+DnCR+8QW4190hnygmdi721na0I2cly5tl09wSUesvdggkAhnu18J4Y1ylb9fRBdynT0hdQ9bg/t7UoGhutCrxwzo3cvyqJRYytjR0Kyu1meNrliTEpYEZrbtzvIO1StUJKi1IeKmIh4bCyr+Jmr1kjDfAzrKxdANSqRdKgi8BjRTh05mu3WOnpLDZDDc0wGMPY1ckhq3P/WDELoK3vC7KrgCEWygiCqmCyKOoZ91GqdvZge2auYm3F2/CE4GctlSQAU/VLWko3JdpmQ+pzvemZRsQANiO36W0SoMAe0wPvQLkCL/Am9hQb1eazuJvNok1ZAjkP6d5uiPZkKlNrmrwldrhLe+HmMqWZbPJH6LD1eEs5owZVByk8FeoRuwznZh3/sXUrM2xyczsqWELtQaelqbwaFULIbmM1MJVFvHPUbjD1iSfd7Vnl3fIcTcDjGKSiTFL/CBWebejkpc1n5aLveJOIZcbSgllScQ8d1+4tiZz3c7qoMS0vFbtJqTiUpNvlCPgIBm0fo9tMd2Fbj0ZbUK2TmclZVidZADMMhBbmD4NwjvkVIe8yABsJORHNXTd0BN1wgj5EC9b2fjwdW3YjfZIQ7bxVjVrg9izSLEqaOnOjtXcOHVtnNvY2TZmirDnuvFEN/+kOwL8kKa2O9DTLR/rmagshzVPVQbUd9ZThgvD0iUDTyU1NEAkXnAzUi2XItONDR9XHTX+9HowMstKCP87Ttu+UjWvJfMQIexiwtxJ7synUsuxMGwYxq/jYHZIIA3oyM/0h2mXWHJzSecemuLGB4wtm25PUUZJ81yfiwXdH1J5apmjYuN6dJ5bp2GUqh1izMVXf29R8zowVcAuqP0iGyejhRj04YijAFAtwJsuqq3bf+25WnKcrVzRi+tRMtlnidlybwVWdM6kkyijRNZW5UrMdP0Y1DHGx5o8sRUTWdd8GzrangU893PyrBl+gRdLuVJXmRvSMJ9LKlL1B+uR6DovJ76wW2BSBpIedsJui3UE7up908qqeB3Agm0CMyGpkgJ4BKgy4HxGqR/xmxVilIjyRyVtMCvgjVkRPjAdUWX9e+8NGIs/t4Ut+j7KrS389ddzWWSAxyVALI9uK5iN+j7nRBB/unA+kN4YJf5et769Jd7x6In06NJoDHZbZIhcKFztUaJ57Bm/Z3AXeBO2RHsjuFugxmNByq2yXC2xeqYNiR8rqe4880wZGk4z7CsPMLg4GZ5+wUqRbh2InyCVvco7DaOp56HU5EHZbowty2WkOalp72Wa3Y80x1bi2UYnC6xNZkFPR5AyHmR118PVdAK9V9mYpS81JvcVuFot7tshS7XxdqzRur61VdNHiDGMzO8bV11lQZdXY7V6Sd/pt0tO43G+Kc6ufT41OyPqgXRhZR6yNYfCLw6eRrmvwDr3szB0nya01TexaxZPyM+Gqp8YiZT3RL4mgIiKoQnp8amt6Aqqw1u7MS62IgSqFh+Uq0RqgiqRrupMUKixufIvf23xaBWsVFr6k+u6cH2qxxph0ahwsN8i1Smt1/FrFSIoYFjYHS9hXfHyvosNuemP1vO/5upbTSTaIPF9a45oanWGpujEUsSAwh1W5VVCeB/qA6IUe31i36A+btUp5A8Empsy51rzejNooHc04JqDSCZmlAjJqUEkt0nxmd8n1XmllcEbmT21xr8TnRildjKMWb4WEhWepQddK6mA5eZrj3I5pTxun31knedUaa5WindcqhXRZtavKrSgEnyUZVDEGq+7jvNnvmZXyTs+ap0Y+EMW+LnawkPPF/nJnlmOsy7Qs35mlMepDXDYeB5jl0qea0jQbwCzvxV87YVgpN3mGPxpJ07QDa8CVjpdn98Du+JN9WG1XywneVT+m0TlJ+ITcZsdsF9+sPd+59uHmabE2XW/XGLLMyWkC/XI9u/5J4rcudckvhuguTuGpT02KAgJTYnorOaFrz8e1cqJtYtLODlkbr5K5Nlnxk6fV6gRQ7xAbljU5RfSpR1uAXy+8aYg7dG3WIEpOP7LlEYb7132qSXuR4nuzeeZUEOZokr9cT0Xyqk/brYtc+MIXd7ZRejlScSnDlMEM9V/2SZ+wxVjqvolnl8gXv4QcQ5V6sj8JxZf9QRTRFU2h6EXZhq6XHMk2aXIsAgbrv+xPOGOLc6sPxWobkGI5rE3y/r7v6qPwqj/IKF5Ekyl7RLag073JJAmGiHndnxOMLRJRH8zr2mQzH0TI5Q/7sXOEV/1Zmyw1wWTSYW0SPjUxlKlNFqxY6VV/dieYukk70CQuITJ9Enl9eyCzxnndnxkZSk22mbRA5L3Q3o6XpkyQdqPLGSllWXUVJP9qkU4xySx+gsr81qSFcMi5mW2esfVckLJ6w1csbjoEvKrsMxRrWkmSriAL2VZu8Q2fwlFjE/fITWICg9KxHbBNSeAxnWPIpnmrb1N0dtvVVlYMntFMUpy96g5KdaGsSYc2kwBczbKbeBws8YYb0IOYp0lWwIeY01YsssyjBizJIjedUnutu3FLjQRrQpCLMzvL8raKoUyquxrDbpcuqJOhRURSaAEGwnQlr9QR9ux/KUaGrK6LoI2JvrUaGqQ3hGrZkRICl15Era7pvoRcSURFQR/jrR6OYL2JxmnggeE8YVyfICgdEZ5yI7YhggObNWZROJIq76PpsSx5yYU9rwnzZpht4IO7NXupS8GZNam2HZY/7nCN2JUDiMtlHn3l0ly45/OMe7bvpZ3m9czGc4xkf4UGvceBEbdGHGBqf+WiorsDuDFD8hzJJXJVqHKfsEI4aGEl931Q6ysA0eLCqNPjYaet+l6sdZDqwzd5LTi4Jklnj82FHZ4QEoh5rx7aLrEOhr4TkE3C6VcjTJsO0pG0NTlGTLUVXk9TU9h97Q7SWLuF/r+l/XpgL6ekXw7WsELTONghl+tGloed7mxDfNRyQpmKUL1DomZUfBtm9kI8hynTXOIVEU5Fwq/ObyEag69ewxUFwpFs06x8Q8Jq61xL5szxq+ezygJMDxF1MS10F/R2fXYx2tg22/uarB+NAn7NWjP1W4CH4CcKni240He40p1FUAJVyn4Ey1XMkLXuvmdFsQXAMttFDp6OCnt3jrBzKpyHsRGtRqJ1SqOOYk0BsKxsmfq8nRO7TiXi3G7PQdWuLchE1XbArRe2Nm9MjRiewihs9pk+XLfB+d7OOiz9nhNzfPLd1s7Be9qQQZIG7OKrw0omGZOsLtLqGw1P/Pjy3JnueXThMDm1YLFFTpfrIO7zlkOrWfXxBan8U12QhnkjtCZsvO3ZF9Udg6BZWqBoodVjQx6qqz17UuGsTqSXkF3gigD/Vv0txCOhT83UyIrr9nqXmBPc7OjprPTLLZSgvNf7cFBpsCdVQF2oOGFyVQa4woe6JesnCwuJY0QOLlsF/LIoVJWe5D1fbz3/FFgjhdCCfk0DDPRgyxepcW6u27ADcGQfZo2tKRS37LGl1OedDKFcZ3SRMGrQ2FRXBi/6YJGlQiC8BG5XNYBihAVlKEPdbgoOxXpKyzMBs+jcUcHeNcn4Gly2IIKhyYxbYLSbkBX1BLwHT9tuF9Y+ZMNxM8EyWIDQnFrBeEEWxf3JMk+axieBfppgjov4CwMUEZ/MTZGshlw3jFAODBAoqnYky6zuLMh8cTBJwYzd6XAQnaH106w5gIm6R3ksBQ44jrN5c87ujYmJlhTJIjR6bgRy94Lih1XNI61UOx7GpgomcaSxY08Hy+hbKk0b+okdYMBh9mZ16RkmclQ1miXfvTjqvNJE2Kb3GNeIkrRBEWV/0J0luLoaj858SE98fwzy9KynvCGl0AHb6ntvl6jLyd6w+dnyxGOxXOu1AbOO3QiDN8Y5ae9xqpX7aqBiAWN41ZIFOzIsNMNqSIBRhAnf+G1ysdzhTJ7vXgUm8X3jyhMriFGEdCQgNHXX6Fh3NkR83s/17kjMNEx5kK3ZLQsXYwmXmYjUk+kebmBxedGNqM6oidF3qHRbbT3eGeaePDnXA1RuXbBqaSUXpmVMHLFGcdx76WU1ogNd5hJQV9vbTZIGXRpDCnFg2KUWcZ8emwFKFLxeIXZiXrWim6sFqVvbMhb/0pxOEEMN6Y0z0m7pj9YxBC4hf0uxyvOO99hHeyx4R3PzS0hf+rnbwXh5sEXvfG7YiFT7uNh0VLe3s20xzH7fyyXxNA78IvBiI9ij6wLmYIZQroRZhBBCzWjIkyS1/Rjlxyj/4ijTfkDxjsdvXnaBKr70kLNP11pg+BU0Hw2xpbtZDmmoPkaOM3cpeYTcJ0Rk074pkM9x0VYVZs8oeDTYyu7YZ9OK32HFfYpjtJ/iGIS+3cVmLRJasJNJeeSWlI7KTgFPmTjIqfC167iAz5CDWkxwOrp7x61PcsJle0UHq9TyBS4QGj1dUJnJ50G0OsyETO52X1/1W8w4ppg7ziDTxWOx8gispssmvrJb/KHZKanoPcX06mq4aZG8W0BPKxDxFhrft3fJ7W7mA1O9bhNntPJkq9lKe4LH51gtPI4KLZeBgKXj2R5HgshE7Xrrs2IPAtniBUkwIml32pzwjY2OlgQ5zjGCZr1bfJ8/1eSc7rSk2TYnoC3nXgRWX+R4dldqjoMlGBISScF5B00/HiVcWoDlofEl1iTsplG9X1P2nuNcoOSc1Tl1tZu2Djnbih2YRMbiRZbPClE0wn2VfN7SEC3C6Xi+FVQvHwxZ3B0OL20hf7T0FB3b4/7ZqfWDIV007GqJIiQPM7yBoCxevQrJxmBub9+wat7El1q9ZfOhPx6xs5u1Qt1sBT5Mr5ZyD2gjZe7x+ToGp6MnONcFOSG4I0nY8nm/4cdiFlt8OUuatlxq/cQvuuAHgrFLkDNCCgGIc12bzDcRR1qwA56wKxoXa84PL+zF2RSRJBpHrVX4caJytUk2F1c5ku5ZOu958ahQClcfPeS+nCMgI2nQuZ86PMJJ1pTz5bKESH7jbzfGh3ZeXXD7anK53O+BPEmAEXiV3/OH62k6LpleWns9Lq1aBlAuqUMmUpURYJqCh+ay2EIcIY/qDStK6+DXkhlI2gnyYA+DWmfyJJYRCDHi+9u8mnC2a1IGua/lCKJCo6sD72wsQ5JGZ743cMh3ZnDQT4Y/RJ+idXuREEX1KJ11RiWl3oGIvuVrwunD+nMbjwxWiY5ULl3T8Xq5pOvciY1pe1usyGQsPVEqPkbocQvNfgSmGN15w6lEZhM6ua7Pm0Irn1qE4rS9NDgI1F6O422s/XzCJUIGACZYLTzoTgU2D1QFMYh4O916U4AZkKGh9HyLwt3Las1Ik3R6k8RjTDGIRO/Ks2d0xFY+nPRTaj5lcqzWHY/Ipz9B0w3lMU4IiziPeG4TqJcguyW51kRgJug26l2x3mBJf8j8dmN3F2HhDbiusVVFSDtf3XR7ZFdaG1PbcfSGBsrbHKlLxNZHYcJaWXKxjRaos1FV+mUuijs/37XZEKFJSZuWF2yRKzhEGPQs21vEPfL5glGI6siRFA/XjABnSjrxWn++hEe1AuHYlqIi4IyOxy0ppghRh6ME12R3COujbwepe4RmPiJkWxf1xs22p87QME8XWnExOsnHTbZSsp0xxGlQXPbFTvIJ+iImA2/Xnaz22lIZrUMdGh1mL4SZn1b8dK7gfCdC2intixvMn4KwzlSk3ENC3lgOfmuFQyQajZcc8N3N3hkqjhI3gXOlSrM++vAP++BRq1LA67JqlUEjrDYdI1aF4HnfNqV7GJcpOFaMuNjZKTAB4gd2pAxre7s5HH09Lc8lzRsEw4ysmwYoZ9TVrQoYVaVmd3V/CV3CNqN4SWmSWW9N1fGoqj3uPN2a2LEMYnovGzU65ce1vf5iECPYD6JKk9g/3fQbVM0Px4N1vUBgjT45qfsJHAE0wTuZWm8avLLe9KAwT0+b/Vw6YziZ6y15ORv86tmc7rcOmw6vHEW/mbsjJXNnm+9E89RhUc4emE6+37J3ASRz3FnoykDppHPOUnIro/5RT6P1lsBwy3qL3jZSFrPUpdyjvbDeYn0IZtilyJ5ubXYgfolwUF/o2dNs7ubiDHDBFt9kG/f2dHOdKFadoHWiqjNx4/HNxxz++DkkdnCTFFexycdZy3XrMvmnfRBqzTS5pH93VIWZ6AvUJu7mbKQgmwDBsmQfX3a7yjHAeqHauPl+dLxta1afdO3q3tl0D2KFjZceQUv1tkJ6XTjdji7Ekog4E8UFoV/WyTxkAG6j0J/R2rvdcSIoRjyglIMV3hGpZmMVPS6GllIZF+4pJ7QsnEkVjRvaWm4u9n0lsTxT1bk53m6wRId0pbqRtNtvajiKbVyThJsfiYK7zBqLoNJTRKki7oueXadvLLaScR92knquUn0093s/XtGVSRXRHqxzDts2yXedfF8JLEJIx1Izm+AMZGqS6vqs/vnKX4IaFaxOgpkPt+h8siR/wa4tVwjX87EDzV1ruAw7HFIRFzoDT7jnNQGxYyJhQ+skcw5TazfeZMsLE3A6j6kgEUcwVkzqSC7I7jFSrdCSzP5jTdvbHgm6l7jbOHO9O7XZ4KHY1Clh9BxPBTPuXHodmTrD0RjbAp9aEl6yXMA4qmqhczS9uUCl5YehbbkRP1nLjdkFkbqv3F1xu0inElvacVYUESAlSpaP+1wEqS05zx5609F2uszptDViBOUuRYffw2R5bgd6r1eRrBN7luUDj2hmIT5tEsAit2OSOnS83jU+n8cGSUwlEJs9jNekU8NHQdjlAwcR41GR8TvD2QWH4FSyPwo9dw4JLPUF/iQbmAtTQm0R+8OhdNMS1QXzbAN4vumfOfqOrsZ4mnv/1iIWAN9ce97hoZwuzdFr71ixp0+YIndsrzefcmhW5+IpljiwiKou+W1j4YfKcvPZSrUyL3BF3Svt9Z6I8ZyRsV1UZywQqTNA2CQo1UGrRyM7dB1vZ5ZIAwoOO+8UJj4mQDSpCHDMYQNer3on6nDzll4UbzoSNwThqvGC0S6lEZtCXul/SBMAEJkQBLi2gU2Y9DVCB5ENaiUFrCKsXk6IjPOOqVKZuSRnub/vEx0ieKAp8h7WjkIAwjpapmFesWK0lUkkPHTnCW274f4EFOk0XKXpVeaM0zUTZnZkeacatKtrVbusyJpG4zpE0JcbYQTKyh/m6qdIIb3HwydgacOV5wylNPhoNkT4hWE2FizfRb9IJEXYe+gyt8YVSINVlHl4PJMj7WieuYfv+bUAhgViTeOUkwpTe6vT/CBasWnUJzoizkAqdlM40jh+Z3WsTks4SWzP01KdL2IwE6LgJIx7YUvJq4mTKE1QQsHqZXsFcW7uCaqCNOFwC/a6EJADJ9draLPDMPQ8fIk8B5omVbtRsQIwLg6+eSJEaZOWfbu6aHuYETnq7D111hbg+WyY2rYatiZ0I1alIksOlCX1bhYBahAcsa6c1deGNA/4J6a3shMtVhQvOT7ru9X8nCUjdPcsmTGldLBXcgGIFI2UzPcaau8SBH4ZEmj9IbX46jGBoO2D1YkAKzgIRCms5ADqnuQZuatwzqn7cdv2bD/Y0ThfKZBmg29sbQk2Gpgm/u5ZYQfOgGLhRnW43CTMTkSU27TbAEf8ALI7ghYWuySOxxMHB2FgmI43VCBjmUAxbqKU5HY4Kcvkg4Rffcbcsqri5bxkzHBSJjvBIhOGY1pZjSHIkBLT9IbtdgQCGW1E3wwSwO0joGPrWhY3BCEIGdITgSBosjFsi02jW9MbEZnJZ5Aofv+OPANmBsys0CFpe0XPqgkmNlFXU53TScjmZc5MaRKz51RLOn1UfKOzdQvjL0WLbFUVbe4RAXqAd41u6WCsza3bXsMwPCE3GjYvSYsgSNHvSIwb73RmCgV2RHE6MdkNFAn3ZSsLLCj0c+udaUq9dqD8ns4oIB3QbxRw6yNqglDRrmx/r4Dhbpf+EIQhZoCLcLM1+1OK41scXFpmQR14HoL8aBUjx9raCwpd0a2CopZ4OlmO0pHLzW2Suq4rHnRrSkfPHkfSprJwQZ8SF4WuorrUw+F+F4nRs2uWabByYiTbjxTpyKAlkFaxMBdRkPpQykB0qDvy2SbhQ14JRzw0z60rwPgU2yrU4bsT3u30VY0yWaMG4S3Pc4fcgrR0lG4ijt6iU0TKcY6gwExfU6KXMyyMzIkok0jM0suyiQj0BnLWdGjYUjBp23oeLceqsGyIOyEwlfi78wwmD0tdanIyRC4NBZAaocEgLmNp8z6csMaxbSvkYLsbsZdR8XrkU2WKQfZMCKdpIdFC3RrFQtM4CF8LZhMLcaQCzb+qAbfRLLinKbxTkqnurxwXhaGoQJIfmGaBX07N4iU86CaWHJO2Lcs0iRZm7NHLdFKf5vIMcyjTkScQIjODSS3PIdjMI5Qxc9vcA2vwBH6l6KbYexEBZZphy4WVb063K8UUXs5gPFSNYKZwMwjZ89j3/AF0dkiyo0+FQAymSzRm3JZoZeoMjuwDBwoI6FiNXJ73V/EMNBjlIIW/FIALjwLDYHaWZTmyVdxloqRlWabzvqtyPjtGOBeB9VX2eF8PCQB3RoyOM0D2BuJywXf3sTEOvIhAmGbm6qSRZttUrsvrNbhN+gTvoQ07GeAJiCF3yyQWqTbe2dwkfCKxJORUs4FgYhyosePUbbuft7QhpjqO9XUHJmq/31+j0w7Hyqy38QvqbC0EBAkZf+f0lsKChW+9gCOSJG/U2AmYfKCwSYWTrPNsNocoincI81jeGvkSAqwcYCkMQhCGkY+79ASMzZQiKJrU1tSyamWC+RwVEQy2uWWz1FEeuY0qMIXXqjzO9Zi7Lxlp0PkSyQ66F5NgTCEyQkYciJVfgGUzGg+v8nyBzjxImU48NMOwUsv08Xrtdv1hut0wveu6nNhuR+gQbvO1t6dseZrbK4h1yElK9RWrNQOliDeSPl+SITCtnCjhktun0DAC8DDAZnBkKAvsCvFRkl4gB9c6nB7uUR/6ilBKOF13zs2D9sF9y4tBYf6qdbZohY9Pr2MYSrYsZNKWjKy2m3EF6ONVtEnolLUu2B8C6Eq4lp10cB4oaiv6xX1J3bJpkdfPqesF3C4gRu8O0jHkzFVFcRRGENUSbhJVn9uNvoBIGjMpwlXbsivGUqaZX1o33DtRdj0EFzmc7ovqQLspVSYdctOEp0tRDOGpaWh3tRUKZRgGdq4qFChMG+wH5mhrO8/axdaJp7gSg1/JTox7lfLO4IU5KcvjcI/WOG5bFgF/XtY2r5zaX2UGPY6jGtEgRHul6ty94Qwag7Bm2HpKQ43nNFYCiHIMsusyx7aGmG40bzqwQEsgaK6gDrSfEtu2C9PaOnhpE5sbrkBY3I9Jdm9o31K3M1A6lVLZI6bZFxUjoWEVOtY8u2SojQQ4YGVFolcI6ORkoLcwAdm4SoJUkEtkWBUaWXcpHu3ojtDPeU5HkdKf2f4u03QHxQ4aVwOgKNJrRwJAdhB3r88opo/jzdxjUXUR0RLr5Kt/JXA8bp6oHw7RhJM65OvSJW0YWshYMI1gcoOkZCY243yKokX64oYEPCFX+sSZpxggg5wgF8phVubOshFSTm5wocmpq0J6h5NnlwZOijYDYdofZ5RE4WS1BiUXj9Josyl7FgqTloB9pp7XX6EdplIkLnilvWR2VYlb13VDsH93JLFTS969knFkHfAZOqZIHafuZDii+Gt5mXrgSa2mJE0Sas9lC+biDVxt+xxNIt+n7C0WQSuUW29Am2OSXIuK2yo7fS+BSQPfkxQoVMcIDCMnQj+fV52QiwWl0llVA/Zp1O1TJ5/STxhiVWrJntzPIfV8RgnAYjgeAVC/YhV0/UlW/o6K7Sk6w1QbKhSQmvM4jsJxWxFuFI31hMPzkg13vxGi9hIFiAb2W8keantkZ0inxLHzYbXRVJ8nhaoKNx3ZwHNmu7t62g570Hsio451dmkXiTiOBgRhGt6It0JZ3eSLsDnNGLbeu5SVbSGHo3I4LG2m7YGPx8w4ceAWbJKOCVbnFTsqhxOwIY4fA2H0Yxvwmxle9kc2Vhs9W2tQ6qrhR+ZW5vZRUNR0oBzdkaLhiZ+KsjkiysjuldMpvzorAS99B455EgyKoiwdw3GcnGr1duC2Y6SchJt5ODzBkdm7xOkFPYF0EhnsEDG8JKdP+UQRvk5sqy3L6s6hJQhx9Y3tJL2OBWe5/QpLmlCNNopNEG4YssNRsi/DdtXfOtjyIBioGW7tCbXtmx+crXI5cmCWc0fIbimywYHSXzuF7QN6xhNynt292p8X73JhK2hUPXb3vAdRLAmbcLGVgAxbirZl+Ye+p2a9TKOi76j42kWhv47Zkzp5ivfLfk5UGA98MUGPEWZ6Hm7AZXwlstIl7i4XcCvoU7HCXxkHxqwfr6PS1zr4CrIwZ0Q7FQku238vQ1a8YLu26+rWhajMw/xdaeIHcRwrhkP/D2/vseQ60HMJPk3v6c1SFJ3orWh2tKL3/umbybrf3zMdMS5meu7qRlWJIpMAzjlIAEkGinLzxlFWmGglHrEXhqn3bQld18cBJ8a2K0GAKOm87OI+tgCSZ3kYSFbPHaIuOf9VU+tl7ztSk+HFVhWtQjdrP35xNYxMolrs6Ev/Z7WczYtJ0P3WATSTJIn7/YruTlTc1tu1y3IJ9j9qRKXwo44F+im/5iUUo+WqFhhC8X9ZD9smmAajKN2qorMhrA0kCdXJvQ5E0PuNDVeej8Nw+XppeIpCkgH1w86uKIyKfrlvLKhE7v+XetRp2rYCb9CtnRQcn4BRwsmPdSeDZqulb9Nlukg42NgPtEN2+Qn7apDMrwkFebt+fU8mLMP0oh57xMSKshVEN2htW3LORhFBL8ZGHtWVny/hgIPxfub2QJC27fCRIElYcr6vpADsitW4lcbjU6+WBFlBtCMjh1WGQwq3o2ja+HW7sMQIWuEd7sk8DcUoqqHbvLoJYDC+4wAPJm5Oc0Wx4n+/M6xpNAxAsBCkNSx9SLK5JJm2fMkoEkU3E0wv+pBcJ/ZGh6Qk6XzeuHqKmwaiFFHo0818O+M6AV2m5p7PYRFJpuvkXNpnCEYUPS/K1m+z94EbEfYOCQhyi6PP92/H6Sbp9IFBO6Klr3f0L5BjB4XcMvmYcFLZ95Ver2rbfAN3Tz+CMmrYHFokWYpgcvrDREYcx+Bi7R1jcn5BgUPQx3U1XHoqJLplFMpBGNanDHtl9881jqLmXzX2CyaiNwAl8HmeeEePrwo6xxjMACL3JwMdf7S+UfUf9NOwGiYdhilm6NZ2lJr3VtRowoEvRo5OJD7XXXeZFyT41C9Vs2vNOB9Hg0mpykfj4WWa4DB+XFos4V97rwjfNmkfG0T8ed53DmJ7B+gGSH5hfeYEJJk/AyGg4Q7XIPd2nvTWNABgs1w1cqvT4ovER5eC739ol9UETymPhH1UuR6gl33HmbeConmhWn/r/aFcyjBihzESu0shCG++pK+laQJRt2g/D2KaK9JEgUmEyl+LFFOs0a2Ckn5osLruZm+M4ngZ2utCkfjpHyJ0of8OQ/Ptecs0uJtCkTF5iwRlkbPy2WI0brxZI0fvUEWWE2Ts4dASBzK+CfZO6+OjKqQka+0L5yWlBfy09v0oDMP15jDnWAJzG0eCzgiDJTZyMoxu0G8hsf+o/ubuRTlkfEedCOJq2LLcGj7f7Eqk6gvDjtbEo0ADAhmUQ0Cb9uAGRXK0dMcUlAKSI3uRDFLGR0oF83dBUd+LqYf9GPRgrH5FV9dFRIqx3P9k4fryVHXHeYX9/Wbkn0I/ujUmURkI4Ju1U8tUUSGxFb5xK5L/F30BL0N+Y7ZbMm8b3fXqf1+ZXgDLlz7frxlMafldnDHQPpD0CS9oH1uHT+vksvEfntDvz7msVbhmaYoQbYtghJzZRxIOb3kW7vtFw040EP2rSD7iC/5SISgcy2hZDMklLQLDv8P1Bv7om7tRWEfJN410gfdVAomf71T6ZMqtkZWC2V7+fUZ8L40mmohXFFbstrTdPDvMo8SIS9sW/6N6Ozk/8obIsXhe2vT+d0UpEMqy/5+vuLeaZEmgcPQI/4ve6Zm26fx2VdfkSNkwhcuwYz/TXnHxJnSY521LGg48OiGfmXYhh+rYTg1DsS2ybBMa8xe/W7c8/6uDgP//uIOAUW7JsifGWaaqHYYD0TTOGOtLQVSjxBWOibbWC00K4vasPgY6VdeNGIub3b8ZsxPOfAPXUBaGmfNKv9iIF/isKNP5a33llHOPCjl7qVPRC7c8TfFMxOkWikPT6utQOFXLNUoN0vQsywhcAMlDBnJJVtcLelwW+s/LQYIihSYe0AvG4DiImlGNGjqngYpG7G7OjqJtjBp52NNL14HciWyusgWFnyAypCJLl6ZpypS8De1KgL/SVwfQUszW7SZVJGlSyFO1IH/xQTNIbzA0R3RWiiCIw9k4lgAdxkN/9kRY2QcOmYRbfV4ACgB45YZ8AnZmlok4T17TNj3ZxoqhjUsyfXBfk64YL9gLilvVgDtD0wIa2lligUlRZ+h0WIZcR8Q7OEV5p2FfCAVRlQYOhM9fCqcvEkXcos7wKsfOV6w9EHXCXlRVTqqLSWXm+lGJyHJLGVCKYeCncytgypo53GFJPGQG8eAkcNE+bCJUxocSQRFFtmgf3B0IEacXlfC74XF67DLxGVw3GSD2r+sWE7emaidauV8TWAm8JW8IjicSO2IoSj8OyGKcyHEwyomVFoobHRVAX+5SQRXafYcPlMibAoB/sCm0M/68A6clsA2YlrKhZ2EPMr3T/X6Bmsh9J9tOUAKGX3iOELdQpIzp5nMgTT8/8rxViAY+zsTWY/+OVhgGoVEepylNC9okDfwzQQibB1bw0TrwHaAP8H+lJT2QiJnEtqaZZAoMqqQkPSNa/JyQuk/RXgU7Bg1fvMGnzIQjIyj0T/3ySym3wvxalHHviV7TVCOAEWhIo2xoWrAhyK89+lWsFXYdNBLiAcewTXwPZU4pmE5s+g2XCDB0n0mzl4gWICnF+0lf68jkOEeRoF8YK0QXqs/gOOfM87xnx6m076/QGqzHUEijm1tHQHO8FVoLSAZDRxTaIudZ5tMrjfaF9jLHQS77pkBTDX+bomjLCpvXtWIawcMQFD0u+EvTtON+85WlRaQAk3/UXb++wzS1X2Qriwa105AiblTYyiBbx+Er/ldkyyMfsAAnap/xUITXdp0X8m+/O9TWtywrBRWR5mVjoQ96mHkZHS/8RvAHLzND6b7LVgdYW//oDBdt8GK2fHuLi7Z84PbpsxERgQOvBrRR3aF2TBxmEQrjpdGABonGb7wpt9Mnn9TuouNTn3L9/TXa2BeW09eU+UnIRh1qK+zdT/yV//XByPiLI1CZd6V2+Wa5Lu4lRaZkTcHvvqAoFvrDVwAI139y3DngyCJsjDuqatVLgNjka+QpjWxaHGfN/qObsHTmTJZfs2Ep52jLAucd896HuHnfXuudnwIB582DcEZbNJHkI4Vj26sa+fB1EvrHdQnR4N6ijjtyyHkR/xZguy1s67YCezUZfTkLVwqXTdq2s/Mz4C0JLXu08vHF+MdP1sLzeuUcr+Ja3bO1eL359CCx/Iub92l86gBP889LzHBTHtRv9JU5+D3eX+AeZ9laD6HaC1gOJ64Qo9X7fmllehrH1dXrUEldq/L+hpq8Cny7ORSs3ayCRPfZGzajsksKRfE4F8nzXk05ovSQuuBaGGiAhsAvcl1Kmx0MouEHI0n5l8dayaz+2FQp2NE9vIGrLXdcOanxOVlOObFlhZQGvpJW5tHPPBdFlsR95al4h99QKa0o6ErJlxqkFa+BRV+/Ol1+JsFbYScj6o7D8GEbWFzvOanoW28aJoSqP2TFbgoG1fPmB4d5bpbY+RzQE+hNwsk8z5tN/dSZSrQISvrh00Wk0uzHBukpUJ97WMUOYl5mdgbdAmPhlRURhJqQX/kZgNBhaKwBtp8YijDdxaEtlad+TQMTJYhcfhSImkc58ljn2xIn8O3NT7c/SDB/tFwo/+xOMCLSTE3lUdztOGmksv36UmiPZd00MRyAOH3LnCSCPbfOhKwefGcRKCBxVMYWtbA6FPjmZQyhZMiE/sXPb6SiuKRsTdoIQudRx/sx86sScRdtfdNV8Exf8qTukNJ0ao2KpGiAk8aDyHf1dp5CtqwQWyJvtAPL8snDCTdSXz8KFT4wkArGyzS6CSL0QkmoHHGIQDhJQ3JFYzHAk2jaADvFM9lgmajATKTt3JwgwCi4PHcbBJmiqW4jIGMmAxfcr8qCbjE6aSFO4jBleuBVfHKwS/RG76+djJQy7DYDedmOJk8gJ79sg3GbcZx036mZITARnhqvsV5wrTkOihpJZLOF3JXUPfNsHDn/lLBdO9BHexeXoSeVeNzI5JH0s59Ly8DnpC60+c8r2I2sdyM9sBJY1UK/5nI8SSgUWLv+4BP8CQNg7I8y73gen8IQp1M8fuAQMP7LiQUxm/zH9WgLoEqBCx0vfT+cG69gK5nHrNiDKfWjaM8GRwlrsmpvYHI1Lz3NIhn4tL5+hjYI4/5W2kXVnKiTafzySkZP1WBg2sFlOBerDUaOkJX9KjGSpeOuCjHGkG9++sEhuB+Whprpyur4Qtjyh2OAtbdfYrDvv4aGwdct0W3H4McUYesjQTFpGmtimw910XK45Ifwcaw10VdnCL99VWytd4tx1FyEjrXo+VydBkD/ENtJWSboxHISmBuxl56exVIf38HtHVce71DtiPIdCEVCfIvu0M62pJYDH0hPb6JLSP9zrPz8LutpaTQBAwynmtv6hCoGu1UTDqEba+mbjTGoiRzGTsR3+Pv1+Z3HDDDAke/IE+Cwyhn58pqXroy8cv2+rvRw1O4Fa7cJxR5SoxKln1KxMeDV5HlNVzhGTWf5Gdz3QAy0BbDQkA0YdzGQpx7yC7j1PK/XLdWEJzmREiSdDE1eSJu/piRFLnASeRZ5ktCqF39qGGg1Svak/QSyuVV74ZUo1VvZBqO4iPIQWN8oU0tr3zHwD9zwQG449KrczPwKhtMLx44amPNSVlV0Xu6AKoNoiCq+tw4ABXqqrwqW7besupB/s1PHubUIa1cubMD9V81shqYoTqzUS19rW0VAvvF+tAyXkM2g5kTVO5q/kIb4SWuS0/KN//Z0fjIoULdnF6//tdesGeNxgszZQd3irjTYC50Hmsur9ePETeZl1zLn2+QgSBjurV1JFWQ4YsP/NlxQpw+XB2d0mObq9pYoii2Q/82AtJ5bfk/sJbooZsH7kTuJ3Mp3VNlQc10mYPArjm5H2kyZioIlbLTb3A2YJL26B9No+JTqXbwqIqQPAx/PrBKNcZdGRA/aJb/yb40Pd8ovq1Ts9O1go6/Igr4WwqptNwDGTBMuTRiToarjhgOUFDgM99CtvnzaBFssauQjq6AbUURQeo8GZaDMlS0SJgUVwAwaaf1ZUYxPykSW4E3lX3CjIxXqaDRriN8ReIONAWGt7gMkhZ7FeXnHEGLAL6+q9KJhIfRSOks/gLcaoRFqvGAOZLOaefZixetFmvKP6Y7i20FEWytU36VycON+/VrL4GwGkLF1UQBYsh6/SnQ8RRFcK8+kmKsq8OK8r/C2JTsmljHL6RqnEnuIcWY3nE0U9L+jBm6At23wUGXmcq4CELHK6qoEwTCHQeA6i/v9DwOXx4YWZuNcyQVQOQqnFZZ/SYgKqH6+hrAYqGey9gjeNllGkxi1+jPgY4xO1KeP2fGp/wCHNKh3Enpcc2z9+ZHQ3fXW2GJg/A6k5wkT3VPpstA4AURrEKbyu3NrbA3cecRBXcYz5PVPXlAMOKeMmfCw7f1XWYHIE4L72fMffL6DebZy1Bcaohc8/tPGq9iXcWW3/3f7YHkPC6q2iZ+Zf9kT/Q+7HRH74QkRe8gtPGf6S8NmYvmGq6gjw+IcYB+49CCX8N6qO9YcsxS23CUmzDl5JWj47dXgEcMkF4WoAotMDwYLgMGk++FoNiTGahfp9SZ3FU83wCaRMEVvTiMMQdKOn6GpuYzQxHxOnnPr59wkwC11nR8RU5sSKoHlxA6sg1+2HP+gMYeC2iQYxkk/V1EsE7U2u2pEOiCo7ghXYQ2QzAqH5KO7bZv7yVo3DVXiOekzL8rfnAqCHfory0ORMfxCQegdq74J+keLgSn5pC46zMbSGkOSU+fHGjdeuiFUMVVKpqeaLSOjgEc/2RVXgGDpxyBL2z6badJLrrIcRERqnjcN2KEMCv0ZYNOb7M8tWZbHANwzt5k2yA2DquI7+lGkgqINrGdwr3cA5dnfD6xo31ItjX+4EJx5wq9rlnkRXNrkoBnGKADFJDAoUMPaL0myD6N/8lGfZN129qpJjpfqIo6ur9tMy59WeyVZJrO1Ac+5Zuy8sZFYQCEP1KJT2NB4oipxPFxsmX9IwhoGjwO8Qw1Ghuqb4ByDKiufMPz3/BtRqIfpgYGvDKIub6CVALoy1HVd5/TZvUDr45zGilwcxGXEvpTJQo/5Hz/fKltmgG8VWRPf7FB8eiT1ry8XFvU9lUaedCTHb777v6LX+8u3nUb1rJt2ViVG/z6L9658lKVltif3ib5nX0Wu86MyzIg8OcjdgaffznhL/CxNLzNqSYLXgwz6Ae5r5FmSpXKvpVV9FOkLg6xxFoYYqf1A0Xw/xjt5MsH+fuf8zN9+WRGRQyHZMQr/kWgKm2sodJwgYvTumjHqpK4VC1p8StodX1CyOlCtz+K2BUaqC5aMYEEQE1NHoU8pcov4Q6j861YBhG+IQcIFun5IdVmaJsopGXSt7DzFdyD0EQTIfBtA3ixM9pk9HR/eurT11G5wv6ts61jIlwPaP1bTW8BGatepVhLKn6H+HpX3Boai4quqz+ZdhPLEq9g3x0MVPl5fLGzErfN6+uj64OgH6AIZrkpYeihvwoSA4JZSDYOQ8X+b06ScgG1oV5d3h4NKAugbFO2G6THPEWnEzxVBBZJgr0qaKNQRFeumYpA/K1fnC4SXzMmkXCm2QKn0DsTrR2DBDx26xLAnxarmeFZIReA+HQ7Safz+NM38W4tBU9Pa1YspCk9aRymoardc+d3EDfqJXBbrDkwR4vxTcFpRIFDrBT4pDb9fA7B8mspF1ESRfg5MwPbFsW4VFQ9yexXUpqoS8ceq6o77/ebVlXaW+FHESEoSnLAdiui67pJXIEI3BQMXz8nJVilor2k8btKaUN7ONKD8qo3SICHaR4Yyf3jHldOW/0/9o+zlqQ/FdL64iUWoqnAvbWelO7YDWsaE0AxZ4fgVd7QH6ibNEfb7kaeJGrYrl7+4t1jOiwhr0vtY35raNvZd7khb+Ae25W6MhLAmnIxbTl3XjXmYkjRdEo14AbjjkXApk7lMzHRsrNG0ycsvK3z+CfXgYlJRGpXZ10SAosoPKCmOM8BNZ7s/eClOSX5g8zLEd+8dj9YBFwLoCKJPWrGLpP2rxGmMFBGH7eNVLbOfB9GB0hD5AxFjmhD5eDDPCNw8SicVbzMAVgaTClVKq20OEnYvtKncpFXkN22uVD4Amxy6gFIWwyNeyxZ04i0M2/bX4SG8CuBitUqg9mBMmRAePdZiA+Msn5InUcN4Kt+iHiRFeyyKxDHBAZWAnjKj848A/LZJFOg4xPHywoBNbgpKS+BYGT5FiPM4iskKeyDkYlB4hmLgNZFsI0Pli7xJ84d0TmkyHtKJKcURVOYdv0lrTmI4JmM8bBSPpvPpyDthEz8VDwTBf3Zm1nQB8fwPYr8VrTqopRtbXb8p1wjdUSffU0CBb4yz1+tJJ5xgHoIg4txgr588vznBNe/XZZudLh+bL7nbvIWtyeXyqEou16i0Q0HxdTQH2v63/2TvPF/GogX4RKL1sg8ikYSvjqIofV8JuV+mK10bURLjiogPZagfAzyS4xK+3YrkvmqnE1mkz/C7wCYwx41n4Jp7hiN++tjK8LKgpK8IAgtYFvgpmLm54oajjZZpCgDORtYXYCMDMg9nPWq/IS83SiEHETGpDn2/7RSqbEf/pRTtqttlEeb6STgdHohiM2a4sUd3x2EU9Z6qYbS9334JQRRGRg78Eqv91SOYwbqQSWIZJhGRGAyXciDWGLF5E1FCK/P9diRhmlKeVi4TAUm3PG+aSu78rRMN/etw6IuWrX1JQcRqIy8K4r200iIrw9N9l96cB5o6OZrDc+3tQHC7vs2pcjmW5IsbPnwvUG1rEIoC5oNbfen6d6Enxfe84qETHHWKPcMmINsjyIwmZAxTIZbiIRUNzZWnPdYP/lTYEyUJlU06sCVriSI5E7LfLJlZ1mlwhyHf1hVE8nWGjwCeb+b+xMUDoa4SuiZabx8+w9j8bp3b9AOM3zQ4kn3Pkmd2PoIqmOvhPALNBDwON8WDVj6zqsCoM1LR/jIjWI10FAR3qG/QrUGAoErnKMgRzBmyadJgqs8wdjHjoqTZT5Z28M6LWGz7YOTC6xRJhIRzVuB1MThY0mdKkBSjDobF9Pxw7t5FcbQcSPEm/5e4+k5O9jbuo4iKcpH6pXv/3Lf6ppbgNE/GMa4DZTfZIEhVX5sCdG2zbSm4dOvJaQMeOeIFjXOBl72N2mMBWcAtJy/yL1QJGUK+Zzzx++EGyWpq0qcgmMIBSIck7T0V178np6Ue4GZto147g1LW8xKlt7tsUo7KXYQLxY5hT9YJzA1nPGlVrQ+8iVeSaaEPIjZEQWEYfYXdf7oFgDx5D+74c/fPCIqRGbsPIjeWy4/NjM0oTa7YTRjms9JhpaLUSa9P9r7En3KA6GVGPNMsHGZr+ftWkiHg3ZCUuSTT2pEiF4UFSG4BXPJ4p8v4CoZPjAF4zGMCicnC2goYgtOvX7Sen3wX7rh08wMYcyS8VHNA0t7SLc29qmNgDBZKkoRDCIJKQgy1ZXuM/WK+tlVg4zxldS0m6UKlg00bSUelTRPOJRfQeoCmhOf5hQWCMPXhfmIC/cAC7NAFQShqVRmFY00obteZKIxx9f2kKOPIskzBaUz6q5UVpYuUbp5ZiTV4GUxVgGP/+MzZnho74w4aLwqj1Pd+wUAbCGOU59Ow7y8xZ5NNWXCq7YSqTD4wBT5PvsgD6hE4z4XWYn9v1hEbVX+Ba3nVAjNdSBTF+EPkb4tKdIqO6NO4bMDnZld+iyCHF76s0X5VUDwCcf5UFAOIgN9UcFUoShpvLUYP5xo/kk3C6OmzQGHJwzOdjdf+McWb5cRo49SSsH3eBoPlgG0xCGGiaexeu4EaB9O2KIQgAaCTNUB78D0DSW5Gvs1CZqt5VZHjaLyXGIUdo6+oaHhb0MSuBbCqJw6AXenIb30ZjcF/p1se/NBpMjQG9ScqIe8LTXGWLfMO34gGTef5g1dzsTbiDEyapg82V3NsIlnqvYKCbcYmZ1rTNMAe+ap18s9Q6GeV/2bMdnBt3R/SyCmICxIM8vtEEHF7z+QHrBX+twvCS96OvGWl4OZtOx3QfkT69K8zR+C19DMRHw0iXdeC+YywmP1+m1X0cRQbL9bwRV0EVSAI6cPFQn/Fws0bbucoII28DoPBWEqG9xxfz2sKYrzzyA+qXbNoBHHLcnZf+wB3uO7YRotTnOSfdSesKC6guhbnJM/g0yS+ytNaWBJDzhZ0GigsiAyYq1QY1G+1YSQS1b6sUB3DfAqLzK6VhMLAE/KV9iR6t1yYHAf7ShKOc3m/bIXj+36p/YAcd+OY/B2nNJofZRYLuOnaL4iOAtr1bynNkrbFKPmj/JCD+4DLsaQ4ZjW0I1Fe7gc/CNlHm60R2PQQqWEopC+QtOEL68AyXI2W7mhN1iXf32ZZh5DKdMN3XUpimANKug7ooTVmSZy8hWE1YYECC6LgjEHIwno+hsVw31ElznTi0Y2fX46TiEj6Ks9PyAUBBMP7fvpudc3GyRAyqOccFxPgEL+nvadihqtqrgMbX3BPBz1+DirgDy5Ae/3z1QELqEPhBsbbW8LkhkprB9pGBAkBPtyOZm1HDH+SVIRyY26U6hH4VXIEZp4blBiAQUADULzrsiAkBubGArcaZGXAGVW61Rb+VSqqnOc3UtysbCInp4WnXVwyY13B3i72Va99EUX/C9WslwZB3aDEtghN32uicRah9lesy0yDCM0vMNXfdi3me2uT/R3MW3bZoD2Kp+ktabkDr0pQmyo6rPGFb6HAYt87NMzdxZIx+UbTkc38nOuFDrdNKD5v5j5We/VPNnqIuUQhM1SqkFlsJ3ojRQy0Spm7cdoJFLLPgQpq6+OiX1UIlTiDb5xJIWgZz4LgNZqJDCqDn40Bj64lBLkjMVjnbMK+j1P24PUErfwn+zwlLm+hc8pgW33LSVxxDbDcLyfMuHw1sowHNVgfcIrGE6+mgn1K9kEK+ZO80oedvKKzDlBwSRw41ykt49llPEc49NXmQezTgMhAj5jA4C1XDZya6sSD2UF6/yoUJOxBtoWJ3qT0ikIl0qhSSD0HT6kUa9Bi9TwwPMFM6SDOny2k9EVO2/bLHbI8UCml1wCh5OM4zokKbX0Zgm8dFPqHomRmv0VlpCJJ8gYhQf0oyr4DqbFOXKKFYYg0FGYunXwLrILVdOPU1Xr5U/H8Gb55LdmeEomCpoH45wRbjkL78wubtuO6qto+JYEqbNzJq0rtu+9Xde1mlp9qrvsDrZHx8HmOaKQtNcZVg8jWdQEiwOFNnNWSzCZCPLoOvC3zz+KSU/vhKMwJ0uWppURvIFxnkaIkaScY+8zSjJdN9Is+3Tq95weleaXxDcO/+UpqsK8UnzfI4qlGqRucnNsxBp50u5FMm1Q8ADvxt/WWvDD42wwz9ratH4lxKoakSIIK3ultgJem66qajE89Pn9AlPstBuvruwsdx0rD1xxYj/vTXVeE9YtYoFu5lJS4PBNEYDhIrmXBfBxvqEfoERC65+H0PlQ5pDWv3tYCRWH/oprPDTMp3dLevJmgppKrI80rferfW+iQ60taYrQ0bfZUozmXtH1SJ6+5avctZ3bNK4uA3d7MpsQ1HOT8Wv/XzDWK50gmC7V1O7NWCe1eckDZgukz8kYLtC6MDkSLNpk1mADd0eqCN2CKMhzHFXWDow+MNl1AUYoxGAgmhDLX1+LfzLpZSHH0c0gEeuu8MEFtoCnpRnXn6iCi7y+TlkniD0d6TkAjm+Sr7hI3xfomv6Y1bwBAt+K8Be5Ga09vlg9b4WCwQE1jB5h0CcWWheiucdLVriP2evK/yilzLsZqaKmXDLfrnAmKDnjzz877hvS+5ekMd7QXq4wv3icEfSTCsdK8kzMrL0ctEiSIfW1bng+cN9h6QmVrsgAX5cl2W+dxTCv4sFgte4MqJGb50rXofz5w3XJ4hZwlThvTs/W4kKAQw73t9rPNuTAAPFFD+SN/G4zDiIXyO63jZVYjnUBSBbtTi/M8iWmrOO0ChLAoElIHUI/M84ogyC1vfdG5fzrG6TIMoaHXuuyBdqJRhAqbpszMD5LwadzAinYz8C3ftDisZ0sPEhzvf7G07n17KthiSLJMzMsEipCOyO7C5mUfei5/PvjT8ZnpaAn6X5jz6OcF55hlZsSuy9B0gOli2vTyXmwept3sLGNExcDrKUo6JhJFesW2nmanjsazZ2x06loOUiyvmGdfBAQonEENsa+VsVmHhY9OAx7G0qh3sLf3PJKG3lpmV4i21O0KHJifCNhdOkbnltZ0exkStnDBZifZYoLpOTAHN8FcE3HvWRjKuHqZajsxDCBJBxPn4r1vmIpVCh0gQgDZXOyCPgK8DD4YxhiLyBn7nqDBSrhhoRr9CgbKXDDllFc8mFlYdwYBkblXZrAXbHordYClVoxm7t5YB7F+8+tj0WPVxCZ2iptUXLFX11gZkeHujNUdfAAc/FJuJRVM7uxtP81U+oOMQhSNjvyJx/ohbgpGUXC7lANSLRzZw01ZglGKjAqb/eJRlNl2Vys5N88Bkl6Mp8kDU+aZ6rdVb5JX6qY/+j18wVF0v57L0cLcJ8qZzU7EQ3AgghAAproLjCtCi8UTxg6sO2TFVdYwHrI3hELVbTRlqvgkcxUXA+Y0/VapO2WGNf+JM9npDmzssdVwR+eBaBnn04MNobqatKfJBb3FSpBEWSCG9juN6VSMxbdeBdAZKYgvYL9UDXBf33xCZjbiyBLsh9CiML+JHDJ0n6LE3nDIEDt0z3KBQ/XIKXAIyn+QCP2x9CxQXePfoU6Cp59I0WDeKePmaxI1CcZTze9ppmY0cauWMALdlYyeFa2eycbnGZ3r6GtdZ9QRiz2scr6BbhS31ssRaWP8ykQiABFm05ft48gu1vdNGnRIaWSHXrFnsvVb430qRRRlozJp1n8qP9jtdV1Vwcb2CWEcmHjJv0UHFRUMeyWPo8YKGLxUsSvSGBIVYOSTBIpDqKPIgsw7Ko4zhAJrD+csw2PfsdRNCMy0xDqWZZ1cno2ipQYkuczv2rtGzdjEqXzNVh9MxVLBxmQXZ0qp5/g27K0Avy3yjKeIvwxBiNNBzufrmUUK3Bwfusrp/IVEish9jZxq6+o+KKfgKxQYQwqvnQZq6zMqrfXQlSMlVrrmWxEs9tGX8NsOLqxkvhFuPq/ePNz2/WK4KaKda1eH3X/T7D8KlOpfzlvNNY4gVPsvrA/iqqbHsQCwifu468RVn1SIkhpYeJL/DmCunMimZjVl9Zb2SjuGLiqT9k5qFFd3/q3jQ8/Ac9kqUhVRQQqQ39Qyc/vIcwzMT33xjcnB3NE4QkTnYYUxyipYtQZaFKQvDhq8LDNyh50GiZ20NwDmJFgVuIaiqtqDqN1z7qpymmtlpwxQ4EFU3T3J/08dJukF8FdmHWsMNwQDkdo2cyWdb+c9Mt22kniSrQuLBmphKKVLKfWFD8JcglTllJs7RU8GARJ0AJ9sqhfF9HI2sCIepn9p7ilv0GJkzConWeH9tkVRSYKX5uQdQrhaJAasLg84TqiTeNHB67jI6yKqjiAJ6VRPRkWfKqAOJUchesK6WLzrKS+zCr/ttPXRV3qANDHVkrOF/+T9GBx0ibhhpmskzrciQaQ+C632EK4FcZg4HK3VDoNew2rtI3ii6jWyZUGtq5v/hzNcv2IL5r8eQmLmzCuPBtM6xr3h/da1pM5RNdPsIHE3/bN33SCY8dDFee6N3OE0tcImeG5wYrXGRHdhN/Ojd4MwSI7IHXjRDm+e6ALaohyw2qlUFBHX2lhqllqZy1xX//RlQ0fX3v/gKlAiiqHNH069fz5B3BzJw/9K0pljG+HwTb2lxfUpqxer9VsKr6q9YASl1CW4XjwJKQft2WJ61FoUY6t7wHTww0AmLHnb1VaeCQ9eb4/XRHuHJqaG3aUpCJtiNNOhql8e+AJ+cZbW7kv8r8cvWXI/IPojzIDJfw7lYsVkM2iapJ9zrN+ZmEiqh/QDjtUBDtSFVEabJIQ11VPHEb73p23x32Ey5tc8HVTKQTkIOKT1DSeckcYQ1JK5dgvVoTOmtZr4zK98PbhkqJCrjk7lU9qhUE6w1Xd/AZ9Ox71eoBxWV9BjwccPTMpP48smvD+33CyPKa9vSDM7+rswmwWoYIT1aevrsZMtp00VPNYZ2PtlNFP7ix1y/70MSLAcQ3Qv9WciHQbsPt2dp2S73uS/nb8SjxqHarsk6V3exEhSyMsvEetXVbIcoqeJfvRBB1Uc+UxZyI6OKGjFbT/js83gxoDrkrlioCD8pms8lYX/WiTvBL++LpLs2L1y5PEPgumv7yMGEhkpcETHNOBqL7XuHH4d9MbrfXMARzIbUm4Ad7Fpk41TpO3ELpyeTuPQ33tqi7Cs7tEmTSVBX0USK6WZOeyNn68FI4UKcN+hPtLL2iBSBs8NTh5nNjiWqcZDkq8QEmR2wk0jqPr3cpJsEJzD7xkCgKtGIwJMnU9n556r8aDfpBuc8ZS5xiZ20Ybuw2R1t3dkbXV8I1ud5d6FQf04em3shpqQh6f98AZgPv5WChaYV133Bf9WrEi7ZFj4BJFbDuRY2Bh4HZ1Pp73dq6ULiydiuZ0E2aX/Z2BwgKt2LA6sSPJFUyJZ6EC+AZbzZeX0Dolh2Kk7WDUvif3b+UDW5ogtWM3UoVprk0YJCJ+oJjz6vm7rCN1gmuXJphChru41QhcgbheDGdL0RsKSW5x/I2hiD77fmOeIeqI88EKKT+uqqmM2QX+sgLTCr16fIxvxVRfAQwiKwHfkHXfICoeF6OixbllvLOk2a3+qZtj83DV0eN/M+coa4cUuRZh+2R46M5Rm6bMzpE6QwJkxTAI3MPyuwOlgQUBZIJFgwMAdYufKrUY7egjMpuG/es3epOJfrgWMKApAVl1t9Pxb5MxNtG6FO3TZzefpmMIgY9XfCWUopmNsmOrFtRPkf11fvMF1o7CzGfm2YgwM9IAxjEY78A4zhvv6wSH8aGjxfV/2oG3m5w5jTQta0x+LIfAHtKQzU6bjYPmNVKnrvGy599v3jFYls2CvMSGSSJwu0mvaxKMLINe6bT4DgM99/Bw+qzKBSVPXK1wa5e68iAOkS5hbqTbd3uZODRr3XcRc0qHZPAF1Lro8ArzjA50D+zY5Se/rL59b3JJHaHeKYFfpgj+HY6vyWLymD7Y+9JjOtRjwylKX7qUsc4VFfdRAnYk1wQtlicDBwtcT0e6/sn+VIVNuDCTwWRqytIRiYAXO2S8XkpQ3qvh3pIWKjT/UfavStRTplH9T1XBNQEFD7Ln/mfKuzJHOMTQdhjgMsr9+i8I3bRdwbTfx7VKuMu+dJ2s2dIkoJglS5sOo/m6XC26rSgGp+5A5sq5zGEcYCMHnr3PZAYxDZaSEURjORebLzU8qS9Z5w27lfKCQCowCare2fMfn8SjoHxroqj+B6OWgJMnqmvNGvxGl9nzg95Gs0D7YTkIylwUJp1qsVGT8aGgMHVw4XK+EoxKW0G51LSYLQm0EWFDymn/zkBY+TVPPlOC1rcBO7W1+6Dc2WGTeWu8V59CBlWZDDDiMSur8s8quklC/DGsWqih0qV/ZVZR783FuLtAAIwj2CUfYpcYgsVDIJMFK+/IwjHAXa5CyGDyemZ2MlUY7t2tmF2OXkLo8yDIxhQVvD9sqOjHPsq+n+99kKAwa3bx5BDXAzKicFq0stUQ3ZPj+xiPPDuL8Jb5o1z0Hi0UmTZhaR0gC8Q++Q2EE+fx7hc/sh1uVT//rVr06KtNVGfNR2vlKcsYaDvsTCZ1AtMuSaEuEX0aN2Ipmtoagy7JUjSH00+CGuKlED7aKrMpgbWOPLExXISTzcPvd5dxK0yA4T7/yHULawKRFxvkKsuMYxuMkbtCfVVb7kReMP4bnkGrleqDRh7lFYfgF5gx04CBuL7ErXuKGrbiXc5Nzab0JjqNyCbApwF9vO7LTjtTwFo25QKPSz1u5/AzZ8Iv1khoa/Rqi8Fv7u7+znOEkXDS9Bpde3nsa0M10TCeC3STyvnY4KHBHdYpXDzodltcklUfj+f73kUvgMz4uzsfseT1piiTMR5V7Hm6GSGFm6XDnDy3VuTZ/JJSFztMLfxXEC/ryhEwFeYxZVpKjrve9QiaVCw+S1nCqhiPpUx0zz4684w2o+b61NfA28J0BepSWaHi6GXym0nnGxbo3f9pER4R/F5GWZETDmi+k9Qc7uI2ju8/yBpkuQSLuJzgzz7kA6epeyxqpAwSJNqy1uTjEOcapsQjya5aSCE2S4qV6ueYFKVZRiXcUUNuYRfTnEGf6qbnJe5BswBG3RJg5w0VU+uTLpkYEl+baKzbUKOWUaLCxnygScdPUqZgQFbNQF7W6il6tsR+GepbWcsfl4e4i78jpMPCcpT9TGE0JTMPdADCmrC22W9c9XVHV/T3Q1HLTzf+ZxqWE4ZVUBvr1rqfzsiT2Lf6GWeR+SYqapqOyFztVRKQR0Np4Tz5K4rzfPzVuvI3SApejATRH3JcAvi7dD7v85Qb99sN23rchKh5P7oU4XzhO2g1HgKYKZjBwAD23t2yuYYxXZdqqOGu/xS3pV567N/n/Yitt/a+Z/F4awiWyY3l6R8OPSyXhSKJR77dcjZ0s5oqttFUHhx4GqfmYgY2HZL55LJt7VNWFoaSimcyC+HKHCC3LJfEiqGvseuvAj4GHwpzDpg8+9imeSqg9OfMH7s0Zs/Rwl4gSjUsIfJgYdG1iv3MowmlCl4WBwG3dtkwtIDSdhu3Uwcvw4vPcEHG8Q7cMcIC29f7bf9oxfauLghPqfMcug3pzd7SpT7uKjzYlBZ6jgHwcQQzAe6T3QhyrTPGs9HSWAAhQlXGQckX4KjyXtaYP4ymEby1iI2J583+gBTY5mYglywbLJ1kXjuMgupVSSo2KafqN43OyQGTVHHKqCmrnIhkSDJFojXK1cNFn1mt09rFOFESKNHlEwRQvctkArisL1sKmzOZNPuqrOzHVLfTyRoZgSZc4s51fhyUUhBfopdSk+KGR9x3xQr44iJY6sPFvZht/M6EE1ahlS1LLspBNrKxtOJ80b9jWe1L6kzx5aoDruUF1rT39tgCL9bJc19EIELQt2Gce2YPu5FC+YUAzSPJ8drl7WpBUgrJhinAFHewqhaOpXDdpAL9kyVux3fKgEbvjo8IDYs2ucslGLWXV07rGbBDw8yI8BjyuVeMpLzHuNRJxttu+XnIyh2GY5IskIts5Y9m+rrw7M+IAk1a6GftAM4xbn8hn5/Y3H6IVy50FMsGOLP7UqD2+DdhFus17g9Pf2cIr1N3rgH45oGOP8IA1NeB8VTEmaDCLLKqPDSUiBlo5Slvnurp5MLq1qJ+3yS82OMIb5tu/33nlnPQthzk0Zd9luohkZom/BPKO/BJXz+QSp7sy6bbw/Yej7YCD/JfeQ/cbPdNMlbdFfgopGnzE0u2iPmJn1oCmhuTA+iUJQDXTWILApgsUxPFRjq1UYvaGM0G5ylMEgaDqHXnvXzU9poCPGk1zS9y2mmrsxgq8Zmt+tBoSbi9hnvw2OP63SHS+3fd2zkumZh7Kn+fexpPGRFDqEDUr47BJRILTQ0kHMYrKJFCkTLm2gfM/5yZN6k21XD+F5ikDdwD7Dk2yZ81imwsRkM7nt+CsdiyC5G2YYZK8rkj4vo9EIJTG/jdBbtMmfPZ11YPm102Gv+KOmfoJNUnexCqJm1gk+AM0r79KNR3OWXQzZU2fNHrfwn92ljHodi6TREfuUz8v0exkjQKx725eq0e2Yj54xZ9yeKrYbppnAQW2bYLj+DRkL21BVdCsSdhGJXwijKUookRP3HHFYKeReF8lC+sUP8jD/NpkPQC84UcfdZ5eak7eamyB4e9MkmBvi6+GMX4PTiS+lv90DCu43GUqBqpm2SACakSJCEtK4XOIIUHj1PNL1cy/9rl9OptpMgzaqn97Dks32z1uf3sbdN+rHYZU5Hk0fQTzT5dp36ZOzqN2LwknhW8g37ClY4LyqxS5TUOveu4ZF1pCT7Kw7M7lhf9nxEKe+PH3qODXK9LxgR0jVfJsNc8kPLz2NZesFiRPmdMuzu5GxWIAD3BHDRuPUihlqqtDBMXDsSDIAZxdYlUGxMlUFq96geM/O8yCLDv+QW4UBJoumFLsU+HI8zgEvug9GXN96hhYw5XlrQ/IB6c+RF/UGhaUA6CNYsNeY6Q8pNkQCnY5Dpi7AycbZve71X8/VFGOD9Zu9RbEG53VbddQeaJeFlwdyO/GvXQ+2kWtPh8j9XgoM+wkyEm8Ez9iZe603nSwQomfV3IwX/M8+XEVL61Nk99GO1JPjKceOWtcXaR0zj3UlpFCobDeflxZ4I+DjvyLoiDFTXPZYBA/bOTTYn1i7tR7EbnNijzH27+iGrYkfvO0KTzlZ5zpZpfvqF1aaXgxRoLlpMgWYqTRf2fYMtpylidazj0D+fPZXdVKDV0TpD3DI8QNCLic4/v5qeYGzJwJOjYBnUoKpPHurcu1j/9jGIvuVPuaMMJ5srnBfSE2Ki1U+49dTud00jn1ay2X7ci2EgI0VwD/f/uCAWvBNgFRn+QpayGkVh5Xj1Azi8UgKtPimPYcK1ZMPdFY/iXZFGgLQXCeg46Sb9HRxCWUZxCYU/YMTQ4oo47caKrPT04XcaJyt0hPk+7HGqJjryLiyjax9R0qJ28kmtC3kuv40YRFWzSirupHCraBycK4nBc93Lf73/4qNa9XhA0gy4uXlT075IYCiGK+ebH9KJnRQJETc8cCIdJlT1lbzsTgO2ai5YSHLCgq4JvMpeMTtV8pXi1dBxlVdZg/B0Z+pQF/dBGF9Kcv1glPew2pGMfynpHu93l9VBAMJlGQl9ExtOYVarU11zFa6K/OzWTYahbMjreqqtbR9w8ReupsdCrS4zQMM+Re2O3Z3q79bIsKdck8pV/BY+2/uv0aCNW8c6KD+Oxdvm+ulzpXTYvaxxOrFtQ/5ClvRGYXyhbYGFejT7jmPLZkxzZnFglSZz1IZyJgt4TDbIz3HuVE6ZwYUnQGTvKeTqNZrmZtfKObHIj81ka2ZYL54MPbEl5uHsyljVTF61oJV0eKmtBE6Skv5mSj2BTg6YT1Bj8w2bwIcT7Xy9w42aVonMl0JvkjFfM6EbJ+/XB5AifYeBS4L7pMHbYn5ltxf02BpCLOi/Tbql8wkxlgV+vMWJm1qw9qSPukX1macjxP9MFcu37m50G4bMuvsgnTIcNP2CZsIn4LJA1pgyApEGqpud5AqREA2T1oiiAdoNT4DshNwJhdIF9JMUvcjmuRlmwQPL5z+VPl/K3Eeydj0zQVjzDODR6TQniKENPf73DhxbMZGubQvTOylKFeJ7VH0XIKqXZiX33tp+YKqvUD0W76rlFfQL/f8BxHjrjRtcnuM3h+HX9q6w43XYSmoe6Xa5xxHIcgCakAzx8DUiqfCUI7N70zG4wkYkxzxzSfrmiKlEhSj/6qwple6kfoTds9er1fsZNU+hSsgoBYYhDdknGi87RaUxON9u4bokXowasqNV0dov4NM2TXvf08z0TPIgChqz7PlqKVKx9+ncZvWMf0jYsc+XEIBORX5KpTlBt1zi96c6xLkapvitqNyxIx+6ZGYD1w+mXg+scj1pCvbP2bq6A5jdH5vPUoyOkt1+CuhlHK+RXxKRCTSDe/19VuINVlu2G8HZ8XPZlRMBYuDZ4c6Yrwu6nKB6Zm08i1b5or8aYrQCaR9IzloQCfGFBLXdJfs2FsQxwnmojrKaEw14fWx5HY55X6ZqKKERR17vQyTaNBkWRRTutfZHwfT38MRW875UIg6yqoa8yBgXqMYRjWvsukpK5y0Zyz4X3kjZPtSnDjPJeMO5aEyITfJP9HcIWVZlklfiR4fVfcm9vyOJGJiwQjDhiQf+DRZqIFwvdCLpSRkWwIckidAQN4jd2+006T3nU4uCnz+H6aIc3ypkEa/ODOmK8OzLrpL2wk4ZV4z0x8hOwKh9/86cw2g9FrkPmqGXD6E0AVsEp51/RsDl4gJbzNmOwFIfNBgM7hg4WUxYerqP42bbd0pYHcBCyWFWJCnm2MWzEBLesAY6aIDXsZ6ROS66YH2Z7jvjuORzUGpgGl/+19dqo6gLSZU7bCuqhIGmiqbBxtngRl8DCdJSm7aY4xStEUPPRCu37obN8DyaGo/X7DSDaPoYUSLcq1+p3fj4tDnQ5Z+VQtx3UZna8L7Dwai6j66wzGaD7YZlhV+breniOSZwrDSI5/x3wOJpxKpcLQAVllkhmCv1g2DAPWKgrq/nf23qtpWmRZF/s1useby6bx3jdwh228hwZ+vSjeWfvstSVFKOLIREh74puYbwqqKaiszHzSepW0zzjdn9L1h5jAfo8o8F+2HTWWY+QHTQZnowUHGy1gtc5KyWSZBEYuSxtv2ttIxsE7AF+mJFBPtKVdMveCzm3qH5SoQJw+CVT3fzkd+/ThbvtE5JbVp5HRjMyOr5tIFJVbLga8mkFQbl1/S1XKMVF/S4sbgeqiq3X9myOhC+s29DCNNGjCzLcn6j5+Hz9AZsD2YAQYLINRLaj9ye++UOlEzYj+RGEiIQ6aR/c0mJGtkCYfiy1gki/0RMgFsHNyDjD95ketppAbNpPq6ezyskYX1GXiRC9ZMbbGLd4JSuJ/24UIu3PrXPciiLgNMtcHGndl89lm3IhI+bBKvRnYjlUbvRs6b4AIEX440JpMlf2jQYD+9AF41T79hyhy85Q1HYEgzPLxJMtD91bqPNbg4zT5EvjnAEHi/CS/DWzD8M0mok85gsiz+73nLFPVufPI95saV7dWUl9D9kgmiw0Qfe4VwHrDDDpdAmIJvaD/3ixg9hF1e6IR7kEe2j6mWH/BgoAMzVX5AAYF9xaavTMt+zoCq+3wI3VN4F4oKRQ7NXKGthSos0JSTv6Q+g3fO0674E2Na6H+SOq8tgWce2PbFu6IikTkRhFl7aIgPlJVFm/1iwWiiPOJBIIhvmtZBG3eLH8cnn9jKlYH1E6T2E6L4COQydVfe7y4+2JQ/9bhjHA8wXahUhW8WG6PdXvfaqpF8xJIHP42jPegIgduhbefJ/O3BHnMlp/7hGUNKF3CcPM4EpY8UbmTR0RlVgNfdJz+2/VUnFublH2O4GQHZueh44Ml0N8qM98iYO+yrcNqZ0XjJgoVGXYyoLBqwviANmVKDHvHYC3Gx/deovDT1eL+s/XA3AnZRDedz/tSu2DUvIgzW9488dcrqXxB/FbXtK314V3GKeG6UjFC66zP1V1tJzn8X683SqK0cphQWuQ2/6w2CheYHAPFTJiWvcGuxhBZFs7i6Lvr8cJGnm937yZ49HXjNXs9jEVY5654Z8tn903QOoNJ1+HYTZEAmg7xu9k4KtkJd5J9lSMqXqsyX79UQ37tAGMxOLK1S06gKNCM6YcOyDWbBHdK9COityWEbkUBciajYzAlQMX7s1iFBF68YIDIR1xwdOGCRJYFH7mxmXWF1hsR6kjWGDvLADL2Z/lNxJ7x6aRLLWhY5scdC2tMXG09YgE20jogyQOXn2qH42mUTnuIvlGVrg9Y1Rs+dr8Uf72D76r/5Ch/2rffKD1KEkoC5bjY2nC/v9mLtOrPltpKVON/Rdk/1L4Xylmn5ff3SztSqNRCKCTDUVtRVT7+jXedgXLr7Pr5Nma6zW47hjnIL583J+5q19df774Pt/3PUO39o/qAax5vdtwq2zdeqG5cGlnIxXm16xhuK1s4Yzre8L/pA/jfz/u//HkiPyUaILbIa+fjHbGswTXfxw3LE/H1slWIxdUh7ndbbv2+eScotLnd5yX3jJ4/HUIzRTW1gj6HiKhwYprmOsuVjZkw1UP0I8aXXK25nrxaWOkOIDx6PcHm0ermIQlUmvKvevxogd+35IDvpsaUYf9C8p3nV7FftPi6tUTDT8piKmOxJ9FsfjHIgCMaY4Dws9ruP0Ch+cKdyYBUdd5D9d3UDSAEFOyzL53Kf3HC6WJg9WK4tRw/cfx5ak/0plxWFVAgTbj7KgWp6zps5O4I6/nswhimmZ7fpUa7EPWIXsMnse1Cit0cHsZaeYtZDc4+6dqtYPPR7weMmE9VOlADgMcobG6dKpwMkz4WnCoRr0WMbJW5oPmZ1rfYmvDWwuT6MuB6qCA8/ELAxSBXTim4HWNw8eWWsL98uq92AzjxTy4J9rvbod+55Mc/7i/A7+RjDTFXY7vF+mTfDBjxgYWpmNEGgTn85sxi8MlPpTJc+gtFT0WnaKk/kfMTE4QCuDYE2REa8fHO4/WlYfazDpc8N1te5Dk+tlE0/HCVQem1dxaPHHtyQEBQfh0T76z1TlxvpmzXznc6GJiiPB1vaIWHOyLPXXNy3vmCmWQ29/wwL4fedMC8ekXdYn8KSt7xL9Gd1Fl1H85GPlZ9NCS8y7fIfYItoWAa01ZbXlb2Z3ZBCaA1RePQvzpb3gsHN0B7TiLh2aJyldmUblUCTzuN4hzjXcg0W09gzzsqAHsD9txf2m/z0RMlkNXXkm8ZFHaVo2rD9pGQf3XqPJhbL9PYU0+Ii6q9dCkkmtskzMQSF2lP7/w9aggoNjWm6AtV/Grm2EQKhOp39u4aIo0jTM3huegl3lifwC1zeOz867I16iL0uwd5ReTcKxB2QQQNTZ4ibiS64y1co1QSPFXD+dDvasHd4qK0JOFalsZIpOt8qb+rKBJV70A13MRNItwwkPAEWvOadUHMgIBzxs7zloI4fHQxNu+rBDkgYCNEDR0PsmAWL0lPrsMjKU80ylnvISdHre7VpBgaCOIB6zUblwPUzB1yS5oCXqlj1FPaSns2ZJU1G47xorufmM1kLESs5Qgn8x3JcV3p9bNDTe9hsreu53lS7YDPZgcIzttbIOI3KARa/OM9BJ7GvaS1d4s2oAY+kyfmOF5Yi2DsrhqbbY4sZCHmbOraqOIp0a219Ar74C2eQfBx9HGtAfzZ8eJpftP8yElVA6zo3GnWSuwH1T3jMUdkZ93rH0pq8p11Xmr8G4M5t+wuefkIQPxmn4gp3GgSPrBdK2pPrEqqmpe6Vx+Aq44c/5zXlSUU9dQMaU0tScY4yCLxB15EEefCODAL2LCFkNfZ5F2e4u5m5GYYuyz1s0y9N28UY5Fz3imB9aZkl+HJPiX7fQt0TmFM1K5iAEUS/mRIcgcs4HJOw/KRXV/lqKo1wy6URKi6ZqWtcYvjwnMNuKsV+7uvjL4DNYWn62PA1wpDuidaRO3YVzmNmm+6RTWTJgXWv0eJFlOiWMPXyWKnWmJwUZM/pjbejBD/3nzU3qzcb61WvBEtbFaohSe1PFaqz6X7eakfv5Vl+zHf6AW5Nu9efzMicmsX0bGOhLJ1khibwn4gpwAQm/ziFikEFG8t1VEnCZpCheOsPZHoK7IN2nLg4Sk14xJ2e88hTRLrprewWjJgHyd68rMAy4eFG2JKotUd329r6B5imq6v1yP2fbxT6mtSd+EctH5dAVX4Baa7BjonmfFZnH/1mVWhHgPaIt/dUoiiKKOBTgO7NUYEmhF8FwNLrX+XlsMqrwpcOIFf1scAc2rqI2Ysig4WObsCMAi+l/yVb0/zG+mpCvJUbt6GQgw7hrCQp2OO//nAKHW9kQJLqxpRVzqZsy6ZW6R4V0RfvM9XINpJLgNpPNHMJVisIpzWyPUNwTz++vV1tKLnX76snnPxGjWNEetR3aj1MaxKqJRoaB32w00GwlcVbZ4lgt9c6rrBc8u35yYlCYOSio6ENnrbI41JGleOPmxRl/SvGV+k5qtEYbSdvFTmC3j2nTc8C71308kOesisA2DIlMKJC6liFnwcfT1VSCGZx8cwbsHdCtS4fWu8PNGC48Kd5oC4omkaT47sG4uPsUjCCarofvnX5stMZGsV8FMAHvJSXJLWWodarwtW15vT7X0QAI2H2Y6koX25INWR6VQC9GpC32LxWo/71+GauWb6Bowy6AnGhzpQnlxEyPAVIzshxy5tAbo26Q7zPnO4deU4B6FvdcqTN/3Yry/UwfbRN1dm8Gm2pcUKkEe3U571FcckHlwfWATniVUBRql46Z9wVEPy+d0bK8QJPCEIqSHiYg8Y6lCemyp4go5E/h+znpS661vBdgzH4dqAZ3WiLNgGwCFqQbZRw55vKeyVvSk4PKKIXJ61drBq5JCNWDFe/sd599zrDRTF4Luxsmopv/BQBeRpJUPq3677CZmIWHo4lzK/f58a68eBpRhJrvbc7P4RRvB9pJ/kaeSX4W8TWeZeBCVtGYygcv5pcn7S4eb4Usm+WfnWHH+9ABsUbcpq+BkjAxgm3ISXv47vPk2L8oDMVfb7xAryhlsGV6R2lSndYsEZje/v4zgj98pTnN6e3Hr093vpJb223Ru/dbNyOGFboo+nIU1RFDdw0/ZSbBvuZCMtpBmXznll8VS/jUEy/V9EfnZTtMHSLFV2QkVxSAI0EfetJXL7dfZC0HfUnCC9eHI5vn9bBqyYF5bfWtKq6ubNQ0MKx9SEPZl9FpkmPXW3lvS9Gdn6iyYq+7P6+nt/xvch6xovxFmRhD8qtKromMRnEW14kKOmJLrCS0JGNvZlXTjTwpjHRntjb0rCSkeKl1S1NBBEb2CztY/b5ttIurRqH0XXXzea1fLXrSvxhsTCdAfMvNjSK0ZWFAFkgnqADLs/jWhA8gYICeLpIt9BUzYmo/dT3Uph/k6L1fl9ClpCgRso4K4jhRpxBtDy5RVd/DRAwIhh/J4u0cj/QWfi6SQqi6wn1awLzmElWL+ZCC+4AfJGnXkW7OA4xop+yQ7+P9ld+X/5C22CdcHj3HqNDemwW8Ocl/0p7gH6Jzv4//RT/v/eR/q/rmGHYCiQOejplHRjrf2l542ixzkThYtgde/l7chK9eqYl9dUlVJJk1R5lscyy+lcRqfsv5Z5+YI1v23Gjt6L8lacHylLHse/0i3RAoT2dzrwgddAy9resDeINVMK9/J5nbcZMzh0JJheSrx3m9KpOMys2dYZ2kjwnsWflzC/jElIgTnN3PcFzk/6IgnCVkfKIcUo2VdUP+nkpgzUbdaJohk5zU6b/fkmKTGwpfhf2MrrhSKwmTgBhOi0JVrcJ4N4y8ulIctdkV/8MaCAPRAMbbxLXcII6HoYmifiDfLN1H7T8gdIrGyDqC1tKR7y98s4lrTNRWh8xvpjjQ1v6JJM+/PJpwJrwcILVP2M/mwTh4a/8uV1jvx0YbcO++ng+en7isxkc03WB3LlhfZbYeRlg2AcIPInFvLbHgVs/eOb1MHX/Ya/4uVFDHGBda+j8rgDbnuusTsgGLXEwH9lQ0FaPhE30ohzXTHIgLNBbjFOLtj1AzbutoXJ+OSysqPbfj3bU+URQ6xdEzXVnupj/HOjDYAZCxBBb8JRVxN6FGJUk5otTttRIe1bMDYEEBcjlzuliHc/SKpkE1YBd/00QlT1jqdOIBmR599TwgXbhbmICK0eNT/ZuLWbx7CieSb69xmeDnZPqWxtJ0q9TJYx/9alTVCTy9Fdy9kyKSHAqAAw4lQg//jLgRioUgryTt3j2qXhBfMo2zVRLte9EFjTAjLUjuAVko9P+Wov0yJZrzRV74J7iHr6oxb+S+y8+Oah+dApvKC5yhpBy4jHnSlOKA26+Jh/1l6+pTuqP+tNeNwzjo/p0dVxhCmnh2s+6QABqzvVWFoququFkgXV2mnA4kcCLMibi27F29HTxAQvJk/pIQHTW5SRTCy9KKcEyzzUHbRMWkaoKKdb3FLgEx3RSQdwfaOJ5K/PcthnHjNVfipHRNwDmdiC1/qtBY0T+1SkCg/MJRUQ81+YBYaCj1gZjSCwTi8po0farWynQ/5mX+0X79HYwr6VP1xTcI7ye5p5pXG0WZfA0+Gn+e6+zf/swBLSLWPzqEwf72oy4V0c66dZtyn2F/jLZ6TLwgsGLf1Oc54WdHHWfU9RxFMTfTHCbRvWPDsYEdql1Fa8fF9enukhCK3bng8olc71BMMvIwokIyWoQFU/TDhDlds4yoghM01Qn+ufc4R+uyCxVg6d4TF9/76R+KGn1A7Z8lk+VYcJ7y6gK8EOm+TTD5mHTWhqxWGNsoEWSd44psurbDh4ChMQ6eeia9qM91txj+khKzG03Iaet9kONt50Ucb+Nl1ssk0vg1DfPpt9K2We9mez4KdCZ20K0MVvkUaKOq4lLT7JugfvSU85F0+WVPeTj39bDwkMP5AaPW1cupD9rC2GQi0P2Tci2r486+tg7wEzAw48UB36+R+LImr9u3+AkcalLzixac7WY3FivodNf1zYxJFxoTm+rgKl8She5F9HHbUNeF6WjB/7pOukiucuCSOrDp4NZ17R29v03Sp4egZD9EKY4OU6QASAr1fQoG+lzfbnsxJwFmOaCxVG8YSmQ7L/GAQbFO+XsZMeFeeGiTRWHbvJ4IU4VfX6VxgF8nmoVev5mk3MUL3HjGbfH6n7HRTQESRkVetYfAelqSQzc1Zp8YNVlyAhO/oSP1LMsn/aaSvwlsAWPCeRsJMpVX/V95fOQBHUA9Kc7rkNn9IuKy77uAHt8JxapIkhh/YzMaDdERZF8bdyc+m3Nq+E2sp1crI8PRmckFEIw3ya9lFpnsfCpL5TWBCwxGPUG5C/W2HxPo61VT0hd6UjmmHPfU5ipor9xtbSFPPQE/Gvobsl061OlMS3h7LAuwFf+SbO7Bz7ap9Jh2AAVajQNKyzR6/0br61+221uRSJd792bLBtRfmTS3DbZw/Q9v75gfCtmmZY5GJI9GP+KP7XJnPwpI3V+svL4sfP/W2J6EsTNzPO34Oz3qcIIC27K5V89KTCcmntl9NCBtx30doEtKD9MsaRIU9yfFlUoFdXX6/zcwloWgk2jOOVOsNBysKi8xtSfeoPCT7DfHBtrtvjf7U6B1Rbl+e4e1VdzcG7+65rozE1cNwh+33eeD1j4m+E+m1r9+HqH6UEEoNuSJIIX90H7c4ZrWq96PiGTi48hcWJnnZhTl/56BuRXgtHbZjZRzk8pQQPORGs59TIUMJP36N+/RpqXf8pM/iAJhHtKsNJbJYvHJPJ4bcBmH1ZE8OILys0FMVVSz+wRkwNgXGIKdmBFYd0hhrzhW8e9Hdt0ISfSIjGVyvKareshQdEzQ8p5jMO43RhKdFWRLFl2iSkgBKyD3xszKT7IZnxtNfy52hP3wsTBUyqIS5hwbr5oQAHaPbOmIFGpRmUGn/ZxPfARAWX2B9/by/1eZqv8ysrD6LTTM53MhtGnFjxm87N13z521ocsfgBEBzSum+mOKMCgoMYi8sHLWfOzXlRzOdx+vFD3k87CaS38FWLgp9HQfti6Z58y5YAhQaZnxRNl5iMJNfKTci/unWfgrLi1uF/8L+RWXGRblfFxY3E//zjTky2QPJ/uMcJEfLsEGzeGzjT4LLS18nxbOn9b5k8JU6J6Pet9aKDsBW0BuNr75e5cR5IXGFOqP6yT0kLr5QZjR7mZCGY6+m0oVNIDwD41CWFwdGvzVOm9AKLO/5Pki1JlsvgJACc5GVAD2Z0NO86b6FjPuLmpzOzYU8sDDgWGlBNONoHTT39E5MD2EK+Cn6ABB9+492bZBuB+dIO7OUSaxGjy5nqzh+zUJILJ9ml9joXWPHl9y1bg2hixlEGGlTYMN2tuTuw78SK0wrvPpre0SroRji/hHYtndaxJsdqkfgbx467d/IrmkOpWX2vq5rR/o73lS6xa7dTfsyYhN7oNUrTyFk3CF3TkHhlCTHyllXfl4dR8ULlviJ3UAjh1fKKP/dn/+9V/feq/ndX9YmbnWntwUy9r4ckbnm958Rop2X7pUKmpJuWRhHr/ICO+R+jOUz8fqdQflFa9SO9wbXJjYKQuv59zKJnuC0bXXzt02c+E/mKuOraDrz/97GSDGbzLSkGU3hr0CBBf8pMl12o8e9jLLILMW95IbBN4fEmL7vROKJO9lP6vxld8xRSvthPoCs4c6M4kyAjRgsP+/cxM6bwwWfuVZL8RnTVj7SxEHrsT8e/j8YQfqNxlpViRM0g3W+R+nuM2VrU/3XsQtaPIFjQAmTPGM/qIjK9QyqkKHz+62i/5oumlUMILIcO/HEjw1UaxEdMjfqvo2ZMR2HE2j+VaCeqO1PElhZyySP838fu7bl+1bv+BrDq43p7q2BW1Kf09V/G6PnoSo8XgPNk+8xVol4jV145TvTcfx0lg15/D4bG7OEaeMind3S2W1HYkP59DNmNe4vuVeZHgiteW0CFS52cxjN7t39r04gxqLtCip4+DC/0ZAdvfuo0r8kyeX9HgTjkbv3QDMzuJyWivymrrSujkqzkikw1tzB2CQ2PdGdutOJuLZv0V37jS2KJbjTreVMLdwdO3kpCoA0vgOspTiATILQeGQB8GuP7CdujlD+tEZ2EZc+/tjCF5Eqs5/z1Qt1hwmmnL9wEDhvodOxPdmxR855GLkNfUD29PRDhzYTmEkR5yQBHD/ODEh9L3IM+BS5znKN0qxcUuE8I2hO+579v5XkDHhN+trqZbq7IRYDL7O2JYS5+Wo7FTadO2M8fCt6BXHIj5x+5tMVmhYjaH946WBv+ZcZQiNf15djTGOQQGokd/HgtuvQAUdBTBF5mPoUSIPRLatUiwIMYiMXRerWHrQ1BxHwyhZIYTjYRx7iHfdzWwiBhOlu6h3l5JyxDTl++7BthcKt0zIuWbF3aJ0+TUy6QeSEM+nfPvA7lhiX38EtOPVfhEcvvnf39st8u3dT3MJ97rsAjv6hxzHv4qXPdsFXwumG22/Hwb/xapsO5zk3M7Dtg+NO2Ox7/jT9Lc5rCvodf74DnzjJr+Aiaf1/OAkH2kXRfYIKWO8psESKuvi94wCMwvrD2x3ith5dZqoQcC5Uc9CT3Wb/2Z6eNF5V5qqQcy40i5Pz3V/l/4auI0LjgsVSFcKyIM56rUf1hzmCZ5WujOza2huLtk0MMFFvcT5M/I3+rCn6sir2rnPFf6Qv3mghlg4up55yy8ta1+DOjgNPb0MC1aFnxBb+UKicfOxM4Yf0N53gSWuOWuLXsh0ck/wnTQrR58BMkWhv4mTHvKfEJEu6tVSdoFeJR9dP5ii5Ct7gzDUURBOVK1qO2286I+sWJDruInAffzA+GA3YAhgaGNykItcErxuwd0g7RSJ1bzzWn1L65Ff9mB6OHG2oRJcnbnLCmIfQS+5k14H++A7fs8b8sBf+qRegO/9jOAqH6BB81OsInuVi0cQzRT2HR21WUo0xgPDvDmGss4UI6WQ/OCygdWyjkbT5oREMwP5TLGfOOtbxsvr/Kegi/Mv9L3rNxJf2khchpK9FnovL3cz7OXGUN79zBPuVTdwyYOvmdwNeqCuyYGv79HhJ2Idl+UNKmgmc7q+JzQf5KiTALTu17g8YGZsEDx2yuUDmipcHxIx79snonAt5cY4/GLzMO+CZbEA/t77PHMufnVoZJR3RQoGEBw7f/qqX+TlEXFNF6AwAEypm6EjGkkwlpuizETauegQMh0g69cmHRLuEWFCb01fn/uOabNNa4NHfybR8ub04kPLFmI8eBR2WcjPb4Bw0/NMQpaeaEiGDwImEJdX3LhXZUx1jp15n8xzb6fqPip8vKb/mEOjDYD/GaNXamuB6Tf+4BlPNb1gM8c9DsYrRyTR8lkLvehcQH2Z/ujoDkTbV2RVlLytoSzI3VDuLrF1JfMl315r3QhcLv1D8VmrR3994YGCu1WVVbp5sLZLEAivrG8gmbY4AA+QGlUd2psMdACHr6rePdm4IZ8iUP+fiTY/V+NMDBh9XKyDArkidlz5L/IeK9MHZc8mFWDNDYGThfFFLHMKtILqQuXNKsKAjpPiU1f0p5tvl4dOIKscVbl7DuZobNz5abLafg1H6vhdzzlw+hUawfJNcJktaiSDd3kj/jGgYmzaq9mWnzK5l2235weghgEndPgp9JUMdJ6Qghn72T2nvS0CUbMiv3pPyexLZGd8LUwa+m3HJ1hsKRox8z1HNhODyTwnXA06VJNmK6J+k5RFUsL9yTsIunRaWF+hTlfVcvZ7RvrHBozmD/hNswentDVvhERItuYHTlMlzn4OTlH5wCU10a6IGbtQMqLk4Yfs+giOMtLD97c4FJ8bIKGH26b074ECTrX1wMJrn3JB9M2p3QKs/+1mqBTlLd084q2sh4Ge9pxD3N8IhEDS7uA1Gd5+ho4bcSKuxOapdV/TctqpC6PU97I6fhngaT7g29PdBnvdYCKJ0CJ7tS39cQYc9zm6kqV87pqCKqFggJd1O7eex+EFkjssY5kdrvesBZc3BGRwpnOvKp0/yQy4qVM3qc8OqJdnMNMG06oaRGZU2yomRe9ECzn2naPe0DptFy+RaVTL83rJKfaYrcket0Nve08Z6Gu+OyFpqduGdkS2hGf7w6jWn5uFmyDqZ1amwEqiyQ21Tdk4LVlL6HPYIU3KOwzzGTUf3wvSuMaRznBEXWQO6uOim+ihvY/7PUOPm4vwAVVz+Uz2qBlpp6LJisudbQiw8E0pkPPEkhzE897ESO9kcAj/6fHQ3Pn0wi9Ip+SqnaTUIv/AfBKQ5ofbr9j0XkT9YdI6jSwhOx/X4Xg0DYuZLi8qCTRF7Q7lWjYBHfjwD1QKn0Hx3U+4TR5T6rqTpgsPldpi4umcbw0czwdjw5Xh7IFurD26WN3cmQ25d0v5DN0zGdWd9TLsHvCCi5qjVGXWuBjwonUbuOQx2DNUo3k9PDx8rNDuXkIh4LEvDhjZmGzuX1SD/at0qxBOXGmOZneqV334XS8z8yn0BrMMGK9mgdgLxNMOHEoNnR5o3UvXqqAWuqX0PLelGOnDjJtGK9OlYKIpE+sVjkQq6wVtu5N4cCuoK/pNgCMfZwehV3XCwIFuePa2Qz/jXEl4ogcvW4kJiFPqQmm5pwJBfIISkKs8TDVbrhR6/l+NjVjbUgu7x5623JSrBDWiT5u2wrFpYzK4G1lA9MrGXngMB6nPtGg3fp3IYaBqRD3ructT0J4x25DN0lQK7EIhu+LCi8cdnqfl3lK+O1W3iwyE1Y1MIC8z6DB+O7Hi37XK0FyglKgyyxTG4WjNGrPQH3XD6JIwjVQTQxYKiUbvWrupVM/0k8440o5tpqT+zUiDNeVhKNZaCeGNgjkmTloF2EAuELt6CO4hU01b5nYRo71Qf8+2rHYCo2tzhK3daK9AL0dykpK3BKExlb0b47bRUc2Pxe5uuYyOLRlIj9wWxgQ+WXmutuzbqVas89bo31Tdtfq7947QUoSGMerMOZQFdgUpt6vQ/yW+s+SPguBe9e+2NG1zFB//ZpAdpc2uy4W6wDzk4DeSC2BLlkUSDaON/i72QStIcEmsU0JdR96x5BogGDPdWFL093MbCqqrciGL4VZLhk3fgUsgS/7/S93RT/9JE1IEBQ3FJ02OquJePoP6tDLim4tGkaP6lR/QvL/ikDSWLMmHyIDO3Pg+IlQsweHnmDwHqGvsziXTtwjOo8DjaMGdxzIofZIKO0coA++Hrdf7iWs3x7J4Ql7ZmGWW0ZfqrsIx/smqNbwRIzCqVZz7iG8/Uvh9ZTSLgCxk6UPYiBmiV79cBdZUAq4E2yf3mmgAqeFb9ZrLtferAx6TnE0phkOZPCjWJs74lXfiy8BaCqP+SaGVLAZu5k3av9H2szs50ArEV+ez/9m10vSKmMbanhSopngfn6L172K+YpR1VFbadHpV0M7ISCig58ndAeA2zcgn/ovfX1Tr0qnSKTL8H3n/Db/4QCwG0Z7OEzVBDEP/Zok8tX0NLvfp/zYYVMrv+uzfpmU8Kz4RlGsDzNwjzijx3bMIGnqsBLGB6ydRSTtxkb+6l4/qoIUq8bGwcYYqQINTf6zUtKMEavOzsbT8X3GKf7vVzsaPvOLttD8aPXG6T6vNwQb4I4rVr/5Y3LCwSOa1GEN2GUOu3HepX7l0OMiEd6afQtXklejFkLnzwShOC/n/P/+HOAT3UTLtzWzfJFNvrEOEAIdQwElwBfuxoU7wGwDcWbokXMZhPxS/BB2CJvHZ83RvwucUEWITx+79qW7HcCf7lhnb60EOHogapyB3H6yTCK7bjI1xv2+xIfRdeJzkwPNczJ2ILtsER9Xxq/mX4L5BOemRpq7PNld5nFKvNnMMsqy9L7Ej4yLNXYDmd1dMgoajyYdpdnVHae0cqw6ddyGq+hB15RiQHEt6TUSvwiWn6lX89uPBBePsD3xVB0P8J9Mf6FtMylXy7qvdc5wRMLYl6C+9IWW09JTs4rOXn/vJwKmVgo6vtCTJcusSxU4rjxRpPxf46BMdV8EQrHEiWei3jJnPTn4vNF74ufRTAdW5QYIYJf2rTGYEGCDMPHfbFrOPO0+f/+iv83fEXkQPURmQW43Uu7dwT7Skv2BgwrDg3Xlpacnedqs4HsZL945GE3UXDAmW9Wtr0k/NbRB50grJJMWfiThgiIlHmaJe10Ulxsaj0SnqHhq37BuLfHXWEPduPk53/S5R6rDPjr7FIhWfH/lCtdoSfTgOUoFOLOTTbRwhsumRN+uUrEef4Z66pw2G76MWIE5MCWdgnRW3EzHjEo+wxkxzXwetOK1f9+fNA/3kT0errEhDPpvPM3KtMp7F/N0dfOWvbtFP5Zdu4bH8coVK/Wr0WbJ1/13Ak5taZjiI8OJScQ7/MPMF4LBPz6j4zdzxA6yfnB96RXBCeirNV2QZKT7q0z/Rv8Jp8PyoIjiAVbunACGzeSsnpkW1av+w0H3HlZbSOZoTWE3fsvP6bx2tfqV/yNtAOg1eoF4dO34hnQFU0NhrSNyj6klp2DL0thbfnktoI18yb4K7Ncs0JJphVFYxFQA/6fpCCP4iV1JnaQfnnMyPDy/1txPmMPtHvqj8x2wbwSVVVXbdl0FmzLqFEdNZvbroP42wIoT4wD3T9W9ERQrH8GPJ8qinwnnxpDYqqi70WSYHrcBqIaiFCyDGFS5v9hAwLfHTJErlh40mPfc1fMCju60bV/pG3bksjugRUTyJVrLqpviFAfmoW5PJBG7Cf4r1Psnm73uglirE/FMPV9Xxa0uHCHpie/haD0E/cHSUBIQrNZsjNi9V1exjQiJv5xYIzUzNMSONHXLtZVn/CA/SnhASARr5fhj6p6/iuN4/V2yc9jcUL8fi2gJJ3z4zO9S3FfnxiQlHk5wkwuBIvt+18sEzXiP1jhcDHWigpa1u07lBf+H2aiJq00/tVOrOOSAhjUIBMy0akUcjpGAHjl4/EgQIzeU8xYAO1NmQWYDp/YoVCt2Ib1qkU0bOcqp7y7LqD2egQR3u8LmBOZIoU6ly9psLCzJ7px78On6tew7yyuXvHljtiSppttV64ay4ueD0s2x9FB+xp4iX+KaD6UqpNJUaDTSZzVrEvDsnU9Cewg+w6OTk1bOszPIDXGRQp6XdfLCwL0OAAWpSjqsvbPZ1uPJx7Dh2HYD0zQNQl2Toi2P6Hnco0uH8isA7oAGdr8oqoZHYom7iLmE28DquqXII68l27K27r2SgJ37tCuE5EtWpBg1ptl0V5vF+B4/ik3kedXOs9JWQHCDRK0S2D79b/8lTLuKf17RTQac/yRVoP57tfro2CMtnIcNW2gCRTfdC93O3PwISw6EeC9VEVzhknSdd1uN8ytWPBREEWk61QVhsBTtG07SmOAk2THQibLs/FW+kFJKVSDVLqFoi24avvCfxzfUnRu3VqZTNzyaIpo7YwhXiJjBGBJyvrqf1cGOe8iPVa6TE+EIpcsIeVo521CnMZSiL6Gebp8SzI7LvNX+C89D+0J/ihg0QQxbagPH4cNqId9eWi5lCyByVhUVQVIN4Ea6kn3eYHTj+0g8hbkPNhykKmEbO7BUFoGoX0HV/qASJk9l/J3pbzk4BBw6UWfDg9xBXrJs+XV6xzWj8n5hX5dUa3eTvaUiNgjBcbzgaFJUhWRQdKMGDAcEs2p9E29RXP/lg9yAYycvnqXoS4FlULVtuX3JBRjlwtSidgLNUMQRjq82HysmuYv0H+JyfhCrVEWfq2OGfqZK787Tu2lb/5DaLIGJ7q4niQNcH4VhcCHEFM0NlFSu96TRDXgx0HpjfQoimjXtEh/fyWGgHjy4Pt+k7Zb8M7d+tphixRZovKy4T3m/4p7I5YdDXq8zrtejL2Rqk6uGKb846Mz2fVzQFElzh7vFcaY86ihS3u+dpm1yuCcpMwQ+JQ9+fj0dYDikJCKgt5/ZKDCb9RVoXfbtF+074K+EBbA8VRvIkqEytMVHnGaXptJJd2vVZ7GObi1IwWqtX17YuxK54Xmn49/ItvWB+AUtwHy8f1EOlPuk5c286pyJIKjYuvnZN3P0pXSX4h/qGU3+e9QhWkKouNgJTI1VUo0j2rHdxdWRLvO9+SJ1Lqz0R1kk4cUGTtuNS8l8IRbyjnG2w0Yhd36D9c438P+6xb21LynMjKLduQaYFp0GCNt4kIlv7oJpSn19FXYb4pALlGsteK09cttCvQwD7ILpzocE3CA+e9JIiX/HnX1UuIkmabst3F/jyDbVtmisJiSKS7hxdUm+mJXmUnVizgmedqJwi/AvVRquWtB0D2Tx0Rx/7Pvqw90As/z8q7rBJhqtOI3BN3IhEHXEgEvcUrrjL7jy8SnScPKlm/hRI12iQea4Hsqf2QB0yex6qXU06O3zT2XHpWB1uzUmZB2kFBcnpumn7vRPiqZ681RyblbugDRTs7zjMx917XmTOz3/9DZH7MCH6VIHo72JKEhaN/3cDARF42TGlLIr2xx6acHnIrhM023JOCBiSAg9AReyaluMVsgiGHSk6KqHfCZreYiIoduGG+xrdFL3WXDI1XnI7C5wxW3Vgh4hvvJ8RZdAUeuvxhWUXkIl36avPBNL/C/8sb8iD4dd1BC+Id9MLLKs9RRoLSflYvYROhT8OXWKmgvmcjastVdfGFUXdeXo9ZuO67R+011/H3SQ62CZGCDSwgcfaOMktQ9+UGQcw2rzZW0y8aRPnBX8zfP5Did8hcfXeuIPUf+k72aWzcnUYanULEMS8qyKwwfCOyWctHcT4B69H2zEcJq0AujpcoMnVyaGqVIceQ/RUIOSvSYi295BsTRBfJ3vGXDLY0lIcdBWqjbaKMp1OFVZMYnQY6nNiKCHGc/LmoboDJZnQ4gfPV6ykVVGATynnDi5r1BcaWKGaS0XXx2yemrZtIwa4SYmtJ8whEhnqMBcACOK5IkcQSZ7ZF0/44QZVG0dsu1WDF02kiMxktzMcQxV599Mofh75J9zdl6hD2z5r3N598vfURP68UNO+jsgnkdmHnLyZEcjKzeoieYLzwYpcLycPdAkZtKPjbVPSZ0WtEKOPrJ4P3IG2rAt0hHbzF2S0EDAcVRSYMnri5vaA3ml+3mOcY30vv6eEryX72pZFlsCLyfxiGFjcH+lnp+wqUArjr3rJ0nhHc8dxlNT/Qb5Dp0cgERPRUZ+zrVytyMPJMo09ZE3Tfg5WJQpOgyawSaeByEfLKwRE6Kikxo2ffEfV3hTAM+AehoMMwO/Q/2Q59KIvVw2bnEzR/vgXJEa4oGB+IQDa+gPfva3+a45cso/uVF8rkJxFU/of08ZRi+7+ZKvZVfn711PNasJWYzXxk7k2f9m9vbVihEH6V4FdjNU6qSXg1A/eGZy+a7akaHZQxfGpBbPbKjvUD3OC5QrFZMOQzQCQMHd4z2iOOO/MmUAEIxvKrp1jvrqdhpwuYhewC3leW6j+/CtY9bo2IP7onxZNi1cry0xBaRvCX1815XhmBHd68IUGO8yJgdu0cLxF42NNTKw8fvB/jLP4WMaTNzjZZXlcDUQGUZxnx/VvKAJ3WGM73t/7r0nIYP2NMe0iTamWh/RnEmayQoKMNQIfTx0LAgPFIv7ITfj+ylyJiA56l3GuTxVE+nt6v+7DiWV38S0MpNASu2cgZkswEjd6u4hHUA60C9bw5HQTZ0ZpeV3Qp4G27FmVKJmabL2qavqBOTJhputXKcvL6rjO7aZWu7rEB56iFWMIc/bYI6IWYpU0sOdgYBZxirgzoIgMxDY1wMH9PIra1qNZJye8fMNiL14HrVFPpUKkqeIz716xOrjWg1zEDSKm2u5qpniDsiGyfqickSRnw+T1BAUmoZWv+GMBUkYlWOHofSN7oWJ9CWO4S4+Z9lmpACai3ySHuTTLIiSBB005gtk6fkUPIk5xD0fF3QelPKTt16ymiNKPdaaFCZYM42yk8yKQaef5SkVPSWPjC0cWqjqnP1PSJbegreQbv2KWCCoKhlXvOnwPxNftUO/6BgC6bWpvEevXWEHHm6z5HhXMm4+ubAPq/yzb0/21+nFjLJH2SA3pJo35Rcfj7lx3kNFzUSxFdJPtbBBE8Kx4dGX39yAZwldI2nbUWqv5Y1jKGu1R8g36MLECQMgtUp+u/mVT/6v9Qs3HbdkQt3GK520SFxWVbX7mm+s6GI/QNnZFLRvd+XFIit6DGVIFsrcuKV7ze/wmedhm98GI5OmGIkzkI/HSfOE13n+cmGhuGu+wTx0+P6j1XkCNIAA0cH9wIAmfC6735Q0ym8F7NBUxRGItiTaaCY67valgF0zmWZbd0hkxy+4P3g+kIJRdY4Y03y7P6HfDKYwfvBiBj2O3eDlinw647MdgiUnhsbh39ZgjifHulopkkc7vvRa5A1RxyqP8ynvQh03fqBDsrtPfCE1pXnBBV7ABA4GZNzzdYb9JDOq/V9+rrx0uruCimN0QC+0bbSx0CTwB9UpGFE/F4QONcG8ff2hren9ZPzRjujh/oKaa3BB0J3+akaxjgPKOoLfc2yLjHkHzbgt0Dfkc1MHirBt8gV9lvseeTpz/P61+2Kz9eiJXdhE0sJ96QjJ5vGjgsqyGope7Hv06Q3a/TduBJkgjAeL6/LFV31FewGFb5c+PYGQfJRqXEVng1QOtpV4qmuEq9TXhRDEvP4S4Wyb/Gb4P2UgdCch1PLE4Ceu/pX9sHfK2a7qQcEvRYSBMPIzWIAQ8xfENk/5KPDfjHnhI5BA8AOGXHrzt0/0UZxa660sXV0SP5BC54mnuZ6Zo+TCDCMQ1X+i5dvwkC11AHVR90Qlr2wkIVZB/+q32+5r/SpPey9TBnwG09mBL0X5JoVIQSPPLRKAdrEG/zs4+zTEweJI37Rdb/GMEkLG3jUkSRKPCELXmEBCc6al8f5EKCzPOawiCvdUZeogxKgDwifQ//B5y8X7a/rAoUseJklr6dtRhBetPmYw4DU2kL02yJr4CeCwp3kAXnkesOJLU5oysCKpM3C4uvhZRUkem5hNL0/ic5wBgEW3125Yv6gMtVHwaXAyO9nogTyvQjD7xa7mMcxA7VWEMT0inzMtw5Du/cNJ4DuktG/HzgoyV9nCA2YSPkB0BBwSqisIEz7/OhX89iSwQkVJJLT9IyiK1MuUvbt4XcB2A4q/m3arRZ/6siiCx2qMMruzcrEn2KngDT3pZZbQLdpuiMjo5vxkuakjbXe7K7BP/VPHS3+BK4vVNCeaglZykuaUc30tMASUfBbjgMOhovaNPrmLjjalDlL4zxDdtM0wduUjG0qAOyWfANgiLSCU4dJWvKBs6EdC/wratzT8BlF0SSnKYL8CRjgjeffKnj3qfKAYeDdgiX2qlvJP90m7b/yQ8Ixuq56k30F9J8gMEDOzE1Du+LTjbcXsqBzzvqtzQpbJXF+573FM2mJWl9aNqAXfmz7p19XE0xnjBKxNJusAa8J3Sv6AnlpsROeRGc/+Nf89ttd7OFtorenJu0CDAUoi7k/m7TqGsLCZG+TK1/IG+Ft4U6Y09ggCdzi9JUFCnnisDH/VaXm+YR05g1EMjOGYRA4PabunNsKiUdZsa8wTdNoIiAENji/gm3Iv+/eLEXEdSE4PkQcJ0TRFKAIH2+QHknTTv+JvhYb69fW0q0EDv/stI/5Ctt/Jp0PovlsjAEHwgd3CvdySOIj5V3VvCRIlCXBghg5tuGeW5Ssw04fiCSriYiGv+HDgWRuq4xpdjVnvFnHm/c3FwWcs0enWRwiip+mprHaOOgkaeyYqFVEPrwFhPx/LjviJwGLkImLOInq8peMGUlFRCUzKoojQqIfgj75SX1su4zzy7IkHBjRITydbYcjVLRb/Jm6amCOd3xeupoorcKNn4Z55wTyiZNYb5CmyiRD6OIcNH5evM+HOG9ZlUTmiwC0tjt9GdDppB7NFeXDrdExKmlqS6/aGy+ReRZFHj51qL/nSgaqiTAuWlVvk3q73Fax7wFDpLd3Hj8rILnGy7hNCfi9LjfuYvQ64YWIJM9z2qD3SWBf41ZNdiVY0e8027vb4ANCavBLIxr23XhWqfnk7IB0qKQG3I7H1nbQRJe1ryQFpEem4h6NUYFT6TqlwVz9cl2oRb4EKpCp+XP24/o8C97fxyYEJU9bCOEKNrNG6TS83IRUzSmhNKodX27jmqYxbVl0C2cZuMQjpPHai+pcmQBcecvzfCLR6Zx2ADfgm7mHMhQTCNxWdPodDcG/3w6PJ5G1DLVQSbH6kR3Sbd267ErpoMuyKGcwXqdUvm/NKknmHoU8d8V/JGAQytagzWvVECNOQwpHVt8PoMPgjU9cWKj3xPuQWFrv4821vJqmNIRszje/ROobu9m7o8LTwrJ1+ZoFnViGigLUnkW7YkTrk6dW5sWS4pVp5HHqUTP27YGVIc8Ld2d5msrfLLvgX6xgtg6YqHjjQro31e4lfqtQppDsWwjPuMiYJ9lxPZH9WLX+vz3rB+g7iIB3nJwh3p6fqfWZ3/MROPA3rzKsq5Q+bGennlsaofccXjACQewsNbBHllZAAdhRTL7IcpF/ru90va0DIXzVN+CWBwNC6ENy2SAQdWDFxHYk247jQEZgJH1qLBCCBuJvCM4kV9JQg3LlaDkYgBTlnc/Z30DroxOfVxC4tp1NKzo7PjWrRDLvgG7Vpzve0v/+pXPmiUfjV3s5QCDjgeEBKdidEJWGy5cHtY/fZtG59GzW4gYUaAyRD1qvdlXU1ac+1p5AT/T3jcda67uL39cmfZyi1/KOV9GzxJ0erC53Op4KsxqQ/40QpiH4XnXEkCPftk1TJGZBktbyOiWn4SRSpfcNRvM+QMqx8QsKkvEajdvWjz3Kd8/2T5naiw2e7eQHtZvUG/VN182nCOzY+n7R49OLfFu4b66ArKumfnN7XGTmjfXH6gmIWeh5DpQAM4hPUYoo3vd928Ex8vvt6koGjzlIhn/YoT2ZLN8CvIQPzAOapzu4VRhtz39Utm0Idz8HGBDPku2rFscTtYYaAMZVT+6/lF5VlUS9+ldojpMXqKGiloirahFQ2XJrgaulfpYa3E90hU3ZDd9UAkD/zgXNdLI1DTFPAAiqc2nNjXEr4fJ0H/NtM9EtI9JRBDfTPO7eHw+unvAlTSNTFCM97bS/6LYHWZzc+/WUUO56mU2jW+oxM6HqkkC/Qlw73fYVdymT2CvK3drCpbIvw3z6Dt5KeBrqH59Nb4RAWa5IfoXF/EkkNpPkI/JO6s1mgJ5GfvN0qtXB3221BioTVRwXNf1Ofpi3688kwqTojnz8r6aoRB3HpDW2uZvvuJAHkIpHN+acO6j43nyLduGc0dLJxjFM4pEfBoHGqyX1Yr6+3aFqijT0BCcJgdZ5hWsgtoqcn0ojk4+7BSNXkazFz+Ewv3vg2J+ApOxZpFihldU41lSlrQF/fzXrjXsHp40tzrg89oqtLD0UVk9Yrp2GxvNaZeG1C+KuWHPSf5dp7AK/KAQ4+8ObgsTpHcaJNi7Od4BeIkVGK1TT2Kf4Ct/HjQk+jvr5ICDekpkgV6ztcj0Egs42JE5y+ylJ8AIJ13yjuAShz8Wf+vmU6vhf2XuPpbeVoEvwaXoPb5YESHhP+B0s4b1/+kHh02+mZ3YdHdGLVlxdScRHEqjKyjwnLQSHW30TmwuitPMYYy0W1+7EMOjL2AyEJJorqooGQdJMHfhxLS+bovZXPFBvYx+oc/zPKrSgIZ8qtCu/dd6rf/ax+pAXDDcz9CaP7semG5gdyq3xKtS/qZuXdVnLOX2pLRppRoqT5A17gXVywGgfpt6jUVnZVdDEv7bQTF/OjRJiOqxeDrpBapJgQpE0gMcTMADAEQypojJ4zY/+SEEzCzQBrnLQdnWtn2V0kzzdQftfbg3o/qu7ZdKbEobWuuWH5CAy8bYWXzizwyz7rJn9/dKTNNMFh4YP59V9iJaXhQQi8DRUDSci/Fk/OJc7h0z2DwMA9NGGZY/owpmouhILx3qVJDPir4/ZAaVbVdOq0TThdZykyEoF8hm59UQQBD//pr4aXk+9wvtMl3YRwMGgoC8U9csK2bDmdbhrrLI8aMXGLa2aPtnVX8B3+iT70fywXbhLxUxOa5mCy2RHXbbWkPRsH2FxAL1yAS43CsT7fZvQiZwIvr6Zw1V3nX98y8Pfao3qwLlvJNjT0MkAoB3SuQ8+/I//8CwMOQkeVeB5uvenNugCMjKr08HNjAAMkwHv4TlG/vkJVDt09lYrXuEg9Wt8cwRWV02G92wWL91F92fe6VGR/0htAkiGJuhJKRxcN75/Id+36xM1UULE3LuKLb8poLEbe+CG7sefJ/aHfieg9mRSEFAI8kk2nU3LIJN348NnczS+LtvC/7fS83+pphPrmBqtnO4E3CKucglktVORjf4qOq0lj6GuI4VpL8jVZ5YFMhrA2uCaBgs0rrE5ISxR1+6fH213naAjKPs9S/ymofR5bfmyElC6A3vI2EB0CehplCDNEOlJJzKjnyTPw68Xf0OpwGtjw61QHjPQrmTdEOijGtLXtjFmvV7HCzAyWd92SmwCWm6FDp0dtzMjcCrdG9/eOhH8CPXPlkMPmd5F4zvNcfSBKEJNESiixwlPbp2Ba5+KPMGm0JO/tdxNIFxzoeqIBewqJ3N43HdkARzFlfgqzSJDLknEHkaayYUQzWgoDGs2WcuOofR3OPM9beSutKwM15PYk7bKc2hVn+VEo7/FntaJBNqM2UQoPMff2vLzTsE+/f7ztTG6mo9aTGEWFc/1jXEBf9mWZ6peuXZmxsmChG+v/EFII9A0rnUrkafqZ46LK8vmdWmLFKHWtuOK3Nn/WDfDwrR4XJe3rRndCgE9Jg62l7m8S1QBmD2TP/Y5+UpMdTDap2lA39GB4GhMt6Wnd4U99vdONdVlLiiOs0lt9VIezv2ZJSM2cEfW/ciH7yWP46bVAX9MKBmkj4A+llxCr94LoKqXcikpQ/IzbmJNMfNPw1TpXDVwC8Xx7gRVdTJhU+Po7zxxBIxOR2EApZp31dqiVHdgq4INbdOtzEBJwnnO/vAkzWbgLhhFJSQVhgh6aWAgfD+/WfCyti97szNj/u0UjQpkt9L0EOLc2Vd4ksd+0rEIkos3jZf4LSCDRDzibt2u87x87gXL/nZ+rsB9b4lxEA4ULE2nGCC6jQxZc+0B7QaxwiTgJEHEBuptOt/3k1wBKTyrFIUXa307+odzRucVWWZ/fr22IHHRxbVJr3IBU5QqLK+n56GsjQddUqeHIOHgQfNBwUU8Vzdm2+QsJD+fK92Vqavjy5RyrE2+5BFQlDYDgX7G2PLYDDTc1WjrbNh4uTXfLEAEADgLIhso8W9xG89vC9ktviB1Imrj/6rrNmJwDIP/qdobPC6e/s+vou6aqwnQNlRZ4p52ztEo46r4/l62w3vnN8ry+IdEnKeLVE/4CGGxdUHDjuORzszdioPU8dy8AbmvTysR4sJ/usiyyB+/AGg2QOJf2x4MOeF918YJm10W9eX9YvDwN9Znq7GhLWUW6yDADZx0bTkI2dg67x6Ahqd/62OfdxvX9FqbdbYJXDewAVghH1cLYRPzieJPo2ewkrl0hGTaxuj4rZZiNW4jdwDDtb9uO/1bZ/IJtj4zTFuw7xtpwVUvefjHS0FLI2YuBIMQ3+IHj8pK7iig/dY0j3CuPmibnTRoU8eBE+M80NDxElf5tg9tVPnA0J27CSlfY3n6YevMO2uSSI2nff9t8GfPjSbeav4F1oait7yQ9uWKURSVNzsX311fH/vcu4kpg2gO+GKyhDMPnc3FJhSevuHK5vwpCOwG9gOIRjY9IDz2IOQSQcfj5nwP5D5H6Xg/GMALJIUdVGOAG8Jjn6oc6jSZLrnpES+RoWXb8BPrfkanylbzOddOfEZhrItGj+2aUCECvNumhk/Ksai0J54h4FHHIenClBJU4+wHAm5CCkPVtmWMaHG8byd8ySJgdu4FFvZ53v8BwFKAeTcDvufkXulxJ1CmsFQDAtmWvwYX+/MZtUTACgL8W/gH8Pmn3iXcBfffWYHwLRsAxwCWZPvQ7NPVhscQ/VXQATZ243DqpX0hmlpinCh4v59Jzbv7DW2xdUZJJm787b/6HwGpL6Fp2dGWw8+3sSQ0GtXvzn0Er/q9oH9vKb1x4HH3KB1hM9Uu4qQHVf1tRUPIle8V972/hir7dfYDud4f2n+JMnJeYnwCkWtesqprBssweZmSaCkI5Y0Pl8J73egO3TLwiO79Z5Odyy1XzE1YgHirl3iBxS32n01d4JBN07bBymnS8+uNqFTwKfMyofAGhC3tUnYaPUbb87LgvwOk86MpkvPTSYcmHCqUdaKx0Uvx6If8dJGf3zp99oH5E+iffYEpQ6OHnsxv16VOoHyUBhIg14nHkPuxZb0R5ES5yy+vsjwtCoLPsSUlvxWZy48u+mIx3HxaBT+oNYduaAHo3xkq7PW0MsivJUbJ+CrbS+XIGzPnwvWGYn9ktlXn+bhPdNocjKJEctKw/aRWX0nehY9Pu820CzqMAjwPOEi6jZaB8kLaepXRA7WGeFi2baozEQnxNFkN1dq6N+gQzlUahlkV/gRS7b2bNttTuffnxi1qTa6bYejUbOldiROnKb+q3i2Rym3ZFaozDXKaiadegEUzqB9qYxJHEC0FhVKVaOYl1/IzcoN9ex2eJEARAduG/iIRYvE1yfScPL1XRV0nBAWbjIMiKutDwyt5zDYKDvV0cQ3CeNl8k1SwSOoGLJuDxq0cuyVKqyFoK8zkOsS5C957HoGH9q2LhLIs8youvpHZFKm/GwSL8vausvmUknj11DNjV5Ihcx60q/1hOqy66WbTprY0JsOFYiG8X9r7X2naDU+fQhhNMFSjWUaffuL4OnFSJz76iN+4YeEajG0hMdcSE8uKyfRTfs7NC1jmfV439mrUMvz29TS4fJiw5Xsy91fmBfKgDrUlu58uHftDeTlmGumv2+D8vUVKhl69pp/kvzA99uqkTDV8XCq/iley1VYdJ6BOqFgqQw+xN4QPIdpVkcSjtJu/J4fO0lkQBGDaZIBI6ECWCB+Es/ZvLQuZjUGfS+sKFEZDnM02LEZB/o6ebGku9RMEZoE9aBuIZgfJ+VWd/Yue5HGg2pjmA9eoPaAK9VQB8deyArquW2boCjbV/qWZcPMvJ4+s2JSSjLltyKnp65DW3CBsVDGEKJho9vq2VrP1N5oAoQ8H+mFLFhatHcnlIXzw5tYBbU42+5NMOZCQFKZsV1d4DZTfZnwF6XWkYoy7qLq+BHInHJ7QRGQCSQe//PcuC6f5xHl+fho2xVQh5wz4NjFFBSJi18BVe4Oe8exXr9EUbvIE3yYSpINzWYzbaAizXp7H3BlYc7YW4BnfE2wb+xsCBVG3eGARFWLyciuRifaoREyiDebQ/WRZFbRDZjrU1z1veqYQT0/H9k+oC47y6P4KZFw9g59Fk933rb6xE+Xwfs1Y5FYsQ3vgkFz75lnrEutQo1XR7ti4cG+3o2EYYl/tWXCfN/vdFlPOjnIUrwHxC1JliA7I0lNf6BrXH9RMklkJMgII9DEH5ZCyxuMYqQHsbWR2Gq1fcELzgMSNncCfGSF+OsExGo7fZHH8nF65uOwcNWgK9BsG5hceeG8kBvqY86mDrA+sJBEmKRq8LPctDHOTJKvogsHNa8+QtD5DP1YBlBslcCoESvy9tjsEJGU1Kgb+jDdnNHTdyUzI6yUFaMsymbzqsrFyOvbXL8oJnScSRejl0L3G7O1ZDpIXABHSL3xGv5jlKIixl8qPAxkfKBiIyrzxTOKXp7ENhtNE3W/Vyl0EhJDUz+7IG67D2+n5wKtOvdKBKK8MTA58JhAAI4DjwwaNVswnj5M1ETcADuAtBwMPucsHO0vTqdc18C9sLntont6CbuKVgEO8vI//AyRMyR6rLeV4yLC9ce3ClVE6Mm1uGMNvEIQ0ti2G4xMow87NFhGqZxrsCoWzeRSfxHnKIiHlDrKGnaLkY0cW6DYrJIXnYs3W1EQ6tT+F0vQM7VubAaRVjOIKGDTeXbOQow+tvD+/RFHSdtvrpoNhmnnzigzpWe+XDYoA39iN0xfbJkKVBii7OtDyiZOtMqY7DPjwqN/YnBI278IgjMzKqOu8wLBzJiooqEwGLTne0HzzWeQ7bzgohGD8dyC8LTt8MIJbp/NaUVG4HkC76yGIOkKhQC7uTbiTuZp9WuZkCe+TamGujoJzvMYbapFlqocH8NNlUF8Rdhh4sKUfHQgyT5JscgnRaXI/iH6v0QfgIA5MnA5PMQnLjblw1/ebG1rmvw0c8lZBtrcuk0l+6llAgdnhjD2GBQLH3hPRo6rYfMNcnuwfEBZfqYW88vexzkhYTHV4uNqJGK0ubeoShXV7EPaGQv2c8W4iV6oIVSzrLbL54sDYD6ahfpbAhHyYy2/xviiznsZ+gz2/Kq75rSn7UYVr/6EBLB6D7yTmF+tbU573TW/0F1M6FSH5Tv+K7pcTZf5VapN9XmXd4dIrcTquCqnJQUNoX/Toe/SqxAsgMMmQ2s3wKFMWdMgFj63Lfi7zLpVfE2aJe6IfJucFfse0ljyuUqjU6DeYn5zi2WZks24BIW2Fg+MvqwL2/BK2CGTbMB3ZSqVORYO/JWM3jcqF9U4LrVCnUG21Y2aWuM2+By58zmFRHHCmLJgtfJkiJNRQuuy+UKgVePQYCggf/o3La6NvDHqrBYBv7Ren8TFDP1l7vU3R7gdf08Xn3l2cwlmxkvSioUCopegrG7RSmMGBYB5GVNVn+PB/sFSL0cOukHniw2+VwxWu4/sMYayF499m6H589x5I8fH6v4C0zq2MAR2tEif0Uby24OyieHe5IXjEtualQUcKut4UqvQ5dYLhZjGqTgPn/DJwOxYKwSGxXZknVC/y0BKwRTT9vCleFk5jA02FRCgYIfKV0Z8DKIfc0IrkvfKOr9jiRzlelvXMsp1ye1n436il+38iifzkrgYNcbgT8ufhfZAnoBFc2ajex85vNVhzNNeBIggFwyh0GxHnqFtJYfIDW8vlpk771TcgwDofdvRmyF/J2/jXguUfu8hDxZLYZ5eEgRUHjv4Wh/IrB3G4WAxzMFGQWGVo7pcZpSzD13ixFOFgL176Gn2Tfn/MdL8cCaSvg/1oyHzEaAGvStuUNDw1NYD8vZRoD9VG6broidcNU9T63Ly/DkplkPeP30kbdY+UP5+n/5J0Ahdz8E8fEw0uCKoDcn7NWlz59aaC8nDtVhN6+rzaweNxR3f/EqSQDnyELEDwx3sszayNHD6aHgcLwVVdF433Pjqgbem9Een1so7VZCuHPjJDswaKACQCR6CY/s65kCWbumrJ+NnQz9FuCOu9oKd84LES4bBNMKc/M7AMQFjpKf2bKQ1M0VjbEpmdNQITaWt2GwHCf4wvWFm3XVcJDEsczL1ik09AwcHmbmzYxs4/19sEgg9V10fU9g+Glpzhf+6z0hJBQpz6galcskYO/bwNgCQiM6QlMZWDf4hieB2hIza/BJNFcF+ZktxfHBqqKZCYyxbkhzNuqasmJ8KwIK8oM8cuLqtu1YB9jPq3uBnQ2e9jps861SmOe7lZnrzLLCz1cvuENvzxa6tGuYnmOZy/0huDRxndT2W+wX6CLq8PqUxdoSA+T3lUu6FPt2C7fTOrlqfIbcuBRvnn68PRDV6NV0a9ECr8Sk4tF25KAp3ypHw6q9+1LZqbBoZJHBLvAk4n2Bl4VQHbISlYT0W55XOAzPVQs3TVRM7c2adXMqdil4Ov8T5JSg8DjsCKXAAl1iyKL16tNnEYMet7zSQU5WuKjyuXt6xnTpVBCxLc3xqqVJYiGYjdNv5959EqNU/nLn6WeAecAto0P6ss1eykKtdg/17B44wsXVh+8feFjiVF8ZL84iUOz1AZCxpfCmqmorM3fSFdrtJnU/eBPxp11TOOeK4mgIQo5NMIfQeOB5NYiS6jBl94qDn7tSBPB5IKslAcUKYXb1AjNG86f/DltpIfYPSbVh86/TwHgMcUxBk+fbgCbafPtv3tbCB0brpIp59Myruzvw44qhLU38+9M6Yyie8ITNBm2GTJd8FXziJPtQ84Hxj6u75YDss+8y5zs+I/h7nhHa0NlwCQkpdoSVs11/YGQG0li0mYBC2HPGCduTAc6Dc3CqHJkaYP6wZBLVi/1RiepBwDVu79ut5y8ZrXBriAJoHKXp+zSjDTQEDWejH36E6kzgmOd9Y4wMyS6NXpx81LExLbHFjYU8uN1aqo3JVyfPQnUmhK3g/ytbIILILGqggtFmd3yFqu+G3vNzGTgd2he6yrfB8sIf2+TUaLnOfsSTvUW5RfvjeUfWndYSGYOIWhlaiuZNQBLSIEq6R+LJaiRrqtokBQO9kIP8Wz6B3KRsZHSLjG8gF3y0W61Cjx0nKEdehKqJrLRuhPqOevJ/PTpGrTDvp8Kz8/qto6V5WnUsB0hM9csVhOuZPxtl+T6CmfI344qtFj2p9DKCCfHExNiMi98U5O2apNYqAiBB8hn18chDHrTnVL8YYFfv6mR6y9FT/ufX0tRz4r1YwQ8asY7Ku8VQNou+aDI+ECcnpLH7B1ID54tMd0rohtBmt62rS2k9nPZ3b536RiZrSAMVapRYUmHoGxX4m3P+U2ZARb0H3Z0O5Q6AgEmm8wn4HfPpmeP/kUwMBpKyoEaKebpTDnok68U9K/0e/ZUqvGfNAxy5Jxags/X4sqJnz45w31Jy+MfO21f0n4TweQHEas+SY5s6rqzkMKjOYQMVNMON74KoJ0TsoleoxpsEQbVBDWfgNTGzHlDcZFc3VR1ogwqNJONFa+VV3Rb/1rx6gdiAsh+iR24DbHUY2mNzdmDCjliqtUAlkeE03DqAA3bzH/Xkl5iCXt8nMy7KjxWoUvINI47ilCZgvo3m03nkXJFa2tAVqmXu3Qa+f998tJcgk7HRHeWx2G4jIVgXyoR9j7CfF9/IEW9VVBmJw5zuvpTu3CaYHfnLQ944tjL92mh/h4lG6XZAxGhaYbXEbGUjfVgt2A9zmwH8BCldmkNoZA5ZRBrcRQ1fz3cRdPZwqfRUrlscMalhC+b5OmwwcQDl0N1n1jqrWeVqBF7EGe8tQgT/Jwt28fERp+0Aa23mKMUCbwXXv+vC8aPuQ55J6mPLrX1TrXAdv9k0ca4P1KuYMeG+mJANkONCElCxIEqzn7jRjb3/5m1FS3sTBYyupjTXn2WjRRFFXNprBbmNgsetrPOR1DySXF5beAulxBAdUrxqQhn5OwNlX07S3pmycQHdKkVMKXLLPX1ZlWLGjZL19u1BKrP1DuwbztoutcDmuY4oId9w3Mq3ZU5idNwNz2p41+tbvf+W2iOjQgriJUImB68lXMp9KKOxP4cQX2jNlGPfSJfQvIklIMRJ+XlRzsgOaCWevlIzDyWAFAkTjtsXtBCIvfAJzDzfGd4zcCICuOWtW0GgDZS2N5FcWRKwpgBcG79tsSbJ+G9WmLG+aIxUP/VftCkmQZcpC45frDlsiAlB8rdmR/ekf1yhoDOCWyC90FUOxT3yR5I/K9QyBDX8sf1DbC3oJR5JwMw5hVG62tR79FRLeTs7vTyrKPPOjFwG5T+Dq+Vvhhf4bMzbKwOQaL0tACnKniB97qMr+ZKIOdAG2w0rAUGT/GOrHAKhSjMx5Rv0laG4PznJf7hGSFuCG24ZPg8vbl6nb+SDv2VWcd+Fo1XqWNRJDf6SWjtbi0k1QKVH2EWT9Q1JoyVwS2pxrj5oQ980Y7J6GKi3LZaKNREk/ELTZUT+UMRS/zTwL+JHOvqM4uTccBiBLLcQNKVf919C9EZiGkXjiLPJX1Gc8Ao8gbN65nTqjVGSua/2zuYJ/+mijjMOJ7R164kOa9bOD6uL3qa7DGOoijnPrNM+9ke8oUB2oVgmaSymeoPLxDqHLdoPcowCe8bqnXRt+xLAz/JL8FX7IfO8RRS5NeT/b8fuYJ9ZUho/wGavkdqk84n11H00h7oB2UJMmU/+suwaBgywObOoj28j8fm7eBsR6JgNsD4A0M14JN4Ezj8NsiljUocO9vXj/QRjUtF/TpeO6zruZ7Q1oFjquZJjEhB8czUXP6CaZyiNE879uDHJgBLEKn8AAOWabPuln9GR3q/aqyKe3NNIT8FThkV4whOg7yEcfGh89MCUuErbifFmKyAES5JZ6qyjdG1eKFY+SwhGW7GZSxAbEfgNZn3XTIfCs8974sEYKhph6QyuPxkPZBzRFQEFd1TqUpZWnnxdO6+YRhJZHKnvgjBZLd7TQsgbv51qbcwW/F5wznzhz9U1eo4zLfriEcIfDbiL+nyN6ctgwN8encf5lgkYTnPTXuKyvg3+7Uy5jKawNNzKVrC5ZGh1APobjaAwWVGWqVQXrgG3+MeCC8fj0+uay5wutHIXFlxOv6K1ICf84CJrxO1EbJ00F71aMG+QLhIfAba4Y5vpq9qxmqOgQDbDKvrjV5HxOwOBTGYsi0i0vJ4aWV3jwYidmfQm1toNwg1YNO12UQVrNdMy+268gKqVI/8wu7YO91ih4BdAXv0NMg4aH7IvdXls0qNhrO5m5i+g0O9Q2J5QKWg1yJUqQpjsoi9Y21wyUlVrn6lpny/73nqK7574oL1nIn1HH+kTvTXlVPH7OxCfcPYbXMFL3Dly/YJMz5XqvrsICRywIt0w400twse//6cwL9VeJL03v7YCEYxzEptnhjVNSBnP1r+2jLBkEb6um3je3+TEdVuarbIO4Y6liE/QCPyuAyQ6X77cDcRcBMfGssvK4LMZThKi3xo4Azci/91ALS9+Mcz48wiHrGwwcYZTbBRT8Z9xyFfms4pPmUXFDrSlW1gKBvL4TX79gp54t1ijxGaS/M/H0QgmJ6laKEhpTX30zRBCpjhAEUbr6vzkqKTcbb0dpopE9bqEBeo3cbcRtHXAmbgn7rICCy/jCqgHnaOJR+MV2dnPDWyVg2qA1VEE3AM4cJ1DbVOhhiikTc3PoOaIGb9DUv/CuPe7/qRakNjvTMig0SVX4T8Rvzmy4j9WrvCn9iFrza0yeD4235N6irTwKpFK8b3Q+Tvz+lt2GJLBEs5aUc7UJYeup46QUvBU+YNBHwCnTgZRSTfvLE3g+IBwftUAOIi4sCbA612xqD3JpOzNUkilP/UO3F5cbrNHW5LztULUM6PtTke/pMu/hMkPKcZ6XY6woLKAf66+XA2OEp/axX8iTq1CucMcJIcgYsFUmvSGUtIbIpZrm8M0+zePLbuW4GfwissI0o0pP/dw/gfX3HuWFIn/Ny6FSrtQdGvM3Vu/VlZz2TNgBnu0SW2HRsKVEEwUiZXl8/4ldWaMpOEcqNsdYeIW5ohvBZlC2Etc5BscbPVeo7In1lPL3WVBM9geKUxhkoBuheNT9+Rtn+lSg8DVYchATuqn9IiCaR4d1uxWhRF8p2z7RP1x6AJPG47J0RMsmiqgHtz53r+G3wYw4p0SwakLvBvdK5fNkgb+aFMwKXMGILJnZWWgKKHNXB8TbIoyDVJDPlxr/rtq6H/dlYLslzB6bKOinpFad+2GRsHte6LqZXEcA9T1MnAVPPQHNdQf+m6gbjbtNy/Js8yDFvBQsWpUVv7VB3fzU9KrjmJrGQio8Wpr42dL0Nw6m6FWMov8jfR9UiaBWFjFCk/W7gyZO5iUwdtcKtZWgsoqBEbLTU20NjJhCA3leSXp4tW4aC/Hpzxk/BU7XQVgjiyOpp6wese3Nz+YPM+JwhOA+K0Pk3LijPfik7NbrY3byb26E9oFjYhwdqiE8j5wnwZH2kon70PjoFOO6Uipq3mtcMNtH7DeiG6enDDF9HGYXRvDgew555JJCd/vgu5pWx/WWfgQAFTyYnKVXZX8i6fNJM28Ds13Ni6BVqQ2ikadXKu0vHp1TQm6EVYkuub+zjNo4VE/kIdwKgPpbjx7b8wtpK3kRcQqV7tem3Aiio8v+ndxOFNAUJeYGbgti9abqkJ+UqVd5fZh2kL9RANFcuPbPDw31HRmFlM9OLpFcPnkl48+59bhT8OVTRgHqj1dp9XFmWcFVAuXvL5ot60FsV0rej2sM/roqibDVAqq7ni5XX8QCcfuLXgaZRyhCgGPS/nwM1OpGcYxT1edp5pnXkryRkXE/nTqSkvORxhTWC04QqiNQ1oLKfgQxVLbtMfzcJbuYC9uXfXVeINNlof6mvQ+Z0p715E4FqQrdPqoV8Ws3+nYCdrQr9leVIsGtsQpdxXCQ5ve11+LExIiq8sFjMvX6TN6aghzaOfPryz3B7BhaCBwQnM4juBQz8c8uDmdkxt0XfNYZW1aQq8/DjpX1RFIWv8T512amlqQCOI+mJR+qmOQ42a+BYLRIRmfCgRVUofjQHSYv9UGrlTXkgRiGxbveBzZ+oGxqja6sM8eXx+1U2Ji1YmtI/Uuve74h/mZaBCmzUA2dwpbYQnqCu9nebFIVAtxgybTuoXO+BKdwMjMV7FCsLtRrbC9I8gnr5cp0b1JYhVyPHoWbmY6IwvlGltJsnauzfVMZ03pM9+DT2yvYf96ocgvxmBMvOr037+nKh26d7y/l7b9BLSCYqG8Q+F7aSjCjMBSpIeKsLE23DVyyADmGmPCheP5yAdrVVo7nffyWEs1FpSauuc2+qKeqzwo9aVw2ECgjOhbxpkGNxBI8G9jjw0Peg8Eg3oIighwbNgBHmzNnngIpQOLolbydUPkTbXZk5IN0haZD3Qoa6mG9rO/xlknD4TrBPVWE1Bp3mBbCxCBALQQLygVPCBItKDRoI7DG8dn5PU4ntJW9u85LZPCdUWhEB4o4pEA53tNqBnvP37S8jbHRmG03uYlq/nKJJVcNrI5cE5p1KxBHO24WSdInxX+22rf6QmDozzOwHqIf+6WsvK1AdGrWfeEvrfI2bIC48tOblUDnIlLHCN1Zb9uwponhajf7c+uYRPvIradL388FvHx9qvhCGYFa9AXC03nEOadT4SrYf/kOeZ6XgFEHHU28KKEWZcuiVEw5OmBvD5TubDWTNjbHiT3VeNp3X6Gr7DWuBGMxi4dZQO45fYzff7V2H7V09fDwHXKdpdRfg7mLrLfkEpPUwBCcnAIoVtzeNHiYVN84PBDOUhWoX0KcGSyFmiEhWBQPv3zuzbI0v48wGzgMK/5PoXwFsqdH5cPuCSvHhaOA7nSd+e1znmRaDcoJNYJGsgKubN/FABScsb2Px4QjsEV0pJU0eNj8NknEfyXMm1FBc90Bu1pXRJJFfhhjOuYUDt7LneTRhi6KzMiXoUEEysNbOYe1myZ5a4QioyPeAGyKvvzc27pAUwSAUHd9djlfH501NoREdTxcP3ycoVHCBz4bSzRQkIrjnvMf5jf20KL25q0WuMx/w50TJ8fQCj4zFA1mgphYUKk80zLes20cHaiyfpaXZshWGR1YUFJqzD4dvG+/nhlTUkwlZvObU5PxTDOqXOVSsWK9eabAl8qp+i4rZqhwmY9ehdCrz1RtG+rdZ6sAfTGpq1zS+njj2Agv/vBvdwlyw+6aVxNimG+YM8UUpr5kQ8ZiWsFxcLyv6JiHEuQWFMe37Rt9dJ/B88TM294tN5FG8NmpeV+onnW86OPzvCxLnc047EkCvp0QBoPDj+NSWsZxqLrBAGESBjPOxeav5rXfAWcwDJ+Xux81IpaoOTbGXjoDlp2OzlZYpcSueVAfZd1GCawDH2ZmC/Ry0OXzhl9MR+84XgMZkIcXnjBv2Zdx8bllkbX1n2hi8u0hR4sAxrFjQP98jLl4SSi6fvBrg5ld/o+49xgJInt8xvJi2eO+AcV5LzPiVucQYFykCuQUCOn0OO+gCw8i/Ys7m85EN3RRmK5zjTENF/nBap7hptucUQ9fZQEkrNW3gM3VibpU1svhGjEr+AmyN+NF2vcjX7zbwGynPhtHtPUb3/EIcdOK9MODEZZZBGfTvj3QXh/5CGEaxJsvURIp70uvUbXkkyB3O411xfyt0A6ucxQT0kLkL7+qgf8OfvPmFHlpPEAU55AX/JIBKDf+LZ2htfN94VZMJSwgf2RZDZH0rDfTXLIADql52hlsbMJvrAHfxYMI2fJ1gqUCWjC+gOehm4ctvHDUXwYo/WDmBiCtvMsNg8BTgckcJ0z04xNVTntfKx41GhbGBQ9ZXOBLdre+DI95wiAFmRvDCaQor9IrDWzqJ+JprpETJKDZQuab6dDNliW44rQvOrq45GJJKipcUQh5jfSiM4eMgh3D4CeSg4gPOv9jvdwsLcHSom/Np1SSQDC71t6xtecQp5i4dWy/KgwtoH+llf98OkRZfblkFXvVu/g9uD7mVApCy6FwRrI5bi9a7eo0c2F47e0B5SezaZOzLMqJDkvYi9G/iqAPgq/WqCNc5u8PcdVSsbuojrfgw++0I8P/7pn2BQeIVPt/kPU3T3puj7wv9dfzsbDgMd7U70++NhXSDpU/brsingU9DDq89CG5mbU5gy07T+6Y1QWiZMwh8iGGKw8dSyVCT7G/1dYCQ1WRbi6CgSHfSwtq2D4zg8KaZQRCLhnYRD8jbgLRBJj4DV3L4vp/Qr8UVDvNyp5AAhBhaBEGghG5FgFST97vaNkOjPiFt46EWln1medhYlZ6xWM1PtNsoj5Wucaf4jFU5BauJ4XRtJbn6MoxK4DXlMqt7R2tYkApfsVvATAT0dKanj12aDuio2jjtuoF+bAlcZ+lxHGRFgXUABCvjyEV9AyaRCXnnExHeuwMIwzMefQSHZWWILjielXwDxr7RB6VgJ76tGg1jNaBXo7d9OOAXLNFV+jxZm5vjOc1tntOrVwkd/Am4u3U4L6onpfcXpXnnwre60wYEIb+EYYwjCCl8XWnZgwO9mbKVSylpLxuP2pf3OA2UV6RQabHoyT8v8K87ItBJiCHNNWufLgMXB7uFkBi+QeTnYlVsrHia+FU93X9qfFXL9qQrDu0fV3nzaK1E9L8062JE7UOsuWHwfjsFrJn7M96CI4DDMG1/yCpi88OQth9QCfH33WB9Wi8bjFD2d/DmZYGf9uogrpEKFjZjLopxJEKvKBWvacvFHzJPz719b2+4FKrGfSWpwOmGws76nBNR/a7O1FrfqwI+p87o1+vFyaFkfTgnFX7Gq4DSrfGEGotct/lr9H9mhLp3hDnAuzF7Kso1rZ07BfMkzeOvvs8ZSRT2gE+SM8tlcOeL+ZV2ewdikv5rTVBvHsVZFO7rPPhUpfih2JrZn/dK+7Ox9wyJD1wOb21GPulCEtB7Qq3+y/w+smhNg7DA1QuXF0Pj9C9BEOk4jTiOEvpEjCLhWNcJbeTOy7/jmxZ9R39e+1xNSvU9OyLxPq05XnuJmmntlUDwsxdG2e07oyNyAb1amWMXvnPhuRFCnJcnbcwC23hnvxK+SEhMfEZieTrT2EccSEqu66/XbU2j0dt7OvrXdcdaB5uMRrEmdOw+1n2SHFhR6uT7PPDdB8pxgLp2+xpH8P6hRK5LDc2XsvTRc+rLWDsrWvD7b07IJRT/G2aj3PYHozI4cQ1EJnIJRS3DMAJXc7mQjV9H1sG/8FVPET46J3fZAxBPQ5DfzOavj5DedH87MYAT6mz09Un1PBzP6f04iCi+zwoU4AEoSIhoPTjSPGKXhBKf3iAyr2DUq1RTu0E/ww2MJw8Fmd9HqSAGdXwu0jLYgIaCHSdzEGVaDw4etSNQSXR545L7RVvrJyd9yyBseWVQnFvCQLXQN93mr2CPk1ZB5Tcw9Kr/ojk5iAWDiy9C3JOn+4fYIRwtdwhtZxljq6xiqyQf4Tp34LjrQEoOIoi5ILED0stAejnmoCOTeWMmYJzRh92ZRGWlNeNGdO6PKlPoU6rbmwcCecoN2qQAPnbRfUfj3PEbYjbzmv5WcJOFKY+dH/E6QRUYc4ux8vEIwKLW1UslwzvAl+MHsBP8bfxNMUfluXspfpf/gkDMMpbpJAj9DnAmfrJZjesr0C7rSczUyfq1STFlPDkeTt5h9735eZ77SVY1BtHvyon4UvWxU/7Dqgl2jtM0BmgyvAylD/7Hf/RJbGgtoaiwfbsIMD9DvKkoLiRQG6Xf06q6dwFWpVZp1WmxWEROWf1iOo0LXO7/KGsFD49XQUvmmOWHkJtlnxyYjTaZW+yTA5SSCKKrd8jvbL3vvbw8SfbD9TI2OsmJq5f2N8VK6M2Qopzjj3WiqRAe80idOjuYuJ8aU+U31kVPIdtDuxkaQG6bgwIMfK1RMpLM++t/biunhut7Ax1xOafz2yYvgcda1yIY/4SfNVCgVnky78Bj+sgOeYHy+gF+e52+gzjClXX9Defb5QNsn/Na9qZhwNkLofPpFemfKgp/ZLD+LPRvipPqZKVtpCqF02MFm8JkCpgd+AwY5cLAwQd5RqYwdq+OAzs6UPtLTjN1xpSPQAwdDn8XCs5bsilmbBjIcnlbrjpT+7c94HtJMr3efocyGMqiecDjCyuLiqxCJPW+ph7To3Ek8B5n8fohBv6UkQ32LxVwebTUFnf0ssyRnnAbziyuWo0RqHGqkFM3sjgIlxEi8EmaoWPFGdW74hW5FUZVUhCE4/CVdjcZ+iawTB6/w4sBzUAN+U+I8qavyh2DQTPoW3uo3UXXode3i+e1XROOWZq2VZ0nyjB11CnFCahIFDSg7QnsQ58Gb2h1Pc9IUsiJisTLMo5rvmYZPj5Zq850JftlI8CzvdBpdmXnda3T35cfG3xDElXbJIuAQg/m0XXNsyxcBI4DSwgA0Rot7fT0X/NQLeYAchWSiLZjX5MAEiMH44aPZj487WIh6mNobLlSvdbUNQbCC9ygdWWSR5qYZFlbXCDCpbmAqNIRoqFi5+2KQXc+yRMBTMHEgBPOkiPtBQ8jQlVGgQ81Qi9bV7zMBPmeF2DpevDk3yLtst6/CMIy4yFMxA/XAlibJx+1cuA5zgB6HGqgjNSvhU+aLcOj0Eltu+DGdtGm55rxM170/i+zeKMXbFS4cWOAJPTb2Txv08JhRePUEAOQGTMusm8SzyyX8/HJpN5wQSOtxyVBULLS1kmSxet3SGPjOIr5aQ6BYM5p5F7uQKA7cugbomgmcDLrJeKhPo08+7Qw7yOkVRjpalwerSej6Guj4Ygran35Xdel67xRHHElQRxbZ6LN/6aCd5qQ0FVhXfa/vDglwCsaRRdf/Q8X3RWPmzflYRDlS3t62YJZPzyroBebwA1MI9lXV5S3c0pRF2ZZltb1M1z9JqbdFxB1cDc3KCtp/Jtr3uhuCS/QeO3wPzY6cH5WAlCMySAEwDj6j2xZdnoj2/FUi0udeGHV0Vw6bVUkByWlpnlNrF3hdb3DiaJ7bYjIbRN0oJxkWSlspdAQIvKPx6/DqnnzTI7qJgkSCeqmnUr41PWj2zZD74vcTVZVCriLy/7JkO3A4APQxXFVIJzkM+Fp2k8OAirKq6CtT8x7XZqmwaFIz+qVAPpm5jWSIpX3m+9qgzSfxuB7ckO0KgTKnaIX3wUqotLzOItCHYiNJj+1uig9AkCaJsty2Scne1aUj0XKo9cJ06LPkhigpkMcYBD8G+EhHj7Q0+uoZh2q6ki45zpyNWf0BmnQFvtpd9Pq8kmX+repnCkYmeJf4bQhY4pv3pbB0NMQqP7cqqGBmxW0D+SQgb4VuJbLul8Zm2Go8NzJOD279lDvwGJyasDltCCgSF0HM0vEYMtu9VMNxRq3nquCKgWOpsM5RSf4DeDpcZ6EXdPbYrRt3TaZAzVpjfAMqHZkNoG5rOYLYg1fKWNV/yPCFD7KFn28Yi/h7Pygw0wmrNTEotbppzr+UiE/NMAX0w8b8s+oLJnC93tHQcCRsuK8E0S37sOFPCWeBM0b1m3dGu87RW6SsAnv0J5wZMy+e4vRruchEK4b5A97M2vAnF/wqJ4Gmnty5im6kXiuPuCinUEtEkr0YTnIuNm1Ilr8cyoMFDxs1woOdZwlGgU4NfDkX5o6L8BvdpD2CvTZzZNAUjWuYngLAuQgctMO70A4yWf62tZ1Gioz8FyRVY/MeMrMQN4gkkdreAgFX7e/oc/sKHvehJ25nsq4L5ohUDVjPwBOrYgSBpZg5wx4HPuUuKGuZV2xIPhLkpArBJR3V5fk99Y/Xyqvt2tf3AzXvR1gk2n7p9GM9EEQ9y84WfR7v1fMauIrTklya5D4nYakHesvQVIpyDvWbePbWk6SyjjSNL+FvZlzWNonjC7fUIN5ProfupkHnhrbC7UFOhDNgpa/tt1OtRwhqkPNMLXZ4bOps76s80oRhFNSXtQSNA6ybdbgxId4B9UiDM4oornAskKrXeheP92J1qgDP6Wj5YbWka5aOCzcum+hgLjoaq65aZpFPp+LI/QKG3by3bcPDpeFYVPT3IwKKgMjfhol3vurXXaew2pF9jMsGNMgzJjigH4eimaIHwpt53pe3z4BKsK1BxwdKcN+FMincqCWKRIvYLwvhqLTGrN1oJDTS0+Wayq0hzwh+x0pR7mjOjOVSgtfttK3XLhWpbbx4cs9xGe+qj4PmXlKLv5MNwCG9zwyo7okA/x7JPKsQa7LkM4+7n6/HlY5B9hHUwQAxjmASqP64M1DrPY3C0Zv0POpg1HrZ5TLM+fC1oZcP+mK+Oh/L0w0hEzFlRAhsmEQx36yNeMd/0V9uajVrPBWqCu8WSsgmHvvPWxtOhbUyPib+/7HxLwThbdj+FSaZyft6Ued/0ilzJhwa8/z08Sc63NnJLpXKX2+tdfqYmZ0Hp+J79/Q2EmQ8fTT1eZqdPzzW5yO25q2QNGiK3+QFNakINCNzQnAp5I1cM1zeK9oSi6BF74dMlEeDfQr8Ow/naQt+c37x5gwo+DlCGTa8LNS9H+bSYZ/CMeYc0YWkI3ui2uo6AlCUjikpbQKs2HWOeU1qZH5OMKoHgf8aQRRDqYXwm9vK06z15MMQhrt3yA31D5m1quPpYy7vvnxsBApZGnziLKGplbRQ4YCSQS2p7tGRaJ7eju5ADY72rCBlgA2EgdZqk8HUfCJJzAyVPG6V3i0RmeMgKNxu2Jd/e3v1P29lmcAMz97T0BrrY4R3rTX5L6lQulLb6C8FOOVWLhVhOTQgyLT000iGJP3izEj6YUGXwBSAJ5UzHONmypy+9wEHqbrqYsU7Stl++DgAmWRehGnlzbSNZeF6ARmgAmiiUHlo1/FMMMiCcYyX5Y1jl5X1X20PfN4Ob27qv59FgPLt6g9DZunKMasbsp6vhQibw/ewu2jnQj/6z4noskn9AAulqNZrbCpqvC75LZqusLkP3M7lvvSahcNe3KwibCn4hvMJB9/0wN1ZHt6qzwdTCctXK5LwaRLpvokjqyRuAnZ09kaOLM2mZN6StSMwk/dFAHmjjPh64epuzTij7Hb0DJWkhAGbKyTq5UCqLG4pQAKX6DIH0z3edoGh+EVF6T9dJtqkPuzpDDNkHk/qfPSiHFTerDLJ5wHHb7PRSNq83/M0dmE7QIntXqs3jrAPVj8UH9aEfTRcSa8Kbg3DwIWpDGMHIpUWIN5WPJoKXe0PEDe6qn/Qngj5ZOCNPNdWHz3cSNcUD62i8ivVylLX1Jmv8SAQpq8MZUhvl3jSZfxNuqvxwR3HvnEzuk0AhbluF/Zj/7WGwU3NOb3aRGtL5srZP0hmXVOiK2wD5jSZVdiBjFQDd6T6MD9jdCa8rVryX74tEDoqvvH7r8Q268SEv6KjRPkVCGl1rvIiP8fN2vu/37P/77vAeeyANDRsW3M1PF39HOmf5PgNlRPNFNDaxpCW7FQix/veo2ZVypGYGTTXBEr4K9fLQ4aYcqOB5tZNVM4Fjf1RbAf/NXf38Pg5uj48J6Vs4rjMfw7iK+Iv8RKHhj4VqQ+tOvrel8K/l1iRVvvmcYdHAG7dH3V8TBBfsC9YIoSK9tZzTbc4gg9aegZj4dAF/wK3ZSlUzYzR244OhH66a1nHh2oWV3qJiueo5k60X1JfU9TdV/S/l0qP9f4Ct3IBYdOvaqpWm/S24t5zbJmWaPDa7BilyH06+qK1bONXrRrtkzKWmjYwcp9hpD3q2s27y0MYlW/SnWsBZe/L9kvgsDuS9W/S/cKEv93Bf9PXsFvkLuh+4zdmp7RQFau4uvTieeFIHNph93XL7FK/Jx5vkXdPzz3WNZU5dX0YFM55KD8lETEnYhFF8v3af2XxgZAwqcAhMV/q5gBDAJSCPjh+MXUaM92PkNiKRnhM9z5bzz1/3hqhZ5/wIArqdrxNgz06WjFfNTDtb7fY4VZNYCSWE+2cQBz/TKwG0/3henlpG0+4meY3zCMiNr7Rojl1SyY036dTLKA1RMXonQq7qbidTdXmbMvNjCkzBOABM/5vS2wV/NU09nQyHcWs7VSHP7XbMAbwj4DziTSUaUUlw+mgvdPEEMQyNCgU5uFjdBH4Bv4ITs5hzAuU91yub6u6h+7B1MGNWB1u2m6LR8GneCZlfxgR/2i1WcwTzazH86L3POAyCxXIY0t6u+uXuQiJqYV7QEc005dWE4JeyLPtc0/MPAf8xZzRM3OH1YS9UoVFieQ6LXvT/ZkV1XPJFzF+QddRxniddN1JwMQ5wm3nraYmRblOXVTJN8UEjA6ycQPiUUN07VHzFxUKNXqL0Rrt0znJZJp+UXYp+2Lq0tQ6Evm6aCH+G/71UfS22CM+gf9A7qDaj9WR873917lj2KIfSp6BdF7AU2NuE8RkEy2ucPT6fxvDjjNTcf9hm6VrJX2N1k9ZMj9HrR/df8+GaD44Xk/qK5h5XBNjwDMTmGcH1fYPzQjkNQak5kg5UBfWXW0BeddjOX433B0dVqFcZKnq1tGbPN4EpdkpCkeWC2npjJMUJazNv/7nf1D9U87HFO4z+He9HG2wOIBcxZtfcZi3i3O/zf30fnbGcAZfjtW0b/NIUDnJOZKrVvhWPoYzy/TdWiAvmg91IslxbiyFIzG2V8fF8ILie7Ad7lOD8LT2+Kg5lkJRjf6p7p/JDHK91Umb7RE4FRaxUcME7DpBDj1nwzEqT/M2H8aL/rm3h/GguGJAtMIuVOD+2J2aLZKmcJvTn7bICybkA6E1f4f9t5z2VVk2xp8Jbz5iTfCg3D/8AgnPIKnb5K1qzvuC3R8HX3rVMTZtbaWIDOnGWPmNEBiAcKlNz/Z/00xHMmntNfejcLwp93JiVm0yTUrz7TdSH1N0/kGW9z3SzKGpPJ1bGqmNCZlsB+xisFlPYu5+hqbJa8nYU7wPRaNJZ0EeOZs/4bc1Mt9pniz1IpeATBWIA6TCHMJzlqq0jWcTot42z3txf0buW7z86KXiG8XjkEehTbefw0u/pstCnbPOx/Um8tjTPrE+hxjlOzUbN2WOlrOnykPst1GvAp6VIpAS8hw6JuJyjvW/G48zrY0j1bZZ/AiQ2jrnie3Aolv4q4FDOW9D/R0RpYHtA3MmWtc+eraFiwk3FlwcsJ4s3eltD1aPwramslbW+21+1cvOfpD/Uq+byy8P8BcBf00DysNW5GemSv7e76O2ngn2esL0mwqkGGWVM+81OH75u+1oLOPY04/qab4BmlFNmAdi/1Y1gsy2cW4t5egKOVyxMGdhPL7KpDIKAhwKy0zvUiya/CnWQfFMNbfH/20dyCGYWyGfW6bSRGSvyIsQjkmcyl0e9v9a0QcQyuY6LOOUHTmlIv8CNXOaEwjJLzsr2MLIvFGXgFTwavpOQVBqQpgAxR6FddyreQ1Q9ap0CwWXCe54CaS9/lCgg4owNAP40RMk4rWoRIw0THYikO6QztptdbaJ6SIGe27yi/LtU0HUeKGDQtJMiUuDYnLfZoNhNYY6933BCGGFXout1EoOULW3YCeiAk958AmkxU6p7CI7DmY6CA+JXNPRfCL9tF6fRt11Xi3yFlpB+mrn0C9V9zsA8SHK7B5INxDEp3+NuhyncthmiebwBufDk/yWFSMI79SV6IRJ1uMiEzoe/JRTHWQsknZm+ZZlT3hbA+Cb2Iwy4kK/96J+HWVOKxzKN7vv1bZoXoR8E5P9ZpSb/bTNs5bUvGvHP6Pb6BuuzMJt6or0agcN6oOEhbGwlxKMeH1sWOM27SbE9ZkSrmBZOud8u6kqihE1O4Lkf8Kr4JJJ7AJrzZHdmWo3/LLFUX7OwxV/haHGgAHlhtaiTDmc0RTbFdBBogmNdP3pF826bJF4I121jMgNh1avpDoIDLBPnfEEFQMBdfnQq3QzQSAAFp2j8kBQh2u3hNqWUHIsqR3NCU5EfoVzubkQEMpaZn4yOjsy1e+xZ9Vu23VM5SV/qdUICyIgGBtgOSN6ZoGt1nmscw04trpCHe3yQaKApJK3ICCHdpAaekZoEWdUHr15ndC6bps1i14yqq4bg7p9aHLyBfReeiZ35CIFBkcibDuCLxCVKiQ4Q7CnSld0bP374/gFvtlZzmyPtjH0Q1o1Z+rA2tB0xjxDeiZM3v/a6RPIm5sJjvOulL3PUDZ2PmZ2dNMhoLZBO5q7EJY3zw7uKVkmHv90lOZMGTFNImWbzRf+MVCczXegLsNB57t+SfPTxODIaijB7aSQSi+2fORNT1M3YDxVZmGgbni25Se8TpRS1CCgq+lQhXV95r/93n/+7z/856HbaXf2Fqqal9O9RxDbJepPccIVTDbTqIBe6etpkIx8nOJscq8kWHxpoDir3vmUr68tymSBcJminjIbVgJ0u9lfDrYY7DhZPhLgr+X3fWEfkVj/j5MlaoYijQJdrIX1OnTjv/29c9oK6s1ZqevNQOKe9wm1G/mxQqPBz7Ev8d0awT4JccWz3kasC5KGjQiF5Dfn+HWiHcc7XlY5zJXwuiipaROgXkWgiAFz3g4ZJVewor+PknXLEPNsl1VdmlYqW5nIqb3Xso2UxRdB3nfteGa79FbmxbWZFzmOJ4HD5yQlTnX173g/v/eG5TmEiGb3SEV7wcap3k/sAc3y77hsh2RkdFo2D8zPu4l//ZEh5S4GEzk5b11r88UxtLBJcU3+f/p/mLqX6cKttfj7ywjZuNlHuQtLGoamvpB0xcqZOeQGno3VcyIs5XhAJy1wc/MLhkJalAaz46v7tMtOBim4HD45+lIOG5fZnLt6NKz8s8DzyHM378x//3G7MEevEjJObzeSucHO274jOi+eLP1AqmF0v9855c7IRKQvonrUfJ73l8SjtF5I9OppPrnvYxK/3uvm5Pd//cpk4HFSzObq5h3p4Asm/u0zQMfi88uFd8aKxSqVL789OLR/GuUCQ11e/HLi+iYra5gmhFAlwSlwWtrIfyfPw+GNERtcCnIRW5N5+JfEJh0tM4DbrAN1MR6Lr8ULWhAX4mbLo7RO8G09X94d1Y015dZIo64IFX//jBsGE9Fw8fmyq2EGaCQgGU/VbBV0bTfrsCrKCe2w+uZBbXjp+l8X7Xm7H6XLJX4wfC6bG8KyjTpEhs68ffa3fyA7sFk54djP5meNQLdAB7c0qqV/SZYVm6CZmZ/wdxe1iWkNvR630DXrGz3fbEw9+s74gK7v/xGxlZ9zn6fN4gOTbmVpSTcdW+6hNZh2jfOOoqtD5kSuNm4ltRP+uqcL3Csd79ax5z5UcUgMaCFLVJI/j/4HGaSfmsci57qxC58NH7t5iLCyGoObqKE/jodqOU6xAm3J+SU+kXIZM1NhGG5EzgsKWAL+kUIV8mfWQkgOwDYFaAkd5WEdFhyBbRyZXpBBiSVXW+6SaY0Af6M4PzkEV80kDu7gIz7QOkOk1M57iqtHpFnoOWe3OJJEf6YRudmExAJcN0CNBVcgz2tkZPDMJ5m2ILackXD0HF/UpiHVNSBEmqz7j/VY9g5xKND3Jr1mZhpZnaPNtzmcEOk0eLPJLH7t3msfH3g8unBHmSicBZApmgc2o2EwIw3L3cUI6zm2xJ3PkjMezF/wBCspi7n+kWB10vfMx+P8iZUbs+wNf2S/lmKfyJbIp6xCCiv4eongaU0YeMCl/gp3/UalRGJpL1JPuOAYDJPPQq8kIWW4ToaRMu9nl0u0dVCO/80lvpr9PbzpFjqUUobibLmLPLSqUHST/c10NSv3ieS3jS7Q3UVlwuvTqEP+C662V8oTdCJmzFlU8SuvJxv4/0kR1l6miLz/+G/RRI5sEq855IqXZlLM5m3Batw6FOlQ+U2ATiUzxjVP6cTtZ96ykynV2zGoAxsGZMZz4TaubIoJWV4KRU8fG27P9hFz/iMQqJfv3+nQMne8Zj6/8Kg/yym1ibt3yUH8+U3ditSIbpCKNRa86VDK/jkut/CDFi+3zL3t4pZdBjoEfOgsyTrjkldO0MHRu48CvF4j63+2AdzcynGJG7SuDuXWDr6ZGNf0oOfkZrPqAPeJhXTQNOxW4aK2423tpnuwQRd+4oq5afo7LpCHwe2D3x42Vzr9bNgdvFrLV+QM5ekyLy9JqHux1qkA8gf8JaIRc75iyw65+2PCE23TeW0bzqo2sl4uLe3y2iqfD/Liz1UoB5EvgLqBy6ygel3fMOWG2GXvrNF3rxpDj6ELIxaDyrqUilVEMNqYekHUpybOQGJMzsZqmaVgoLb1wlXPzz93jv7drfviSz7ThtKxekuKsYJFlHrzfBcTgshJw5ARJTqBZuXxvSzGG+zAKlfrKuq8pww2/wMF/Q4hm/e3RX0H9nSQmZslxpeldCnSVplFfb3cvH5ivlBnKu3W9gEsmggJERTLbNwmKgWcLzUFwefL1VW8jT46z5GuUzAZp9lTcDdhXuzQFwU5t5cZxBfEptCZXhm+wXBKpVaxozZ5+P/5ACuwS7iq8CwnxCbteC2JFv13hznRTRa8m+MIgR9GOWnZX43g5yK4+Urylo6YvfU55+Rw+hJt70O2MbxFBOVti3WL0fse4nrCmMabmgkiZJRa8b17ikHUJ2H6A61QvUcxW60ZhZsrKPi/H0EYKNOmzu06t2Rfn/KuhYq3za7tykqQzr9KWwknvfRfC9ca/+OJrq3yQLAwcBaRufI+3CgmFoPrru3KRTANj1txCGbkdTy3qZILwLib5MmfZ2fNryeqzCNsN6btEmWVjLju/rAtBD4/xprqbXA/KYAm3mfH7q9en9ch0Cqv+MBgOQGvhisZvD4ra8TPl6xdh+QK8LPJFvXdpnMbbeEQmz8l2CiMLT38TzbpDIas7FuuAapkh3rfTjO+RxOCe9CJVahch9NY7Wb/RwN/p+kYujHZg5tuQ/GGU6Ued0b1WUO2CgQ1Uwdhon+35Vj7ze/wRPT7gmTHGqXvEkzc/dvWcOG8Klrb80ZH5/dL4yBaNC8fzQuWf8wbghW5T8lRn+258eE4vqJtA2m5YSuSkmQN4j8L2rWD+WOgLdV0GT6SVZayUlNqAa+LX+2YF9LMqQ1En8cHTmUBLDRhB9ByS2sUNw2l2t9wJtKX9XECp9QBkVIm6PtN7ZxIuul+Roccv3pxjP5WNsP4ZbO7ROa24OlhvxqMviryx8hQEGCFLuUYQtsifvyPn+YIC0Xbzu3seTc6okbiqKOFVNOvism0RzSW9N/USv4X2EQzva0FDnWvJfgsoWokgiJJf8Sd6tuc6Uwvy4jbdEeZjiJN5ZSmiG0Q/OPZuzhsKrcbhmDwosNTmiEUnU+buSrcS0SieJ+p9z01kiEYwT4EimiBRsDx9cSxxqenDs1/XLdG+6zvZEda2S/cgPAhE2+xdP53D+jkcfjSV5eVOgJ+MFgaKQwXqiH5P8T+XuFJ9ax+LiJy0S9PYg+mO+VKvbNBEGAbbw/MeR9o90fG9ix2++PgVuY5qAPuz3Sb2a/dYsag10b6OmP9eADW7d71z5hwH9r1f5ba7ntpEydb+ZSbz7V92AXs6ZwhJrFr9femFhkSCXqAr7iuxH5pmX7E8dMfNMl27NRuo7Ory+RkZMTSzn6iZb6WYoEhsLfnv5nKzkZyAqhFsXT56q3Lw6iQWankbiJrrZjIefUL9Wbbwfjc3mZlpQ+VoO+kdV/UsBzhNsrthy4AggfNhsVGb0HuRQ1Ll7DQir4nB7dBwGuGhR4J0Q2frGzeTHXaW6Bluivcrof8HwOo2K6Nv5mhbOZ7znFSCTivi0oaij6pb0NSYepL5+q/GqN3hcB/6MCHbyVx+sExLMg1VaAToZ0kOfeo0TPMcD7Oj1DoRSoyzT0M+a9q86VyoTA6F5xvVnO8/zpJNX1N37Kj4Ip9+dwveK4yWfz7+d/YEo6mL3b1lRCRIahsDvwO6Wzm22CCo0r0l8qX944C70um9ZDGQcqZtMh/0mdV9LrN8DyWH+Rv0ShfkdufTdTlHklbtk/zut5hR1LcygjRTeOLk5TB9gpKoL3kpuJBVbkJispk4Di1Aqe+v5X2BREde/sswvUQCbUTDvdcqL0s7r9fMzEFxhP/0US3LOW2wg/xTsYmBzGdjeqnyKGqE7ZX5bla8J+TOkrxF6HKEeqLtZuaUFeZ99cSfwq7tsW3U5iJAfL4//itSgBSiYOOyt4Zdm27yeqQAojENvb0h8r8Bd7TyTI7ar+FfaDX9ifYtqXZT+15vYzvoVO8X9zqcCHUupLnQq4iCPWneSFhhdxAjhpiIwfKdzDdXhYEJUjNlMk1EetdLzOP6FSs0i6ZZG9aLuRG1z4qp3ri+iZsWqNnpPS+8Xm51eydo4nEUSpDvQ+Nq5FyScA8bSoCkLimIbbgQKES/jCFjO8XG5Y5YAhUaxLBMLmc/zgLdgRJ6GZkIEwvblrsJeIShNZiklEGd8u2dr605x+4sU4JbT7R/jt18EtKPlhxbHEtTFy8bdi5ubVsN2Y4fMauaOtmTn3a37rN4uHPfp0jrY5zO/Ct6/ud/t3u3/Q28wPJTdFCMCEtHbraq2zb3ROmroUdFe/wfWrYxYc0iRLQupfX9Cf170UXOBlqyeq/12dfEzBC5/U/W0sss9z2lDLnsRw57dfJ87w2lvz8heL2BayEyyNXBdcn24QZFbLf3h5wONSZIJctZP69k1b5QjX0YwNw768SJ+Shh2z1/UbHNnnVtBLnLzZSwv4rZXIfa5x+Dd7q6c/IFCIkJUfBM91G1Nb9j879jjSNxOfjdvUvp65GN82S2W8XoqK/m7eo5iD0T/MaYXeZ3EoxJvfqmDfeaZXwhg/BHVi1vJ68kWA5q8I/svM7tVlECMnVfHmmKYQHDOqtJRayp/v+2CsGIibkT5v4EuHGPiFj2y9lFuy2TjJH1QikyROebsH8CAIBB6L1XxceJZalIrZ8ZrnaR1Mfk0neeWhCGQpOO4JGrWL3JGFIP3kppTaXAR5OYJrqwva6eTR/z++z8JqgN5cKmaATt6ux/8XiUJ2cqe/n1zGb2+A3kDJgr09faXuAkbqsOLTNWSkVo3eHE9PbO+3CyDLfZcoQqdzmRZ5k8Gas7cctnbFUzF1Re0izppX56f2LTPg6d+aSJ7bwjrDYltq+o6Vel8j8+yH6Q6L50f9GVhOzfiOmEzedWZmxAEk+mIYpX+PTPe0ZdvXLUpvlIQhanO2nka8XUOhhonIRQi4nloN7ufB4HlI0NBUDvBb/XlK1js/AhEXZK/3MAiAzRzc3yt6d8Pt8szit/NyBrXJYTXgl0hZlmtgDOliLetg2/Ym1cElNk2ZZdi44NVyc3su3UDEEyTc1nlZckJWmJwFwOrvulgMFErtIus4mvSMqP9lv6H4d8a0DqrJzjDHcRq102bE653v5ipKk7fv487H4slXCI0Mq8cAG20LUZYNT4PqDxjv9vrplJ5xp0JHwGjQdPXzvAv2TdO1bcj6AmMvVdVBSjoO+wlPmuZwVZVdSCJMU3p48C1eloe1tvQFI/x/b3WOy7r3fRTfoouHPu+nAhWAi0cDl8BWlmlq0D8Mgm4WcnuCKLuBSXWGtxMH4sh2H2oyup+wBsyCNmcxju/SuVXi3luhBHnK177v9dIPqAdIpDCa95voc9zCbgjqZsVEi8Cbf73r6bvH67VMwfJV0igMP53JQusjLzpGhMdeBj2dJ+YNFIwr28xnyhyCqqpUa46EvKL4cNuXzZaGR/1VpOmBJSkvZeLV3/fbxgQ6DOjg3biVq9BrD/6mswIqUJ/bZp2vxbJPFwvXlabZ0aj74a9fIssGMtgtJ9Of3iIM+X69bkUkoExo0Uc8ZOzCFcvxI8oQBAjKFAxyw53xilvhGgfzu66mXnJKak4Lah6s2xMrmazJk6tv4CzASmcZL0EBwwfJiKBAYnLmaXp45ZcEkrxM99ItywJ75hF6x+sMRgDhRRKzx+P7VY9elof428s8xxQCul/96CzoQySA+ZpfglYLOOtQOXwtCouFIq1NbMX8BLDE6os3ufUT2/H86gJb6zkt7rYl3eDsgmGpC4F9oJZiqpekP19rhHvmv3F0fhgoODxr8zRN+8kQNHhmB/wOvGtWOwPBLfddpWj83n94MvOyip/WQy+Q80K9m9C7eCMsU5Iz2hU9jqTsyusKvPKEKtEzIJZnqw9oo8/G7/L1V7vMLp9mk812hEeKMF9Kvl+ggO2Gvhux+36HrNvuWh0VCdMTDsppPD73LY5xbEySeVh/pLlF7Vvhz+3J2xmcWoieyuUQFkfuBwE1dIHU0rUWO+gxhOGwrvnTi5AmSvSYWZKE4P5pSwi4MgWRw9O3zj5A9RJr/WjYdzzvhEEY2IzQnRokjxdh9nNoH3sSwI+DwbokEJsBjyMRULsVGHpNzp/8DUEQmSaw4apfgdK+OnYDcv9v0+WfSLhC8tqcH02v1twbJB3LT5kNFSkRwKmT5ggyPhIP2QS2ixrTc6cfyHmobksF4K33xjLvf/RkvS3A4yJWwDZ9H8Z9LW2qag0VtCMjFQhRLoLhNuI3B8jzKWye0TDNvZKLq4nD1G/6vdeOO+ZJQe8oHenatCxRmLx8nmdcOkYZtRCwULCJ5DXQ/qM9NVe/A8auS1gontqT9/AFJ3FLFC6hwzYdx4F5YknKxE+jSgj0gIThQpYeraG7p2LSIlE4VstAfHnYr4nGXmYnef5+owySJlO1ULIANr0sQeMz9t0eAs9utmw4JBwT78OOSio6nW1tpEkJxjq6nPMzwVL74tUeLFcQhebLchzCS8t7DTn9mA9p/4lvU4mnzG/s8lVcAnA7u/sOM6lvDvE6+8P6JVmtJowDH0TLe+HiQTPZUDfiDsBOVC3tWYUzPMdSHjdJus73p9+2bX51r/A3eYDc0btd/XqAZ6bwD4sgLX8tzzg7GAaef47H46j6eU7LIgDve/X5Dn6V4yVgclCNVdCSev7m5gy/DygsFk+3KCc1TvO5m275RQEj9TVGNIayJRWVAGzhG72Ddh5pIgHyUetTO1kzrmf5+0ThH1JMr38JX/HXs+gFfmZ9vFPt1rNziNi2PDBIT4XdmE+ToaDb13IRiRkkObeDBXyZtDuvbhoYVg4cApIFOtBYLyJ49ZMinod9wURzhAHxJPGzRV76d4yWZSSLoqojAyoazf48iRFF0X4vqgW72ZBUNjjYKK+zhPccey0v/9My3AUQyo1Pgb8cV2W+KHTjg6c/ypmgX8e0hZJ+rpus5l3SJI7DpabN69K5GQhQmM6J9Ktq0+wXbzOTw5/aXH4GzW0eS54ttrKU9OdzosGbB4F4mi/e5uKQX5rwDjuc44DeZ8J4wy5BZuJrHekRx3HoVerjBtZ5lvk6jtEGGgAdDNtJPZiI7BbAWUn9trwzDx4okN9IZhfVsBWubO3eaTOjEg9NvFBUkJ+SshLaei6KnTQEboTfbnLeVDMsNirzRqb3ZxJ2Drx4R5Ok56vEed50Tv0VOwJJQE3dm2+Mt60y3+PG8LRfxpMTAyEkSPLC1eo4IOsI6bj8LuCncQNmU7DdGYq9vqwp34eK/i4OO1nqpJUcoTvkqs3pYQcYCVF4DwEWimXZCQKaB4yV9yRjXae5clT72RL5JjRvIIo+Tj5nv6/aQfIWukD+ldEgcQ+ohuK7EHKwTxMkazUbuPtZs54jo2YRuzH4n3AeQHwIYjyG8DWNkl7joOfL3sdguKb4yj4ovybcwSW3rVLTZqoKXl+TFPnzONge3hYpydtKT1kABG/jhvef+QCx9r4HjGlTLkVG92XGfFCb3XclI4r4r/zIoesdUQmf+dMVipQ5cHgX+E1T19EDyNW24kvrUIEF3AEtvXVDlTeB/Hy4olyjQYpQgvcFLag+TL8xlNRY1UC+8d5aoPqGn6pcxvONbsFpilEpVUlsPVMNgBYwyVpPlei8f4dsVwXsAtH4QonxqpIk7JMb0RVOLltMkpEeB4MzKvLsRvfvncIhy8E+lEWxEyJNjC96MmdGjGX8Lp4x6AxEUaAIRaGgmdjpI9ZuwXo31Asaws7DaAn0VdoFmkRpFxxT8zt7ZbL0GSIg7hrJFzu21HGewJbJc+R7jkqrOlJ9ZFAxKzrIp+ObBYcx3eD7eGIMUGABzOU4EfWAo05t9sKw1Fs2OGWdIpnkfStNwTrFKWMA6z+L/CFLIOOXPAkWwHCfa36mHgJRIUUpI8XLHycLAVmIpZOKLxbWqZV+WgmXRckvFGXydFTwzCdTP58DFVGwFJaxLSMzy5I8fvcOduA2yRzDHZnwyqGup38ry/4oJWAFDYKOK/VrrI38Rki+/ggqBFgUvZrL4ymsENwr50WQQMx63le92B153/xMpsyqaTA7DFEM9/ihEyXZcZzEsQoMt32crRuZFDaC/DPC5EF3617DXT+4b13ylPZD2dSLB2GFb0gv6FHW8v1E/RlVu5gMB3CVlcG5A+Tm0C1OKClS+TBzatWu+ov9OId16xIEAGrk+LZTRnvqJc+ut02i3MLVwqUvWuYvCClK9dNCPwgP1PO8yLxkexOHapjyr1mhIcZMRV4WnKRLlhj1bDN4SmSGzmWdOpEWzLvwgI7le1dSWb6Cu4EbNcZSYd4rx6WASHR7F8G7hxhbpAINilE/keoSHMX/joMRXDAL3tP8/CaTco1FaIji7JUcN9GRuegrFVzbQdCkXPTe3Ob4qWeEFB3Tr2riWYxipewbt6DtG1ue8jMXryqsEIYpK8sOSl0o7HThkhZYLckosHs3KsKob3sQievV33cY1PWQHcnL30FbO5GHlPB9hNe9zZ74fRwQpaoffvBh4BtkScQOLGsucHFAS1OS5vt0YIKiYZ2ApMkz2NtEAMrEELQVYwrHi/NzEkig1p9sxLpPc6tCBiYKNi0WlfxE/BksdeiaPqavq+lTUmXTxJTEo7V4+ZjCUB5HAEBYOkVDn6SWEg4kyeRPxlyCqzQMw4RRXTz7YEXnxGbU6ak7jSoT6bAcRtEKvi4el3sZNZG3I22lnKrlUy2Se+9L5UMikLt0bLTf06EIAnBDOb6bAMyRpN9MrCcilo3qCjoI3jbQZEE0GoNrxP3xv6ffAIww/mIeoEtozQYBGsKL/nqdWHZjh8O70BULmKb7FWN5KeF1VnWM0/V2ryRiLX89FDaDPy3EDeFU2VnBvaxS/sF9jwaIbpTVFrGGmRUec1xP5n7jcoec4pRpHyAzxTXk/Cn5pg3DqpOiKOp5PQR2pM4iTUVN+Ne75MLh4d2mUCOUmU0IfN7ss0PZPX3TD+4RDwAeuSTOTeV4Zo9fNdH3AzNCy4fNnRh2Vr1LCP2pk+5eGL3O87ELg8zSCALGN3LQ2tqWs1i/2nAl8JViREb5OI5QiMcokiniXkc4iGPFT3UJpV1+D/LZJMyA0Nyvq8oOf974tbk12Lom/ZkEp3tQ66OdvNdKeXibS9Z/wLJ8JTEDxquzwBgC7Xpa1H8KJFpdf4qgHkOellAkTF5IVoRQRNK3PAytTJKuelz3P/XWD0GSODUe+1sSxzRxIAYvHjOCwcQLIPyyUxZmZW7CxHS7fpCHdrn7Cl4BPK3dH74CQRioMxKdozAtwyDc/RhKiZdf5gZggyE1x+lgKS3WjqfrcvOLImN8EDGMcr+qYqfft65N0yIlJzXANyGSabH2r3obUXj4nTn02Z57LAWB2zqR8/Z9GYwIhJdCH6jubXR/LFYPWmRlFodowm08h6F3ad2iPrR/vsVXURzsxlnvkpCOhUTR8IkRyIqCfvOuaLyChoo/4Akwg4hZ2JGsDlkA2THFKrIk95dQFA1aB4CfCS0ok39dXprmhnU84QITDGhWpgCjtQMjJGrZT4q6GTfUpKZBUxQhm1+XUybq5XmEkhflPOpSY+eSBIQv5wvJ7c7eguIYPGGrCvnrk9etOBWgUOlLhMqU2IQzKnNxpHwiEQv0pz/C+71kUWVBDPKkUIqjjlDXJMmYWtTjSXx8gcTPth0AMPUiZdxtiTb8wgZn/BXSX1rQXDjwuw44pp68t4sMTqpsGoLdPkPApQa4a5l4wHtZGwavo/6qo4ptp8u8/YcC64yXegBMGi83ZulBHxEXAv9nWM7lSGeTL4ry8yg14RmzQZZ3jSGUEJg1rixVeU2k/7jZezvPRZLRRW2E2fdwzSwfd/0JgjGYTvT2D28PsKP39MyF8OjN9pfzRJBJ5r6D5INU+qZKaaUAMbmOWxVuS6yivxH7MGD7oKp/zLw/loXA4wEP0Y9TJ7MHxs2xjVQt0oAtXokarlG83y1cynKYZ3nZ1DVc3J9KRqJVtA8mMnWGs3SZpiRlf0g3mImvommLg9ev4CNPvQnHTG0TI7AXuxLCdt78EJu3i9MqyQnq+9frlax8FqfVU56Hu5ivVi9XJgr9Rf4Nq2YpoN1Kx8/Sbj1doIrP/R6h5/GwsSxS695qk+VxN6DorYCXlTxoxQ+6Mi9u/YKdm9G/e4JHwZ5l1E3Qn14MVZa9NZTmd8WT0zAvwVfTrrG8YT9OUx9MLhUzjJxWPwxJVIGwe2WdA8CJky/vMCIF6CxicE1QfxGbJdpBEEoN2IWRXn9zg40pgASNaW6Wnny/owoEIwgQDD9j1PpmKg3H95M+3WiaBRrD+7udyvsFIXEr3ulT30HMIHNh3Vs+XeBLQS72TUj1G9iHhuXPiTpYVoZ3DegOYk4KRlK9d6QvqHgfGaBn7BbsZRcAf5gx9ItqaHIAQmOj0zDJJOjK+wZx3rAJ01QzZ3lrPsaK0YuIgohiLjUTaBb1iQfuDbRe2rsJDQH1CVL0FrhNLHsSjS3sd7zO+2TxbnX7vsgqKoOL29KqV20v9dRRtZYbhoRTF2F+Mv9VZDdPTSr6ufgGnUgJglNVBPvO84vHHqb9GEEum96GLJg5ubY7tNMoYdDAMBCcJPtrWWaCCcvmasJPa/Sv/Q6n1832mklzy0t2mqO/sBuZitfb+33yWprXGSVux8pxXGzReiraaTqr6hpqGknpfC8Ey71mlKD/nOFolv5sGVcd/FALt8nHJ14YffVyR5JqNwyfGxm5dO46FNEytVWlTq7B8rbeTjhoU/M2tp+1pqHJDPSr+13X2MGKjOtaL917fcrLJQZy9cnzMo2jxboo6dh/ah/XTs4FlHiK+nIAsXnRE2s+LTbaneLFil7zCr4XxPYGPotC5n8C44I0oNh4c+8R3n6R6fNxS+4G7AIFwAFs+ClvpqGPqpbTs906+frv93SRGIymWf1bhaZ1+h4QOn1SlkIYnCwc4MKHUkyqC+SGw/eK4WDdijiZSjguLaF775vmPf0HbpcOYm2t9EN9npzJsJdW3zBo5O1rFw13K4Z3RtBCEBGEozSryyL++RHsd1EeeiIoyqmdPr5/lgWmgII8cXYTrBJ2wReX3xTTF5wMUSoLn3rmJ+JpGFgnwkHkqS5mxkBoVBzT6nlHtn6Zcw13eyo59VxmqtxH993Mk73mDyz81+qrod50eWnXMZhdLSMcbhqWyJxZvl9XO4yTxwE3H64JcNc0PcE3Xgj7ji9PbW4YZn2rKo7b69bp5bdEYFoFaMbJq+L8ggtyz95w8Ji5ECiYgWOqaWbCZaUwEU7lbU/Ljs3bROGypXm/dN5Xf4fc1GWkz6ejZ7NII2+j4V3PIPuviwloh8UyJP0XwGEt29nAZkyFnA5eD8i5YZkCCWaj1jU2mxZ8QQUxaBNxIq+PMtL+thB/qFvcb0uNfdvhZYM8Ala/2RvslUjJkAWA+5elezfo77qelE2kX0Pvun6Ocu4zPR63nMyh9v3QfU4ECnHvCkHY607zF8Hk/d4kH66Yc/DGvEVM6Q0RnCHiAnEUCOFJgr804kXuyzKBYYQi5d2PTX9PSIpUS0Kl+hlBb5FAk1s/cTobaqk0XYwW0I2idlVbNgWOTIZx7dvkfcKcQPEmfOSiQ+TUVCnDp5oYbjeS9dAMWUh/AdpBk1JZDeSVJNuyvmnVMqtb/HWZF99IQJ+3ev9Qje2SXa3HKC1NmoST/TeM21+7PAumaqK4/RQquZR1Zk3TfDihDc+jzJGgBvGk8vjZrjr2/XW6bpD7ME170XnO6O2G0gKEzC5BIa/32sOjBHe/ho7mzoHRQHCEOHxChq+kyF7zfLuF2y/4sOovuGPt1gLhauEZeNDzrFY6SQ8CM3HJAIPuCV4NglP3zmhwRJIayxuZJDwfcbuu/pz215ue4pcBjBnyjxXcGbHAyrnvHDfDK6y8fUgt06rN8SP37EqZJlBBA/dOkSqlmKq337WXAxJgnxOYA3Hfyr4n5qf5ZYB3F07/sqZUp6ya1Wqii68YgyvkOaBfO+VB/I2XojJc+VsHyRp/ssNVTW9ALaAovUuG1fepsQDAhfYI26mQtoTIgWeHNr7fQHvaCbn49xgv9dsBd060VB+09fja+Fpm3rGefmSB/J6qz/HNDL62NX/qyFQQGG4h+N7IStPxyo9n+nPUvis3YSrk5F4jxDRhJN50QwkX92WrRmXHMSu+icr5vqPsV30dvmtGzHfr9Wi/w+ImtuKynujdZqn6TLnNQEG2Q+IpYZ/bkfgfqIkE4fgzM6Xtp+3mNVUX2IXbRWMO0I3x3hhMqdJmmp/BsTQJrTeOcV032VY9ZG7oWRDr2vX98Z2LUfv3XWm+Qg23ZFkOAoo/oLv2e0Ls23t2ze+WS4u/DmUoyXEUZACNn/ycpsLdIGuf4RudNd70lKIknoayMg9d+CQwEjhii/31yaylOXfgrL9E0lPbCWKKjYSrPbKbZjLl+TrG/P5runb9pgpbtl+CkCbF4vMWdIVvipmf1CJ78xeJRQBopuLHwWartQ3TiH8JiZbD4NO1874+P2nc89xxHEwcBmTqpaBwN7n6MT2+esrAf77WEXXvVz5OrdQ4X0EEAAG5pVxBkevypBSvzRyustKR9FT2/LzUBxHS9RP2zXXZlQmORVn2hbxS5EVhft/eI2+P2e6MRgqXRaIRukpoipi3Lpci+rQp2OfZ/SYcZlPm07DwumiSlr86f5mJejvLICAgglo+TcbL4CpiDZ9eihz1tfKUIJCdbpmMRMxqX3GCgFqKOaPsLSEYFkXRxtLJDbd+v/6TUZ4KCFc+QhCFHrPlwod8/caspbQUfUMtOHOmIxCSbY53RcWsIMve+GF66TpP92vWRo9j1m0p1JfqAOPxZMPCBbTQ0IlGH8IaPjqwOsLItEQyrqu/Iaa3XtXx1ttyut7+uq4bDum2kRcxDk/NhSIMiAeeQl169B+tfuIHMPgegINrhgrxWk9QxSWUfciH5bBTo9NY4978m/9iSzH0W8GIy6Tn6w1yAZZcp2o2KbGHoLVQ8Q5rt3mMcVzNPJI4JfMTn1XvaFfz81TPS965d5Btou88vy+quWTEkuoC1LwT5j6tn6VZ4u9Tcn76xiVFo0AQRmUKnZMkz/3V1me8IW0Y/yrIKSt8rCNor8mp88MgDZNSX9Zp2BvWA4Ktf9i+Q6kyMrknZ20uxRri1d9op7aMV5dUoRufSlCVfyUc6Q1Dky+F+lEOsdDq64Wtuc6YOpf4+TShv2qkbl68vY/3QRJL/c3kHBkb2vlETeASDPMpDL5yNVNZmXd62xewpT/ltZP0jNKhCN/66Lwo8uZUE73u23VT7+NxxlNRlLcXF1jjhkYk8YNRH6gmCanH9uLXd/B+k88277vCp4IX87dwqe059b2jDmk4fvA45DrRzxg+0qtUCfOEoErX2vAL2AdW3a+FEH/wk9MoDuq5YI2VY/yScgqSZ1gcezVOk7yk8ucmiX504H5/pt7CkRkIlbE/2r4cbPjIFUJx6AhJXVKgK4mXg6VnRQYomwfndXcUBU1cX5RBluHWeCQah85NskjgRfqrrRqaaF/hyENjiKM0sQJBUjxyi0t8/twotZzINCqV5WVA9WubW78PAzeP5e3yymJSkxdp7zAAGBAmsQU+q3vS35BQiM7DQImvDR3Fqygpur5Ui94GPiUuJcdLi8BMy2SZwXOEIojI7KNIsgXhIJipZ7yS4qmZCKXbyPSX8nggKaz5jC5qd7/pH4gqOCZo3uvApxxjbPFktJ0twXp7CCVGhgVmVgUcxROtlPC3Ou7Krl73+RyV77WSIA8UCQkoSEzcnPLguVSwpZqpHX/4fqYF7WXbJELQGkPc0IskSZ+5bTfLm22gn8WHT27HBd+uTBvlvgOQn7wxZHBj3veKxTTRT8MTU7mEnrI2cehlfYa1iH0JJOqYLEhuoSj95dbk0A+vlaC2wQwomA7q9mmOXmYUwdA1gt5Y74+ZE20RYXylC5SdrfoBCTRB3gxaB5bwEQiEe5PTcV24h/K7/N4vsgjc69E6PqNuynmfJPJ+5uDg96/VW0hPL9MS5k7T5k7IX5d0BqBAdlz1nCo9bhlWtI0F02p+KuMLWqtz8rgsw3xguqggp/x0aaqaQIeAVR5DR/xiiZWPwtSA3nzsp1DLQ9SPwT8HnqvKNTK2cd12pvcmddeqG3oCRxwaKTTgOCxs21CWBZl/ZEw4Z54mo1RDiJdpTiE3r0UNNqYI44Ie/dBAmXoo25RAUS0ZZFRM9urzXffLx2cBf8lYue99eygeenn805McodOdyfA2e/4jm+ZZUVjCUE4m5TuxWwFcQ4o+CD7lW5IGJxXo7liCJzbxwJ9XyVDNjj1NAWlelM/Iw2vp1JQbuu9saQyhNr3yUCK4zL2wVtcmdqnKlFE3c00p4EcVNsOMdYUoZLLQsvh23YDc6I7PFlWbs+NX0I2j/ooPlJPP3F3ir8+7v29NeTWQddGZzFvnMJuk8DWmJz6Nrun1snFr3aXSkZEXvi4UgvSCmzlcXL+4jL/imS5vByMh9gdbDe3L8EJ5OeFPvEEai6fdNpsR/hMceZlUL8g+8ZVHij4O6VeF1Uyu/HbM+1Nk6ybNHF5iqEDfwjLFtqCixmtcQDBuI8VPBmyngwm3Rwd6pY/i+9A5SpXhv6SCzy7R0vLS5hunjAOo4CIaovCaAoyXZemUQMKc181Z8WdJai25BP5Y7BPp+wTgwpe07ZprIFxffck8aL0MP6A24Wpb60o4F/wMeyNe8VXc+ieXAfka/MUd+1c0cWLfCQUx/kar+mR/uTp4AWmRZ876iTBPN1CZY38UZxuGSq4hKsSt7qNCcAE/rx8HRvDJtwCcj9gkWcYjQGrZFo1u7yHzRN4GRyX7im7q1y81DD1LU3wHa8yBYI1qhDwmaml/GVX5B/dzfMApP99Ls3bZ0AY2LwpGwtw88F+aBedDgy+gz+bWDwMkN+cB4sSTgJG05/A+nRcZhn6x4ObSGN6XJIHx16LzHqoZvYf8PryLqoZlfVperrCvtogMZLle0yPEMmgvOx8tlIPXA2XZzytcn7FI+LScUID+9n2/ac/oVvgtiIAvNjZcAFeXpC+jY1jU4WjrdIT0doD4CMNd211Pr1nWKVDFDNJICzRzrexiWB18vj3RL0sK7/oh/4IQwa5iv9upciqJ49BJXLZ7UW7KYpvxqkNvidhiWw31t44GMVKGbsyJx1EkFjgZJkAXATibxjvSWBnG7PylzbOGcKPB6F1IM5g9CXysdUJWkLm/m6CHFEGNivPzPNTweY1Z7SoMkJ9zvCnySm1/rfqe5pXdY/NOXWyd2MMwBBt+XnjQxLkUf3+YeXE1yJbwUvvHx3XjE4cQ2Tf8ddj6swvEPttbCqvQcdCy+QTarkmZiRtUGSx364dIbJ+mgUii8Jc+83+4eJRPGzhxx2M/EPbLfGaWv41kpzdvulR+ajt+6CXd5vMD1TTqdWPwW53z33URASb4b5jbMmV962H84qdPntTAJjhu6CPuyXG3h3PP3erSGDAT40ILBAmSeWH9V8AhG4KQpESPZ5igHx0KuF+MvKg+XeUz+1kjF0cf/8TXrwT965uEfb+RfJ12BrWN2cbBQUwMFB173a5W4Bt6GDUS+2jtZyC2cTdx8FhaCOI2BW5Mzl5iCRE28TIQ43RiufNQc9vxvMznntquD37y6ofcb6QyFoRuFbbiSEBrXpZdxW9f2CoC30BkTkOaqO9qGnfXshu8ZTNNk4glw+HX+kKprjOzMES8YQeUkK5EskbC67reYV+Yv9O1yQkdSJS057TEfATvhoBwt/KzqzNSNQBvvBcSeSHXTxVB/OaJeL6GT4DgEr070iyvC5k3N7MEH4WRL+5MhicEZkPgGrSTTpPkNi7c5sBmPSypfp60FtXnc8yuyuvyN/qI42EYZYilkeXb10yBZHIRtSJevaFoN3RIUVc/2hDDXLgZROnU6x5IojaRbqpAhdmC4Lf48deht2PTUYGDsm4kXu6zdxw77Snobwa6y66//o1rEo2HVkNmOuZOKub6766FpX/DRDCZ6u2+GUyFb0mkDzRwNSECpnTrd81MnhV/fTaWcRTp2+GbIMhvmyYAvoDsvta34N7c86K6jOgoR6CDxtny0yVCy9pXvRiBGh0B1kMU9WTzzkMIEtD38+XKB0eQBNsZqdwZhTSdlZuWHNjsFB+2r/IXOnvdENWh3mfL8w6L3AfE855Iv9vc4OFMDTGLzrRMwpvjEGPLzQxW0pwU2wllcJP3kQqvzxG1LtqLXBbzK3NkReTxeNv5LkZLCFd4ZfTbTzcIZ5nXT8MFAoxn8xK/JEPWmY+JNqwLiV9jnsGmAGf1UnsqqL7oZsZvKS1lwSTmYYkZCDkPGIxSFUMNMT/ooIdhiuNRL5Hl+vP9czRZ9R15YE4veMpz55C48nvL1/CQ3APIK/oDyawhRmD2kAEnQR3njbPREEV3THZomTmr+RNDXnk2naNJZ0GZL/HTDDcPxC8ojvMSXnmmmdC+Qsig/sE2uJo9Yv+NYZn24vTmeuZIkMRLdjNPpJjt9CPQuFMkQC8AFg4h/YlGc291RtFtgr/FvqbDVs/k1LCDntnNJrsgPBor5FuBtJH7fadOp+odbwKQBSWSrsPhdBrTjY2sdFvxgzfoTxlbXEPt8sxEY6p33sze/jOCNXZQvT9xxqUIYnTL1FDVRXjXP8T6EWGiDYwdFC/LISHwYutpAT5gWmk9jR607+AuCFxHyqQ2ohKiIBDI32V1C8o4Ffxl+h3ac+Rb88mWM/tEJbJn2iP9JNee89uf6zgKCIp6p8+4g7ihWnX4KpIyMS/QLZDduJvJm9EU1hzejNHtmcBKg/KXcfpyfVrAA8Jw+mkwuNQQq9vm21pEfXAZ+4JnQX1qcC30E8cfKlWc8xuQtMqXjYzsgO3CapmfHUU6diDluoNeAu6pQIsxLI+18WulLx0dP594+JwxhMCuHrQDb/MoSaZraU8pK9mJO30kV2Ab0g4OSNEsyxoWrfKAYIFwVDq0Kw7DlB+AnIAcZI7fQIMLvARRWzR/DLa5cu3N5L8ERqClr/bVASFvVvmAQAX6ho4bFPOqNt4IilA+AE6bWtcYbUwYnheGqLHGr81MJZf4rSLUpSL9GaQSgjKZVeXf0YrGrjZA/KsKz6nN6OoNKQLfDXH/FVKWQxQic9lxwHEU3M1f4iXWVkFiwg+ErNkNxTM65AcGgsyEpUVcR3NeTPXWUExSpnBnoTrvm0xEkdVukQsH2Yv1QvjWCzWOZM3TrDYHZSmfVqZgpLW4bexobehhTPjZD7ddfjrUCtgwitvy/XYptViEN1RuHBi89DuGjwIgZFM/F5ATZMiet1EBIXGpe1jGLwyqM633H/GNMLzlEFrECCRHJnCrkb5lWbKPUlI01cCya+Xyqv6yv7gaGQNpyjhXTQ6S+soNuxQqnCBVNhuBz1itAINw3rnRhm9mf+iVBmfQK7PiNE7WbdbDAoUksxQdhxBlcDWipphqCGdMOvKvnb59UFXR58sAZ9GQmGbyLMWJpaIbLPtvNPfNEp+OhiZsnimS7gcE7nejlzy/I6mP5tu08ORCqiNJDq1uKEJ6fKX+UUmVM1/y94v/ukTURThlTUkCPLehaGTISXhG6P32fpu/HWmKbnstl8o11V/f0ayjYEuSpLjV23Ecid5bGEqc4fu7M5M2NXATohOIW33OiQuZJ5W4SfNctFjxXh7GcqCNqlg+xM4gPYkldv1NRJujfIK9KIoI8bU/BMN7Lp7HT1l4ruB5Hog4aoRsmilarTVEbK40dOwAsRvd+9O5Nzi3+t8Pw5ibkUz6pzNlkzNmYaNChxxZRLWjOpdPZHh5LbcBjLMwAeyKjK4pvFWLnPSECA0iQS7L8VECbeTBGVq83MUZjVL+KF8hj3a1BWkveRWJ+vNVMoym4d9C5JANYs7ycpvrXOq37VVA+/p/sfcdW45C0XZf4zk5DEFEEUWGGTkjRIavN5dq+73lsGzP3asG1VUlJLgn7H3i4d9SputPPT69JYgoNJSuq8JVzaSbxtw7G866tPRgord1gMqOu2jJkr55LMQe6nxJ1/zmE1SBM1sLs2fOXv1EVToX+ZVfEj9n5kS/oqKc0/Rb+w6MWt7rWbADL5CWiQWLT2EWggeKMsc3xvvVQBnctwvp/Jj2nxxxpIl+apBDcApQpdPewCjIim5DHM3Ne+U9f4dEQkGqqmN+jHkMWFme17MNui7LfY6ZY6vWTPZ/Dvyylizy0afHwuBNB5SDGEADerR7OhKBZjTybmswDN5y0WoOtHdc0GrGO0kuv6ZYsZ6XCzmZYO8PVIipIEFmu8NQW4563R5QSs0LRu4xT+I8it52DAK00KObxQhBxaHAtfPUvVGxb8T47TvlJ67hp/7j7d1eH2sL2T1w1BlVfMPrGQt500SOLFNA9b+z6sMI89HgyC+JNGgAHEXPKd11Uzbr+nGOb3i9ePnUU0Cw+mEkROrwnir7rwop4TO+WG9jNl2/YEpMRss9ZcK+JGj3STIdZ021yPPf1g403epiOvXdNRK5Pe8Ucuk/XWoomXJHSrP7Tip+xgozPE2SBDe5LAEdcAFz0Wf9lDrf+74ffKMOOLfZ9bI9R1+9wtBi8koWH6yxYdVlrX/DrYNe/tZQWOIhOy+JT87NjbKo0BIGlxYk8bddi6pCXaFmtL4ahJFf1F73b9avx4oULjD04S9lyku3DAswGIeNNFEV33I5rZKpaWEhY7I5vUKKI0K1OTRZkrDjRooEjKXYa2vYa5FQbx0bb1xwA6Xgs2Fl04OWNrIe5grsAhIvp6xqWXDmewdkRSvUkvncHvb73c5lvdEsP5NUkblcb7aNStEBzC1S864waBCsMp84FbnBq4Ep03hTb+A4b48bvVp6aN8Kz1w77CQ1GnUuk14tXXXRjiXdbUU1m/4L4bwyyNSfDkG4G+TUYGcfs2n1lTm2cB9C4AI0tNw39/32xnD6BdwLpYdgVVI3GnUjfB2U3m3f7sqIUzVVWtoSxSZ9u+V3jF5+7xHKSc37JGXy4Rgwv0ZQKDC3RjgMGD7Wps4rheS3+nyD5zJpQ2c0H11PpmHCFSv/3GBGlwxo8H9SffKX0X/44DbJITIDq9pfz2D1ZhC6Ie6h1G+/t6G+Whiayt8xmRvcPjl7goAGEPoxjKOfnTimST2J9kBvvkafOmmYlYk+3hYcOE54A8/RxWAOYI72Y6eDvT0LhoUk/qjGgH1HA034dR6Po7+2CXK6cMtfK79/L9WDM/aUpb5+ZmkFrmsKBx6hE+bMPVc5qm/Q/PhmaHS+2X820ZRpoqmQSubbuvT7tHiw35KCgCsxTvtyXxjn5MB4AZ1fJN+7mPfGH063dtuITX1ZbuuvXvsqtOT5dRWKddmkOUa4VLKMdOSZsrZbipIpdVJfMYmMoy3IhB30Zxpd6AbjT6uAQpnfmgPFCjXyrOq2jwziB1qvuNJbxjSYHMq2ms6WIuNTJNpMEQoUQM9WSx2shmG58AQkan97tvnqOXDSedW4Gy411i8j9SZBjgX+iTl6De+zyTsPhrOsMNpOnrWr+kCSNcPBbg5uY6n4m79uXBZN4S3ip/SmzhlwrleVgmpDmRyauib+TDrrsvgP1HyweWGdTIjjgiQpvUWxuQaDwP3FUIDWPZPO+twZGCzGfuq3BptjKhzQ1SwamuNYTov/wTwA6+8DCnECQdH3FGmIKVp5NW7MDQtIGjK17CByK3DQIMKjFJ9kNYXaKwlkOUthh11UfQmFK41TcaebZ3E7cLQB+nrC4SNTzQn88hPO7KlTy6Hk5yymD8bN8a/l+j0YKPOtuC2EE+mjW/sz3MGvMinO4hnWp5r0W6NX4pXX86kO2TH0ZbsP9EnN2sjmEtL0sWle5GkX/JK/XyZaY0aAGQ2PpGlShPkyR+9EbmKYZHlI0Vj72ghMOxfd+xBzMV8ZXoptplZx35NcDOneuqhFtNWL89a46boti4eMwWsrSA1DOPyYUi84HcdJfWnGSnfWmgZppP5Ffc/tR2ndJVE+A+P+WZ6uCVXh8iYpU/mmE4opHHZDmb0LODdExUsuWAzC6IR/eXGwvl8D5yBuJKIXOPyjC6XTpFj8nXwPEqPfzzg5qZECh/ZFEj+HF0YWBtZE5h6BwNCgkvb75xQl92YqesMsORkC3AAAIc6t6D3L1Jyz49TDcDc6kUbi6oesRVJdQcPVt1iQeTetI4U8yfAWE8M2biUmOleTYxiCHrHgfMLYXcBodjltpmp7kshvuJVnXQSCM5bmTEldzrTva8C6E8hpYcfg5KvDVTsVID8mGpf0+6wkw4eoJPDQzSiE2gI6xEmiKZapghSgjpKUrmwEws1petn4sSRRP2BJLJLClOjWhl+W9R77/XeeGW2ZB0jfsePYP1u7w7wpvLGOBfyjzdgZp44Ii8T4FEYc4dMMdtGBFnwYhmcY27VYj8NC96EajLNAJoCd2vAxtB0vzKbUdUprBsc+rfrNBCks+ji+YeFtcDiw/5LFbns0oGdP0TEWFQQEoc5EvnCKCbJd/wrXsXaD/gbRl2h75o7n1FAOk3V2vBNuDuLB7zrWL5iCdsgDdQ39/qx8/JmgG2HYXCe6yX7fh/a6bkNdxwWJY1gMX6YJpwzYLMcWmAuubyr6ZA3f/Af9NA0NUrkXokC1u3lEE7DpWthArFz3SR3PzVh/i13uEV8yKYQBrdHK4vAsj+shCn7kQKrTYFK+RHX0PIQQP/3KXYWvjsijfB+i/F899O9H0FAobgDn0f9y5l9PRczYffr4RI7FZvRAwfcAfwUMhA7hzd6/xCulIYdYtiHXZyC/mR0+OwhIQJme+teWbNZ1nbvNe1DaGrThtnGaWtw0/7+ALcs35AXhAJTuz0gts2WaSKhVR+xY8mzTxp2KpBWWZnJI840e37Wmc+v72YJ++svTuN2r08S7LyAV67J8x3NIl3zpZvOqpV/T3LR+YscCIpQBJUXCkfG1KUVI3dM3lTeJhaO/qGlEL29m6CCOy2xGAzmhE8uHJDGS9XUfyn2DTI0u/NvwGvtpjXE/wJBbOdhjxEagZNVU3rmHHXK5Tn65thKINuXeEBnl0zqH8mML8KWkjkNlTwpdPAsrtGfrLoDjI4xIHOTBoPqWu/XsV8D4YUk9ZAmrgIBI7ZnPmy+gOEZH0bNk0menBQq+5PLagmRvA5uz47zNPYAbBDXqOrLiYdE8Idt1rmLkfyRs7kXt1tmFKrgqdZtFm99l2j6ZY6nNUzJj5uvn1L6r2pGwVdVbaFZE8yzdE9rcPkCEZzZTj693evedLg8w6uMSjvrJzN9NgnhnzCUPg/r52rA3jr3WdP1UUEHUnBBCt9hwMg07X9RB4vbj2J/dH4r+/b1BQ8kY+gI5QvuSnjHSSatQ0jO2WsvrL9c4OV95XNU4998bG/sykuA0pa+ii5RIY30i7SnR51X6DGSbroMd3YMvdOdgwi+YVS2SHpV8yd9tFBb3fhvbNHSxIGEYpjbCTzqdVPFjTjhk2e3xA3HeOO70F8ZxPc4cR3R2uk9Bz0DAYViCSLBOQLeFx4OEX1ygmFjsI2kFYrmFo0rq4eVaeClVzFDeDU6rtwtvCCbGG+c5Jj4uYpG9cMKPgbaBWRe8dO4KcnX8Orl4XBFKzr6rL7du2cG2EhzqrxfAoDbIDFBMKF9T7kRP6SrN41PRvRTDvyTyBAFnR0lMuZaBBEZOPXU4/MMBu9rmFyJnIjZqZKGcvxVQ4qTrCMW8UfI24rHLkyRKSgvOISZGmhSmySJmYOqVxARFszI97Yy//HJRuS/w+jMVNytAudkqA6VMrrnMgaJrv+tmDWnUMH+xgslV49FZUij+pOohnAmrCpTBE/kbrlqDasUu87BCk25uC4zP3C0+dTWq1wj23mvychMLHP3k8lIQxdS+e0ehYnMbvzwqWWYQ5/OMF53uZSpdr5Geut5lzfv3WbByhUDKQS/lQTqelVHYLcZWgWgtgigOM4Zl9fZK9kN//jqJBOolr6bZv0Y5G/uSQema4J+B2tTPUAOQE/rC6qzGH79B5O9+nGr0QkZq6n+xuPVERma859gslh9kMwQuArYHMFUYFVLyvdkIYh4Q54QRCv7JE9TvX76nA12ACPlC0dL+WK//m0nsZlAVujq1QGnjr0lKSRr8PG28cV0KTAoFPmk2SBPJ4iRZB3OwJSmKQ1PiOKYCzUkTRDKok5xs9EaSSpf6jhNtQ4gm5qlXyud6gpGDPKO8P5ABShfJlo23UfheGM0nin4RWzu9XaY3O9UkDEOOmh8s/BSa+JmBdxuvp1Xtvsrp298hx+wV2mHKDoK36J4VLTzDDqPuSDG3jVTl8xsEBC79+EMhllGisl+r5A+TNdMae33wUN7MsjSFg2kyNTgw63Ogr9mR4OuEmOs1/qiHA4E4w2WBGFzTvqPeuRofZSrGALmvrLgPUPX9qE81Ivm6eJREeC8yOxkGJH98SjJenYmnJZmUrpVcmA2mULjK1+EiSzwfxMIsrfSV9t4G0zDSZlCdrQl5c0g4Wz8yFqVX/3tHIX1ALQR5DYHkaPotKPtQC0r2k3aV3k88nI1lE3xEaXXmjAb7aqJtegl48mIDPDnt2/HfOkrxaZGSlsgUPZRZCYYuZC5wPhsEhWc7Xm602UDUJLtgbmQC+VDPlPiug+/figB3bRrU+sqB94GWd2W8t6Vbca22+BUpUv4JESRl1qdk+A5fRblThAWaPQT0GlVxKgx3JrbYfMtsIDgY1MQ39APMFNtojlg39KatcivDfUdev1ALso2nD5MI7k8Fhy9LQaYO9MiWEpx5LhbWIB61xqPis1H/KXuQjRFs3bwuOEjnLOY04u3EFkkQBb001RrTH4L5DV+Yayycm4wbIdwvHUdjOyE+lfNizem5ZOHuB/ADMVNLh3z5G3V+EMZ3O+k7RJIF4j+wpqvSuhGqR+a/362n1c+yfFBUQ9ke/nHq/9VmA4jCfv1xxuzVMpET3X5m/rlGHUs8YgOb5vUYY3dqRoX9GJJv7POJQgm/qUf4/c9z7JmkXrIycliopeevEi3ejNu4kr+l13+aXk9Zz+uQ4hkHgDsLXBw33kMBhKwZSHjPWnxe1/IbQzAFiMMaMKa3pvOxwEtg75MD7r6ms+UyA7T2M0eAOjZhtUysVHMKgNb8JF88Fe3SUPC9/OJnfbk+YpnHKP/zIufdFDH7fvUUHNmZos76u/XWtHBuoW4mBB2WwnIV+X5k7xlKrPXJrsdLVATjm+tTkaMg1lHsSdhtZyx+L5ycvgQGNFJ9V8pNRF/4O0TQKlDVeSjSZvYxdU5mjPsjxGlUtAnZPTNttJlZtaSjg88RpsTGRyuCDzMAk+9WU10+GDZ6eapH/k1+cjo/WwlcPZtjwQLK01Yp/e0ErE/wx6mYtnWSaDk1/DuBlnQ2b3H+7R9Cuifpt58M4qR9/WVy36ZGT8naVwKMcQPWgp6qRViDLSJ99r0OZ/1pqeKELmWQWDbPVH/Tu48IytdBCPJpQmvl7eb2aC2RI6KFfC9hokNxuOdgjkmzFC4Is40Fa8a2n0a7DbLWD6k/ax13+zbSOvv975Zc6AdRnFkAHX29KocuqDyr6bA82QOFQlpinZFGs4FcoK0eX3EAj44Rr4PnG2imXdArBUeuImP0+dgHw6+xny87c0rJldYUacTGvwZmBLMfwtUKKXTNsRIN8uKm3N8WCYd86x/hf9gBgS8/NjWX2qxF9W+jPcDBju6d3u1pRaDlm/ZxDOXRluPDItePxA9yRt6hgaKir32d7YZtnze/9tzoDANKG7heVp6Sn+tk1OM1jeNbPzMdaQRPQIiuA8LGpcpx8sQafN3NeGlPQfnRXqcrXu925k/XdZJDBE25bPze2/CXh1l6pk44Gs17fQptAX56ImQ8DapahMKgMNUR48mw6/okLN2U+qvTTQN2e77dg8IirNDsgykxhEwmOYqeIycjoR5G+8L6BQPNiJJencRJjWnVPtBGZ+cWRHmoH+dYsOXpDQQwmq7TR4itZnYDw6sMxE0dsiKSujzm3ijKKLTmY2D8GQtlSAKUwpRM8wlHnz6S4/OtHeCDn1h2XIwLoIrgZcEbz/Z4ue1P6CoIhMVAvKepoZulPQn6Da3FYMfNbfrq2rYAR6m+2zOxArCH+4ZRAAq1U9Y5O3I568ddzomWWMgZgVsqpa+LGvfK8/wtUwUQ72q8VDCMiaV/Y7sdq4o4DYj+ZN8Nh0SPW745ATKLLGFzPxLJNwT53kyo77IaPgZVuo/jkga6eoq56karNCpCJL1vfUUOQ4mwj5xzOccp9oXXo7TXSlpcIQUncCiLKskTQIo1H4P5wytDj6NR7bB26zPUAXL/5RDfDmeNX6Ma9U8HmLR9sTDjlc48P1+muKUe+0KkndxPgXrd3I1QzWysEBSRJBJa07T+MOGH/HSpy1eva5ijVry9j/pa/2Mvip79jI36281CBPqgC4Z7tEmxfzDIa/UR4dVehIvxTXwZJbdDji+e30X/D78TN5lz2HBVyGpkI3PUeOniY4gL4obvll/WqCyMNW5afFISMmSok0P4YJLwqou80G+kO45UMGs0HllOrBmMjxQ6YiWCBpEoIN1K/wWhEoRCTYQUcwlHkHMDWAhddpCbj+36lVz+ePlKHizVM7K+0r3XVd1WJ3P7ELbun3SrxvaelAp0vgGmMZeoG2B0nRf1Z48HuWZNJB5E9JNRrxFJgj1ouOqZ6wsk5gyu7oQpa11o5OBnUO5oHnhKqP0Nmyvsosh2uYILnKKwyWqCQk2HQyjmVi2VK8T36D+Qz9Lfdvim7Fpp4l5QTpYHSY93Fsxkowm0aKGrn30BiQeQ+JAM+QYRKg4r/PmjqXYYDo5Ch23ovMnZfTpx+4jmSkUIfF+71gIYGfCBuRfqyJpkf6/pdqbd0ugWlRrLppwQCNtm3cRbF6g1qj/EGt98cfKbU58ygEyGFhsedg/xo1DkQ2o3o4lt1nZTKI7OXw4nXJYuq3+KM/gwiK+9x9W6XxmBcKHol0pP9R+/8ylYbggKeX0WisxVTqU2GcLekLrnDAbdKkrPpHKdyenux7FRP29NOtfz6QBKqftZKSIWRdmKDoNFuUaIu8UZDb/mwjbF1qmWc2xvFzwTW5GbRdiiBKPWwf+a5I12lVc1abIeSRUMYBcfe57Yd238PB7FQ1qEo62Kay1vc4fBVsQVPNnqzxieqhvwusw68cPbCm9IyieSEoxrsttxTvHncf3AVX3b34f54u/ZtDKS+d/9vhsraM9dlSTtPssVk3v0HgE+55hftz8tolrWflNQXFOcIAQMD0ojiEQWRXsav8KIAfVIRBcTbvFpp4q+SSpOp53GG3qdenlbIK9yoYk+pam/wVyMjP/0OVmQ5bLSCtnCCgARqXf56wVW5gjrdotSf6JadPVIZn54FOFDqD+AzKlfiVizuVzPSyoTs9NpzdNqQp8gauXE9xNTEYzTlYxC0GAWUWu3/Ix2+DNfiZ2sm1Gao9JkF9j/zo4AG0nJMUZ6lhU40npn2jih9BvpciuIYVl+RyhaHVYuN0siIOpmHwgyKSccPPWGdEwCH0i+xpczHylOG5P2o/yqfUbyn/Ngf87A/NNOO04WClvXtayNRPuslkqzD/R3SiCkx5f0DG7nMnrZ6/MZq8fXwpjm2FUQlCYPyIjghrxV2EvVtcsJgoFXbi+qHcxClM7RGpXUVy2eT8EeteuK+v59In1ANFCeRk4eqd5lj7VaGMR36jezmdnC6NJ6FLiJCHoT6zrSavgBPRXb6Jskd+EK7m691N/YNSHYFLEE8DCb03l0YNp5rLxnRgbO7l8rWpPOYh2SxkfHuDAGEyDYUDuMgWoczn4myqA4CwqIHL88DTkXXPNsqf1nI9xYUdfbLN4NxEck/V4O+DSnhPi8tfN1niUPYUGAnlaEQQDLjb/w1gm3o6+D2Nk6JZx3MiJZlMcUQxUQqHIYLMFFuXiwEYzaxf5n5cXWdW1H5oRCgseN2CCILKuY2STXYdAXBBEpKXrYWA0g8PTFEGqcMPwz2LkvdMVJvl9v9j5la6dZdP6IGnJ7iCVS8eNnUdA04/xF/6Kd5Pp8b9v28Ljh3fasp/lPhyuRhNy7nlOx3n2hKiJxkIzZIPW2dvJnA03wGxGg3cNKV+bUdIcYQWr4N6xSIEFGbrnpxA/lvsj47HtAydOlQJYKxIa8m7oFnlxrFKWS1Kue5SJflwXCoOl7vi+fQYGcSkYt7FmPANQT2FKL6cHNyfYg8iOwsVKA3pw8ghmyrI9hczch7xaZJiLCjk4/t4otaRkCudG/MmmWf/19zzKSedkWAZ7o8QEt+wJS8OlyY+iiUdPqGQ96FnmsKZtGRm//mw0M5JOU3m00m/Z/w3eFeuSUmJvpufgqumH4m8yCqgZDwSf3kxbEsWNFcOC3mh5wZu4tb2oYr685uU1kkH+gdtiTuJE0WdBMby/0NjQHX6oTicwLKtHiQQ8CL/rMF4b1dIYwSUaC6HtDnzjfWECQBZ5HTDh5V6Iaq7MnE3Z/vP5VHun7jekyoMidxoK4wJiz0GYadJ6vXccx8s3nv5LwL5x/EsjVGqISdc8Q9r4BAO8Zw/7uRiXNLDMvWojdgOGjcssROiiOjcm/wo8fcgt1YNsIHJ0im2ZXSapSv6LfEguKjo5JKJOZup2vIvowJ9HnWQSDkWzCjTaSPrBGWeyTEdYJpoGyDLsFPoOJHsnPb2nwZ6TT3EKYSaUbIjXpBfrM4whFMLqSHZ7NTK4lhxqWrqS1J5L1skgmIY+jWoVjc36OtPbUEpXIRtkcqwDIHxSx5aXV1VyXKvcav+zG0dT+QQcbSd4/a7hojq8D70H1DZu1AYS8HIvcGgNxcc+Nl5R9stoz/sK/+9WmmfkrkCsw941YnjkC4OF1SDoOub031yLPF7r3MYHCb2/wXCuGTQDzdcOCV+YX0KszgsTJM6BPwb9f+DwcbGaN2s+L/IiB+Gt7ZK6DkIQenO0ceNQeHyD0C8VWQkE7TNNloUFyux2byXPQis0UAhjrywbhvSQYgND6/gDx3+PMIrMNv+gwYWb9hin7rEEmnWnFzd+eosQ31R5f9ZtjCZPfVokogN+5KZgXBE+/NAc02gQqmDy5o98ioOt5JLmPqu8bL/9+8dW9O3Mm2wcVzuR9QCqlDT1tj8UPE5E8/1yzFWUD/9kKnODY2JLARaf356dEODW1odi/KeY2IKpaWkCxasddzC2F1CQKt2+B6hEKZH0X563R6IB3s/JwMbExOlF73bTZuaJnEIkBgCppzufNZCS0yrA1hufQ9o0irLg4vf2FRvqq4zrdektg6m3b+eabKiKh9qOxL1RjwgLZLFoeuhpsa2Z9QRrQ29ZS9Hfu6+MZLkLZ4nXQIQnYFwI0itpRPB6oLDYTmbdQZ9oHq70/LKu2umsnGuK+nHyotxLNmzlWzcE+QWb/M0x8XEsw0A+Wc+hxp894W9YjbMGQPWTb5C/KRC37cpXRpFtlQ81rOOxjQHDSpvxzIg71Zl1J7szixhjPAL0R8DNSK5oM4l0/qO0OuJQNnSsSGa6sElPcgC64PUX2DXDqZ9BgSZH2PJ4Y6vZk4/HdmKSjQJiJb+e1DvkJu8wJY1cgOKzKQ1MDtcVAqxOxxeltTCZ12yFq+3kavthZoLMUkm1iVDzZpZ9EMey69avxFNZUB4ttSeRCFC7k9Txcl28dCBN8SMT7wMZvHUhQYchGnzy4iWi29LHVF4xxfPt+TcAH5lwYvaElo7yjkIDeAzcbceCCgitBVrrSxni+0+XsM0cjmUx4nbkh/5P51yArb4VtyYo5nUWfil6Gq+/nW4ZKypTtt3dWvtFNqB/psLH5muRlViG4noL8UIIiXeEDn5Uvh37/ipuCVbL7K2UlY5h26rlaanQV2p76jYmFTE1AaBuPWqoY7ycBWynW1NcJbASAtuOFtxdXV7J0vbFZO+t09IUfxKoHgAdUmvsKOP/4fj2dUIwvHKNwpNX2o28gqB6BFyxcSjZdvyi45n7J5B2UfW3+cCm67cT2okQhK09+A/ZPc7TvE5YL++mEl67GG8T67fpiBdom+bUiFRxHwtixNWBmir62E2TlT73s/SUy0STwUhwcNy3dYM9bqlYTIScUl4O8hG5Z49w0MpS11UJQREPScSsyk92yJrPVCEg8ELn/nIwkHpizf54NHR94u3KCLaw1zp7wxXQcHotuMmLHOVNMOzE8CfXtt4HHsTnYSmkiQmSf0NhjJlu12DhtiC38vAbaqKiyqUZVM7hNmezUmouvFc0Gwe/Wrsrz95MctYYDy4vlfOVSnAfaSzcO9JbbZCGszvh8Ou77DcqIZ5eZPC+aLlERqJGDmHMQq8oTOurXE6FS7wJ2KHrjY23xu5QQY1v0dikWYsomRZIfhfhGRgMwFBha/WuoyEocvCWftGM90vXmND7+nTtwJD/kGkfgQ4JFSJVnsJR14Lgn2YOQIgCxgOkDrwUjlq8H4p/pDMzbqaXNbEDQj6SYvUQ9AuvlD3YzNYKhyDF9c1SD/mGfz0mSmIy+lhuFSMFlKy4Yxw222bME3WVCA9CdruzjmVWhFuhCaw7ONCn1xzmHUIe39wVJLZcVhWuK/VEMPbRjCAmEGHXWnH3P+NdaCxjBEDNC1O7ddI0tl7ailWMrv+zVNWVDQfObZAOdquwfU44Qc/8ivDG2Bm15OHi9Ltesukf6eo72Gr8tmgtM/yCpdC2Rh5WV+HIMLhqA/GMSEHkRNCDX9Tfr3P5dnPIsIU9PWu6jikxvvrL2zTK2MFJ33S0CfGju/X5YcVLvNwwA9jbq821ZQqFvDerkn0UrEYmB66joKq5MWR6FQ7F6AsM0ucX7rUebIeVqY1Au2R5Z+lqdjZwJxajZcb9WrVkLOo/n33lpA/5kpaj9GDv0mV3lpdQOyrjYpswOcsFGxQXh0AIHuf/xCzyuyVE0x+q7qWfwUfVDuDNoh+8BLiPAcKrhme06nI5Ri4MzRJ/OIORw82ReMOCd0d3kZHig1Orap8Lc2B6oV++XcSTilpIv857mEN/RWfCiDjnOaBxGjiYj3OO0pBTBl9xMmblMKpBHBuKjcOAbk4BXznwhCw59rRuTw5vgsMUzEsXfCiWooY9CMsaXQnlcNSVvXbRB9F8+Iel0sd1MCXs6CmtZbKRf8dGwSv5cEL1451yVvEsXDNna4AYcFpCR4XbSuqEMR4s9c2LC0ayMgrW0En6a48568VAqw0NzmZsScvPbv6h0CmThV91Q2bFrYD6sXC2v81rUYfGFJg98P/XOr9ZR8+NDihn3fQTqst6g/eMGVTDQWPJVXeaLnrKBAiiI3TXRefOZLq0RDh/dhA+p0FbEHhQadjmkaBIejONRFLDCZmSUdcEICEcjCXCe0I0ACGuDi6dwRIvC4TaxumFWw4ZFbRs4x2YgoYbSGyFtCQ6EcivET44DjlOdMN8LTva28SQxPgOFQ4KVo32i46jZPU3znqIq34BJ+PN5epLccjA+WDShacJpYmogjUDiOHrlOIInHd3hTF3hYhhWudQJ7XeWjvuuwW+WJZeb1WQ1+3AvJo/p6pAhjO/Z7+tb00SWrb9Rno0mEJaG3Rty7/YZCRRVzXQhlC4E3YQERXbJEKULzWnaIUmvqEGI73cDomxzjiE9T+3F9+ZDcNugQCiTj4I368i3c4AXF318ciUzYPo1vyvMa+y/bjmm8hFu6GcA61v5ATtGql4X/FUBzk6ZBB3JhXrzpl4fPx2QSVY15fjyTAAi2+J8OoduVPMKahc8DdcdwjZrefTImp6FFBLGKcL1Z0tBzBS3KLdwvgNs1YNuMn1CSWsSCDjQI6SU1ATKx2l6k3McJB2xfqbUYl+m/3T7qSfLfmWdrj8YrdHQyNFuKBCEa9J5J9x+kNPIANjdLkjxrzwCx3W8I5+kSdHRtT5nekCjFWPtvCyyYmKKmhG2HF1N/c0yt21uQpKCeaASYpprItNgTpV30HfqW43H+bIYp8SM64SjQO1OWqZAzrVVMgTtBwbxsXZO4eM4YW+Nv4gstF6k72+oy231ByXWvUpbsihjgaGzbZrW9UPMEB/LgUZP5Jc3hY5k6DcNtCQKPn3QMoPaYGOda/gkRxSXZDLdnsxJih99fdYe5EvdgYEqt6vIcY6pqPw2QfBGl+vgIa0GJhQ+RF0QwrNA/jo9kt6TukNMsbUOzKERXfTUT4rDtgj0L1YyjadxiI2hm9vPRrJnxsRQ7U/iZEpJbmmkeqGP83WYbHZZG/sjhr7iN3MQDJMyR61+DRYJrPMN2aBGTAyTkBynQZZ+aP1sfJ3FLFM+OqCzujUuleAefqqdFQg+eIbacN04AIgDkqOuNJGQ1CTPyOubMBU/aobt7UN+ZAuAYjWGcMsLBDgIVmnkVFdU8e9J3pxY13XKXNkFqu8bfgU0Q8M5hkW08z59j2mNrPsYv1P/Snt7Sbr4tvy0srF+QxIt+xyGTbDUWzsyj2g6a4/r6Zk7LZGAn61VoBsbUe0ICSjhVM9dtXSTbBGnVXaPS3D/Qhp6uGXsQH66Qgkpmwq0Az9kvBmmq/gCYCD4MH5RHTKD0j5Mr18wTZHukAkulwWZ8TOoQDpdjaz/LmexJprtlLAJwXB78E7rtpuENZqzbIv+9n2HyLN1fCbyUpXO/H5IIwXaOF0cRZYj6EEU0oT4nvjE70B5xRSUUrL1l8QoynvZKOlveOBLfGc4OS2J5PV+E0XMcbRWKOIbT9kyyrlRDZrgU+LO70xvVf3RxedyjuyUOPuDH8t3ubzF/Aa49mM4jmKZGTJxjPipC6Cu55YocpCbtZoZVKn5Q5X7goEK2JvXkekE+QVUmEY1ta5m+QaQ+YZHeFfGzxf7+aWTnbsJj7hR8ST9gDkDh67MsKkXZmeNzuLGt20pvVguOO8DRMfpGx8MdjIGu8tp/7bR7cOB5NRu0sD/yNp7QRNf8p06SMoeqMtN0ZMG/YQahSu1UxMFlH3egamkua68W2NgWdcgvffG6D0AkEVBHRd8WYvgbtKZFzt+HE/fXjSwOUEmDdd0ylBz289Wjq8xpLi58vD8OcLFvJYnzQNw5M+nyQlFh356yvBbOLdIzad2wIBBnGrbCN7pXgTxEqNJQp19j/6axgStR5PliKrqQETrdaN7dN1uwL28Fs0mgRW0LSKZDbA5QX1GIyOWDGV+TC/k02mkNLl6BYgo4Pa2Xs1r+KUCkvB7nz+hS0rj6P2nnKvpBsyDCYF25nloq1wZJ4Fz4cZPUl+BYbQmWBLKXpiKUmA+ZOTX2gbzN9wI1BuM0Guhk+zNj4ET4Zm9kCvJtQX+1b2yBO7QPaamnxasDAEDew/q1thzenamv0+xgliTxwhLCUv2rQvjayddNrKvpHq6hembWibe9ougZxQSmeSC4GWp7V7qd7biMgmbkHEBIfk9vSDArIl64hTNMy0di8ipaZaGqypGeyU9pPccrzy99gDqSA28eOlLXLWd9m6f8V+epn+XZIbNZsjbm1Nz23P2us39xCyk3c5mb2fGeRy7myYMnSSTBxvGqmmUous0FUJMq/gvSOslV1nNkDxWpJun8fajMxxIEi58xKv88Ffh3+cSpWoAVz8i5IxPB2qVg2Fb6Iwh5ybTjMJzmcRgvVYH8bU00+NVBMPdTY3CqA9e0UvTn2VZ0tTccFgYJkl5YVtvheHa1GHhwnB5bps5nrswMKWqowwwYVWiOBcaFDlXlGGKNr+fnVtD2XJRt3H1jRAEjvkqhQUKbuGGucGEZkqvejaqehYkfSumX0xy6MlxMKwb8uZvbflCFCYxWcZKz90nnIoLnqWUKqOKtVWcPeYWr66DKUM0GXFQaG4DAEcsP5o0gg3JwtnPWwXrpumgZRVe5/Z7O3ACoqjsKv5ovBsYJJoYhIw5rxHK9hroNR/fOAXKdQQS/n1fHnlx3JcXqptqBy/XEC9d/xYwUDo88pkeJbnbd2g8yrCWe3LEXHSSL2HMTomJMBsP7RyhX1GaqblyS2XfkPaUNjbzoFa51lOkoVjnpprW2mfHmgUOpcreYGK08Z3C6P63I9fFWZfdjM1OuBzjGAMTQRMuznkoguQoIz/jtwMCe/Id4P7Gs+PeumbyA5OoiPujM0tqC8S8sXJYOXtuM1ZerIu+ayu9tyEmB0JVYgutmnSR952w6/khij8lDjVEig2ENxlSXDDe7FNBciUGqkp7WxqEDL4HHB5yIUSh60XJe7BflqB9sH+B8HwtMB+EPdYTLaFyaNMjdhvSfUU39L+Gf3+EN9jrPDH3XaYX/Trrp7ZkBUubdq3XKLW2z93Cxyd8PDrNSOlg6q7LzhTuUa/BsNnAGGx/7zyAw2ufoouQZEARSFEy+CFyR6u9QS+fSYWpF50rO88gr23lJonRfcKM2PUaJFMTP4muvcqfkhvyyRA5Bi0l83WK6iJUkqQGF9jfvS0d8qpuSojoCK4Pw8pcNyNP906GE9ehxcq9EQxIzwBItCnxbC4pl6yGCNbFsU+1/d/XrvusptT15SQKM5UIr2D0+JvG+Lq2RCqZcq0nBYreY9eGHXuh1gAKOtdmx0zIfInJF2u3cgvfz5rTz7c9+l2r6P3zQX8ukKCYSHhWghgTPJbh51EqZIsZip/KW+Zwk+8JmvYptkN8gbWyXBFfghn74p8ZE24XQSkwDN7yczQwOS3V2koO6+9jVaI/cmeC5Cg6xJC/f/FzYcRvq2cuLUg0hYogwG2IskHc+nlrRq28dFXbuu4TvQGqDF/DMFTrLp/qPGugTCIToDTHlJuSk9cLgg5D8LyCy3EHGXH2MpQ01Dg+HQG0ynEcYRJutK0A+/hQlFIlF9VlOyXGKKF4sXMm/awr1D48Gv8WiopXK/QWgI44M7//T1HwGcasguyu0Q2bTp+XreXGZWkMN44yQhcT6VBp32uclDvIOY1tQdp8QRsitdJAdr/7pwCr7QCF0rjB1HUbtJBRtiPVhKA4miHs73m7snTT9TAraZqayIJqSvlp4R7HGxdP9qBI+tdBOmxiCeGVy85s5OZJDi9GPZsOXirE4dRL1ZWDa1bQ1OSaP3qsnskqBQY8/mWYzSNOELSTUoZnJQYSycKzqQ1VzcovPk2qZcJXIn5tEWZJXHgHuq3rVaSNmCuMT4dx7nlMj4nk05IuAkOEiEyefl03kkKc0JUB2lDWMXWims1ABhyXMYbDW4wMT04vqIwctM5REAGhfrNpSW9uM0d9X3IFecBFT05R8G1M0zjZhifqQkkrcgj5kCH9Me6IuXsMKIDJarlk14XByKx77XY6U2NpBadmJynA1qoQMUcWDuKhZz1gmYZ0Tg7b00a5czRxRQF1HlIBnbdPQUU1WDsV2ZNXrBjeRvz6vlOxyGt6leoLPMLf++eSFMLXK206Eg/uultfTyr+2zYswNsJwlCv+kVtJGpsTBvfaIRr4h+OvUcAcoY4tCLyVxf0h+KsrDrtW0wVmkQZwowjprSiTx3ccDBHDybP19LCxT6m3pCu76PjXDdVMde8IEC6+ybcWakpSUF9MW3CDCPB+Hf7CkHFpnpV5o/PVb77JjE3keXUFYjbLMOZTZ5KaqrHGO82NqmBrW2Ztf+MTPa+ZNStu116lj6cqUy+ZSVADky7wo8QONAMvy0q+z0SJIqvdV3Gn2SvQwNsJCd3F6WowCMO9fHB8Wi/5eJ+j5dx2q+OnulmE2Jfu5GXEZWhmU5cbfOs5zTramicOWgfTdfSq5lIkgdTdKvUcGM2bV7v/G1LPKS9Oh0gYwcBOuFpYwFv5Bux6OQzB4CDG1FjmNc+oLAs4oLjqrlfcXN4ERo1iNppKwNNHc32xp+Ne43Q23X+HbrvO4NqykJvkY6zf8+ieLuDt76C9EPXpYnjpeb2ACmYWpIgE72qnw7V3vit7lUC1f/uTkFpgo7tlCmaPLKl+XT1//aqEGTN1uN532EI17fH4t9hQI/Tbhl6sdTbYNbgWSkKIyhkRA1aQPd/E/JT/DMO8YtYbCfdgzLHc6Ef+AmcpgonbYfyUvi9zjS83vKvQC8RJJ0xYDzpKNwHzz23ivsOqAIQbzA7YgvfdzR3duZTclZxqLJfbX8zr/iZLnPonTal1e3J7xMy7Jj7e+pAm4QaFbWpXyDBAJfPSpDvMeRb8M5bjGEGNr+LpHk0f45cDV4A/upX7PaVcnJz6HynD9EulDJa1HxZG1WiVv70jBVJwJ5iIXmSV+sqGsd36iREUtUpNaMIUKyu0WNal5D8HKmrZm4buobT77Pj2eIEc1R2O/VmRXmIEN3zgxP61yNiXWFqy8fFkXqdhn6UTNOwzFOB3q+PJzL9fRYkA/ybfPre4NPfVEjOyKwfR5eK0kaCaYCUHv4LpA+o+IBKAKsD6ltUQi9KUpDKoJGR6rnLuuoktpMJt29s1UcDPP6DL8DQPksHZqWIloH2oRmQTU1eYSCSrPO7YUcsKltvUPN7m4ddTQnWO07kVleVcS0mJ8oDvVH/DFlCeOLS5iS+x43uBh60C0Jmaz7nCoInITEVBH49RVTuQLPPsrjZq7IdSQ1mvu1wyzrK3wb7+fOy8gCcPQ0y5sWtPEspHPZloEsu1Qzd+15cyVKkgrQOSK8A+hfrggjj07/HAvRvpf/tcvefBwPOHthqk7Wgpx9c34aBKPCbn4InUcYxghhlHn5vYLydNChcJ5OFO9FPt9EmqPQ5B8GF0H00zqe3PxGz94tASVWHoRrWed9b/t7vXfHM8fOxifO4odtKt765KVKqwFIB29zuJYPB7xQev9V13sQ/UuUssQUYbJ0W7I/NpHa7xhTywY8YE/ihNZbpRWxb8WZUZmXtYPETOd2XtK6tU/Lhir5tD18KZSBPqt+Y7foBZ0z/zmsn/m2oLaH9qnrRMJz1c47VecE3jDm9PXayOP7GDlOKYfv59Y0an3+iSvoywa4Bdl+Sk7q1dFfLUoj/+D0E1Yx8qKnXTcIHowiFBVSApwwBJ3V9oeZBUk8lUIzfK7blemnORiCvwM8vPXADaC/fL8R+BNdx3haBstFKZ3qKltM7+psvsxYF6I8SKyQV4j3+hQQyqzmFJe9v7Q9LQrirf6qJ0irdaSl4M8XgE2wYWn+YXZ3vD2YNJ8oogfztUgtewuJp8bMYJgRREeWEywNPvoLKu1Kl/QiGPE/pBrWZarD2KKt6jBBKN9az7mqF5dA4eHCrHhRgCIAAwCtVBAy4VHN2PyeGbwejFszoljVM/x/l4Q1A+TpPjuy7zriyGbJ9ofb1Tq/OXssfOlfM8fbnTv4aH2Uuosq8kHT2rEFBPJ2jjW6ciqO6DYnomzDdL+/J0idWde08EQqOI4DUoyZ3wdaLWju8pS75pfrz75bXiVwePBa532QWxmcht6NIiDyyVK3UjI+rwQPOb+sTgF3tAvKUcDxl4jRKgi5usliH/sWE4G3yzadFjUTQq55rmZZL7G2633bXC7HGurx81/qLSHFxZErKbfwNrkNQYWJHKgl0uEfx+pz3X7C3XwXg9rAchwa2trh9cYdtxvQvWwqYk4q3IB/x/T29IQsC/jLysQsOKX4xqEIL/ctCvuh/fxWwC0DTt6HIQa4LK+PwvjUZO/bu1+AOgbHMKx4B+sukln0XWhSpOBBvcKZdLv1ePMJRXC2W1daBLaTsGPwNm2ESA+A98FAkdCgYH5bdgKYk5eobXP203WcXy2/N1L+6/DIOO3BCJZe9ctOyX9T2BcvfPq4dP3zasS5/tLxedjaFV2Ue6B6R/xw0gC/9RJ/ZKVLFgcQiTUSGF6Fr/xsSCC2Iq91N0Rt6iQO2/ho8JmuYr87Ck/o6CkCqU4r3hHhc1djUpQ1hJHyPuU+mnAdsHonUTyS4WYSv33J0LjL18PGQyQb2vgw0qZJL9xn+M8DEI622+/ALyKnOb4gmRUzOfRbjODoVlFOwfvVoRep8y28f8/NlmXRTIR/psytOmsb2c17dtwDCM8ikHL/bapIhENb6kFnLIjeDhSR/BlYbG5ab7cfb0hqJbDYXh67pyGC3iScnCG/any+T2fuHOzD9nYSWLc90ZE0RatsfQ5vxL9nEDo6mK4i+zVYehiL1wWV++p2K2/MQYsPRvhYLHOm8QNrXHS2bjOmOyI7aUt3CNGnu9cSY2elpRlyKBxDIx7BPM88aWNuCE4nyIQ1b+xBfM49VsBkPp8/1VJlZFEy3O2vyS62dDVnZyCmPp6e69FxAmiqZ2AbSFttPjpTeTW0t7huOrEltlc12gjP9Yl1DJ0fql36c9415BGmQoAhy83H4YqQoghTQo4VOd00J97KSlxhAvJ8V1mZZv1/RcDfYBro1kb+HoVTYokq16JJlCZLFl6wk3+BHJDX3tQCgoYv6717JWRmKApetRs9ezsVDFmIc8UY1eb63mlXn2EMupL4R8cpEAgpe0KTLrPsCOHPyMN/4EK5piEu6089eWroARyxn9haeDPjgQr89FiTfds9Ee5H7qVmdWPCIhlkQro52sDZZPDAH/nM1zxcrdxV6YJkKpinibuAWAb9+u0nLChKw+N8z5gc42N8/fw+YgWXTw2rpKzGTqzHNgYy7rONrbtu5uogE8KHyzMQJvueLotYddD06mw8Kx81EhWpf1brPBUK0muZRlFefwRQethd23nVbSPAmAYY9HZS3pa0pzyGHp34MXOA1FMB0eiR4vMRzw1m45zy3WKUms5pnez5dnM2zy94Fw6nBjR6FZopIfX7o17oLcaf4JctuEy8ba82pvjv+GEE0SqckTlVbLLv/yn6osCanyuvZsWr8GeNSBiFFLa0JhdeW0e6+zCchjhc3fcsT7pS4ZK3my2PGXF+qe1/yJfSv0tnPU53Hj420iv99sWYjyWtdcWrsjjHDg0lB86ckCFlbovttGTckWIOb5PmEhfuSVVR+eUqba1Jx+WV8dT+mdLDjVL/fz4n8//v4n+6DEwXcyszRTAK4f9srvbIQoXBUaG/CKWCm2L7MhcpcDJpF/UVVxgfLbrut9stkirkzV2IyzFqRhP+VvT9ZdhRotgXhp6k5fTMUIED0oocZrej7/umLYOf5zrk1q7J/8Nu1m5ZpubeEUEQQ7r6Wu4c7LBNIb9h9AA7c8Pj02Gui5rJv1T+ZPgjXfi7BntWoEclEHKCPKF5hED21KFkQ572RPL1U49b6nFBkX1Aea6wWm5acDUiVYpQZygFj1L/W6qCHEvppIwcRAods+PgnpRsqorSm0j2UTFRAHpx02Tqa0X2PWUJ76TtS9ufnDDX0tfiY9pSuyCeAyflKoUpiNC48bNTqU0rjimzoRME5sdHPcXS0VNC3AsRcsMMvoS9mG6eruW3Bo6UB0vL+9PSDVgApWBDgdNphgn13YWmhMnHfupImAzU2AjAR1k0gCMt+nw77QQH2lH6ZsCC4cXhbM0eufSc+ka55OlD9SSZ9vzkImpXREBdGnaz6kqt90hwKaCnIQr4jPgcnDV2e3wyvJ0gE9Gnk5y4qk9AvLqfIbExoVYy1MmJDtF7/pZ23paIy8DS6j6PzYmmXlZhefUB3h3VSS8/8R0MDn53R+SUAj65gVVeC6h7HhKdFLx8GaBkDnaqJ2iEWpDaAedKvSMBpVRQyRteFd3+P00y41Wdj7iwc6/WZSOrGmPt5BKmNQXVhOSXsfzz9LT997cGmWv9jBz/5uWMlUk9JEJTFo+zwkmz+tgBtb0CZTXSEIgJt3or6ZBwvAQ4ZYmtE0Lv0tu3/HLW1sbSQiX4gmYZ11dyg7ZPc1vepKvhyDAdFUnWHYKNMcy2m8EsP0c8nSXOlEUGQhZrq4T0NmwZsob61MzAqt6HDcYmnMk8OUcTpbszHix4GUdcuml94w0WolbtAQhHXWP82CyCZY967kWHpLvGkAs4uiVSqhftWlvvUgBsGslHU+ABqliHNmIdXToUNoeQSIuBQuqkhYvrcVmbh47GRHXg1PvgT0pD+pBHH1Dm21ccLIapmJRV0QpaEHrnTRFk5TrJfQDGhkzFAZ1sGBO+C/+uP5//nl1tYP+MEXa+oXjsBqUNv7D2n+c93wcY/6wMbmCg6N7QnLv/MbruHuP3oDt/YRvRIHJF/9/TAyRpGCRqiEDox2497++Ihg24LNPG4fiynUIJQufMhyEP7qrQcdfhC2Ng6PdhjaUwjx5/qPxq5RFzaO18ae2DgvdK+unC2JPyghjj+XiKBKon77UnaBlUbmaegHmJvtVVFYtY7AXXThfopJ3c/nGv6VrLmITA3W3qfpkI6VyQn7B12Q4xVmRMpYqlxXFBeOkTOXdRwC93WntiN9sd/mvCWLUMb5zitTL57SohXO2e9Vr17xpm9+3I04G1rB+vRdFAgia0T/+1Q2qxMpXF9Op/R1JyAnFM3N3cXvg0FZygvqJYk4AvISUn/tn1+7xIatKrjyc/YipVYzNqb+8I9WwEB/sqfmOnrskjfC4RybJ3Rv6RvSg2F0Oz9Xk0IUgZ+LTEjqyu61G5izy49Ag1lYcotXeONrYHQdr43jg10IVrYZqzf+umCsaIp5S+Bf2+iTsQ1TTz6J2NvBLZM9Iod99vp4PT+V0m1M6MfqTEY9+M9uBxNguvgZWh0/msELXjZr8daeXDkNGohVjYRr/NQiX4Tf7M6cltiys76orV7f0059n3z8JP+SCAp/VCb8UjRz5dVDCNxIx247X5ZD3s8t8/QKt+shrvecuLC0wfOZ/B1YMw8hdeVWB6HM7t+gDmfXBNeM9B9/2TV5BTTDQRvkWIvREQrCSIbjJAKqipox0dPqWXlhPgviFTpGT0NGtUNyO8GVuM+LzcZqY9Zn7HdAfXPeGwQBfOROwmJ+fZ3XK8dCqx/CvwfoqQr+DJFyZ2L4Q1dkLPs5KiPICzbJole5kY1Ty/lIm5sgq+j2HJawYy/nts9hSRSPHWhbxXE8FtU2+v1cvGI/435ewEPVJj6unCSfg6CmXgLEVvm6sMvotsO0uY4iX6/tr9+dd+a6uUsxzOm+Bu9Fihb5Xs9TVNtvhfOOrLq0ShZsbWlHyPPA7e7/+FnFb1w2iCUfWRBpVaWXG2HPStTDbvDRthl0W3xj5VbnktsqGSK8uq/YS9/KJ596vG5NWCIjb+A9SSMZfn6HRRHzA+E0jYWSbY2J61stdfHL+9DNFs6u3r8zCnn6h1Xy/6fQVlsfGRoXEw/hFudQeoaOUFsMCGkuvtmPJ39j+Zz/ksNbuhTnx+QsvSfucnBodTb4h6s4G3ScOT/jL+bG1NsHPhrBKia/CIS+eoOlDkJC8mse531jnPra3GgZmMQhT5howKJkwMHwfkxBcAuU3k+M7qIPFUOtvE2AWR9XVYF91r9dXIwjjPt+th+TlgR/4YEVdyo/ffoHgaAVCibAXdBkufrOueVAeXi6g3bzvdpPXR5p0guofPeKrpFyb7L1yRDsNTjghVKedtL64k7+Ei9wk/cNrLQI46kzBBDI47ip9bvSl9JvdhQW9aS1jZ6Vrax0trCtDN1s0Z1HVRSUq+X80bDZE1h/aXV2mU13DeSnQaJvgQhu9s6vMLtp1veMNqm9W5PCzW+dtqc0uVir/s3jfU0sx4tPxy5eB49negY4WTxG88OGi9bpHZbZ15H0HetoT9OUpz/M4f/fecgoNZAqaPCEKWLf91Fb2Z5miN3Pcm+fKlFvn5kVZXQgI6dzRNN/puOUvuDBeFNrtlS+lP1+/UJ+dPVedhvGG8vrZ2+v3wjIT0iCA42Z8vgkcpafl4nyLmdf/2+d2kFLxYdpG+XQfo24pwgsFecQKQb57Bsca0v7Dw7poRpHQk1yHx5ThdVQRDa5UEgTTyvxOfHySJxshXHpojhwQpcQKYjRmXYa2ZR6GQ33ZgIwz6yqGNcpciJoPgQGdao+WZCKx21omh0RZz+M9s+n/7PbP/fzXY1CzymBAfZisv8KEqFmja3JZEfT7pwKgfiyNqSovL2Why25kxJqQDSuhQEuS3ZU95BrL6UUx4Y9o0Gt3sMfTImLPqNPsbb6f+0cLYdkkdUYndQ8o0irq5/02bUuu5mNIs/u6sUSDdus3O++IehRaRWHtfO8SVExKCDq1EgUXOmatHy1c2YqNfeQhn5iEGBOtT8pZY1hZerVFvj0C1+7ly3QM8gZSPw5zjICsYBPqQdJf213m6TMoU0IwvO8uH0M9pucnqUVPsdeGB9bnJxW+xjv+i8EN/zpPeEoxEeb62WmP2HsfyXCaR+hzPR/xxM6ZZPdbB1II2McRLwMs1svoGOpPHnlKoAmgGf+x6R7OlSBrqE8R1ILrwp7BodSV6XvXECn8BmnprduYQPO9zoMlzm9CJLYuOrppGiVkuiuc3n/t1reTln3epkwyzjyqn+z7X/59r/r9cOLivBcUhA3/IY4Vl0RyobhR9/+p+mwye4pqVgCE6gCYKou3BYjf5SJBq5heOP2J5SCOe7saG4uyg+UAnVlzOdG865mbE97HiQ8MJQZV2sXu4ZTTeuA7RDzAPtJjXw6N/87h+HJdDnAxXhl0q/p/JzYQMZNfpEEiFmmLwhqztcY5goCUTpJKDM8xxN91s4rHRb0SPbSEpu+WaL+T8oKTpRUUh9dSz+9uSXqDGi0z6mLHLDN/q78H9weRwkw2J2oN7KuGyLSv+pJMXsxM1bDRnXAS6/Bxr/N1u5wMDDFziUt1vnia30JyvOY41rag4UUnXD1JMg0zrex428kVOvYc3v6Fae1+kdfaaA/ZKqfsXiaT8FRIRPnev8iHajxUOQiUO71frpzE3NDOPtIsuLXsnRp8u41yZoV8cDz1MONX+ltq4IFhzXo/r/5RsBmoZthx7SuCTkOat1aejLFFNZKkD1xNfWfJDlm/0cES/4rgDdH8/2p+1qkILwvnHfAqtvykjjaVTK8pY58mcIygRTQYguEEFdX/4iwdEzEfnp9vHL8RelFjt+moWjzZQmiYTjKrZ3QZ5qQL9M+O7Z8dOZIl24vietcvACJGsHylB/ubra369zBpjAI5BeVve0m7AKiWjD/8U0WCj7fPf8+DFv6nphUR6a3qLZqllT9J8Lzf07Dssnny+WMi9DUEWOlDnTRBreouCZ1uxqOv58fT/FxAruyV1/ve1L5YqBbvgIgtVDsB/GB+4JJtftO2SevogFNXORJdcaDtvsu24xepN8Rkusr+LeZHjfywGCvdFSPeaelOrKo0Tzj2eOTC8qPg1nGnBCVlWCl7T2ivMYl91efvyulP/FTrkZ+xB0L3gSGaI/myK/JRbmsYNtHrbzxGL+XGs+aZIj+1A3xS7kk2BLDpkU8fNr/af50Ez+i/D0RYc4YlLFaCh0PrTQtJ3GtgbXqLiYX+sbxkGiRcbHbXYv8fPnLV+cb7xjhYqTa7JyvwX5CWqHtnZ0vl9/X54XK46ty7HqNm8/Q6/U1I7J8d8O6yG+4lSsFCdu90Y96EpD7SAd510LHiJyXnXyYcJ3PIqAt/iDvLFL3VqweFnl/VbKoxqEOFcSwjD+fvFNe8Lvq2wvPjR5dKFQx04imD/4V7M0J/zZSw/lw0NDVsyr1NeLeb3ezfvrmouKenaSwTwjMc1S7HBPFZ7PhEdKg7fMJDtuDCbVi0lB/Xy/ZaZXqjUqKqRpZr6YMJwXU8WG+fAcMyVzDX6jwmIm5leKw2Sx1E+zHF7yv+Nawv8brh1yEZu88vGxSg46vlHC5ryTC0yYWnthfCL6Y5kB+BdZ8iqoURSU9OL1cE4hlJGmOYLTyXuf+FqVT+5Pk9S/C1TU+gtFbHT8eDlHGqGI0WWZrRaJIpMTyeg1cIVuZBtuPgm4u+ZOxnPM+6mw38/71bZ7/NK3TchyLJjaEdJX+adubfGxbTnaxKKfVhyt5ZtlDun0+2KNcGtP0pRKqzroJ+KT+T5I2RI1GxkIUNecyRkE2j+pVdkORlAZuEh2RWl9a/g/HfN47yQcWbN82R3jphOO/ykOClVStjDfxr+oz3PK/3EUQVXuxJ1JAn/RBaxBrmosVI3XfC1Pw2rZQApK+EGiNc+ZSV/XXlqtISx+W6Lkm6N6jemzJgiI7lCAh+84dr6tJFlVRFVvwW07vXyRAmNFVeV4DbDYR4ITH5mF7cRDMkLM559x/BzhR8DNwyxK6B4o2qS19D6/RxOFZP/UOAa3+Ubc5HnCRM7XddvEPGbNd6PiVpNFSj5/9fMHCQXhFtWYoQbVcLGPYJ0p2aojxW3pkyI333ar99oAQ5rlSTX+Pn7dNYS+ji1xXtgDJ2ABkR9uDnhp1LUQOLkPI+peEJrHrwzZc1EK/QS3mFNeJtgfLlFyZhOJYv/sitj0uA/H1KTFesYgSR4TZajjZ5oykm6rXyZIlWbKxDH0e39UGoPBUlN+oGQNvywHjEJoj+E+fEse+U8WB+BoXkq02uPBpf0KmNL/xD1MTqx87MOmpiQtGUHrAS437JsPhoN5kum0WhUN/EdUuHIpP9ZT4nfYiL7cgNZDtexNZICOASLkIGp28ynoe7rqD6sDWBNEVERC55sPfk+QLdtTMWyHCfZrLVEm2HsS/dX3sGkcB6RqOfUHrZhG6TxIqEvkrW7jZz5BcyOmrFwIfYt/K34/Y5eec+6wPOP9HtBbO9HC10O/232Ryn8rkgZUTbvniqIBrAMme34rISKETpDGanbLpw/wm0qHT8ABrySIh1SJoQnGxebrjTfiFZWJKkL5k6mlUXoiZpiA3sx6uG1nlKKoCykOqX/fP3CviQLVqWi9sw9qBn6DdltIcmo6Srk6A6gTsv+bHgADwC7vBZSoxuaFoGUhg2SG6v0IXdfLER48fCLg4ZxRtR7kGrVNSDV/GXcmi55OOpeW7pJbxTHJZAYKLcD+g4+wNJXQIVyDafYl4VsNAKPCOWqIMC2g4CgpGVENiFbz/jX9G8q/zAjib3D25JOSPYKOYExNrEW8pbbc/w8PMU8CpDzQZy/X4s9A2N7J+ZoXEPESi5G7V5NlTDeoXduoi3Ca3MDBhx8H7pfXooXbLZB1Isu7iggKVWiU99OKMTF/0PhUBpcykLZDxE/fUIbjiobCWTUXvJO+0ojDUBzpdMJYwODNmKejDDJdyX0a7/6lhfBodmvqBV4a6r3Z29QEmqb5N17C5WvZDvJvqrnd6T5HERQBIRWAWkjmF9g3ez2ZPzeu+rtXV295Tvo0gafZOhz2v8AqkA2P+ncMgAYLYxDXz4s7Aae6q9p59vN9ESGyhzzi9JFi3dri9ULhshZds2gG2aFfLGdY0ve214LXD7bzVlTt1TFN7YdR2WgvZ9nfrxy0pGM8Ge+DSFfqG392Hf6Z2kE9G9Q1w5pKdqyG36+PSn8aBq6d58NXxp43K8k7UbwV1m0U4aWYte42Ln6rYvk0xXmG6bKiFBOFV1nHuQQ6fnfoRJgo8EBOB2cYojwxIANDJ/8/jp7NBcFMG1nwoMKCXi+NDgYGdoIwshrj+/pdUOmInmk2o+zADHtxX2kP7wUDN/zySsK8uqO4b2gVzfJxlteLyz35m7qC4NWDDf0/5vz9zjvEJoJ3f+V9Q4ix0NdXooLnCYTx///MIYj2TVYGWiKjrD5kF132W2/8cFoMoW5ZyZQnt5jfA35q9qz7fgMIKJ9EeinIfT0npEWpXpm/qETDkwnXQz9+6Wg/DD/Aah64pHNeqrh2lDVEvZkA00BHZYF0Bp097pnAkYkkbpt0YQDfZvrJa9H0/LVDCjlh/cwPggA8a0pzmbSOfsIkOW0bFPw8N/dN8SyNtm8eZoiEK2BsEq6hcrkq28NyGOTjIG4m/r0HviePlpX7HAYCAjoxMp9uN5vlxaMNVahoQdjPpS+QdVNaozHhOBp90kGLCzIq4XESEcSwvi0YYqIbfITAxu+4uXhd0lNF2iLwUnCa+7FrXfR/yEw46P5ku9wMrCarv/fvF0AIkMu1SHsKNzsDxU371pGm+ZnFdyNNENq4tfk+z83ImqOjdVDagxcEhEo60basQI3jqShUg2LZ/cq2BUV+iOe3585dbgftKDai9qTYB36mhj3sXtt1NobR+HkxtJ5lgrwfvKRwKQlXXaLyDY4FUXbADqNnD/jBQgpZjZ+Rwy8KwMdd4bg4i2SDYYqVcjr5A2EdAv2uddU1ffUnrKWEpyY7fDPaohWmCxosKo2VOAx7Dcdx2D814LEggTrb/BG+KMvzReXYC9MRmIOmqHujeAhmkvN88wjkujIOQRAq9UWRv5Umz8NNPWthFCEdKKDGl0Wxx52PMsU8QriuZ0ly2foidDYzrvM8T7/Lo0EN3KJwwaH887aAZDfN7+bxE9iYoj/49Pq17boK2XldF8E15FFPCeh0BB2eIEuH79/46b7O7tBhTNQgDPdL193lVFdE2pNba1US2NT0iornCcOaPAzdRfnW4HvAbUGSacw8sVQEmkWavqhd189934ML/AnkdVmOcV6yMFoBYlM7aBlGxRr/Zx7qRRBc7sB48drz0bCPgO7OVl8q+MmEEYHVBD/Kfz86AFvZ8Jt8in7cP7uc0xD/sDUVqMfr4G0VvHBbLtEcmmEPGJsvHCmE9FOH3nQeLaKIIvOaV2V5jCBfXPaNOPW6qP04zFThw7A+RpEEdZFtYIiY9Jzzxfq5ZIQHkVlnMcDoVzNRKOicA2ROfffYEIJ0FP6d5wbH4Ou9fi2WZToQC0ckKSpK1TZVgHQ+7mwogz68aX8DMi5PjJ3WzLIsvI8HGu9BYt8tH89pjxLb743gozFbMZ7fxKhu/UpFeQ7VhItUI5HEwiEJMWKJ4G1Xmde/wyL/138dctUPRvmrTMpklq1nqXX5XdfR1AzmcZwZe4CDX1bJ5WRLL1sHDcD8prZIE1kBwHAMnDTkwjfjaHmuAtOq/4sbF6YXOovbFqHW5dfJ+bYtMLUTCLFH4Gl5HFjHe/9fWdeJ40hRf0eHmPO8QRu6ZcAld51WtC1xbwLAgm3P6ZQTH1iOWwJiWXBCBudoL/gfnHDvFYgnnqcoHN0WQy4MJE8IDG8BsqSJd643lLAmK5hQAPAGMzGXQoDqBoszg2q+bpPJFYIEmWe14bQtS0tlWWYWE3D1/fKMJsFmjauPBnI/BLtQ9ASsk1Wui2uwAOfLSbcrb6ImgjIbP+mhF9WU6b7fjK/gRDKN8U2QJQn9TJDNh2G4MgxfB3Pb3HLfprF81CCPHinQJIVF2fkI9fbTjaWY2L2A0ok6IdBLzUdbGmXi0yBvWTeWAH+y+arJxaDGfNukxd3Qv6qgtW2O+OdJsOQcbxr64iWtiB7lzJuSsAcPphGVaymlBVs9TOk4c9t6FeekIvXYl3n0DYA2h59C9kTk8LoWl5armkb61oPN0oyTCJ/ZoU57M1bI5NabDqXRAfKF6eXFiV51DPL46ZBaULdNFKVUe2Z2jdZQSnjPXCLh6dstJImH8aUiQTBPv79kqb2BGp6K+pnb4oX/Y/UKehLBgCHo90mAiAv1eC959EoAC5qDvIH2Z4768N/rFxBo1cB49BPUpau+LgQW/B0DFqbr1jM7Puz+ewWROc0vy9qXfVI9+x4s0Rfom3gSMX1OfmZH1Od/VrAifd1TJ/cwiMbBoaCvUSFVsECUPBt7J1Gnc2UhkoVphsM0UfqLJM1+2F229+hmjHE0A7vylpPjnzBg270FiQTeuIK4FWTZKSSuwqN8hDg+4Gv81LtoDmTljuM5T0dOa3yGage6o0L1vKDpgYBUEdSQDUKSDmx7EXYX5Re65SntNiGaoNex0DDinRCJEOel/UvshkHIQclTXx5kIlhikqZ3qKXM7k2TH2l+jWh5ilGwXU/CbFH8dvG2vsoCyvsgsIsaDe8mCI4fpD0ZA3HFbp59XtyuaUC0v+LP/lNFa9ikRjurFUdiOxD08WOst97Ax6q6zlOIw5tzjasDVoYansIvhtW1R7ZtylOzmiMJKKCQBoYR8wscu6spdUvq+5BRxrHykUY4x7dnZS/0MyYxk8IwTsaW5r+HzSC371Oj8Dw298aAjb8iXpZP6e4bD/+5nP0ctYVoESSy0at8UyjI5r0VCp0trwQx3yS/TtWGdl9ez76p/9Szn5wBH25t1DZr7nkevqQ3G+IJfK7BsQRIUabje9LHjRFQbbRlLE8FxSeqAxy/junnYHN6EMRNCQyYEtCtoqCcQMHZTGZZIDp1fe36mE5sOO96k7eegibcZeYXGUBYxvysGEgf9vEV9nGT0MyCrviCXD6ZP/Xgw8fblH4NVu5ILypCblF9A6jyi7GI4sx1kCFaCBEGlSY8uz0WbWcizotyvu63QdlUfhj1dI4O1IkyVZDlJ5zEJCr175rn1/aDI8ekZGYInUFfIsh2w7Fy3z72CvJuyeCaIiFZCB715ZIVo+h+qnqsU4W+f8fxWjik239e0Inxu8HDnSA2jt/PGdGmVxDFoFYlHVG4D4nHK2HYF830wOZG6AbDRrtCMiKzYMfEcXxuEhNRgNhifd3lMgtY9L2Yi8ib2xE2utqD3YcXtxSLgVPEB3dtEMSwC0tvh2pxxU8Y29aJIiCcthwvwPjMmZKD2EtC+0UIb6I3LdBgolSxJjKMKxxNN3Itj4P5lGPeSJUmMZvqfkkk8PyiLA1oz83wH6G1UVpmkCmQ4IsHMjRKmsY6/8Yo3eDQQ1A0NtXqOawfxwlEUdhV4RFOWWGCeUgEsA1Ykadq1Qbqeok2JiScoWm8/rQrkxUNVeJxK8QWSvIstkLwTQS+fK7kGpcmFxMAZ/u/ioa8IMLPgTiahPPcA73Up/DBdDS1gf68/HcnBT6Bn0qEvhdFr6HJ0SVGQx6cxwzvPd/xwVpF7+3C/9rmMkVox5kgwGYz2Giapk/7AqTr8iCeiWVaJDHOUqAhdihFondMlxNuJorjvaDurW8khhHOOGIA8zdtG3mh2rryWFJ8UDMCERbDLofNZADlUm+x6FtzQ2POmUWNVXTI+jcIWpo9271IenZKZIFgTDZQTWrDw5G9jaSIA6aa7sknzFoOtU835IKdLe36HJq/jNMF1Kx8Uvy2CzUsxp4w+V3MQehL1hBdy0Ua3pml12RA2t4m90oMRWKu/QIy9eb0Z+ex/dmPV70x2Ighs9VMxBX9Vv3JHkbWrq6qUByHIQzH7lYklv8mFSlTI7W2tskFT6X7TMpu3IITwa/vHuae7zbFjdxRF/4AMIVEeW0KsU2RT40XiFCqx8R3zybpX6c2/63GbYp0gis38W1iu/sQmifwBU8+njF2MwkC+MSVnzBn0ffXpXFVykF4PLWaaE2D1yMNbkZ1JtJ31LN5RiVpXFDjLNcgTemzeEFawsYXOU0rPp64mar3itAmUvDLNjtnqjf+9M1ngsL2C2ZCv7oI4efDWb7FNngKjXusnu/DhJaE9K/Wk6UFm31eAai+Vhocrx1QHBcbBX3dHAsVt3vDTvecgKWmWIFK00BoN+boPw1DViiK/hbbL+2f6OcUYNVzUltr/e8oKxAxBo9TGIojg2wXTae6dC/yJx8cQUjDpdVQxFwU6Evj8hbKw4CTl2fwYWve/DjHJrLKzuh6EF21pUxFOl2yrnzQyblo36i7uslydgjOry4WbCfuHqeq74LuWRAsThScGY8pO7Oi4LXH7SchSdmDpqj8wonm35X/jDUPGDVsoDgPHYO+Xs3byBTnEgdVN7osoSi6pf/SNRl4UcIv3SGacxpyitEaL2roDc7jRPfmzS5uUO2NMUmSyB8MSGV/o79JbryKyzVsYBU0WgnwNTNLaqhgk8q3/LZccb7dxuxML4U5DhiDOwucJaH3SNRASyU+Gih+WdXIS4FOO7YN3B+jMLKd7qcxzjjW/6rv0AS6LgGgL3zNwD5y+rCnElzaPEdpexO/KpK8v7KasKemZqweserfUAHNrk/YSYK//w3csu2zFruLh3z5TWIbx7WU1nQ7sA/f1S4KCthpzPuQfdfFCE20tHcb3CiMmBbmwrZyEWg4Ut/ieTfs063auR+yuqvskfEVpCtIdSpu5gsW5CUa96Y2OeTRll2HvFn7BesV3kH/aBbLpeKKIH4f3w+AckNMPH6UAlT2jZGXDWAtACpMFjDa+kWTdl6u3DlZ80xhxLpR52jy2vU4kMDm7LwlHcaRbLU0bIpF6X6/3wL9I3JhhhARzTRa/NJ1wU7kmUX8NCV4/JNd116gyEHCJa+5hhV7gYWHNPrkfkv2a2lvsgbUgwk25DEx3WDtv7BTmg5NvImMroz6fKFRAseJy09u771ZxHCF8hEgQnZzfOsJQYxs/j4ZG8IweObXk3AKDfXb+pNE3SvUksFmdVXNOudvfd4b4sL1J+sk9Pj17KKzBx7yK29QKo3U8Ba8oykk2ALxoz3UdphWZH/sT6oehMqkr9dxQEUUoYZBmX8GkKcTchter87wzDgWbbI0zdvW3Hug3zoaqDMxPG1zqF87prrsyfoya2dQdfnD2Z7dtOX6uuLTgufxaNlS+FaH3/wZzpmCEI806FugtmlC2+jhfPi5FQikr692LIsuXLEiyv30BvxumItfnY438rhVaxkP7to53m+jivr7qY4QiC8rqGOjuA2K5h6Eiu9/8Ls9NRUmstq9Bp1/gVffj0rYdyypr1am6SpnWOM7aNsVp0a1ly4Mm8xtff81naO9jbolW9Dm4RBBln8QPpGDtocjZbgifpgSqjQ9bHbPGymNE+VpAttc5IYGMTsNLES+/86PMAvjwir8gZSyjFb1NwmxTx/p0c+dzMJPh79mzBCDQiw8CazbwuiBBx5/3W9AT5tJZL4aMNdDN1o42PqTvE0kcCS4Nxz1U34mgwPmBLdqJd6T5FtAS4ckiyGvR1OaRaf7plCylDBfO9Kg0hpuXuYU8RANzGkBpE6/jePyi6KUpLsNVLRiZRCOePtWrPfJyOD75y0SK9PDrutFzr+K7aHd4pKmxzX4HbdsRrqSzrFpiZTlmx/heOLMLnq6fo5RoKzcUrX+wyRcUv9wwscUMe2VRZ5szxFw5oDDdQkVqsRbg+8/EIm7azzQC0lXNfIkR0YRQbqIjzvJ+x6t8QntFATXxpnuz9l0W7A1B1R3vhjrbFnQrMfZdt3nU67jaccDSisR3bPgEfc9mLyT+5VbFBt81DkzEgTlVO11bR9qhCqPRJ9IMQpEC4hnu8Q7qm24arPSDc2KwoqCRHZ6/ihlRf+utUdOP7gZ8Vm+xndcwIsFC7WjTWq3mWPEo0+nzMrnDMW/vHI6X5d82/coa9CnXUFO+WjF3IZS1QbytgjyIOEzdwvcaya7H+rn+QaO1tO4k3uW/1p/HfZZ6PYB6Cs65mIQAPhz05pYuMLRuIVHhHOkmj9UxeM7I9cfz5rSH3KhBC0UF/Z+i/cY6vO69ZqNi+pDEqnLf5qdR+j688WZMe9tM6KOmmg/0qNCGiBRcohx3i0iEESqe6LEvQsJKbe5HM8q6lBrjv160pK8bDFDpCQVkRRnKNaeBt9rNo4jVByezxR2LsIHYuBKk6axA1XTrtigGSGf6bhOHaP0sfa2Zj45X1zLYv2UdJ6+lOKhN+jdrvZwy87PbyubQSV4TMF7ulcKYPynQs9olCeCeIyQ19CS5bPf7MRwZjDf9XbFf5N7TpCNABr8JhiWInjG5NnRjRmdAMcQGYD5DnuLO4oqFxb6qW/CrGczKVjCja3Em2ff9PHq9Lobx9DUGWk2istXJTSbiSOEwEdXO36dAXrNUZDRV1U2l+cgLaD5vNnTCmXii0U+/rny4wXfmdfW+pcRFRZbetO8yjZSfcbZc6y1aKy8iW7rLEPq3x8VkXkleynQ4tqOBCe+N33r1HAjRdOXoScOHYacJS77elthhJ1QKaEk8N1pVPCgOpYczC83TiSF/lXwB3+/cPtKYteE9qTQPceBaC/PRZ4fXL/ljgUQAHg6MfQmkEN+lfA/PTC1Kp1Gv+h+Sm3GNHBRsjYaqMo8881VfKEzGWVBTjcsz3LLDIcsTZFe/2e1geEjKL3rUF13gZ/sin3vthZwlf6AErp18RY0CRTaYaQ0O7zUrjtx3VdpB/pNk0EzRp6fA/apd0ZOyLh3W+sV9ZHlhjSO1pNt5KP+50FMhupHcjzFgOT9ALUtb+OHk5N+2dZHZfQhR8/fBfT8B6xV89uhzf4QcuTO2d9QNdcdhoDkkVwr8S2zgm48lvBk3dr5zZMTddwx7FC9FK6R6En4achKsrApm80xMYSOs6xbQukdP+rSXwWARxtL1FhfiHmodnJNZ+uGknuyQEvpvSVhZL9juNXsgoIw2fL3sVrF7FNb9GVowCeFE7/j8vTYOXZ6fhs5z8Gc3R6ePMWfh80D5fVv7Ci68IBrvEQw/xmwFOy7RDe8o1OnHJQRyld5J7P6fWyZ2UPxz6LhiqyoN9CYlT8T53kZQOhD84zYv4ffGDkujWC1Fr1d0jWbm0PCBOzABqOd6+AVl3MLkxD0FB5Flixy3URLKe7ruVHv2ldMmR37BSxNopXkqaeBAZEggA+N99IuKMJmz0N0v9HMgKYNLoQRhwse5EcGHUADRhy5Wcf2di/Yeq057QP3DYfzEY0VNtnyGUiQkEM1xaadI/MUuF92Aq1MbDJmwoQM7IA+B1lqNzqzMt8/Me6yBBIUQtppGNEDgz5uyizmEqEtO4/3AqQK/fY6k8Vd0GKV9B/IaGSgLB7DTb9qZgUgShepqSF+nERCjj/b6vfvoQO6L2mtungNuzE9SG+ioBgOwyz021XXdYyOeh6h+V1PL0IG2IVgArh8DrmDs9oCXJcCyRr65JPIEc8ZwxAWE/a7psgA+YXdrtW6DH2JeaUyr18q9Lskbz3FSnw/llZWB44wS2uMuQ7pQRnUtzCyP2Pw9J6yncZ08dPpb+sZMH6CvE5RO9hs3jRQ7Mdk2RyFJAA+zN/+EgeeNBt1bG/Be6dztHe+cEPm2H6aZngLScCtv66rOajzzdrVJEnQZEdREnjFCWQmIfav7DPfCdoPvi1dvtOoFxAwflxa/4Q12rLtLwU3iQxsNxD7Gm+2X4/2K0JgfBjD9mDlQAvDTZWbVVbwJ6LRN2c3rvnNSa6pyTdv8pEma4aa8r7ZZQ2cDDcbypsTqV3TpAjDhssT71CQ0m0Ui4fUghwfP7i+VnbOS7G3r1V8nb1AVz3X1IHfwID31kG0bX7DmPiZ1Ml/ncs0JEvvszB3YKWWrBT+7zOUL6IAkgTEUqIxrO+ip2vcmzb4wb6+j919iQtwDjGxhhrppyUzikND+TVs1Gf7AMHlA6NjeeRkZ/MBU6q3bPq3n/0TqQrKxSMB4nTwBk2usI9unU6TgwHFwrOarLER+1VeMk19RceueShY/Rbf1Nb4AR2ag3I/vPQ+oGvtRyMMy4WS1D4u0IPPGXy0OqA2lK+oU1TIG907f+PBD1jbhslTP/lOdPi+aeaR6bov4P2kUKPS2oYnoijkhHVNcD3OUKCw37/+3LynHQNZQBw+VC8AeK+dA4YGwz59/uwjJdF88D/5chCwcZowYSlBq1ob3ZMXha7Owt54t4nKTQbMyoCjE9fTAy98eHcO99re3au+vm8XlxmYUwsf8fV6vPHeSOYKhgEkqJj1m4Ak89prDThNPpHsCDtLBencNJkc7Kh4BTxXDSCxgOeoYgPA6/P+sGAjRL91GUP3NyGPEzcqF2cc2q1rWmXirsTSn3DOBkC8+M/hAFAH6t9az0lgaLpGN6JHsZv6p7YgFU9VUbThsaVSv8HAYn/i7JWkDShuDiWUMVlj8NgoLJDqb1R/MW8mAYi2Q4I79L+GB3zu+xqutd1i/syVufBsyP2nOzeOk21hKwNXTQoYEQo4I4Pf+BqBYSNGbszNTb+eIsv3Dq5HZJkg+q291ZMT4yFON/UuLKsTov2nwxgi90FwQxMb/o0+JcCSXmt/SZ4tTIWiVVXlQ+Zu1dN6gYoDoAOXnFI1jbaDVZl+JWku6+FWYEo8okQbaR10liYKoBa6xPgsjoXnUub6TdGIaKiNdj+3t/IaXHVgfSXbjlh5KldiVPqNi6Pq6XMG/vA1qyQvWSXjR4qaS17FU7g6Acaf7PQB+8f3dtwbcLNANiSfEnKgL2u84S5406ZVI1fnee6PS6U/hN28EFAF+BjBZpWe2yFw05HslgnwUzZQMqqROhJCJlr6ZYt/Gi48qsivqRzmRR/4/uCYxCHu+v3mzWLagMqxCvhUqk3hUJxEWUEwFFFfDexL3JAX+GVgy/P66yZBH3TgVTzCya2HIMlmmD17qrrk2Co6HvCwx00Dw23gKpQgakLGXYdqvStqIyQxn8UWrD/YqnaeK52YXJTyUjy6T5+ijlcIFBRZMlw+sXRiGxefwNxKr4nydHEjn6KKafUi4TquqcBY+SdRxswvhK6tlPKXtfM84cu+F75yKFTS5cqtqcYMjoXBF/+t0zu1FBTMAsNMv0iLGHzz/UmLAk6+JMqdxLvldeAcAjYnrjXuDe3gq4F5W6hu2jX8Ri75+MoBgjIMYwLLrgZ+7OEQEpkpvegJRVHIKJ5eAtXhKYooHvh1di1DFHrf6LuqPvw9/twFjgKyVOae33MExNo9xN5ArIw5EOTXnl8Ocm0p2BEO7nxFYWhqWzNh0qavbqzA8I/lukZF5MAE/nsrV//pZygT8pauPn3Xrdlq2+tTfSPWVFhHOQsr+yKO07y+V+7J+9s2KLzlR/Bf4FwnkjgEEWH1zFxHRJs47qJ+FMeZj7/QTqsok95vhPbRNihV3nnGYDKeswZMfRJJyZtgFpdNKk2+oVaVWoGqreENmS4cAt2WGFg+KWoes5pSDQOS8ZjLqH5s0WscRPcpF+CmknfyZsTo84DywFH/uLQR7kS60H3/7ec4UcYBl8ujiGwMOM/cnyziO4qHGSFKMIiCMDkrOHApOek29Zn7CjWhGPTvVw139oK5TZDIztwucnCeoN2jv4ANnKueQo18G2wY+NzPBoygR4csLJ9eVLUvZVsuTBSS/UjlYp+aED14h60+ktRX+6kO2taqQEffkgXw3g60/EcnPjmcH0Zry6AcTd49PI/YXpthOSBlLHPira5QerSrqshECKjpE8dhlM9ifc1rdoU5qkN3KYzjsYvz8EhUv3jT7+QoUZCazAB12u/fJEZZ/tx/dacxWZKjpPE5ntAMYpztuN77cQim/skOcMA2TLrpav8dzrqB05aHkweyVYzqoMK8y8cJ78qBQ8FKn4WryCgJ2f5GUUFgzyc0GftUrjsAZsZYSymwUrSPdjwfT5pEbTNx0yO7Rl3EILgaZme0Y4xcvIyuJDawNCPu638nhhjrye5YHi2QJ0t0M5Zzo70rqbYdo6h7ZYlF3DcsfGucwRkNfpWex+Tf7RDxXBP05JwvykM9tAJwnu+/AgJ9UZTyh15s/yrLMu+lY9iEire8TP17bGuR5bAlCJBFmrjvShTKRYAhDuK1KlwC9pwiVpf8ZPDQB3x0PZYBdaDoX6wxI9P5GOgMFo8qsDWuURK3CHSaOMOIn8pIm7DbCFDF0D/TqIjODfBAZJHCXlnEIzvQJRsvG1oFHgjF0TcNMAzJ7PYb2ePrhS00TpQH9rSKvTjwoG3PyfaRLsQntZj4zPEBp4153vdbEbQ9N+DChsSSL/XEBJJIp52pNNcZdAioz8QftZPkWr9hSb6nmfwCcrbZhWgTQEfP6aHINjXDBKQ+vN318KfWJog8gn/daM12k9N5vBmYBpLHhjQxcgl4ofmoxatQjrHsImUt1lPfR2GNop54iPzB3XxkwT2+7Be+bmJ5Pfld4vXkjWSoIRhg88uiALVymL66J0u4ebrNeE8XuKsw9moTSV2WtRPdIicvsen0VF5ncw7cWXmCvdQQf/0YNfEe7Tqxy3uveJ7ToyAIs7/losY5TDSIJjcN3SD12snQoaJoP0NUlEy49WPsPK10XWmTWZiqVd+CFPSbKPIO2uZdcS7micRXyTdmCW8mHnH11wNteMZe9T7svNpOW9WhVDcPWGtJpbFbUK6gWCOnj6rwW6/XV0TD75LCMqdBzGnBhhXJ3xqJHJyQw3kN1Wh7CZZ3Y2CzVJvTIrWn5AKOOtgL+aKa7GhMPxTB07znpwLF8n5dJLmf4/vIBZBVlxIjqqX+R7m35mm8tMykFL8lOal3DQqfwJpB8RHEy1IyF2Jz7OdAuIPRf/P8CUwKcuBt24yFvMybtBACBfMrlTg+UJLMLFXD5BG7A1f5p0lZI1+PinDDdCU/RBY2DW47mE+Gcebq7/KvQC/jaXRTNkuAzQoGgMx0VIiTN0sqFVNhggfeKijXT5TPw/T7woNaFhkGJZ+aoy+scUhSMssOzBUokS85xCBU9vvRehgWpPRGj48qngN2IeP6VhRDxQAl06IrDweuQzfx2zTH3LybhKj5hVYCTFQd+bc6CSN6SDPzqwkNX0goOwWpnQvUoLDLWf1+2/+suOXft5r6p5E9UTNACF64FLWDxn+sWFISmtEQ/9NpPih0kUBSAAvWIKVtD9pABuSYvOOP0jayV0POoMxQcUAOGSfWF81ear38v90797LA95bJEPLHCQO7jR7oQNsEqd+U3CdZeF5bGOryZsQdUNe5MW6jnSXYzzfIvPytXhb3FnwgZyQcTU+8hQ+6MQ6SoInlSytpkkjWyKdm6V2xCJtTGJSlt63pxDO70pGYUwmpHoOHiiR6desx0CbKysDapLz3TZoNJNJgKJsbEwp6AjMrheP2G6gNrjuAjiXee7a9c5H12fbfZsniCOTjKh5G+naFBv2Aqh9WT70182E6BKTt69ESLFbDuoOILt+RN8qjeqbR+7k+pchdtiV2V5bMfPWplmd27vfit5VgqLB12k0HmoFYUIu3pgQ3X8XXtwAdW9L0Xs+rqMBDdnT02svPQBekqIN8LY0sZD+LGbmV4EYurm3r2hqB8ijHZhRGMSf0bkpsodjro2h1Lp++mThSnEfwbROrty8fliSjSa9fab5GnuoAfP5kvohJEH+PbkkcbpWiTk8eZ2nVarSKzNcnjNix22QFF5xdkIzAOPhuGb7Vn3b7UHXaNgN805+u5n0vB/5SuLxCEE/yKXE/JMNO1U8RA40xqVxd4pdyacom0ee62Q1O066XPriFLMhDJr/b4Hk5WFqNyMyYQGeLS+p6XuUseVvXU3gpfTC87XWH9CFriuMlviIqOf4dZpjaRpNclgzm1/PXoh33c5P9rfkeT0Ypz8MItIqrEoZhlrWtRh4GWd7f/rcBWI5D6rQ8CQujdpjGj/F7gHhAfCqd0Bzfm0H8K/z9kYkvtt/bjqaXNuaoccIOHMNevPUjrU/1nqXXL9LnG6EXOGZ7vfjjQtbdHCfrEwnB7M6dMtv8JJEYZwSOt5he0x0BmX8n+fkM3giy7shnE5F9TNICQLZVEJdPDIP401z1deYg5ZjBHIWj02VGnwiEJf5BgDKj5ZAn4BjZWJ/eWssdHduuRwrIjI5Oj8FOhwr0wcCFruV2FULR1JIqqi3RzcjoK+xHbH86IUmKojWUm/YQcqysyt6A/Tm4DIHeD3yIL7A2NXHcgY0Bd+evdcZDYX0UPRcXOF5u0yLgg1WMWsPepuWVpzfHlcPJJH4+qJ1j/P+2do7s5aW83uofpmb8eqFSojTsTUmVen5zowxkmaAsakphbq+7TFD5wcSxj0iCfqAMKio2ztK4jC9DWEN7KSkUhg05aG7LeJ5EnnaNLRdrFRIh8YKQumFIi0cQsRLjQlFeNuIAM8hTj0AGiTSEDdGCM+h+fa2x0CL4SqlPnujnYl83i1z1/6p8w57e/cQqTk4Qw4NA5Nd8cwMUTy9Z1SUfpGPHdeeKjGSns4Ad/0vlm8gViV3+f1S5ibl1dvoda1KbXq3USd4NQywstp8314IXFg7T+6O3hZ/48X9dcWsCT873JUlF/bEye1/6yz3la2JhMs5m5I8QLP3O2eEVNNAmbRnLUses+hP3Suhbyi4fHbTGBmkd/kjAS7ErFaTO20fFyQswYONT5kUUEWfzxslUYlPHEzmrCPXW86LU9IIyJ9OMyas1JanqSUmexy5KWI0+HuF8aA0aRFEKNIlABMpzpDSMjcuutuk01Q9HDRKpjhNiaOb6dmGg41Ckpm/8iAehfFGFhLQXAdVRWGUau0dRCVxPoyGuRn2VVINWx2Hm9hsSoTFbWyGm8xvO+NonSZK+mNFL1PB/B88yc4bY5wuefqkZCCdsvG4AmTzfeT0VdvEEtwHMvdnyX0spPiFZ8KJN5FsDMk4/sJdRvx8gXDBJIouv8MSLTacPwsW3btrVh3pIatG/TzMmN/ppnAow9abB3cUEB8g9AI8PIPR10KF1gZJtpc/zlqxwokpoHNhonh2h7VXhdTgUCBus8F9xDhf5Y8HMINx0QTPqWxlJt7EY1LTB8y9HLNZNat15SevIhVxT+mEQ5ndwhOznRzmIpxy7e6//IilZJC4uhRlQTrHLv5DaeaU0dsqJZZNFRT55u6l1k83PR43aff8YCwgFQxB2viSfukqBa/rH5yurZ0GpB1WQ52ZuiRP4wIUSvCgI332EDv5WMwf1O1sk4XILuDlcPEz2tYLeCllbpwg1QviJyZ70e9GXXBFMHB4XuX3rqhajBzdjK+TDh3REm0y9VZIEPZr4STjyrXdp92ZTq7KM77i9ogG/tTe5eyqdUKvxtK/Fd+vmAla1aazXQG4TqFeb2a/vLsBG8X+3955LjxtJu+DVnIjdjdAEvPlJEJ4AARKW+PMFPOG9vfpF4X1bUqtbGmlGmjlnVx0d0U2YQgHpnsysygR+hRSJYlfr4SYJyPiekxkJZn66huJ+Lk4J6WiJSbChh2HDMrwk0nxzlitGKqJ4ZhNpXLnOU+T78bzzHohf8jfgPBXShXrxmjjdzsf4ko5xjx13gdKK6MQMs9lmxl6NlOe7kWjfPUwCe49Dw7q9boBlZYe9YucqEFVkGg3LFnQFoQenY8/dZXtPLb6q5XZF4xs8+T026D3FmmNmBm+KqtYpzA1ZrUynj6kXjm5XluhAUyg+1oTnzTOZF0O77IE6lClaAecjVLaelZv4GYC3hNbkNLrWbm5dE3HVnFsSColgn/kyOyj42IrwCwZyP7WOploSLX09Be/0kpA7HuSx5yVRNVM1B8vZkM3QYZtzMVGajsxTNzEHDT9UJJ0uAuckQyMWF52Hl2tfxto4KEPhI60/aNst9e5c7Hvme73h9Z3dcuLpGMy7XNKbaXhsAxu9fCNsjO9Tywi8C8NA6WYeNzSb0ZcafpxpUujx8kLpUkDIg7hb3WZMZcyAZiYV8rg8IkktkMoi1KLDzbOilnTnBMG9XuT7vTi+8W0o+tZNnYl5ymNx4USDle9tqeXybbIOM5U6Gfs853Q1c93rManM1/dBWfrNSKwsDQ05Owt2+EktxxTFZhLR45heV8arg/FFBj0er/CYBHRM4l5064/TExDzcvFkFeja2rodE2xBjTDGk0aOE8TrMQ35mIZ3G6wvE2TGYjkMvCnrLXw9JtjZJB+kjqE8Gea9HJ/GOD4NYvT8l4/2CDz18i/M6qYCCTOm6cDqoANzv/J49FIZtsCtUJ/pMWCSy6END20kK7ddSIUJbB5IG5gu0rmVUnm99AbvKoD5DMToVkSAoWZB4i2M7Du0gMwC8/I8otNBB5/ltTH6SpMkqvvge3SoEQQx5JnuwNsSiW/AHk+dPu9ZGQ8ymfT4OIyWkUylXOrMauW777iu0Tg3MsZnXmK1EIBh1QoBzGW4sywFgZsA2t6Ae4J0FN9ujwZiuGG6vPnpYjkBeF8L2fJ7Scb8R4yHkYsDupQw0IrMC2Wm+kq2JjK9gsiRu/BWs3MmM2/Xvb5mFa4ml11s1LLoXKIZXpOH+S4Kce74lHVGiEUcihVclaTQRNo7+DqV0ye8HLxKUDWnxq4USJyl4hrDaxJzYAbL1U8dp0rfdaQBKEmahlEHYvHAoOoptddXLj7eZxudvq6N2vLSINlfziVwUe6qICS97Pj7IjV9AK/pQrBXPr+sBLFGUGwMw5ITHlmXXLDh+LUr5jfOXuE8vIF0DbVUEQEMaJZl8D1ynm+lQ7Bpjp5T5MEy0u0ayfTdI15OULBxwRQGnnVttQUsH7UlJjAZLkkJgsoZ37zYNbmVeAiFYN+DYt7xsFb7vm1lPUmUs89WU50xNbAaK1jKQNfwcqx5uG+YEB5HGq9K3wKsI3Gq2Z8pMytBLliIR5dLE4UOFWYj9nTVtYrP2gg3UcS5uPD3SrSdpMhFT4oMEB7d952OwiRy7ae7h/rnfpsbfJrnt4BrFbxbzINjKlQ2UZSAQIUsRpnrXI5lWYPviy+pYWBeDsTuvfOxrv0ryQ5EL+s2Ou18yoJlWo9SH/skP9NVhzZTODcI9oicg7eqVY+B3bhicziXXBF0d9/VQ5Q3LFxuCUEGQdCOlD3GIL+YIdhFlpkiucdEM1CERkiAuw+kRoqG5/XqCicUZT5g+HinLopZUQHhk9ss6aiAuknQ+7Rv8r7r1m2rYlT4pN+JLiqXaQ4+u1eMXUyUIeySAxRGMgyduVgygyy/0L14u3lF9xigPv2uplTnYtH6LoK8Tsfuzla+r/fxLuKHPy+GJsSlbXjbBskBZGXtoX08hOt9J3cg1axqIoXMhfZBS8Rir2WTpCu6Ztm8eVfV4vg8EMm7g6zU3hgwx99De+JfSHNjYRXJSvZicfAciDO7oAgwcb3bx/LhZKUyd24dsiO6vubveguTgQOJIhJT9nDSHiN72ZlyV2rmlV3gELLiSE9m2RwVllP7ep7nse3ZRD7r2r+QCdIvqxMK2s0ZqTCuEa9s4+zc1yU7sMxVczW8gK+RTuOdvQrprCu7YPCBAtf12X00QUgy8HvA4Xx4Fg5z7d0MqERQhVgaNLT0BB9468Yiguiy1G3lmRZEIeatEWc5mRYoshOLaXbxOPRUz68egJ+bO+uPsk6yBkdFL4AXekEDyp5kbw0KShM53OrwFAss06NaG6A1bEP61RNn9wJgoUCTKDJjCDnrS77vRuQ6zbmNPkCRe08I+8wnQ4L10X1e9OvmJto82HJnrOJ9SIJEa7PJ54YovtMvQIXR86IdbptNv5/rKJDIrxWs0khIIPOlESrUbHuxXKJnQi7E+x5QswdYGm9D2+0VsFmCyXJSf7g7w6Uu0bL8ujDuNM/JC3KqmfbJ0s8E9CpeqjWRMTVTQg982ZCIrUWsz6plVLBGu04ZBl1eqP1cheljAphew+k6e0mCMwQysioIqnJUHUdvSFzMcS3r4UDNDnB58FZBDTLZd4oU6Jzfn09xDaHXvloRMflnd+GssPckljCsydm7QtBo9gDort9DoyHps/UpTibvFHthAO7rImsCdWnkIOygncvfKGvWKcY7M8VWAXdT1qNOXzz0O632tBDTZd9vZ58a1nUcBR2LKkwSAvh7prJJ76t6oCsjR4PVR/O55aw7hUrIMeGYZIU+b2WfEiBVrDLOSJacPfxR+ko3d6rWhpnlDkt81XHP3zu8qgnu4j5bE+ojocfub0G8J11hytj90sOYRDhhL9YNWil3G4MFOf7wBG5jstQ1Z8/rS6+U+knorjocuKRrlamT5XtUh1V4UZlw2t2imQ1JCozyirku/yjqAVLflQt1RTHdOO4dDTazOfhz08zQqCKj+TOvtXBfTp88jl6vdctzKyJrx31ao3XXPuzZfvNfL0ZdOxvS0/K4TTxvM2LyuQnP0OhiA5NeBi/griibhdjA57W3/+K1jQg7chGRqzE7waHml4cYE2Ryu3Vzew3rELTEYHKt2jEXCAVNJ65LlVco5azCXhfLvWCQGrHZY/IUd5a2c3/SYX2ACwTvcBTRKBy3Lgif9kBEYJh1iHLlarOn7hcm19ule7G7/fDQBlIFhAB6ipxDvBldoK0Yzs2c5RS0oXJlcXsdRk2erjMIDzt99f7gIE1NgFYKcOdpVZ7KhZfujT6CcuT56BUmNwmHGBoLSczwnuS1vvbugZGvBxLzFUnCpjtijKZO72UvPWUDpI34wxSQVyxynGVCbukmTTy/RfOs3G74kGzTPsST1MlUjPGeOOajpLy7FiO2dQLBx5eBgnqFjEuX7oggruv6FcA0fOCdi3OI3L8mnL2TGVh51yPvTEzvOneZgEeuyAOwGgr0rGuk1IU3RlPOcEwis3feQSL845V1z0/YOzWus5HSYvoeb9wdNWu8mXqkIh2PKPUb6oGVBJE7OQVbpNiggIav/NVxWkCmiXzuZKGTutdhB3qkF7AkyCQfLqj1z9hQo9Y3+Tph9tNE5jKumxcx0kKfGIq+9/3YlNGhLFxG6fBH21dZpeXCm683LDCb6Y2KjmPvT1bn8M/yi0+qWSsAZni0PJBd5cROhMJABVLqcOJFsg0jKYqwIl84oN3GuexqPNSvhtq/88uDAZXWGIIfHuiMm+8GS9Jefw39dZLPeJdUOZw22eHo2JFDLHpHgk7T+gbbNi4/6S2i2LWhL5kQufwVzfypQhJodfg20ltjAmFdFB07pNMoR0xB22p9ZlLapnJ7fz1FIAm6/t7jfbqSynVVCSnAqkt642DzHaRsM7EwilyeD8CaKhLFXHh4lve7UkTC2+gF9XoXns/JXtcCezOcPN0NrYQk/dblpnlVqrGCPG22sOJ4+FlHtiwNMj4bUrfCy5/XCSoCCJf3znofbCCNvkIYVnTWbMvhdqSBKXCwe3A/Phg+8cpxYXYQQRlxEzcK77ywnuUKVg2KKD/HT38cHyc3bzS5WWsN2fOsCW308vaszhZuMVUpdjHNU47Gg7JWk+Tt3qo8A+VWVriNZ4MH+cFMt2oCY43HlSqcbRtVqIGYPXkt0WTbjuyWx0qUPhDGO9XvFkTfkT3wRXEeBvsmok+/toYWmwNTuRpkt8bXZwZhgqy8ZuDV3IdxetIXs6dHIkSvG53ZUOuJOOku83gI4p7f2Qr3kbA5E4O6N3OHs3nuaXIbE70TQjzf+3HoMUQV5q0p2rEd3WTUhC57Z3IYR2o/FyXAGxvuvPLJ7PbhvjtEXQKdBNaAEarOg3w5rAgjMbXpzqxuWXeOXFGA/1w+jRDFJqVe3SOtNrp2S5x3C8JBz1ILNewA3uZZE/ZNSf1MGkV/ZU6B13vJovs+qBwgKCqngfVafHx/PT8icbPuci0121JxKNTDbYbjKM8lZJ3zAqMDm+TG+IppHE2mcNEPM3nYKaCtFSUgOIhXaa6kxOoNtIqvlAb/DFW3vIAsFuZQiKCUsTYN+5t7Xpca2V5M8RDGwQEo8spbiPw6Kw+F9QJWCdG5SCpeh/LknQlpsPAu9m/e9qjTlDUf7vU41rSAXk9Yi5UD6QwRjbDcEEM1jgeVQWKzSL8/4rBM6s2PAqw4Y5ZEVu9qh1LrS1n1bHlBspwwH5qD16w5Zw5OwUn6WSMYNrZAQ2fGLgo66c71jY4Q3qNGgGwJNHnIRLbPLxGstg91TWWVPrgB5n8x6HzHiNNLrIH7cFPl/o2tvTwjTp7nEVjLYSm9EWvi3Lt0TOmFQo+i/R7CZyCPAn4XuHjtKH9FT1x6wxMbcYczbQqqIzHd61xhju5OMMLxs1du/N15tnMrPxGxz3BapjeuQ5m2eaecKjrFbAmHaYMiQb4Tr3246HrzUn13lx4DLO4BdLhAq2RycGu9RGNwLa1ZbxzbWvGI53Rx3/yqoNO1fnFRfPnousZUEuLP0952Kp69ClkxmkUb/UmyzQySRAOgVj8p55M/+pcpeHtRQnN4uAim34g1JPFGvcXoPklubOxnAuSDWldkN4R0CUN3AhIGwNvAkWGD1kVdOStHAusVTUlFs1KHhzcGJ7KihuhHGMXPZzzc4OJutJ5jG4J6GP66fe5IbAZmpGKyihS4yiAG4vU3MmRoYEXjfqEumCtnNQO/tzjQdJ5C4zH2cF2Jn5zovM+1StJeUe8XobmpTXCJSLXFygtqi+DEnNhOPHmSuezR/AoSsQpc3oCswxFIM8B1F8FGo3VoNtnPLx7H4/AwWA4QjEy+Ccl1F23+8KL0GvEjxSZKLHqCZDHA50m+vHci7G8gcQ3CHZvV2Zqih2KdE8OkMaJARsyKPd37cyWZgeJwHWRG+KI6ELGkwTBMBPIbSqXgNXWP95Rjzn1HclCK8Ca5yMEM9fWtHG7xw63oJGGzpKiiQrUL86NEPo97nlTpwFVXDYcCOQr+sYjLmtv1MF0ljZFbAhQVZZpGQNJt7mnRIl8oezghUPZil5mMEbQpGXIJ/SuheVuMGzfncEMqs1Qr9CF75DComkjzH5uqGaPGpTZGwIdLWC0DgEZb+trLUs6BKSgKhvG9NuScyVSGXURmBmEA5pGLOJ9Hc77ScOUtwnxjbQ3DTdS4V8ShocCuGWYiqAZdEPUZSdrCsylQkrZDsMD9SS5apl4fzoEHGqbSEzqAW2gsmmt9aDV8t3u/QBUCOOdRuWVkIrUssYFv9D4LHNTFPt9yZrb3fS9Q23Q7Dq/fZBO+EiqMAp/FL0jRvw3NXiXa1diXpk3vy6FQo6HJdJzoyRIW6fTFjpRFeInWoMQzSMvYcF7bsurzmCQ61ixUdZlqV7RKeB5TI34s0S1b+7zbICJsLJm2FoHXi2dEdZX7JIICGrzvVjjfxwBko2ziWkTmqhSVNGvaIXcuaxgH/HPtLOKs4amGlRgNeXvGnqqhC/uX5UWWyVvQU6MEsNqv0pS6cmHtORBW0+FE83XFdCDUVdOFimV7tPXkLeqphQIbD2ap9XPt2seNt7Dr5K3zpdq/ggT0XdgB1U0CQogLBddnd3RxpMhMD4NImFy82x+kf/iRWwiPAYF2fqzikGqMA70kO+PLxZaoXtsnM7X70n2Aq+oMpzm744QguSB4IeKgtocZIMq3ddME85G6G8YsQqJ67vdWd5zaHt1SYvA4T7CDb1Q5TOLhlRn+DLT6Z1EEYFXMPXQdrGre2LRkwIzn4xoEZs/RSufFNKuEeb4/g4CE4QUv6d2ZVZjEEY44U2yMe98YfAxuF7wA1oTp6lCcSbau8+aCigesT5DduZYRFDzlksx5fqa02I3TpMDpA/yW4l7LeS/u6AQB2xTyMo0Gm1sNU3c5e7KqV8Tbn2KWTQNB5SmvgGpuZDDifCkBGGl2FIyhUT6OZDSTFLmmD8I8q6eldWGXqZQg795/CC+Eek0wR4NFHdPx5P3NwEUjlduTGouHLOjOzr9XlMIhYfPlSC4ezNr4fWlVB0DdxfTcxq7bsJsF8UjcLLfW6XaA4BrbhF1zWy/eHCdZ9YM8FlAp1s2Mmj0XLrnO02p29ZxrICQwT5DRhgOQ7tUPUYvWZqdIbcjQa3W5+vf4lqv1C6tXaZ8dBtl1XyIlAgN0EZ3SvyAxhtboi7dQXNRNx3EslGq6fDyXqoePxI81NSdufZtVAZ7fAlXrJnRPyeKJlewjHob7eAnvlzCJFFZYoKft6xoGAK78nkAjb94QX13vIudei+xSOUZSTBEVJnvD7vWmceZaj6Ml6NMK215AmELgnrchnnm5j9SOJPQUBEzDugiptGD3IJ9eLiLKy0/ukDxuRhu6u9Euj1EXB1TpH9NBH5o7L/qWrSi25x48Oz2aV6g1qXzdy6my7VKw+rzkmwFAh/s8PQovnNJULvZFZceG5nkfsqVd2NHqumwiW0nMEv406iE7UHWlFpHtJObx0AfsLvzULwAQ6zjVSMzroauLJtSIZfemtXNnU9WHwjav+3HqsmhIDh2nnlauBnr8Ulgp1F6pyACp3FLIitqnNd8D3Xgp+wXMn2dAPmFPoVdynBqPU88XSR6n0pR/X77uLABAB34ZtHcKH6fYC+nrz+MUb6jrQOPpoL4XwvI7Cl2chytQ4c1p3+WNqcqnq8GkTq1nB3NVheAb2ksaMA/l+uSIS2BKab9JzCyq+kjKI4D7MEL1yqrRCL2bQUCk9X7zE0mDFDOdk5GKEAEAFSwcKYR3LkWc6AwT1u5Mm9JznyOvv5dUuM/ngl2gBnDrYjL3TcvWkjvbpI0B7sJyDN8DtNuDAaz6oQ64Ncsm2aO+nMNDqvrspVv6LJyuDEOUNOm6Wxn0zUpOqfYqFPdt6nK8RKbZmn0Kh8/KPCzGHFSvtoVWwu02VTH6nQpyKqcjuxL71WfsJD+3WpfvzVoG1ZVDGVN9sCHhICQTgCUJfHctYalMUSZQIkR6b7OvtuRdd/DhyePJq6Fn5MUPQXA9iw3RYQaj3bhMpjF3S0IKq483r+6wiWYyT8M0DQ2UU1UnkcUlagE2kXyoXejnuWtWaZT7iu3V0MjTYAYhRc1qZ/H1U33yQaJd07sbr+eaiitYReLo17lEYmkCIefRoSbXZSVa2yqTvudXMrzY+yOCtNULFJ7CmVei+ww2Dwu3HxbfySbZs4ZFyM5C1FAxzNZ+d4xzU1N+f8Hkq4AQzyOFHolARHbqB4oQdJOkqKsG7ecev8G5p2AmTcclwJ7RGhetMoGW63XO6DFN4eSWL24AtOHchpN4MUNvxy8NqN2Ws2RNPuTYsJBOloYbiN+mVTIIK2CKMydp2jewv5kHEGmiigo0aWUiLQkte18vsd8xwjxTHPJSGY/kToXUOl5vJq+XdK80srpXHYUBz4y3YTtwwyGhSfZtYZC1Ck82E7GmrwY7i0ntuTPGPb5Eso5ZvI48hiE0M2d6QdMxP2UEawtmoZHPPrxxH54lriul9Qyv15N8fTKx4UXTwfuSHNuoNmu9foGgWlDoFwQlXtcr6WOqwZsdlsWOXvf+vduKr/Vst2t17crY5VW+Js6S48og6MBQD7DTqK7uD1VDvUZqlJ3izJXVdKU1hmyg0MIePLzMBh0gIL9l5L1h0GNOz3paWNmjv/eVPfOH5Hp388crTHqKzv3+fb6zbclQTYKQPVgyD82YO1KYE7g3aErMDbCCwXnQc+5NFG/F5eCjvuvUZduikOmVPLtSql3TXOWOopq7HRSHNIl2am0dMss9NW+92G63+g96VbKnBMryM5XmJv61HfBqe4t5QVJZno/KuEAAi0hkljXSUF5Tld35M3bZEA/DDfwnHGYTxdgqyq63tx7qXU/etIHETRifFOs1TxJuPoacpGu2W1qhatwyjgLR89lX5t2x4WNf4ll55k7Qjzvsuvu6PrHgco3HzyJY65vyz16OY4JkcW2m1VNpnSqeYptGsUwfsTEr5x29ig0iaDqU9Evc6mJ7WBLe39B6WB63XYxRvMc5gnax8EFNfF9WG1aCXb2mfb3edt0Foh6hLKbnJGbmpNW867dNEV1R6w88w+6U02EIOye5ervhRdokLgJtnjq4+E0SA9+s3xxOmhTZDdpQBmpzADdyTtrETsPMSwrgiXYdQQ94u+7m7IoOkjAY8rzEKLlmMKZqKHDWeDG7ooISUBrJzmo2DtAs9cp9O1tJRxHI+QT7qz1XuJ+q8N48Nf1AOucG90pNO2XYlWE4sBcCMYmwEAzekokrzA85fNE0dOCrMjTIeJ5FG4GBiXiivBerhGH7avmsveXc6ijJsyhNvd2HXnAtZyU0knnBVKOfiwvSsolz0ek+XRfxs1DrIStICphZaRo+7Lw60DVq9DxlC1KAKatoTnzVm2v6LJAUJb61tAaJLwRQk/RcHRp+QqdaodzDc5bjXQf7AKXD88AljmBqZAF4S/eM8kw+N1zVV5lDPbnZpQXLQSKzkmja5HFRquAC92wXAjOLRFthy2tpQs1uTbfDaVs9Dp7nCZRF4oWVFIAbvEH9FnL7yXCXSJomfthI2W8a42x+gC6SdXgN/Esx4sNIbdMH0+KbkIWs8H4N9os/JPMRGGW/5cYOtJK7bPVnLxFzfEi9eYeFe4HUt6g1bHE33vt7efivKrjfTcwAW2u4zr86phzsdSsUEWyud7g0X6vkxGpuxVA4GhBOrCWephVR+lkd+N6kdrjHGc//DXun/IG+M480nv2bV4/8wEfCFRBhzWRkwvTHYvbMUqbkyLL7NMS+StlYZjZWve2Dsu/7Z2KNb9T7FQGRay4p4eFsCsBYF1dGr1fzOT13FHlSSZc9QwFwW37WmEZhGI8YgtZa/uL7qD3WkxPpWzaadkHOb/VpTx1N4cZMiAxWUfR7WeCkpSfevgCcJ0tXcY6sF+hdA+CnTIiCGZUC7T9KEs2jhwEi8i9Fyfxrfm5Ffywl2kdYxy3QeOOoATseWbuOE7swKPBg2bkj4ujSmU0IFgEom4M+dfxCHL5bz2oBhgFIMI8lhmEIrKTbNdRut9vGhxOI3rkv6k3kgdyHMljNs//oHQIjP2LMdG5IVJYGZ9SS7Isk8utxLlZuz1CImJ5velku/Ma7rO4VFd665vv9jJ24DR4dRa3nysTnqo0W3uK12NZ3ehj9HUuKLTN9pn95IOrDFk8LF1qXpF6HRZ19qf2g0PGXYVFS6p7vAkM9BheBERGcPXNsSL3MQewDs4Yv1axfw4hRk/tb7UAWuhoxMsNzZNfmdvgwCDw8jrCHw6M9ntXuGd/CD419TZrYf10+ZJPYdiJGemZd0stSepSAizOnOt7h7wSmS5NgDSxjQ89wAyA7Go3JR6++RcFgJc2gdhn5Ssg9ZvhEPd4XFfviNrfA7VymQGow/PDBai5o49jHMLyC5iupck12H69gFdW5wRIl0QkfWC+al5QysmzbDUUhOS5F4jjxAm9DLFcGuWkjqTGS5ZThDiG60boQeoCA96oPKbLvpg+CD+P1ggEHxUTIlQsa/qz9x0lzvG0bfL/LE0gvLQ8pImkkem7BWOVklJTvJsR8OCGR7JYI5IEhkN1NTBXvxxveNc4WssWEsireqcyrdBHjgqpUtWa7z8FL8rZBVS66nt4qs0NCHIa+b22+u/hvBLLJxzizL9ELGOqWIzkSENnmydSz1WbxJdJzPc/QobgsANrP9RnP1lcGkiQ3cxbbRh8RBHgtCrch97GhqDHLC0BdtwP08LZwbggEX4Bb+nKIB4h7MXGUPEQkZKV8jYx7+YwOfSWrjHF8TGwm3Lpkqux2H98dLt5BtC1zxcKDl06IbMG8KdV1YJwLpdiq7jSTOKSRf/Gic8Mib0jB1Z10AAG4iSUUQw7SA8aE9GZ9tGLhIT4loMtGsD0JwvVQulGpaGl9nvp4ScD8QhXs/GCV5uBTICqG5Wmv60R+/Tyfbiaw+1fahji8MRKGgNr62R03tvc93icfQ0VFuftxFJVtfSWDOH4PdGEOuHkJODJ8HY7yrBDr4TKuAOH3EK1BWSDTOo3NKTXPwF6tgX9pSSQB+nBMjLF13xTTCTCVciqbvQ+o817FlXMheFsWCHUk7v0R3OUrXL2nPuF7YQ5jhjm5r4O7nREDFeavN4VzlDfDoPortMqWoExbIN8TYeNIOCZ9lsCxO5cyb09LqMrkdpa4crV76kaH0mvX6ZXH6swXZ5e8qGZXPBwyBn5VTJkRb/HWRzFBzUTKemMhIo4FNsKormjwdhSi3TKlWx/i7zXHEm8MWa7l0uAVR+MKhuNu+6sG6Zir6pI5hasmVl8o6cK/xqns9mVmGiG5DJN/TTzlgDWFOA9UUkKrvUlOcVWi0ms6MvXvVW1QyOFMTSGGg7oo7CyRvoKiaJywauNAZ7oIdGoYza0DT3YcBO9sXGqsF4GN9bnta1IlqGhJ+Kz3RlEU7vJIHM1+VKsdS+JCk2PT3bNsquAED1urYdqBjhd8HveSPdaukwzKJvFuzTKMpYh650c2m/sJv8VjbCCdZyJvSlTGKAolQDfWsTCxeuPicuBfM9EYVn8TB57suDKcqGifmqqqvG52zwZi7namdXDjLC8yKWJcoQEpl16p6pdz6ZVcAbB1TdtnWqe7FLSwDRKCyiqEqL6FIK01NWYGXq83Za9jbarpzfh23deFNaCl8ZIxS1GliLSyLCtc0/M6yp5iN83dYWqdiznjZ/1YINa9p+i+JosyD1+FOA2CwDionDeYpN9r0EeTL/QIDkCV+8Nx5UMn3PParfW+K8OoeKk1/Vx5KyajUInoOCi5iqfCJOlZgr6daUx3FdwxvGoobt2YsEWiroLAW/qOXogL0UgEIWuWK/F93L9VWpXewlpEGG8nvSIZu3mwkRPGVeqIytXckWsn4U9b0B9hwz3OPmnHS5wdtUCvKbA9nyHys95pqcPVWS1ynIAN8imVoQqH5gPE1eBHM539gMBp0a26KbNhrLBcEEg4tBmEdQrwsUn2weNM6hQ7Q0zzEAfpakz04YZKl2juJs309lCA/EgV1FK8v+6B5/WaLt28QLlQVKjTFzmzvE12udRca40/y2A9h8OqwWQ9kJ+7r+kFAp3eGFmmQoiuBE3TXoeZoleSRBH2x6JlDL2cXeBeweYpwSsN9xfxBG8Q5AO0PtDbx45zfgTci+eq5ggCYWjaw+/7Xukel5eAzmQhC/Oynqa70wlPhiC/V2qnanRQSQNIPSuZxLRC7mUt1OZlbrvruiyKbZ979tvOcdy6a5dwPNwY79J3qVZXRCwOWa8opvrI6Jd/vbAuyGYn9IFp7jNNUQsVJ/qVSR/rAY+URvTWfc+rswAOQfjbhS1U8eyt6W+Ltew7vQTikmi6d9tDzJp3zLqXJQhwv8OP65oNfcEk2cO1dKNkwdgoSJUNuFb2RURR1iMeOyIwzDN51PwKQaHFCvGj9wjt7mb2KpdV+IonansVTxZUD+YPfB/e8/z4VKoQlvaiglUR0BuO5vnOp3Re18O0EtwjPuhCqzWrnsW79p1JQXcp5s5cgn6IAiV/PxsNxyszwB6+oFWPBPesPD0XzkVTnRZhmIjGhhC3G4t7UYD2yX5gqkk82CCK2xTIrOhv1Db0xr16nZ5xdzhz7etcBTtvT++gOBdv152FygMC1j4BC9foUEfBLj0IjUKlsNZ2XO8MsHcGK5C9pClVsm3cQEb8xQuMnCTVI3ZF0cMfGi663UKpN4U9fITXy8mCAeB0kjzeDb2Ro0vECkyTdSgaxobbIfO+7BDybkUEI6MDgyDJzcNM5OyOeOugzoMH3FtcJWqsJ4gwzJtIgg1w1foaMEqlkgbHlVBYtcqQXGTxWduOgihgb0Y2K+Pg6mdJTqMBiJtR+3ygskGgatJKHzvHLKOczy0qIVCQ1e2LWbEkC+a67gcxrBPxIQQMPIHGG3xix6oHFiQwGRw9Wr6ZNMdxluOL1hkjnht5+kCfXlJRlvsV7Q+9hWIzlBrimie9Sq2UUJXe3oPs53pB8Kd6TSHROZz7GK7Ft1Cqse7rd3FGLiJ+vMYjum0rrGfOm2aNOUTEXRYsT8XOdkpRyGyLwV7a+7o2ykYnmGbRXCn77cR6Os2OyEPEltsxyMH/ZzcmXwRV8LXDzfUkf7uDSB6kod4lyi/NnYEPWXNp54mDRB41j28pM4SwNz7n4/00H68+YEx/bhILgUgyjBC9c0xJNK2azBur1AXY20YOhMtazc2L+Ty2PRnZhmy9IeFwuHTKuosBw315f+VMcnnv500Ya3d6EE5EYGzxpU8kIuLA47TuijQP+Mfd3iX88d0BmGyEl2M8Zw/uD71Tx9MF/9L3kQe5FNCltGSNrUfVSNHo49s9xBRSzCEMrlReds9OKAoXiQhQ4GlvUu2ZWlWDwXwb6Jap3uQXOhDwIm777F7CmjmmBvWKsCQS4VvGu/XOoIlW5vH0wCU29fm20u7v2N5tRWzfFkQ80GJMz6plwiSQG8ZtVNeOJG23piJZYTAN6PunHtb6PlieD4kqyFa8E80oX5jD4UK+27dw3WzFOmtvtS8H/2hj/dnPmriL6Rt7sWL2zGkzZoDvaL80tTRgnRAJRwRoN63pfN912Y5FNbpSvnLVXNm9llgnRiGr25dTMzvDprNn3uzKHcBK2QX34JvZ2EP5VhLXIn/L54JJ47KX69hv2cRzCPBciKfRJu0NLP2/67TOjuy5kdf6cF6bd9F5z1VaO1/VJ2XYzlyX8dm/EUOnemlXRi00C7LiMHnWzydRxXk+vD57QY+aK4yDKvOD5CWPpwfTAs0LHXBNVe191g3256knwYY9MYOGWPLM6XplSTVtp3fct5OkvcVP08ZRoNc4f5nVeon9uLD2/EV2wCgC75xKCjcFpU/4qA2npj77PiTA/5E/MFfDA+QhvG5gSyaPO1tjZPeZSwT5BvqMM9arQUPsuupI4hBn8Ml95JF6GyM4ukK05PRUBaLVgCqggzX4J6kH/GlqsDbeYLLzv3SiPNcCuZ8Qo5uTL0acJyicvt9pGKJ0hmUXStOSxyOGsUnELW8EijcbJ1Ltu3sfupLw6HoxOdeKnzVLgNL6XNdEGyx1B6F3HrcY8w5dBHpajPe83x5uSEkBp63jK5rxbl3ySr3faM4nRZjhkvpaXfi7S879tqomdfa8vkqwXY1Kf9gW27CdE0uBByL6B4RyCRZ93RH2/sK3S7VEW204jyzH4STewPYoOkdcNhzr2413bOcOtk6Pnx/q7F56/qAh5q7oQz0/8oEj3qcE6AVYZpUgW4okNYdgUMpdFIjJ5eeiNM1hwsubkzLM3HMS6HCoOFbbXXhBS82U2BR1fBpVIzmvG6Ozh2uxlYziP1o/lcAydjXMiBunjq1RNpdHQKxXtm/SDS5vfso884bDtCHbFesY8spX19Rctk0Z2oeBgL6KV0bPRWnK3qziW61/4UCufHikBCGpo3c89nI4HYzG9tKwwfwx5NtLG45Sh4y8WdzYXsvukpqgfgwDWDDQ6KE0UMnMxFMZ4/BoYBKbGW88GoeAwFKYpsJyCzcjfr85G3z2+8B4T7urLO+inFEH/fCXnl5r34zr0yT3jek5x7GNoqgs7Ww+Rax8yVud7JWXITnm8v4/+n4DnujmPcP5KRcdcqHm13C5UJ6jeBque8I9P7cmUxYJPnE3XNoyevQvCfTi5LcepPQHr7Y7M4OgpDQ9aEp4RtSCTbo8dG4earCip+wUa24Gv7MTfl3jVbow7Uz0xVmekg7XMqIfCFjvsQI0419BzUn+kjHpAqZsJmfPhGimrWucSf24axWptQC1NxK+xQzHHkqULvCaHp+eH4dqQqQhTNuUrYnOE0sfqqoj6HbuFYNAFooHViAgwldAy4ocO09SKW31bnI7mkfGWwChB0KHS6DLCKAY6TuFc3eqxnVEXto4KRD7Kb7adU8qgLtMfQciiJ+QqC/eco4Dbdle9yjiWoegG9B5YaoCak+Qs6bGvI8QJuHL+EiDN66u9ynX3iZATMJc5egqQiDACWSMf7BWDUYOb/26JEDjCnw4WyKWvGtHl+wNd+ELkwBwWGHN1h+OCU0+xOs2abZmCN1dt+YkKPo2cNZtRp40+SbS+Hw180fHp+gR2KkehHxTUBypkOFcGHmdJrdiCeCjtmelcwDHsS3J32x6GFAQ/IphN0dGKgG4p+HxGRQaYLx5X+n6Ga5+Lat0YF0EDJTp5PlJ7BjNBdRFk8o0d7NH4YR6W0yS7CJBbsbLJrBL3QML+IKBQddkSfTBHhwpDW0ykHDeF3TAU90oz7e3cyOKF6Ua7cebmEkN0Kin720uPNmbHS1hhud4dRBqSj4cct5Jpnl/7QEfSOvlGmsHXgbUWtb9o4Yf6IaXgNxxVNH7+wXo30YrJM6tbnm4YN7dfS2hzjR8Kfdm2t6JltbVsWRSu6MfY3WZsxthcI0KOPSZALOXQOD/j8SklTLgaxBo7WxME1hOA4q8vrQGq+n3ugyteLHPz5xs3gdntdBqm1GLW6ZYGeniW1AlY96Bx+FAr8xzR/e842fvtBs+u+PzXIwFv5iAPitH8XblT7O3GHKCn8tGx/AswGL1QPpgYPCIHAtXcZiz8uI+VFwTAWp5bm11ooIbTIyDUpZbidQOYPdd/zI3nNqutA1skEryXkxaa/jm3TjDCFSKU1jdDioDnj97b0rCOzwAv9ljKGncJppfselCM515mqkSiLrWjQEp0XxIgc8CYuFbOql+QfuAF8Aojjsn4J2HemT2p3sqpfAqbg6YUTk+sRol+p2K9ID2oz228KYH76CiuLO6mH8ppYIOyFqhQH1KPqq9szKGDbyZ8BkVJm7ijyoDuggikoA99/bFIIt4MnYyB72FJ6j+EaifFDzcySes0n7g9mMWu1FseE5+g/ZtygPvOu6m3iG5O0nJTL7LTEnuHkqbJO/MZ/2jFrO1AkfQcJ1jV4MQ6PhwqJ/dx8d0VuLq9SussICneaqba7VGkZiu5mrfalYwKORFkIBUymMl3Td4z8pMHE7YFGVAAPsx8K0Byxj4gaTwYHI++kkxeDstXr97uYK4FYk36YzbZD5aLZPOKS8Q4AMDe1Af4ilS6ErRBSegDFUAhfGkFp16k9OMEPBqcJMOz2G1dl48ConZBfAy3awavaPnxfmBmpKopM5vt7rD0vqvkniGkPYCOvhs0/LMSgQ5lUysA3Rf0fwT+D+pfc87GVAYstpoPNka7Q+YnhvW5rb8/sCznKR5wn15HlBPBIXuL67VS/+ZQNoDPGB+JqVwc4Okm+9gEuzNhO2ixpmC0BsI/Xj2DH9W8QHczWfEC5QG5qtcy7gRC4wOQQ6mQExPTug+MW6fleZfVkc+k+UF2WSkjAhx2JswdA4vgwNx0/e5u/M0cP5ZQQi0VWfc0X9DHbFg2A0ZsIPUFJnsgLsMpgt7uh1hUGwxVscCw3bW3G2I8t8HpVrYWcuOQzR6xGbkQwaXNnCvxKunvRHNdV+buAVagB3ijcLyM0+HG9s/7hpI7U4jB8+gx8NoD70D5sW7rEyMmPHgawv66EGPmZ4OZeoehilY7ubuNhswOtJdWzb64EbQTI858bhZ6nq8C34QAK1JsCZ1ad0+IuXkNBr2w6Hs1r5MZ0uVFMSTL4p9A2WUmHKCTztC+cRC7vXYlsLWMTgiuAQDotsWiYDtO+0cefme4P2b5t4tD9a86NeUItiAvuBJF5H3BKppG0T1JwoCIenqCWXvuH5bccQAvycNe1g7BAVYOKb/ABH+KzAlwn7O7NLr2sfEbjaBxpt79q2ym/h0JAAfkEv0IsL5sLCUyog4HosgbZP0YYjAieoFH55bnMkHG/LS+8RGoMGox+00MEW0I1CGSl7jUnWr4bCKcA/jtymQH4+8Cz90J1BYwXx2mACcnc8oCrqa8vt8FxNIxtS5/Kg3CqRRd9eG1IlQcVcauY/8fM3Xci5ot8JdEr1Sq+5S6BUFfFmGGAtuKR0dAXPpdKo4dA4iVB/CH1DzEwu9mcTIA6dUlAoVYLntQz5cHMA8cU9FoP7Cp64A06ToLlc+5gajZw/lBLCDSO0e0MtYEdU7jr7P3i1eF6xMrbWW6lAg9Dihr9X0Asl4PDfhZlPpZmRslV3DnAOxM8+yJR4DSszyZIbfn4z2yLxk6Pz0StnBTYXS7FBQlcSoM2Lfn0x8G0shfjzV5Wlal8rtb3WTHiB3q3VJql4S3l1MZS1MyLIzJTLqgjNUK4QuXoltHnG7vZm5NTdWFm6af5E2yjvMjW2laNEeSqHmxDvHeZpUxlvXPZTt7/n+nzVf4JMm2NqoisADzk2VUR0V2b3tziO+cqyarcO2gS4g3izrzPWCiN59ydb5BUeTPN8vF0GeYJzx0MGfUG6v0gfLjdVgeHURTKi0V4/wyoka+R727V4HpZdLxzBXxPXU5efjVvcB9mQfHYwBLdAivSjiWDXPr8ZFb9h7ILe7G1TeflGZTEBdWf35uGwVpYiHO+iwDXAhpu9LoAzVy/vZuKxwWJF0CJBjmCzYHxcmQ3ZL/mpc5fDZjmEEctxUvBAvbzboi+r183F3DfLVYQ4Q2Qk2c3mk9zdihtJX4wZj/HAiUujHVcUb7rIqXs9VPx+X2ScsUKncQ3DntbHHMGOJmKr6s3HlPLg7hjOSXH/H77h0ua6k13FfjXvfJinQqNxG8Cpc2MvrPcKb+dW4eP26V7E9kmp/9xhMeggbKVfCOe6Hfe7FeRFdKVG24EVA2XI5/jDy08K5vpDTNP1fKHv+PTT5AfKg1u/jegRHEMSRNqdUCkKQHwfjWczNvNs/wB/XzXE/xuvHdechlPtf6LVahbip4rE/zBj05SwBfdyyffymIfzj95JF4/vjGIaR/wAL2cHhd5yl788ZwOjnvf7wcSD9cfizH+D5UGA51sPGlF/mcP4fgbLo45625Pc35pEXhC/iumgsT/jhy2sM41bGH5fFURobnz+bfnw3aVP7JffTUaZvpjqKwajQ8euna5SmaY+D8HEwj8fxkOQdDOJPY3Mceo9V+Xk2XrPRBbf/A//89frZGXb9HPn8sX3++GNkAS/xm0Tp49Ifs/nnF33/637eqjcZKNnxhZjUl1s+iUn8gj5DM/Vh/HnTTyT6w+OMfp/G4zfjnKT+8W3+dep/4bXZL6fPT3HtsxHUj/4FVwxLVpV+DcifNPX4hUEAzfwyS+vj/+FBmbg/DgCZyEK/vHyeGAFbMOE7KyPF35oJ0G8Y/bD48ot5N322H8P6XzjkON2Pn/yDEF9dYYA7P5mij4fjGv0LU8C/OKT661cXKv4wfh4Im7L02yELfnyN6vjUWc0049hUnxclWVlem7Lpz2+A8jxLE8Q5u74p4p+diYiAwIm/UnnA9NfaA6Wwb7THjxrmK9UB/1WqA8G/YZ4fjp/hJwNBdTz1fnn+Z1yavviGp453H7/5mnXzwWQ/+/Sfh77wWRkn469y2dD6YVanynkNi/105Pn5ScCh5rg3KZvl+PnOoiiuT6U2+qP/wQ6A9i2QufOT4czx9/iIV6Cu8GPi1+M3/NNvnD0v78drUx/v4mcn6eOD15YY8Nt3mOK3hfGfc8UnE4DWhb+HB76M/OezAPENC3xD5TI7qfdBZedzpvC/ROLqINZpgT5pap725gf4G7qj39Id/Q6NSz+IS70ZDoZtwPj9x7W/oP0/I+/X/JvV7/iQgL+S7Dj0+8hO/VVUJ79D9eN1oaTpF7+P/q//+29R/9NpTv1OUSf+faIPZPGqHyMX/I/gkze0uMpa/n1RB0Qf/Dn+nw+l/zfh/3zC/147/2cQ/vLa9uoe84R5TQQY8zilTH6A/iseQn1M3f35j9dP/gL4+ZOTcP764iX85FlQEPG7fQvwQz+09vHBAIA9j0X+8D7f4GMG0aXvT2Zq2pOVjiN8Bj7kv+CcfDgHn9epMG08BYzGvNdzfW6yWdx++DSiH+D/47q159FH/mqJp3i9+yFS1Yr1A/4f8nZgjPoafWL0LxDFr/g7f5afgn6rfC7heCiOv92U/73cFJT8l90U5C9CK+i3aAW4Kf4H//ztpfxBC/ajKP75XsqfYMG+P2Xqby/l3/VS/jjZceS/66Wg9N9eyn9a1Inf6Zn+GaL+W8jp+04K+BZjnG5/U/7Ppzz1H1Ty3/VPv41kf+u3fIPivwL5P3M/vvZffnQqfuZQ/JS6+BWX4l90YE5E+YtZnsf+FG/jfyQzcDqkI1G0aaqN6n64jT+Apjy/y934Qrt/6m58jwn+TReEoPB/0Aj045+v0yYYjP+DhKhfnv2j2Rgc/Rq9Eij9D/pnf35hqn4lOXNQD2zv/vGyT/H91VfDfxHaJ2D4t2f5i+txDPuF9HzM4F/1wL4rXt/G+t/H+x0v+VtSFmV+1dSR+c6+dpk/XeovjhP2m8L3b0cC0J/HAf4BkeQfjgX86wL3e917wa2ujbp08GglFHKrOcQSf/gvihuMYb9gMuj3efzfjgQjvxyJ/kvECMZg6usHEdQ/mdkvohp/+AaM/EpS/xrJ+9Z9hf/xj//n1xDMz+TonwY8fnRUzrDDrwIOgClAhG74EFf4D+CkPyY4fyQdiuJfUQLF6W/wB/Yd+PFnrKP4LpW+ZFl/RiYl9vv6gHvgcWXaHO7Wu/qGbH8HrH4rYEUhAUr8pQEr5Bci/SNj/VMgC/8Gkv183jMOR79Oj0/x4wN/+Tz8ey4T+js41y8PpqgPf4YB5nL4hoH/BM0Dw9/wNIicvbc27g9S+KeJHH5NE/3/0Jf6bc3wv2XA7Pu5vu8T/iPJ9zfB/3SC/95Q2V9H8G/jJj/GyP+m959Ob/K/nsz/Fq38cDoNbXli7mBKkgNy/E34P5nwMPQfzOZ/f87fi5D+nQv5tVzIn0V39Dso7y9Khnx/yuh3yA4C4wCvHyj1f5pgiPvZB591+Ds+/ldwwH9b58Pfi5D/nAUOa/839f8i6hP/db3/bQD3g/pTGx1O5N+5sb+S+vTv1P5/Hd4jv6Hqf3Kbz//6F/Jm0D+QH0P8r5+f/JV4/39ovd5vCtfPI/q/ufzzz1uw9++xBfXfZAv4X2ML8v97bIH+3kzPf4Yt0G9dgv/kkt+f5fb+MIV/T/r8Z1zwabL+HS74p1nzP13kfyU59U0km/p9Gbs/mmcjMfi7z/mzkmDEpR5Vm+cMzkpfVVHfZj/6L6af/4NrP/519fNdxiOIbzn0u3nmL/L+T1n0kwN+gP6BojT665jm32Zl/GtWJqhfAKPfn3z+RRobpfGvR/qzks8I9PWDSOg3pOKn27+M3yTJEP/BpfNgU3vTjD+/vPfbt9pEMbji/wU=&lt;/diagram&gt;&lt;diagram name=&quot;Version2&quot; 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pointer-events="none"/><path d="M 428.14 487.06 L 421.12 490.51 L 428.09 494.06" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1318 445 L 1318 515 L 1080 515 L 1080 418 L 1055.99 418" fill="none" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><path d="M 1030.99 418 L 1043.49 410.65 L 1055.99 418 L 1043.49 425.35 Z" fill="#000000" stroke="#000000" stroke-miterlimit="10" pointer-events="none"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 515px; margin-left: 1161px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; "><div style="display: inline-block; font-size: 11px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; background-color: #ffffff; white-space: nowrap; ">has a</div></div></div></foreignObject><text x="1161" y="518" fill="#000000" font-family="Helvetica" font-size="11px" text-anchor="middle">has a</text></switch></g><g transform="translate(-0.5 -0.5)"><switch><foreignObject style="overflow: visible; text-align: left;" pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 1px; height: 1px; padding-top: 321px; margin-left: 1055px;"><div style="box-sizing: border-box; font-size: 0; text-align: center; "><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: #000000; line-height: 1.2; pointer-events: none; white-space: nowrap; ">1..*</div></div></div></foreignObject><text x="1055" y="325" fill="#000000" font-family="Helvetica" font-size="12px" text-anchor="middle">1..*</text></switch></g><path d="M 1180 108 L 1180 82 L 1340 82 L 1340 108" fill="#ffd966" stroke="#82b366" stroke-miterlimit="10" pointer-events="none"/><path d="M 1180 108 L 1180 298 L 1340 298 L 1340 108" fill="none" stroke="#82b366" stroke-miterlimit="10" pointer-events="none"/><path d="M 1180 108 L 1340 108" fill="none" stroke="#82b366" stroke-miterlimit="10" pointer-events="none"/><g fill="#000000" font-family="Helvetica" font-weight="bold" pointer-events="none" text-anchor="middle" font-size="12px"><text x="1259.5" y="99.5">Learning algorithm</text></g><g fill="#000000" font-family="Helvetica" pointer-events="none" clip-path="url(#mx-clip-1184-113-152-26-0)" font-size="12px"><text x="1185.5" y="125.5">- hyperparameters</text></g><g fill="#000000" font-family="Helvetica" pointer-events="none" clip-path="url(#mx-clip-1184-139-152-26-0)" font-size="12px"><text x="1185.5" y="151.5">- critics</text></g><g fill="#000000" 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)" + "![architecture.svg]" ] }, { "cell_type": "markdown", + "id": "5dfa1c37", "metadata": { "id": "dDn1blWvPM7Z" }, @@ -56,6 +59,7 @@ }, { "cell_type": "markdown", + "id": "296fa058", "metadata": {}, "source": [ "### Single-Agent Learning\n", @@ -87,6 +91,7 @@ }, { "cell_type": "markdown", + "id": "7b8f0839", "metadata": { "id": "OMvIl2xLVi1l" }, @@ -114,18 +119,20 @@ }, { "cell_type": "markdown", + "id": "345e905e", "metadata": { "id": "OeeZDtIFmmhn" }, "source": [ "## 2. Get ASSUME running\n", - "Here we just install the ASSUME core package via pip. In general the instructions for an installation can be found here: https://assume.readthedocs.io/en/latest/installation.html. All the required steps are executed here and since we are working in colab the generation of a venv is not necessary.\n", + "Here we just install the ASSUME core package via pip. In general the instructions for an installation can be found here: https://ASSUME.readthedocs.io/en/latest/installation.html. All the required steps are executed here and since we are working in colab the generation of a venv is not necessary.\n", "\n", "As we will be working with learning agents, we need to install ASSUME with all learning dependencies such as torch. For this, we use the [learning] attribute.\n" ] }, { "cell_type": "markdown", + "id": "e0770442", "metadata": {}, "source": [ "**You don't need to execute the following code cell if you already have the ASSUME framework installed including learning dependencies**" @@ -133,32 +140,23 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "f8459b9c", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m0DaRwFA7VgW", - "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b", - "vscode": { - "languageId": "shellscript" - } + "outputId": "5655adad-5b7a-4fe3-9067-6b502a06136b" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR: Invalid requirement: \"'assume-framework[learning]'\"\n" - ] - } - ], + "outputs": [], "source": [ "!pip install 'assume-framework[learning]'" ] }, { "cell_type": "markdown", + "id": "3b53bcf9", "metadata": { "id": "IIw_QIE3pY34" }, @@ -170,6 +168,7 @@ }, { "cell_type": "markdown", + "id": "c09764f6", "metadata": {}, "source": [ "**You don't need to execute the following code cell if you already have the ASSUME repository cloned.**" @@ -177,69 +176,51 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "id": "5b00f9b5", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_5hB0uDisSsg", - "outputId": "1241881f-e090-4f26-9b02-560adfcb3a3e", - "vscode": { - "languageId": "shellscript" - } + "outputId": "1241881f-e090-4f26-9b02-560adfcb3a3e" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cloning into 'assume-repo'...\n" - ] - } - ], + "outputs": [], "source": [ "!git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { "cell_type": "markdown", + "id": "32169f9f", "metadata": { "id": "Fg7DyNjLuvSb" }, "source": [ - "**Let the magic happen.** Now you can run your first ever simulation in ASSUME. The following code navigates to the respective assume folder and starts the simulation example example_01b using the local database here in colab.\n", + "**Let the magic happen.** Now you can run your first ever simulation in ASSUME. The following code navigates to the respective ASSUME folder and starts the simulation example example_01b using the local database here in colab.\n", "\n", - "When running locally, you can also just run `assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"` in a shell" + "When running locally, you can also just run `ASSUME -s example_01b -db \"sqlite:///./examples/local_db/ASSUME_db_example_01b.db\"` in a shell" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "id": "3d31a7b9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3eVM60Qx8SC0", - "outputId": "20434515-6e65-4d34-d44d-8c4529a46ece", - "vscode": { - "languageId": "shellscript" - } + "outputId": "20434515-6e65-4d34-d44d-8c4529a46ece" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "^C\n" - ] - } - ], + "outputs": [], "source": [ "!cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" ] }, { "cell_type": "markdown", + "id": "72d7f32a", "metadata": {}, "source": [ "**Select input files path**:\n", @@ -249,8 +230,13 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": null, + "id": "7f3dd8f1", + "metadata": { + "vscode": { + "languageId": "python" + } + }, "outputs": [], "source": [ "import importlib.util\n", @@ -266,13 +252,14 @@ }, { "cell_type": "markdown", + "id": "33e527b5", "metadata": { "id": "zMyZhaNM7NRP" }, "source": [ "## 3. Make your agents learn\n", "\n", - "Now it is time to get your hands dirty and actually dive into coding in ASSUME. The main objective of this session is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions. Our emphasis lies in the bidding strategy class, with less emphasis on the algorithm and role. Coding tasks will highlight the key aspects to be a djusted, as already outlined in the learning_strategies.py file. Subsequent\n", + "Now it is time to get your hands dirty and actually dive into coding in ASSUME. The main objective of this session is to ensure participants grasp the steps required to equip a new unit with RL strategies or modify the action dimensions. Our emphasis lies in the bidding strategy, with less focus on the algorithm and role. Coding tasks will highlight the key aspects to be a djusted, as already outlined in the learning_strategies.py file. Subsequent\n", "sections will present the tasks and provide the correct answers for the coding exercises.\n", "\n", "We start by initializing the class of our Learning Strategy. This is very cloesly related to the general strucutre of a bidding strategy.\n", @@ -283,9 +270,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "id": "04d17e03", "metadata": { - "id": "xUsbeZdPJ_2Q" + "id": "xUsbeZdPJ_2Q", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -308,9 +299,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, + "id": "383bbbfd", "metadata": { - "id": "UXYSesx4Ifp5" + "id": "UXYSesx4Ifp5", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -380,6 +375,7 @@ }, { "cell_type": "markdown", + "id": "b07c7ad1", "metadata": { "id": "8UM1QPZrIdqK" }, @@ -400,9 +396,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, + "id": "12e33c9e", "metadata": { - "id": "iApbQsg5x_u2" + "id": "iApbQsg5x_u2", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -455,9 +455,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, + "id": "a2907489", "metadata": { - "id": "_4cJ8Y8uvMgV" + "id": "_4cJ8Y8uvMgV", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -483,6 +487,7 @@ }, { "cell_type": "markdown", + "id": "91134e29", "metadata": { "id": "Jgjx14997Y9s" }, @@ -498,6 +503,7 @@ }, { "cell_type": "markdown", + "id": "dade95c0", "metadata": { "id": "PngYyvs72UxB" }, @@ -516,9 +522,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, + "id": "7c7be3eb", "metadata": { - "id": "0ww-L9fABnw3" + "id": "0ww-L9fABnw3", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -639,6 +649,7 @@ }, { "cell_type": "markdown", + "id": "64c091f0", "metadata": { "id": "kDYKZGERKJ6V" }, @@ -656,27 +667,20 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, + "id": "8d1e1548", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "id": "PYoI3ncSKJSX", - "outputId": "4b4341d7-5a21-49c4-ee25-b8c55f693cd1" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n#scaling factors for all observations\\n#residual load forecast\\nscaling_factor_res_load = self.max_demand\\n\\n# price forecast\\nscaling_factor_price = self.max_bid_price\\n\\n# total capacity\\nscaling_factor_total_capacity = unit.max_power\\n\\n# marginal cost\\nscaling_factor_marginal_cost = self.max_bid_price\\n'" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "outputId": "4b4341d7-5a21-49c4-ee25-b8c55f693cd1", + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "\"\"\"\n", "#scaling factors for all observations\n", @@ -696,6 +700,7 @@ }, { "cell_type": "markdown", + "id": "79379f1e", "metadata": { "id": "rW_1op6fCTV-" }, @@ -716,6 +721,7 @@ }, { "cell_type": "markdown", + "id": "dfe2ee77", "metadata": { "id": "Cho84Pqs2N2G" }, @@ -728,9 +734,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, + "id": "9a2f135a", "metadata": { - "id": "8ehlm5Z9CbRw" + "id": "8ehlm5Z9CbRw", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -792,6 +802,7 @@ }, { "cell_type": "markdown", + "id": "2c792a7b", "metadata": { "id": "OTaqkwV3xcf6" }, @@ -805,27 +816,20 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, + "id": "d06d6dba", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "rfXJBGOKxbk7", - "outputId": "06f76c52-e215-4998-8f61-f7492b880e4d" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n#base_bid = marginal costs\\nbase_bid = next_observation[-1] # = marginal_costs\\n'" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" + "outputId": "06f76c52-e215-4998-8f61-f7492b880e4d", + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "\"\"\"\n", "#base_bid = marginal costs\n", @@ -835,6 +839,7 @@ }, { "cell_type": "markdown", + "id": "8fb00a28", "metadata": { "id": "B5Hgh88Vz0wD" }, @@ -852,9 +857,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, + "id": "337833a5", "metadata": { - "id": "Y81HzlkjNHJ0" + "id": "Y81HzlkjNHJ0", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -956,6 +965,7 @@ }, { "cell_type": "markdown", + "id": "86d9cd3b", "metadata": { "id": "3n-kJeOFCfRB" }, @@ -969,27 +979,20 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, + "id": "3f84f013", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "id": "wB7X-pFkCje3", - "outputId": "ff905a9d-e3f2-4487-9e8a-9dbf4e855ab7" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n#calculate actual bids\\n#rescale actions to actual prices\\nbid_prices = actions * self.max_bid_price\\n\\n#calculate inflexible part of the bid\\nbid_quantity_inflex = min_power\\nbid_price_inflex = min(bid_prices)\\n\\n#calculate flexible part of the bid\\nbid_quantity_flex = max_power - bid_quantity_inflex\\nbid_price_flex = max(bid_prices)\\n'" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" + "outputId": "ff905a9d-e3f2-4487-9e8a-9dbf4e855ab7", + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "\"\"\"\n", "#calculate actual bids\n", @@ -1008,6 +1011,7 @@ }, { "cell_type": "markdown", + "id": "b452178f", "metadata": { "id": "hr15xKuGCkbn" }, @@ -1043,6 +1047,7 @@ }, { "cell_type": "markdown", + "id": "7f3afb42", "metadata": { "id": "aGyaOUgo3Y8Q" }, @@ -1055,9 +1060,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, + "id": "43e813e2", "metadata": { - "id": "U9HX41mODuBU" + "id": "U9HX41mODuBU", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1164,6 +1173,7 @@ }, { "cell_type": "markdown", + "id": "c02e38ee", "metadata": { "id": "gWF7D4QA2-kz" }, @@ -1193,27 +1203,20 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, + "id": "bbe8a015", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "id": "e1XdVXPSCo_k", - "outputId": "585d94a5-7475-4e96-d0a1-5e82b711c6a5" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\nscaling = 0.1 / unit.max_power\\nregret_scale = 0.2\\nreward = float(profit - regret_scale * opportunity_cost) * scaling\\n'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" + "outputId": "585d94a5-7475-4e96-d0a1-5e82b711c6a5", + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "\"\"\"\n", "scaling = 0.1 / unit.max_power\n", @@ -1224,6 +1227,7 @@ }, { "cell_type": "markdown", + "id": "4a72845b", "metadata": { "id": "L3flH5iY4x7Z" }, @@ -1236,9 +1240,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, + "id": "6aa54f30", "metadata": { - "id": "ZwVtpK3B5gR6" + "id": "ZwVtpK3B5gR6", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1282,6 +1290,7 @@ }, { "cell_type": "markdown", + "id": "947ec7f3", "metadata": { "id": "cTlqMouufKyo" }, @@ -1291,9 +1300,13 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, + "id": "23b299f8", "metadata": { - "id": "moZ_UD7FfkOh" + "id": "moZ_UD7FfkOh", + "vscode": { + "languageId": "python" + } }, "outputs": [], "source": [ @@ -1319,6 +1332,7 @@ }, { "cell_type": "markdown", + "id": "132f9429", "metadata": { "id": "ZlRnTgCy5d9W" }, @@ -1333,58 +1347,20 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, + "id": "53219009", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ZlWxXxZr54WV", - "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_02a/base from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_02a_base_1 2019-03-31 00:00:00: : 2592001.0it [00:29, 88492.68it/s]\n", - "example_02a_base_2 2019-03-31 00:00:00: : 2592001.0it [00:31, 83508.91it/s] \n", - "example_02a_base_3 2019-03-31 00:00:00: : 2592001.0it [00:30, 83653.36it/s] \n", - "example_02a_base_4 2019-03-31 00:00:00: : 2592001.0it [00:30, 84041.85it/s] \n", - "example_02a_base_5 2019-03-31 00:00:00: : 2592001.0it [00:30, 85265.01it/s] \n", - "Training Episodes: 100%|██████████| 5/5 [02:32<00:00, 30.56s/it]\n", - "example_02a_base 2019-03-31 00:00:00: : 2592001.0it [00:35, 73343.42it/s] \n" - ] + "outputId": "e30f4279-7a4e-4efc-9cfb-61416e4fe2f1", + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "log = logging.getLogger(__name__)\n", "\n", @@ -1430,6 +1406,7 @@ }, { "cell_type": "markdown", + "id": "3621dd9c", "metadata": {}, "source": [ "In comparison, the following cell executes example case 2 of [1] where the same capacity of the reinforcement power plant in case 1 is divided into five reinforcement learning power plants, which hence cannot exert market power anymore." @@ -1437,261 +1414,14 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 0%| | 0/5 [00:00 wait_for=>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 349, in __wakeup\n", - " future.result()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py\", line 198, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py\", line 84, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 279, in __step\n", - " result = coro.throw(exc)\n", - " ^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot reuse already awaited coroutine\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", - " raise KeyError(key) from err\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", - " raise KeyError(key) from err\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "Training Episodes: 20%|██ | 1/5 [00:32<02:11, 32.77s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:mango.agent.core:Agent eom_de: Caught the following exception in _check_inbox: \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 20%|██ | 1/5 [00:32<02:11, 32.81s/it]\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'energy_cashflow'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4485\u001b[0m, in \u001b[0;36mDataFrame._set_item_mgr\u001b[1;34m(self, key, value, refs)\u001b[0m\n\u001b[0;32m 4484\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 4485\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4486\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 4487\u001b[0m \u001b[38;5;66;03m# This item wasn't present, just insert at end\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n", - "\u001b[1;31mKeyError\u001b[0m: 'energy_cashflow'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[34], line 32\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 32\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equals a test run\u001b[39;00m\n\u001b[0;32m 40\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:959\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 953\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m episode \u001b[38;5;129;01min\u001b[39;00m tqdm(\n\u001b[0;32m 954\u001b[0m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mtraining_episodes \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m),\n\u001b[0;32m 955\u001b[0m desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining Episodes\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 956\u001b[0m ):\n\u001b[0;32m 957\u001b[0m \u001b[38;5;66;03m# TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\u001b[39;00m\n\u001b[0;32m 958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m episode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 959\u001b[0m \u001b[43msetup_world\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mepisode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepisode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 968\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:706\u001b[0m, in \u001b[0;36msetup_world\u001b[1;34m(world, scenario_data, study_case, perform_evaluation, terminate_learning, episode, eval_episode)\u001b[0m\n\u001b[0;32m 697\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msetup_world\u001b[39m(\n\u001b[0;32m 698\u001b[0m world: World,\n\u001b[0;32m 699\u001b[0m scenario_data: \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mobject\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 704\u001b[0m eval_episode: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m,\n\u001b[0;32m 705\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 706\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 707\u001b[0m \u001b[43m \u001b[49m\u001b[43masync_setup_world\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 708\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 709\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 710\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 711\u001b[0m \u001b[43m \u001b[49m\u001b[43mperform_evaluation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mperform_evaluation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 712\u001b[0m \u001b[43m \u001b[49m\u001b[43mterminate_learning\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mterminate_learning\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 713\u001b[0m \u001b[43m \u001b[49m\u001b[43mepisode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepisode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 714\u001b[0m \u001b[43m \u001b[49m\u001b[43meval_episode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43meval_episode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 716\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:92\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 90\u001b[0m f\u001b[38;5;241m.\u001b[39m_log_destroy_pending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopping:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:133\u001b[0m, in \u001b[0;36m_patch_loop.._run_once\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 130\u001b[0m curr_task \u001b[38;5;241m=\u001b[39m curr_tasks\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 133\u001b[0m \u001b[43mhandle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# restore the current task\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m curr_task \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py:84\u001b[0m, in \u001b[0;36mHandle._run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m):\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py:434\u001b[0m, in \u001b[0;36mAgent.raise_exceptions\u001b[1;34m(self, fut)\u001b[0m\n\u001b[0;32m 430\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fut\u001b[38;5;241m.\u001b[39mexception() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 431\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\n\u001b[0;32m 432\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAgent \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maid\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: Caught the following exception in _check_inbox: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfut\u001b[38;5;241m.\u001b[39mexception()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 433\u001b[0m )\n\u001b[1;32m--> 434\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m fut\u001b[38;5;241m.\u001b[39mexception()\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:92\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 90\u001b[0m f\u001b[38;5;241m.\u001b[39m_log_destroy_pending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stopping:\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:133\u001b[0m, in \u001b[0;36m_patch_loop.._run_once\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 130\u001b[0m curr_task \u001b[38;5;241m=\u001b[39m curr_tasks\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 132\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 133\u001b[0m \u001b[43mhandle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 135\u001b[0m \u001b[38;5;66;03m# restore the current task\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m curr_task \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\events.py:84\u001b[0m, in \u001b[0;36mHandle._run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_run\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 84\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 85\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mSystemExit\u001b[39;00m, \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m):\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:360\u001b[0m, in \u001b[0;36mTask.__wakeup\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__step(exc)\n\u001b[0;32m 353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 354\u001b[0m \u001b[38;5;66;03m# Don't pass the value of `future.result()` explicitly,\u001b[39;00m\n\u001b[0;32m 355\u001b[0m \u001b[38;5;66;03m# as `Future.__iter__` and `Future.__await__` don't need it.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 358\u001b[0m \u001b[38;5;66;03m# instead of `__next__()`, which is slower for futures\u001b[39;00m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;66;03m# that return non-generator iterators from their `__iter__`.\u001b[39;00m\n\u001b[1;32m--> 360\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__step\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 361\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py:450\u001b[0m, in \u001b[0;36mAgent._check_inbox\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 447\u001b[0m priority, content, meta \u001b[38;5;241m=\u001b[39m message\n\u001b[0;32m 448\u001b[0m meta[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpriority\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m priority\n\u001b[1;32m--> 450\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 452\u001b[0m \u001b[38;5;66;03m# signal to the Queue that the message is handled\u001b[39;00m\n\u001b[0;32m 453\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minbox\u001b[38;5;241m.\u001b[39mtask_done()\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:525\u001b[0m, in \u001b[0;36mRoleAgent.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 524\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhandle_message\u001b[39m(\u001b[38;5;28mself\u001b[39m, content, meta: Dict[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[1;32m--> 525\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_role_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:401\u001b[0m, in \u001b[0;36mRoleContext.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 392\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhandle_message\u001b[39m(\u001b[38;5;28mself\u001b[39m, content, meta: Dict[\u001b[38;5;28mstr\u001b[39m, Any]):\n\u001b[0;32m 393\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Handle an incoming message, delegating it to all applicable subscribers\u001b[39;00m\n\u001b[0;32m 394\u001b[0m \u001b[38;5;124;03m for role, message_condition, method, _ in self._message_subs:\u001b[39;00m\n\u001b[0;32m 395\u001b[0m \u001b[38;5;124;03m if self._is_role_active(role) and message_condition(content, meta):\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[38;5;124;03m :param meta: meta\u001b[39;00m\n\u001b[0;32m 400\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 401\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_role_handler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py:239\u001b[0m, in \u001b[0;36mRoleHandler.handle_message\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 237\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m role, message_condition, method, _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_message_subs:\n\u001b[0;32m 238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_role_active(role) \u001b[38;5;129;01mand\u001b[39;00m message_condition(content, meta):\n\u001b[1;32m--> 239\u001b[0m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:211\u001b[0m, in \u001b[0;36mUnitsOperator.handle_market_feedback\u001b[1;34m(self, content, meta)\u001b[0m\n\u001b[0;32m 209\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[marketconfig\u001b[38;5;241m.\u001b[39mproduct_type]\u001b[38;5;241m.\u001b[39mextend(orderbook)\n\u001b[0;32m 210\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_unit_dispatch(orderbook, marketconfig)\n\u001b[1;32m--> 211\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite_actual_dispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarketconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproduct_type\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:348\u001b[0m, in \u001b[0;36mUnitsOperator.write_actual_dispatch\u001b[1;34m(self, product_type)\u001b[0m\n\u001b[0;32m 345\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 346\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_sent_dispatch[product_type] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mcurrent_timestamp\n\u001b[1;32m--> 348\u001b[0m market_dispatch, unit_dispatch_dfs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_actual_dispatch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mproduct_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlast\u001b[49m\n\u001b[0;32m 350\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 352\u001b[0m now \u001b[38;5;241m=\u001b[39m timestamp2datetime(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontext\u001b[38;5;241m.\u001b[39mcurrent_timestamp)\n\u001b[0;32m 353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[product_type] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[0;32m 354\u001b[0m \u001b[38;5;28mfilter\u001b[39m(\n\u001b[0;32m 355\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend_time\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m now,\n\u001b[0;32m 356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalid_orders[product_type],\n\u001b[0;32m 357\u001b[0m )\n\u001b[0;32m 358\u001b[0m )\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\common\\units_operator.py:327\u001b[0m, in \u001b[0;36mUnitsOperator.get_actual_dispatch\u001b[1;34m(self, product_type, last)\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m valid_outputs:\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01min\u001b[39;00m key:\n\u001b[1;32m--> 327\u001b[0m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m unit\u001b[38;5;241m.\u001b[39moutputs[key][start:end]\n\u001b[0;32m 329\u001b[0m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munit\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m unit_id\n\u001b[0;32m 330\u001b[0m unit_dispatch_dfs\u001b[38;5;241m.\u001b[39mappend(data)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4311\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4308\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m 4309\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 4310\u001b[0m \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[1;32m-> 4311\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4538\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 4535\u001b[0m value \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtile(value, (\u001b[38;5;28mlen\u001b[39m(existing_piece\u001b[38;5;241m.\u001b[39mcolumns), \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m 4536\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 4538\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item_mgr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\frame.py:4488\u001b[0m, in \u001b[0;36mDataFrame._set_item_mgr\u001b[1;34m(self, key, value, refs)\u001b[0m\n\u001b[0;32m 4485\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[0;32m 4486\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[0;32m 4487\u001b[0m \u001b[38;5;66;03m# This item wasn't present, just insert at end\u001b[39;00m\n\u001b[1;32m-> 4488\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minsert\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_info_axis\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrefs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4489\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 4490\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iset_item_mgr(loc, value, refs\u001b[38;5;241m=\u001b[39mrefs)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1385\u001b[0m, in \u001b[0;36mBlockManager.insert\u001b[1;34m(self, loc, item, value, refs)\u001b[0m\n\u001b[0;32m 1383\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1384\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_insert_update_mgr_locs(loc)\n\u001b[1;32m-> 1385\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_insert_update_blklocs_and_blknos\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m new_axis\n\u001b[0;32m 1388\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (block,)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1422\u001b[0m, in \u001b[0;36mBlockManager._insert_update_blklocs_and_blknos\u001b[1;34m(self, loc)\u001b[0m\n\u001b[0;32m 1419\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loc \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblklocs\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m 1420\u001b[0m \u001b[38;5;66;03m# np.append is a lot faster, let's use it if we can.\u001b[39;00m\n\u001b[0;32m 1421\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m-> 1422\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blknos \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mappend\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_blknos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblocks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1423\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m loc \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m 1424\u001b[0m \u001b[38;5;66;03m# np.append is a lot faster, let's use it if we can.\u001b[39;00m\n\u001b[0;32m 1425\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blklocs[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m0\u001b[39m)[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\numpy\\lib\\function_base.py:5618\u001b[0m, in \u001b[0;36mappend\u001b[1;34m(arr, values, axis)\u001b[0m\n\u001b[0;32m 5616\u001b[0m values \u001b[38;5;241m=\u001b[39m ravel(values)\n\u001b[0;32m 5617\u001b[0m axis \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mndim\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 5618\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " - ] - }, - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", - "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", - "\u001b[1;31mClick here for more info. \n", - "\u001b[1;31mView Jupyter log for further details." - ] + "execution_count": null, + "id": "b01d2e8b", + "metadata": { + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "log = logging.getLogger(__name__)\n", "\n", @@ -1737,6 +1467,7 @@ }, { "cell_type": "markdown", + "id": "c21c085b", "metadata": {}, "source": [ "The following simulation represents case 3, respectively." @@ -1744,112 +1475,14 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_02c/base from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Training Episodes: 1%| | 1/100 [00:22<36:39, 22.22s/it]\n", - "Training Episodes: 1%| | 1/100 [00:22<37:32, 22.75s/it]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for=>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pygments\\formatters\\terminal256.py:180: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", - " self.xterm_colors.append((r, g, b))\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pygments\\formatters\\terminal256.py:180: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", - " self.xterm_colors.append((r, g, b))\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Time must be > 1552158000.0 but is 1551398399.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[36], line 32\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 31\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 32\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 33\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 34\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 35\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 36\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 37\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 39\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equals a test run\u001b[39;00m\n\u001b[0;32m 40\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\scenario\\loader_csv.py:970\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 968\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 970\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 972\u001b[0m \u001b[38;5;66;03m# -----------------------------------------\u001b[39;00m\n\u001b[0;32m 973\u001b[0m \u001b[38;5;66;03m# Store updated information across episodes\u001b[39;00m\n\u001b[0;32m 974\u001b[0m inter_episodic_data \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mget_inter_episodic_data()\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime must be > \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# set time\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time \u001b[38;5;241m=\u001b[39m t\n", - "\u001b[1;31mValueError\u001b[0m: Time must be > 1552158000.0 but is 1551398399." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:assume.markets.base_market:1552161600.0 Market result [(datetime.datetime(2019, 3, 9, 20, 0), datetime.datetime(2019, 3, 9, 21, 0), None)] for market EOM are empty!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] + "execution_count": null, + "id": "400495c9", + "metadata": { + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "log = logging.getLogger(__name__)\n", "\n", @@ -1895,6 +1528,7 @@ }, { "cell_type": "markdown", + "id": "2512bd0b", "metadata": {}, "source": [ "### Result Plotting " @@ -1902,123 +1536,28 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting matplotlib\n", - " Downloading matplotlib-3.9.2-cp311-cp311-win_amd64.whl.metadata (11 kB)\n", - 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identsimulationtotal_costtotal_volumeaverage_cost
0EOMexample_02a_base1.444376e+084.263112e+0933.33
\n", - "
" - ], - "text/plain": [ - " ident simulation total_cost total_volume average_cost\n", - "0 EOM example_02a_base 1.444376e+08 4.263112e+09 33.33" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "execution_count": null, + "id": "1b425f0d", + "metadata": { + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "import os\n", "from functools import partial\n", @@ -2052,20 +1591,14 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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timePricesimulationnode
02019-03-01 01:00:00.00000031.146341example_02a_baseNone
12019-03-01 02:00:00.00000031.146341example_02a_baseNone
22019-03-01 03:00:00.00000031.146341example_02a_baseNone
32019-03-01 04:00:00.00000031.146341example_02a_baseNone
42019-03-01 05:00:00.00000031.146341example_02a_baseNone
...............
7152019-03-30 20:00:00.00000031.146341example_02a_baseNone
7162019-03-30 21:00:00.00000031.146341example_02a_baseNone
7172019-03-30 22:00:00.00000031.146341example_02a_baseNone
7182019-03-30 23:00:00.00000012.500000example_02a_baseNone
7192019-03-31 00:00:00.00000012.500000example_02a_baseNone
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720 rows × 4 columns

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" - ], - "text/plain": [ - " time Price simulation node\n", - "0 2019-03-01 01:00:00.000000 31.146341 example_02a_base None\n", - "1 2019-03-01 02:00:00.000000 31.146341 example_02a_base None\n", - "2 2019-03-01 03:00:00.000000 31.146341 example_02a_base None\n", - "3 2019-03-01 04:00:00.000000 31.146341 example_02a_base None\n", - "4 2019-03-01 05:00:00.000000 31.146341 example_02a_base None\n", - ".. ... ... ... ...\n", - "715 2019-03-30 20:00:00.000000 31.146341 example_02a_base None\n", - "716 2019-03-30 21:00:00.000000 31.146341 example_02a_base None\n", - "717 2019-03-30 22:00:00.000000 31.146341 example_02a_base None\n", - "718 2019-03-30 23:00:00.000000 12.500000 example_02a_base None\n", - "719 2019-03-31 00:00:00.000000 12.500000 example_02a_base None\n", - "\n", - "[720 rows x 4 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" + "execution_count": null, + "id": "3415447f", + "metadata": { + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "sql = \"\"\"\n", "SELECT\n", @@ -2272,20 +1678,15 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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7AwA+/fRTnH/++bj77rtx6KGHxt4P03qRlccffxzXXXcdZsyYgSFDhmCTTTap+v1pLFiwILYeua6Ljo4O/7P+/fsHjlHvbVtbG37zm9/g1ltvxVNPPYUHHngAra2tOPjgg3HhhRcmGqwJIYQQ29CARAghZKWmra0NY8eOzeVaAwcODCSk9njhhRew5pprYuDAgRgwYAB+9atfRZ6/zjrrRH4+ePBgzJo1q+pz77OhQ4cCKIfHjBgxAk899RRGjBiBlpYWf9ertrY2OI6DY489FgcccEDVtcKT3Hoi630LM2TIEACIDCmbNWuWfx/zYvLkyZg1a1YgNNCUgQMHYty4cYHQOpUkA8PAgQOx00474bjjjqv6LipUshZ6enqw995745BDDsEZZ5wR+G6dddbBhRdeiEMOOQSTJ0+uyaAWV7ZKkufUK6+8gnPPPRdHHXUUTjjhBIwaNQoAcPXVV0fmqopj8ODBsfUIKLfJL7/8Uutao0eP9pOWv/nmm/j973+P++67D2uvvXZkrjVCCCGkKBjCRgghhOTEtttuizfeeCNgRJozZw6++93v4plnnsG4ceOwZMkSuK6LsWPH+n8ffPABbr755kAuJpXtttsOr732GqZNmxb4/PHHH8eIESN8A0pjYyO+/vWv4+9//zuefvpp7LXXXr4HR3t7OzbddFN89NFHgbI32GAD3HTTTXjxxReF7krtZL1vYb7yla9gxIgReOKJJwKff/7553j99ddjPZyycuONN6K1tRXf+MY3Ml9j3Lhx+Pjjj/GVr3wl8Nt///vf46GHHvJzWcWdO3nyZGyyySb+eWPGjME999yDv/zlL0ZyNDQkDxkbGxsxcuRIPPzww4Fd0jy8pPR5G4/a29sxc+bMwGdJOZJee+01lEolnH766b7xqKenxw/pK5VKANJ/73bbbYe///3vAU+jnp4ePPnkkxg7dqy259DTTz+NHXbYAbNmzUJjYyO22morXHzxxRg0aJC/AyQhhBBSL9ADiRBCCMmJY489Fo899hi++93v4qSTTkJzczNuvfVWrLrqqvj617+OgQMHYrvttsMpp5yCU045Beuttx7efPNN3Hjjjdh1112rwoo8jjvuODz++OM49thjcdppp2HIkCF47LHH8O9//xtXXHFFYLJ78MEH46677kJDQ0NVqNpZZ52FE088EWeffTYOOuggf6exN954A6eccorovamF8ePHZ7pvYRoaGnDWWWdhwoQJ/j2YN28eJk6ciMGDB0d66ujwxRdf+Nuud3d3Y+bMmXj00Ufx3HPP4dJLL60pl82xxx6L3//+9zj22GNx/PHHY+jQofjjH/+I3/3ud5gwYULiuaeccgqOPPJInHTSSfjWt76FlpYWPPDAA/jrX/9qvJPZoEGD8M477+Cll17C5ptvHhnydeGFF+Koo47CoYceiqOPPhqbbLIJSqUSXn75Zdxzzz048sgjsf766xuVm8buu++OJ598EltssQXWWWcdPPLII4GdCMN4ubYuvfRSHHbYYViwYAF+85vf4L333gNQ9l5qb2/HoEGDMHv2bDzzzDOR4ZGnnXYann32WRx99NE48cQT0dzcjHvvvReff/457rjjDm35t956a5RKJZx66qk48cQT0dbWhqeeegqLFi3yvQcJIYSQeoEGJEIIISQnVlttNfz2t7/FNddcg/POOw/9+vXD9ttvj+uvvx6DBw8GANx+++244YYbcNttt2HOnDkYNWoUjjvuOH8HrChGjBiB++67D9deey1+/OMfo6urCxtvvDFuueUWfPWrXw0cu/HGG2PDDTfEvHnzsOOOOwa+22WXXXDnnXdi4sSJOOOMM9Dc3IzNNtsMd999d+LuWkXT0NCQ6b5Fceihh6KtrQ233XYbTj31VLS3t2PXXXfFWWedFUh+bsJDDz3k77rV0NCAIUOGYIsttsDdd99d9QxMGTVqFO6//35ce+21uPjii7F8+XKsu+66uPzyy3H44YcnnrvxxhvjN7/5Da6//nr88Ic/hOu62HDDDXHzzTdX1Zs0jj/+eFxxxRU44YQTcPfdd/s7mKmMGTMGjz32GG677Tbce++9vlfN+uuvj/PPPz9V3ixMmDAB3d3d+MlPfoKmpibsv//+OPvss3HhhRdGHr/99tvjRz/6Ee6++248/fTTGD58OLbffntMnDgRp556KiZNmoTx48fj0EMPxTPPPINTTz0VZ5xxhr/7n8cGG2yA3/72t7juuuswYcIEOI6DzTffHL/61a8i700cI0eOxB133IEbbrgBF1xwAZYuXep7Be6www413RtCCCEkbxw3a4ZEQgghhBBCCCGEELJSwBxIhBBCCCGEEEIIISQRGpAIIYQQQgghhBBCSCI0IBFCCCGEEEIIIYSQRGhAIoQQQgghhBBCCCGJ0IBECCGEEEIIIYQQQhKhAYkQQgghhBBCCCGEJEIDEiGEEEIIIYQQQghJpKloASSZNWtR0SJkpqHBwbBhbZg7dzFKJbdocUiBsC4QFdYHosL6QFRYH4gH6wJRYX0gKqwP9UFez2HEiIE5SpUOPZDqlIYGB47joKHBKVoUUjCsC0SF9YGosD4QFdYH4sG6QFRYH4gK60N90FefAw1IhBBCCCGEEEIIISQRGpAIIYQQQgghhBBCSCI0IBFCCCGEEEIIIYSQRGhAIoQQQgghhBBCCCGJ0IBECCGEEEIIIYQQQhKhAYkQQgghhBBCCCGEJEIDEiGEEEIIIYQQQghJhAYkQgghhBBCCCGEEJIIDUiEEEIIIYQQQgghJBEakAghhBBCCCGEEEJIIjQgEUIIIYQQQgghhJBEaEAihBBCCCGEEEIIIYnQgEQIIYQQQgghhBBCEqEBiRBCCCGEEEIIIYQkQgMSIYQQQgghhBBCCEmEBiRCCCGEEEIIIYQQkggNSIQQQgghhBBCCCEkERqQCCGEEEIIIYQQQkgiNCARQgghhBBCCCGEkERoQCKEEEIIIYQQQgghidCARAghhBBCCCGEEEISaSpaAGLO8q4eXPWbV/HZF4tErr/F+sNx+mFj4TiO1vHvfToPt/7+LXQs6Uo9dtigVvzPNzfH3X98F5/MCMq/xog2TPjONujfUnu1nDxtASY+8h8sWtyJdVYdiAnf2RrNTY1Vx/3u75Pxl5c/h+M4+MZuX8HXtl/HqJypX3bg+gffwPxFy2uWOY51Vh2Ia/5nt8jvHvrHFPzppc9QKrli5QPABmsNwf9+a0s0NkTbnN+cMge/+MPbWLKsW0yGtHr54jsz8as/vYdly3tkBHCAncasihMO2LTqq5Lr4roHXse7n8yTKRtA+4BmfP/gMRiz3ipax7uui5se/g/emDy7pnL7NTfiW3ttgN22WL2m63jc+thbmDqrA+d+e2sMGtAv8phps8rtat7C5VhteBsmfGdrtLU2G5Vzz1Pv4Z9vTgeEmka/fo04ep+NsOOYVau+mzFnMa574HXMXSjXLwBA/5YmnHTwZthz+7aq7z6avhA3PfwmFi7uFJWhfUAzTjlkDDZae2jVd29/Mhe3/f5tLF6arhtqYXB7P/zP4VtgnVUHVn33yntf4u6n3sOy5XJ9EwCsMrgV5xy5JUYOHaB1/PLOHlx57yR8/mVH5jLXXW0gzvt2tG4z5Z1P5uLOJ9/Fftuvjb23XSv2uF/96X08+/p0uG65YW28zlCcfeSWaNAcL0yZvgATH/6PSL1samrAYbuNxj7j1o495t4/v49/vFaRX4qx662C/zl880h9dfvjb+PFd2eK9U0AAAfYcbNVMeG47au+cl0X1z/4Bt7+aK5Y8W39m3HywZth03WHJR739Iuf4dF/foTu7lLuMgxq74czDtscX1ltkNF5f5s0FQ/+fTK6ImTacoPhOO3Q4DhE7e9XXWUAzvv21hgYo9sk8cYhi5d247zvbI2WZrN+4ee/fwsvv/elSL1cfUQbJnx7GwxojR7ff/rFItzw0BtY0CGnr76y+iCc+99bo7kp3neiu6eEn/z2VXw0baGIDJuuOxRnHpHcXy5d3o0r752EabMWi8iw7cYj8f1DxiQeM79jOX7ym1fx5byluZY9oLUJJx60GcaO1hvHRnH/3z7E3yZNRUODg0N3G419E/p7oDwOuevJd/G17dfGXgm6jZhBD6Q+yEfTFuDTLxbBBUT+Xp8822ji8/J7X2LRki6ta89ZuAyP/fNjfDyjWv6psxbj/c/nZ7wrQSa9/yUWLu6EC+CTLxbho+nRyuAfr01DT8lFd08Jz74+3bic1z6chXmLlos9C0/+92MME8+8XpZfsnwXwAefz09UZi++MxOLl3WLyvD65NmYl2Coe+HtL7B0eY+cDC7w/H++wPKuagPVzLlL8M4n80R//6IlXeXBnSZzFy7H65Nn11zu8q4ePPfmDO1yk+hYWv4NM+YswTufxE9evD7IBTB99mK892l0/Y+ju6eEZ9+YDteVex7LO3vw3H+i78vrk2djzkLZfsEFsGR5N/711heRMkx6/0ss6O0DpevlSzH18qV3ZqJjqZ5uqOVvfkcnXv1gVqQM5X5Btm9yAcxesAxvTJkTKUMUH06bj8++7KipzI9nLMLHM/JZSHrp3ZmYt2g5/vlGfFsvuS6eeW0aSm5F57z76TzMmLNEu5xJ788Sq5dd3SU8m9BXua6Lv4fkl/p7c8oczF6wrEqGxcu68O93Zor2TS4A1wX+9dYXWLKs2ng7a/5SvPXRXNHyO5Z24aV3Z8Y+C49/vjkdXd0lERkWdHTilff1dabHc2/OQGeMTK99WD0OeWPyHL+/nzFnCd411Fd54Y1DPp25CJOnLTA6t/y8vhSrl9NmLcb7n8ffl0kffIn5HbL66qPpC/HJF8mGoU+/WIQp0xaKyfD2J/Mwc25yf/nB5/MxddZiMRnKc7ZkQ93bH8/FzHlLcy978bJuvPhOer+QxD965z1d3eVxXhovvdOr23Iax5Iy9EDqg3Qr3iYH7LhObisd02cv9htjT0l/Nai7p3zs4LZ++NoO68Qe97v/m4yS6wYm4F/bYW00Nzbg8ec/KZfb48acbUZ36DrdMR466nE9Gbx4vPP7NTXg0PHrGZ+fxKIlnXjyhU/L5cQ8D6/8zddbJXWlLwuzFyzFX1+ZCiD5/nj1ZZVBrdh7u3wt/NNmdfgdf5IMXj1cfXhbbt4yHh/PWOgrvShvL7Ue7bn1GtpeCLo89e9PsWBxp1m7VI7dbYvVsfrwai+VNF54+wt8+sUio3ITZeqpXCeprYfbr2nbVJ/RdhuPxHprDDY6P43n3pyOqbMWx8rlyd/U6ODw3dfPtWyPv036HLPmL4t9Np4Mba1N+PrOXxGRwa+XMc/Sk2HowJbUVcKsPPbPj7Cssyf2WXifjxo2AHtstYaIDA/834dwXTP9pdbxA3daB+399fX4go7leOrFzwAAPT15tc2yPEltvdS7WAEAa49qx2czO4xl8PqAvOvlpPe/xIdTFyTK4rrlPwDYZsMR2GCtIbmV7zFjzmI887o3jqquD2od2XGzVSO95mrl0y8W4oW3y/oq3JeGP9t9qzWw6rB89dWfXvoM8xYt12oPXn3YcM3B2HqjkbnJ8PhzH2PJ8u5MY0pPd45efRDGbTIKQPI4pDtU5/Iax5oSGM8ayqD+pp3GrIq1R+VTL5cu78bvn/s4VSZP9v4tjTh4l9G5lO0xv2M5nvb7y+T7oj7L/catjSEDW3KRYea8Jfj7q9PKMqSMZ9TneNDO62KAofd1HJ/PXITnexec0mWo3Idv7r4eGhtr9zf5y8ufYc7C5ZnmWio9hvO2im4rpl2uqNCA1AdRG8Eum6+GUTlNVv/z0RzFgKTf0LzJ2uC2ftgnwXjwyDNT0NntBlyVdxm7Glr7NfkGpFJObuXhSX5ciJf6eZbOxZO3pV9j4m/PwpfzlvgGpLTJ0YZrDcm9fKC8YqNnQCp/N2xQS+5yvDlltj9wSwrV874bNbR/7jL8660ZvgEp6j6oco3bZBQ2zHli8vx/ZvQakMzbJQBsu9EIjMngMvzZzEW9BqT822XSswyXZ1q+evyYrwzDrjkbFCdPW4Cpsxan9ivNTfn3Cx6vfzir14CULENba7OYDM+9Wa6Xsfeht38c0p5/v+Dxp5c+w7LOnlgZvPszckj+/YLHQ/+Ygu6ekpH+UuXdbYvVMXxwf+1zp89eXDEg5awzddvlmiMqBqQsv7utf771cvb8pWUDkqb8m647FHtsvWZu5Xu8/fHcZAOS8tmWGwzHdhvnZzTxePGdmb4BKcogGDCwbzQCm+S8+PTvt78oG5A06oUny7qrDcq1Pvz1lc+xZHl3pvB+75y1R7b7MiWNQ8Lvi5qo6urXtHO3XH84ts2pXi7oWO4bkJL6Ca/8AS1NuffT02Z1VAxIKXVSvQ87jVkVa45sz0WG9z+b5xuQ0p6Nep/Gb7kGhuZkxJr0/pe+ASlVBuX7vbdbC005GJBeee/LfAxIhvXcu580IOULQ9j6IK7SCHTzDuigXsuknXmN02lIlsX7XvWMaGhw0KCcl1cun7CiSpvgRJ2jVU7vdfN8Dh6B5xEjv5fHQaJ8AFBTHukYb8Tvg8YApAgZ1M8kyzdxBFKfV1rbzLPcJAID3IRnGc5PYtovqOc3ZPztSXiXjPsNlbqYe9EVGRqSn41uv1ybDMGyqmTw7oPgSKM+nkWwLB1KNejxoM40OjVent77p9PHAghMKIz6pd5L5N1PevckKbeR+tuk2oV6WTeiPgT6JqE6mTamKkn3j37fpDO56z1HqD7UMq5T60jSGCDtvS1qGc8Gxgs5PgtHc3zv33OJ8ZMiQ1SbDMihfJ1nH+FojmOBkG7IUQadOYX/vfJ1Xm3T+ylpzyCJLG3N+621lEuqoQGpD6I2mMY8OxeDTjYoj54sjb2dUHe3G/hMPU/MAyll0g9k61y8a0gOwoB0DyqJ8oGg4kgamHtfFXUfAGUgKixDVD2RUvjha5okflXFbMw4AMhSbhLBAW7CcRrtN7mcymuJ59GYMkHy5M2zfw6T9mxsyNCYMknzZRAycAPpk1VXsI/2SLsPUdSixxsEdWaSMUita82qASmDB1Le9bJiMEgvG5Crk2nPRlpXAOGJYvX31gxIGtVCagzTaCBDmKh+K2kcUjcGpFo8kITmFbrje0l9ZdJfWrkPKQZ3G3M8EyNWXl1lLUZdD93oksAxGosjxBwakPogPYGGnad1OroMXXnSrNRe56HG1jY0OIFyc/NA0uhk8nA71v3tWVA7+8iwKbeSj8LGSqZOaICMt0f9y9ATmBTkXrx/zawhbFnviXdeXq6/uq7H4fJM+4XA8xD1CEs27Ip6/zjJz6bSN4mJoH0fJI03/n2IGRxK9gthGbJ6IJnWEwmd6d2npEG2WteamrJ5Dkt5GqS1B0DecBK+bqSuEPZWLcuglBcxWy26f4ySJW+9WZHB3EUvqt9KGgPUqq/yIqBfa/BAyvNZ6I7fJHWFrgzh7/MUxSTSomRBBt370OA4ufXVeYwnMxmQeo9hCFu+0IDUB5EaBGVd1XT9jibl+r3fqwYkJ9Q5iYWwRVy2ehXJvBy396eIGA1S7ov1lUyN1SNpQ1pStazIkLsIqW3DFZ4U+IPhjB4OWWVKCw8yRa3Gyd5ktbVN6bbhTfjTPG+kJojqtWM9bwSN2x7+fUgzpFnwQIrzILXxLBzL7dMkFEEXTxwdL08gHMJm/rtzNxhohBHWEjaoLUdauHMd6G1XsamIhvhq1AupMHy/TWYI8fS9mWM8kMK31A2VUZQBSR2H1OKBJJUaQ2/8JmtASqsPdlKE6BuQ8nUSUGVIPtYV6KezjGPDZBkb+t619EDKFRqQ+iCqsirKvVFFd4U3ygOpsUEohC10mbQ8AFnLrgyG829KRkYLoYGortutVFgCoF8vRVewGpIVr/SkIC1kKoo8PZAkPAMTcyDVOCCXNuilhUd4bcVK+Fia4URSBiflPvR+biOULz4HkrwM3rXNPASrzzctD8hTZ6av0sbmQMpgOMs9hK23LibnQFKOt+CBFO7HbMkQ1NvJYx/Z/lHDO0Con8oSVurLFOWBlNDmal3wyIv8PJCE5hWJfUv+ZUfJkBaOb2WBPs0DqQ7SlEi0yzzGk+F5iJaXY+9vYQ6kfKEBqQ8i5mKZcVVTt6Pxvu/qCXbQEkm0dVyKs7hCxpUj4vWiut1GbD0q7YYOZEmAmL8M2i7Q1lawIp6F8IC8sppqbtgFsq8i5bFipKKboyEcjlRTCJugd2DaDmhWvH+KDN1K9UAqycug+SzseEHpn1PLJF63XzbBu3+JBpiAASnjeEGobeiER9j2/knbAc2G3k4PuRb00DQJYcu9PkBbhjBRYXV9IYRN7VNq2bk0V8+bQDhlgs4X1JlZQrfC59Usg3Ipk53gxGQwCGHLr/wcDEihe6ez06NnOGIIW77QgNQHEbOQSxuQ/CTaSg4kx8ytUpewpTk6iXbofYbCZRP/VZcTKFt1Qxdzx0+WIfydzOqRUo7G5ED8WUQaI9Vj7Xu8RJGHF07uHkiBJNp6E720Y9PKkTQopoVNyeZACpZVLYN33Ioug1dHo7+XCpkKyhAsS4daco5Ibjyh088DNXgglWTaho6x20oIm4m3qlCzSA1/d2VlMFl4kPI8qWXxIyqsLmkMUD9JtCuvTUVQjxebVyR5HQv207rj2PD3+RrSTLx/lPOEZNANo8uz/FoS23uE256OV5F3CO1H+UIDUh9EahCU5vacJk/WJNrqaRLJeuOum8eqkaSngfo84pJoRx2brwx6eS5EjTeahk3JxMWNaQNy4RXdiiFH38Uhj5W0LKFzSajX0fUUyFK+erxkWGVaAusiw6asJLDu7R9SE3kXGsIm/yyyJAetpX2aJIXVRSfRaFwIW5bfnfcuaDp9VcBTVGj0m+ZRHdTbMkKkjh2E9ZWJ3ijSIy2OqH4raRwSfl+Up4Oufo1CyvOmnOe0/DqxbQrqCpO5jY1QPhMvqDybRZYwulzrQg7jSZ25Xdw5RXkGrqjQgNQHkfJACrjFG7QzbQOS54GkhrD1JtHOw7UxIJPGilCVJRvm25VLehqkJRcPJNqTGgybeiAJux/Xqwz2VnT1z8ljJc3JUG4SAQ+kpIlqjSu6wbYh55GWnkQ796IrMujugCYqQ7CsKhls3IeUUBWrCc0LSKKdl7ODv9VxUq47pbDmJnVxQb8c7xJ5TxR1kiZLJchVSdOZdvR2wQseBuM5Ke/lWsaUUX1GYg6kDF4REujq17Rzczfm6XgHCvbTJhv1SHn2Z0minecOaOYy9J4jEEJXi4deeI5mkmeNSbTzhQakPoiU8s/qFq+7whuV1NCT388hkXNCUI8ohR5VVtaJqnjy6IJWMushAaJuvSwykXfQ40UuobqtJL3h82SSaCccV2MOJPXwvD0dgHSvF38HtCLzDwmG14ZliOu3bdyHxpTJiR1PLPN24t0bxzHPz2Symq1LxYCU7iUABHMgmehtG0mT4+Sx47VroreFjFgGCx6y/WPycZL3otI3mZ8blXg/aRxSPyFsqn7NvuiS97PQGUdIjt9M5jZSdVJ3QxpADeeTeQ5lGfTug8Q9qEVnVRlrXY3E6PRAEoEGpD6IVAibSYK1gDy9h6at9EcNkisGJPNyE2XKEMIWdZ5uOVJ5VryrprqhC82NdCcqogkQdUPYvJVtYRnSwgllPJCqy0kjDy8c7zSdRIU66CbRrjUkoOgVdu9+iXq9pEzSJEM6wzLURQhbmgw2noVJKFcNdUQ3L5wJWjmQcgxhkwpZAuKNBj059Impcph4/4jlYaq8jtqAw1bIdVq9kBzD1BLCFpV4v0+EsNXigSQUNgXohS5F3fO8MArdCrSN/GRwDLx/opK454FJnlsJD+a03JE6RI1FdT26aEDKFxqQ+iBeG5DaBhcwa+C6luqoztArU3K3p/J1I45JSUytVY5ntBBeSSxsIFoHCRCDOw7FHyfpaZC6NbN4yJS54s3DqJWl3CTUy5gl0TYtR7ZtaIePSRpO0mSog9AtGzKk7VAotZqrkimErYY6YhKKoC+P8jrOo0z5uNYk2rkbDJTrpXmjATKeN4CZt2pRelttKiITds0FD0m9mXVM6bpupJdc0jgk3PX0SQ8kodQYgPosksqXKVstP02G8veqIS3HBfos+YdybpsmaUokDHppnts6RM+FUs7pLc+tsWwShAakPoi3NWzeij+rW7zuVvZhA1OUe7BEqEzcdaM+y+rpIO0BFLUdcI+gwo+6rl4CxPy7FHWgr+UFJbhte7mciGch6P4NZA1hU2XKdlNqWcWNQr2ObqhM2rFp58vuilegASkthE0wJMAjrd+2cR98t/iYZ1EPXlBR1HJvdBPTmqDTNoMhbHobLISRqhM64xfJSbKuHNb1doQHkqrDJPVVWr0I6M0CkqpHoXYjgTFqwjgkjw1Z8kBXv0ZhI4QtOUF/SaRsAEYb9UiNHbIk8pZ6DoDGfRAIYctjPKk7l4v7nl5I+UEDUh+kYqnP97omWzyq+HkuNJNoe6gGMCfjalEcOitCafkJ9MqRnZwkraJJu6Gr5ZdliD9OMlluMDQhXoiorXclZEjzZhP1eMmcpNdeuUnoutiHi6sln4OkV1z81vG9ZRfp/SMUKqSSlrjYyn1I8ZKz+yz0z6lVrrzbpqvRNgNJtJUcSEYGJOGkyUnySCXIjZUjxcvZht6OMqy6wjLo1k1X0JiW1cAapzeTxiHhvsfUkz0v6jeJdvl/0vhNsp822ajHz02Xsyw6HpLh73N3EjCQQSJ/YRY9GUY3miTunLzy7BIakPokXsOWSjoIZJuopnU04e+jPZC0i9WSyX8faSxKPy+1HOHV7YaE+6LKL1W+7qqJrfAxrV08CpDBFX4WSfUgjjwMjGoi0jwUrzrQ1nmW/nvjVWRZg56uB1Kh3j/+QoMFGQq8D2mDUpteUFk8ceomwb2b3jYDOZCaMoawCSV311losO2BFBnubEEGk0Teov1jaqhO5bVkUnUT4vRm4i5sdZJEWy3WtFuQNGzqeKTZGkunJlwW0ptmeZjK/0X7SN0wOgEPpFpSImRZ+A96IGUumoSgAakPIpaE0iDBWqQ8aQakkLyqZ0CWEAAdmTx03R6zhspIbwkcFTZlI5eCetnkBNaC3j+a7seyibwrr9NCH+vFAykPV+y8c62sKCFs+vmHci/ax9sCvNjQrfL/1BA2Cx5IsWFLJfln4V07kydORrl0EtOaoNM245JoZwphKyAEX9ozEQh5/6SMMaTqpJMyWe0Rvg+6dVNSb2YdU8bJlDQOqccQNtMwIRvPInH8JqwzdTfq8cbakh6SaZuSiIX5aqaDUL/P1wsrvR6kETUONUnWX1SC+xWRQg1In376KU444QRstdVW2H333XHHHXf43/34xz/GRhttFPi79957C5S2fpBzb1QHHSbyVJ8fef1QZxilnHNzxw8rdM3M/Vk9HeR2U4kfBFkZiKq5NjSSHot7/ySuYFUfb0sG6YlJphwrOawyB393pksE0PFyKJdV24qufEhhbzkpE1XRHdDqwIilm0TbERxppOkO6TBj9dpZkkln3yGx9nCAKHmSrqn+vmASbZNyyv9Fxy9x8ltYdAnskFdYEu3K6/RdQ4tb8JAMw68liXaUTEljgHrxQFLlMvUYlgwn1HkWEiFTpjKUvw8en1v5Wbx/cr4VJlEm/ng61yTaemUnETXe0b2ftZZNgjQVVXCpVMKJJ56IsWPH4tFHH8Wnn36Ks846C6NGjcLXv/51TJkyBWeffTa+8Y1v+Oe0t7cXJW5dIeUC3mjQuQTk0bTYVxmQIpSzhDt+1Pu4srK6O0uFaCQakBRZsyZJ1qGxwUF3j5t4bzzxRBJyanrBSHo7NKbIIB4SoBm/r5JHWF3WPiEOXVfi8OC3Fnd8ySSxLsr3JfzM/T5a0PMmLUxEekCuXjvNiCUawpaaA8meDEZJ7muUSycxrQlqm9Px4DFJChu4ho3xS0pdACyFj6XoCqk6qY4HIkOuhT00dVMSSIYaZwn7BoK6JjBGTRgDVOdAKt6AVFMOJKHQqaiQznD50u1SNweSaAhbyqOxsdGAbn4yiRC2WtpH2q6SUQS8a2lAyo3CPJBmz56NTTbZBBdffDHWXXddjB8/HjvuuCMmTZoEAJgyZQo23XRTjBgxwv/r379/UeLWFXKdS3UZevL0nm+YRDvogdQ7GM6pcae5GMd9Zuzu7K+wy66apMkqu8Kf3un7q+nSCax1wugK9EByHJl74GRYucnDJV03hFGXksYkNeq7kuEsINg2iqsPosablBXVSkiAvAxxz1I6xBcwCWEr7llEUeu98apWXklBtULYQsaPLCEJlbaRRcp41H5XJwRPLuxb31tVbtyglBe5C5usAUm3PUiG0mUNl4kNYUvo8+slhE1Xv0YR/N25idR7vfTxvbSu0N2oR3dXaVOMwseEdLdJmhKJXY3z2PghOiQ3eXxYS3J5Ek9hBqSRI0fiZz/7Gdrb2+G6LiZNmoSXX34Z48aNQ0dHB2bOnIl11123KPHqGgnXQiA08BFIihn+OsoDqZbkakGZgu+jVj6iBt5ZPR3EPJCSdmET9nrx0MlnIJlnRCs0QXhV10mRQTIHFJAtIWgeYXV550BS9XzyjizhAblhOa7cYLh8TcUdPcG70U67jP7eihHL67djnqUVL6jU3ejK/yUMu74MGfSXW6Mez99rt/I6Ngm1UtecBsfvV0yMWFJ9ZVroWPhzaa9hIN1zWGyyrOaDShnniOx6lZIbrSKHpAdS+b+pgTU2iXbCGKD6ffEGpLySh+eBXhLt3mPF00EkHye6EGm4E5zsTpXJx0r003ls/KC7IZJKYPMWGpByo7AQNpU999wT06dPxx577IF9990Xb731FhzHwc9//nM8++yzGDJkCI477rhAOJsODQ2O6MBVksbe/AKNjdUzPxcVg426E0qtNDQG75XutX0DUlOyPOHvVPm9jsU1KDeJ8EDehVt93YiO0XHMynddmWfh0dj7TEo9blVdUCdE/ZobRcoH9J6NVweamxpylyPchqOu391TCnyftwwt/RpTZQDk6oGXb6RUchP7hgDKbevX3JhpAt3cXPndjpPDb1NXxhF/H8Nj38j2q1mORNtoVq7nRDxzrzY2CtTFsAx+GG2oPnhdYFOjnAzedUtu9LPssSFD7+92I2Rw3UrobXOznAx+Px1zH6Lw9XhjtnbVENMva/cPIQKeMXFtPdSuGhocoMc10tteOXn30/3UviquH1b6QMn64KD8XBA5nqjI0NJPRm+3KPeiVCrF1gUpfdXs6Ss3ud92As8j33vRpClDlUzKeEMdzySNQ8JGqqi+qAiqxtxJfYOgzqyMIeOfhXcPJcZvQRn0no1E22hscMoegSnzDFc5Pk8ZAuNYJ2WeKaC7s7bJABnmbaoTZqxuKJCsOrto6sKAdOONN2L27Nm4+OKLceWVV2KzzTaD4zgYPXo0vvOd7+Dll1/GRRddhPb2duy9997a1x02rE101dEGgwZVh+019ys/tqamBgwd2pZreY5T7jhaWvsZX3tA/+RzWlqC1U2V35uoNjU35vObQo+9uV9T1XUHfLm46rT2gf2NyvcGG62tzbk/C6AySexx3aq6MGD2Ev/1kCEDRMoHKvkUWlrif6M392hra8ldDnVwFnefl3f1+K/bBWRo7Nfsv+4/oLqet7SUv29sdESew4AB/XpfOX49iOobVFpbyzI1OMCwYdnyxw1qb628HtQfQwe1JhydTmv/fv7rpP4rHNrR1FTdfpMY0L/Ffz1saFvNcodpD9yXAWjr3xz43ptstLaYyW1C24Dyb/QMJOH64Km+/v1l+iag3OcD5QFvZBm9srVFtJm86O/de6e67amhAhL9gkdrb/tvaNTXyRU9nk3nNTWVdWa/CN0GpPcPYdQpcPug1shrDhigtqsBvbqhhBYT/ddbMfunjBdMGTSwos/b2+PkX+C/HjqkDUOHDsitfJWGBgc9JRctLdW/Ue0Dhw1tQ2tL/sPwlv5d/utSqXrs0OLphgYpfVWuJ64b0y/0snB5RW8PjqlzWenv3eeIfiGJ5YpnxqCBFZmSxiENIRffxrzGsYZ44xAAaG6O1z1RfUN/tV4Oa0Nrv/zqpTe+T5LJ7xeExtKe0SCuv/Tw+2WDvlyXhkYH6EkeSwPleRBQvm95ytCpjJNb+/dLHEs2+vcrPxkGtPWOF1L6heRrdFR9NjBt3qa03dRjC8RUZxdNXRiQxo4dCwBYvnw5zjnnHLz66qvYY489MGTIEADAxhtvjE8++QT33XefkQFp7tzFfdoDadCg/li4cCl6eoK+hsuWVgYH8+ZVG0FqocFx0OO6WLx4ufa1u7vL8nV1diee09Md8pl0K/J7ynnZsq5cflN3qKylS6uvu3Dh0qrz5s9fgnmtjVWfx9HV2yH3dPfk/iwA+KP6Usmtqguq/B0dyzBvnr7cJnhNaMmSztjf6OWoWb48n+dXLYODkuuiI6ZeLl3e7b9etixezqwsWVa5/qJFy6quv3hJJ4DyiqrE7+/s/X1dPSUsXLg0tm9Q6Vi8HEB5kpBVpqVLl/uv585bDPT0JBydTkfHMv/18uXx/UVXV7CcZYb1aqFSzqJFS2uWO8yyZZ3+67nzFqNzWdCA1CndL6Dc1oCKkSRcH/x+uUtOhs7Ocr3s6SlFluHJ1pmiG2rBqyvdEfda9UxcnpNuiaKnuyyDye9c6ulx180kl9OrM5csDfZ3SWOHJNRj581bgv6N1WOnhYsqOmfRwqW+bpAYL5iyZEmlr5q/YAna+1Wv5i5cFOwXmh2ZcAbPgLR4SfV9UfvAhQuXYqnAari6oNJTqu4bFnu6QUpfdXp9U3S/4DF/fmURbIlBHdKhq6vcN3V3J8uQJNPSUNuKG4d0hvSV1DgoDU/nA8DSiP4uqW/o6Kicu2jhUizN0xtCY3zf3duHdnXJ6AqvqYefaZilSyu6Pf/5Vfl/VL+gsmxZuf24GXVDHGquoI6OZYljyeW9Y85ST34yeOPYnhquqeogj/nzo/t7j27FBWne/MUwmOJZIavODmPbMFaYAWn27Nl4/fXXsddee/mfrb/++ujq6kJHRweGDRsWOH706NH497//bVRGqeT2+XjHnp5SlTGkq7eCOU61oaRWvIFPV3eP9rX9WGs3WZ7wcFSV3+n9trvHzeU3hZPUdUfdx67qcjq79H93uJy8nwVQ8SIoldyqutCpyO+W8rlvSTJ0RdxDD/8+pNSBrDQ0AKWe8rWjrh8wOAjIoCZx7oqQoav3fYPjiD2Hshyur2Ci+gaV7hxkUj3zTdtGFF3K+XHPEigPMFSi2m9iOUp9KAm0DXU1urOzB93NwRGJKr9YfQglhgzXB69NOkJtUpWhJ6bf9mRwXcH+qfc2RMmgTqQlZfDoMahr3TXqcc+7Ok5npvUPYdSxUldMW1fbb6nkKjLol6VOYvJ8HmrYemdnjPyWdKaXNyRJVwBlvdLdjdxRE2eXSqWquuDrhgahvsFrkyn3OPA88u6nfBnM2oFqDArXkbhxSHjS190t39dE0R3SO/H6NaVe9rjoTtoyzRBv7J/UT9gaSyeNY8tlV3L/5D6/8vrLhGcDVJ6jg3xlUMctXd2lxLFkj8Q801sQr0EXZ5m3qfnAOrvM+gObmOrsoiks4G7q1Kk47bTTMHPmTP+zt956C8OGDcOvf/1rHHvssYHj33vvPYwePdqylPWJN1CS2CK6kpBU/xw/+V2Kt1c4nDCYRNu7Vl4JQcNJeJOTWSZ9lliOcJLYxF3YhBMF+9dO2y7cdf25rFgCxLREuWpSUMHkh3Ey+FueCj0H/xlkSKJdy04/OgnMTQgkM0y4nk77TUI6UW1afZBOqg6EkvUmJPIW3aExpV7aSOSdtEOhjV23gGwJrWutI3kkJI2Sp/w65phQot0sW6WXhHSFWsfiuknJrcqDsvTKUVAS7bQddaX7J9OExRKyZE0yn7RLXtw4JO29LdxAG67hd+dcLbQ2YrFUJ9M2OvDkkNghUXcnOMmdKr1flX4fyv/zfB55bPygO5eLOyevjZpIgQaksWPHYrPNNsP555+PyZMn45lnnsE111yDk08+GXvssQdefvll3Hnnnfjss8/w29/+Fo899hiOP/74osStKyS3js/SwHs0JwnhXU9UA5j3OrfBsMa2qrqfJSG99WjS85DcNSMgQ8qzsTEoT9uqO48t63XKB6Lvg24byFy+4ommiydTLYbmtN9tirojUClhdlHrtsjSbaMx5b74ia0t7IAGRLeLHgsypBkxSjnUQV0ZUo3skvchg/6qtY44OetM9f6lPU+g3M9mMWJJGRXT2oNatkT5AVmSFn48w64jtzNg2pbh8vpKr170CD6PrAZWVaZwvxU3DqlVX+WFThtOO7fBcXKvl40J7SFcvvTuiEkySMuRpKtUJPWmfx9SjC4SfYR3KRfZjaxR5+neT51jiT6FhbA1NjbilltuwWWXXYYjjjgC/fv3x1FHHYWjjz4ajuPghhtuwI033ogbbrgBa6yxBq699lpstdVWRYlbV0huEZ1loupqrhyEremqASyLh0USOitCaSvWOuj+9qxoG5BsrK5rbO1c1IBUemKQujWz9IA8k4dD8Nxs5SrXy0HxqtdI9EAKD8gNiw54pEl7ICVNEi1MUuNkkO6b1GvHeeV5n9q4D6keSKJeUPY9BBsSPK9MUb1Ik64ZNsj544UMvzvvx5HmFaiWHT4+b5J0pg3vRMdx/A1RipBBdzwXbJ/5ylDx9jA7TxU53GfEjUPCZRTlgRTUrxnHsgJuBb73T4JIUlvX+zLoev8IepNXvKCSj/NlEBrL9pTS07tI3IfwODq887cOkWOdhPtZUsYh3nuSD4Um0R41ahQmTpwY+d1ee+0VyI9EKnjtR9JCbtLIdC3VVTtVRBmQcvNACr2PuGy0K6RhOYLPAkheLVCfkRUvg5h7Y0OOhjQZpD2Qel1/3RgZpAfkWTwc3BwGIY0akzITAgPchN8S3ha5Fnd8iTqZGj7myq0geqi/K8n7RjZUx6uX1d/Z6p+SQhPUj2z0kSarm77uqIMQtnAV1vHgaQyEsJn8bpkV/jSvQLVswFK7SDBqStZH7/rdPdETRVcz7UAtZXuUXDdWL6r9fN66M2v7SKojceOQcN9TlAeSrn6NPFfYaKGWEV1+77EFh1VKGrK0jViSMqSMpT0k7kPVQmyGZNamqUeqjb00IOVFYSFsJDtSK3iAXqxyQBaDPDzh71VF4bt859S401yM4z7L6vYrlWckyfMmKVZfRoZozSsZO+/LkBYqExj02ZdB2gPJe74uqo0rcfTk0E/kHcKmXiKprecZwiZRJ9PCZWzk/knzQJIOr1WvXXLdaqOfLW8PjXAhWzKYVNNa64juZEQH3fYW9uzLorel2ob6eHVD8KTQ0duSOluVITGETSyErvI6qe+WDGHzLmc6pkwKh48bA9RLCJvaF5iG6kjWiaS6WFV+wR5IXn2RSYXQW0bKsxGVwTC8NN8QttoXJHXnch5J4xJSGzQg9UFErdOGg1KTlf4qZay81U1wp0u400hLZpn0WRLSq4lJIYU2knECaq6N6O+tJAX17oNGaIXUs0hKgCi9ehZe0dUhj37CyUHhR8kEJLf1qpCAjKupUnlG0pKLS9cHIGi0jp6o9spgKQ9TuHrYCG1Vr53mZSqVbwaoPGcT/VVriECeXrvhth1npA4bZrPobSlvzTSvwPLnldeiXnEJOtNG3wAkLwZWvE1ky44rPywHIFcfTMeUSWF1ceOQ6vdGReaGWq6pui46NYZk6BhQeZapoVt1ML+S9YIq/9cPYctPhqCXaLZrRN26ZA+k8HsakPKCBqQ+iKSracXtV1MWAy+YOHdgINsKrq5c5etWXziyIzIUQDrPSGKC2FL1cZIy6OSVkJJDN1kvIP8sikianJYUNYo8BgB5KHyV4E5PSUo/vf3qlCMdUgnEGKcteCA1KrObnoiHYyUHUsKk3ZaBWyffDGAnjM4ohM2vI9mGYTqr+qayeKRtVuAl2s22O2T5v1TS5CR5bHjLqrIUGsKW5AXl1lb3dMtWy4pCcgyTNa9mUlhd3BigXnZhU73EzXVm+b9k2FSS97R0DqRGTeONlfuQarzpPV6gk9JNUyLRT+fhgRQdkht/rSrvQNqPcoMGpD6I7EqB2aqmySQh0YCUYQCeJFP4KtIhbFITJJ1VRMnygfSVGxuJatN2HLIRzpd0Hyo769gwWOidk4cRI/cQNuUaSW29aoCecRXZRohG8i5HIsVXyRBl3JMOCQjLkBQ2bOM+pG40IClDhoTWtYaY6iSm1UV3AhxOtGtqxHJdV84DKSWkE1BCzoU8Ez20vH9k7UeJYyrJNAhq2WpZUUjq7awG1qSwurhxiK4BVhpVD5jKYCPvTqEhbJoem6LzK815juS90E1TItFPp3lN62Aawlal22hByg0akPogkh5IprvJ1BbCpnogwahcXZn8zxJWAtOOSyxLOFGtbg4k2QlaigeSW31s7jKk1MukrXdzkyExhE3WYJDNA6n63FrKzTvXSpJHU60rutJ5RtLCZWzugAZUeyCZ5KbLS4akVXkrHkhFGtkLSHKf66KLpsE2vGCSFKoVhdpUcs+BpOEt6d1zae+fpPGMvRxI6TJIj1vK5ccfJ9lP+WGlrn7ewGqZohc90yaleaViMCWoXzOOZQWqhE7olvRGJGmLkL4cgsbVutgJrsBE3kGv6WxthEm06wcakPogkhZy091kTIwHOh5IeXs5mH5WT6s2QPLzkExAqZI2UVEnr9L3Ia5+SE5MwtdNWtGVC5lSytI27pafSy0ypSWLNiVrCFvWVWQxY2LKCrsV75+k8DEDw74NGURD+RImBrb7SIndS2PLzDGJdrUBKea4kNHL9HdLPo9GDSO7jcTygJ7eFjdieTL0JOgrC/2jrqdpY86rLzo5sdJlCt4f3RC2vDaDMUU1lGX12hXd3Tl2/Ob6YzjxsbSm8UZy99Yi8zDpzvEk+gj155gYdVV053L+d+G2SQ+k3KABqQ8i2dGbegKZTBLCXwc8kPI0INXgbWTu6VD+Lxcqk+71Ill+QIa48DEDI2LNMmgk0S5iFw/p1bMsoWRe3awlVCMQJpXDoFgNvzNR+qb5l0rCg9E0zyzp+gAk1wkbIZ0mMsgmE+/1NEDyjiv15oFUax3Juk15tCzh9zH9bNgDSTMxbdR1c/c4UcN8U/SEpDERULxfEkPY7BiQkvonKx6amv28ZH0w0R/qseH7EzcGqJdEvboLNJHn+uGd+deJutiIRTf/kKCHoG6uVxuJvNNSIVT6iBzLziElQtImNpHH14l34IoIDUh9EK/+i3Rwmp2sh8mKYtxqDqCf4E6HpJ1Pgp9Vf6ibXwYI5XMoIG7b9RW+8A5DKbk2XIM6UKsMcQOQpK1385Yhqo5YDQkwnKzl5YGUh+Lt0Rzghp+z6WpVreFBaTSmDIQ8+W15/1QnilRW0QX7hmDi4uB3thJYJyVPtuGZqMqQJZl01nvjPVYJr934xYJguzLV25JeaVq7sFnyQEoy3rhCScSrZEhMol3+b2PTh6S+W7I+ZNm5NHxsuO+MG4fUyy5sgRC2jOkYRD2QYttl9bF5o2288e6DZALrNCOWoAy6XqMS9SEQdm/LA4khbGLQgNQHqTX5ZhJ+4kHNRhbcsSLl2qEDVAOYbmI3HbQ9kGoMYQtMToTGgpWwqWqrhfeMbA2G08ICyscKyZCSENNGPqgkGaRDI7LkIqoYtWooN+8QNo0cDaphNmvZkn1k+bppBiS51VwdGWyFj+nKIOoFlSCDrRA2J2ZimUStdSTrLlNRhPV92kSvKoQtkwdSzgYk5XKxesKWB1JSCFsd6G1xfaXoHJ3EyeVz8pVF/WlGnoEJMsWNAar7vhy2LM2Ajn5NO1cmiXawjLiypcpXr6sbuiXj/RMsI00GSScB7UTeeYaw5eGBxBC2uoEGpD5IPcToepgMAKqTaFd/l0fbtpVE28YKe5LCkfZ6qciQsnpkxf24/D82NMGKB1JvWZFJk71j5D1e9POT1T4AyDuJdmCAG3O9qI+zuuOL7QqY4HljwzMRSJ4w2wgrBYLGj+qVPjsyJIWq2AvzLf83GZzWmng/zxC2sHdhmqHe+726iWnD5wPCHkix8suUXSVLwnjGut5O8F62YmDXSJwcPsemDMkyha4ZMw7RzSEmTdCAZHiuoGHTZAwp1U3rpueQ9FLU9/6BnAya98EV6KfyCWGL+kzPSJ12LDGDBqQ+iJUOTmBFMdwRBULYNK3iOqjXaGpsiL2u95l3DGDWqanXlM4lkLgVr/BANG2iYiNhb9qqiZUcSL2jxyKSJjsZFG8eMqWFapmiPicdjzavbWZeTRUajSYZ9NTxiWzoVny/ZcvzJnAfElb6REP5EpIn20om7j0Lo92eSrUtBOWZRDvNo8L/PLR4pZuYNuq6eT8PnTDfsAFMisSdAYX7Jg+dRN5FhfhGfScZwmYyrkzqO6PGISXXhffO11cFTVLVn5nZa7cArxcbukJ3btMjuPhj6v1TpJOAiAeS5u6MSUTN2xIT9Ye+Kio/2YoIDUh9EMnVbVNPIJMBYbUHklP1Oo88K+ogvrkp/rreR94x5c/MJwCAha3jE+SXHoim5doIrh4J3weNFSxxb7ACkiZnyeeQx0TFyUHhq6iyx7X1UkT7zZrPQS4nVXVZUe8lJ6pOws581kLYFBnCzzOQG03wPiTtUBgIo5OUIaWPjKLWPiMtL5wJSXU48LkXWuEn0TbT25JeaVo5kAS9twOyJNSHWj3PdEnyDpPuH3UXPGzVB5NxpU4IWyBZdSlCXxU0SQ3oV2Ov3fJ/UQ8knQVA4RyW6eFjweNzlUHT6O/mMHarVQZ/fpHjfchjQdKr17rztnDbL8o7cEWEBqQ+SD3E6IZlAdK9cJJzIPVeL+cwmaQVIa8jyuqBFMj/JL1qkhCCZ201VSMBovgOaBpeUEUMQKSfRZYk2nmE1eXhcqwSDGFLPyazB5KwcTWQJDbBe8NW7p/wVt0muenykiHRkGbpPhQlg7oTnKmBN2sdScsLZ4JuEu3w6nwtC055LzborG7byE0GpOhtazmQguXZlEHX00Ayd2FWb4ckY0bUOETtawv3QAp5RpngCho2KxuxRMtkZSMWbcOJ3FhOeyc419MNcjLoJvLO8z7kkRLBk1udtyXdT4awyUEDUh9E0gXadDcZkxUkayFsEQo9yY07q1Xc5s5f4QkiYG81tZ5C2NJyWwByk/bGBCOWdDhh0ONF75y6DGFTrqETjpg9hK1cIaTrI1D9OwLu+NZ2QIuXQdYDSe8+2JIhyRBibSc4zbrq65/MHkjl/6aeBlHo7iLlhnSmbkiGh6qb834eOs9AMjxFRWexwZbejhw71GMIm6AHUtQmJFoyxYSwxemxrPoqL3T0axz1EsJWvAeS5AK9Xn9ZDyFsEvch4DWdsY2Yh7DpLY4Qc2hA6oNIuh+b7oZmFsIWfu9Uvc7fAyl+hVTtICuGM4NyVONZAasmkjs16MoQ/lxc+cetLKsTEyEZ/LaRtKJrwWBhmp+stiTa1derBVX0tDAZILn9JpbTO18QC6lMMN7Y8EwEkr3S7CWPpgzha+sadNwa+ww/J1suOjP8Prmv935vUlhvFEFPA0MhU9DZdStsAJOi3vW2dB4m0yTajlOMR1q0TNHXAKLHIWrbqeirggxIMaF1JufKhk0llw3YGEsnHyd6HzTnGUU+C18GAQNSllQMYaIWzhO9HDXDs4k5NCD1QSRjlRsNDTm1JNFW35vmUkiWqfLas1JH50CqdNKO4e8Gkleq8iLJ+8daPoeUXBtW74OG0UFehojyhZ9FkqdHHHmsdEuGsMXnQKq8zh7CZu95JHlvFOX9Y8vzJpBnJPSIXAuhrUDyoNRGeG342vq7JFafa1SmY1Zesix6q7RhLwXzHEhy/bTjONqLHbYSWCflLpT0TgRSvKA8GQpe8JDsp/PIgRSWK2ocEu0xayZrXsSF1pmcW4TXiw2dqbtrpZX7oJsDSUAG7fvgtU2pJNoZ9Zar5KjS+S264dnEHBqQ+iCSSRiNd2FTjkuL19VJop13PofmhBC2KA8ko22YA263mURNJem+SOySYCoDEAxLkL4PekkYZWVIrEs2cu6Y5lipQaRAnp2cQ2XStgoHlPabcTAslag2aSBkwx0f0A/dEs3DlBjCVplFSd6HpIS91p5FoD7onVNr/53WJ5qQ5LkVPC5YtnEIm/Dz8PP+pBjAxHMgFRjuHJYhKnxL2gtK7XcTw0sE9aauDGGSEu9HPVfd8aYNdPRr7LmCbcP3VNTQ+dJjKF0DkkTbSLsPHpJjex2vUdd1/QWhPO9DHgakHsOFfxqQ5KABqQ9iI0bXNI8DoBHCVuWBVP1dHm07sIKUkNTQXwF2nEo+CZOBhgW3WydhQOwKeqKppOXasJEAMW0nOBv5VrzLRnqzWQyZ0vdwqL2fyLqKG0d4++MootqvadlWk8QmhP/IhrBFlwmEVnRtGU6K8sRKCFWxFk6YsBNcHLUaOXVXs01kiXvvfx5avDINPZeeKKZ6IPW2VWmvXSdhPCOZpFclyXNYWgbdBQ/ZyXp835REUlhd1DgkED7v6auCwmSC+tXs3Fo9IpNI34hF7adzLx6A/kY9VkLYUr1/gsfniU6aEqnQ78A4tsYk2g0NTuqCQdR3tB/lBw1IfRBR67S3omiYx0FHnvCgLSqELZfVVEWm5sb466qJdrN4QNlYNUnyjLKXEDR5Vc2G8aYxpV7amLQnrbZLJj0Esq3c5B3Clrd3oOtGD7R7ItpvPSUEBdLCpix5vdRBCFtSslxbhrTGhPtQD4m846jV+yJPnRnuV9MmeuEQNtO8bOq5eZLmESXpva2SqLf9eygrROVeVFuQxPWVpt6oNZF8Elk3gEjSm1HjkKAHUjZ9lRfqozYO+xbceMJoI5YCxtJRsojcB0MvKJEQNo25jtT4Iagns11D1ZtaIWyhr0wS6pNkaEDqg3iTLtlYZb3jTSYJySFsvdfLO4l2U4IHkuI1kmU1N7DyJGw4KNYVvre8OkiiHZ83x54MUfdBcgtcIFvywXySaMd7d2QhbDCKvJea7TcJ8ZDChN1EgpNkkeLL1070/rFjxEpKXGwjrDR87XD/YE+GGtpnRsGybPwQR9V9SzPA+Em0zWSQzkmVGupcBzqz0jeJipB4L1zp/lE7iXbv8RKeFjV6IEXdm6hxSNR407TMvHBjDFs6qB75eWO0EYv0WDrVAyl4fJ4kbcQSlEGuj/DrcKJnYPXxeZYNZG8f6iKAzjPV3WGUmEMDUh/EToZ+vVbWYzBRCXdE6qS40dtRJmcPpKQkvGoCxyy7wNnIM5JktLCVEDQ1AaKibKRXNItMoq31LIQsBkl5XuIoKckGa0E36aIO1Z4y1cdEtd84b6U4vEOt7IqXYLyx5v3TE76v9r1/kjyxbCXyLkqGLN4OtSYQdlKMJSaE71tafjJPZtO+Qbpt6O7WaUtnRi141Lr7ni5JG0/kEd6sUzaQvBgpeS90ZQiTZGSMGgNE6Sv1OjbRCRGPQzQ1RsrCtBUDUsoipC+LpPePptG/0s/mP57U8RoVC2FLWHDSJTBv0zDIhXVZHqkYSBkakPogfiJIkY6+/N90klo+NwcPpJzDZJqTciD5iiJbOICNBK1aiZuFW7HJ6pFYDqAUt1v14yKSMFbCSkWKDipeXeNuTqvMWXYojENnt6eeiPZrWr50CFsguXhS6Jal5NHhEKRg6JaYCNqJvOshlE8ycXItIabZk2iblZdE1X2LM8B4Y4+wB5Lhb1bPzZP0xQ5LBiRPX0WF6FrygvJ+YnIYnYwMugseknoz6w6iSXozahwSq68KmKgGDFuG5VeSE+cqUu81K2OIyLD1gsfSUbKIbFKkKYOkl2JS3+QhNX7II4m22ofr3M+wwaio8NIVERqQ+iCSiSBrSoqZZkAK1TZ1kOG9dlF7AsJgEt74lQ91NbIyENYvRxVT3PMmwYOqeA8kC6tHKc/HhqeBVjihcPw+oK8A8wqrMzUqJ5EU5hR1jNd+y58blCPcNhzHgXflpPAf0R3QAoOx4M0xyU2XmwxJYXS27kNBMiQZ8+KoNfF+Wl44E5ISwauE8+7pJqaNuq5EtUwNt7YVwlbnelsyTKd8XbWsBE8HSa+XzCFsvedHeSBFjEPU2xvQVwWEsMUl99ZBcuv4wGYcEWK5wqGt6nXThjGuYPv0rpnkBeO6LrxvZZwE0uc6Uh5IWVIxhFET71e8yuKPr14coQEpL2hA6oNIdnC1uKSn9XXJIWz5KV5VdD0PJCWbf8YcSEV4vUgm+1NJc7u1kgsqJW5bemICJN8H6bCETB4OOYXVJYVCmFI1UY3MJ1V5ndUDyUbbiBuQBiYUgpPEekhgnZQUM+AVaEmG8EAy8CzqKITNdd2aQ9h0QzJ00MlNppZVFcJmmPdJPTdPUpP1JhgH8iRpsmprJ7ikeyFpLCiXreeJY2PHqzQZwiTp8qhxiNr+gh5I+rLmRdADKdu50gakpJBKQHAsrdlX2bgPujl7inISkNpZOUsqhjDeaY2OXuqRpEUlUhs0IPVBJN2PdbZ4VAl6fiRXp+QQtto7Fg/V4uwn4U0MO3L8SXbmEDZhw0lSCJuklwOQnmsjGJYgI4P27jqOIxaqktQ2xEOmAgMwvXPyCg8wDVNJQifXSlT7NS2/EmpjKqE+cbscWTPeBMLogt+Z5KbLT4bwsy1FHicrQ8KzsOaJlX682gyy1pEsGz/EkRT6F/g85LliunupdNtI11VyO02pVPrM6ll8OAxQWobEELYV2gOp8trESy9Jb0aNQ2L1VR14IJl484vu7qw+ixSdL7cIV/6fNo6Q3eU6WEYU8mG+1eWEkQopzNMDSV34T7yfGmkTSDZoQOpjSO8qYxzCZuKBVGVAUl4nrGSbEkhqmGAYUhPtprm+R5YjFCeskjQQk3ZDD8ugY0CSy4HUW1ZqaIJI8akyiIcEZFi5ySusLtccSCHZo7wnotqvafnSSWIBta+MLls9RqT8wE5wYcOJepwl7x83/Gzty1D1LCx4JgKhflqjfeahx02NN7ryAPG/wc+/2CuzqReUdNtI3WzBms70DEhRMsjrKiC535aWQdfTQPJ5BD0Tzcd10SFsvcfEJdHW9LySokq/Gojgb7ohGLpVLifNAyn34svX1Vwcz2vzkUgZdDxmbO1Uqem1k+subAkLTrqoO9Tp3c+wbstULImABqQ+hvQKnnEImzpAT5EnLK/6Po+OJUqmpqbeAS7ivR8alFhak7JtrG7rhKnYcoWPW8VTPy8iFxQg6wrv0ZhQR6Rza9QWwlabTEm/2xQdD6So9mtavtUQtirPG0seSHUWwlbtgWTLkBZ/H9TVZNEk2oYG3jzqiJ9Pw803byCQZIAJtitTI5Z020gbv4QNYFIk7oBmW2/32NdXujn7JD3CkvqmJJIWXqL6/Dz0VV6EizT63UWGsFnQV41Oct8QlqWIJP+AvN7UmeNJPQ/192RVWZV5W4PejnKa+f2IOTQg9TGkO9q4VfU41M4ubRCQaEBSV9NzHAwnbauqGh085WK0UmUhQWvSSp5qiZckbZVZKl46IEPKSoONnW2SEiBK7poB1OiBVKNQOokKddFJaBjXfrO0Tdmwpd6yEo03YsUnGt1trOiGr52cwLoYGfJKJJ8qg6Frfh6LD6Zl6soT9T78uVe2bmLayvmV15KeDroGMCmchDARe0m0g+XZlEE3gXWtieTzkCFMkiElqn4FPJBUfVVACFstCYNFwwlTxi82DEi6G/WIGtI0jFjSelMnTYn6VZ59RFajroo61tYyIDEHkhg0IPUxrLmAC7ikhwdtQQNSjoNh5fzmBIWurgT6nWqdJdFuTBgEVRS+bDPWDQtQj82b1OSogqtGHkn3QXpikmUw7N2qWmUyDWtNotoIGnFMTPs1GW9IuqF7xHkZ2NoBzXEc35MiKQQpLTddLSR6SFrwTNSVwVbS5CgZogjUkaweSEJh31HvK+XEGZD0GmcevzuJtB2GpJNHe+h4IMnXyfjwfWkvqLRdt3w5BPVmY8b2kaQ3o8Yh6uvmhAVLG9SS78V/FoJeL+Vy4suWKr9ahmQDc/j4vGVIeizSC9ONGuO54PghTwNSdBkmqEnuTX9L2rHEDBqQ+hhSDdtDJymZikkyUFtJtIMuxZUqHpco1HGyhelIPwtAL0RE3AMpJUzBRqhKeghb8DhJGYoIYTPd5Uk9rtbVXdM+IYk4I27cZ8H2qz8LkE4SC8Sv5NkKYQPiVzRteSA5Cf22jdxoQLLusFEPqmTQGKDmUUdy3bm06tnFHBfyXInzwotDum3o6qqi2mVABku5CyP1lUUPJJ2EwSKeFlk9kHorf7YQNkVfFTBRDXvpZvFAcgSeRWoOJIte7HEyVMkhIIaW94/weFov7EvmPmT1ClRR+3A9b6r48RmpDRqQ+hhSroXha+q635oYD8JfixmQAkkN41eh1NVp09C98PWkdkJLui/SW8eHZYjLtWHD+8dJeT42VnWT6oj4gFzpqU13PKr1luTpgaSz21Ns+zUo3kboUtx9sZVEG4g3aprkpsujfCB59dteEu1oGawakDQqah56XD2tdp0ZfB+722XoftbmsWwqZTpee48bv1gLH0voM23r7egJe/m/2LhF09NA8nlkCfsuH1v+H2X0jhqHqK+bMpaZF7q7KUYh+SySFhrKZUcfmyc6HpvSetM3uGt6zMiG+cYfI+UFFfCazhrCptRTnd8SbhMmqRBIMjQg9TF6Ah1c/tc3TSZtku8j7KWjvs/iYRGH7raqqhu36UpquJwict9YW01NWem2ESaS5iFmIx9UZbJUPfqQfhZZVlO9FdCaQ9gMJ4lJhC+RtEIPhNpvhrYpGd4ZVydteCZ66CTyLix8zFvRhp3QUiA+B4itkCVA04CUw/PJYzXXo2qQnRKu7N1P3cS0/vniHtQpesKWQTFBDtt6O0kGKyHXCXVD8l5kHVMm3ZuoPj8PfZUXtYSwSdaJQFL1iL5F9S62USd1vOllQvniw0o9iu4jw9/l3TZrXZBU5206kSNhg1ERnoErKjQg9TGkc1uYNm4Ta3m4I1INYIFk0TU2cPV03STaWSbJ6iBbKs9I4hbV1pJxJq/cSLqh+zKkrNxI53Qoy+DVkeDnruv6dU46hK9cvl4dzSvfR64GJA0Xe/X+JrXfxHJsGBT9pOqhsi14JoZlSE6iLd8mymUGvwsbG+zIEJ5A2ZHBSZkghckjvE93pysdkgyQUZ9791M3MW1UOZKJk9Py9dky3kTqS2FdUS1D9b1whccOSfnZVCQNeuojNhlTJnmvRo1D4pJo27YfqeOQLDJI5gdT72WUB4grvHV9WIa4+iCeo01joVqVTcQLKiHBf5QMebdN052+w6gegt7tSfIqCuvjIgy7Kyo0IPUxggla879+Y8LAJwqvo3Gc9AFhuENWjS5pSfZMiE1qmDC5ME0GGj5WzAMpMcdI+b+t7YCBZA8kG1umxymKvPL9JBGfNLnyWjJ+37uyfhLtfAaEOokKddExIKl9XFL71SnHRuhUUgibVEJQ//oxgzEbdRJIDqMqIoF1lTHPQt8Uvr6O7swjWWvwd9e66JLeLtVyvHptmofJWpLaNA8ka2HfUQZyOzIUncg7qXwPf+FF0NMCyLj4ECFT1DjEjRtvWp6oJj1nvfPL/8XDCRPGkFLl68hQ/jz6+LxlcDVlEPFA0khTImnQ08lblISq03XmbTobt5Bs0IDUx5BOQunHeOu6pBsM0KuTaEd/V+tqqq5LsZrMMouXhY1kuUm5BGwYTcrXj5dB/cxGmEpaCJvkRDFua2ZbSZPTdhgKk1ey1loVvopOQsM8QgJseOfp5B+Sn6j2lplwX6Vzk8V5QRWRwDruWQg/BmNvB5PQ7/gy81t00c2d4i8YNUTIYBi6V8T4xfdULTCEzVad9O5FT0+1cdD7xIaHZlEhbIF+wWDxIanfinquuikTpIkaF2T63dLPIkXnS3my6/RV0npTx6hpS4bkELZKZcrdAylDvlkVNeJB57dUeUbTgpQbNCD1MaQt9aaeOCbGg+Rd2Cqf17qaGrfKGZ+EOltcrvWdI2JWiSVDx9JkUD+zsdOS7vbSNmWwteOVSX6yPMPqsiSYj6OqDUaGeCgD8oyhrXbqQ29ZBdUHoGLcq5okWhiQh68fXtF0S5b6p4TQBFtGdvMk2rXr8WDuJ4N9ylPkAdInN+Ek2uVzdMqpvJYdv8SVHzSASZFkULTmmRfXP1kycJvskCQ9WTcZUyaNaaPGAPGbttg2IFWXZ/S7BcO+U3dhs+GBpNFHS7cNVYY4DyD1mUk+i0SvHbWfzjsHksE4NoroJNoJv0VzcYSYQwNSH0O6gzN2SfcG6DoeSKFj1Pc67qW6uP6kIfn3VAZyDVqrZWFsKD0d+e2GZ0StqFYflze+668bExZgweMkLjzC2m5TGq7HHnmGMNUas66SZGzxP4v1QDIpp/y/iLBKmx5IcWEiRewEF9u/WgrjS5LBVjJzwJ4nTp4eSLpu/uH7aZrIO5gDyVTKdNLCpgqpkzGhnbb0dtzCGSDbP+l4GkjmLkzKH5lEUlhd1DgkYEAq0gMpJTQsDdkcSKrhJL5sqfIBvbmNK6w3tXaCE26fOmlKJGXwLqe703cYdXEwLbWFerz/3nK7XJGhAamPIW8hjy4rDn/nBh0PJE0DUs0hbKqFOuG6ldXUbJNkGxNFJ0F+2zvKqGVGymHBeAJEKwAbO9sUvW27ycpNnmF1/kp2DgakuDaoEjAgZU2ibcHzxA/dSgofE/cyiK4Tld9vz/smtn8VvgdJW0Rby3ljuACSR5hC1hwvUVR7qUTPLsL3U/X+MumXGhxHNol2igeVtPHGSXg2Re8EZyu8VSu8ROmnci8/Y1qEpH4rahySh77Kg7TQMN3zpQ1I6SFs8mNprfAxiV2uDXeCKypNSXBzpHzLr9UDKZB6JGbsoVLLzoQkGRqQ+hjSLuA6nWyUPDodXfiQYAhbfh5IAQt10i5B6mA2xfU9shzhZxG+bmwOJEuTVCD6/ljZ8Spl5cZPiC6Z7yVm5caeB1J1eXHkGUaVJcF8FNG7xCSvmjY1ZusXbOQZicubZtX7J2YwaMvToh5kSDKkWJPBMIQtDw/BpPx4plT1aTGXCxtmTfV20g5XeeCk9JGSXhYqOjsD2jLsxm2+oR4jU375f5KngXQ4Xy2e5Uk5kMrXDP4HQvrKdghbzUm05frK9DQI0cfKyRB9jHyKkOiy7MpQGc/FtU3JMW2tu/q6Sp+RFrIcVQ49kPKDBqQ+htoYRHYxMVy1cQ0GAGF5Y0PYah4MV3cwUddVBy+ebCZWcelnEb5uXPJmWwlBy2VWW29seBqkhdGZ1MNaZUhKOCu5sq2zq02UTDV7INW4YuQRJXaSRxsQXNE1Wk21ECYSa0Cy6YGUIoO0p0VAhpiVPhuTda+E2I0S6tkDKaNsjQKLLnHvK+X0lu17IJnpbenn4ffRMffDVmL3OH3luq71sMqwN5n0Tnjh8nU8kKTkyLL4kSRT1HMNLngo+sryRDUqjEf3dwdyJgqGTQHJOt/G+AlI2IxFeCyn01/aTFMSVz0C9yHnvjJLvlkVVYfoGKPogSQHDUh9DFvWaUAvRlUNA0sjvOKmnmO6gpuE7xXlOIkheUFPJe/cbAYkqfFo0sqNDaNJtQzV39tY5VcvnRzCJiZCbA4iG6tngNluaHn2E7UqfI+0cDX/M+WjwLbIdZLPwSMuubit+gDET9JseSeqMlR75vV+b8MLKmYl0pYMpuEyeSfRzjuELW7yG/bsy5o8XHqr7ricGCYe0zXJEaMz88xNpytDyQ3qrMC4RVJfGkzu5OoDUmUIkxRWFzUOUa8d0Fe15bU3Jlq/6p0rPa8I3LcEQ5dsyHnldWz/EGgb9j2xwjLIeIMpZel4QdWZB1LU/C5pbKi7wygxhwakPoa08jdNPGiyyhy26Dcqs/1Gw3J1ZGpscAJlVOeuqZTtGwcyuPxK5hlJCt2yNhhWjYpR3j+2PZAKSqJdtMeJiSEnz5U0nUSFOugm+QzI3phc99LKslEnk+qDuJdBbPhYb/kWjDdpibyL9IKyZWTXWdVVyaOOOBqTEV2qEi3HGmCC9zPrphtFeZzYCLdW5QCC91Y6QW6cDOrtCHogyU0B9HZI6j1W2CPNaGEwod+Kqu8rQhJtdWwpEtmQNn6z4IGk4yWqdnsSujPNE6tKhqK8oAQN3bXu6qvOOXVy14Z3+qX9KD9oQOpj9AhPTrKupOpM3MMdkWoAM829pCdTsIzYHEJKsm0Tt2M7Sq/yOj4JuPRANF6GgBwWjCdxMlh5Fr2XdhEcgKh1pohd4KLINYRNI1GhDmnhah6BJIkabudR2GgbcatfNjwTfRm8FfaYRN7Sk1RAuQ9FekGlJvIWLt8wH1EedSQ4Gcl2DY+w3ktbHffqlXnORFlPg7S+qoik6qoswQTWoiLEyiCZIDeq/KR6Id1PZ8qBlFBHosYh6r1tynEca0qkd7imDNKLYPobseRedKQMOiFsMpsUaRhvLHpBxSfyriiUvNtmrbv6ql6wOu07bmxEaocGpD6G3W0m9QfCekm0ndj3Om6VuqgTl+RklkpHlKFTs+L1oly7KnTK1uQoNQGihfuQ4gUVTu4qLUMgr0Vg0CFWvJEhJ7CCVGsIW0x4kClpyTM91ES7WZLrB/I5FBBSaMMz0cOvEz0hDxJLnhZA/K4utrw9gEq7C/cNtpJoOxqDcpU8PFHyDGEL37dUA0xkEu30cqQ9TioTirjyLRmQlMvHhY/Jh1VWXpcKkEHH00A+qXp2D6S0JNpem/H+O8i3TZqiu0ATea5yfySqRF2MIXXCx6RD+bS8oGQNvDqeWKrXTt59pc4ucEmoXrCVzY/SjdTh80nt0IDUx5DegtU8p4G+LOFD1BVUnQR3uqhb9SYmoVZclbOsVHnlSK6wJ62ahAfzUugmQCzUA8mrh7ZcoONWlQXLN9kpMLCClFMIW17J7VXSkrLruHxXlWMpTKQxxqBnM4F1XIiGTQ+kuMTFtrZMB+KNq7aehWkS7TxC2PLcubT6vlUfU3JdeEdFhbBpGc6En0dqCJs1r90Y75+CQtgC+spW/+jrjXj3uEo/JTMVybI5SlLfGemB5MboK9shbFH6VVMG8ciGlH7Cihe7xlhCeiyn01/afBZa9yHvJNoxXtO6BFOUpLdvJtGWgwakPob0AERnm8mAPL2DA52OznGCHkFxr/PKtdIQKi8uz0NwO0iDlSqvI7O1ahK3ui48EE3LtWFiRMxK2gTNpB5mliHmPtjatt0ohE05pPYk2t418zcghePT1XIaHCdU93TLqby2MSCtTqpuZ5IKVIyKYUOcLU8LtYxwv+1amqyrMsQNFm14gnkl2EpynzW8M02eqPdAtOdKwNPG4HdLVcskY7cbYQCTIlZXWFpsqJIhLoRNcsEjxRsMsFcfovRMHEmJ96PGIWpfm2cqBlOi9WuGRRfxhen48q15IGmFsAnLEPNo3Ih+VkyGuPvgyt2HmkPYIuZ3SZeKmzeR2qEBqY8hPTkyT4pZ/q87QFdljntdawN3FTf5pKTgqgEmzfU9ChteL0nPw1aS2rRcG/YTIEbIYMGIFXcfbCVN9g05GorXzXGSUKvC94h+bvET1TQPwvhyLD2PuBA2m94/qeFjBRpvLBm4k2Uo/y8ykXcUeejxuBClLFTvoBdtgPHw7mdWzyuxpMkJIUu2DP1AvNeuelut6m03WgYrHkiJSbRl60OWHUSTEu9HjUPUcWChHkg1JNHOc7wQRTChe/xCkr2xdPQxNlOEpG1UED4+L3Tug+QYqtZdfUsR87skQyk9kOSgAamPUQ9J3gLyGBoxVGt2nAGp5hA21UKdcF01gWOm3TosTBSTVrSKcMdPDGETFCOtXtoOo4vzQLKRg0mnjgZdsWsst8aYdY+khNmB45REu1lySthKYu2HTcWFj9k03sQYAOwk0fa8oGLug0UZqvvIkj0ZDAyteYQI6ExGdNHzQKou21Rvq7pZAm/TjLR8MNLGmzi9bTUHUlwYnSUZGhKehS+LcH3wZJAIYfN+lzoOM92EJk+i2qyuDOKpMXRD2CzkLATi66R0CJt6SZ0QNgkZdPL1BcdQORuQaliQdF030gMpMYQtZt5EaocGpD6GdII1411VfGWrd/24xNl55nNQJ09xq7ThRLtJA8/YciwkiU1aZTb1/souQ/KzseFpkLbLUY+FZxG3o5+1kCkDQ06eq+06iQp1iMrHkDRRTWq/SdjyNIg13rjB7yXxiqgKYVP6NnkZUu7DSidDej0NrHRn9UDKM4RNY5DdE5hU9MpgqLfVsHEJkla3bfXTqhxlWVQZ7Cw2VMkQa0CSL7+veSAljeuixiFRu0IBQI3q0pjo3Wn1zhVPjZGixytjyNyL1pYh/Ln4JkWFyVB5HesFJWjEqmVXX/WWlT2Qyq91+hj/PUPYcoMGpD6G9OpRY8zAJ1YewxUkdeUvzgPJJF49Taa4VdqqjijLQMPCCnuj0tsHdv5SLPFWE8QW5P3TmDJRkZ6YAKFnEeOBVETIVBR5rrZXvPNqukzkYCVyRz2lXtfqgWQjJ1a4PiaFQOQuQ2+djDMA2AzdCocm2GiTHnEepEXkgjLRm+p5xuXlueii44GUEsKmo7elFxuSwm2ld5qKkiMsiy1dAYQnq1BeqzLITQF09JV0iKlOiEuYpD4jahxSq77Ki6j2FxUuFoV0WGPqRix+vyBXH3XC4VWDm3gC65hnI7kDGqCXpsS7PQ7yn2dWxgvm54b1po5XfvhRm6QpIcnQgNTHkB6AmCpA0zAFddUtLol2Xqup4W3A1euG3USz5HmxEUIW5/IaNoBJEpAhyk3aRgLElHppZRcPdQWrkJCA3pUbnVwjOYbV1bJipJLkuq4St6Kru6OMrSSxcSEaPYr80lQ8kKINJ9JeDkB8mIjN++DE9N82PBM94p5FFMGQkWzlZckPFkdcHY47ppJEW9Wv6SNz6VCVJKOF9O5GATm0kmiLihDyQCoprxXdIOrxka43pPspX2dm8iyvlilqHOL/BsUjwrTMPEja0TQN6TFMYDOMFJ0vhWkSbYndlXVksLnTdloIm2Q+rCztI6w3dbx+6YEkBw1IfQyrHZxJUsyak2gr18xpNTXsgRS/c1aNrs6CWi8QUhiziig9OUrLtWHD0yB9JzgbxryYVeWI0A7J8vU8kJTzanwuXrl551kpfxZxnDKpyJLnJbCaaqE+1IfXS/QgSXqSCiTcB5ueWE6wTA+bXlBxnlhR5GHkzLJDoY48QLJnIFCROW1zg+pyes+XyoGUsCuPzSTaseHOFmUIjqnU13YXPJKag7ThIG/P8qhxSFROFtMy8yCtzieeK2zYTPO8sZLD0jh8TECGgndAA8JzimQZJAy7tSTRDhs6tQxITKItBg1IfQzpVTTTvAp+HiFNWeJC2CRWU5NcisMD+EwrVRbCRIL3pWIVkE60p1IPCRAbU2QoWdjFI3AfFKWkvrZRF0wMu3nIVMuKUZxMSZ959zPcfvUTglbaiZVdhmK8f+wm0Y6WwUYIW+p9sChDoYm8DbxY81jxz1Nnhr37kgy7gOKBZCiDrRC2VA8q4foQp6/qQW9bC/HV0BtqXy8pQxbP8iiZop5rj3K84zj+IpLtiWqSYSb1XLVOSKTGSAth8+6hJS92neTREuGdemF0su2zUcOI1SM4fqhlV9/wIobJTo8etj0DV2RoQOpj2EywJhHCph6myp/mYWJCwKVYY+cs1VPJJD7WvteLUrawm2usDAleJLaMN1Gx/laeRYzyl94C17+2wcA0z35CJ1GhDrrbDKteI3HJaJPLqby2satLnIt0kTug2WgPHk7MKqBNGeK85IqQQWeAmksOpBwNSOH7pmuACYT1mngsC4084zzR1LLLx9nTmapHWmEyxC2eFeChqWIvqbr+Od6xUd4XUeMQN+RVV+s25VnRXaCJPFc8iXayHvc9XkTHT8V7IMVFFMTJIOIBpKE3XMF+uhYDa1gHORrztrAu080LRtKhAamPEch9Ixwfq+WKbzhAj/M6ynMw7J3e6Dghi79yTMjK709AjFydy/9tbN0OBBVLHrv46BJ3D8Ny2diBTC0vIIN1F2ilbEvPQkdZeuS52q6TqFCHyLqTECrjlWu6YmXLoNcYM1Hw+5+VzPsn/HiKSOQdt1NlkYm8o1DvVdb7Y2q80ZUn7npR/ZypF5SfcFg4541uCJ4UsYsNbvQxEsQlyy1ZkkGn3y4yqXqsTAn9VtQ4pFZ9lRdpieMTzxVuG2pzj5az/N+GRxwQn/Bf2ktRK4G1sAwmoXySO9FlCmEL6U0dnRue0zEHUn7QgNTHkHaBNg0ZMZ0kxCXObnAceO/yCmGr9mCIWYVryBbCJunm6REXM23TFT41/5AfsiMnQ1q9tOICrfMsJMP4DDwc1JCUWp+LSe6lJLRD2EIhYI7him6wbRiLqU18+Fh5dGrT+yc+gbVF403IQlhEKF+RnlhmOcpq7zOyhHfGEZeEPe6YqCTaJr9bzOOkTkLY4pL/F6W3iwij0/E0EE+qnsHbISnxflSbC3vhx3mmSlOLB5L0GMZR8tUUlQZBJ3m0+PxKuWRaGJ0DeSeBtPsgaUSr2QNJMwdS3NiI1A4NSH0M8WR3GZNo6/ZzcYmz1e9qbd8lZQAQF5IXDnPJsppb2a47u6xpxCUXtxWmU5YheZJgxfsn5flUXKDFRAjKEAgJUI+RX0HT8ZLL0wsnNwOSZghbONFu3G5nOuXYCdEIlW/BM9GXIebemOamq0mGuPvQ+14yLCFVBoueWI6B/soj8X6eCXt1Eo0GPVe8/1nHC1IGg3hjc3E6U5XBTt9ULYM6dshvcUGn/Njtyl230k8Je6SZtI+ksLqocUgpZHDKEjaXB7qbVEQhvXV8+bq9ZSWFrYvWx8prvRA2WeNN3KYgJWHdHZwTRR8j6oGUUxJtR5m3mSXRNi6WxEADUh9DOn49s0u6rgdSTNia+l5nO+AkKtbzhthV2nCi3SwJSe1sHR8tv61EmOHr10MCxCRXbdkk1pXuMjaflgUXbJPcZHnIZBKak0SaV4N/XI0hAUUnibXhmegR92yKSB5d5H1ITSZuwZhnUk/VFfesxpRgSEa+bTMpNxlQuZ+mXlC2kia7bvVEVXoDkig5gPBiQ/F625YnVlp7sKE3a/EsjzQgRdzTsJE6KZG7JFHFZdp4QtiYl+iBZKE+Agl1UtoDSaO/9J6FdF4woNpruPK53PPIkm/WI7xhjd/fI8EoWAq/pwdSXtCA1MeobK8os4oXSPJmkGtFP4m2E/lafV+zB5KymhGfRDtYrqOhXKrKsRAeEberR9gSL4m+909xBiQbLtCxWzNbGpD7LuAGucnU87JiknspiSi5k/NZld+brlhJJ6H0iPf+CcoviV8neqINAHZCt4JlVssgLkLsSqQNz8Q0GaLIo47kGcIW5cGWZICpJNE280CylTQ5Sp6AV6ZwnYzTmXl4nunLEDP2EV6EDF9bZ2Inn0Rbv30k9Z2RBqTQ8d4htpP1Ri286o9lK6+LeBY28mjqbNQj7aWol39IrvxqGaKPCXuB51q+QSqGMEHP+vR5QdTnzIGUHzQg9TGkJwamoVzGSbS9vCaontz5Lq41DoZdRRk5Sm4l9bpVHVGGcABbOT6iFG9w9U60+KDiTQhtsLYbXZILtCVvsPgd/cSKN/JAyjOsLs5AYEpUu9bZUc80B5KNiQlQkS8uSaOVHEgxHkg28pJ5xCUudi31j6oM8UYsG4Y0/Xqah1xZdihMkyfpsyjDrE5i2uA1yv+tTI7CK882k2jH6EybMqi3uAgZKrszRn9vQ28mJVWPIi2sLjKJdmjsUVQOpKj2p2vEsvIsEkLhbfTTpsaGolKESO6AViVDinegyD1ICGVMI7wwqrPpk27qBGIODUh9DOmdbQJhOiau+IYhbNHx5dkt00ky+a6zCckss4Sw2cqvEeUKXpQrfJQXiY1QlbTnY8MDSSckwEZCdZPQUqD2+un1CbUOiJNc11XCfZxpSEC9hLBZ2fkrps+0mfsn9j5YWFUOyxCuo/UQRhdFXnUkrx2fdBLwRrUrnYSsKtJtI0lP2ExgHbsDWlF6u8AQtrRkwZJymI4p0/RmVP2qlxA2Xf0ahZVnEWPkV8uXXvDxrq5lQJL2QIoNYZMdy5rsBNcgYEHyrpmlfYT7cB0v3FqSy5NkaEDqY/hJKMUUrlKWVlJM7zxdA5L3P8KAlKBgTKhyKY4ywCRYsnX7Fn811VI+hST5bZQPxHmRyBtvnJTnY+NZxK4qWwon9EOmDJLVls/LyQOp1kmq6sEQ8Zl/nNfHhdqvjodD+JpFhGjY8kwE4o0WNkICfBnS7kOBMtjwTPRlMNBfedURU++8WHl6T1el0fHg0UlMG3UNqccR8LpJ8KCSbptxIfGFJdFWboW1/jHF08CGHKYbs6R5r0aNQ8JedbUkCa4FXf0aea5NA1KU17Etb/6U+iCdIiQ4hkyWQeo56KTrkOyna/Foj9uFrfxdzDneM425DskODUh9DK/eSyUF1bGQq7iGnV1U8s1w2bUn6+0tK+SBFBhEhVYCTX+3KqethJxx8tsyYIVlqHwmP0lLTeRtQQadAbkNLyxTD6RaZdJJVKiDquCbmnq9miLdi8v/w14O9TQYBtTnEfzcD4Eo0OvFVt8UlCH4uS0DOxDfNmyE13oYhZjmdG/y9kDy2iUQbxAEKvdTZzVbRdqglySPjZ2m0uSwFV4bvr4bI4Osh2Z8H1+WQ15vmo4p0/RmkgdSeLypu+CRF6rsvn7N4HklNbdI2owjrPOlSKsP0npTy/tHeCzb6GjIIOi5a6Inw6jiVkWOpPyWJN1GskEDUh9D2gXceFeVrCFsCfHleQ2GnfCKUMxKoOPouULGlWMrB1JwFzmLK5kpYQqVOiAoQ5oByX8WdmQoIiTAxB0/ONmrsdwMxtUoAgPcxvgBbim0C4lXr7R3lLGUk8qJ6a+s7oAWI4NVL6hYD6SSfRnq4FmYtM+avQNjErmb4g+yGyudeFxYZLncstxp+fHCiI9fEnSVjZ2m0uSoB71tz0OzumwVOyFs1WUlkZbkPDmJtldmbz9g2wOpFKFf62jRJelZSEdWVGRI7qOl9WbAY7MwGdLnOpIy1OKhFw5hi9vYJuocVbfRAykfaEDqY7ghZZU3psmkTV0dK0ad+LJr3e0pnAAuSnGFVwJVebQTD/qGquyy6hCVCDJsiZem8ASIyqWLi6GPlkG9JbK7wFV7osWRZ1idTqJCHYID3KT6FCzXdMDhWhgMq9cuuW5whd9m4uYY45rXv0nv0FiWIc6I1fu91WTiwc+L2AlOp5rmJVde4TLe+V67jLpmsJ/r/W9oXBbfBCQh3LpkUWcG+0wor+30TWEZ4kKuRRd9UhYEbehNU2+HNONa1BggbPyoxcOiFqL1q+a5iqhSOiNuwaVcvp1+Om2jHmlDltZOcMJ6M1CHU0LYJO5DLe0jOfVI9PXcSN1mXDSJgAakPoZNDyStAaGhPOHEuFHf5bWaGi4rKZllFg8kb4XJVjLMpCTg0hSeADHl+fjPvIBdPFR5JOfKjSkDchW1ruQVwgbUlhg0coU00aMt24DcVttQ76vaLCTdv8NUJmnBmYLN5NFx9dJmGF1c4lqbeZhMkufmVUfiEpiborNKG9WudBLTqkg/jyQPpKLCvmM9kCzKEKevbBnYowh4hAmH62TRHVFtM+q59oTGHnl50psSFRKo/7srz0I6fKuojViAdA+k8LPMvXwD7x/psE61rDCSz6OWeV64jutsfhSl22wnuF9RoQGpj2FzAKbl6WC4oui7vkfIH7cltSlV24BHdFhZLNlhbCVo9UQrKhknkBwqYSNcJun5uK4L7xNJF+i0Fd0GxxFOoq2vePP0wtFJVKiD+tyaE1zsq0ICcl5FzotYjzRLnolA/CStkptOXgYnJrl7ZTVXXgbfy7TARN4m+iscZp2VvD2QmhMMSHE7VJkkKg57F+ZNME9dgvyWPB2AJL0tLUP0vbCXRLt4DyTTMWWa3owah4TzgJpsdpEnattKyjcUhY38YFGe9JXy5ceQqgzx+YeCx+VevmJwj/WCEvT+AZL7yMrnvccKPI/Kxg/m54ZDTOPG5IFzej9O0m0kGzQg9TGkXcBNt7M3HaBHJd8Ml117Eu2gTGkeSFXJ2AwnqtJKrzEiGaWNBJRBGRI8kFx5OZJDE+wMiOMS9pkmks+KyWQxz3AN00S5cQQ8kPwk2tXHhQ2zjYaTZHtJYmMMioV43gQ/r4Txyqv4uL6hiETecX2DpGeih4m3Q173Jq+Evd59S0o0qt5b9X6aJCq2uroeEseqB1LMgodNvR03pvGeo9ROU+Hy45qDjVDjNBnCpBm1osYhOuNNG6jJl+tx0SXRi93WGCrl2dgYy6UZ3KX1ps6CoOTiuL/YU2MIW9XmRwZJtGtJxUAq0IDUx5BeUVV1h4kBSbezS06iXf6flzt+OIdKwJU8tBKos7VlbDnig9Hy/7g8BlY8HZLi131PAzuT9aTQBMlBeVwdsZFEvHx9/UFhnsladRIV6qC2OS8ePTkpu9NbfvbBsKhHWFySWoteL2khbBZEiA0LKCKRd0/IsOu9lU7OCpi1z9ySaBsk7k6i4uYfrwfjQp8yJQ8Xy+8RL39ATwjXScdxoj2HS3b6JiDY9qPGPtLtMinnTVgmKd1p2j7SwuoiQ9hC97OoEDZ1HGKayLvoZ2FvLK1XJyXFqIQdR1tvpGXQCaOTlCEplDGN8CKASUigqtsYwpYPNCD1MaRdwAMDHwNPB93BkB+WEmlAil5FNqXKpdi/rnJMuCMyDN0DKtZ7a263ca7wNsIzErxAbCRATHo+trZGjl1VtjQgN3GNz3OiliW8Mwq1zXnuxFFtPXw/K55XeuXY8jQIrEYXlEQ7bpJm1XiT4v1jU4ZCw3yNPATzuTd5hct4ty2QAyncz8YYZtMS00ZdQyqsMWmXI9s6M6o+2JQhLmGvLQN3mqeBFa8XwzFlqgdSxDjEHwdmXPDICzUMLClcLAqbzyLKbiI9r9GRofy5fNtIG89Ih/PpbIoi6RFmWjdVklOPJJ8T1G00IOUBDUh9DBsJWk1ccDN7ICWs7uQ1GK5KwpswkMsSwmYtdCnC5TXsyilNJfFd9XeVOiDbncQl3yskhE25D7aSJpusbOZZN7O0jSjUU5O2GQ4PyE2TLtpqG3HhMmH5JVFDNNTBoK2+CYgfENu8D5F9pKVQxmoZdPRm8JzsZZpt1x0rT9QgO6GfVe9nFsOZWKLeBGN3wDhgVWeq447q76XLL8tQ+Tw8PpIuPy20RD02b0zHlDphdeHnGjYGNxoueOSF2rYkfnet+IaDCJls5RNNrZMWFj3SDCjS4Xw6KQlsGJCy6Kyq3bMjQkqrztHQbSQbTUULQOJZtrwbH06dj56eSmWf37EcgI3QDBez5i/D5GkLEo/t7k2+odvP6BiQFizu9Mvt19SAtUa2J/7eru4efDazw0+k3NnVo/yOynXnLVruX3fa7MWVckOW7M9mLkJXOKlIBMs6u2N/S554sgXkn7W46ntRGXp/49yF1XWi4u0gK4Pj18ulARmWLe9W5JQrX73PX8xd4sswZ+EyRT7B8ntvcFdPCe99MhcLFy0N9A0qs+Yv7T0nh3KV3/XxjIWYu6jcB606bADa+zennr9kWTemz1mMmfOW+J819roTL1rSVVWf/HbleyCVP1+o9AtJzJhTKceWV9yUaQvQ1nsvlobkl0Ttez74fIE/OF/eVar6XlqGru5S4Pn4usGiEWt5Z7cvQ1e3/C5PARl6y1iyrDu1ns5fXG5DNXsg9Z4+X9ENbf2bMWTIgMTzlnf14PMvO/z3Pb3Pqllx8//0i0VYovStX6jtSjUg9b6esyB9vNDp1UsLSbQ//WIRlnX2+O9nzLarMz1PFHUcNXOu2jfZ0RdASF8tWGq1/O4eN7JeqPVJSnd6+m95V4+W7pjde2/Uc8OExyHe+MM73vvdi5ZU9FVzYwPWGtWufc87lnbhC6Wu6DBnQWUc4pUzvyOoMxsbHQxasBwNbglD21sU3az+btlnsSBCjy8PjdmlcCLmAirzesc2sh5I5f+zY/rLBYs7RWVQb/HMuUsix5KLl3b1yipgQPJCGUP9wloj2tHSr9F/X3JdfD6zIzAXmza7I3Ad9RZ9OnMRlnVV+nsP73epuu2TXt2w9qh28YXvFRkakOqU7p4STrnm/zBL6dhVJOu818n+47Vp+Mdr07TOMU2iHXW49907n8zDO59M8j/fa5s18d97bxh5Pdd1cdkvX8FUxaBSkcm7bvn/65Nn4/XJsyPLVV3q737qPZ2fopxvdLgx3r19Y/JsvBEhv5X8Hr1F/Pudmfj3OzMjj5GWo6EBQA/w99em4e8x9VJ25ajy+o///hR//Penoe/tGBI7u0r435v+aXROLajP9eZH3/Jf92tuwNUn74RBbf1iz126vBvn/vxfWLxMMfIpA9zJ0xbgil9Pijw3bGx+//P5scfGym5hJREAbnjozYjvxYqulKH8vMt/9UrE9/b6huVdPZHPx2b42PyOzkgZ7OSJK/+fMWeJdj2ttY54dfC1D2fjtQ8ruuGIvTbEATusHXlOT6mEH935ImbNX1b1XaOySvuLJ96JL9dRX5ffPP/WF3j+rS+M5M4btb3f+eS71ssPlNEry7NvTMezb0yv+l66Tqrt7ql/f4qnwvrKUvndPaXU9iC56xVQXqgw1R1x/VbcOCQ8tv1walC37br5ajhu/01Sy124pBPn3vqCb1QxRU3J8OaUOXhzypzI475/yBj8+k/vo6PXWOCfLxw69e6n8/BurM4XKVqRofz/1Q9m4dUPZiUcJz9ueO4/M/Dcf2ZYl8FLU+K6wJ9e+hx/eulzqzL4YaVAoH0MG9SCq07a0fcU+tXT70f2m/51Qh5ISf09ENJtfyjrtrGjV8GZ/7WF8W8gZWh6q2OSJj8brDlErNwNDa/tAFhv9cF6116rfO0o+TdYK/oaU6bHrxx1dpUijUcOgPXXGNx73eqyPIYPbsWQgf2w+iptaGvNZk9df029356VjRLkX2VQC4YNbBEtH0i+h0BZKejWgayk1cvGBgdfWW2QWPlNjQ1Yd9WBsd9vIFwP1l9jMEzVeR79xDqjBqJfU7WqKLe9jogzKsyctyRgPCrLNDi1PgHABl77zfgb1hzRjlZlRStvvrLqwEBixjCSfbTH6NUHJ4Z/eH2gJEn10nGA9daQa5MeSW2vscHBaMF+wZdBo05XnVNjHYnrE9//dF7sOQsXd0UajxwHGL/l6ujfktxm1hjRhtaWiq40/d0OgPWE6uWaI9owoCVZj6+2ygAMyKjrTdgwoU6uv+ZgcY9Vx0kem0j3T+utMVjLSNbW2oTVV2kTkSHr2KylXyPWGtke+V1cm/PuZ1x7mDJ9oVbZ077syGw8KssxOHYsrfLCW19UGY8kxzAbavQT0vpKRwZA+D5otru6kEGjHpkyeo1BkYapuQuX+xE2QPK8b+SQ/hjU1ow1hren9vdARbeFx4MfJZRB0nHcFXg/u1mzFhUtQmaamhowoL0V/3l/ZlWYSr+mBqwxok1sAFIqufj8yw7tTPVD2vth2KBW7evPW7QcQ9r7Vcnvui6mz17sh1889txHeOujuVh31YH40bHbRV5rWWc3TrnuWQDAgTutgy3XHwEAGNzWD6sMbvWvO3XW4kBIg8eaI9rQr7ncqSxd3h0If9GhfUAzRg7pb3SOKY2NDhYuL2H2nI6qurDGiDa0NMtNkj1KroupX3agOyZkatigFgxplzVk9ZRK+PzLjtgEiMOHtGLQgHhvmDzo7inLEO41GxscIxf1rMxduAyLlnZh0KD+WLgwPoTNl2lkey6rux1Lu/BlrzfkrPlLcdvjbwMAzj5yS2y27rDY8z6esRCX/bLsGXPMfhthnVUHYq2RZbfh6bMXB0JMVNR25boups9ZguUxx0bhOMBaI9sDce8SLFrSGTkZb+/fhJFDk8OI8qJjWRe6XKeqPgxqa8bwwbJ9k8fchcswv6Oz6vOhA1sw1IKBGwBmz1+KhUu6qj5fZXArBid4yeXJzHlLsHhpd/qByEePh3XbA//3IT6cugBbbDAcZx+xJbojdN68Rctx9s3PAwAOGz8am6xTbr/es1qyrDs2fMZxyobZZsWg7IUa6I4XVN0sgan8UsSNo2z1TeXCgPlLu7FgQbBvyFM3JDFv0XI/LCiO1VYZgP4ak8CszF6wFAsXV/cLSaw6rD8GtEaHZ0eNQ9T+3nVdzJizxNdtf/z3p3j1g1lYbZUBuPx7O6SW/c4nc/HT+18HAJx40KYYmRKOquKNQxyU0xx0htp/Y6ODq3/7GpYu78aW6w/3PfKP3m8jrDNqoGi9DI/vwwwa0IzhwmPppLmAR1Ojk5o2oxZ05lctzQ1YfbjcHM+rw47jxI4l+7c0YjUhw67aL3zw+Xz87u+TAQBXn7yjXwcuuuNFTJu9GFttMBwH7Lhu4Hx13pbU33tUdFsXvpi7FM++MQ3PvjEDA1qaMPHM3XL+deY0NTVg6NA2zJu3OFJn6zJiRPwCtwQMYatjWpobsd4ag2uqUFloaHCwToKnRa3ETSgcx8EaIyqrPgN7c4okDUvVifyIwf0xevXqlWbHcWJXk1T6tzRFnl80juNg3dUGYXBro/W64NHgOFh7lN3OKUxjQwPWXbXY59PU2CDq5ZTGsEGtGDlsQK+y6WetPrT3b/bzHbU0KwNMg+WHNYa3B57f6sP1BieO42ANzWNtM3BAPwwUNlqmMaS9xXp9CDNsUKvRIoIEw4f0F5+ApDFq6ABgqL3ywrqtrXfSm7QsqK4Zjho6oErnDWg104MNjux4wRRT+aWQHkfp0NTYgA3WGop57cX0DTYNyHEMH9w/V0N62jjEcZyAbhs0ID1PoIradNca0R4YE5uwZsSYt6mpwfeaVZMYr75Km/i4Jjy+LwLduYAk9dAveHW4Yriw2z+o/cKCxRUDs1r3vdcDB/RL7M9N+vsBrc0YvXoz/vPRnKryiDkMYSN1TK/1XbeVW8hzQchKj7oNbErjDExk2T4JIYSsTHg5X3THsQGdKaE0q+WxkSOOkCgcqOPJCt5Ch0TdjC6RmEIDEqlbHN9+FN/IA0qQM1RCxAm0shT9q7Zdtk5CZPF1ZqIHUvXxhBAZvCambz+S1ZmVPkIthx0BKQi16kUoLpGaqaEnSTo0IJG6xe84Ehu5ogSpAwkRR21nqfqXHkiEWCdx0YWNkhB7+BYkzdmqsIE3yoDEboAURcB+pL723gg0AlOjLomGBiRSv2hEsLEDIKQ40sbEwakqR6mESOLohMvQA4kQa5h7IMni6eES7UekDnBiLEi+/UiyUE4ga4IGJFK3OBGx2mHojk+IXZw4jR8F2ych1tBpYvQ/IsQejs5KqEJwTCuXAIYeSKQ+iMlI5NVPwRxIaTk8STI0IJH6RS+GTTmcWpAQaVJC1gNQQRNikajJYYjAN1SZhMiikcsziHAOJK8U5g8ldUAgJYJSKSU9kBxTt0ASCQ1IpG7RaeNcRSHEMgY5kOghSIg9tNKtMHkuIdYwToEk7CIYmUSb3QApiNiq5zsgyVVO2o9qgwYkUv8khbDZk4IQArNd2Agh9QU9kAipX2ypVKpuUm+oxlMb3uvcha02aEAidYvpqgjHwoTUL/R2IEQYuhIQUl/U0CRlWnNvblGRaxNiSFolF/HCo57MAxqQSB2joeiYEZQQuyjKN22VKCkXCyEkXyrhMnpuu1SZhMiisxlMgMCYVmALc4awkTpCXVhUq75gDu24tN3EEBqQSN0SpejCcJtwQuySNYSNg1RCZNHZnVh4fkoIUai0MT1l6TKJNlmZSNuVRbARcH2zNmhAInWLVr/BVRRCrBLQ9ynHUkETUgBJDkjSWXoJIVVoq0JpD0HH84iicibFEzeerHggCXjhUe/lAg1IpH5x0l1/qQIJsUxg29XkQ4PeDlTahEjitTHdBKRskoTI4hh6O0gnuffkKXGHVFIHxHu0l99I1k3aUGuDBiRSt/iutgnHBLcJpxYkRBrHxAfJlXXHJ4RU0Nky3JX2cCCE+Jh6O0iHlmnlSSPEFoGcmoh8LVWkjZ3eVmRoQCL1i85oOOJwQogcTkYPJDZQQoQxzIHENkmIMBq5PIMIp2WI8Ozn4ispimAKJFd53fu9hBeeX0j+116ZoAGJ1C16HkjsAQipV5hthRB7VAbGei5IzAVBiCw641gV6SFtlAcSewFSFGkGIhEd5Yd6k1oo1ID06aef4oQTTsBWW22F3XffHXfccYf/3eeff45jjz0WW265Jfbff38899xzBUpKisDRWU5Vj6cWJEQcJ8blOBJu+USIRdIHxvRAIsQiphYk9VQBnRmZk4n9AKkD1DrpVrJo5w6rez4UZkAqlUo48cQTMXToUDz66KO45JJLcOutt+IPf/gDXNfFqaeeiuHDh+Phhx/GwQcfjNNOOw3Tp08vSlxSBBpxqi5Hw4QURpoHoPSWxISQCloJe5kDiRBrOBpGXRVprwhPnhI9kEgdEGck9WqnzE6ESjmMYslMU1EFz549G5tssgkuvvhitLe3Y91118WOO+6ISZMmYfjw4fj8889x//33Y8CAAVhvvfXwwgsv4OGHH8bpp59elMikIJJ3YWPjJ8QmRouibJ6E2Edz51JOHAmxhOZElRNasrISmM9ZagYuqAezUpgH0siRI/Gzn/0M7e3tcF0XkyZNwssvv4xx48bhjTfewKabbooBAwb4x2+zzTZ4/fXXixKXFIBpo2aEDCH1C9snIbKwiRFSX9Si92SSaJf/uQwvJ30BhrDVLYV5IKnsueeemD59OvbYYw/su+++uOKKKzBy5MjAMaussgq++OILo+s2NDhoaOibVaWxsSHwf2WkobHy7Jqaou+Den8aG53Y4/oyrAtEpej6oLaxhobkNhduwyti+yyaousDqR+88Y4LN7Y+NCptspFtcoWGfUPxVNpk/Dg26nggf53Z2NgQmZKJunnlpB76h+B4sroeNkZ8Vivq721qbCjcTlAPzyELdWFAuvHGGzF79mxcfPHFuPLKK7F06VL069cvcEy/fv3Q2dlpdN1hw9r6/PaUgwb1L1qEwmhtbQZQVqhDh7ZFHrO8VHk9sL019rgVgZW5LpBqCqsPTY3+ywEDWhLbXFtbh/968OABK3T7LBr2D6SlpawzXTe+Psxb0u2/HjSwP9vkSgD7huLo3788l3Gc+HGsSltbi/96yJABGDqwNVd5vClRgzI3GjyI/cDKTJH9w6D5y/3XAwdW5nDe3L1//+bc62bbgIp9YciQAXVjuOlr/XRdGJDGjh0LAFi+fDnOOeccHHbYYVi6dGngmM7OTrS2mnWkc+cuLtyymJXGxgYMGtQfCxcuRU9PKf2EFZDly8sD3Z4eF/PmLY48ZsGCJf7rxYuXxx7Xl2FdICpF14eFiyuG/I6UNtfRsaxy3sKlGNDUN/vjeqbo+kDqh87OinEorj4sWFjRmR0dy1ZInUnKsG8onmXLugAApVL8OFalo6MyoV64YCnQ3ZObLOWJclkHdyv1YdGipZjXrz4m0cQe9dA/LOqozPUXLlyKefPKxp2eUlmeZcu6ctdRS5ZWxrBz5y1GU8EGpLyeg20jcKFJtF9//XXstdde/mfrr78+urq6MGLECHz00UdVx4fD2tIolVyUSn07IV1PTwnd3Sun4nd7n50LN/YeqJ/3lOKPWxFYmesCqaao+qAOPNNkCLRP1l9ReH+Jl4DXdd3Y+tDTXRkTsc6sHPA5F4c/jnX1xqfqBLJb4Ll5jkfq3KinZ8UeO5NkiuwfSj2VeqjWdy9Hl267McFV6n53d6luNnvpa/10YWa3qVOn4rTTTsPMmTP9z9566y0MGzYM22yzDd5++20sW1ZZvZ40aRK22GKLIkQlBaGzJTF3lCHELoF2lqJ42T4JsYevMxOOUXe66esh/oTUO6ZNTFpnVvoIpR8QKIcQLdTKF6m4JLJoV67JTQ+zU5gBaezYsdhss81w/vnnY/LkyXjmmWdwzTXX4OSTT8a4ceOw2mqrYcKECfjwww9x++23480338Thhx9elLikABydjkNp/BwLEyKPOul0jSxIbKCEyNKbsDdp0YUDZkKso93uAmNauW3YXK7ukDpAneepVdLzphXciDCiVGJCYQakxsZG3HLLLejfvz+OOOIIXHDBBTjqqKNw9NFH+9/NmjULhx56KB5//HHcfPPNWH311YsSlxSB74EU38CD31ALEmKTtEExVzkJsUdlvqk3KKZNlxBZPCOQvv1IdkLrh7C51M2keJwUDyRBG2q5SNqPMlNoEu1Ro0Zh4sSJkd+ts846uPfeey1LROqJqO1Gw6jGJQ6GCZHHqJ2pq6m5S0IIUfF1pqYHkpaXLyEkM9H+FfG4wl71kX0EB8+kDlCNp5KGndSoOaIF0+6T+kXHghRxOCFEjoDyNYhgYwMlRBgnIjwlhEurLiH20MjlmXJqrvgeUfRAInWAE+MN5Prf5187A9ekBSkzNCCRuifZA8maGIQQAME11eQGyPZJSBHo7TzBiSMhdtAOYbOkM6mbSX1jp4JKh4yuyNCAROoWY/d6joYJqVsYLkOILGxhhNQXtbVJOy2a/QYpirRxIetm/UIDEqlbHI2EDsGNJNjVECJNWtLDIMxRRog1NMJlGFZKiEU8xafp6OAK60xfHJeuiKS+UOuk/1IwD1igHGIMDUik7kls31SChBRGmu6lcibEHlppA5lEmxBrVNqktgVJFK/Nl9gPkDogzkgqaD/iXDEnaEAidYtjuJrKPoEQebKuikokQySEVPAngppJtNkkCZHF0bLqVhDfHC3CA4n9AKkHAnO9Shbt3MtRDaZc5MwODUikjtHoOOiARIhVgso3JYm2tDCEkAq+/Sgh7JuNkhDrZGl2Ep5BOguzhNhCXVgM2o/K72R2IlTfsSFkhQYkUrdUUiDp5UDiMgohFjDYAZWrnITYQyNtYPB4tklCRPEmyLptUjo3UdS4mt7BpCiCthyl7ve+lK6aNB9lhwYkUrfodBwBJSgoCyGkTNbFG7ZPQoTRyNcb1JlslYRIEu1fEY94WganOgcSIYURsyApWT3VuSU98bJDAxKpe7iaSkj94Bh5IMWcSAjJnUoOJI6KCakLTJuk6oBkzQMp/3II0SFuU1/X90CSzYFEskMDEqlbfNffhGM4TibENvrLN4GEvVLiEEIA6O0Y7gpPUAkhFUwnq8G2aycHErsBUhixK5JyOZCCQ1hOIrNCAxKpW0xdfxnHTYg8Jh5I3CaREPvo7lxKCLGDvgeSrGeQZ9ByaUkmdUDQA6lSJ/3qKeiFVy6TZIUGJFL3JA+G2fwJKYq0QTFbJyEFkKg0mTyXENvojlVt6UzqZlJ3FFEp2RAyQwMSqV84tiWk7sg652RzJkQW07bJNkmILHVno42Qp95EJCsPae1DJF+RiRc9iYUGJFK3+Mn+Eo6hFy4hdUygfbKBEiKJH56ScAwHzITYw9d6mg1PekwbeUmqZlIHRCXRloBJtPOBBiRSv+hkBFUPZ6dAiDhqO0tLQMgQU0IsorHjU+ArqkxCZNHYDCb2VJEk2tXXZDdAikKtj6re8saOIkbUQBIkjlGzQgMSqVsqHkjxDTy4TbioOIQQwMj9lx6ChNhDa+MJ7r5EiDVM25hbwPZo9A4mRRGseW7VS+maSfNRdmhAInWLoxHDxm3CCbFLoJ0ZaF+2T0KE0fJAolWXEGsYbhkuvSbKJk/qikD7UF7738t64dEBKTs0IJG6J7F90wOJEKsElG/KsS7jZQixhk4OJHogEWIP4y3DhfMGRoawsSMgBZHWPkSMqALXXBmhAYnULZ6i083nwBxIhFjGIAcSB6mEyKLntRtxPCFEhIDBRsOCVIRDBLsBUhgR7cNmGKeOVyCJhgYkUrcY53OgFiTECjo7JOodQAjJG+28gYQQa2htKtHbQKWGs9HjZA6eSTEEPZDc3v/R3+dWJqt7LtCAROoX03wOhBA7aLRNgN4OhNjE0WqXqlcgGyUhkgQcLEw8kISaZpSnPrsBUhSROTWFwzgD7gmcQmaGBiRSt+iEpHGXJ0LskyVclCGmhEijEfbNHEiEWMN8FzbvPJnWyXEyqSsiIjylHQOCUXO0IGWFBiRS99BCTEh94Xs6pChfxpcTUgQJIWzqG04mCbEG1SEh8Xjtw2o7YZvMDA1IZIWBHg6E1DFsnoSIYtrE2CQJESajy49NTyF6JZGiSJu3SdRN450RSSQ0IJG6RcfLgR4OhBSISQ4kUUEIIVo5kKgyCbFGUO+lNz6b4Tv+Z9TOpCCcyCRIyvcihUpcdOWDBiRSt/htXFOfchWFEDtUjLspMEcZIdZJth+xURJiDcMk2l7zlGqakcYidgOkDogMYRNoCGob4HpKdmhAIvVLb8eROBhm6yekALxkvSk5kCLOIYTI4Oi4IDGJNiHWMA2XUfZIzF2WuMuyHyBFEb3LmnArCDRKTiKzQgMSqVt0Oo6goZpqkBAbaDc1RTmzeRIii9fEdCPY2CQJkcUJbvmUircoI+eBFPEZOwJSB/i7sAkrKeZAygcakEj9EnD9jWnmrvh6DSEkhD9RNciBRAgRRscBiRYkQgrBJL+RVNOMXmhlR0CKwYmY50mrKIcWpFygAYnULTptnFsSE1IAmjmQ1MkqVzkJkUUvs4O66MJGSYgkptEy/jFiFqSIj9gNkIKIrHqBcaOICSmqKGIIDUikbtFx/eViKiH2cXRcHeLOIYTI4OUN1PRA4sSREGEM21jFfiTTONnkSV2hKCE/ibbFnQi5k3d2aEAifYLYDkU4Wz8hJAJtDyRm7CXEFjo5kAgh9gjs+KSVRdvbhk1InohxMofOpCiCkSa9IWzCw0ZW93ygAYnULUErcfQxrnS2fkJIFX5bM8iBxPZJiCyGm7Bx4wlChAk2sXQLknQEWxT0DiaFkZarRNiCRAek7NCARPo2bPyEWMfR9EBi+ySkABJGxXTZJ6QYtFoemydZSYnchc1SmcQcGpBI3WJqeOZiKiG2MG9s9HYgpL5gkySkPpFqm9yEjdQTaVVPwjvOoQtSLtCAROqXiORqYdj0CSmONG8Gtk9C7OEZadnuCKkPdFIxqEgnZYiakNN+RIoierMkpRUwCVLdQgMSqVuCbTxa8wZ3lGGvQIgNtFtabwNlyyREHj+Jtu4ubKLSEEJMPShcYZ0ZNUzm2JnUA34SbeFy0tIuET1oQCL1i9bKDZNoE2IbnWS9gNI62TgJkUcju31gR1NOHAmRJeOW4WyaZGUgykPPFVZRhnntSQw0IJG6RcdKHNDHVLiEWCVN91Z2JGbjJEQar50lzlPpgUSINUy9HaRTsoQn5OwDSJEUkQMpkB4l/6uvNNCAROqW6NjYILQfEWKfStvUU79cTSXEHpr2IypNQqQxtCB5h0iFlVVdl30AKZTqXLeucJx1oEkyiXZmaEAifQJXy4JETUiITdJD2KicCbFFxa6blARJOV5UGkKI6kGhpw3t6kx6B5MiCfoJVOdAEsmhzSqfCzQgkbolaCWOPsZlDiRCrKOtgL0QNjZOQsTR8QsM6Ew2TEJEcXQGshGHSDXNKgckdgGkXnBD/wHxiR0dkLJDAxKpX3Q6Dq6mEmIdHUcHgCnuCbGKk54DiQNmQorBpOmJ7cJGXUzqiKhMJUH7Uf71lQsn+UADEqlbnIjY2DDM50BIAXgT1ZQhsb8lMdsmIeKYNjO2S0JkUSerOsZb/xgxF6TQW/YBpEAiDUSu6iUrWz7TLGSHBiTSR4hu5FxNJcQ+GruFE0IKJC45qHB+UkJIDXBCS1ZWPJ1lswVwDpkdGpBI3WJqeaZrLiGWMG6bhBBxjBsaWyYhkmRtYXIhbLZKIkSDAqofve7ygQYk0ieINxLbc3UkhJTRSdYLqO74gsIQQgDo7RjuUmcSUghaW4YL68xw/hf2AaRIonRWwEtWoILqpEch6dCAROqWQHK1uBxIbPyEFIdmEm16BxIijxOVkTQMdSYh1jCd/0qvuYSvS81MisRJWfUQqZ+s9LlAAxKpW0wnncysT4gdHM0k2pU9iYUFIoQEiGubtB8RYhNDbwdPZVpKok3dTIpFaR/ef2HPgKDNihoxKzQgkfol4IHEwTAh9UaanmcEGyH20PHajTueEJI/QafA9EYpPaENL8zSO5gUSdADqbrui+gonVhvkgoNSKRu0crnYHG7R0JIGe225q+miolCCOlFp5kFdCYnj4SIEmhhOimQhHVm1XXZBZA6ISoHkkQFdSK8nog5NCCR+sXQSkw9SIgd/CTamh5IbJ2EWMBJD5cRHpsTQlQ00pKlnJYvTuJbQqwS5TUrvdFDWt4logcNSKRu0bESBwbJdHMgxBJ6OZA8bwe2TELk0crtoO5wIyoNIcTU26EyppVpnVUOSOwESIFEesFa1FHMgZQdGpBI3ZIWGwuELNXC8hBCyvhtU1P3cpBKiAU0Vla55kKIRTTGsSremFYuhI0xbKSOiMh1K+0la5orkERDAxLpE8S28cBo2IIghBCfNN1L5UyIPbQ8810qTUJsYRwtY1lnsgcgRRLVPoIaSjYHEskODUikT6CVz4EQYgVdDyS2T0IKgh5IhNQXOkm05aUgpD6JzqItWyRXOTNDAxKpW6pdbVOOF5KDEBLEdAXHtC0TQsxhOyOkvsjaJG3twsYugxRJqs5i/axbaEAidYtWv6EmW6MmJMQOvU0tPQEhV3cIKYK4tukGdKYlYQhZaTHcMtz1zpJKos1GT+qT6BC2/GEOpHygAYn0CeLcDJlBn5Di0M2BxIkqIfIY5uslhAgTnKzqJ9GWsvNUeyBROZNi8Wug1z64yNEnoAGJ1C06HQdXUwmxT0XhGx5PCJFDS2eqO5eyZRIiiWkL81onWyZZafA92oP/A1/mWZxj6BVIIqEBidQxSiPXaOVUuIRYolcBa+/CRusuIeI4GjpTeotkQoiCabiMsM4MexxRNZOi8fSW74CkLnII1M/AJemqmxkakEjdEnD91cjnwNEwIXaocjmOwWu3bJmEyKOjMwPhAbLiELLS4xjmQJL2QKoKYRMqhxBdkoxEIvUzoCdJVmhAInVL0EocfYw6SOZKCiF2cEIux7HQH5+QYtDwQGL+E0KEMU1M1nuMtabJPoDUCd58LtBMhD2QaEDKDg1IpH7RsRKz9RNSGGnjYdqPCLGHo6UzqTQJsYXpZFW6dVaFsAmXR0gafpWszqEtk6dPS1GSNGhAInWLjutvcDVVVBxCSC9OWOPH4e/CxsZJiDTBHEhxO5d6xxJCpNHxpA8cIqwzq67KjoAUTiinZmB3JKnSeouiBSkzNCCR+sXQ9Zc7yhBiB998lGo/onImxBomO5dSXRIij/GOT8J5A5kDidQZfkqEXuUkHMEWdDbgEDUzNCCRPkGsBxLd8Qmxj2YOJDZPQoohre1xwYUQu+iMV6kzyUqPxTbA5pYdGpBI3WI8vOV4mBArmDY1RrARIo9JM2ObJESezM1MqH2GL8vwclI0STVQunbSYJsdGpBI3aLjZijt6kgIiUJ3G7bA0YQQSXRC2LjmSog1TO0z0hGmNBiRusMPYev9H/gu//rqMIYtF2hAInWMRuy4mmuNepEQq6RNRiurO2ychEijk0Sb42VCikHL20FYZ1Z7IIkUQ4g2nt7yqr6qu0RyIAlcc2WEBiRStwR2WkzZUab3DElxCCG96G/C5gaPJ4SIobM7se/hwDZJiDimHj/iOpNJtEm9kbAri0g7CMwtBa6/kkADEqlbtPoN1VJNTUiIFTTtR/R2IKQo4hyQuA0bIYWgFT7ae4i9EDb2A6RYwuNJaaMOA9jygQYkUr9oWInZ+AkpgFDMehz0diDEHo7BluFsk4TI4xh6O0hHfTOEjdQbSR7tIjm7dFx1SSo0IJG6xdHIgaQqZCpCQuzg6Pog+aupbJyESBNYWY0L+xb2cCCEVDDVfa60zmTDJ3VHKAeSsFUn6IFEC1JWaEAi9UtwNKxxODUjIVbQ3ITNlfbHJ4QYwbz2hFjEON+KrM4Mj5O58EqKppICye39r3wn4YCkvqH9KDM0IJG6RSdONbDKSkVIiBX8pqYbwiYoCyGkjFa4TO8XXHAhRB5TbwdpnRmekLMfIEWTZCQSqZ+MYMsFGpBI3aJjJVY/phokxA6OpgcSkyARYhONsO/qQwkhUphm7PUckGy1T/YDpE7wFj1c4YldID0KLUiZoQGJ9Ali2zgbPyEF0BuznqJ92TwJKYiUnSc4byTELgb2I0JWOmzlQIoulZhCAxKpXwyXYESy9RNCqjBtamyZhMhj0i6pLgmRJ3sIjkwDDY+T2Q2Qokmau0nUT9OdEUk0NCCRukVrRxk7ohBCFLSVupdvhaNUQsTRyhsYeTQhRALHMITNFdaZVZelciZ1gxv4B4Bqqo6hAYnULVp6zU8ISgixhpcDSTOJNiHEAhqTVZc6k5BCMAnNsZdEm5BicULjyaD9KP8aymiVfKABifQJUtI5UAsSUgBpw2Gv3VJhEyJPIDkozbeEFI5puIx/jCWVSdVMiqaqCqqbawvXT2rJ7NCAROoWnUmnP0GlBYkQazi6LkigtwMh1tCYrFaMuvLiELKyYzo2rdiP7ORAIqRwHG9TlvJb6cWPoFGXJqSs0IBE+gRpHQp1IiEW8exHKYfZXk0lZGVGK+pbXApCSBRak9XKqqgIVSFsHDyTgvFqoDfPc4U9kFjj84EGJFK36Lj+0k2fEPv4TVMzBxIVNiHyBHRm7FFekl62SkKkMW1mtnUmewFSOBULUsRXEhYkJdSbU8jM0IBE6hatjoPu+IRYJ0HfB2GSMkIsog6MY3Yu5YCZkELQanuyDkjVhmOqZlIw4fGkdFhZcK8JKsSs0IBE6hctD6SIgwkhsvgx68nK1/W9HcQlImSlx6SdsU0SIo9qsDGwH4k10PBV2Q2QovHbSJQHkkQF1ditlKRDAxKpW3SsxP6WxNSChFhDu7kJr6YSQioEdGZaEm1xaQghgXam4Vnhj2llxIm4MHsCUh9E5UCSgPajfKABifQN2MoJqRv8BSPNHEiEELvEtT2Xcd+EFAL1ISHxVHZhs1mozcJWLGhAIvWLiTu+nBSEkFphAyVEHupMQuqLjA1Nyr4bzi1KOzIpmqQ6KLHZQzCslBakrBRqQJo5cybOOOMMjBs3DrvuuiuuvPJKLF++HADw4x//GBtttFHg79577y1SXGIZVdHFrqZymydCrKOr1CvhMmyghEjjmCQOZJMkRBzTcBnx8B3m0CZ1RlUdVBqBdP3kphLZaSqqYNd1ccYZZ2DQoEH4zW9+gwULFuD8889HQ0MDzj33XEyZMgVnn302vvGNb/jntLe3FyUuKQBHQ/NWxsJUg4TYJn23DCZcIcQaqv0o5hDajwixiKPRKCNPs5NEmx0BKR5vU5byO7f6q3xLY53PhcI8kD766CO8/vrruPLKK7HBBhtg2223xRlnnIEnnngCADBlyhRsuummGDFihP/Xv3//osQlBRPrZsiMoIQUhqb5iM2TEAsEE/bGHMQVV0KsYbpluPQW5uHZMxdfV1y6u7tx55234ZvfPBh77LEjDj30ANx003VYsmQxAOCQQw7AI488knu5d955G0477UStY7u6uvDllH8BKLeP0047EY8//Cv/e+lN2Eh2CvNAGjFiBO644w4MHz488HlHRwc6Ojowc+ZMrLvuusUIR+oCh974hNQl2is4zNdLiDV0wmW8SayUhwMhpIKOJz0iDhHLgRS+LruBFZZbb70RL7/8Is499wKsscaamDZtKm644af4/PPPcfXV1+Puu+/F6qsPx9KlPYXJ+Ne//glT3/oz1tlja8AFrrjiGnw4rQMf/P798gEiDUFJj8IFlcwUZkAaNGgQdt11V/99qVTCvffeix122AFTpkyB4zj4+c9/jmeffRZDhgzBcccdFwhn06GhwUFDQ9/sHRsbGwL/V0aalN/e2Oigqan6XjT0di6Og8jvVwRYF4hKPdSHBkWpJ7a73sMcJ7r9ktqph/pA6oNGpY01NES3Oc9w5GDF1ZmkDPuG4lHbWEPMOFbFU60NAjqzsbGhyl4kUQ6pD5566glccMH/ww477AAAWGutNdHaegFOPvkEzJ8/B6NGjUJrays6O5fmWm5Dg6M95lPtQ06Dg2HDhmLAvJL/WVNTQ+71s7k5XU/apK/20zUbkDo7O9GvX7+aBbnmmmvwzjvv4KGHHsLbb78Nx3EwevRofOc738HLL7+Miy66CO3t7dh77721rzlsWFufX2UbNGjlDdubu7jLf93e3oqhQ9uqjmltbQZQVoJR369IrMx1gVRTZH1obm4EADQ1Nya2u37NZRXT3JR8HKkd9g9k0JzKRKCtrSWyzbW09OrMxga2yZUE9g3FsaS74uLQ1hY9jlVpairr1uYU3ZqZ0JSoqYn9wIpKQ0MD3nrrdRx00P5oaCgbJ3bbbUc8+eSTWGedNbDvvvvitNNOw6GHHoqjjjoKu+++O5577jlMmjQJG264Ia699lrccccdeOKJJzBixAj8+Mc/xrhx4/Diiy/i6KOPxvvvv++Xdd555wEArrrqKvTv3y9Qfx988EHceeedmDp1Ktra2rD//vvjwgsvxCuvvIIf//hiAMAHT/wQu256M84442SsNXozAJsAAP79/F/x8AO/xrRp07D++utjwoQJ2G677QAAe+65J0444QT8/ve/x7vvvovRo0fj8ssvx5gxYxLvS8uyytxywIB+dVP/+1o/ndmAdN999+EXv/gFvvjiC/zpT3/CHXfcgVGjRuGUU04xvtY111yDX/7yl7j++uux4YYbYoMNNsAee+yBIUOGAAA23nhjfPLJJ7jvvvuMDEhz5y7u0x5Igwb1x8KFS9HTU0o/YQVk0aJlgdfz5i2uOmbJ0k4AZbffqO9XBFgXiEo91Ifu7nK5XZ09ie1ueWd3+fie0grbPoumHuoDqQ86OhSd2bEM8+ZVD/GWLevVmSWXbXIFh31D8SxcWDHqdnREj2NVurrK4UTd3fnrzLIHUnBO1EPdvMLyX//1Ldx++63485//gp122gXbbbc9dthhR6yyymro6OhEqVTuExYuXIqurh5MnHgzLrjgRzj99LNw7rln47DDDsM3v3kk7rrr17j11om45JJLce+9D/hzM7XeLF/e7X+2dGknurrKY8NXX52Eyy77MS6++DJstNEmeO+9d3DxxRdi7NitsMsuu+HMM8/BzbfdgTV3Oh3NrYPR1dWD5Z1lA8+Cz1/BTX/9A374wwnYbLMxeOKJx/G9730PDzzwKEaOHImenhJuvPFGTJhwEb7yldG48srLcPHFl+IXv7g78b4s6x2XAsDixcsLr/959dO2DWGZDEh/+MMfcO211+KYY47BHXfcAQBYb7318NOf/hStra04/vjjta912WWX4b777sM111yDfffdF0DZxdozHnmMHj0a//73v43kLJVclEp9O8Cxp6fkT9ZWNtSG1N0dfR+85+sAK/x9WpnrAqmmyPrg5VEpuW6iDJX+N/k4UjvsH4iqM+PqQ48yJmJ9WTlg31AcOuNYFU9nukI6Myoog3VjxeToo0/AqquujkcffRC///0jePTRhzBgQBv+53/OxgEHHOTn/+npKcF1Xey00y4YP/6rAIBddhmPv/3tzzjuuBPhOA4OPPAQnH/+OejuLvl1Wq03XvL37u4SSiUXbu/YsF+/Vpx33oXYddc9AAAjR66KDTbYCFOmTMGuu+6B/v3b4DgNaGodCBcOXNdFr10L8z9+DvsfeBj22Wd/AMBJJ52GV1+dhN/97n6cfPJpAICvfe3r2Hnn8QCAI474Ni688NzU+tyjeAX2lOpnbNrX+ulMBqS77roLF1xwAb7xjW/grrvuAgAcffTRGDBgAH7xi19oG5AmTpyI+++/H9dddx32228///MbbrgBr732Gu655x7/s/feew+jR4/OIi7po6jhh7EJQfu2fZCQPom/ipnSACtJ7vumJyghfQlHZ8twJrYnxBqGObSt60x2Ays2++zzNeyzz9ewYMF8vPjiv/Hwww/gqqsuw3rrbVB17Oqrr+G/bmlpwaqrrubrlJaWFnR1dVWdk8bGG2+ClpYW3Hnnbfj44ymYMmUypk79HOPG7VB9sBt80dnxJTbccJPAIWPGjMWnn37sv19zzbX81wMGtKG7uxupBDZo4iQyK5kyNn388cfYdtttqz7ffvvtMWPGDK1rTJkyBbfccgu+973vYZtttsGsWbP8vz322AMvv/wy7rzzTnz22Wf47W9/i8cee8zIs4msYMQ0cjZ9QuzjTT5T2x+VMyGFkGI/IoTYRsuCxBZKamfy5A9x003X++8HDx6CffbZDxMn3o4RI0bi1VdfrjqnsbEx8D4uh3DU5z090Tu5vfjiCzjhhKMwZ85s7LDDTvjxj6/G2LFbRB4bsh/BaWyOKKcU8Oprbq4+xgS2tuxkMiANHz4cH3/8cdXnr732GkaOHKl1jb/97W/o6enBrbfeil122SXwt/nmm+OGG27A73//exx44IH49a9/jWuvvRZbbbVVFnFJH8VkZaSvJ0snZIWGzZOQuoIqkxALZGxoUu2zaqzMfmCFpKenBw888Bt88MF7gc+bm5vR2tqKIUOGZr62Z7RZsqSSO2j69GmRx/7hD4/igAMOwg9/eAEOPPAQrLPOupg2bar/fdLcrV/bCHzwwTuBz95++z9Ye+11MssOhKo8LUiZyRTCdsQRR+DSSy/FhAkTAAAfffQRnnvuOfzsZz/DMccco3WNE088ESeeeGLs93vttRf22muvLOKRFQUNb3y2fkLs4zXNtMXSijs+IUSaQARbWggbWyUh4gRD2NLHq9Ij2nCrZz+wYrLRRhtjp512wXnnnY2TTz4dY8dujjlz5uDpp59AZ2cndt99T9x11+2Zrv2Vr4xGS0sLfvnLu3DwwYfiH//4Gz744H2stdbaVccOGjQYb731BqZMmQzHcXDvvfdgzpzZ6Owsb+bQ2tqK7s4l6OyYhVLPKACVNjB09K546olHsNnGG2LTTcfgyScfx5QpH+LCCy/JJLeHTqQ3SSeTAel73/seFi1ahLPOOgvLly/HSSedhKamJhx55JE4+eST85aRrKToxI57g2SuphJiD22PP3+ySgiRxtHQmi4bJSHWMPV2cIWbZ5XqZj+wwnLppVfhl7+8E3fddTu+/PILtLb2x7hxO2DixF9gwIDsO3a1tbXj3HMvxG233YyHHrof48fvicMO+y/Mnz+v6tjjjz8JV1xxMU466Vi0tbVjxx13xiGHHI4PP3wfALDNNtuh/8AR+PTZ6zF79I8C5w5cfQvsv+1w3HHHzzF37hysv/6GuO66iVhnnXUzy15GZ6WFpJHJgAQAZ511Fr7//e9j8uTJcF0Xo0ePRnt7e56ykZUdAzMxdSAh9klLQOh/SwsvIfJojIs5XCbEIhm9HeTSMjgJ78iKRGtrK0466VScdNKpkd8/9tiTGDq0DfPmLcbEiUFvpBNOOCnwfuutt8Vzz73iv/eSc0ehnjt8+HBcd93EWBkHDRqMbQ86D9NnL8bw1VbHORNvx2sfzsJND/8HAHDgQYfjuKOPijz3oYf+kChjHByO5kOmHEjLli3DhAkT8Ktf/Qpjx47F5ptvjoMOOggXXXSR75ZGSK3ouP76g2T2CITUHZ6Bia2TEHm02hkdkAixhuoVqOPsIL0rFFMgkXqjar9tN+o7Gbigkp1MBqSrrroKr7zySiCp9YQJE/Diiy/i+uuvTziTEH0cjRg2z7BEJUiIPfxd2DS1L+27hFjAxAOJjZIQcRzTGLbI8/KjOoSN/QApmNB40o34LtfiGMGWC5kMSH/9619x9dVXY9y4cf5ne++9Ny6//HI8+eSTuQlHiEdsG2eWXkIKI033UjkTYo+At0PsqgsXXQgpAj0PJGkpGMJG6gt/Uxbvf8ADKf8aysTx+ZDJgLR48WIMGjSo6vNhw4ZhwYIFNQtFCBCMAU9bTWV3QIg9HEMXJLl8DoQQDz2v3YhjCSEimOq+SvuUaaBVIWzsB0jheONJ731FeYnUz4AHElc5s5LJgLTlllvijjvuQKlU8j9zXRe//OUvMXbs2NyEIys3wX4jeTTMCSoh9givGMVB5UxIMaTtXEoIsYtW0xP2EORImdQb/npkbwuR1lE6O3yTdDLtwnbmmWfimGOOwYsvvogxY8YAAN5++23Mnz8fd911V64CkpUYrXwObP6EWCe8YBQDvR0IsYdebofeCSobJSHiOIbeDpUcZSLiVF2X/QApGr8GRjQPifoZuCankJnJ5IG0+eab4/HHH8cBBxyAzs5OlEolHHjggXjqqaewxRZb5C0jIVxNJaSOSFL4hJB6IHnnUk4bCak/qFLJyoob+m+zTGJOJg8kAFhrrbVw9tln5ykLIQFMBrhcRCHEIoYNjs2TEHmMkoOyURIiTtZmJueAxIZP6owiqyS9EDKjbUCaMGECLrjgArS3t2PChAmJx1555ZU1C0YIDNwMqRIJsUclB1Jyw3QZw0ZIIXDjCULqAI3NYAII60wm0Sb1hmfU9NqHGuopVT8dlJsazUfZ0TYgTZ061U+aPXXqVDGBCPEIJjpLdsfncJgQezgVC1IiXrtl6yREHq3BNnUmIdbQGcci4hhbSbTZCxAJ7rzzNrz22iRMnHh7+sEJORG8r373u9/it7/9NRYvXow999wLZ575Q7S2tgIAZs36Ejfc8FNMmvQKWlpa8NWv7o0TTzwVLS0tyWW66Ubdww//Oo4//kTsv//X03/HSoa2AenXv/61//p//ud/sPnmm6Nfv34iQhEChBRbShJtrqIQYp/U4TDdHQgphLiEvdx4ghCLmG755O8sLCFMxIU5eCYFE16PDKgux8E//vE33HXX7bjoosswbNgwXH75Jbjllhtw1lnnwnVdXHjhuRg4cCBuvvkXWLRoIa688lI0NDTi1FP/J6FMh7qwRjIl0T799NPx4Ycf5i0LIUF0ItjY/gkpjLTVm4r9iINUQqRRd5dJ05mcNxIiT0b7kRhV9iPh8ghJI+zRrhp2HAAPPng/vvnNb2HnnXfFJptshv/93/Px5JOPY9myZfjss0/x9tv/wfnn/z+MHr0etthiK5xwwkn4y1+e1iuTZCaTAWnYsGFYtGhR3rIQEkCddKbmc2BnQIg1HO0YNnoIEmILPa/diGMJISKYbhnu75IolQMp9QPSF5g58wuce+6Z+OpXd8bhh38dd911O3p6enDZZRfhv//7MHR3dwMAnnzycey773jMnPkFAOAPf3gM//3fh2H33XfAvvvuiUsuuQQ9PT0AgMsvvxi33HIjfvSjCfjqV3fGd77zTXzwwXu4/fZbsN9+u+Mb39gf//d/fwUAzJgxHbvssi3+/OenccghX8N+++2On/3sp365Yd544zWccMJR2HPPnXH00UfgH//4m/Jtbw4k763STnpKPXj33Xew5ZZb+59tttkYdHd3Y/LkDzBs2Cq49tqbMGzYKoHyFi/u8F+rv/mAA76Ka6/9CVy31FtUeqP86KMpOO64/8aee+6Es846DV988YX/3Ztvvo7vf/8EfPWrO2OvvXbBOeecgdmzZwMAuru78ZOf/BgHHPBV7L33rjj33DMxa9aX/rnPPPN3fOc738T48Tvh8MMPx6uvTkqVpZ7ItAvbbrvthpNOOgnjx4/HOuusUxVneNppp+UiHFm5cQxGw/RwIMQevvlI0wOJEGIBjbmqSwsSIYWgFzIjnDcwZJji2LmaJcu6MWPuYqtlrjasDQNa9abkruviggt+iPXX3wB33/0bzJ49G9dccwUaGhpw+uln49vfPgwPPng/9ttvf9x88w045ZT/wahRq+K11ybhZz+7Bj/60WXYcMON8eGH7+Hiiy/E2LFbYddd9wAAPPjgfTjjjLNx4omn4IorLsEZZ3wfe+zxVdx22z148MH7cc01V2D33ff0Zbn77ttxySVXoqenG5dd9iP0798fJ510akDeOXNm44c//AFOPPEUbL/9Tnj77f/g8ssvwdChw7DFFlv5VdILu1ZbyZLFHejsXI7hw0f4nzU1NWHQoMH48ssvMWbM5th++x3970qlEh555HfYZpvtAKDqN7/33ju47LIfYeQW/TFg1BitQepjjz2Ec8+9EOuttz5uuOFa/PjHP8LEibejo6MDP/zhD3DEEd/GRRdditmzZ+GKKy7FvffejR/84H/x8MMP4LXXXsV1192M1tZW/PSnV+LGG6/DZZddhQ8//ACXX34xzjlnAsaOHYPXX38ZZ555On75y/uw5ppradWDoslkQPrTn/6EVVZZBW+99RbeeuutwHeO49CARHIn3gPJtyARQmzhKfyUw1w2T0KsEQiXibXuehNUtkpCpAk4IBl4IEk1z6ok2uwGAixZ1o0f3vovLFke7UkjxYCWJlz9/Z20jEiTJr2ML76YgdtvvwcNDQ1Ye+11ceqpP8AVV1yCY4/9Lk477UzccMNP8dprr2CDDTbCwQcfCgDo338AzjvvIowfXzYArbXWmvjd736Ljz/+yDcgbbTRJvjGNw4HAOy993648cZr8YMfnIOWllYcfvgReOyxhzB37lxfllNOOQNbbLElAOC73z0Zt956E0488ZSAvI888iC23XYcDjvsCADAmmuuhQ8+eB+/+91vywak8A9U2knn8uUAgObm5sAhzc3N6OrqrLo3t9xyI95//33ccccvI3/zaqutjvvv/w2+XDQTA0aN0TLpfuMb38Tee+8HADjvvIvwzW8ehE8//QTt7e045pjv4sgjvw3HcbD66mtg9933xLvvvg0AmDFjBlpaWrDaaqth0KDBuOCCi7FgwQIAwP33/xpf//oh2Gef/dDU1IDNN98Ezz//Ah599CGcfvqZGlIVj5EB6YsvvsBf/vIX3/to1VVXlZKLEC0XXi6mEmKfhE0zoo/nKJUQeQx0JpUmIfKYNjPpMS1Vcd/n008/xsKFC7DvvuP9z0qlEpYvX44FC+bja187EH/84x/w4osv4L77HvGP2XjjTdDS0oI777wNH388BR99NAWff/4ZttlmnH/M6quv4b9uaWnB0KHD0NLS6r8HEDDcjB27pXL9TTF//jzMnz+/St7nn/8n9t57V/+z7u5urLXW2uU3vgdS739lYNncu1lXV1dX4JpdXV3+Lmwet9xyIx588D5ccskVGD16/cjfPGXKZEyd+jmGb7hqb5npg9hNNtnMf73aaqtj0KDB+OSTjzF+/B742tcOxAMP/AYffvgBPvnkY0ye/AHGjt0CAHDQQd/AX//6Jxx00L7YaqttsNtue2D//Q8EAHzyySf46KO/4vHHy8/HcRx0dnZi3LgdqwWoU7QNSK+88gq++93vYtmyZQCAAQMG4MYbb8Quu+wiJhwhHqlJtKkVCbGIF7OerHx1lDMhJB+CHkgxB9ErkBCLpOfyDCBu4Q2HsBGVAa1lT6B6DmHr6enB2muvi6uuurbqu7a2dixZsgTTpk0FUM495BmFXnzxBUyYcA72229/7LDDTvjud0/C9ddfHTi/sbEx8L6hITlVclNTReZSqaf3nGCt6unpwT77fA1HH3185LlOKAeS2k4GDx6Mfv1aMGfObKyzzroAysanhQsXYJVVhvvHXX/91XjssYdx0UWXYvfdv+p/Hv7Nxx13Iq699irMWJ74swI0NgbvQalUQnNzM2bN+hLf/e5R2GijTbDtttvjoIO+gX/96zm8/fZ/AACjR6+Hhx76A/71r+fwr3/9E7fdNhF/+cvTuPnmX6Cnpwff/vYx2G+/A9DY6GDw4AFYsGAJmpr6zu722gakG264ATvuuCMuueQSNDY24tJLL8VVV12FJ554QlI+QgAkbUlMCLGNbg5tQkgxpNiPuOZCiGV0ciBxa/HiGdDahPVWH1y0GLGstdY6mDnzCwwZMhTt7e0AgJdf/jf++McncOGFl+AXv7gVgwcPxlFHHYeJE6/HDjvsjKFDh+IPf3gUBxxwEM4++9zeK5Xw2WefBRJUm/Lhh+9jq622AQC89967GD58BAYPHlIl71tvvRnI7XPfffeiq6szaFSKmOc1NDRgk002xZtvvo6tt94WAPD22/9BY2MT1l9/QwDAXXfdjsceexgXX3w59thjr8D54d/c3d2NadOmoqE3p5KOUXfKlMl+CNznn3+Gjo5FWHvtdfDss3/HwIGDcfXVP/OPfeihB/zXTz31BPr164evfnUf7LnnXnjrrf/g5JOPw7x5c7H22utgxoxpWHPNtdDU1IChQ9tw6aWXY80118bXv35IulB1gPYubO+88w7OPvtsjBw5EqussgrOP/98TJkyBR0dHeknE5IBkwEux8KE2MO0vXGySog8Zu2MjZIQabLqPimdGb4uw8v7HuPG7YBVV10Vl156EaZMmYw33ngNV199BVpbW/Hhh+/jkUd+h7POOhcHH3woVlttDdx003UAgEGDBuOtt97AlCmT8dFHU3DZZf8Ps2bNQmdnV0qJ8dxww7V477138PLLL+KOO36OQw/9ZtUxhx76Tbz33ru4/fZb8Pnnn+HPf34at99+M1ZddbXyAQlV0AHwjW8cjvvu+zWeffYfePfdt/HTn16Jgw46BK2trfjkk4/xy1/eie9851hsvvmWmDNntv8X9ZuvuOISzJkzG26pnONKx1z7wAO/wTPP/B8+/PADXHHFJdh5512x5pprYdCgwZg58wu88spLmDZtKu699x4888z/obOzHOK3eHEHbrjhWrzyykuYPn0a/vKXpzBy5CgMHjwE//Vf/42//vXPePDB+zF16ue45557cP/9v6mE9fUBtD2QlixZgiFDhvjvR40ahebmZixYsMC3gBKSJ1pqjduEE2IfwyTahBDbxHjtUmcSUgwGIWx2AthIX6SxsRFXXXUdfvaza3Diicegf/8B2GOPvfD975+GU0/9HvbZ52t+Hp6zzvohTj75eOy33wE4/viTcMUVF+Okk45FW1s7dtppF3zrW9/CBx+8l1mWr351b/zv//4ArlvCIYccju9859iqY1ZddTX85CfX4dZbb8J99/0aw4ePxGmn/QD77PM1AMquvt7/QDtxsNde+2LGjBm45por0NXVifHj98T3v38GAOCf/3wGPT09+OUv78Qvf3lnoNznnnul6jfvuOPOOOSQw/HXf70ZVVgkRx75HfziF7di+vTp2GGHnfDDH14AANhzz73xxhuv4cILz4XjONhkk01x2mk/wJ133obOzk4ceuh/4csvv8Rll/0IixYtxEYbbYKrrroWjY2NGDNmLC666FLcddftuOWWG7D22mvj0kuvqMkbzDbaBiTXdass1Y2NjSiVSrkLRQiAwAg3fhc2Qoht/Jj1lAZYCZfhsJUQaRwNnekfKywLISS0C5vG8dIpkKo9kGTKIbKsscaauOaaG6o+v/vu3wbeb7rpGDz77Ev+++uum+i/9kKn5s1bjO7uEi644OLAufvv/3Xsv//X/ferrbY6nnvuFQDAjBnTAQB77bUvjjrquCo5TjjhpMD77bbbHtttt33kbwlvyqKGcXr186ijjsVRRx1bdW7c5x7Dhw8P/GaPKdc9g+WdPalt8qGH/gAAOO6471V919jYiHPOmYBzzpkQ+Py//uu//dennHIGTjnljMhr77XXvthrr32rnkNfQTuEjRDbBBKCxq6m9h5LLUiIPXS3YfO8HUSFIYQAeu1MeptwQkgFx7Ch+WNa+iCRlQUnmETbhmeA6U7CpBptDyQAuOuuu9C/f3//fXd3N371q19h8OBgsrHTTjstH+nIyk3QghQDJ6iEFEVqCJv3gg2UEHlUbwd67RJSPIE2WXwMG3MgkXpiwoRz8K8XXkDJdfHRnxz8/tYG9PS46Oope+L83+bn44D9D8y9XMcBpr38S1z/58m4qTHal+Z///d8P8yOVKNtQFp99dXx1FNPBT4bMWIE/va3vwU+cxyHBiSSCzr2I66mEmIfbQek8PGEEDF0vHYrXoFslYRIo7UOGnGMmP9R2IAkVA5ZsVHD2Wrh7LPPQ/ODr+Cj6Qux6bpDcfS+G+Pld2fi4Wc/AgDstNNuNZcRjYNRY7+BPbccib22XSvyiGHDhgmVvWKgbUD6v//7P0k5CKnC0Qge5wSVEPt4TTN1QMw9wwmxh4HOpNIkRB4nowVJTmXSgkTqh+HDh6N98Ej0W9AP7YOHY80118KU2Q3o17YQADCgrU2kXAdAU+sgDFllVay5ZrQBiSTDHEikTxC/muq9oBYkxB5eEu3kEbHLEFNCrGEQ9c02SYgVlMT2Gkcr6YMFZKEHEqk/wmGUajux5YlHzKEBidQtgcXUWPsRtyQmxDba7U18NZUQ4hEwIMUoTXrtEmKPoAdSugnJa7dSOrPqslTOpE7wdZbSTKSrp1ZaMhIJDUikbtHqN7iaSoh1vPaWpnypmwmxiIYidF1adQkpAhN9KBfBFrwyewFSNOGUCMF2IuWJF9r5jRhDAxLpE3BHGULqCE3ly9UdQoohre1x4kiIXXT0IXUmWemx2gjY4LJCAxKpYwyGuBwNE2IN0+bGrYIJkcdkZzU2SULkydzOhBpo+KrsB0jRJFVBhrDVLzQgkboluKFMTD4HP4SNWpAQa+jGsDGJNiHW0NiEjQNmQixiOjaVzlFGgxGpOzyP9uoUSNJFkhqgAYn0DbilDCF1g28/SjnOlR4NE0KiSbEU0SuQEAsENoMxiGETS6IdyoHEfoAUTGU8Wa77ajORTibPBZXs0IBE6hat1VTvWGlhCCE+/iBUM4k22ych8qiTwXgPJI6YCbFFYBM2jePFdaaT+JYQ+ySsSIpFl/h5PKkPs0IDEqlbtDoObihDSGGkKl//azZQQqzCjScIKRxHZyVURTgtQ9VYmaqZFIxX1/1d2AIuSFJlklqhAYnULxquv5VP2R0QYg3PASnVftSbA4nNkxBxTOaqbJOE2EXPfmQ5hE2mGEK0cULxZGo7EffE44pKZmhAInWLjuuvZ1jiYJgQe2g3N6YoI8QaAZ0Zt+jCNkmIVfy2phE+Kt4+qy7MnoDUB27VC7kcXbQf1Q4NSKRucXQsSN6xopIQQgJoeyAFjyeECKIx2HYZ902IXfRSBkaekzdVEWzsBkjBhBNa29mFLUujJCo0IJE6RichaNWhhBBhHM31m8pqKhsoIdIEPZBiDqIHEiFW8XO8GG3CZicHEvsBUjRVXkZeZImFsplEOzs0IJG6xdEYDVfsR1SDhNhCf/GGs1VCrGGwcynbJCF2MPPykU7LQAsSqU/ccA4kwbrpj2FpP8oMDUikTxDbxtn6CSkM7RA2Qohd4hqnv7rLmSMhNtHxdqDOJCs7nNb1DWhAIisGHAsTYg3T1VE2T0LkMWlnzH1CyMpHdQgbOwJSLHG6SLJuhvMuEXNoQCJ1S2BL4rjFVO9YcWkIIR7aip35egmxhppLIjWEjRBihco25enHusI6k5uwkXolPM+THTf25iWjRswMDUikbtGapDLFCiH28ePHU5Joh08ghNghOYKNLZIQa3iTVZMzpJJoB6/LfoAUjVcnvfZhw6hjYtQl0dCAROqXgAdSchJtujgQYg9d3eu1WzZPQuQJeO2mtM6qnW8IISI4mgsugWOkmmc4hI3dACkYvwp6dd+i5zrtR9mhAYnULYFN2GKOcS1u90gICaGpfdk+CZFHY+NSrUksISQ/TPSfdFqG6utSO5OCCe3qa8NznYbT2qEBidQtTnA5NeVYWVkIIQohhR+HP1dlAyXEAmxnhNQdJluGC1uQqkLY2GWQggl7tNvwXHd0B7EkFhqQSJ8g3gPJqhiEEKjKVy8HEseohMijtfEEE9sTYhXHIAdSRWfaaaDsBkjhONHGHAs5tJlEuwZoQCJ1TVVsbNxxHA0TYo0YfV8NM/YSUghxA2MOlwmxjMme4cLeF1XX5diZFEzFA6lc910LK4/MoV07NCCR+iZlosp8DoQUR1rzowcSIfYIzAVT3Ha56EKIHUwmq9Ij2rBnE3sBUjThHdGseOFpr4KSOGhAInWN7/ob547vHUctSIg1tNub74DEBkqINGo7i7Uf+ccSQmxgsmW4eIhplQeSUDmEGOI3Dwue6/RAqh0akAghhBjiGXbTciBRPRNSBGmLLpw4EmIXakNC4nFDHkhWCyXG0IBE6hrdVRiOhQmxh3F7YwMlRB6DdsYmSYgtsrQ2mRZKByRSb8SFU0vWTUaw1Q4NSKRPEJsQlDFshFhHt7kxhzYh9gi2M8Z9E1IPVHJop09X/S3MpWQJXZjh5aRoqgKvLe4USgek7NCAROqatNhxz7BEFUiIfbSVLxsoIeKoA+74EDbqTEJskmUiLLYLW7jlsyMgReN5A1WFsMlVTsckMRmJhAYkUuf05lqJ+5oZQQmxjqd801RvxQOJDZQQeTTamcXVXUJIBZ0FF3GPCNqPSJ0RNuVIe+EFyqT9KDM0IJG6xo9TjWnlVrZ7JIQESN/rKfg9J6uEyBPwQIo5huNlQuyiu+CiHhOXF6Z2WcIfiBRDiAHRCYlEx42s9zVDAxKpa1LbuMsJKiFFkbZ6w8kqIcUQu+jCRklIQeibkMRyIIWuzMVXUjSVhNblum9DRzGArXZoQCL1TSg2NgwbPyEFoLmDhctwGUKsodfOvEUXNkpCbOCkjGNVKhvDSAkTestugBSMXwVDOZBkdRS3YasVGpBIXeOk5EDiLk+E2Ces8NOPZwslRBq1ncUuulBnEmIVk7Zm2X5ESPFU2XLkrTphrydiDg1IpL7RzHTG1VRCLOLndEhuly5nq4TYI5ADKTlvINskIZbw9KVZEiQhUUIhbOwHSMH4jgKeB5IFz3XTRVBSDQ1I5P+3d/dRUlRn4sefmhmBAUQU0Z+JKy5G8A0B8RAjYtSjkviyajTGI3p8WWPMxphNXKOJ0Y2LOcbFl41rfIue1ZVV40tE3d2cuOZoPJoorq/HN2RQolkjogEE5HX6/v6Arq7b3XWrpod67p2u7+ccdKa76S5m7q1766nnPjdoWetU0+o8AChO3sGXTRIBPVY/y6iiTZ8EdNTmsdnzVVNwDaR6ZAfDu5QJZaEtkxVs/UYACQMDvRwIRpR38KXfAl6kx48I6wJe9CUDCSiZatPXzAsgB6F1BJAQtLwpjKThAgGjfwKF68tSbsZMQEkLfa2o/tnwvpwH4FlqEyxwkIoy17cgCwEkBC5fEW0AeqKcNR1quQ7MUgFNqX2TJWyAqr5cqhY9pa0fizkPwLeoroOoLONkCVu/EUBC0Go1tN0FQSmiDehJ7PXkfqGpbhle5NEAqMrqmxTRBnRFudd8JwsIF1VEu/6BQj4G6IO6RAGFVdYU0e4/AkgIWuYYahQi1QBs1flwzgwkAEoy+mZtY0RGTUBTvi3DC57TRvXfch6AX3F8ddPgpFGlrw8xXaQggIQBIXUyXP2CMRBQk3fSqbEdK4Ca7L7JGjZAU5Tzhov1mqJqINV/z3kAnjU0wYKz8JKfyk7erSOAhKDFtVZSnmc/GcCf/GMvPRTQkHWxynQZ0NXK6FdUZhDlHhCcupqa+TL1NstHoh8IIGGAyJgNczYA1GUN9IYlpoAXqX2TBCRAWb5NJ/K+ZnNi6gzf6ovMa2Su0+z7jwASgpZ9N5ULVEBb/a4ZuV8PoFBZfZN7LoCuWr2V7AEzntMWtYSt4X05EcCz2m5JTR8u8jNZwdY6AkgIWuYJhBorgLq88SMGZ0BXdelL6rLvoousAGgufw3twnpn/dI4zgLwLS0DqcjWmTVOIhsBJIQtcqf+0vkBD3JGbGvZDkxTARU576zSJQEdfdnxqXbtrFNFm/MAfIuDmnU1kBRqaHOXsx8IIGFASEv9pe8D+pLjunsXCzoo4Id7zOS6EVDGcAikMrUIkuJnolUEkBC0vJNcMhwARX3sbvROQEfuvsaYCahoZUe14pawAYHx0CjjjySC1DICSAhbZrEVimgD2qwMJMfrKLcCKMvceMJ6GYCi5SyibWXzFlZEO3J+D2irX02msdFDH/eBQRMEkBC0rE7OBSqgz5p0OkZguiegKzPbgTVsgKrcm040+Tub/VjqayAV9DlAbvWNMB6iiowgVevrEkJqFQEkBC3KqD7IBSrgl/OuKnuGA6riITNlYsyYCSjLW0U7mYCkNWZyIoBn8Y5odUW0C40fFffWpUEACQNC6kWqwnaPAGxWApIzfsQSU8CHjFXfxd7dBRDLn4FUfDZEwxK2wj8RcKs1SZP4b7Fts3ajpcAPaXMEkBC0rE6ust0jgNZUL1bpn4CKKONqVeMiFUBNrU9m1UBq8nc297E0PMDgjDCY+i9om0EjgISgZZ4+KOcAqLOKaOdYwQZAR7wcIOV56gYCfvRlPCyse1IDCYGpTxTQyUCi5fcXASQEzl4bW48VbIAH1uCbPi2O6/UyWAM6Mmog1b0MQMGiyD2PrbKeL2jMrH9Xhmb41rCc2uitLKGIdusIICFotdqDKQVBqecAqMubgSTUQAJUZfU1MpAAXbWulr+KdnG7sNHxEZi6mx4aIZ28de2RjgASBobsiqAAtCSLaDtexsUqoCwz26Ea1KVTAipyFuy1M5AKOxoLASX4Vl+2TyNznVbffwSQMCCk1nNQPQoAInWDL50QCE7WmMl1I6Arf/4RUEINVbQVPpJO1zICSAha3kkuc2FAT1/vDJHtAOigpwFhaaVPFreETedzgNxSGmGhbbOaqUvYtmUEkBC0+MIz43Yqd1MBP1wDMHd3AF1ZW4YbxkxAWc6CK4nni1q+0/C+nAfgWf3OoRqlD+K3Zo7aMgJICFtWEe36FwIoXN4i2tV+y8UqoCtrXkxWIKAjazOYKo1siIZd2DgPwLP6mx4qpTMpot1vBJAQtPriavWM4naPADbJ29+ocQ+oijK2lzGGTgloypvtkLwZU1j3pN8jUI0lkIosop2zsj1SEUBC2HLutciYCOhJ3rV0ZyBV/wI9FNCQedOl7nUACtZKtkNBHbQ+44ihGb5FdbEcjcz1nJeWcCCAhKBlZyBVX8goCGixu1v2EEzvBJRkLZchggSoimu89CkDqagaSHXfF/IpQF/UtUKSZAcErwGkxYsXy3nnnSdTp06V6dOnyxVXXCFr164VEZH33ntPTj/9dJk0aZIcccQR8tRTT/k8VHiSWRC0+jqVowFQL2t5qYjQQQElWctluOMKKMtdsTdR1VNrzOTmKzyrXebZNZA0imizgq113gJIxhg577zzZPXq1fIf//Efcu2118rjjz8u//Iv/yLGGPnWt74l2267rTzwwANyzDHHyLnnnivvv/++r8OFZ6l9nHoOgFdpA3DyYbonoCOKtydOUa0bSK8EVGRl0kvO5zeH+l3YOAvAt/oYZu2yrsAaSARO+63L1we//fbb8tJLL8nTTz8t2267rYiInHfeeXLllVfKgQceKO+9957cc889MnToUNlll13kD3/4gzzwwAPy7W9/29chw4N4Mkw2PhCMXGOvwpbEAJrLHDPpkoCKKGcEyUraLawGUtYDgB+19q+XGGBIQWqZtwyk0aNHy6233hoHj6pWrlwpL7/8suyxxx4ydOjQ+PEpU6bISy+9pHyU8C1Rqrf5CxQi1QBsdhHttOWlDMyAtqwxk/kyoC0jK7Dp39CJIDFzhm/1WbMaiQHcQOk/bxlII0aMkOnTp8ffVyoVmTNnjuy3336yZMkS2W677azXjxo1Sj744IM+fUZHRyQdHQOzlXR2dlj/L61Nv74oiqSrq8nPovp8hzR/vg3QFpAUQnvo7IwSX3c07XtRxX59u/ZP30JoDwhIVPuiab/c9HxnB32y3XFuCEO1z0Xinqcmx9WOAsbMzs4OWd9rh7GK+BwMDKGcH2rX6Ua6ujqkY1OHSb3u2wySWfG+238ov4e+8hZAqjd79mx5/fXX5f7775fbb79dBg0aZD0/aNAgWbduXZ/ec5tthg34pRMjRnT7PgSvujZ1qEGDumTrrYc1PF898QwZskXT59tJ2dsCbD7bw7DhQ+KvR44cKlsNH9zwmg29tQjS0KGD275/+sb5ASIiHR0bx8zBg5uPmdUIU3f3IPpkSXBu8GuLrk4RSZ/HVnVsUbskGz6smDFz7dLV1vfDGJtLz/f5obt74/V+FEWy9dbDZItN/aCrq6Owtjlo0MY+2dnVGUz79/176KsgAkizZ8+WO+64Q6699loZN26cDB48WJYtW2a9Zt26dTJkyJDmb5DiL39ZNaAzkEaM6JZPPlktvYkLsbKpVDbeLVm7doMsXbqq4fneTXdT1qU83w5oC0gKoT18umpt/PWyZZ9KZf2GhtckA0irV69r2/7pWwjtAeGoLilNGzMrm55fs2Y9fbLNcW4IQ3UsXLvOPU/9ZFXtJvmqTzf/mNnZ2dGwdIexubxCOT+sWbOx3VcqRpYuXSVr160XEZHe3kphbXP9+l4REdmwvtd7+99cvwftQJj3ANKsWbPk7rvvltmzZ8uMGTNERGT77beXnp4e63UfffRRw7K2LJWKiQMQA1Vvb0U2bCjvwF+t11CpmKY/h+pkuWKaP99Oyt4WYPPZHpLn1fUbmh/H+sRjaf0Xmw/nB4jUVrBVKintobpVcgnGTGzEucG3TfPUjHFwgzVmFvM7izrsZTKMzfB9fjCb5pNGNvaB5PyysOOqXlsGNA76/j30ldcFd9dff73cc889cs0118iRRx4ZPz5x4kR57bXXZM2aNfFjzz//vEycONHHYcKj6t2StIK8Gts9ArBZdzFTq/LWHqd3ArpSe+XAvqcGDDhRziLayee15rSMzfCudqFnP1zoZzb9SPSBtwDSwoUL5YYbbpCvf/3rMmXKFFmyZEn8Z+rUqbLDDjvID37wA1mwYIHccsst8sorr8gJJ5zg63DhSXwCSe3lG58Y4KWugAEr14Uq/RNQEaVMxqtM/esAFCuuEZxxuZp4vqju2dDvOQ/As1oJ7Wp2bN0TBX4mEaTWeVvC9tvf/lZ6e3vlxhtvlBtvvNF6bv78+XLDDTfIxRdfLF/5yldkzJgx8vOf/1w+85nPeDpaeJMRJabvA/qSk9C0OTHxI0BfbTKexlivA1Cs7D6Z7/nNob7fcx6Ab1FdB6nFj4qMIFWzArmKbJW3ANLZZ58tZ599durzY8aMkTlz5igeEUIUp/6mXaRWl7AxCgJhSfRZsh0AJdWbLimDJkvYAF15sx2MxphJBAmBipt/rTZJYWj2/ee1BhKQKefISw0kQI9dAinlQpU7O4C6rJEw7pUMmYCOONuhD3+lmCNpmCtzGoBv1WCpachAKvIzxf4w9BkBJAQtq48bhUg1gL6z7qb6OwygXCJ31q4wZAKqaiWQ3FerWc9vlmNpKIHEmQB+JQoiWP/TaJnEj1pHAAlBy4oSa0SqAdiSc85cc146KKAi86aLsO4b0NRKV6N7ojTiZdeb/h8/XlwnqM96Qt8RQAIA9BGzW2AgowcDAMqNCFKrCCAhcBlrx0lBAtRZGUgpvdNewkYHBTTUsnbdabtkOAC6srId7OeL6aD1S9Y4DcC3+vlhdSlnoTWQ4s8q8EPaHAEkBC2KUxvTCvVueh3DIKDG6m2Z0V0uVgFtGSWQACjJW2fIKIyZDe/L2IxA1F/mFTpvrF5bFvgR7Y4AEoKWef6oRqoZBAE9VgZScwzMgL74YjUtAcnUvQ6Aiswi2QobTzTGjzgPwK/akLWxA2hkBeXc4BsOBJAQtrriavXo+4C+qHHfjAbWEjbmqICK7N2Ji18eAKAmI6YbU1jB1jAYMzbDt7RgTrE3OarlUbiKbBUBJAQtyqiBVLubqnM8AMSe3Oa4XcRdTkBJ1rJv6gYCqvJ2tWSPLWrMpNsjOJF9nZeZqbf5PpIshH4ggISw5a50xrAIaLHiRymvMUYhHx+AJW9XI6gLKMm7ZbjCmFl/s5Wbr/CtvgnG9zgKbJvEj/qPABKCltXJNar1A3DIsbyU/gnoiDIuVtl1BtBVm8e6O5+PMZNAMrxL1tRMDFCFtkyafb8RQELQ8hY64y4KoCfZ33JswsZgDShLu1il5gOgLG+6g0LdwIa6MozN8Kw+o90opCDF5VEYDltGAAlhq1sbW4/OD/iQKKKdVmvFejWzVEBDZm0H6gYCqrJqeVbZz+vUQOI0AN+iujuS1ZscOhlIXES2igASglYrgeS+SGVLYkBPru6WTEWmewKqMuJHXDgCSmpB3YwlbBpjJhEkBMxsjCBtRA2koBFAAgD0CXNOYIAjqgsAKDMiSC0jgISgZc9xKaINqLOKHjZ/CeMyoC+riHb8OoVjAVDTlzGxsASkukk1y8vhW1Q3n6wlIBVYA2nTWzNPbR0BJAwImTvKMAYCapIDe576ZCwxBXS4eqZR2CYcgC3nCjb7+aKKaNd/z3kAnjU0weru2gpFkNLKoyAbASQELevCk/gR4IG1bUb2AEz/BJRU76w26ZZsjAjoy3sDRWPjCQJGCE6iUSYzkJQ+Ei0igIQBITVKrLDdIwBb/barzXBnB9DnvPAkKxDwJnNMVMkQrFvCxnkAntkt0MTdoMiWmTcrEOkIICFoWetUVbZ7BGCx+luOAZg5KqAjcmYgMVsGtOUd/zQyBOuPhaEZ3qXU1Cw0uFmtFVjcJ7Q9AkgIWs4a2lygApqSKccpL7EvYOmggKZmwSJ7cq54MACysx0UEpDo9ghNfUa7RvZ6/JmkILWMABLClrGjDF0f0GeXQGreC5MXsFysAjrivsYubEAQopzZDvY9l6KqaNcvYSvmY4C8rEwjrZsctPt+I4CEoNXmwikXqXEGEmcDIChs+AR4kH6xys1WQF/ebIfkzRi9DCRGZ4TDJGogFSnnfRY4EEBC2DJ7OTWQAG3WDaM82YF0UECFu24ga9gAdRm1PJv+FZ0EJE4D8K5+U9/a3kjFNc6olU4JCwEkBC1y3E0VSVy8MggCQdHYkhiAzZXtoLLJEwBL3mwHlcyL+iVsxX8k4FbfCI1CYkAcPyKC1CoCSACAPmHJKDDA0YUBACXGsu7WEUBC0GoFQd29nAwHwI/UOzikOwDeNK2BlPiaLgkoca8rTfkrSj2UEwE8S16/JZewFdk2afb9RwAJA0K+rcIBaOhrDSQGa0BH5LpYNU1eB6BQWZvBSM7nN5fI+przAPyyh6JaL1BZwsY1ZMsIICFoWQlI1VMNc2FAT65JJ/V6AXWui1XqPQD6apn07tcZrTEzSvka8MzE/5FCO0GUuzIZ0hBAQtAy75Ju6vuMgYCivu7CRg8FdDjurLKqFPCnT7uwFdhDk+/NeQC+1We0x4kBCp9J+Kh1BJAwIJiUq9TaWlmGQUCLte1q2obhiT5L9wR05O5q9ElARfVGaNZyGaO07js5HjM2w7eGYGk1MUChbbKErXUEkBC0vCcQxkBAkR1B6tPLARTHdbFqZyDRKwENiRLBztclb8bo9U7OA/DMykDSWWhN4LT/CCBhQGg+GSYfH/AhyjEl1rqbCqBR82k4WYGAurwFe8lAQgnV3480cQaSzjJOtIYAEoIW301t8hzXp0AAcu3CRg8FNLgK9pKtD+jLW65Xa8ykBhKC0pDRXnwNpFpQl1GxVQSQELT4BNI0BSnxOm6jAGqsooepESQyBAF9jpsuzJUBD3JW7GUXNpRQfUZ7PE4VmYWX+Dy0hgASwuYYd9mSGPAvzy5szFEBHVHO5TLccwF01HZ8yl8DqUh2/IgTAfyyxqJEDSSNDCQuI1tHAAkA0CdcfAIDGxeOAIAyI37UOgJICJorSGy00n0BWHJdfLKCDVDnKm/PxhOAvlopBvfr1Oa0LGFDoBIlkKTIxhlJzlRdpCKAhLBF+daOMwYCfqQVIbQeJcIL6HDMi1lWCniQswSS/Vcooo1ySNawNaa2lLPIaWPOS0s4EEBC0GoZSM3upiZfyDAIaMnT3ZKBJXonoMN54UkECVBX7ZNZyQ5aO0Ilx29OA/CtoQ2alMcLQAJS6wggIWiuLYmTDzIIAn7kGYCJ7wI6XEW0tbYJB5CQdw1b8q9odU8GZwTEJIpoF7oLG82+3wggYUDI3JKYkwGgxko5TnkNd3YAf5ru6JTMCmTMBFTk3fBJa8wkAwkhqR+LTJyBVHwhMK2sv3ZEAAlBq16oUs8BCIfV33IMwBFXq4AKV9YuU2VAnzuTvsauyqBUA4mhGd7ZNZA01rDR7PuPABKC5sxzsHas4HQAqEl0t9QMJC5XAQ823XRp8oxhzAQ8SO+TFq26gXR9BCSy5pO1mWOh+UdswtZvBJAQNmc9B3o+4FueJWxcqwI6mBgDYan1SXentHcuLexwrLcmkAzf7Ix2qSUg0TSDRgAJQXNl/loXqBoHA0BEmgz4ma+nhwIaXFm77IwI6Mvb19TKMnBljpDUZbRrjFJxeZTCPqH9EUBC4Kq3bvK9DIACq4h2885JkXvAAzKQgLA4anla7CJIxR2OzscAuUR1tz2qNzqKbJvZ28AgCwEkAECfMOcEBjYuHAEAZcaNltYRQELQ4rXjTdPxE6/jkhZQY118pgzARiURGUCSayw0WkVWAMRybsKmNmYmx2/mzvDOmk/qFpInftQ6AkgIWnwCadrLEycaxkDAi9QBmCVsgDpXwV7DmAnoiyNIGZerPjae4DwAz+rvR8bdRGMZJxGklhFAQtgcUWL6PeBHcueWtDmxHT9ilgpoyrjnQo8ElOTPQNKRHL85D8C3+oz2OH6k8KHs5t06AkgIWvXCs9lFKtuEA37Y3S07gkT/BHREjqtVVrAB+qKc62XsOa1OEW3OA/CvrqR1tSNoFNEmftQyAkgIW85eToYDoCi57WqOGkgAdMQ3XZo8R1Yg4IGjlqdNt/7Lxi85D8CvyJpPGpUMpChfTBcOBJAQNFfqrzHk4wM+5KihTYYg4IOjBpIwZgLqcpdAUqobmHxrxmb41tAE4wSk4hsnu7C1jgASgpY3HZ8xEFBk3TLK8XJ6KKDC1dMYMwEPcnY2rf5Z5PI4oM/qMtqNKCxhc11cIhcCSAhcejo+s2HADzsDqfkA3DQDAkCxonxjJteQgA5XLU+LNafV6aCcB+Bb8gZjche2QoOoBb53WRBAQtDcWxInXsfpAPAjTwYS3RNQ4VouQ0gX0Fcb/9w90CjVQLKWsDF3hm/WHUmlZdbxtWWBn9HmCCAhaM7zR6Lnc4EK6OlrDSQAOur2s7EYrSIrABpkDolaJcro+ghI/XyyloFU5E6EdIL+IoAEAOgbxl5gQOOmCwCgzLjR2ToCSAhblL52nH4P+GGtWU/piHY5B65WARU5U/PpkYCOKGeftJ4udBe22pszNMM3qwma+D8qbTOthieyEUBC0Fx18u1twhkFAS1R44jfyOjUcwBQ40rNZwUboC/vchm7/EuBy3fo+whJZC+81gjpsAlb/xFAQthcFUGbvAyArjwZSHRQQIcr24GNJwAPHJvB2PTrenLzFb7ZNbRNrQZSoVl4mz6vuI9oewSQEDR3BhJdH/Ahz6STZAfAn6ap+Vo73ACI5b1Y1ZrSJsdvTgPwLW06WehNDgKn/UYACUGLTyBZ9Rw4FwBepHZNuwiSwpEAcKXmE9QFPGgh3aHIzKAo9RvAL2MSyQEKGUgbP5NkhFYQQELY4vhRsy2Jm7wQQOGSc9u0wTfZZ+mdgJZNG080e4oEJEBd5OqTCWqXsVHTLwEv6oOl1X5QaNtMzmGL/Jw2RgAJQXOVQDIe1osDyDmwW0XuizoSAEm1GkhNbro0eyGAQkWuiWxCss9q1H8p/IOAPjJiVCJIefaBgRsBJITNdQLhbirgR3LXjDxFtAGocA6Z7IwIeNOXMbHY7AtqICEcdkZ7Mn5U5E6EtPz+IoCEoMWpvxn1HBgFAT+aFuuVunq9DNaADscubAD0uXZGTLKeV6qBxNAM3xoCRZs6glbbTJvDwo0AEoJWO4G40/HZkhjQky/9l2wHQJtrLDQsKwU8yNfZtOoG0vcRlJQMpEI/su4z0XcEkAAAfcMEFAAAACgdAkgYEJrvKEMRJMAHawvUlNfY6fgFHgyAmKuItv06OiWgoVZDO2sNW5O/VDDOA/DNnk+aeO6oVUieDKTWEEBC0Fxrx7k+BfywJp05imjTPwFdzboltR4AfXkvhLXGzIgi2ghJ6h3JIiNIeW6DwoUAEoLmrG1EPQfAu9SLUqWCoABq4ovDZjddGDMBbzIzHZQ2nohSvwH0Ja/zjNQy9QoNoia+JgOpNQSQEDZHOr5p9kIAhctTgFCrICiAmni5TObr6JWAhmowKDt+pHQlGzX9EvAiLRmo0Jsc2Un0yEAACUFzTYaTQSXupgJ6cnU3SpQB+lw3XVhXCqiz8yvSaWUI2hlInAgQjmQNpCLl20kYLgSQELact1MZAgFNiSlxjhVsdFBAh6urkRUIeOCo5ZnxVwpBDSSEpD6j3cSPF7iMM0qGdYkgtYIAEoJWTbNvnoFkvRCAEruGdvPB194kkQ4KqKgul2leRTvxOpWjAUov77JS4+GuC6cB+NY4Pyy+BpL1acSPWkIACUGLL1QzaiBxgQp4kjr4ssQU0OZc9m29jk4JqHAUtk8ySmOm9d6cBhAQYxJL2LSWcaIlBJAwIDQdd+0UBwAepM2JuasD6HPddCFTH9BXC+pmRpDUEUiGb/XBUoX4EdeMmwEBJAQtvnGTMRfmXADo6evd0SLXsgNo1DwDiaxAQFuUcw2bNactNAMpUQOJ8wACYowkiyAV9jnJwCk3O1tDAAkA0CfctQQAAMDARgSpFQSQEDjHhSor2AA/rF0zUopoKx0KgJrIUW9Fa5twAI36MiYWeZOGro+Q2Dui1TJli92JsPY1c9XWEEBC0GrlHNxFtJkNA3qSvS29BhLLZQBteXd8IosQ0BG5dkZMMFp1PRPvzfJy+Ga1QGPiwUurabKErTUEkBA01wnEukBVOBYAG0V5IkjJ1xd2JAAscd3AJjddmCkD6uz8inRadT0JHiModdlAGqMUcdP+I4CEASFr3svJANCUPSW2+iwdFFCRt6fRJQEljs1gLEpLTKOo+deAD/X3I02cgaRVRJsbK60ggISgxam/TZ6jzwN+2BlIaTWQyBAE1DmWyzBmAvryZvzY3VNn1GRshncNBYmKr4FUn/WEviOAhKDlTf1lHTfgR2rP1MrHBxCr1UBy1w1kzAR0ZWcg6dQNtN6b8wA8szOQTK0bFNkHkt8QQWoJASSEzZH622yCDKB41g2jlG5I/AjQF9UiSI2oGwioq22MmL8GUrGiJl8BfqTV1Cy0VhcZSP1GAAlByzkX5iYKoCjXwG71TzoooMOx7LvxZQAKFne1zF3YEn9HLQOpuM8B+ipZA6lI1hyWtd0tIYCEsNVu3bhfxigI6LHG3uwaSAB0OHcuTb6u8CMBICK1umR9+SsF9lDiRwhJ8gajMbW5o1oQFS0hgISgOes5MBsGvMuzCxuDNaAjHjObr/sGoCzv8Gf1WaUxk+xg+Ga3QBOPU1otk2GxNQSQAAB9wpQTGOC4cAQAlBwr2FoTRABp3bp1ctRRR8mzzz4bP3b55ZfL+PHjrT9z5szxeJTwwVUQlG3CAT+ihm1XM15f3KEASHKs+mbMBDzIseRbRG+JKVlHCEpdOaK4HxTYTukD/dfl+wDWrl0r559/vixYsMB6fOHChXL++efLcccdFz82fPhw7cNDIJoOuSxhA7zLs4SNDgroiNwRpNrr6JKAivpNplK7ntLGE1YNJM4D8CxlE7Zig6jJzyQFqSVeM5B6enrkxBNPlHfffbfhuYULF8oee+who0ePjv90d3d7OEr4VB1Es8o5UEQb0FXbWSa7iDaTVECHc+dSzQMBICL5M3bV+mfU9EvAj7T+QeMMmtcMpHnz5snnP/95+e53vyuTJk2KH1+5cqUsXrxYdt555369f0dHJB0dA7MFdnZ2WP8vq86O2nS4q6sj5TmRrq6o4fl2QVtAUjDtIRIRIxJ1NO97HVb/7Gjb/ulbMO0BQYj7nWkcM+mT5cK5IQydnbV+19kVSWdH899H9eFIpJC+WW0HHVHyPNDJeaCkQjk/bJH4/ChxKJ2dxV3XdSbet6PT71gYyu+hr7wGkE4++eSmjy9cuFCiKJKbbrpJnnzySRk5cqScccYZ1nK2PLbZZtiAX+c4YkS5s66GDh0Uf7311sOs54YvWxN/PWJEd8Pz7absbQE23+0hiiIxxkh396CmfW/o0MHx11tvPUy6B3tfMd3WfLcHhGHwkC02fhFFjWPm8JXx1yO3Gtr2YyY24tzgV3IsHDlymHSlXCh2d2+c70ZR43x3c+rq6oy/3nrroTK0es5AKfk+P6wztev0YcNqfWXY0MGF9YMth9c+Z6sR3bL11kML+Zy+8P176KsgZ/Rvv/22RFEkY8eOlVNOOUWee+45ueSSS2T48OFy2GGH5X6fv/xl1YDOQBoxols++WS19PZWfB+ON6tXrxeRjatkli5dZT33ySe1ANKKFWsanm8XtAUkhdIeqmfWTz9d17TvrVq1Nv56+bJPZc2gzobXoP9CaQ8Iw7q1G0REpFIxDf1yxYramPnJJ6ulu2tgzo+QD+eGMKxevS7+eunSVakBpE8/rb4uKmQ+W20Pvb298WPLln0qa7m5U0qhnB8+Wb46/jo5Rq1e3XxuuTkk56fLln8qW0T+Fnhvrt+D9g2hIM8axx57rBx88MEycuRIERHZbbfdZNGiRXL33Xf3KYBUqRipVAb2qv/e3ops2FDegb/6+zNGGn4OyY5W6TVt/3Mqe1uALZT20FtpfhzJ/rmhtyKdG7hYLVIo7QF+VQuCGmkcMzck+iTtpTz4XfuVvA5Zv76SWuyot3fjE1HU2HeLQtuA7zZQbfciIhsSX1cqxV3XVZKfuSGMPuD799BXQS64i6IoDh5VjR07VhYvXuzngOBNdQWiaTLiUjkf8CdyVesVaiECPtT6ZdbOE/RKQIPd09Lnrc3mucWIEl9xHoBfVg3txLhVaMvMV9ceDkEGkH72s5/J6aefbj325ptvytixY/0cELyp7fSU8TrGQEDZph0S055my3DAm+a7sClNzgHUWBfIjtdteq7o8dJ6f04ECEjF6EwcCZz2X5ABpIMPPliee+45ue222+Tdd9+Vu+66S+bOnStnnnmm70ODtij9IpUEJMC/tExA+1EGa0BDVEvbbcSYCahLjn454kdS9HhJ/AghSW52ZcWPCv3QxNdcTLYkyADS3nvvLT/72c/koYcekqOOOkruvPNOufrqq2Xy5Mm+Dw0AIGQVAQMZ3RcAEBQPsRzCR60Jpoj2/Pnzre8PPfRQOfTQQz0dDULhmuTa5RyYDgOaMntcci073RNQUStN1qRuYLMXAiiUNT91XK1Ws3kLHy8T78/YjJBY41aBbdN6ayJILQkyAwmIpRRX2/RAs5cB0FBdKZOjiDYAJY5+aS8PYNQEtOUplF18/IhFbAiHXUQ78bjWZxb4Oe2MABKC5lo7zt1UwJ/qJDRt8FWqhQggwd3VyAoEtOUttxI/p1hEm/MAfEs2wYqVuV5oCCn+ih29W0MACUFzpf6yTTjgkWu78IaX0kMBDdUxMysDCYCSnMNfLX7EeIkSSSmirfSRaBEBJAwYDam/dhEk1WMByq5Wa6U5o7adBoB6WUtlqBsI6Igk5wVy9UkykFAidoaeTmkSNmHrPwJICFra2lgRe4LMGAjoijIiSGQIAvpc/ZJ5MqDPDtKk90KlFWzWJ5DtBO/SrvOUIkiMi60hgISgOc8f1FgBPHLXQLL7Jx0U0JHeLw07IwJeOS9WqwlIihlIxI/gW3oGUnGNM0qNWiEvAkgIm2NtLF0e8KdWAql5T6R/AvqyMgPj1xV+JABE3Jn0SVo59cSPEJLkDcaKVmIAGUj9RgAJQbPPH3Y3N2Q4AN5kdrlNHZSeCeipxY8ap8X28gB6JqAhbyaF0RozrRpInAcQDq0d0Wj1/UcACWFz3rmhBhLgW9p4Hz9M5wT0bOpvTXdh414roM+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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "id": "6a884224", + "metadata": { + "lines_to_next_cell": 0, + "vscode": { + "languageId": "python" } - ], + }, + "outputs": [], "source": [ "# Convert the 'time' column to datetime\n", "df[\"time\"] = pd.to_datetime(df[\"time\"])\n", @@ -2307,38 +1708,24 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "1698b545", + "metadata": { + "lines_to_next_cell": 2, + "vscode": { + "languageId": "python" + } + }, "outputs": [], "source": [] } ], "metadata": { - "colab": { - "include_colab_link": true, - "provenance": [], - "toc_visible": true - }, "kernelspec": { - "display_name": "assume-framework", + "display_name": "ASSUME-framework", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - }, - "nbsphinx": { - "execute": "never" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } From 139f1fac6560cc3cd8956bdba2d0a674cf2a9706 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Sat, 28 Sep 2024 16:21:57 +0200 Subject: [PATCH 09/27] - adjust seup of algo tut to match new format --- ...forcement_learning_algorithm_example.ipynb | 35 ++++++++++++++++--- 1 file changed, 30 insertions(+), 5 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 5631a93f..4b83906c 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -68,7 +68,7 @@ } ], "source": [ - "!pip install assume-framework" + "!pip install assume-framework[Learning]" ] }, { @@ -105,7 +105,7 @@ } ], "source": [ - "!git clone https://github.com/assume-framework/assume.git" + "!git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { @@ -538,7 +538,33 @@ } ], "source": [ - "!cd assume && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" + "!cd assume-repo && assume -s example_01b -db \"sqlite:///./examples/local_db/assume_db_example_01b.db\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Select input files path**:\n", + "\n", + "We also need to differentiate between the input file paths when using this tutorial in Google Colab and a local environment. The code snippets will include both options for your convenience." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "colab_inputs_path = \"assume-repo/examples/inputs\"\n", + "local_inputs_path = \"../inputs\"\n", + "\n", + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path" ] }, { @@ -609,7 +635,7 @@ "\n", "**Evaluation Loop**: Nested within the training loop, the evaluation loop periodically assesses the performance of the learned policies. Based on average rewards, the best-performing policies are saved, and the function determines if further training is necessary.\n", "\n", - "**Terminate Learning and Save Policies**: At the end of the training phase, the function saves the final version of the learned policies, ensuring that the results are stored for future use.\n", + "**Terminate Learning**: At the end of the training phase, the function saves the final version of the learned policies, ensuring that the results are stored for future use.\n", "\n", "**Final Evaluation Run**: A final evaluation run is conducted using the best policies from the training phase, providing a benchmark for overall performance.\n", "\n", @@ -715,7 +741,6 @@ " range(1, world.learning_role.training_episodes + 1),\n", " desc=\"Training Episodes\",\n", " ):\n", - " # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\n", " if episode != 1:\n", " setup_world(\n", " world=world,\n", From 91488256b10718bfa9ab8c07102ccfefaba0b86d Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Sat, 28 Sep 2024 16:25:56 +0200 Subject: [PATCH 10/27] - spelling proof read algo tut --- ...forcement_learning_algorithm_example.ipynb | 710 ++---------------- 1 file changed, 59 insertions(+), 651 deletions(-) diff --git a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb index 4b83906c..8fb45fe3 100644 --- a/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb +++ b/examples/notebooks/04_reinforcement_learning_algorithm_example.ipynb @@ -2,31 +2,34 @@ "cells": [ { "cell_type": "markdown", + "id": "d3dfcafd", "metadata": { "id": "4JeBorbE6FYr" }, "source": [ "# 4. RL Algorithm tutorial\n", "\n", - "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement leanring (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL alogorithm. It ,therefore, start one level deeper, than the RL_application example and the knowledge from this tutorial is not required, if the already perconfigured algorithm in Assume should be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", + "This tutorial will introduce users into the MATD3 implementation in ASSUME and hence how we use reinforcement learning (RL). The main objective of this tutorial is to ensure participants grasp the steps required to equip ASSUME with a RL algorithm. It ,therefore, start one level deeper, than the RL_application example and the knowledge from this tutorial is not required, if the already perconfigured algorithm in Assume should be used. The algorithm explained here is usable as a plug and play solution in the framework. The following coding snippets will highlight the key in the algorithm class and will explain the interactions with the learning role and other classes along the way. \n", "\n", - "The outline of this tutorial is as follows. We will start with an introduction to the changed simualtion flow when we use reinforcement learning (1. From one simulation year to learning episodes). If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we dive into the tasks and reason behind a learning role (2. What role has a learning role) and then dive into the characteristics of the algorithm (3. The MATD3).\n", + "The outline of this tutorial is as follows. We will start with an introduction to the changed simulation flow when we use reinforcement learning (1. From one simulation year to learning episodes). If you need a refresher on RL in general, please visit our readthedocs (https://assume.readthedocs.io/en/latest/). Afterwards, we dive into the tasks and reason behind a learning role (2. What role has a learning role) and then dive into the characteristics of the algorithm (3. The MATD3).\n", "\n", "**Please Note:** The tutorial does not cover coding tasks. It simply provides an overview and explanation of the implementation of reinforcement learning and the flow for those who would like to modify the underlying learning algorithm.\n" ] }, { "cell_type": "markdown", + "id": "16b738b3", "metadata": {}, "source": [ "## 0. Install Assume\n", "\n", - "Frist we need to install Assume in this Colab. Here we just install the ASSUME core package via pip. In general the instructions for an installation can be found here: https://assume.readthedocs.io/en/latest/installation.html. All the required steps are executed here and since we are working in colab the generation of a venv is not necessary. " + "First we need to install Assume in this Colab. Here we just install the ASSUME core package via pip. In general the instructions for an installation can be found here: https://assume.readthedocs.io/en/latest/installation.html. All the required steps are executed here and since we are working in colab the generation of a venv is not necessary. " ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "id": "d2e2b8fe", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -37,42 +40,14 @@ "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: assume-framework in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (0.3.7)\n", - "Requirement already satisfied: argcomplete>=3.1.4 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (3.4.0)\n", - "Requirement already satisfied: nest-asyncio>=1.5.6 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.6.0)\n", - "Requirement already satisfied: mango-agents-assume>=1.1.1-8 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.1.4.post2)\n", - "Requirement already satisfied: numpy<2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.26.4)\n", - "Requirement already satisfied: tqdm>=4.64.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (4.66.4)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.9.0)\n", - "Requirement already satisfied: sqlalchemy>=2.0.9 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.0.30)\n", - "Requirement already satisfied: pandas>=2.0.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.2.2)\n", - "Requirement already satisfied: psycopg2-binary>=2.9.5 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (2.9.9)\n", - "Requirement already satisfied: pyyaml>=6.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (6.0.1)\n", - "Requirement already satisfied: pyyaml-include>=1.3.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from assume-framework) (1.4.1)\n", - "Requirement already satisfied: paho-mqtt>=1.5.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (2.1.0)\n", - "Requirement already satisfied: dill>=0.3.6 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (0.3.8)\n", - "Requirement already satisfied: msgspec>=0.14.2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (0.18.6)\n", - "Requirement already satisfied: protobuf>=3.20.3 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from mango-agents-assume>=1.1.1-8->assume-framework) (5.27.1)\n", - "Requirement already satisfied: pytz>=2020.1 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from pandas>=2.0.0->assume-framework) (2024.1)\n", - "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from pandas>=2.0.0->assume-framework) (2024.1)\n", - "Requirement already satisfied: six>=1.5 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from python-dateutil>=2.8.2->assume-framework) (1.16.0)\n", - "Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from sqlalchemy>=2.0.9->assume-framework) (4.12.2)\n", - "Requirement already satisfied: greenlet!=0.4.17 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from sqlalchemy>=2.0.9->assume-framework) (3.0.3)\n", - "Requirement already satisfied: colorama in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from tqdm>=4.64.1->assume-framework) (0.4.6)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install assume-framework[Learning]" ] }, { "cell_type": "markdown", + "id": "55ab1d3b", "metadata": { "id": "IIw_QIE3pY34" }, @@ -84,7 +59,8 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, + "id": "d5e77f71", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -95,21 +71,14 @@ "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Cloning into 'assume'...\n" - ] - } - ], + "outputs": [], "source": [ "!git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { "cell_type": "markdown", + "id": "7bf23b1c", "metadata": { "id": "Fg7DyNjLuvSb" }, @@ -121,7 +90,8 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, + "id": "de097384", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -132,417 +102,14 @@ "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario example_01b/ from examples/inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling demand_df successful.\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Downsampling availability_df successful.\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - " 0%| | 0/2678400 [00:00 1553886000.0 but is 1551398399.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[27], line 47\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 44\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 47\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 48\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 49\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscenario\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstudy_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 54\u001b[0m \u001b[38;5;66;03m# after the learning is done we make a normal run of the simulation, which equasl a test run\u001b[39;00m\n\u001b[0;32m 55\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", - "Cell \u001b[1;32mIn[17], line 102\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 99\u001b[0m \u001b[38;5;66;03m# Give the newly initliazed learning role the needed information across episodes\u001b[39;00m\n\u001b[0;32m 100\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 102\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;66;03m# Store updated information across episodes\u001b[39;00m\n\u001b[0;32m 105\u001b[0m inter_episodic_data \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mget_inter_episodic_data()\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime must be > \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# set time\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time \u001b[38;5;241m=\u001b[39m t\n", - "\u001b[1;31mValueError\u001b[0m: Time must be > 1553886000.0 but is 1551398399." - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:assume.markets.base_market:1553889600.0 Market result [(datetime.datetime(2019, 3, 29, 20, 0), datetime.datetime(2019, 3, 29, 21, 0), None)] for market EOM are empty!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "example_02a_base_6 2019-03-31 00:00:00: : 1556474401.0it [00:29, 53550828.33it/s] \n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -1543,38 +968,21 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "id": "df2ba59b", + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [] } ], "metadata": { - "colab": { - "include_colab_link": true, - "provenance": [], - "toc_visible": true - }, "kernelspec": { "display_name": "assume-framework", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - }, - "nbsphinx": { - "execute": "never" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } From 303fb882d6f833b2a391069ab31488a2d8218ac7 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Sun, 6 Oct 2024 15:11:41 +0200 Subject: [PATCH 11/27] - added juppyter notebook for xRL --- examples/notebooks/09_xRL.ipynb | 1071 +++++++++++++++++++++++++++++++ 1 file changed, 1071 insertions(+) create mode 100644 examples/notebooks/09_xRL.ipynb diff --git a/examples/notebooks/09_xRL.ipynb b/examples/notebooks/09_xRL.ipynb new file mode 100644 index 00000000..3be4b14d --- /dev/null +++ b/examples/notebooks/09_xRL.ipynb @@ -0,0 +1,1071 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e62e00c9", + "metadata": {}, + "source": [ + "# **Explainable Reinforcement Learning Tutorial**" + ] + }, + { + "cell_type": "markdown", + "id": "fb3aa803", + "metadata": {}, + "source": [ + "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "0d793362", + "metadata": {}, + "source": [ + "**Table of Contents**" + ] + }, + { + "cell_type": "markdown", + "id": "87bdf688", + "metadata": {}, + "source": [ + "1. [Introduction](#introduction)\n", + "\n", + " 1.1. [Multi-Agent Deep Reinforcement Learning](#11-multi-agent-deep-reinforcement-learning)\n", + "\n", + " 1.2. Prerequisites\n", + "\n", + "2. [Explainable AI and SHAP Values](#2-explainable-ai-and-shap-values)\n", + "\n", + " 2.1 Understanding Explainable AI \n", + "\n", + " 2.2 Introduction to SHAP Values \n", + "\n", + "3. [Calculating SHAP values](#3-calculating-shap-values)\n", + "\n", + " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", + "\n", + " 3.2. [Creating a SHAP Explainer](#32-creating-a-shap-explainer)\n", + " \n", + "4. [Visualizing SHAP Values](#visualizing-shap-values)\n", + "5. [Conclusion](#conclusion)\n", + "6. [Additional Resources](#additional-resources)" + ] + }, + { + "cell_type": "markdown", + "id": "5e8c7fec", + "metadata": {}, + "source": [ + "## 1. Introduction " + ] + }, + { + "cell_type": "markdown", + "id": "06e91420", + "metadata": {}, + "source": [ + "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." + ] + }, + { + "cell_type": "markdown", + "id": "47b1e7ab", + "metadata": {}, + "source": [ + "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." + ] + }, + { + "cell_type": "markdown", + "id": "ec0717c1", + "metadata": {}, + "source": [ + "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "0d59bb0a", + "metadata": {}, + "source": [ + "### 1.1 Multi-Agent Deep Reinforcement Learning \n", + "\n", + "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", + "\n", + "\n", + "- **Observations**: Each agent receives observations comprising market forecasts, unit-specific information, and past actions.\n", + "- **Actions**: Agents decide on bidding strategies, such as bid prices for inflexible and flexible capacities.\n", + "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", + "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", + "\n", + "For a deep dive into the RL configurations we refer to one of the other tutorials, such as \n", + "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", + "\n", + "Agents need observations to make informed decisions. Observations include:\n", + "\n", + "- **Residual Load Forecast**: Forecasted net demand over the next 24 hours.\n", + "- **Price Forecast**: Forecasted market prices over the next 24 hours.\n", + "- **Marginal Cost**: Current marginal cost of the unit.\n", + "- **Previous Output**: Dispatched capacity from the previous time step.\n", + "\n", + "\n", + "Agents choose actions based on the observations. The action space is two-dimensional, corresponding to:\n", + "\n", + "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", + "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", + "\n", + "\n", + "#### Run an the simulation MADRL Simulation\n", + "\n", + "Similar to the other tutorial, we can run Assume in the following way. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee220130", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install 'assume-framework[learning]'\n", + "#!git clone https://github.com/assume-framework/assume.git assume-repo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfd1daf2", + "metadata": {}, + "outputs": [], + "source": [ + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "colab_inputs_path = \"assume-repo/examples/inputs\"\n", + "local_inputs_path = \"../inputs\"\n", + "\n", + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path" + ] + }, + { + "cell_type": "markdown", + "id": "3f0f38fb", + "metadata": {}, + "source": [ + "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "201251c6", + "metadata": {}, + "outputs": [], + "source": [ + "# @title Overwrite run_learning function with enhanced logging\n", + "\n", + "import json\n", + "import logging\n", + "import os\n", + "from collections import defaultdict\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import yaml\n", + "from tqdm import tqdm\n", + "\n", + "from assume.common.exceptions import AssumeException\n", + "from assume.scenario.loader_csv import (\n", + " load_config_and_create_forecaster,\n", + " setup_world,\n", + ")\n", + "from assume.world import World\n", + "\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "\n", + "def run_learning(\n", + " world: World,\n", + " inputs_path: str,\n", + " scenario: str,\n", + " study_case: str,\n", + " verbose: bool = False,\n", + ") -> None:\n", + " \"\"\"\n", + " Train Deep Reinforcement Learning (DRL) agents to act in a simulated market environment.\n", + "\n", + " This function runs multiple episodes of simulation to train DRL agents, performs evaluation, and saves the best runs. It maintains the buffer and learned agents in memory to avoid resetting them with each new run.\n", + "\n", + " Args:\n", + " world (World): An instance of the World class representing the simulation environment.\n", + " inputs_path (str): The path to the folder containing input files necessary for the simulation.\n", + " scenario (str): The name of the scenario for the simulation.\n", + " study_case (str): The specific study case for the simulation.\n", + "\n", + " Note:\n", + " - The function uses a ReplayBuffer to store experiences for training the DRL agents.\n", + " - It iterates through training episodes, updating the agents and evaluating their performance at regular intervals.\n", + " - Initial exploration is active at the beginning and is disabled after a certain number of episodes to improve the performance of DRL algorithms.\n", + " - Upon completion of training, the function performs an evaluation run using the best policy learned during training.\n", + " - The best policies are chosen based on the average reward obtained during the evaluation runs, and they are saved for future use.\n", + " \"\"\"\n", + " from assume.reinforcement_learning.buffer import ReplayBuffer\n", + "\n", + " if not verbose:\n", + " logger.setLevel(logging.WARNING)\n", + "\n", + " # remove csv path so that nothing is written while learning\n", + " temp_csv_path = world.export_csv_path\n", + " world.export_csv_path = \"\"\n", + "\n", + " # initialize policies already here to set the obs_dim and act_dim in the learning role\n", + " actors_and_critics = None\n", + " world.learning_role.initialize_policy(actors_and_critics=actors_and_critics)\n", + " world.output_role.del_similar_runs()\n", + "\n", + " # check if we already stored policies for this simualtion\n", + " save_path = world.learning_config[\"trained_policies_save_path\"]\n", + "\n", + " if Path(save_path).is_dir():\n", + " # we are in learning mode and about to train new policies, which might overwrite existing ones\n", + " accept = input(\n", + " f\"{save_path=} exists - should we overwrite current learnings? (y/N) \"\n", + " )\n", + " if not accept.lower().startswith(\"y\"):\n", + " # stop here - do not start learning or save anything\n", + " raise AssumeException(\"don't overwrite existing strategies\")\n", + "\n", + " # -----------------------------------------\n", + " # Load scenario data to reuse across episodes\n", + " scenario_data = load_config_and_create_forecaster(inputs_path, scenario, study_case)\n", + "\n", + " # -----------------------------------------\n", + " # Information that needs to be stored across episodes, aka one simulation run\n", + " inter_episodic_data = {\n", + " \"buffer\": ReplayBuffer(\n", + " buffer_size=int(world.learning_config.get(\"replay_buffer_size\", 5e5)),\n", + " obs_dim=world.learning_role.rl_algorithm.obs_dim,\n", + " act_dim=world.learning_role.rl_algorithm.act_dim,\n", + " n_rl_units=len(world.learning_role.rl_strats),\n", + " device=world.learning_role.device,\n", + " float_type=world.learning_role.float_type,\n", + " ),\n", + " \"actors_and_critics\": None,\n", + " \"max_eval\": defaultdict(lambda: -1e9),\n", + " \"all_eval\": defaultdict(list),\n", + " \"avg_all_eval\": [],\n", + " \"episodes_done\": 0,\n", + " \"eval_episodes_done\": 0,\n", + " \"noise_scale\": world.learning_config.get(\"noise_scale\", 1.0),\n", + " }\n", + "\n", + " # -----------------------------------------\n", + "\n", + " validation_interval = min(\n", + " world.learning_role.training_episodes,\n", + " world.learning_config.get(\"validation_episodes_interval\", 5),\n", + " )\n", + "\n", + " eval_episode = 1\n", + "\n", + " for episode in tqdm(\n", + " range(1, world.learning_role.training_episodes + 1),\n", + " desc=\"Training Episodes\",\n", + " ):\n", + " # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\n", + " if episode != 1:\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " episode=episode,\n", + " )\n", + "\n", + " # -----------------------------------------\n", + " # Give the newly initliazed learning role the needed information across episodes\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " # -----------------------------------------\n", + " # Store updated information across episodes\n", + " inter_episodic_data = world.learning_role.get_inter_episodic_data()\n", + " inter_episodic_data[\"episodes_done\"] = episode\n", + "\n", + " # evaluation run:\n", + " if (\n", + " episode % validation_interval == 0\n", + " and episode\n", + " >= world.learning_role.episodes_collecting_initial_experience\n", + " + validation_interval\n", + " ):\n", + " world.reset()\n", + "\n", + " # load evaluation run\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " perform_evaluation=True,\n", + " eval_episode=eval_episode,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " total_rewards = world.output_role.get_sum_reward()\n", + " avg_reward = np.mean(total_rewards)\n", + " # check reward improvement in evaluation run\n", + " # and store best run in eval folder\n", + " terminate = world.learning_role.compare_and_save_policies(\n", + " {\"avg_reward\": avg_reward}\n", + " )\n", + "\n", + " inter_episodic_data[\"eval_episodes_done\"] = eval_episode\n", + "\n", + " # if we have not improved in the last x evaluations, we stop loop\n", + " if terminate:\n", + " break\n", + "\n", + " eval_episode += 1\n", + "\n", + " world.reset()\n", + "\n", + " # if at end of simulation save last policies\n", + " if episode == (world.learning_role.training_episodes):\n", + " world.learning_role.rl_algorithm.save_params(\n", + " directory=f\"{world.learning_role.trained_policies_save_path}/last_policies\"\n", + " )\n", + "\n", + " # export buffer_obs.json in the last training episode to get observations later\n", + " export = inter_episodic_data[\"buffer\"].observations.tolist()\n", + " path = f\"{world.learning_role.trained_policies_save_path}/buffer_obs\"\n", + " os.makedirs(path, exist_ok=True)\n", + " with open(os.path.join(path, \"buffer_obs.json\"), \"w\") as f:\n", + " json.dump(export, f)\n", + "\n", + " # container shutdown implicitly with new initialisation\n", + " logger.info(\"################\")\n", + " logger.info(\"Training finished, Start evaluation run\")\n", + " world.export_csv_path = temp_csv_path\n", + "\n", + " world.reset()\n", + "\n", + " # load scenario for evaluation\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " terminate_learning=True,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfadf522", + "metadata": {}, + "outputs": [], + "source": [ + "#!cd assume-repo && assume -s example_02a -db \"sqlite:///./examples/local_db/assume_db_example_02a.db\"" + ] + }, + { + "cell_type": "markdown", + "id": "e77c41cf", + "metadata": {}, + "source": [ + "### 1.2. Prerequisites" + ] + }, + { + "cell_type": "markdown", + "id": "cbf18570", + "metadata": {}, + "source": [ + "To follow along with this tutorial, we need some additional libraries." + ] + }, + { + "cell_type": "markdown", + "id": "a5ac592c", + "metadata": {}, + "source": [ + "- `matplotlib`\n", + "- `shap`\n", + "- `scikit-learn`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae266ecb", + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "!pip install matplotlib\n", + "!pip install shap==0.42.1\n", + "!pip install scikit-learn==1.3.0" + ] + }, + { + "cell_type": "markdown", + "id": "5069ff93", + "metadata": {}, + "source": [ + "## 2. Explainable AI and SHAP Values \n", + "\n", + "### 2.1 Understanding Explainable AI \n", + "Explainable AI (XAI) refers to techniques and methods that make the behavior and decisions of AI systems understandable to humans. In the context of complex models like deep neural networks, XAI helps to:\n", + "- Increase Transparency: Providing insights into how models make decisions.\n", + "- Build Trust: Users and stakeholders can trust AI systems if they understand them.\n", + "- Ensure Compliance: Regulatory requirements often demand explainability.\n", + "- Improve Models: Identifying weaknesses or biases in models.\n", + "\n", + "\n", + "### 2.2 Introduction to SHAP Values \n", + "Shapley values are a method from cooperative game theory used to explain the contribution of each feature to the prediction of a machine learning model, such as a neural network. They provide an interpretability technique by distributing the \"payout\" (the prediction) among the input features, attributing the importance of each feature to the prediction.\n", + "\n", + "For a given prediction, the Shapley value of a feature represents the average contribution of that feature to the prediction, considering all possible combinations of other features.\n", + "\n", + "1. **Marginal Contribution**: \n", + " The marginal contribution of a feature is the difference between the prediction with and without that feature.\n", + "\n", + "2. **Average over all subsets**: \n", + " The Shapley value is calculated by averaging the marginal contributions over all possible subsets of features.\n", + "\n", + "The formula for the Shapley value of feature $i$ is:\n", + "\n", + "$$\n", + "\\phi_i = \\sum_{S \\subseteq N \\setminus \\{i\\}} \\frac{|S|!(|N| - |S| - 1)!}{|N|!} \\cdot \\left( f(S \\cup \\{i\\}) - f(S) \\right)\n", + "$$\n", + "\n", + "Where:\n", + "- $N$ is the set of all features.\n", + "- $S$ is a subset of features.\n", + "- $f(S)$ is the model’s prediction when using only the features in subset $S$.\n", + "\n", + "\n", + "The `shap` library is a popular tool for computing Shapley values for machine learning models, including neural networks.\n", + "\n", + "\n", + "\n", + "Why Use SHAP in RL?\n", + "- Model-Agnostic: Applicable to any machine learning model, including neural networks.\n", + "- Local Explanations: Provides explanations for individual predictions (actions).\n", + "- Consistency: Ensures that features contributing more to the prediction have higher Shapley values.\n", + "\n", + "\n", + "Properties of SHAP:\n", + "1. Local Accuracy: The sum of Shapley values equals the difference between the model output and the expected output.\n", + "2. Missingness: Features not present in the model have zero Shapley value.\n", + "3. Consistency: If a model changes so that a feature contributes more to the prediction, the Shapley value of that feature should not decrease." + ] + }, + { + "cell_type": "markdown", + "id": "21d49573", + "metadata": {}, + "source": [ + "## 3. Calculating SHAP values " + ] + }, + { + "cell_type": "markdown", + "id": "d16712fc", + "metadata": {}, + "source": [ + "We will work with:" + ] + }, + { + "cell_type": "markdown", + "id": "2c0c1262", + "metadata": {}, + "source": [ + "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", + "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." + ] + }, + { + "cell_type": "markdown", + "id": "2fb6fc14", + "metadata": {}, + "source": [ + "Our goal is to:" + ] + }, + { + "cell_type": "markdown", + "id": "e0b69db6", + "metadata": {}, + "source": [ + "- Load the observations and the trained actor model.\n", + "- Use the model to predict actions.\n", + "- Apply SHAP to explain the model's predictions." + ] + }, + { + "cell_type": "markdown", + "id": "f870b3e8", + "metadata": {}, + "source": [ + "### 3.1. Loading and Preparing Data " + ] + }, + { + "cell_type": "markdown", + "id": "aaa7c3d3", + "metadata": {}, + "source": [ + "First, let's load the necessary libraries and the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6ee4f28", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import shap\n", + "import torch as th\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "aa268f14", + "metadata": {}, + "source": [ + "the simulation common.py contains utility functions and class definitions\n", + "from common import load_observations, Actor" + ] + }, + { + "cell_type": "markdown", + "id": "f866385a", + "metadata": {}, + "source": [ + "**Define the Actor Neural Network Class**" + ] + }, + { + "cell_type": "markdown", + "id": "ff7cf03a", + "metadata": {}, + "source": [ + "We define the actor neural network class that will be used to predict actions based on observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7216677a", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "from assume.reinforcement_learning.neural_network_architecture import MLPActor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a4d6e17", + "metadata": {}, + "outputs": [], + "source": [ + "def load_config(file_path):\n", + " \"\"\"\n", + " Load the configuration file.\n", + " \"\"\"\n", + " with open(file_path) as file:\n", + " config = yaml.safe_load(file)\n", + " return config\n", + "\n", + "\n", + "# Some Variable definitions:\n", + "\n", + "EPISODES = 3\n", + "NUMBER_OF_AGENTS = 1\n", + "SIM_TIMESPAN_DAYS = 31\n", + "ACTOR_NUM = 1\n", + "EXAMPLE = 1\n", + "\n", + "SIM_TIMESPAN_HOURS = SIM_TIMESPAN_DAYS * 24\n", + "\n", + "# actor 1-5 are the default non-rl actors, so we just skip those\n", + "ACTOR_NUM_ADJ = ACTOR_NUM + 6 # 6 #9\n", + "\n", + "\n", + "# Get the current working directory\n", + "current_dir = os.getcwd()\n", + "# Go up one level\n", + "one_level_up = os.path.dirname(current_dir)\n", + "# Go up two levels\n", + "two_levels_up = os.path.dirname(one_level_up)\n", + "\n", + "# Paths\n", + "path = os.path.join(\n", + " two_levels_up,\n", + " f\"assume/examples/output/{EXAMPLE}/{EPISODES}_episodes_{SIM_TIMESPAN_DAYS}_simDays_{NUMBER_OF_AGENTS}_rlAgents\",\n", + ")\n", + "actor_path = os.path.join(path, f\"actor_pp_{ACTOR_NUM_ADJ}.pt\")\n", + "\n", + "# DEFINTIONS" + ] + }, + { + "cell_type": "markdown", + "id": "ddfe95d9", + "metadata": {}, + "source": [ + "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44862f06", + "metadata": {}, + "outputs": [], + "source": [ + "# @title Load observations function\n", + "\n", + "\n", + "def load_observations(path, ACTOR_NUM, feature_names):\n", + " # Load observations\n", + " obs_path = f\"{path}/buffer_obs.json\"\n", + "\n", + " with open(obs_path) as file:\n", + " json_data = json.load(file)\n", + "\n", + " # Convert the list of lists into a 2D numpy array\n", + " input_data = np.array(json_data)\n", + " input_data = np.squeeze(input_data)\n", + "\n", + " # filter out arrays where all value are 0\n", + " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", + "\n", + " # filter only first 700 observations\n", + " input_data = input_data[:700]\n", + "\n", + " if NUMBER_OF_AGENTS == 1:\n", + " return pd.DataFrame(input_data, columns=feature_names), input_data\n", + " else:\n", + " return pd.DataFrame(\n", + " input_data[:, ACTOR_NUM], columns=feature_names\n", + " ), input_data[:, ACTOR_NUM]" + ] + }, + { + "cell_type": "markdown", + "id": "444b8679", + "metadata": {}, + "source": [ + "**Define Paths and Parameters**" + ] + }, + { + "cell_type": "markdown", + "id": "7f218d15", + "metadata": {}, + "source": [ + "Adjust the following paths and parameters according to your data and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fa78b12", + "metadata": {}, + "outputs": [], + "source": [ + "path = (\n", + " inputs_path + \"/example_02a/learned_strategies/base/buffer_obs\"\n", + ") # Replace with your data path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f9189ca", + "metadata": {}, + "outputs": [], + "source": [ + "# Define feature names (replace with actual feature names)\n", + "# make columns names\n", + "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", + "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", + "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" + ] + }, + { + "cell_type": "markdown", + "id": "6c5f1986", + "metadata": {}, + "source": [ + "**Load Observations and Input Data**" + ] + }, + { + "cell_type": "markdown", + "id": "36ae8f9e", + "metadata": {}, + "source": [ + "Load the observations and input data using the utility function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d522969d", + "metadata": {}, + "outputs": [], + "source": [ + "df_obs, input_data = load_observations(path, ACTOR_NUM, feature_names)\n", + "\n", + "df_obs" + ] + }, + { + "cell_type": "markdown", + "id": "5d8b9dcf", + "metadata": {}, + "source": [ + "**Load the Trained Actor Model**" + ] + }, + { + "cell_type": "markdown", + "id": "b1b50488", + "metadata": {}, + "source": [ + "We initialize and load the trained actor neural network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4da4de57", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the model\n", + "obs_dim = len(feature_names)\n", + "act_dim = 2 # Adjust if your model outputs a different number of actions\n", + "model = MLPActor(obs_dim=obs_dim, act_dim=act_dim, float_type=th.float)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37adecfa", + "metadata": {}, + "outputs": [], + "source": [ + "ACTOR_NUM = 1 # Replace with the actor number or identifier\n", + "actor_path = (\n", + " inputs_path\n", + " + \"/example_02a/learned_strategies/base/last_policies/actors/actor_pp_6.pt\"\n", + ") # Path to the trained actor model\n", + "\n", + "# Load the trained model parameters\n", + "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", + "model.load_state_dict(model_state[\"actor\"])" + ] + }, + { + "cell_type": "markdown", + "id": "d4a63712", + "metadata": {}, + "source": [ + "Get the actions base on observation tensor we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6460cfb", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = []\n", + "for obs in input_data:\n", + " obs_tensor = th.tensor(obs, dtype=th.float)\n", + " prediction = model(obs_tensor)\n", + " predictions.append(prediction)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be91cd97", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " input_data, predictions, test_size=0.15, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b21c93c7", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert data to tensors\n", + "y_train = th.stack(y_train)\n", + "y_test = th.stack(y_test)\n", + "\n", + "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", + "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", + "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", + "y_test_tensor = th.tensor(y_test, dtype=th.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "ddd1ab1e", + "metadata": {}, + "source": [ + "## 3.2. Creating a SHAP Explainer " + ] + }, + { + "cell_type": "markdown", + "id": "ae7b108b", + "metadata": {}, + "source": [ + "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d9be211", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "# Define a prediction function for SHAP\n", + "def model_predict(X):\n", + " X_tensor = th.tensor(X, dtype=th.float32)\n", + " model.eval()\n", + " with th.no_grad():\n", + " return model(X_tensor).numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0f7857b", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a subset of training data for the background dataset\n", + "background_size = 100 # Adjust the size as needed\n", + "background = X_train[:background_size]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84bb96cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the SHAP Kernel Explainer\n", + "explainer = shap.KernelExplainer(model_predict, background)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a7929e4", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate SHAP values for the test set\n", + "shap_values = explainer.shap_values(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44fa277f", + "metadata": {}, + "outputs": [], + "source": [ + "shap_values" + ] + }, + { + "cell_type": "markdown", + "id": "c1f3d550", + "metadata": {}, + "source": [ + "## 4. Visualizing SHAP Values " + ] + }, + { + "cell_type": "markdown", + "id": "3a0f0cbe", + "metadata": {}, + "source": [ + "We generate summary plots to visualize feature importance for each output dimension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6af4983", + "metadata": {}, + "outputs": [], + "source": [ + "print(shap_values[0].shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e318a5b", + "metadata": {}, + "outputs": [], + "source": [ + "# Summary plot for the first output dimension\n", + "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", + "plt.title(\"Summary Plot for Output Dimension 0, p_inflex\")\n", + "plt.show()\n", + "\n", + "# Summary plot for the second output dimension\n", + "shap.summary_plot(shap_values[1], X_test, feature_names=feature_names, show=False)\n", + "plt.title(\"Summary Plot for Output Dimension 1, p_flex\")\n", + "plt.show()\n", + "\n", + "shap.summary_plot(\n", + " shap_values[0],\n", + " X_test,\n", + " feature_names=feature_names,\n", + " plot_type=\"bar\",\n", + " title=\"Summary Bar Plot for Output Dimension 0\",\n", + ")\n", + "\n", + "shap.summary_plot(\n", + " shap_values[1],\n", + " X_test,\n", + " feature_names=feature_names,\n", + " plot_type=\"bar\",\n", + " title=\"Summary Bar Plot for Output Dimension 1\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9a888f8b", + "metadata": {}, + "source": [ + "The SHAP summary plots show the impact of each feature on the model's predictions for each output dimension (action). Features with larger absolute SHAP values have a more significant influence on the decision-making process of the RL agent." + ] + }, + { + "cell_type": "markdown", + "id": "c6c4ce8c", + "metadata": {}, + "source": [ + "- **Positive SHAP Value**: Indicates that the feature contributes positively to the predicted action value.\n", + "- **Negative SHAP Value**: Indicates that the feature contributes negatively to the predicted action value." + ] + }, + { + "cell_type": "markdown", + "id": "86545200", + "metadata": {}, + "source": [ + "By analyzing these plots, we can identify which features are most influential and understand how changes in feature values affect the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "06f3977c", + "metadata": {}, + "source": [ + "## 5. Conclusion " + ] + }, + { + "cell_type": "markdown", + "id": "dadd0a0c", + "metadata": {}, + "source": [ + "In this tutorial, we've demonstrated how to apply SHAP to a reinforcement learning agent to explain its decision-making process. By interpreting the SHAP values, we gain valuable insights into which features influence the agent's actions, enhancing transparency and trust in the model." + ] + }, + { + "cell_type": "markdown", + "id": "37633c16", + "metadata": {}, + "source": [ + "Explainability is crucial, especially when deploying RL agents in real-world applications where understanding the rationale behind decisions is essential for safety, fairness, and compliance." + ] + }, + { + "cell_type": "markdown", + "id": "8735d66f", + "metadata": {}, + "source": [ + "## 6. Additional Resources " + ] + }, + { + "cell_type": "markdown", + "id": "d6b0332f", + "metadata": {}, + "source": [ + "- **SHAP Documentation**: [https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)\n", + "- **PyTorch Documentation**: [https://pytorch.org/docs/stable/index.html](https://pytorch.org/docs/stable/index.html)\n", + "- **Reinforcement Learning Introduction**: [Richard S. Sutton and Andrew G. Barto, \"Reinforcement Learning: An Introduction\"](http://incompleteideas.net/book/the-book-2nd.html)\n", + "- **Interpretable Machine Learning Book**: [https://christophm.github.io/interpretable-ml-book/](https://christophm.github.io/interpretable-ml-book/)" + ] + }, + { + "cell_type": "markdown", + "id": "a8cdea5f", + "metadata": {}, + "source": [ + "**Feel free to experiment with the code and explore different explainability techniques. Happy learning!**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 9b6e4ab93e427b7c7417d6dbf0768cbe90d657cb Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Sun, 6 Oct 2024 16:03:22 +0200 Subject: [PATCH 12/27] - added example sim of market splitting tutorial to xRL tut - should be runabele so far but I have some problems with glpk, needs debugging --- .../notebooks/09_example_Sim_and_xRL.ipynb | 2588 +++++++++++++++++ .../09_example_Sim_and_xRL.ipynb.license | 3 + examples/notebooks/09_xRL.ipynb | 1071 ------- 3 files changed, 2591 insertions(+), 1071 deletions(-) create mode 100644 examples/notebooks/09_example_Sim_and_xRL.ipynb create mode 100644 examples/notebooks/09_example_Sim_and_xRL.ipynb.license delete mode 100644 examples/notebooks/09_xRL.ipynb diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb new file mode 100644 index 00000000..cc7b905e --- /dev/null +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -0,0 +1,2588 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e62e00c9", + "metadata": {}, + "source": [ + "# **Explainable Reinforcement Learning Tutorial**" + ] + }, + { + "cell_type": "markdown", + "id": "fb3aa803", + "metadata": {}, + "source": [ + "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "0d793362", + "metadata": {}, + "source": [ + "**Table of Contents**" + ] + }, + { + "cell_type": "markdown", + "id": "87bdf688", + "metadata": {}, + "source": [ + "1. [Introduction](#introduction)\n", + "\n", + " 1.1. [Multi-Agent Deep Reinforcement Learning with Market Splitting](#11-multi-agent-deep-reinforcement-learning)\n", + "\n", + " 1.2. Prerequisites\n", + "\n", + "2. [Explainable AI and SHAP Values](#2-explainable-ai-and-shap-values)\n", + "\n", + " 2.1 Understanding Explainable AI \n", + "\n", + " 2.2 Introduction to SHAP Values \n", + "\n", + "3. [Calculating SHAP values](#3-calculating-shap-values)\n", + "\n", + " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", + "\n", + " 3.2. [Creating a SHAP Explainer](#32-creating-a-shap-explainer)\n", + " \n", + "4. [Visualizing SHAP Values](#visualizing-shap-values)\n", + "5. [Conclusion](#conclusion)\n", + "6. [Additional Resources](#additional-resources)" + ] + }, + { + "cell_type": "markdown", + "id": "5e8c7fec", + "metadata": {}, + "source": [ + "## 1. Introduction " + ] + }, + { + "cell_type": "markdown", + "id": "06e91420", + "metadata": {}, + "source": [ + "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." + ] + }, + { + "cell_type": "markdown", + "id": "47b1e7ab", + "metadata": {}, + "source": [ + "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." + ] + }, + { + "cell_type": "markdown", + "id": "ec0717c1", + "metadata": {}, + "source": [ + "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "0d59bb0a", + "metadata": {}, + "source": [ + "### 1.1 Multi-Agent Deep Reinforcement Learning \n", + "\n", + "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", + "\n", + "\n", + "- **Observations**: Each agent receives observations comprising market forecasts, unit-specific information, and past actions.\n", + "- **Actions**: Agents decide on bidding strategies, such as bid prices for inflexible and flexible capacities.\n", + "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", + "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", + "\n", + "For a deep dive into the RL configurations we refer to one of the other tutorials, such as \n", + "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", + "\n", + "Agents need observations to make informed decisions. Observations include:\n", + "\n", + "- **Residual Load Forecast**: Forecasted net demand over the next 24 hours.\n", + "- **Price Forecast**: Forecasted market prices over the next 24 hours.\n", + "- **Marginal Cost**: Current marginal cost of the unit.\n", + "- **Previous Output**: Dispatched capacity from the previous time step.\n", + "\n", + "\n", + "Agents choose actions based on the observations. The action space is two-dimensional, corresponding to:\n", + "\n", + "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", + "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", + "\n", + "\n", + "#### Run an the simulation MADRL Simulation\n", + "\n", + "Similar to the other tutorial, we can run Assume in the following way. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ee220130", + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install 'assume-framework[learning]'\n", + "#!pip install plotly\n", + "#!git clone https://github.com/assume-framework/assume.git assume-repo" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "75cd480a", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# import plotly for visualization\n", + "import plotly.graph_objects as go\n", + "\n", + "# import yaml for reading and writing YAML files\n", + "import yaml\n", + "\n", + "# Function to display DataFrame in Jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bfd1daf2", + "metadata": {}, + "outputs": [], + "source": [ + "import importlib.util\n", + "\n", + "# Check if 'google.colab' is available\n", + "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", + "\n", + "colab_inputs_path = \"assume-repo/examples/inputs\"\n", + "local_inputs_path = \"../inputs\"\n", + "\n", + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path" + ] + }, + { + "cell_type": "markdown", + "id": "636ea9ae", + "metadata": {}, + "source": [ + "Load the created example files from the tutorial before:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "233f315b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input CSV files have been read from 'inputs/tutorial_08'.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "# Define the input directory\n", + "input_dir = \"inputs/tutorial_08\"\n", + "\n", + "# Read the DataFrames from CSV files\n", + "buses = pd.read_csv(os.path.join(input_dir, \"buses.csv\"))\n", + "lines = pd.read_csv(os.path.join(input_dir, \"lines.csv\"))\n", + "powerplant_units = pd.read_csv(os.path.join(input_dir, \"powerplant_units.csv\"))\n", + "demand_units = pd.read_csv(os.path.join(input_dir, \"demand_units.csv\"))\n", + "demand_df = pd.read_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", + "\n", + "# extend demand_df for another day with the same demand profile\n", + "demand_df = pd.concat([demand_df, demand_df])\n", + "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=96, freq=\"h\")\n", + "\n", + "# Read the fuel prices DataFrame from CSV file\n", + "fuel_prices_df = pd.read_csv(os.path.join(input_dir, \"fuel_prices_df.csv\"))\n", + "\n", + "\n", + "print(\"Input CSV files have been read from 'inputs/tutorial_08'.\")" + ] + }, + { + "cell_type": "markdown", + "id": "6985289b", + "metadata": {}, + "source": [ + "**Let's make this a leanring example**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b205256f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nametechnologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
0Unit 1nuclearnaive_eomuranium0.01000.00.00.35north_1Operator North
1Unit 2nuclearnaive_eomuranium0.01000.00.00.36north_1Operator North
2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
3Unit 4nuclearnaive_eomuranium0.01000.00.00.38north_1Operator North
4Unit 5nuclearnaive_eomuranium0.01000.00.00.39north_1Operator North
5Unit 6nuclearnaive_eomuranium0.01000.00.00.310north_1Operator North
6Unit 7nuclearnaive_eomuranium0.01000.00.00.311north_1Operator North
7Unit 8nuclearnaive_eomuranium0.01000.00.00.312north_1Operator North
8Unit 9nuclearnaive_eomuranium0.01000.00.00.313north_2Operator North
9Unit 10nuclearnaive_eomuranium0.01000.00.00.314north_2Operator North
10Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
11Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
12Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
13Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
14Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
15Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
16Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
17Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
18Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
19Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator South
\n", + "
" + ], + "text/plain": [ + " name technology bidding_zonal fuel_type emission_factor max_power \\\n", + "0 Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", + "1 Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", + "2 Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", + "3 Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", + "4 Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", + "5 Unit 6 nuclear naive_eom uranium 0.0 1000.0 \n", + "6 Unit 7 nuclear naive_eom uranium 0.0 1000.0 \n", + "7 Unit 8 nuclear naive_eom uranium 0.0 1000.0 \n", + "8 Unit 9 nuclear naive_eom uranium 0.0 1000.0 \n", + "9 Unit 10 nuclear naive_eom uranium 0.0 1000.0 \n", + "10 Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", + "11 Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", + "12 Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", + "13 Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", + "14 Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", + "15 Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", + "16 Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", + "17 Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", + "18 Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", + "19 Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", + "\n", + " min_power efficiency additional_cost node unit_operator \n", + "0 0.0 0.3 5 north_1 Operator North \n", + "1 0.0 0.3 6 north_1 Operator North \n", + "2 0.0 0.3 7 north_1 Operator North \n", + "3 0.0 0.3 8 north_1 Operator North \n", + "4 0.0 0.3 9 north_1 Operator North \n", + "5 0.0 0.3 10 north_1 Operator North \n", + "6 0.0 0.3 11 north_1 Operator North \n", + "7 0.0 0.3 12 north_1 Operator North \n", + "8 0.0 0.3 13 north_2 Operator North \n", + "9 0.0 0.3 14 north_2 Operator North \n", + "10 0.0 0.3 15 north_2 Operator North \n", + "11 0.0 0.3 16 north_2 Operator North \n", + "12 0.0 0.3 17 north_2 Operator North \n", + "13 0.0 0.3 18 north_2 Operator North \n", + "14 0.0 0.3 19 north_2 Operator North \n", + "15 0.0 0.3 20 south Operator South \n", + "16 0.0 0.3 21 south Operator South \n", + "17 0.0 0.3 22 south Operator South \n", + "18 0.0 0.3 23 south Operator South \n", + "19 0.0 0.3 24 south Operator South " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create scarcity in south germany\n", + "powerplant_units = powerplant_units[:20]\n", + "\n", + "# assign RL power plant and give it market power\n", + "powerplant_units.loc[19, \"bidding_zonal\"] = \"pp_learning\"\n", + "powerplant_units.loc[19, \"max_power\"] = 5000\n", + "\n", + "powerplant_units" + ] + }, + { + "cell_type": "markdown", + "id": "cce0e8b4", + "metadata": {}, + "source": [ + "Change yaml to configure learning" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9c555ce9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Configuration YAML file has been saved to 'inputs/tutorial_08\\config.yaml'.\n" + ] + } + ], + "source": [ + "config = {\n", + " \"zonal_case\": {\n", + " \"start_date\": \"2019-01-01 00:00\",\n", + " \"end_date\": \"2019-01-01 23:00\",\n", + " \"time_step\": \"1h\",\n", + " \"save_frequency_hours\": 24,\n", + " \"markets_config\": {\n", + " \"zonal\": {\n", + " \"operator\": \"EOM_operator\",\n", + " \"product_type\": \"energy\",\n", + " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", + " \"opening_frequency\": \"1h\",\n", + " \"opening_duration\": \"1h\",\n", + " \"volume_unit\": \"MWh\",\n", + " \"maximum_bid_volume\": 100000,\n", + " \"maximum_bid_price\": 3000,\n", + " \"minimum_bid_price\": -500,\n", + " \"price_unit\": \"EUR/MWh\",\n", + " \"market_mechanism\": \"pay_as_clear_complex\",\n", + " \"additional_fields\": [\"bid_type\", \"node\"],\n", + " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", + " }\n", + " },\n", + " \"learning_config\": {\n", + " \"continue_learning\": False,\n", + " \"trained_policies_save_path\": \"null\",\n", + " \"max_bid_price\": 100,\n", + " \"algorithm\": \"matd3\",\n", + " \"learning_rate\": 0.001,\n", + " \"training_episodes\": 10,\n", + " \"episodes_collecting_initial_experience\": 3,\n", + " \"train_freq\": \"24h\",\n", + " \"gradient_steps\": -1,\n", + " \"batch_size\": 256,\n", + " \"gamma\": 0.99,\n", + " \"device\": \"cpu\",\n", + " \"noise_sigma\": 0.1,\n", + " \"noise_scale\": 1,\n", + " \"noise_dt\": 1,\n", + " \"validation_episodes_interval\": 5,\n", + " },\n", + " }\n", + "}\n", + "\n", + "# Define the path for the config file\n", + "config_path = os.path.join(input_dir, \"config.yaml\")\n", + "\n", + "# Save the configuration to a YAML file\n", + "with open(config_path, \"w\") as file:\n", + " yaml.dump(config, file, sort_keys=False)\n", + "\n", + "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f0f38fb", + "metadata": {}, + "source": [ + "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "201251c6", + "metadata": {}, + "outputs": [], + "source": [ + "# @title Overwrite run_learning function with enhanced logging\n", + "\n", + "import json\n", + "import logging\n", + "import os\n", + "from collections import defaultdict\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import yaml\n", + "from tqdm import tqdm\n", + "\n", + "from assume.common.exceptions import AssumeException\n", + "from assume.scenario.loader_csv import (\n", + " load_config_and_create_forecaster,\n", + " setup_world,\n", + ")\n", + "from assume.world import World\n", + "\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "\n", + "def run_learning(\n", + " world: World,\n", + " inputs_path: str,\n", + " scenario: str,\n", + " study_case: str,\n", + " verbose: bool = False,\n", + ") -> None:\n", + " \"\"\"\n", + " Train Deep Reinforcement Learning (DRL) agents to act in a simulated market environment.\n", + "\n", + " This function runs multiple episodes of simulation to train DRL agents, performs evaluation, and saves the best runs. It maintains the buffer and learned agents in memory to avoid resetting them with each new run.\n", + "\n", + " Args:\n", + " world (World): An instance of the World class representing the simulation environment.\n", + " inputs_path (str): The path to the folder containing input files necessary for the simulation.\n", + " scenario (str): The name of the scenario for the simulation.\n", + " study_case (str): The specific study case for the simulation.\n", + "\n", + " Note:\n", + " - The function uses a ReplayBuffer to store experiences for training the DRL agents.\n", + " - It iterates through training episodes, updating the agents and evaluating their performance at regular intervals.\n", + " - Initial exploration is active at the beginning and is disabled after a certain number of episodes to improve the performance of DRL algorithms.\n", + " - Upon completion of training, the function performs an evaluation run using the best policy learned during training.\n", + " - The best policies are chosen based on the average reward obtained during the evaluation runs, and they are saved for future use.\n", + " \"\"\"\n", + " from assume.reinforcement_learning.buffer import ReplayBuffer\n", + "\n", + " if not verbose:\n", + " logger.setLevel(logging.WARNING)\n", + "\n", + " # remove csv path so that nothing is written while learning\n", + " temp_csv_path = world.export_csv_path\n", + " world.export_csv_path = \"\"\n", + "\n", + " # initialize policies already here to set the obs_dim and act_dim in the learning role\n", + " actors_and_critics = None\n", + " world.learning_role.initialize_policy(actors_and_critics=actors_and_critics)\n", + " world.output_role.del_similar_runs()\n", + "\n", + " # check if we already stored policies for this simualtion\n", + " save_path = world.learning_config[\"trained_policies_save_path\"]\n", + "\n", + " if Path(save_path).is_dir():\n", + " # we are in learning mode and about to train new policies, which might overwrite existing ones\n", + " accept = input(\n", + " f\"{save_path=} exists - should we overwrite current learnings? (y/N) \"\n", + " )\n", + " if not accept.lower().startswith(\"y\"):\n", + " # stop here - do not start learning or save anything\n", + " raise AssumeException(\"don't overwrite existing strategies\")\n", + "\n", + " # -----------------------------------------\n", + " # Load scenario data to reuse across episodes\n", + " scenario_data = load_config_and_create_forecaster(inputs_path, scenario, study_case)\n", + "\n", + " # -----------------------------------------\n", + " # Information that needs to be stored across episodes, aka one simulation run\n", + " inter_episodic_data = {\n", + " \"buffer\": ReplayBuffer(\n", + " buffer_size=int(world.learning_config.get(\"replay_buffer_size\", 5e5)),\n", + " obs_dim=world.learning_role.rl_algorithm.obs_dim,\n", + " act_dim=world.learning_role.rl_algorithm.act_dim,\n", + " n_rl_units=len(world.learning_role.rl_strats),\n", + " device=world.learning_role.device,\n", + " float_type=world.learning_role.float_type,\n", + " ),\n", + " \"actors_and_critics\": None,\n", + " \"max_eval\": defaultdict(lambda: -1e9),\n", + " \"all_eval\": defaultdict(list),\n", + " \"avg_all_eval\": [],\n", + " \"episodes_done\": 0,\n", + " \"eval_episodes_done\": 0,\n", + " \"noise_scale\": world.learning_config.get(\"noise_scale\", 1.0),\n", + " }\n", + "\n", + " # -----------------------------------------\n", + "\n", + " validation_interval = min(\n", + " world.learning_role.training_episodes,\n", + " world.learning_config.get(\"validation_episodes_interval\", 5),\n", + " )\n", + "\n", + " eval_episode = 1\n", + "\n", + " for episode in tqdm(\n", + " range(1, world.learning_role.training_episodes + 1),\n", + " desc=\"Training Episodes\",\n", + " ):\n", + " # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\n", + " if episode != 1:\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " episode=episode,\n", + " )\n", + "\n", + " # -----------------------------------------\n", + " # Give the newly initliazed learning role the needed information across episodes\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " # -----------------------------------------\n", + " # Store updated information across episodes\n", + " inter_episodic_data = world.learning_role.get_inter_episodic_data()\n", + " inter_episodic_data[\"episodes_done\"] = episode\n", + "\n", + " # evaluation run:\n", + " if (\n", + " episode % validation_interval == 0\n", + " and episode\n", + " >= world.learning_role.episodes_collecting_initial_experience\n", + " + validation_interval\n", + " ):\n", + " world.reset()\n", + "\n", + " # load evaluation run\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " perform_evaluation=True,\n", + " eval_episode=eval_episode,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", + "\n", + " world.run()\n", + "\n", + " total_rewards = world.output_role.get_sum_reward()\n", + " avg_reward = np.mean(total_rewards)\n", + " # check reward improvement in evaluation run\n", + " # and store best run in eval folder\n", + " terminate = world.learning_role.compare_and_save_policies(\n", + " {\"avg_reward\": avg_reward}\n", + " )\n", + "\n", + " inter_episodic_data[\"eval_episodes_done\"] = eval_episode\n", + "\n", + " # if we have not improved in the last x evaluations, we stop loop\n", + " if terminate:\n", + " break\n", + "\n", + " eval_episode += 1\n", + "\n", + " world.reset()\n", + "\n", + " # if at end of simulation save last policies\n", + " if episode == (world.learning_role.training_episodes):\n", + " world.learning_role.rl_algorithm.save_params(\n", + " directory=f\"{world.learning_role.trained_policies_save_path}/last_policies\"\n", + " )\n", + "\n", + " # export buffer_obs.json in the last training episode to get observations later\n", + " export = inter_episodic_data[\"buffer\"].observations.tolist()\n", + " path = f\"{world.learning_role.trained_policies_save_path}/buffer_obs\"\n", + " os.makedirs(path, exist_ok=True)\n", + " with open(os.path.join(path, \"buffer_obs.json\"), \"w\") as f:\n", + " json.dump(export, f)\n", + "\n", + " # container shutdown implicitly with new initialisation\n", + " logger.info(\"################\")\n", + " logger.info(\"Training finished, Start evaluation run\")\n", + " world.export_csv_path = temp_csv_path\n", + "\n", + " world.reset()\n", + "\n", + " # load scenario for evaluation\n", + " setup_world(\n", + " world=world,\n", + " scenario_data=scenario_data,\n", + " study_case=study_case,\n", + " terminate_learning=True,\n", + " )\n", + "\n", + " world.learning_role.load_inter_episodic_data(inter_episodic_data)" + ] + }, + { + "cell_type": "markdown", + "id": "dcacfe26", + "metadata": {}, + "source": [ + "**Run the example case**" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bfadf522", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.world:connected to db\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/82800 [00:00 exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:07, 10368.18it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 01:00:00: 9%|▊ | 7201/82800 [00:00<00:04, 16445.89it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:04, 17504.61it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 03:00:00: 17%|█▋ | 14401/82800 [00:00<00:03, 19578.01it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 04:00:00: 22%|██▏ | 18001/82800 [00:00<00:02, 22414.37it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 05:00:00: 26%|██▌ | 21601/82800 [00:01<00:02, 23810.62it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:01<00:02, 25770.59it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 07:00:00: 35%|███▍ | 28801/82800 [00:01<00:02, 26101.95it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:01<00:01, 26708.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 09:00:00: 43%|████▎ | 36001/82800 [00:01<00:01, 27188.48it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 26675.60it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 11:00:00: 52%|█████▏ | 43201/82800 [00:01<00:01, 26480.14it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:02<00:01, 26144.45it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 13:00:00: 61%|██████ | 50401/82800 [00:02<00:01, 25340.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:02<00:01, 25567.01it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:02<00:00, 25677.37it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:02<00:00, 23504.02it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 17:00:00: 78%|███████▊ | 64801/82800 [00:02<00:00, 25143.88it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 26110.73it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 19:00:00: 87%|████████▋ | 72001/82800 [00:03<00:00, 26124.17it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:03<00:00, 26331.15it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:03<00:00, 26564.47it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", + " raise Exception(f\"None of {SOLVERS} are available\")\n", + "Exception: None of ['gurobi', 'glpk'] are available\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:03, 24831.41it/s] \n" + ] + } + ], + "source": [ + "# import the main World class and the load_scenario_folder functions from assume\n", + "from assume import World\n", + "from assume.scenario.loader_csv import load_scenario_folder\n", + "\n", + "# Define paths for input and output data\n", + "csv_path = \"outputs\"\n", + "\n", + "# Define the data format and database URI\n", + "# Use \"local_db\" for SQLite database or \"timescale\" for TimescaleDB in Docker\n", + "\n", + "# Create directories if they don't exist\n", + "os.makedirs(csv_path, exist_ok=True)\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + "\n", + "if data_format == \"local_db\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "elif data_format == \"timescale\":\n", + " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + "\n", + "# Create the World instance\n", + "world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + "# Load the scenario by providing the world instance\n", + "# The path to the inputs folder and the scenario name (subfolder in inputs)\n", + "# and the study case name (which config to use for the simulation)\n", + "load_scenario_folder(\n", + " world,\n", + " inputs_path=\"inputs\",\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + ")\n", + "\n", + "# run learning if learning mode is enabled\n", + "# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + "if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=\"inputs\",\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + " )\n", + "\n", + "# Run the simulation\n", + "world.run()" + ] + }, + { + "cell_type": "markdown", + "id": "2194f71b", + "metadata": {}, + "source": [ + "**Generate same plotly figure**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdb21cbe", + "metadata": {}, + "outputs": [], + "source": [ + "# @ title make makret price plotly figure\n", + "\n", + "\n", + "# Define the path to the simulation output\n", + "output_dir = \"outputs/tutorial_08_zonal_case\"\n", + "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", + "\n", + "# Load the market_meta.csv file\n", + "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", + "# drop the first column\n", + "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", + "\n", + "\n", + "# Extract unique zones\n", + "zones = market_meta[\"node\"].unique()\n", + "\n", + "# Initialize an empty DataFrame to store clearing prices per zone and time\n", + "clearing_prices_df = pd.DataFrame()\n", + "\n", + "# Populate the DataFrame with clearing prices for each zone\n", + "for zone in zones:\n", + " zone_data = market_meta[market_meta[\"node\"] == zone][[\"price\"]]\n", + " zone_data = zone_data.rename(columns={\"price\": f\"{zone}_price\"})\n", + " clearing_prices_df = (\n", + " pd.merge(\n", + " clearing_prices_df,\n", + " zone_data,\n", + " left_index=True,\n", + " right_index=True,\n", + " how=\"outer\",\n", + " )\n", + " if not clearing_prices_df.empty\n", + " else zone_data\n", + " )\n", + "\n", + "# Sort the DataFrame by time\n", + "clearing_prices_df = clearing_prices_df.sort_index()\n", + "\n", + "\n", + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices\n", + "for zone in zones:\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=clearing_prices_df.index,\n", + " y=clearing_prices_df[f\"{zone}_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Simulation\",\n", + " line=dict(width=2),\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Simulation Results\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price (EUR/MWh)\",\n", + " legend_title=\"Market Zones\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", + " ),\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", + ")\n", + "\n", + "# Display the interactive plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e77c41cf", + "metadata": {}, + "source": [ + "## 2. Explainable AI and SHAP Values \n", + "\n", + "### Prerequisites" + ] + }, + { + "cell_type": "markdown", + "id": "cbf18570", + "metadata": {}, + "source": [ + "To follow along with this tutorial, we need some additional libraries." + ] + }, + { + "cell_type": "markdown", + "id": "a5ac592c", + "metadata": {}, + "source": [ + "- `matplotlib`\n", + "- `shap`\n", + "- `scikit-learn`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae266ecb", + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "!pip install matplotlib\n", + "!pip install shap==0.42.1\n", + "!pip install scikit-learn==1.3.0" + ] + }, + { + "cell_type": "markdown", + "id": "5069ff93", + "metadata": {}, + "source": [ + "### 2.1 Understanding Explainable AI \n", + "Explainable AI (XAI) refers to techniques and methods that make the behavior and decisions of AI systems understandable to humans. In the context of complex models like deep neural networks, XAI helps to:\n", + "- Increase Transparency: Providing insights into how models make decisions.\n", + "- Build Trust: Users and stakeholders can trust AI systems if they understand them.\n", + "- Ensure Compliance: Regulatory requirements often demand explainability.\n", + "- Improve Models: Identifying weaknesses or biases in models.\n", + "\n", + "\n", + "### 2.2 Introduction to SHAP Values \n", + "Shapley values are a method from cooperative game theory used to explain the contribution of each feature to the prediction of a machine learning model, such as a neural network. They provide an interpretability technique by distributing the \"payout\" (the prediction) among the input features, attributing the importance of each feature to the prediction.\n", + "\n", + "For a given prediction, the Shapley value of a feature represents the average contribution of that feature to the prediction, considering all possible combinations of other features.\n", + "\n", + "1. **Marginal Contribution**: \n", + " The marginal contribution of a feature is the difference between the prediction with and without that feature.\n", + "\n", + "2. **Average over all subsets**: \n", + " The Shapley value is calculated by averaging the marginal contributions over all possible subsets of features.\n", + "\n", + "The formula for the Shapley value of feature $i$ is:\n", + "\n", + "$$\n", + "\\phi_i = \\sum_{S \\subseteq N \\setminus \\{i\\}} \\frac{|S|!(|N| - |S| - 1)!}{|N|!} \\cdot \\left( f(S \\cup \\{i\\}) - f(S) \\right)\n", + "$$\n", + "\n", + "Where:\n", + "- $N$ is the set of all features.\n", + "- $S$ is a subset of features.\n", + "- $f(S)$ is the model’s prediction when using only the features in subset $S$.\n", + "\n", + "\n", + "The `shap` library is a popular tool for computing Shapley values for machine learning models, including neural networks.\n", + "\n", + "\n", + "\n", + "Why Use SHAP in RL?\n", + "- Model-Agnostic: Applicable to any machine learning model, including neural networks.\n", + "- Local Explanations: Provides explanations for individual predictions (actions).\n", + "- Consistency: Ensures that features contributing more to the prediction have higher Shapley values.\n", + "\n", + "\n", + "Properties of SHAP:\n", + "1. Local Accuracy: The sum of Shapley values equals the difference between the model output and the expected output.\n", + "2. Missingness: Features not present in the model have zero Shapley value.\n", + "3. Consistency: If a model changes so that a feature contributes more to the prediction, the Shapley value of that feature should not decrease." + ] + }, + { + "cell_type": "markdown", + "id": "21d49573", + "metadata": {}, + "source": [ + "## 3. Calculating SHAP values " + ] + }, + { + "cell_type": "markdown", + "id": "d16712fc", + "metadata": {}, + "source": [ + "We will work with:" + ] + }, + { + "cell_type": "markdown", + "id": "2c0c1262", + "metadata": {}, + "source": [ + "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", + "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." + ] + }, + { + "cell_type": "markdown", + "id": "2fb6fc14", + "metadata": {}, + "source": [ + "Our goal is to:" + ] + }, + { + "cell_type": "markdown", + "id": "e0b69db6", + "metadata": {}, + "source": [ + "- Load the observations and the trained actor model.\n", + "- Use the model to predict actions.\n", + "- Apply SHAP to explain the model's predictions." + ] + }, + { + "cell_type": "markdown", + "id": "f870b3e8", + "metadata": {}, + "source": [ + "### 3.1. Loading and Preparing Data " + ] + }, + { + "cell_type": "markdown", + "id": "aaa7c3d3", + "metadata": {}, + "source": [ + "First, let's load the necessary libraries and the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6ee4f28", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import shap\n", + "import torch as th\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "aa268f14", + "metadata": {}, + "source": [ + "the simulation common.py contains utility functions and class definitions\n", + "from common import load_observations, Actor" + ] + }, + { + "cell_type": "markdown", + "id": "f866385a", + "metadata": {}, + "source": [ + "**Define the Actor Neural Network Class**" + ] + }, + { + "cell_type": "markdown", + "id": "ff7cf03a", + "metadata": {}, + "source": [ + "We define the actor neural network class that will be used to predict actions based on observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7216677a", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "from assume.reinforcement_learning.neural_network_architecture import MLPActor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a4d6e17", + "metadata": {}, + "outputs": [], + "source": [ + "def load_config(file_path):\n", + " \"\"\"\n", + " Load the configuration file.\n", + " \"\"\"\n", + " with open(file_path) as file:\n", + " config = yaml.safe_load(file)\n", + " return config\n", + "\n", + "\n", + "# Some Variable definitions:\n", + "\n", + "EPISODES = 3\n", + "NUMBER_OF_AGENTS = 1\n", + "SIM_TIMESPAN_DAYS = 31\n", + "ACTOR_NUM = 1\n", + "EXAMPLE = 1\n", + "\n", + "SIM_TIMESPAN_HOURS = SIM_TIMESPAN_DAYS * 24\n", + "\n", + "# actor 1-5 are the default non-rl actors, so we just skip those\n", + "ACTOR_NUM_ADJ = ACTOR_NUM + 6 # 6 #9\n", + "\n", + "\n", + "# Get the current working directory\n", + "current_dir = os.getcwd()\n", + "# Go up one level\n", + "one_level_up = os.path.dirname(current_dir)\n", + "# Go up two levels\n", + "two_levels_up = os.path.dirname(one_level_up)\n", + "\n", + "# Paths\n", + "path = os.path.join(\n", + " two_levels_up,\n", + " f\"assume/examples/output/{EXAMPLE}/{EPISODES}_episodes_{SIM_TIMESPAN_DAYS}_simDays_{NUMBER_OF_AGENTS}_rlAgents\",\n", + ")\n", + "actor_path = os.path.join(path, f\"actor_pp_{ACTOR_NUM_ADJ}.pt\")\n", + "\n", + "# DEFINTIONS" + ] + }, + { + "cell_type": "markdown", + "id": "ddfe95d9", + "metadata": {}, + "source": [ + "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44862f06", + "metadata": {}, + "outputs": [], + "source": [ + "# @title Load observations function\n", + "\n", + "\n", + "def load_observations(path, ACTOR_NUM, feature_names):\n", + " # Load observations\n", + " obs_path = f\"{path}/buffer_obs.json\"\n", + "\n", + " with open(obs_path) as file:\n", + " json_data = json.load(file)\n", + "\n", + " # Convert the list of lists into a 2D numpy array\n", + " input_data = np.array(json_data)\n", + " input_data = np.squeeze(input_data)\n", + "\n", + " # filter out arrays where all value are 0\n", + " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", + "\n", + " # filter only first 700 observations\n", + " input_data = input_data[:700]\n", + "\n", + " if NUMBER_OF_AGENTS == 1:\n", + " return pd.DataFrame(input_data, columns=feature_names), input_data\n", + " else:\n", + " return pd.DataFrame(\n", + " input_data[:, ACTOR_NUM], columns=feature_names\n", + " ), input_data[:, ACTOR_NUM]" + ] + }, + { + "cell_type": "markdown", + "id": "444b8679", + "metadata": {}, + "source": [ + "**Define Paths and Parameters**" + ] + }, + { + "cell_type": "markdown", + "id": "7f218d15", + "metadata": {}, + "source": [ + "Adjust the following paths and parameters according to your data and model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fa78b12", + "metadata": {}, + "outputs": [], + "source": [ + "path = (\n", + " inputs_path + \"/example_02a/learned_strategies/base/buffer_obs\"\n", + ") # Replace with your data path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8f9189ca", + "metadata": {}, + "outputs": [], + "source": [ + "# Define feature names (replace with actual feature names)\n", + "# make columns names\n", + "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", + "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", + "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" + ] + }, + { + "cell_type": "markdown", + "id": "6c5f1986", + "metadata": {}, + "source": [ + "**Load Observations and Input Data**" + ] + }, + { + "cell_type": "markdown", + "id": "36ae8f9e", + "metadata": {}, + "source": [ + "Load the observations and input data using the utility function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d522969d", + "metadata": {}, + "outputs": [], + "source": [ + "df_obs, input_data = load_observations(path, ACTOR_NUM, feature_names)\n", + "\n", + "df_obs" + ] + }, + { + "cell_type": "markdown", + "id": "5d8b9dcf", + "metadata": {}, + "source": [ + "**Load the Trained Actor Model**" + ] + }, + { + "cell_type": "markdown", + "id": "b1b50488", + "metadata": {}, + "source": [ + "We initialize and load the trained actor neural network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4da4de57", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the model\n", + "obs_dim = len(feature_names)\n", + "act_dim = 2 # Adjust if your model outputs a different number of actions\n", + "model = MLPActor(obs_dim=obs_dim, act_dim=act_dim, float_type=th.float)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37adecfa", + "metadata": {}, + "outputs": [], + "source": [ + "ACTOR_NUM = 1 # Replace with the actor number or identifier\n", + "actor_path = (\n", + " inputs_path\n", + " + \"/example_02a/learned_strategies/base/last_policies/actors/actor_pp_6.pt\"\n", + ") # Path to the trained actor model\n", + "\n", + "# Load the trained model parameters\n", + "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", + "model.load_state_dict(model_state[\"actor\"])" + ] + }, + { + "cell_type": "markdown", + "id": "d4a63712", + "metadata": {}, + "source": [ + "Get the actions base on observation tensor we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6460cfb", + "metadata": {}, + "outputs": [], + "source": [ + "predictions = []\n", + "for obs in input_data:\n", + " obs_tensor = th.tensor(obs, dtype=th.float)\n", + " prediction = model(obs_tensor)\n", + " predictions.append(prediction)\n", + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be91cd97", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " input_data, predictions, test_size=0.15, random_state=42\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b21c93c7", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert data to tensors\n", + "y_train = th.stack(y_train)\n", + "y_test = th.stack(y_test)\n", + "\n", + "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", + "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", + "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", + "y_test_tensor = th.tensor(y_test, dtype=th.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "ddd1ab1e", + "metadata": {}, + "source": [ + "## 3.2. Creating a SHAP Explainer " + ] + }, + { + "cell_type": "markdown", + "id": "ae7b108b", + "metadata": {}, + "source": [ + "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d9be211", + "metadata": { + "lines_to_next_cell": 1 + }, + "outputs": [], + "source": [ + "# Define a prediction function for SHAP\n", + "def model_predict(X):\n", + " X_tensor = th.tensor(X, dtype=th.float32)\n", + " model.eval()\n", + " with th.no_grad():\n", + " return model(X_tensor).numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0f7857b", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a subset of training data for the background dataset\n", + "background_size = 100 # Adjust the size as needed\n", + "background = X_train[:background_size]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84bb96cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the SHAP Kernel Explainer\n", + "explainer = shap.KernelExplainer(model_predict, background)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a7929e4", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate SHAP values for the test set\n", + "shap_values = explainer.shap_values(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44fa277f", + "metadata": {}, + "outputs": [], + "source": [ + "shap_values" + ] + }, + { + "cell_type": "markdown", + "id": "c1f3d550", + "metadata": {}, + "source": [ + "## 4. Visualizing SHAP Values " + ] + }, + { + "cell_type": "markdown", + "id": "3a0f0cbe", + "metadata": {}, + "source": [ + "We generate summary plots to visualize feature importance for each output dimension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6af4983", + "metadata": {}, + "outputs": [], + "source": [ + "print(shap_values[0].shape)\n", + "print(X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e318a5b", + "metadata": {}, + "outputs": [], + "source": [ + "# Summary plot for the first output dimension\n", + "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", + "plt.title(\"Summary Plot for Output Dimension 0, p_inflex\")\n", + "plt.show()\n", + "\n", + "# Summary plot for the second output dimension\n", + "shap.summary_plot(shap_values[1], X_test, feature_names=feature_names, show=False)\n", + "plt.title(\"Summary Plot for Output Dimension 1, p_flex\")\n", + "plt.show()\n", + "\n", + "shap.summary_plot(\n", + " shap_values[0],\n", + " X_test,\n", + " feature_names=feature_names,\n", + " plot_type=\"bar\",\n", + " title=\"Summary Bar Plot for Output Dimension 0\",\n", + ")\n", + "\n", + "shap.summary_plot(\n", + " shap_values[1],\n", + " X_test,\n", + " feature_names=feature_names,\n", + " plot_type=\"bar\",\n", + " title=\"Summary Bar Plot for Output Dimension 1\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9a888f8b", + "metadata": {}, + "source": [ + "The SHAP summary plots show the impact of each feature on the model's predictions for each output dimension (action). Features with larger absolute SHAP values have a more significant influence on the decision-making process of the RL agent." + ] + }, + { + "cell_type": "markdown", + "id": "c6c4ce8c", + "metadata": {}, + "source": [ + "- **Positive SHAP Value**: Indicates that the feature contributes positively to the predicted action value.\n", + "- **Negative SHAP Value**: Indicates that the feature contributes negatively to the predicted action value." + ] + }, + { + "cell_type": "markdown", + "id": "86545200", + "metadata": {}, + "source": [ + "By analyzing these plots, we can identify which features are most influential and understand how changes in feature values affect the agent's actions." + ] + }, + { + "cell_type": "markdown", + "id": "06f3977c", + "metadata": {}, + "source": [ + "## 5. Conclusion " + ] + }, + { + "cell_type": "markdown", + "id": "dadd0a0c", + "metadata": {}, + "source": [ + "In this tutorial, we've demonstrated how to apply SHAP to a reinforcement learning agent to explain its decision-making process. By interpreting the SHAP values, we gain valuable insights into which features influence the agent's actions, enhancing transparency and trust in the model." + ] + }, + { + "cell_type": "markdown", + "id": "37633c16", + "metadata": {}, + "source": [ + "Explainability is crucial, especially when deploying RL agents in real-world applications where understanding the rationale behind decisions is essential for safety, fairness, and compliance." + ] + }, + { + "cell_type": "markdown", + "id": "8735d66f", + "metadata": {}, + "source": [ + "## 6. Additional Resources " + ] + }, + { + "cell_type": "markdown", + "id": "d6b0332f", + "metadata": {}, + "source": [ + "- **SHAP Documentation**: [https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)\n", + "- **PyTorch Documentation**: [https://pytorch.org/docs/stable/index.html](https://pytorch.org/docs/stable/index.html)\n", + "- **Reinforcement Learning Introduction**: [Richard S. Sutton and Andrew G. Barto, \"Reinforcement Learning: An Introduction\"](http://incompleteideas.net/book/the-book-2nd.html)\n", + "- **Interpretable Machine Learning Book**: [https://christophm.github.io/interpretable-ml-book/](https://christophm.github.io/interpretable-ml-book/)" + ] + }, + { + "cell_type": "markdown", + "id": "a8cdea5f", + "metadata": {}, + "source": [ + "**Feel free to experiment with the code and explore different explainability techniques. Happy learning!**" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb.license b/examples/notebooks/09_example_Sim_and_xRL.ipynb.license new file mode 100644 index 00000000..a6ae0636 --- /dev/null +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb.license @@ -0,0 +1,3 @@ +SPDX-FileCopyrightText: ASSUME Developers + +SPDX-License-Identifier: AGPL-3.0-or-later diff --git a/examples/notebooks/09_xRL.ipynb b/examples/notebooks/09_xRL.ipynb deleted file mode 100644 index 3be4b14d..00000000 --- a/examples/notebooks/09_xRL.ipynb +++ /dev/null @@ -1,1071 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e62e00c9", - "metadata": {}, - "source": [ - "# **Explainable Reinforcement Learning Tutorial**" - ] - }, - { - "cell_type": "markdown", - "id": "fb3aa803", - "metadata": {}, - "source": [ - "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." - ] - }, - { - "cell_type": "markdown", - "id": "0d793362", - "metadata": {}, - "source": [ - "**Table of Contents**" - ] - }, - { - "cell_type": "markdown", - "id": "87bdf688", - "metadata": {}, - "source": [ - "1. [Introduction](#introduction)\n", - "\n", - " 1.1. [Multi-Agent Deep Reinforcement Learning](#11-multi-agent-deep-reinforcement-learning)\n", - "\n", - " 1.2. Prerequisites\n", - "\n", - "2. [Explainable AI and SHAP Values](#2-explainable-ai-and-shap-values)\n", - "\n", - " 2.1 Understanding Explainable AI \n", - "\n", - " 2.2 Introduction to SHAP Values \n", - "\n", - "3. [Calculating SHAP values](#3-calculating-shap-values)\n", - "\n", - " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", - "\n", - " 3.2. [Creating a SHAP Explainer](#32-creating-a-shap-explainer)\n", - " \n", - "4. [Visualizing SHAP Values](#visualizing-shap-values)\n", - "5. [Conclusion](#conclusion)\n", - "6. [Additional Resources](#additional-resources)" - ] - }, - { - "cell_type": "markdown", - "id": "5e8c7fec", - "metadata": {}, - "source": [ - "## 1. Introduction " - ] - }, - { - "cell_type": "markdown", - "id": "06e91420", - "metadata": {}, - "source": [ - "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." - ] - }, - { - "cell_type": "markdown", - "id": "47b1e7ab", - "metadata": {}, - "source": [ - "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." - ] - }, - { - "cell_type": "markdown", - "id": "ec0717c1", - "metadata": {}, - "source": [ - "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." - ] - }, - { - "cell_type": "markdown", - "id": "0d59bb0a", - "metadata": {}, - "source": [ - "### 1.1 Multi-Agent Deep Reinforcement Learning \n", - "\n", - "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", - "\n", - "\n", - "- **Observations**: Each agent receives observations comprising market forecasts, unit-specific information, and past actions.\n", - "- **Actions**: Agents decide on bidding strategies, such as bid prices for inflexible and flexible capacities.\n", - "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", - "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", - "\n", - "For a deep dive into the RL configurations we refer to one of the other tutorials, such as \n", - "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", - "\n", - "Agents need observations to make informed decisions. Observations include:\n", - "\n", - "- **Residual Load Forecast**: Forecasted net demand over the next 24 hours.\n", - "- **Price Forecast**: Forecasted market prices over the next 24 hours.\n", - "- **Marginal Cost**: Current marginal cost of the unit.\n", - "- **Previous Output**: Dispatched capacity from the previous time step.\n", - "\n", - "\n", - "Agents choose actions based on the observations. The action space is two-dimensional, corresponding to:\n", - "\n", - "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", - "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", - "\n", - "\n", - "#### Run an the simulation MADRL Simulation\n", - "\n", - "Similar to the other tutorial, we can run Assume in the following way. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ee220130", - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install 'assume-framework[learning]'\n", - "#!git clone https://github.com/assume-framework/assume.git assume-repo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfd1daf2", - "metadata": {}, - "outputs": [], - "source": [ - "import importlib.util\n", - "\n", - "# Check if 'google.colab' is available\n", - "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", - "\n", - "colab_inputs_path = \"assume-repo/examples/inputs\"\n", - "local_inputs_path = \"../inputs\"\n", - "\n", - "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path" - ] - }, - { - "cell_type": "markdown", - "id": "3f0f38fb", - "metadata": {}, - "source": [ - "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "201251c6", - "metadata": {}, - "outputs": [], - "source": [ - "# @title Overwrite run_learning function with enhanced logging\n", - "\n", - "import json\n", - "import logging\n", - "import os\n", - "from collections import defaultdict\n", - "from pathlib import Path\n", - "\n", - "import numpy as np\n", - "import yaml\n", - "from tqdm import tqdm\n", - "\n", - "from assume.common.exceptions import AssumeException\n", - "from assume.scenario.loader_csv import (\n", - " load_config_and_create_forecaster,\n", - " setup_world,\n", - ")\n", - "from assume.world import World\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "\n", - "def run_learning(\n", - " world: World,\n", - " inputs_path: str,\n", - " scenario: str,\n", - " study_case: str,\n", - " verbose: bool = False,\n", - ") -> None:\n", - " \"\"\"\n", - " Train Deep Reinforcement Learning (DRL) agents to act in a simulated market environment.\n", - "\n", - " This function runs multiple episodes of simulation to train DRL agents, performs evaluation, and saves the best runs. It maintains the buffer and learned agents in memory to avoid resetting them with each new run.\n", - "\n", - " Args:\n", - " world (World): An instance of the World class representing the simulation environment.\n", - " inputs_path (str): The path to the folder containing input files necessary for the simulation.\n", - " scenario (str): The name of the scenario for the simulation.\n", - " study_case (str): The specific study case for the simulation.\n", - "\n", - " Note:\n", - " - The function uses a ReplayBuffer to store experiences for training the DRL agents.\n", - " - It iterates through training episodes, updating the agents and evaluating their performance at regular intervals.\n", - " - Initial exploration is active at the beginning and is disabled after a certain number of episodes to improve the performance of DRL algorithms.\n", - " - Upon completion of training, the function performs an evaluation run using the best policy learned during training.\n", - " - The best policies are chosen based on the average reward obtained during the evaluation runs, and they are saved for future use.\n", - " \"\"\"\n", - " from assume.reinforcement_learning.buffer import ReplayBuffer\n", - "\n", - " if not verbose:\n", - " logger.setLevel(logging.WARNING)\n", - "\n", - " # remove csv path so that nothing is written while learning\n", - " temp_csv_path = world.export_csv_path\n", - " world.export_csv_path = \"\"\n", - "\n", - " # initialize policies already here to set the obs_dim and act_dim in the learning role\n", - " actors_and_critics = None\n", - " world.learning_role.initialize_policy(actors_and_critics=actors_and_critics)\n", - " world.output_role.del_similar_runs()\n", - "\n", - " # check if we already stored policies for this simualtion\n", - " save_path = world.learning_config[\"trained_policies_save_path\"]\n", - "\n", - " if Path(save_path).is_dir():\n", - " # we are in learning mode and about to train new policies, which might overwrite existing ones\n", - " accept = input(\n", - " f\"{save_path=} exists - should we overwrite current learnings? (y/N) \"\n", - " )\n", - " if not accept.lower().startswith(\"y\"):\n", - " # stop here - do not start learning or save anything\n", - " raise AssumeException(\"don't overwrite existing strategies\")\n", - "\n", - " # -----------------------------------------\n", - " # Load scenario data to reuse across episodes\n", - " scenario_data = load_config_and_create_forecaster(inputs_path, scenario, study_case)\n", - "\n", - " # -----------------------------------------\n", - " # Information that needs to be stored across episodes, aka one simulation run\n", - " inter_episodic_data = {\n", - " \"buffer\": ReplayBuffer(\n", - " buffer_size=int(world.learning_config.get(\"replay_buffer_size\", 5e5)),\n", - " obs_dim=world.learning_role.rl_algorithm.obs_dim,\n", - " act_dim=world.learning_role.rl_algorithm.act_dim,\n", - " n_rl_units=len(world.learning_role.rl_strats),\n", - " device=world.learning_role.device,\n", - " float_type=world.learning_role.float_type,\n", - " ),\n", - " \"actors_and_critics\": None,\n", - " \"max_eval\": defaultdict(lambda: -1e9),\n", - " \"all_eval\": defaultdict(list),\n", - " \"avg_all_eval\": [],\n", - " \"episodes_done\": 0,\n", - " \"eval_episodes_done\": 0,\n", - " \"noise_scale\": world.learning_config.get(\"noise_scale\", 1.0),\n", - " }\n", - "\n", - " # -----------------------------------------\n", - "\n", - " validation_interval = min(\n", - " world.learning_role.training_episodes,\n", - " world.learning_config.get(\"validation_episodes_interval\", 5),\n", - " )\n", - "\n", - " eval_episode = 1\n", - "\n", - " for episode in tqdm(\n", - " range(1, world.learning_role.training_episodes + 1),\n", - " desc=\"Training Episodes\",\n", - " ):\n", - " # TODO normally, loading twice should not create issues, somehow a scheduling issue is raised currently\n", - " if episode != 1:\n", - " setup_world(\n", - " world=world,\n", - " scenario_data=scenario_data,\n", - " study_case=study_case,\n", - " episode=episode,\n", - " )\n", - "\n", - " # -----------------------------------------\n", - " # Give the newly initliazed learning role the needed information across episodes\n", - " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", - "\n", - " world.run()\n", - "\n", - " # -----------------------------------------\n", - " # Store updated information across episodes\n", - " inter_episodic_data = world.learning_role.get_inter_episodic_data()\n", - " inter_episodic_data[\"episodes_done\"] = episode\n", - "\n", - " # evaluation run:\n", - " if (\n", - " episode % validation_interval == 0\n", - " and episode\n", - " >= world.learning_role.episodes_collecting_initial_experience\n", - " + validation_interval\n", - " ):\n", - " world.reset()\n", - "\n", - " # load evaluation run\n", - " setup_world(\n", - " world=world,\n", - " scenario_data=scenario_data,\n", - " study_case=study_case,\n", - " perform_evaluation=True,\n", - " eval_episode=eval_episode,\n", - " )\n", - "\n", - " world.learning_role.load_inter_episodic_data(inter_episodic_data)\n", - "\n", - " world.run()\n", - "\n", - " total_rewards = world.output_role.get_sum_reward()\n", - " avg_reward = np.mean(total_rewards)\n", - " # check reward improvement in evaluation run\n", - " # and store best run in eval folder\n", - " terminate = world.learning_role.compare_and_save_policies(\n", - " {\"avg_reward\": avg_reward}\n", - " )\n", - "\n", - " inter_episodic_data[\"eval_episodes_done\"] = eval_episode\n", - "\n", - " # if we have not improved in the last x evaluations, we stop loop\n", - " if terminate:\n", - " break\n", - "\n", - " eval_episode += 1\n", - "\n", - " world.reset()\n", - "\n", - " # if at end of simulation save last policies\n", - " if episode == (world.learning_role.training_episodes):\n", - " world.learning_role.rl_algorithm.save_params(\n", - " directory=f\"{world.learning_role.trained_policies_save_path}/last_policies\"\n", - " )\n", - "\n", - " # export buffer_obs.json in the last training episode to get observations later\n", - " export = inter_episodic_data[\"buffer\"].observations.tolist()\n", - " path = f\"{world.learning_role.trained_policies_save_path}/buffer_obs\"\n", - " os.makedirs(path, exist_ok=True)\n", - " with open(os.path.join(path, \"buffer_obs.json\"), \"w\") as f:\n", - " json.dump(export, f)\n", - "\n", - " # container shutdown implicitly with new initialisation\n", - " logger.info(\"################\")\n", - " logger.info(\"Training finished, Start evaluation run\")\n", - " world.export_csv_path = temp_csv_path\n", - "\n", - " world.reset()\n", - "\n", - " # load scenario for evaluation\n", - " setup_world(\n", - " world=world,\n", - " scenario_data=scenario_data,\n", - " study_case=study_case,\n", - " terminate_learning=True,\n", - " )\n", - "\n", - " world.learning_role.load_inter_episodic_data(inter_episodic_data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bfadf522", - "metadata": {}, - "outputs": [], - "source": [ - "#!cd assume-repo && assume -s example_02a -db \"sqlite:///./examples/local_db/assume_db_example_02a.db\"" - ] - }, - { - "cell_type": "markdown", - "id": "e77c41cf", - "metadata": {}, - "source": [ - "### 1.2. Prerequisites" - ] - }, - { - "cell_type": "markdown", - "id": "cbf18570", - "metadata": {}, - "source": [ - "To follow along with this tutorial, we need some additional libraries." - ] - }, - { - "cell_type": "markdown", - "id": "a5ac592c", - "metadata": {}, - "source": [ - "- `matplotlib`\n", - "- `shap`\n", - "- `scikit-learn`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae266ecb", - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, - "outputs": [], - "source": [ - "!pip install matplotlib\n", - "!pip install shap==0.42.1\n", - "!pip install scikit-learn==1.3.0" - ] - }, - { - "cell_type": "markdown", - "id": "5069ff93", - "metadata": {}, - "source": [ - "## 2. Explainable AI and SHAP Values \n", - "\n", - "### 2.1 Understanding Explainable AI \n", - "Explainable AI (XAI) refers to techniques and methods that make the behavior and decisions of AI systems understandable to humans. In the context of complex models like deep neural networks, XAI helps to:\n", - "- Increase Transparency: Providing insights into how models make decisions.\n", - "- Build Trust: Users and stakeholders can trust AI systems if they understand them.\n", - "- Ensure Compliance: Regulatory requirements often demand explainability.\n", - "- Improve Models: Identifying weaknesses or biases in models.\n", - "\n", - "\n", - "### 2.2 Introduction to SHAP Values \n", - "Shapley values are a method from cooperative game theory used to explain the contribution of each feature to the prediction of a machine learning model, such as a neural network. They provide an interpretability technique by distributing the \"payout\" (the prediction) among the input features, attributing the importance of each feature to the prediction.\n", - "\n", - "For a given prediction, the Shapley value of a feature represents the average contribution of that feature to the prediction, considering all possible combinations of other features.\n", - "\n", - "1. **Marginal Contribution**: \n", - " The marginal contribution of a feature is the difference between the prediction with and without that feature.\n", - "\n", - "2. **Average over all subsets**: \n", - " The Shapley value is calculated by averaging the marginal contributions over all possible subsets of features.\n", - "\n", - "The formula for the Shapley value of feature $i$ is:\n", - "\n", - "$$\n", - "\\phi_i = \\sum_{S \\subseteq N \\setminus \\{i\\}} \\frac{|S|!(|N| - |S| - 1)!}{|N|!} \\cdot \\left( f(S \\cup \\{i\\}) - f(S) \\right)\n", - "$$\n", - "\n", - "Where:\n", - "- $N$ is the set of all features.\n", - "- $S$ is a subset of features.\n", - "- $f(S)$ is the model’s prediction when using only the features in subset $S$.\n", - "\n", - "\n", - "The `shap` library is a popular tool for computing Shapley values for machine learning models, including neural networks.\n", - "\n", - "\n", - "\n", - "Why Use SHAP in RL?\n", - "- Model-Agnostic: Applicable to any machine learning model, including neural networks.\n", - "- Local Explanations: Provides explanations for individual predictions (actions).\n", - "- Consistency: Ensures that features contributing more to the prediction have higher Shapley values.\n", - "\n", - "\n", - "Properties of SHAP:\n", - "1. Local Accuracy: The sum of Shapley values equals the difference between the model output and the expected output.\n", - "2. Missingness: Features not present in the model have zero Shapley value.\n", - "3. Consistency: If a model changes so that a feature contributes more to the prediction, the Shapley value of that feature should not decrease." - ] - }, - { - "cell_type": "markdown", - "id": "21d49573", - "metadata": {}, - "source": [ - "## 3. Calculating SHAP values " - ] - }, - { - "cell_type": "markdown", - "id": "d16712fc", - "metadata": {}, - "source": [ - "We will work with:" - ] - }, - { - "cell_type": "markdown", - "id": "2c0c1262", - "metadata": {}, - "source": [ - "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", - "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." - ] - }, - { - "cell_type": "markdown", - "id": "2fb6fc14", - "metadata": {}, - "source": [ - "Our goal is to:" - ] - }, - { - "cell_type": "markdown", - "id": "e0b69db6", - "metadata": {}, - "source": [ - "- Load the observations and the trained actor model.\n", - "- Use the model to predict actions.\n", - "- Apply SHAP to explain the model's predictions." - ] - }, - { - "cell_type": "markdown", - "id": "f870b3e8", - "metadata": {}, - "source": [ - "### 3.1. Loading and Preparing Data " - ] - }, - { - "cell_type": "markdown", - "id": "aaa7c3d3", - "metadata": {}, - "source": [ - "First, let's load the necessary libraries and the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b6ee4f28", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import shap\n", - "import torch as th\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "markdown", - "id": "aa268f14", - "metadata": {}, - "source": [ - "the simulation common.py contains utility functions and class definitions\n", - "from common import load_observations, Actor" - ] - }, - { - "cell_type": "markdown", - "id": "f866385a", - "metadata": {}, - "source": [ - "**Define the Actor Neural Network Class**" - ] - }, - { - "cell_type": "markdown", - "id": "ff7cf03a", - "metadata": {}, - "source": [ - "We define the actor neural network class that will be used to predict actions based on observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7216677a", - "metadata": { - "lines_to_next_cell": 1 - }, - "outputs": [], - "source": [ - "from assume.reinforcement_learning.neural_network_architecture import MLPActor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5a4d6e17", - "metadata": {}, - "outputs": [], - "source": [ - "def load_config(file_path):\n", - " \"\"\"\n", - " Load the configuration file.\n", - " \"\"\"\n", - " with open(file_path) as file:\n", - " config = yaml.safe_load(file)\n", - " return config\n", - "\n", - "\n", - "# Some Variable definitions:\n", - "\n", - "EPISODES = 3\n", - "NUMBER_OF_AGENTS = 1\n", - "SIM_TIMESPAN_DAYS = 31\n", - "ACTOR_NUM = 1\n", - "EXAMPLE = 1\n", - "\n", - "SIM_TIMESPAN_HOURS = SIM_TIMESPAN_DAYS * 24\n", - "\n", - "# actor 1-5 are the default non-rl actors, so we just skip those\n", - "ACTOR_NUM_ADJ = ACTOR_NUM + 6 # 6 #9\n", - "\n", - "\n", - "# Get the current working directory\n", - "current_dir = os.getcwd()\n", - "# Go up one level\n", - "one_level_up = os.path.dirname(current_dir)\n", - "# Go up two levels\n", - "two_levels_up = os.path.dirname(one_level_up)\n", - "\n", - "# Paths\n", - "path = os.path.join(\n", - " two_levels_up,\n", - " f\"assume/examples/output/{EXAMPLE}/{EPISODES}_episodes_{SIM_TIMESPAN_DAYS}_simDays_{NUMBER_OF_AGENTS}_rlAgents\",\n", - ")\n", - "actor_path = os.path.join(path, f\"actor_pp_{ACTOR_NUM_ADJ}.pt\")\n", - "\n", - "# DEFINTIONS" - ] - }, - { - "cell_type": "markdown", - "id": "ddfe95d9", - "metadata": {}, - "source": [ - "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44862f06", - "metadata": {}, - "outputs": [], - "source": [ - "# @title Load observations function\n", - "\n", - "\n", - "def load_observations(path, ACTOR_NUM, feature_names):\n", - " # Load observations\n", - " obs_path = f\"{path}/buffer_obs.json\"\n", - "\n", - " with open(obs_path) as file:\n", - " json_data = json.load(file)\n", - "\n", - " # Convert the list of lists into a 2D numpy array\n", - " input_data = np.array(json_data)\n", - " input_data = np.squeeze(input_data)\n", - "\n", - " # filter out arrays where all value are 0\n", - " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", - "\n", - " # filter only first 700 observations\n", - " input_data = input_data[:700]\n", - "\n", - " if NUMBER_OF_AGENTS == 1:\n", - " return pd.DataFrame(input_data, columns=feature_names), input_data\n", - " else:\n", - " return pd.DataFrame(\n", - " input_data[:, ACTOR_NUM], columns=feature_names\n", - " ), input_data[:, ACTOR_NUM]" - ] - }, - { - "cell_type": "markdown", - "id": "444b8679", - "metadata": {}, - "source": [ - "**Define Paths and Parameters**" - ] - }, - { - "cell_type": "markdown", - "id": "7f218d15", - "metadata": {}, - "source": [ - "Adjust the following paths and parameters according to your data and model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fa78b12", - "metadata": {}, - "outputs": [], - "source": [ - "path = (\n", - " inputs_path + \"/example_02a/learned_strategies/base/buffer_obs\"\n", - ") # Replace with your data path" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8f9189ca", - "metadata": {}, - "outputs": [], - "source": [ - "# Define feature names (replace with actual feature names)\n", - "# make columns names\n", - "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", - "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", - "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" - ] - }, - { - "cell_type": "markdown", - "id": "6c5f1986", - "metadata": {}, - "source": [ - "**Load Observations and Input Data**" - ] - }, - { - "cell_type": "markdown", - "id": "36ae8f9e", - "metadata": {}, - "source": [ - "Load the observations and input data using the utility function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d522969d", - "metadata": {}, - "outputs": [], - "source": [ - "df_obs, input_data = load_observations(path, ACTOR_NUM, feature_names)\n", - "\n", - "df_obs" - ] - }, - { - "cell_type": "markdown", - "id": "5d8b9dcf", - "metadata": {}, - "source": [ - "**Load the Trained Actor Model**" - ] - }, - { - "cell_type": "markdown", - "id": "b1b50488", - "metadata": {}, - "source": [ - "We initialize and load the trained actor neural network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4da4de57", - "metadata": {}, - "outputs": [], - "source": [ - "# Initialize the model\n", - "obs_dim = len(feature_names)\n", - "act_dim = 2 # Adjust if your model outputs a different number of actions\n", - "model = MLPActor(obs_dim=obs_dim, act_dim=act_dim, float_type=th.float)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "37adecfa", - "metadata": {}, - "outputs": [], - "source": [ - "ACTOR_NUM = 1 # Replace with the actor number or identifier\n", - "actor_path = (\n", - " inputs_path\n", - " + \"/example_02a/learned_strategies/base/last_policies/actors/actor_pp_6.pt\"\n", - ") # Path to the trained actor model\n", - "\n", - "# Load the trained model parameters\n", - "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", - "model.load_state_dict(model_state[\"actor\"])" - ] - }, - { - "cell_type": "markdown", - "id": "d4a63712", - "metadata": {}, - "source": [ - "Get the actions base on observation tensor we just loaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6460cfb", - "metadata": {}, - "outputs": [], - "source": [ - "predictions = []\n", - "for obs in input_data:\n", - " obs_tensor = th.tensor(obs, dtype=th.float)\n", - " prediction = model(obs_tensor)\n", - " predictions.append(prediction)\n", - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be91cd97", - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " input_data, predictions, test_size=0.15, random_state=42\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b21c93c7", - "metadata": {}, - "outputs": [], - "source": [ - "# Convert data to tensors\n", - "y_train = th.stack(y_train)\n", - "y_test = th.stack(y_test)\n", - "\n", - "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", - "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", - "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", - "y_test_tensor = th.tensor(y_test, dtype=th.float32)" - ] - }, - { - "cell_type": "markdown", - "id": "ddd1ab1e", - "metadata": {}, - "source": [ - "## 3.2. Creating a SHAP Explainer " - ] - }, - { - "cell_type": "markdown", - "id": "ae7b108b", - "metadata": {}, - "source": [ - "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6d9be211", - "metadata": { - "lines_to_next_cell": 1 - }, - "outputs": [], - "source": [ - "# Define a prediction function for SHAP\n", - "def model_predict(X):\n", - " X_tensor = th.tensor(X, dtype=th.float32)\n", - " model.eval()\n", - " with th.no_grad():\n", - " return model(X_tensor).numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0f7857b", - "metadata": {}, - "outputs": [], - "source": [ - "# Use a subset of training data for the background dataset\n", - "background_size = 100 # Adjust the size as needed\n", - "background = X_train[:background_size]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84bb96cf", - "metadata": {}, - "outputs": [], - "source": [ - "# Create the SHAP Kernel Explainer\n", - "explainer = shap.KernelExplainer(model_predict, background)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2a7929e4", - "metadata": {}, - "outputs": [], - "source": [ - "# Calculate SHAP values for the test set\n", - "shap_values = explainer.shap_values(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44fa277f", - "metadata": {}, - "outputs": [], - "source": [ - "shap_values" - ] - }, - { - "cell_type": "markdown", - "id": "c1f3d550", - "metadata": {}, - "source": [ - "## 4. Visualizing SHAP Values " - ] - }, - { - "cell_type": "markdown", - "id": "3a0f0cbe", - "metadata": {}, - "source": [ - "We generate summary plots to visualize feature importance for each output dimension." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a6af4983", - "metadata": {}, - "outputs": [], - "source": [ - "print(shap_values[0].shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e318a5b", - "metadata": {}, - "outputs": [], - "source": [ - "# Summary plot for the first output dimension\n", - "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", - "plt.title(\"Summary Plot for Output Dimension 0, p_inflex\")\n", - "plt.show()\n", - "\n", - "# Summary plot for the second output dimension\n", - "shap.summary_plot(shap_values[1], X_test, feature_names=feature_names, show=False)\n", - "plt.title(\"Summary Plot for Output Dimension 1, p_flex\")\n", - "plt.show()\n", - "\n", - "shap.summary_plot(\n", - " shap_values[0],\n", - " X_test,\n", - " feature_names=feature_names,\n", - " plot_type=\"bar\",\n", - " title=\"Summary Bar Plot for Output Dimension 0\",\n", - ")\n", - "\n", - "shap.summary_plot(\n", - " shap_values[1],\n", - " X_test,\n", - " feature_names=feature_names,\n", - " plot_type=\"bar\",\n", - " title=\"Summary Bar Plot for Output Dimension 1\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "9a888f8b", - "metadata": {}, - "source": [ - "The SHAP summary plots show the impact of each feature on the model's predictions for each output dimension (action). Features with larger absolute SHAP values have a more significant influence on the decision-making process of the RL agent." - ] - }, - { - "cell_type": "markdown", - "id": "c6c4ce8c", - "metadata": {}, - "source": [ - "- **Positive SHAP Value**: Indicates that the feature contributes positively to the predicted action value.\n", - "- **Negative SHAP Value**: Indicates that the feature contributes negatively to the predicted action value." - ] - }, - { - "cell_type": "markdown", - "id": "86545200", - "metadata": {}, - "source": [ - "By analyzing these plots, we can identify which features are most influential and understand how changes in feature values affect the agent's actions." - ] - }, - { - "cell_type": "markdown", - "id": "06f3977c", - "metadata": {}, - "source": [ - "## 5. Conclusion " - ] - }, - { - "cell_type": "markdown", - "id": "dadd0a0c", - "metadata": {}, - "source": [ - "In this tutorial, we've demonstrated how to apply SHAP to a reinforcement learning agent to explain its decision-making process. By interpreting the SHAP values, we gain valuable insights into which features influence the agent's actions, enhancing transparency and trust in the model." - ] - }, - { - "cell_type": "markdown", - "id": "37633c16", - "metadata": {}, - "source": [ - "Explainability is crucial, especially when deploying RL agents in real-world applications where understanding the rationale behind decisions is essential for safety, fairness, and compliance." - ] - }, - { - "cell_type": "markdown", - "id": "8735d66f", - "metadata": {}, - "source": [ - "## 6. Additional Resources " - ] - }, - { - "cell_type": "markdown", - "id": "d6b0332f", - "metadata": {}, - "source": [ - "- **SHAP Documentation**: [https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)\n", - "- **PyTorch Documentation**: [https://pytorch.org/docs/stable/index.html](https://pytorch.org/docs/stable/index.html)\n", - "- **Reinforcement Learning Introduction**: [Richard S. Sutton and Andrew G. Barto, \"Reinforcement Learning: An Introduction\"](http://incompleteideas.net/book/the-book-2nd.html)\n", - "- **Interpretable Machine Learning Book**: [https://christophm.github.io/interpretable-ml-book/](https://christophm.github.io/interpretable-ml-book/)" - ] - }, - { - "cell_type": "markdown", - "id": "a8cdea5f", - "metadata": {}, - "source": [ - "**Feel free to experiment with the code and explore different explainability techniques. Happy learning!**" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 678410bbf24a38f79643d24644c74993b305c336 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 7 Oct 2024 10:05:46 +0200 Subject: [PATCH 13/27] - changes to paths in xRL notebook - fixed installation of glpk for colab --- .../notebooks/09_example_Sim_and_xRL.ipynb | 4214 ++++++++++++++--- 1 file changed, 3443 insertions(+), 771 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index cc7b905e..b77601c9 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -3,7 +3,9 @@ { "cell_type": "markdown", "id": "e62e00c9", - "metadata": {}, + "metadata": { + "id": "e62e00c9" + }, "source": [ "# **Explainable Reinforcement Learning Tutorial**" ] @@ -11,7 +13,9 @@ { "cell_type": "markdown", "id": "fb3aa803", - "metadata": {}, + "metadata": { + "id": "fb3aa803" + }, "source": [ "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." ] @@ -19,7 +23,9 @@ { "cell_type": "markdown", "id": "0d793362", - "metadata": {}, + "metadata": { + "id": "0d793362" + }, "source": [ "**Table of Contents**" ] @@ -27,19 +33,19 @@ { "cell_type": "markdown", "id": "87bdf688", - "metadata": {}, + "metadata": { + "id": "87bdf688" + }, "source": [ "1. [Introduction](#introduction)\n", "\n", " 1.1. [Multi-Agent Deep Reinforcement Learning with Market Splitting](#11-multi-agent-deep-reinforcement-learning)\n", "\n", - " 1.2. Prerequisites\n", - "\n", "2. [Explainable AI and SHAP Values](#2-explainable-ai-and-shap-values)\n", "\n", - " 2.1 Understanding Explainable AI \n", + " 2.1 Understanding Explainable AI\n", "\n", - " 2.2 Introduction to SHAP Values \n", + " 2.2 Introduction to SHAP Values\n", "\n", "3. [Calculating SHAP values](#3-calculating-shap-values)\n", "\n", @@ -55,7 +61,9 @@ { "cell_type": "markdown", "id": "5e8c7fec", - "metadata": {}, + "metadata": { + "id": "5e8c7fec" + }, "source": [ "## 1. Introduction " ] @@ -63,7 +71,9 @@ { "cell_type": "markdown", "id": "06e91420", - "metadata": {}, + "metadata": { + "id": "06e91420" + }, "source": [ "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." ] @@ -71,7 +81,9 @@ { "cell_type": "markdown", "id": "47b1e7ab", - "metadata": {}, + "metadata": { + "id": "47b1e7ab" + }, "source": [ "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." ] @@ -79,7 +91,9 @@ { "cell_type": "markdown", "id": "ec0717c1", - "metadata": {}, + "metadata": { + "id": "ec0717c1" + }, "source": [ "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." ] @@ -87,7 +101,9 @@ { "cell_type": "markdown", "id": "0d59bb0a", - "metadata": {}, + "metadata": { + "id": "0d59bb0a" + }, "source": [ "### 1.1 Multi-Agent Deep Reinforcement Learning \n", "\n", @@ -99,7 +115,7 @@ "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", "\n", - "For a deep dive into the RL configurations we refer to one of the other tutorials, such as \n", + "For a deep dive into the RL configurations we refer to one of the other tutorials, such as\n", "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", "\n", "Agents need observations to make informed decisions. Observations include:\n", @@ -118,26 +134,221 @@ "\n", "#### Run an the simulation MADRL Simulation\n", "\n", - "Similar to the other tutorial, we can run Assume in the following way. " + "Similar to the other tutorial, we can run Assume in the following way." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "ee220130", - "metadata": {}, - "outputs": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ee220130", + "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting assume-framework[learning]\n", + " Downloading assume_framework-0.4.0.post2-py3-none-any.whl.metadata (9.9 kB)\n", + "Collecting argcomplete>=3.1.4 (from assume-framework[learning])\n", + " Downloading argcomplete-3.5.1-py3-none-any.whl.metadata (16 kB)\n", + "Requirement already satisfied: nest-asyncio>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from assume-framework[learning]) (1.6.0)\n", + "Collecting mango-agents-assume>=1.1.4-6 (from assume-framework[learning])\n", + " Downloading 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This behaviour is the source of the following dependency conflicts.\n", + "google-ai-generativelanguage 0.6.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", + "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", + "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", + "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.27.2 which is incompatible.\n", + "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n", + "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed argcomplete-3.5.1 assume-framework-0.4.0.post2 dill-0.3.9 mango-agents-assume-1.1.4.post10 msgspec-0.18.6 paho-mqtt-2.1.0 protobuf-5.27.2 psycopg2-binary-2.9.9 python-dateutil-2.9.0.post0 pyyaml-include-2.1\n" + ] + }, + { + "data": { + "application/vnd.colab-display-data+json": { + "id": "5bd36a4bde024ee1bea1d8fe663e99d9", + "pip_warning": { + "packages": [ + "dateutil" + ] + } + } + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.24.1)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)\n", + "^C\n", + "Cloning into 'assume-repo'...\n", + "remote: Enumerating objects: 10788, done.\u001b[K\n", + "remote: Counting objects: 100% (4342/4342), done.\u001b[K\n", + "remote: Compressing objects: 100% (1458/1458), done.\u001b[K\n" + ] + } + ], "source": [ - "#!pip install 'assume-framework[learning]'\n", - "#!pip install plotly\n", - "#!git clone https://github.com/assume-framework/assume.git assume-repo" + "!pip install 'assume-framework[learning]'\n", + "!pip install plotly\n", + "!git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 1, + "id": "Hn_DvrqR7oK2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hn_DvrqR7oK2", + "outputId": "391046fd-90d9-4967-b919-52c49f13a9f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pyomo\n", + " Downloading Pyomo-6.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.0 kB)\n", + "Collecting ply (from pyomo)\n", + " Downloading ply-3.11-py2.py3-none-any.whl.metadata (844 bytes)\n", + "Downloading Pyomo-6.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading ply-3.11-py2.py3-none-any.whl (49 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: ply, pyomo\n", + "Successfully installed ply-3.11 pyomo-6.8.0\n", + "Selecting previously unselected package libsuitesparseconfig5:amd64.\n", + "(Reading database ... 123620 files and directories currently installed.)\n", + "Preparing to unpack .../libsuitesparseconfig5_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n", + "Unpacking libsuitesparseconfig5:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Selecting previously unselected package libamd2:amd64.\n", + "Preparing to unpack .../libamd2_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n", + "Unpacking libamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Selecting previously unselected package libcolamd2:amd64.\n", + "Preparing to unpack .../libcolamd2_1%3a5.10.1+dfsg-4build1_amd64.deb ...\n", + "Unpacking libcolamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Selecting previously unselected package libglpk40:amd64.\n", + "Preparing to unpack .../libglpk40_5.0-1_amd64.deb ...\n", + "Unpacking libglpk40:amd64 (5.0-1) ...\n", + "Selecting previously unselected package glpk-utils.\n", + "Preparing to unpack .../glpk-utils_5.0-1_amd64.deb ...\n", + "Unpacking glpk-utils (5.0-1) ...\n", + "Setting up libsuitesparseconfig5:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Setting up libamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Setting up libcolamd2:amd64 (1:5.10.1+dfsg-4build1) ...\n", + "Setting up libglpk40:amd64 (5.0-1) ...\n", + "Setting up glpk-utils (5.0-1) ...\n", + "Processing triggers for man-db (2.10.2-1) ...\n", + "Processing triggers for libc-bin (2.35-0ubuntu3.4) ...\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_level_zero.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_adapter_opencl.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libur_loader.so.0 is not a symbolic link\n", + "\n", + "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n", + "\n" + ] + } + ], + "source": [ + "!pip install pyomo\n", + "!apt-get install -y -qq glpk-utils" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "75cd480a", - "metadata": {}, + "metadata": { + "id": "75cd480a" + }, "outputs": [], "source": [ "import pandas as pd\n", @@ -153,10 +364,24 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 3, "id": "bfd1daf2", - "metadata": {}, - "outputs": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bfd1daf2", + "outputId": "1edeb31f-bc3a-493e-b518-01f4188c44b6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "assume-repo/examples/inputs\n" + ] + } + ], "source": [ "import importlib.util\n", "\n", @@ -166,22 +391,42 @@ "colab_inputs_path = \"assume-repo/examples/inputs\"\n", "local_inputs_path = \"../inputs\"\n", "\n", - "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path" + "inputs_path = colab_inputs_path if IN_COLAB else local_inputs_path\n", + "\n", + "print(inputs_path)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "4sfWyPgJ-Hq5", + "metadata": { + "id": "4sfWyPgJ-Hq5" + }, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "636ea9ae", - "metadata": {}, + "metadata": { + "id": "636ea9ae" + }, "source": [ "Load the created example files from the tutorial before:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 4, "id": "233f315b", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "233f315b", + "outputId": "f98da7d4-0080-4546-c642-838f722965b0" + }, "outputs": [ { "name": "stdout", @@ -195,7 +440,8 @@ "import os\n", "\n", "# Define the input directory\n", - "input_dir = \"inputs/tutorial_08\"\n", + "input_dir = os.path.join(inputs_path, \"tutorial_08\")\n", + "\n", "\n", "# Read the DataFrames from CSV files\n", "buses = pd.read_csv(os.path.join(input_dir, \"buses.csv\"))\n", @@ -218,21 +464,39 @@ { "cell_type": "markdown", "id": "6985289b", - "metadata": {}, + "metadata": { + "id": "6985289b" + }, "source": [ - "**Let's make this a leanring example**" + "**Let's make this a leanring example**\n", + "\n", + "We place a learning nuclear power plant in the south zone that has a 5 times hihger maximal power, to generate a scenario where it has a price impact." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "id": "b205256f", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 676 + }, + "id": "b205256f", + "outputId": "b9bb887b-f534-4a50-dd5b-229be1012600" + }, "outputs": [ { "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"powerplant_units\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"Unit 1\",\n \"Unit 18\",\n \"Unit 16\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"technology\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"nuclear\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bidding_zonal\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"pp_learning\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"uranium\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"emission_factor\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 894.4271909999159,\n \"min\": 1000.0,\n \"max\": 5000.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 5000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"efficiency\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.695323946259567e-17,\n \"min\": 0.3,\n \"max\": 0.3,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"additional_cost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 5,\n \"max\": 24,\n \"num_unique_values\": 20,\n \"samples\": [\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"unit_operator\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Operator South\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe", + "variable_name": "powerplant_units" + }, "text/html": [ - 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\n" ], "text/plain": [ " name technology bidding_zonal fuel_type emission_factor max_power \\\n", @@ -594,7 +1122,7 @@ "19 0.0 0.3 24 south Operator South " ] }, - "execution_count": 16, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -610,25 +1138,46 @@ "powerplant_units" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "QBTGrw62_5I7", + "metadata": { + "id": "QBTGrw62_5I7" + }, + "outputs": [], + "source": [ + "# store power plant units to csv again\n", + "powerplant_units.to_csv(input_dir + \"/powerplant_units.csv\")" + ] + }, { "cell_type": "markdown", "id": "cce0e8b4", - "metadata": {}, + "metadata": { + "id": "cce0e8b4" + }, "source": [ - "Change yaml to configure learning" + "Change the yaml to configure the learning specific hyperparameters. For a deep dive explanation of the different parameters we refer again to the solely RL tutorial. **TODO** Erklärung einfügen" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "id": "9c555ce9", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9c555ce9", + "outputId": "473126ae-3c3e-4698-e3a5-347cc00e5108" + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Configuration YAML file has been saved to 'inputs/tutorial_08\\config.yaml'.\n" + "Configuration YAML file has been saved to 'assume-repo/examples/inputs/tutorial_08/config.yaml'.\n" ] } ], @@ -639,6 +1188,7 @@ " \"end_date\": \"2019-01-01 23:00\",\n", " \"time_step\": \"1h\",\n", " \"save_frequency_hours\": 24,\n", + " \"learning_mode\": \"True\",\n", " \"markets_config\": {\n", " \"zonal\": {\n", " \"operator\": \"EOM_operator\",\n", @@ -690,16 +1240,21 @@ { "cell_type": "markdown", "id": "3f0f38fb", - "metadata": {}, + "metadata": { + "id": "3f0f38fb" + }, "source": [ "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "201251c6", - "metadata": {}, + "metadata": { + "cellView": "form", + "id": "201251c6" + }, "outputs": [], "source": [ "# @title Overwrite run_learning function with enhanced logging\n", @@ -906,23 +1461,34 @@ { "cell_type": "markdown", "id": "dcacfe26", - "metadata": {}, + "metadata": { + "id": "dcacfe26" + }, "source": [ - "**Run the example case**" + "**Run the example case**\n", + "\n", + "Now we run the example case similar to before in the market zone tutorial." ] }, { "cell_type": "code", "execution_count": 19, "id": "bfadf522", - "metadata": {}, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "bfadf522", + "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" + }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from assume-repo/examples/inputs\n", "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", @@ -935,810 +1501,2774 @@ "INFO:assume.scenario.loader_csv:Read units from file\n", "INFO:assume.scenario.loader_csv:Adding power_plant units\n", "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/82800 [00:00 wait_for=>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "/usr/local/lib/python3.10/dist-packages/sympy/geometry/polygon.py:237: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", + " def angles(self):\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[Agent.raise_exceptions()]>\n", + "/usr/local/lib/python3.10/dist-packages/sympy/geometry/polygon.py:237: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", + " def angles(self):\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "ERROR:asyncio:Task was destroyed but it is pending!\n", + "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "Training Episodes: 0%| | 0/10 [00:00)\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent clock: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent learning_agent: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent EOM_operator: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent Operator North: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent Operator South: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "ERROR:mango.agent.core:Agent eom_de: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", + "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", + "handle: )>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", + " future.result()\n", + " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", + " raise exc\n", + "asyncio.exceptions.CancelledError\n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", + " raise fut.exception()\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", + " result = coro.throw(exc)\n", + "RuntimeError: cannot reuse already awaited coroutine\n", + "\n", + " 0%| | 1.0/82800 [00:00<4:56:01, 4.66it/s]\u001b[AWARNING:pyomo.common.numeric_types:Dynamically registering the following numeric type:\n", + " torch.Tensor\n", + "Dynamic registration is supported for convenience, but there are known\n", + "limitations to this approach. We recommend explicitly registering\n", + "numeric types using RegisterNumericType() or RegisterIntegerType().\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpf0sv8yov.glpk.raw --wglp /tmp/tmp5asnipvw.glpk.glp --cpxlp\n", + " /tmp/tmpfmwxc953.pyomo.lp\n", + "Reading problem data from '/tmp/tmpfmwxc953.pyomo.lp'...\n", + "/tmp/tmpfmwxc953.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:07, 10368.18it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:07, 10193.16it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp55pxy9xx.glpk.raw --wglp /tmp/tmpql_afw06.glpk.glp --cpxlp\n", + " /tmp/tmpx6mdl1uh.pyomo.lp\n", + "Reading problem data from '/tmp/tmpx6mdl1uh.pyomo.lp'...\n", + "/tmp/tmpx6mdl1uh.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 01:00:00: 9%|▊ | 7201/82800 [00:00<00:04, 16445.89it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:04, 16817.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpb1j_ikxq.glpk.raw --wglp /tmp/tmpezp3razm.glpk.glp --cpxlp\n", + " /tmp/tmpfbbnpt7q.pyomo.lp\n", + "Reading problem data from '/tmp/tmpfbbnpt7q.pyomo.lp'...\n", + "/tmp/tmpfbbnpt7q.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:04, 17504.61it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 20289.50it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp8r2jsxua.glpk.raw --wglp /tmp/tmpa6724_sx.glpk.glp --cpxlp\n", + " /tmp/tmpgjwy19ki.pyomo.lp\n", + "Reading problem data from '/tmp/tmpgjwy19ki.pyomo.lp'...\n", + "/tmp/tmpgjwy19ki.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 03:00:00: 17%|█▋ | 14401/82800 [00:00<00:03, 19578.01it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:02, 23320.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpe8846btc.glpk.raw --wglp /tmp/tmp0ilf7_dz.glpk.glp --cpxlp\n", + " /tmp/tmps6gfx_nx.pyomo.lp\n", + "Reading problem data from '/tmp/tmps6gfx_nx.pyomo.lp'...\n", + "/tmp/tmps6gfx_nx.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23916.10it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpond8pqlm.glpk.raw --wglp /tmp/tmpyx1d7zt8.glpk.glp --cpxlp\n", + " /tmp/tmp213byyn9.pyomo.lp\n", + "Reading problem data from '/tmp/tmp213byyn9.pyomo.lp'...\n", + "/tmp/tmp213byyn9.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 24674.58it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp019fpb63.glpk.raw --wglp /tmp/tmpenhlv7h2.glpk.glp --cpxlp\n", + " /tmp/tmpbknr_2vl.pyomo.lp\n", + "Reading problem data from '/tmp/tmpbknr_2vl.pyomo.lp'...\n", + "/tmp/tmpbknr_2vl.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 23638.63it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpa994dw_w.glpk.raw --wglp /tmp/tmph76tkpxl.glpk.glp --cpxlp\n", + " /tmp/tmp0riz9f2a.pyomo.lp\n", + "Reading problem data from '/tmp/tmp0riz9f2a.pyomo.lp'...\n", + "/tmp/tmp0riz9f2a.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23175.57it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpdwhwi8rf.glpk.raw --wglp /tmp/tmp60iy59cg.glpk.glp --cpxlp\n", + " /tmp/tmptof5okg8.pyomo.lp\n", + "Reading problem data from '/tmp/tmptof5okg8.pyomo.lp'...\n", + "/tmp/tmptof5okg8.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 24335.43it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp55hci9a1.glpk.raw --wglp /tmp/tmpi0psaqav.glpk.glp --cpxlp\n", + " /tmp/tmpd657t8ua.pyomo.lp\n", + "Reading problem data from '/tmp/tmpd657t8ua.pyomo.lp'...\n", + "/tmp/tmpd657t8ua.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:01, 25381.64it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpgn729qmk.glpk.raw --wglp /tmp/tmprb78b6rf.glpk.glp --cpxlp\n", + " /tmp/tmp9w3g0mmd.pyomo.lp\n", + "Reading problem data from '/tmp/tmp9w3g0mmd.pyomo.lp'...\n", + "/tmp/tmp9w3g0mmd.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:01, 25367.58it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp_i9sychx.glpk.raw --wglp /tmp/tmpvhnix6t0.glpk.glp --cpxlp\n", + " /tmp/tmpzisrcyck.pyomo.lp\n", + "Reading problem data from '/tmp/tmpzisrcyck.pyomo.lp'...\n", + "/tmp/tmpzisrcyck.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:01<00:01, 25805.98it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpko0qf5mb.glpk.raw --wglp /tmp/tmpwdbqp01a.glpk.glp --cpxlp\n", + " /tmp/tmp5z992p5h.pyomo.lp\n", + "Reading problem data from '/tmp/tmp5z992p5h.pyomo.lp'...\n", + "/tmp/tmp5z992p5h.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 26004.28it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpfikht9v2.glpk.raw --wglp /tmp/tmp14s4vgy2.glpk.glp --cpxlp\n", + " /tmp/tmph8p4znaw.pyomo.lp\n", + "Reading problem data from '/tmp/tmph8p4znaw.pyomo.lp'...\n", + "/tmp/tmph8p4znaw.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 24577.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmprgvhrh6y.glpk.raw --wglp /tmp/tmp1_6dn0t4.glpk.glp --cpxlp\n", + " /tmp/tmpyapsxfai.pyomo.lp\n", + "Reading problem data from '/tmp/tmpyapsxfai.pyomo.lp'...\n", + "/tmp/tmpyapsxfai.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 23890.35it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpe870v_oj.glpk.raw --wglp /tmp/tmpvudm7qsl.glpk.glp --cpxlp\n", + " /tmp/tmpwi094v0g.pyomo.lp\n", + "Reading problem data from '/tmp/tmpwi094v0g.pyomo.lp'...\n", + "/tmp/tmpwi094v0g.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 24453.78it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp47qng8zx.glpk.raw --wglp /tmp/tmp03j5ii3a.glpk.glp --cpxlp\n", + " /tmp/tmpb7hqi91q.pyomo.lp\n", + "Reading problem data from '/tmp/tmpb7hqi91q.pyomo.lp'...\n", + "/tmp/tmpb7hqi91q.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 24706.79it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp51erzb_a.glpk.raw --wglp /tmp/tmp0iq0mabf.glpk.glp --cpxlp\n", + " /tmp/tmpu5xxl4xp.pyomo.lp\n", + "Reading problem data from '/tmp/tmpu5xxl4xp.pyomo.lp'...\n", + "/tmp/tmpu5xxl4xp.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:02<00:00, 24981.59it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpvkcz_hrl.glpk.raw --wglp /tmp/tmpznf0t9y6.glpk.glp --cpxlp\n", + " /tmp/tmp2ueg9oj5.pyomo.lp\n", + "Reading problem data from '/tmp/tmp2ueg9oj5.pyomo.lp'...\n", + "/tmp/tmp2ueg9oj5.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:02<00:00, 24948.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp0x3xdbk0.glpk.raw --wglp /tmp/tmpc99m191o.glpk.glp --cpxlp\n", + " /tmp/tmpowun19s9.pyomo.lp\n", + "Reading problem data from '/tmp/tmpowun19s9.pyomo.lp'...\n", + "/tmp/tmpowun19s9.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 25113.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpbvai91ye.glpk.raw --wglp /tmp/tmpfb_2w9mc.glpk.glp --cpxlp\n", + " /tmp/tmpk5x4i7yq.pyomo.lp\n", + "Reading problem data from '/tmp/tmpk5x4i7yq.pyomo.lp'...\n", + "/tmp/tmpk5x4i7yq.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 23751.99it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpsneoef5m.glpk.raw --wglp /tmp/tmps4qx99k7.glpk.glp --cpxlp\n", + " /tmp/tmpnfa4gfd_.pyomo.lp\n", + "Reading problem data from '/tmp/tmpnfa4gfd_.pyomo.lp'...\n", + "/tmp/tmpnfa4gfd_.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_1 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:03<00:00, 20862.94it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpmpzml_n_.glpk.raw --wglp /tmp/tmphy7mcb3t.glpk.glp --cpxlp\n", + " /tmp/tmp13ub5l10.pyomo.lp\n", + "Reading problem data from '/tmp/tmp13ub5l10.pyomo.lp'...\n", + "/tmp/tmp13ub5l10.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:03, 23250.79it/s] \n", + "Training Episodes: 10%|█ | 1/10 [00:03<00:32, 3.60s/it]INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "\n", + " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:05, 15063.49it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmptw07elbf.glpk.raw --wglp /tmp/tmp0x6dxvb0.glpk.glp --cpxlp\n", + " /tmp/tmpambcx7ro.pyomo.lp\n", + "Reading problem data from '/tmp/tmpambcx7ro.pyomo.lp'...\n", + "/tmp/tmpambcx7ro.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:04, 16453.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpbse10e5u.glpk.raw --wglp /tmp/tmpyw23oq3v.glpk.glp --cpxlp\n", + " /tmp/tmpnivr7kob.pyomo.lp\n", + "Reading problem data from '/tmp/tmpnivr7kob.pyomo.lp'...\n", + "/tmp/tmpnivr7kob.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:04, 17221.35it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpolvkucm1.glpk.raw --wglp /tmp/tmpi_t4m2lc.glpk.glp --cpxlp\n", + " /tmp/tmpvus_8u99.pyomo.lp\n", + "Reading problem data from '/tmp/tmpvus_8u99.pyomo.lp'...\n", + "/tmp/tmpvus_8u99.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:03, 18374.44it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpgtsp63b2.glpk.raw --wglp /tmp/tmpd68kmvyd.glpk.glp --cpxlp\n", + " /tmp/tmpnawahz9p.pyomo.lp\n", + "Reading problem data from '/tmp/tmpnawahz9p.pyomo.lp'...\n", + "/tmp/tmpnawahz9p.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:01<00:03, 19350.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpnlu5ux23.glpk.raw --wglp /tmp/tmpyxwz4s_p.glpk.glp --cpxlp\n", + " /tmp/tmpzos92ejh.pyomo.lp\n", + "Reading problem data from '/tmp/tmpzos92ejh.pyomo.lp'...\n", + "/tmp/tmpzos92ejh.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 20968.10it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmplq8gqt0k.glpk.raw --wglp /tmp/tmpxq0098y0.glpk.glp --cpxlp\n", + " /tmp/tmpufnkio4j.pyomo.lp\n", + "Reading problem data from '/tmp/tmpufnkio4j.pyomo.lp'...\n", + "/tmp/tmpufnkio4j.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 22155.97it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp5dt2b0oc.glpk.raw --wglp /tmp/tmp8o6m8qmi.glpk.glp --cpxlp\n", + " /tmp/tmpgygidn7m.pyomo.lp\n", + "Reading problem data from '/tmp/tmpgygidn7m.pyomo.lp'...\n", + "/tmp/tmpgygidn7m.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 20999.51it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp3gzdb_r1.glpk.raw --wglp /tmp/tmp51k72zan.glpk.glp --cpxlp\n", + " /tmp/tmpd3g3oslf.pyomo.lp\n", + "Reading problem data from '/tmp/tmpd3g3oslf.pyomo.lp'...\n", + "/tmp/tmpd3g3oslf.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 20242.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpe9x2tjf9.glpk.raw --wglp /tmp/tmpcozapllo.glpk.glp --cpxlp\n", + " /tmp/tmpr9ot36qy.pyomo.lp\n", + "Reading problem data from '/tmp/tmpr9ot36qy.pyomo.lp'...\n", + "/tmp/tmpr9ot36qy.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:02, 19402.14it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp9t744uu7.glpk.raw --wglp /tmp/tmpstthnk19.glpk.glp --cpxlp\n", + " /tmp/tmprixpurt5.pyomo.lp\n", + "Reading problem data from '/tmp/tmprixpurt5.pyomo.lp'...\n", + "/tmp/tmprixpurt5.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:02<00:02, 19923.94it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpibjig1ky.glpk.raw --wglp /tmp/tmp_0lbb30q.glpk.glp --cpxlp\n", + " /tmp/tmprkynpgr1.pyomo.lp\n", + "Reading problem data from '/tmp/tmprkynpgr1.pyomo.lp'...\n", + "/tmp/tmprkynpgr1.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:02<00:01, 20449.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpr_3fyj2x.glpk.raw --wglp /tmp/tmpfi6fol7b.glpk.glp --cpxlp\n", + " /tmp/tmpde1k0uo2.pyomo.lp\n", + "Reading problem data from '/tmp/tmpde1k0uo2.pyomo.lp'...\n", + "/tmp/tmpde1k0uo2.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 19941.06it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpoiy1c0yc.glpk.raw --wglp /tmp/tmpr8jxzs11.glpk.glp --cpxlp\n", + " /tmp/tmp6k_39sav.pyomo.lp\n", + "Reading problem data from '/tmp/tmp6k_39sav.pyomo.lp'...\n", + "/tmp/tmp6k_39sav.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 18864.41it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp1sj1evtg.glpk.raw --wglp /tmp/tmpf60d7krd.glpk.glp --cpxlp\n", + " /tmp/tmpz2yoze5t.pyomo.lp\n", + "Reading problem data from '/tmp/tmpz2yoze5t.pyomo.lp'...\n", + "/tmp/tmpz2yoze5t.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 18663.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpnnwilo2t.glpk.raw --wglp /tmp/tmpbntwet5y.glpk.glp --cpxlp\n", + " /tmp/tmpwn0651x9.pyomo.lp\n", + "Reading problem data from '/tmp/tmpwn0651x9.pyomo.lp'...\n", + "/tmp/tmpwn0651x9.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:03<00:01, 18298.76it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpd_uy11ih.glpk.raw --wglp /tmp/tmpzpdoz7rf.glpk.glp --cpxlp\n", + " /tmp/tmprayw9lsw.pyomo.lp\n", + "Reading problem data from '/tmp/tmprayw9lsw.pyomo.lp'...\n", + "/tmp/tmprayw9lsw.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:03<00:01, 18869.52it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp14nuv6mg.glpk.raw --wglp /tmp/tmpo3r87q3g.glpk.glp --cpxlp\n", + " /tmp/tmp3vdza3z8.pyomo.lp\n", + "Reading problem data from '/tmp/tmp3vdza3z8.pyomo.lp'...\n", + "/tmp/tmp3vdza3z8.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:03<00:01, 17994.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp7k5tzmso.glpk.raw --wglp /tmp/tmpc8pc38cn.glpk.glp --cpxlp\n", + " /tmp/tmpc7vcg1bb.pyomo.lp\n", + "Reading problem data from '/tmp/tmpc7vcg1bb.pyomo.lp'...\n", + "/tmp/tmpc7vcg1bb.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:03<00:00, 16828.72it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp4l672sh5.glpk.raw --wglp /tmp/tmp6nlohwjm.glpk.glp --cpxlp\n", + " /tmp/tmp7o8efpx4.pyomo.lp\n", + "Reading problem data from '/tmp/tmp7o8efpx4.pyomo.lp'...\n", + "/tmp/tmp7o8efpx4.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 17908.43it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpmzs68lr2.glpk.raw --wglp /tmp/tmp0mj_rh12.glpk.glp --cpxlp\n", + " /tmp/tmpt2fkk745.pyomo.lp\n", + "Reading problem data from '/tmp/tmpt2fkk745.pyomo.lp'...\n", + "/tmp/tmpt2fkk745.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:04<00:00, 18990.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpivomrkn6.glpk.raw --wglp /tmp/tmp627sqbtz.glpk.glp --cpxlp\n", + " /tmp/tmppve9nq1t.pyomo.lp\n", + "Reading problem data from '/tmp/tmppve9nq1t.pyomo.lp'...\n", + "/tmp/tmppve9nq1t.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_2 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:04<00:00, 20269.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpt98xz8ht.glpk.raw --wglp /tmp/tmpgascxfek.glpk.glp --cpxlp\n", + " /tmp/tmpbwkx71rj.pyomo.lp\n", + "Reading problem data from '/tmp/tmpbwkx71rj.pyomo.lp'...\n", + "/tmp/tmpbwkx71rj.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:04, 19574.35it/s] \n", + "Training Episodes: 20%|██ | 2/10 [00:07<00:32, 4.05s/it]INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "\n", + " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:05, 14355.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpt16tq7d0.glpk.raw --wglp /tmp/tmp0zw37ric.glpk.glp --cpxlp\n", + " /tmp/tmpmc211vqy.pyomo.lp\n", + "Reading problem data from '/tmp/tmpmc211vqy.pyomo.lp'...\n", + "/tmp/tmpmc211vqy.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:03, 20554.01it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp8e0esoa2.glpk.raw --wglp /tmp/tmpx7bh3kjd.glpk.glp --cpxlp\n", + " /tmp/tmpdwx9zyjz.pyomo.lp\n", + "Reading problem data from '/tmp/tmpdwx9zyjz.pyomo.lp'...\n", + "/tmp/tmpdwx9zyjz.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 04:00:00: 22%|██▏ | 18001/82800 [00:00<00:02, 22414.37it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 22373.84it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpmeyemsh7.glpk.raw --wglp /tmp/tmpcw2adg8v.glpk.glp --cpxlp\n", + " /tmp/tmprhgc3wes.pyomo.lp\n", + "Reading problem data from '/tmp/tmprhgc3wes.pyomo.lp'...\n", + "/tmp/tmprhgc3wes.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 05:00:00: 26%|██▌ | 21601/82800 [00:01<00:02, 23810.62it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:03, 22644.80it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp3sjdrw0k.glpk.raw --wglp /tmp/tmp_fjd3ap6.glpk.glp --cpxlp\n", + " /tmp/tmpjcy_smhw.pyomo.lp\n", + "Reading problem data from '/tmp/tmpjcy_smhw.pyomo.lp'...\n", + "/tmp/tmpjcy_smhw.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:01<00:02, 25770.59it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23629.61it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp87k4tn__.glpk.raw --wglp /tmp/tmpth4qqh4l.glpk.glp --cpxlp\n", + " /tmp/tmpdeadx1am.pyomo.lp\n", + "Reading problem data from '/tmp/tmpdeadx1am.pyomo.lp'...\n", + "/tmp/tmpdeadx1am.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 07:00:00: 35%|███▍ | 28801/82800 [00:01<00:02, 26101.95it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:00<00:02, 24455.20it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp2zjwiw40.glpk.raw --wglp /tmp/tmpk20m7gf8.glpk.glp --cpxlp\n", + " /tmp/tmp6smnf8i_.pyomo.lp\n", + "Reading problem data from '/tmp/tmp6smnf8i_.pyomo.lp'...\n", + "/tmp/tmp6smnf8i_.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:01<00:01, 26708.36it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 24087.81it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp0q5hh2yz.glpk.raw --wglp /tmp/tmpo2vjz_wd.glpk.glp --cpxlp\n", + " /tmp/tmpgv492qod.pyomo.lp\n", + "Reading problem data from '/tmp/tmpgv492qod.pyomo.lp'...\n", + "/tmp/tmpgv492qod.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 09:00:00: 43%|████▎ | 36001/82800 [00:01<00:01, 27188.48it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23952.26it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpkagy58tc.glpk.raw --wglp /tmp/tmp2j6qp_jl.glpk.glp --cpxlp\n", + " /tmp/tmpuaxsiqyo.pyomo.lp\n", + "Reading problem data from '/tmp/tmpuaxsiqyo.pyomo.lp'...\n", + "/tmp/tmpuaxsiqyo.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 26675.60it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 23760.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp5yhim4_4.glpk.raw --wglp /tmp/tmpbna7142e.glpk.glp --cpxlp\n", + " /tmp/tmpkpy85ovz.pyomo.lp\n", + "Reading problem data from '/tmp/tmpkpy85ovz.pyomo.lp'...\n", + "/tmp/tmpkpy85ovz.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 11:00:00: 52%|█████▏ | 43201/82800 [00:01<00:01, 26480.14it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:01, 24322.08it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmprfjtvhm9.glpk.raw --wglp /tmp/tmpw9aexzmn.glpk.glp --cpxlp\n", + " /tmp/tmprfdi8val.pyomo.lp\n", + "Reading problem data from '/tmp/tmprfdi8val.pyomo.lp'...\n", + "/tmp/tmprfdi8val.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:01, 23845.30it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpkrrbnupn.glpk.raw --wglp /tmp/tmpjr7l7_wh.glpk.glp --cpxlp\n", + " /tmp/tmpgc8en5f1.pyomo.lp\n", + "Reading problem data from '/tmp/tmpgc8en5f1.pyomo.lp'...\n", + "/tmp/tmpgc8en5f1.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:01<00:01, 24326.83it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpdxeyze3w.glpk.raw --wglp /tmp/tmpvkf8l8a3.glpk.glp --cpxlp\n", + " /tmp/tmp4gm8ynp1.pyomo.lp\n", + "Reading problem data from '/tmp/tmp4gm8ynp1.pyomo.lp'...\n", + "/tmp/tmp4gm8ynp1.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 24439.56it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpxhb916fg.glpk.raw --wglp /tmp/tmpt3mn5wjy.glpk.glp --cpxlp\n", + " /tmp/tmp_bhh30i8.pyomo.lp\n", + "Reading problem data from '/tmp/tmp_bhh30i8.pyomo.lp'...\n", + "/tmp/tmp_bhh30i8.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 23578.78it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp40s83njh.glpk.raw --wglp /tmp/tmpwygq4gh3.glpk.glp --cpxlp\n", + " /tmp/tmpazkbcvor.pyomo.lp\n", + "Reading problem data from '/tmp/tmpazkbcvor.pyomo.lp'...\n", + "/tmp/tmpazkbcvor.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 23041.29it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpejpk04ty.glpk.raw --wglp /tmp/tmpscq2syqp.glpk.glp --cpxlp\n", + " /tmp/tmp90xyfpnk.pyomo.lp\n", + "Reading problem data from '/tmp/tmp90xyfpnk.pyomo.lp'...\n", + "/tmp/tmp90xyfpnk.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 23733.55it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp22jjnsyh.glpk.raw --wglp /tmp/tmpp6191ieu.glpk.glp --cpxlp\n", + " /tmp/tmpx_injkqi.pyomo.lp\n", + "Reading problem data from '/tmp/tmpx_injkqi.pyomo.lp'...\n", + "/tmp/tmpx_injkqi.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 23844.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmps__k6p1_.glpk.raw --wglp /tmp/tmpi5excchr.glpk.glp --cpxlp\n", + " /tmp/tmpl7vwmgrb.pyomo.lp\n", + "Reading problem data from '/tmp/tmpl7vwmgrb.pyomo.lp'...\n", + "/tmp/tmpl7vwmgrb.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:02<00:00, 23188.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpxzs6sg2y.glpk.raw --wglp /tmp/tmpwft8utsc.glpk.glp --cpxlp\n", + " /tmp/tmpi78di4gj.pyomo.lp\n", + "Reading problem data from '/tmp/tmpi78di4gj.pyomo.lp'...\n", + "/tmp/tmpi78di4gj.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:02<00:00, 23099.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp1_t4ho6s.glpk.raw --wglp /tmp/tmphfck3df2.glpk.glp --cpxlp\n", + " /tmp/tmph6zwnbnm.pyomo.lp\n", + "Reading problem data from '/tmp/tmph6zwnbnm.pyomo.lp'...\n", + "/tmp/tmph6zwnbnm.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 22975.09it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmptgk_g10l.glpk.raw --wglp /tmp/tmp_jq8n4g4.glpk.glp --cpxlp\n", + " /tmp/tmp95cudyvq.pyomo.lp\n", + "Reading problem data from '/tmp/tmp95cudyvq.pyomo.lp'...\n", + "/tmp/tmp95cudyvq.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 16696.03it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpau6n4wbi.glpk.raw --wglp /tmp/tmps7yujjug.glpk.glp --cpxlp\n", + " /tmp/tmpqenz8kjz.pyomo.lp\n", + "Reading problem data from '/tmp/tmpqenz8kjz.pyomo.lp'...\n", + "/tmp/tmpqenz8kjz.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:04<00:00, 11555.84it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp25vtvn5_.glpk.raw --wglp /tmp/tmp_muaqg1h.glpk.glp --cpxlp\n", + " /tmp/tmpi6twmn6s.pyomo.lp\n", + "Reading problem data from '/tmp/tmpi6twmn6s.pyomo.lp'...\n", + "/tmp/tmpi6twmn6s.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:04, 19678.81it/s]\n", + "Training Episodes: 30%|███ | 3/10 [00:12<00:29, 4.19s/it]INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "\n", + " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:04, 17203.09it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpf88z2bg0.glpk.raw --wglp /tmp/tmpn5vl3w_u.glpk.glp --cpxlp\n", + " /tmp/tmp8veys5hk.pyomo.lp\n", + "Reading problem data from '/tmp/tmp8veys5hk.pyomo.lp'...\n", + "/tmp/tmp8veys5hk.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:03, 21178.21it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp9eab_s13.glpk.raw --wglp /tmp/tmpbi8f4t70.glpk.glp --cpxlp\n", + " /tmp/tmpfr_nlni3.pyomo.lp\n", + "Reading problem data from '/tmp/tmpfr_nlni3.pyomo.lp'...\n", + "/tmp/tmpfr_nlni3.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 22659.63it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp1a1zgm32.glpk.raw --wglp /tmp/tmpw9le5n2d.glpk.glp --cpxlp\n", + " /tmp/tmpza1_7q9r.pyomo.lp\n", + "Reading problem data from '/tmp/tmpza1_7q9r.pyomo.lp'...\n", + "/tmp/tmpza1_7q9r.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:02, 23171.51it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp_dkto4l0.glpk.raw --wglp /tmp/tmpllvpamz7.glpk.glp --cpxlp\n", + " /tmp/tmpiv_2d8u5.pyomo.lp\n", + "Reading problem data from '/tmp/tmpiv_2d8u5.pyomo.lp'...\n", + "/tmp/tmpiv_2d8u5.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23065.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp8veh0e3w.glpk.raw --wglp /tmp/tmptblsb08m.glpk.glp --cpxlp\n", + " /tmp/tmp9u3ab1tn.pyomo.lp\n", + "Reading problem data from '/tmp/tmp9u3ab1tn.pyomo.lp'...\n", + "/tmp/tmp9u3ab1tn.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 22615.46it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpnzkw03m6.glpk.raw --wglp /tmp/tmpwqh28yg0.glpk.glp --cpxlp\n", + " /tmp/tmp1p3ur3wm.pyomo.lp\n", + "Reading problem data from '/tmp/tmp1p3ur3wm.pyomo.lp'...\n", + "/tmp/tmp1p3ur3wm.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 23336.23it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpdp4qi5fo.glpk.raw --wglp /tmp/tmpfgmya78p.glpk.glp --cpxlp\n", + " /tmp/tmpa9g0a688.pyomo.lp\n", + "Reading problem data from '/tmp/tmpa9g0a688.pyomo.lp'...\n", + "/tmp/tmpa9g0a688.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23990.62it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp4pkjmlts.glpk.raw --wglp /tmp/tmptzg2n0ca.glpk.glp --cpxlp\n", + " /tmp/tmpaghu_aj9.pyomo.lp\n", + "Reading problem data from '/tmp/tmpaghu_aj9.pyomo.lp'...\n", + "/tmp/tmpaghu_aj9.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 23543.97it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpsqv4_zsp.glpk.raw --wglp /tmp/tmpakz0ri0t.glpk.glp --cpxlp\n", + " /tmp/tmpledf0plv.pyomo.lp\n", + "Reading problem data from '/tmp/tmpledf0plv.pyomo.lp'...\n", + "/tmp/tmpledf0plv.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:02, 15692.21it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpy6i75ktl.glpk.raw --wglp /tmp/tmpfh5yv0u5.glpk.glp --cpxlp\n", + " /tmp/tmpr4_muo_c.pyomo.lp\n", + "Reading problem data from '/tmp/tmpr4_muo_c.pyomo.lp'...\n", + "/tmp/tmpr4_muo_c.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:02, 18019.01it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpr0_v6zyz.glpk.raw --wglp /tmp/tmpn3ph9rps.glpk.glp --cpxlp\n", + " /tmp/tmp9vu2z9a1.pyomo.lp\n", + "Reading problem data from '/tmp/tmp9vu2z9a1.pyomo.lp'...\n", + "/tmp/tmp9vu2z9a1.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:02<00:02, 19771.91it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpn1albn02.glpk.raw --wglp /tmp/tmpsvktmfis.glpk.glp --cpxlp\n", + " /tmp/tmp0_kkve0b.pyomo.lp\n", + "Reading problem data from '/tmp/tmp0_kkve0b.pyomo.lp'...\n", + "/tmp/tmp0_kkve0b.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", + " result = coro.send(None)\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", + " instance, results = market_clearing_opt(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 20960.54it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmphhqqze9w.glpk.raw --wglp /tmp/tmpw4ldhuqq.glpk.glp --cpxlp\n", + " /tmp/tmpzfpsmfsl.pyomo.lp\n", + "Reading problem data from '/tmp/tmpzfpsmfsl.pyomo.lp'...\n", + "/tmp/tmpzfpsmfsl.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:02<00:01, 26144.45it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 22076.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpkeg9zxuz.glpk.raw --wglp /tmp/tmp98s139q4.glpk.glp --cpxlp\n", + " /tmp/tmp061b8nni.pyomo.lp\n", + "Reading problem data from '/tmp/tmp061b8nni.pyomo.lp'...\n", + "/tmp/tmp061b8nni.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 13:00:00: 61%|██████ | 50401/82800 [00:02<00:01, 25340.05it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 22468.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpx5ybax39.glpk.raw --wglp /tmp/tmpdhvn1d6k.glpk.glp --cpxlp\n", + " /tmp/tmpaiyc4qr8.pyomo.lp\n", + "Reading problem data from '/tmp/tmpaiyc4qr8.pyomo.lp'...\n", + "/tmp/tmpaiyc4qr8.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:02<00:01, 25567.01it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 21997.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpd4925kmg.glpk.raw --wglp /tmp/tmpdu2nlsyh.glpk.glp --cpxlp\n", + " /tmp/tmpyhur6zbz.pyomo.lp\n", + "Reading problem data from '/tmp/tmpyhur6zbz.pyomo.lp'...\n", + "/tmp/tmpyhur6zbz.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:02<00:00, 25677.37it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 21782.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmphktz2rxf.glpk.raw --wglp /tmp/tmp8rc49vwj.glpk.glp --cpxlp\n", + " /tmp/tmpxlusthcq.pyomo.lp\n", + "Reading problem data from '/tmp/tmpxlusthcq.pyomo.lp'...\n", + "/tmp/tmpxlusthcq.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:02<00:00, 23504.02it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:03<00:00, 22494.61it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp_h0elnwc.glpk.raw --wglp /tmp/tmp7vryum3v.glpk.glp --cpxlp\n", + " /tmp/tmpylug0ode.pyomo.lp\n", + "Reading problem data from '/tmp/tmpylug0ode.pyomo.lp'...\n", + "/tmp/tmpylug0ode.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 17:00:00: 78%|███████▊ | 64801/82800 [00:02<00:00, 25143.88it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:03<00:00, 23176.81it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpgnzrqsxy.glpk.raw --wglp /tmp/tmppttqbekb.glpk.glp --cpxlp\n", + " /tmp/tmpcyjqei4n.pyomo.lp\n", + "Reading problem data from '/tmp/tmpcyjqei4n.pyomo.lp'...\n", + "/tmp/tmpcyjqei4n.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 26110.73it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 23074.36it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpnkm3nfui.glpk.raw --wglp /tmp/tmpr46eydah.glpk.glp --cpxlp\n", + " /tmp/tmp5ehx5m8b.pyomo.lp\n", + "Reading problem data from '/tmp/tmp5ehx5m8b.pyomo.lp'...\n", + "/tmp/tmp5ehx5m8b.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 19:00:00: 87%|████████▋ | 72001/82800 [00:03<00:00, 26124.17it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 22627.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmp6ans92ru.glpk.raw --wglp /tmp/tmpk9egt30z.glpk.glp --cpxlp\n", + " /tmp/tmp_3wuknzb.pyomo.lp\n", + "Reading problem data from '/tmp/tmp_3wuknzb.pyomo.lp'...\n", + "/tmp/tmp_3wuknzb.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:03<00:00, 26331.15it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "\n", + "tutorial_08_zonal_case_4 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:03<00:00, 21647.41it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write /tmp/tmpfvzvcpv2.glpk.raw --wglp /tmp/tmpds05wu1f.glpk.glp --cpxlp\n", + " /tmp/tmpcdzc3n9q.pyomo.lp\n", + "Reading problem data from '/tmp/tmpcdzc3n9q.pyomo.lp'...\n", + "/tmp/tmpcdzc3n9q.pyomo.lp:24: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" + " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:03, 21847.46it/s] \n", + "Training Episodes: 40%|████ | 4/10 [00:16<00:24, 4.09s/it]INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "\n", + "Training Episodes: 50%|█████ | 5/10 [00:16<00:13, 2.67s/it]\n", + " 0%| | 1.0/82800 [00:00<3:00:09, 7.66it/s]\u001b[AINFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "\n", + "\n", + "Training Episodes: 50%|█████ | 5/10 [00:16<00:16, 3.32s/it]\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:03<00:00, 26564.47it/s]" + "ename": "ValueError", + "evalue": "Time must be > 1546304400.0 but is 1546300799.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"learning_mode\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m run_learning(\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0minputs_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_learning\u001b[0;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_role\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_inter_episodic_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minter_episodic_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;31m# -----------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/assume/world.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m return self.loop.run_until_complete(\n\u001b[0m\u001b[1;32m 687\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_ts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart_ts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_ts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend_ts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 688\u001b[0m )\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m 96\u001b[0m raise RuntimeError(\n\u001b[1;32m 97\u001b[0m 'Event loop stopped before Future completed.')\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/asyncio/futures.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__log_traceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception_tb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 202\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/asyncio/tasks.py\u001b[0m in \u001b[0;36m__step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;31m# We use the `send` method directly, because coroutines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;31m# don't have `__iter__` and `__next__` methods.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 233\u001b[0m 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\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 652\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed_role\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;32mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/mango/util/clock.py\u001b[0m in \u001b[0;36mset_time\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m 68\u001b[0m \"\"\"\n\u001b[1;32m 69\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_time\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Time must be > {self._time} but is {t}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;31m# set time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Time must be > 1546304400.0 but is 1546300799." ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "ERROR:assume.markets.base_market:clearing failed: None of ['gurobi', 'glpk'] are available\n", + "WARNING:assume.markets.base_market:1546308000.0 Market result [(datetime.datetime(2019, 1, 1, 2, 0), datetime.datetime(2019, 1, 1, 3, 0), None)] for market zonal are empty!\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=Exception(\"None of ['gurobi', 'glpk'] are available\")>\n", + "future: exception=KeyboardInterrupt()>\n", "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/world.py\", line 686, in run\n", + " return self.loop.run_until_complete(\n", + " File \"/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\", line 92, in run_until_complete\n", + " self._run_once()\n", + " File \"/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\", line 133, in _run_once\n", + " handle._run()\n", + " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 498, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 495, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 450, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 242, in market_clearing_opt\n", - " raise Exception(f\"None of {SOLVERS} are available\")\n", - "Exception: None of ['gurobi', 'glpk'] are available\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:03, 24831.41it/s] \n" + " File \"/usr/local/lib/python3.10/dist-packages/assume/common/units_operator.py\", line 411, in submit_bids\n", + " orderbook = await self.formulate_bids(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/common/units_operator.py\", line 475, in formulate_bids\n", + " product_bids = unit.calculate_bids(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/common/base.py\", line 109, in calculate_bids\n", + " bids = self.bidding_strategies[market_config.market_id].calculate_bids(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/strategies/naive_strategies.py\", line 42, in calculate_bids\n", + " min_power, max_power = unit.calculate_min_max_power(\n", + " File \"/usr/local/lib/python3.10/dist-packages/assume/units/powerplant.py\", line 349, in calculate_min_max_power\n", + " max_power = max_power.clip(lower=0)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\", line 9098, in clip\n", + " return self._clip_with_scalar(lower, upper, inplace=inplace)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\", line 8838, in _clip_with_scalar\n", + " cond = mask | (self >= lower)\n", + " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/ops/common.py\", line 74, in new_method\n", + " other = item_from_zerodim(other)\n", + "KeyboardInterrupt\n", + "\n", + "tutorial_08_zonal_case_5 2019-01-01 01:00:00: : 1546308001.0it [00:00, 3195660900.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n" ] } ], @@ -1772,7 +4302,7 @@ "# and the study case name (which config to use for the simulation)\n", "load_scenario_folder(\n", " world,\n", - " inputs_path=\"inputs\",\n", + " inputs_path=inputs_path,\n", " scenario=\"tutorial_08\",\n", " study_case=\"zonal_case\",\n", ")\n", @@ -1783,7 +4313,7 @@ "if world.learning_config.get(\"learning_mode\", False):\n", " run_learning(\n", " world,\n", - " inputs_path=\"inputs\",\n", + " inputs_path=inputs_path,\n", " scenario=\"tutorial_08\",\n", " study_case=\"zonal_case\",\n", " )\n", @@ -1792,19 +4322,52 @@ "world.run()" ] }, + { + "cell_type": "code", + "execution_count": 11, + "id": "DhYXvzaP_iyK", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DhYXvzaP_iyK", + "outputId": "73259557-0218-42da-ec23-ab7a9713ed54" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "world.learning_config.get(\"learning_mode\", False)" + ] + }, { "cell_type": "markdown", "id": "2194f71b", - "metadata": {}, + "metadata": { + "id": "2194f71b" + }, "source": [ - "**Generate same plotly figure**" + "**Generate same plotly figure**\n", + "\n", + "We use the same code described in the amarket zone tutprial to generate the plotly curve displaying the market price." ] }, { "cell_type": "code", "execution_count": null, "id": "bdb21cbe", - "metadata": {}, + "metadata": { + "id": "bdb21cbe" + }, "outputs": [], "source": [ "# @ title make makret price plotly figure\n", @@ -1884,7 +4447,9 @@ { "cell_type": "markdown", "id": "e77c41cf", - "metadata": {}, + "metadata": { + "id": "e77c41cf" + }, "source": [ "## 2. Explainable AI and SHAP Values \n", "\n", @@ -1894,7 +4459,9 @@ { "cell_type": "markdown", "id": "cbf18570", - "metadata": {}, + "metadata": { + "id": "cbf18570" + }, "source": [ "To follow along with this tutorial, we need some additional libraries." ] @@ -1902,7 +4469,9 @@ { "cell_type": "markdown", "id": "a5ac592c", - "metadata": {}, + "metadata": { + "id": "a5ac592c" + }, "source": [ "- `matplotlib`\n", "- `shap`\n", @@ -1914,6 +4483,7 @@ "execution_count": null, "id": "ae266ecb", "metadata": { + "id": "ae266ecb", "vscode": { "languageId": "shellscript" } @@ -1928,9 +4498,11 @@ { "cell_type": "markdown", "id": "5069ff93", - "metadata": {}, + "metadata": { + "id": "5069ff93" + }, "source": [ - "### 2.1 Understanding Explainable AI \n", + "### 2.1 Understanding Explainable AI\n", "Explainable AI (XAI) refers to techniques and methods that make the behavior and decisions of AI systems understandable to humans. In the context of complex models like deep neural networks, XAI helps to:\n", "- Increase Transparency: Providing insights into how models make decisions.\n", "- Build Trust: Users and stakeholders can trust AI systems if they understand them.\n", @@ -1938,15 +4510,15 @@ "- Improve Models: Identifying weaknesses or biases in models.\n", "\n", "\n", - "### 2.2 Introduction to SHAP Values \n", + "### 2.2 Introduction to SHAP Values\n", "Shapley values are a method from cooperative game theory used to explain the contribution of each feature to the prediction of a machine learning model, such as a neural network. They provide an interpretability technique by distributing the \"payout\" (the prediction) among the input features, attributing the importance of each feature to the prediction.\n", "\n", "For a given prediction, the Shapley value of a feature represents the average contribution of that feature to the prediction, considering all possible combinations of other features.\n", "\n", - "1. **Marginal Contribution**: \n", + "1. **Marginal Contribution**:\n", " The marginal contribution of a feature is the difference between the prediction with and without that feature.\n", "\n", - "2. **Average over all subsets**: \n", + "2. **Average over all subsets**:\n", " The Shapley value is calculated by averaging the marginal contributions over all possible subsets of features.\n", "\n", "The formula for the Shapley value of feature $i$ is:\n", @@ -1980,7 +4552,9 @@ { "cell_type": "markdown", "id": "21d49573", - "metadata": {}, + "metadata": { + "id": "21d49573" + }, "source": [ "## 3. Calculating SHAP values " ] @@ -1988,7 +4562,9 @@ { "cell_type": "markdown", "id": "d16712fc", - "metadata": {}, + "metadata": { + "id": "d16712fc" + }, "source": [ "We will work with:" ] @@ -1996,7 +4572,9 @@ { "cell_type": "markdown", "id": "2c0c1262", - "metadata": {}, + "metadata": { + "id": "2c0c1262" + }, "source": [ "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." @@ -2005,7 +4583,9 @@ { "cell_type": "markdown", "id": "2fb6fc14", - "metadata": {}, + "metadata": { + "id": "2fb6fc14" + }, "source": [ "Our goal is to:" ] @@ -2013,7 +4593,9 @@ { "cell_type": "markdown", "id": "e0b69db6", - "metadata": {}, + "metadata": { + "id": "e0b69db6" + }, "source": [ "- Load the observations and the trained actor model.\n", "- Use the model to predict actions.\n", @@ -2023,7 +4605,9 @@ { "cell_type": "markdown", "id": "f870b3e8", - "metadata": {}, + "metadata": { + "id": "f870b3e8" + }, "source": [ "### 3.1. Loading and Preparing Data " ] @@ -2031,7 +4615,9 @@ { "cell_type": "markdown", "id": "aaa7c3d3", - "metadata": {}, + "metadata": { + "id": "aaa7c3d3" + }, "source": [ "First, let's load the necessary libraries and the data." ] @@ -2040,7 +4626,9 @@ "cell_type": "code", "execution_count": null, "id": "b6ee4f28", - "metadata": {}, + "metadata": { + "id": "b6ee4f28" + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -2053,7 +4641,9 @@ { "cell_type": "markdown", "id": "aa268f14", - "metadata": {}, + "metadata": { + "id": "aa268f14" + }, "source": [ "the simulation common.py contains utility functions and class definitions\n", "from common import load_observations, Actor" @@ -2062,7 +4652,9 @@ { "cell_type": "markdown", "id": "f866385a", - "metadata": {}, + "metadata": { + "id": "f866385a" + }, "source": [ "**Define the Actor Neural Network Class**" ] @@ -2070,7 +4662,9 @@ { "cell_type": "markdown", "id": "ff7cf03a", - "metadata": {}, + "metadata": { + "id": "ff7cf03a" + }, "source": [ "We define the actor neural network class that will be used to predict actions based on observations." ] @@ -2080,6 +4674,7 @@ "execution_count": null, "id": "7216677a", "metadata": { + "id": "7216677a", "lines_to_next_cell": 1 }, "outputs": [], @@ -2091,7 +4686,9 @@ "cell_type": "code", "execution_count": null, "id": "5a4d6e17", - "metadata": {}, + "metadata": { + "id": "5a4d6e17" + }, "outputs": [], "source": [ "def load_config(file_path):\n", @@ -2137,7 +4734,9 @@ { "cell_type": "markdown", "id": "ddfe95d9", - "metadata": {}, + "metadata": { + "id": "ddfe95d9" + }, "source": [ "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." ] @@ -2146,7 +4745,9 @@ "cell_type": "code", "execution_count": null, "id": "44862f06", - "metadata": {}, + "metadata": { + "id": "44862f06" + }, "outputs": [], "source": [ "# @title Load observations function\n", @@ -2180,7 +4781,9 @@ { "cell_type": "markdown", "id": "444b8679", - "metadata": {}, + "metadata": { + "id": "444b8679" + }, "source": [ "**Define Paths and Parameters**" ] @@ -2188,7 +4791,9 @@ { "cell_type": "markdown", "id": "7f218d15", - "metadata": {}, + "metadata": { + "id": "7f218d15" + }, "source": [ "Adjust the following paths and parameters according to your data and model." ] @@ -2197,7 +4802,9 @@ "cell_type": "code", "execution_count": null, "id": "8fa78b12", - "metadata": {}, + "metadata": { + "id": "8fa78b12" + }, "outputs": [], "source": [ "path = (\n", @@ -2209,7 +4816,9 @@ "cell_type": "code", "execution_count": null, "id": "8f9189ca", - "metadata": {}, + "metadata": { + "id": "8f9189ca" + }, "outputs": [], "source": [ "# Define feature names (replace with actual feature names)\n", @@ -2222,7 +4831,9 @@ { "cell_type": "markdown", "id": "6c5f1986", - "metadata": {}, + "metadata": { + "id": "6c5f1986" + }, "source": [ "**Load Observations and Input Data**" ] @@ -2230,7 +4841,9 @@ { "cell_type": "markdown", "id": "36ae8f9e", - "metadata": {}, + "metadata": { + "id": "36ae8f9e" + }, "source": [ "Load the observations and input data using the utility function." ] @@ -2239,7 +4852,9 @@ "cell_type": "code", "execution_count": null, "id": "d522969d", - "metadata": {}, + "metadata": { + "id": "d522969d" + }, "outputs": [], "source": [ "df_obs, input_data = load_observations(path, ACTOR_NUM, feature_names)\n", @@ -2250,7 +4865,9 @@ { "cell_type": "markdown", "id": "5d8b9dcf", - "metadata": {}, + "metadata": { + "id": "5d8b9dcf" + }, "source": [ "**Load the Trained Actor Model**" ] @@ -2258,7 +4875,9 @@ { "cell_type": "markdown", "id": "b1b50488", - "metadata": {}, + "metadata": { + "id": "b1b50488" + }, "source": [ "We initialize and load the trained actor neural network." ] @@ -2267,7 +4886,9 @@ "cell_type": "code", "execution_count": null, "id": "4da4de57", - "metadata": {}, + "metadata": { + "id": "4da4de57" + }, "outputs": [], "source": [ "# Initialize the model\n", @@ -2280,7 +4901,9 @@ "cell_type": "code", "execution_count": null, "id": "37adecfa", - "metadata": {}, + "metadata": { + "id": "37adecfa" + }, "outputs": [], "source": [ "ACTOR_NUM = 1 # Replace with the actor number or identifier\n", @@ -2297,7 +4920,9 @@ { "cell_type": "markdown", "id": "d4a63712", - "metadata": {}, + "metadata": { + "id": "d4a63712" + }, "source": [ "Get the actions base on observation tensor we just loaded." ] @@ -2306,7 +4931,9 @@ "cell_type": "code", "execution_count": null, "id": "e6460cfb", - "metadata": {}, + "metadata": { + "id": "e6460cfb" + }, "outputs": [], "source": [ "predictions = []\n", @@ -2322,6 +4949,7 @@ "execution_count": null, "id": "be91cd97", "metadata": { + "id": "be91cd97", "lines_to_next_cell": 2 }, "outputs": [], @@ -2336,7 +4964,9 @@ "cell_type": "code", "execution_count": null, "id": "b21c93c7", - "metadata": {}, + "metadata": { + "id": "b21c93c7" + }, "outputs": [], "source": [ "# Convert data to tensors\n", @@ -2352,7 +4982,9 @@ { "cell_type": "markdown", "id": "ddd1ab1e", - "metadata": {}, + "metadata": { + "id": "ddd1ab1e" + }, "source": [ "## 3.2. Creating a SHAP Explainer " ] @@ -2360,7 +4992,9 @@ { "cell_type": "markdown", "id": "ae7b108b", - "metadata": {}, + "metadata": { + "id": "ae7b108b" + }, "source": [ "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." ] @@ -2370,6 +5004,7 @@ "execution_count": null, "id": "6d9be211", "metadata": { + "id": "6d9be211", "lines_to_next_cell": 1 }, "outputs": [], @@ -2386,7 +5021,9 @@ "cell_type": "code", "execution_count": null, "id": "f0f7857b", - "metadata": {}, + "metadata": { + "id": "f0f7857b" + }, "outputs": [], "source": [ "# Use a subset of training data for the background dataset\n", @@ -2398,7 +5035,9 @@ "cell_type": "code", "execution_count": null, "id": "84bb96cf", - "metadata": {}, + "metadata": { + "id": "84bb96cf" + }, "outputs": [], "source": [ "# Create the SHAP Kernel Explainer\n", @@ -2409,7 +5048,9 @@ "cell_type": "code", "execution_count": null, "id": "2a7929e4", - "metadata": {}, + "metadata": { + "id": "2a7929e4" + }, "outputs": [], "source": [ "# Calculate SHAP values for the test set\n", @@ -2420,7 +5061,9 @@ "cell_type": "code", "execution_count": null, "id": "44fa277f", - "metadata": {}, + "metadata": { + "id": "44fa277f" + }, "outputs": [], "source": [ "shap_values" @@ -2429,7 +5072,9 @@ { "cell_type": "markdown", "id": "c1f3d550", - "metadata": {}, + "metadata": { + "id": "c1f3d550" + }, "source": [ "## 4. Visualizing SHAP Values " ] @@ -2437,7 +5082,9 @@ { "cell_type": "markdown", "id": "3a0f0cbe", - "metadata": {}, + "metadata": { + "id": "3a0f0cbe" + }, "source": [ "We generate summary plots to visualize feature importance for each output dimension." ] @@ -2446,7 +5093,9 @@ "cell_type": "code", "execution_count": null, "id": "a6af4983", - "metadata": {}, + "metadata": { + "id": "a6af4983" + }, "outputs": [], "source": [ "print(shap_values[0].shape)\n", @@ -2457,7 +5106,9 @@ "cell_type": "code", "execution_count": null, "id": "2e318a5b", - "metadata": {}, + "metadata": { + "id": "2e318a5b" + }, "outputs": [], "source": [ "# Summary plot for the first output dimension\n", @@ -2490,7 +5141,9 @@ { "cell_type": "markdown", "id": "9a888f8b", - "metadata": {}, + "metadata": { + "id": "9a888f8b" + }, "source": [ "The SHAP summary plots show the impact of each feature on the model's predictions for each output dimension (action). Features with larger absolute SHAP values have a more significant influence on the decision-making process of the RL agent." ] @@ -2498,7 +5151,9 @@ { "cell_type": "markdown", "id": "c6c4ce8c", - "metadata": {}, + "metadata": { + "id": "c6c4ce8c" + }, "source": [ "- **Positive SHAP Value**: Indicates that the feature contributes positively to the predicted action value.\n", "- **Negative SHAP Value**: Indicates that the feature contributes negatively to the predicted action value." @@ -2507,7 +5162,9 @@ { "cell_type": "markdown", "id": "86545200", - "metadata": {}, + "metadata": { + "id": "86545200" + }, "source": [ "By analyzing these plots, we can identify which features are most influential and understand how changes in feature values affect the agent's actions." ] @@ -2515,7 +5172,9 @@ { "cell_type": "markdown", "id": "06f3977c", - "metadata": {}, + "metadata": { + "id": "06f3977c" + }, "source": [ "## 5. Conclusion " ] @@ -2523,7 +5182,9 @@ { "cell_type": "markdown", "id": "dadd0a0c", - "metadata": {}, + "metadata": { + "id": "dadd0a0c" + }, "source": [ "In this tutorial, we've demonstrated how to apply SHAP to a reinforcement learning agent to explain its decision-making process. By interpreting the SHAP values, we gain valuable insights into which features influence the agent's actions, enhancing transparency and trust in the model." ] @@ -2531,7 +5192,9 @@ { "cell_type": "markdown", "id": "37633c16", - "metadata": {}, + "metadata": { + "id": "37633c16" + }, "source": [ "Explainability is crucial, especially when deploying RL agents in real-world applications where understanding the rationale behind decisions is essential for safety, fairness, and compliance." ] @@ -2539,7 +5202,9 @@ { "cell_type": "markdown", "id": "8735d66f", - "metadata": {}, + "metadata": { + "id": "8735d66f" + }, "source": [ "## 6. Additional Resources " ] @@ -2547,7 +5212,9 @@ { "cell_type": "markdown", "id": "d6b0332f", - "metadata": {}, + "metadata": { + "id": "d6b0332f" + }, "source": [ "- **SHAP Documentation**: [https://shap.readthedocs.io/en/latest/](https://shap.readthedocs.io/en/latest/)\n", "- **PyTorch Documentation**: [https://pytorch.org/docs/stable/index.html](https://pytorch.org/docs/stable/index.html)\n", @@ -2558,13 +5225,18 @@ { "cell_type": "markdown", "id": "a8cdea5f", - "metadata": {}, + "metadata": { + "id": "a8cdea5f" + }, "source": [ "**Feel free to experiment with the code and explore different explainability techniques. Happy learning!**" ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { "display_name": "Python 3", "language": "python", From 1d3abaeb0c58069171cbad517200a26ec37ff4ae Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 7 Oct 2024 11:05:30 +0200 Subject: [PATCH 14/27] added tesnor conversion to learning_unit_operator, so that glpk etc can handly bids, which were so far tensors --- .../learning_unit_operator.py | 40 +++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/assume/reinforcement_learning/learning_unit_operator.py b/assume/reinforcement_learning/learning_unit_operator.py index 3b39715e..3314f983 100644 --- a/assume/reinforcement_learning/learning_unit_operator.py +++ b/assume/reinforcement_learning/learning_unit_operator.py @@ -252,3 +252,43 @@ async def write_to_learning_role( "data": rl_agent_data, }, ) + + async def formulate_bids( + self, market: MarketConfig, products: list[tuple] + ) -> Orderbook: + """ + Formulates the bid to the market according to the bidding strategy of the each unit individually. + + Args: + market (MarketConfig): The market to formulate bids for. + products (list[tuple]): The products to formulate bids for. + + Returns: + OrderBook: The orderbook that is submitted as a bid to the market. + """ + + orderbook: Orderbook = [] + + for unit_id, unit in self.units.items(): + product_bids = unit.calculate_bids( + market, + product_tuples=products, + ) + for i, order in enumerate(product_bids): + order["agent_id"] = (self.context.addr, self.context.aid) + # check for tensors and convert to numpy + if isinstance(order["volume"], th.Tensor): + order["volume"] = order["volume"].cpu().numpy().item() + if isinstance(order["price"], th.Tensor): + order["price"] = order["price"].cpu().numpy().item() + + if market.volume_tick: + order["volume"] = round(order["volume"] / market.volume_tick) + if market.price_tick: + order["price"] = round(order["price"] / market.price_tick) + if "bid_id" not in order.keys() or order["bid_id"] is None: + order["bid_id"] = f"{unit_id}_{i+1}" + order["unit_id"] = unit_id + orderbook.append(order) + + return orderbook From 73039a6bf5919ed45f9534418d0523bde1ab5832 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 7 Oct 2024 14:27:22 +0200 Subject: [PATCH 15/27] - make prices exact same value type as rest --- assume/reinforcement_learning/learning_unit_operator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/assume/reinforcement_learning/learning_unit_operator.py b/assume/reinforcement_learning/learning_unit_operator.py index 3314f983..2c52d9e1 100644 --- a/assume/reinforcement_learning/learning_unit_operator.py +++ b/assume/reinforcement_learning/learning_unit_operator.py @@ -278,9 +278,9 @@ async def formulate_bids( order["agent_id"] = (self.context.addr, self.context.aid) # check for tensors and convert to numpy if isinstance(order["volume"], th.Tensor): - order["volume"] = order["volume"].cpu().numpy().item() + order["volume"] = np.float64(order["volume"].cpu().numpy()) if isinstance(order["price"], th.Tensor): - order["price"] = order["price"].cpu().numpy().item() + order["price"] = np.float64(order["price"].cpu().numpy()) if market.volume_tick: order["volume"] = round(order["volume"] / market.volume_tick) From 723cc25e3a0a67b44f51496ab2e9cb0250827d76 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Mon, 7 Oct 2024 14:29:08 +0200 Subject: [PATCH 16/27] - path and file changes to xRL tut --- .../notebooks/09_example_Sim_and_xRL.ipynb | 3611 ++--------------- 1 file changed, 337 insertions(+), 3274 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index b77601c9..b16964f3 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ee220130", "metadata": { "colab": { @@ -147,204 +147,104 @@ "height": 1000 }, "id": "ee220130", - "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting assume-framework[learning]\n", - " Downloading assume_framework-0.4.0.post2-py3-none-any.whl.metadata (9.9 kB)\n", - "Collecting argcomplete>=3.1.4 (from assume-framework[learning])\n", - " Downloading argcomplete-3.5.1-py3-none-any.whl.metadata (16 kB)\n", - 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This behaviour is the source of the following dependency conflicts.\n", - "google-ai-generativelanguage 0.6.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-datastore 2.19.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "google-cloud-firestore 2.16.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 5.27.2 which is incompatible.\n", - "tensorboard 2.17.0 requires protobuf!=4.24.0,<5.0.0,>=3.19.6, but you have protobuf 5.27.2 which is incompatible.\n", - "tensorflow 2.17.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.27.2 which is incompatible.\n", - "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 5.27.2 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed argcomplete-3.5.1 assume-framework-0.4.0.post2 dill-0.3.9 mango-agents-assume-1.1.4.post10 msgspec-0.18.6 paho-mqtt-2.1.0 protobuf-5.27.2 psycopg2-binary-2.9.9 python-dateutil-2.9.0.post0 pyyaml-include-2.1\n" - ] - }, - { - "data": { - "application/vnd.colab-display-data+json": { - "id": "5bd36a4bde024ee1bea1d8fe663e99d9", - "pip_warning": { - "packages": [ - "dateutil" - ] - } - } - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.24.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (9.0.0)\n", - "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (24.1)\n", - "^C\n", - "Cloning into 'assume-repo'...\n", - "remote: Enumerating objects: 10788, done.\u001b[K\n", - "remote: Counting objects: 100% (4342/4342), done.\u001b[K\n", - "remote: Compressing objects: 100% (1458/1458), done.\u001b[K\n" - ] + "outputId": "ffd98b47-2b07-41cd-dfe4-ff0381571825", + "vscode": { + "languageId": "shellscript" } - ], + }, + "outputs": [], "source": [ - "!pip install 'assume-framework[learning]'\n", - "!pip install plotly\n", - "!git clone https://github.com/assume-framework/assume.git assume-repo" + "#!pip install 'assume-framework[learning]'\n", + "#!pip install plotly\n", + "#!git clone https://github.com/assume-framework/assume.git assume-repo" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "Hn_DvrqR7oK2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Hn_DvrqR7oK2", - "outputId": "391046fd-90d9-4967-b919-52c49f13a9f8" + "outputId": "391046fd-90d9-4967-b919-52c49f13a9f8", + "vscode": { + "languageId": "shellscript" + } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Collecting pyomo\n", - " Downloading Pyomo-6.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.0 kB)\n", - "Collecting ply (from pyomo)\n", - " Downloading ply-3.11-py2.py3-none-any.whl.metadata (844 bytes)\n", - "Downloading Pyomo-6.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hDownloading ply-3.11-py2.py3-none-any.whl (49 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.6/49.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: ply, pyomo\n", - "Successfully installed ply-3.11 pyomo-6.8.0\n", - "Selecting previously unselected package 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c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (2.9.0)\n", + "Requirement already satisfied: pyzmq>=23.0 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (26.2.0)\n", + "Requirement already satisfied: tornado>=6.2 in c:\\users\\tg3533\\appdata\\local\\miniconda3\\envs\\assume-framework\\lib\\site-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert) (6.4.1)\n", + "Using cached nbconvert-7.16.4-py3-none-any.whl (257 kB)\n", + "Using cached bleach-6.1.0-py3-none-any.whl (162 kB)\n", + "Using cached mistune-3.0.2-py3-none-any.whl (47 kB)\n", + "Using cached nbclient-0.10.0-py3-none-any.whl (25 kB)\n", + "Using cached pandocfilters-1.5.1-py2.py3-none-any.whl (8.7 kB)\n", + "Using cached beautifulsoup4-4.12.3-py3-none-any.whl (147 kB)\n", + "Using cached defusedxml-0.7.1-py2.py3-none-any.whl (25 kB)\n", + "Using cached jupyterlab_pygments-0.3.0-py3-none-any.whl (15 kB)\n", + "Using cached tinycss2-1.3.0-py3-none-any.whl (22 kB)\n", + "Using cached soupsieve-2.6-py3-none-any.whl (36 kB)\n", + "Using cached webencodings-0.5.1-py2.py3-none-any.whl (11 kB)\n", + "Installing collected packages: webencodings, tinycss2, soupsieve, pandocfilters, mistune, jupyterlab-pygments, defusedxml, bleach, beautifulsoup4, nbclient, nbconvert\n", + "Successfully installed beautifulsoup4-4.12.3 bleach-6.1.0 defusedxml-0.7.1 jupyterlab-pygments-0.3.0 mistune-3.0.2 nbclient-0.10.0 nbconvert-7.16.4 pandocfilters-1.5.1 soupsieve-2.6 tinycss2-1.3.0 webencodings-0.5.1\n" ] } ], "source": [ - "!pip install pyomo\n", - "!apt-get install -y -qq glpk-utils" + "#!pip install pyomo\n", + "#!apt-get install -y -qq glpk-utils\n", + "!pip install nbconvert" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "75cd480a", "metadata": { "id": "75cd480a" @@ -364,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "bfd1daf2", "metadata": { "colab": { @@ -378,7 +278,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "assume-repo/examples/inputs\n" + "../inputs\n" ] } ], @@ -396,16 +296,6 @@ "print(inputs_path)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "4sfWyPgJ-Hq5", - "metadata": { - "id": "4sfWyPgJ-Hq5" - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "636ea9ae", @@ -418,7 +308,51 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, + "id": "988d3e15", + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[WinError 2] Das System kann die angegebene Datei nicht finden: 'content'\n", + "c:\\Users\\tg3533\\Documents\\Code\\assume\\examples\\notebooks\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[NbConvertApp] Converting notebook 08_market_zone_coupling.ipynb to notebook\n", + "C:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\zmq\\_future.py:724: RuntimeWarning: Proactor event loop does not implement add_reader family of methods required for zmq. Registering an additional selector thread for add_reader support via tornado. Use `asyncio.set_event_loop_policy(WindowsSelectorEventLoopPolicy())` to avoid this warning.\n", + " self._get_loop()\n", + "[NbConvertApp] Writing 188324 bytes to output.ipynb\n", + "Der Befehl \"cp\" ist entweder falsch geschrieben oder\n", + "konnte nicht gefunden werden.\n" + ] + } + ], + "source": [ + "#if used locally\n", + "#%cd assume/examples/notebooks/\n", + "\n", + "# if used in colab\n", + "#%cd assume-repo/examples/notebooks/\n", + "\n", + "#!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", + " \n", + "#%cd content\n", + "#!cp -r assume-repo/examples/notebooks/inputs ." + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "233f315b", "metadata": { "colab": { @@ -475,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "b205256f", "metadata": { "colab": { @@ -488,15 +422,8 @@ "outputs": [ { "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"powerplant_units\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 20,\n \"samples\": [\n \"Unit 1\",\n \"Unit 18\",\n \"Unit 16\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"technology\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"nuclear\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"bidding_zonal\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"pp_learning\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"fuel_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"uranium\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"emission_factor\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"max_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 894.4271909999159,\n \"min\": 1000.0,\n \"max\": 5000.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 5000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"min_power\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 0.0,\n \"max\": 0.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"efficiency\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.695323946259567e-17,\n \"min\": 0.3,\n \"max\": 0.3,\n \"num_unique_values\": 1,\n \"samples\": [\n 0.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"additional_cost\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 5,\n \"max\": 24,\n \"num_unique_values\": 20,\n \"samples\": [\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"node\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"north_1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"unit_operator\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Operator South\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "powerplant_units" - }, "text/html": [ - "\n", - "
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"17 Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", - "18 Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", - "19 Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", + " Unnamed: 0.3 Unnamed: 0.2 Unnamed: 0.1 Unnamed: 0 name technology \\\n", + "0 0 0 0 0 Unit 1 nuclear \n", + "1 1 1 1 1 Unit 2 nuclear \n", + "2 2 2 2 2 Unit 3 nuclear \n", + "3 3 3 3 3 Unit 4 nuclear \n", + "4 4 4 4 4 Unit 5 nuclear \n", + "5 5 5 5 5 Unit 6 nuclear \n", + "6 6 6 6 6 Unit 7 nuclear \n", + "7 7 7 7 7 Unit 8 nuclear \n", + "8 8 8 8 8 Unit 9 nuclear \n", + "9 9 9 9 9 Unit 10 nuclear \n", + "10 10 10 10 10 Unit 11 nuclear \n", + "11 11 11 11 11 Unit 12 nuclear \n", + "12 12 12 12 12 Unit 13 nuclear \n", + "13 13 13 13 13 Unit 14 nuclear \n", + "14 14 14 14 14 Unit 15 nuclear \n", + "15 15 15 15 15 Unit 16 nuclear \n", + "16 16 16 16 16 Unit 17 nuclear \n", + "17 17 17 17 17 Unit 18 nuclear \n", + "18 18 18 18 18 Unit 19 nuclear \n", + "19 19 19 19 19 Unit 20 nuclear \n", + "\n", + " bidding_zonal fuel_type emission_factor max_power min_power efficiency \\\n", + "0 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "1 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "2 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "3 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "4 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "5 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "6 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "7 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "8 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "9 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "10 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "11 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "12 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "13 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "14 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "15 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "16 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "17 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "18 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", + "19 pp_learning uranium 0.0 5000.0 0.0 0.3 \n", "\n", - " min_power efficiency additional_cost node unit_operator \n", - "0 0.0 0.3 5 north_1 Operator North \n", - "1 0.0 0.3 6 north_1 Operator North \n", - "2 0.0 0.3 7 north_1 Operator North \n", - "3 0.0 0.3 8 north_1 Operator North \n", - "4 0.0 0.3 9 north_1 Operator North \n", - "5 0.0 0.3 10 north_1 Operator North \n", - "6 0.0 0.3 11 north_1 Operator North \n", - "7 0.0 0.3 12 north_1 Operator North \n", - "8 0.0 0.3 13 north_2 Operator North \n", - "9 0.0 0.3 14 north_2 Operator North \n", - "10 0.0 0.3 15 north_2 Operator North \n", - "11 0.0 0.3 16 north_2 Operator North \n", - "12 0.0 0.3 17 north_2 Operator North \n", - "13 0.0 0.3 18 north_2 Operator North \n", - "14 0.0 0.3 19 north_2 Operator North \n", - "15 0.0 0.3 20 south Operator South \n", - "16 0.0 0.3 21 south Operator South \n", - "17 0.0 0.3 22 south Operator South \n", - "18 0.0 0.3 23 south Operator South \n", - "19 0.0 0.3 24 south Operator South " + " additional_cost node unit_operator \n", + "0 5 north_1 Operator North \n", + "1 6 north_1 Operator North \n", + "2 7 north_1 Operator North \n", + "3 8 north_1 Operator North \n", + "4 9 north_1 Operator North \n", + "5 10 north_1 Operator North \n", + "6 11 north_1 Operator North \n", + "7 12 north_1 Operator North \n", + "8 13 north_2 Operator North \n", + "9 14 north_2 Operator North \n", + "10 15 north_2 Operator North \n", + "11 16 north_2 Operator North \n", + "12 17 north_2 Operator North \n", + "13 18 north_2 Operator North \n", + "14 19 north_2 Operator North \n", + "15 20 south Operator South \n", + "16 21 south Operator South \n", + "17 22 south Operator South \n", + "18 23 south Operator South \n", + "19 24 south Operator-RL " ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1135,12 +904,15 @@ "powerplant_units.loc[19, \"bidding_zonal\"] = \"pp_learning\"\n", "powerplant_units.loc[19, \"max_power\"] = 5000\n", "\n", + "# assig specific RL unit operator to plant\n", + "powerplant_units.loc[19, \"unit_operator\"] = \"Operator-RL\"\n", + "\n", "powerplant_units" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "id": "QBTGrw62_5I7", "metadata": { "id": "QBTGrw62_5I7" @@ -1163,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "9c555ce9", "metadata": { "colab": { @@ -1177,7 +949,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Configuration YAML file has been saved to 'assume-repo/examples/inputs/tutorial_08/config.yaml'.\n" + "Configuration YAML file has been saved to '../inputs\\tutorial_08\\config.yaml'.\n" ] } ], @@ -1249,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "201251c6", "metadata": { "cellView": "form", @@ -1472,7 +1244,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "id": "bfadf522", "metadata": { "colab": { @@ -1484,11 +1256,11 @@ }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from assume-repo/examples/inputs\n", + "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from ../inputs\n", "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", @@ -1501,2774 +1273,65 @@ "INFO:assume.scenario.loader_csv:Read units from file\n", "INFO:assume.scenario.loader_csv:Adding power_plant units\n", "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for=>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "/usr/local/lib/python3.10/dist-packages/sympy/geometry/polygon.py:237: RuntimeWarning: coroutine 'MarketRole.opening' was never awaited\n", - " def angles(self):\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[Agent.raise_exceptions()]>\n", - "/usr/local/lib/python3.10/dist-packages/sympy/geometry/polygon.py:237: RuntimeWarning: coroutine 'UnitsOperator.register_market' was never awaited\n", - " def angles(self):\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "ERROR:asyncio:Task was destroyed but it is pending!\n", - "task: wait_for= cb=[ScheduledTask.on_stop(), Scheduler._remove_task()]>\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "Training Episodes: 0%| | 0/10 [00:00)\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent clock: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent learning_agent: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent EOM_operator: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent Operator North: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent Operator South: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "ERROR:mango.agent.core:Agent eom_de: Caught the following exception in _check_inbox: cannot reuse already awaited coroutine\n", - "ERROR:asyncio:Exception in callback Agent.raise_exceptions()\n", - "handle: )>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n", - " future.result()\n", - " File \"/usr/lib/python3.10/asyncio/futures.py\", line 196, in result\n", - " raise exc\n", - "asyncio.exceptions.CancelledError\n", - "\n", - "During handling of the above exception, another exception occurred:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/agent/core.py\", line 434, in raise_exceptions\n", - " raise fut.exception()\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 234, in __step\n", - " result = coro.throw(exc)\n", - "RuntimeError: cannot reuse already awaited coroutine\n", - "\n", - " 0%| | 1.0/82800 [00:00<4:56:01, 4.66it/s]\u001b[AWARNING:pyomo.common.numeric_types:Dynamically registering the following numeric type:\n", - " torch.Tensor\n", - "Dynamic registration is supported for convenience, but there are known\n", - "limitations to this approach. We recommend explicitly registering\n", - "numeric types using RegisterNumericType() or RegisterIntegerType().\n", - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpf0sv8yov.glpk.raw --wglp /tmp/tmp5asnipvw.glpk.glp --cpxlp\n", - " /tmp/tmpfmwxc953.pyomo.lp\n", - "Reading problem data from '/tmp/tmpfmwxc953.pyomo.lp'...\n", - "/tmp/tmpfmwxc953.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:07, 10193.16it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp55pxy9xx.glpk.raw --wglp /tmp/tmpql_afw06.glpk.glp --cpxlp\n", - " /tmp/tmpx6mdl1uh.pyomo.lp\n", - "Reading problem data from '/tmp/tmpx6mdl1uh.pyomo.lp'...\n", - "/tmp/tmpx6mdl1uh.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:04, 16817.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpb1j_ikxq.glpk.raw --wglp /tmp/tmpezp3razm.glpk.glp --cpxlp\n", - " /tmp/tmpfbbnpt7q.pyomo.lp\n", - "Reading problem data from '/tmp/tmpfbbnpt7q.pyomo.lp'...\n", - "/tmp/tmpfbbnpt7q.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 20289.50it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp8r2jsxua.glpk.raw --wglp /tmp/tmpa6724_sx.glpk.glp --cpxlp\n", - " /tmp/tmpgjwy19ki.pyomo.lp\n", - "Reading problem data from '/tmp/tmpgjwy19ki.pyomo.lp'...\n", - "/tmp/tmpgjwy19ki.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:02, 23320.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpe8846btc.glpk.raw --wglp /tmp/tmp0ilf7_dz.glpk.glp --cpxlp\n", - " /tmp/tmps6gfx_nx.pyomo.lp\n", - "Reading problem data from '/tmp/tmps6gfx_nx.pyomo.lp'...\n", - "/tmp/tmps6gfx_nx.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23916.10it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpond8pqlm.glpk.raw --wglp /tmp/tmpyx1d7zt8.glpk.glp --cpxlp\n", - " /tmp/tmp213byyn9.pyomo.lp\n", - "Reading problem data from '/tmp/tmp213byyn9.pyomo.lp'...\n", - "/tmp/tmp213byyn9.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 24674.58it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp019fpb63.glpk.raw --wglp /tmp/tmpenhlv7h2.glpk.glp --cpxlp\n", - " /tmp/tmpbknr_2vl.pyomo.lp\n", - "Reading problem data from '/tmp/tmpbknr_2vl.pyomo.lp'...\n", - "/tmp/tmpbknr_2vl.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 23638.63it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpa994dw_w.glpk.raw --wglp /tmp/tmph76tkpxl.glpk.glp --cpxlp\n", - " /tmp/tmp0riz9f2a.pyomo.lp\n", - "Reading problem data from '/tmp/tmp0riz9f2a.pyomo.lp'...\n", - "/tmp/tmp0riz9f2a.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23175.57it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpdwhwi8rf.glpk.raw --wglp /tmp/tmp60iy59cg.glpk.glp --cpxlp\n", - " /tmp/tmptof5okg8.pyomo.lp\n", - "Reading problem data from '/tmp/tmptof5okg8.pyomo.lp'...\n", - "/tmp/tmptof5okg8.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 24335.43it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp55hci9a1.glpk.raw --wglp /tmp/tmpi0psaqav.glpk.glp --cpxlp\n", - " /tmp/tmpd657t8ua.pyomo.lp\n", - "Reading problem data from '/tmp/tmpd657t8ua.pyomo.lp'...\n", - "/tmp/tmpd657t8ua.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:01, 25381.64it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpgn729qmk.glpk.raw --wglp /tmp/tmprb78b6rf.glpk.glp --cpxlp\n", - " /tmp/tmp9w3g0mmd.pyomo.lp\n", - "Reading problem data from '/tmp/tmp9w3g0mmd.pyomo.lp'...\n", - "/tmp/tmp9w3g0mmd.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:01, 25367.58it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp_i9sychx.glpk.raw --wglp /tmp/tmpvhnix6t0.glpk.glp --cpxlp\n", - " /tmp/tmpzisrcyck.pyomo.lp\n", - "Reading problem data from '/tmp/tmpzisrcyck.pyomo.lp'...\n", - "/tmp/tmpzisrcyck.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:01<00:01, 25805.98it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpko0qf5mb.glpk.raw --wglp /tmp/tmpwdbqp01a.glpk.glp --cpxlp\n", - " /tmp/tmp5z992p5h.pyomo.lp\n", - "Reading problem data from '/tmp/tmp5z992p5h.pyomo.lp'...\n", - "/tmp/tmp5z992p5h.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 26004.28it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpfikht9v2.glpk.raw --wglp /tmp/tmp14s4vgy2.glpk.glp --cpxlp\n", - " /tmp/tmph8p4znaw.pyomo.lp\n", - "Reading problem data from '/tmp/tmph8p4znaw.pyomo.lp'...\n", - "/tmp/tmph8p4znaw.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 24577.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmprgvhrh6y.glpk.raw --wglp /tmp/tmp1_6dn0t4.glpk.glp --cpxlp\n", - " /tmp/tmpyapsxfai.pyomo.lp\n", - "Reading problem data from '/tmp/tmpyapsxfai.pyomo.lp'...\n", - "/tmp/tmpyapsxfai.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 23890.35it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpe870v_oj.glpk.raw --wglp /tmp/tmpvudm7qsl.glpk.glp --cpxlp\n", - " /tmp/tmpwi094v0g.pyomo.lp\n", - "Reading problem data from '/tmp/tmpwi094v0g.pyomo.lp'...\n", - "/tmp/tmpwi094v0g.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 24453.78it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp47qng8zx.glpk.raw --wglp /tmp/tmp03j5ii3a.glpk.glp --cpxlp\n", - " /tmp/tmpb7hqi91q.pyomo.lp\n", - "Reading problem data from '/tmp/tmpb7hqi91q.pyomo.lp'...\n", - "/tmp/tmpb7hqi91q.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 24706.79it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp51erzb_a.glpk.raw --wglp /tmp/tmp0iq0mabf.glpk.glp --cpxlp\n", - " /tmp/tmpu5xxl4xp.pyomo.lp\n", - "Reading problem data from '/tmp/tmpu5xxl4xp.pyomo.lp'...\n", - "/tmp/tmpu5xxl4xp.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:02<00:00, 24981.59it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpvkcz_hrl.glpk.raw --wglp /tmp/tmpznf0t9y6.glpk.glp --cpxlp\n", - " /tmp/tmp2ueg9oj5.pyomo.lp\n", - "Reading problem data from '/tmp/tmp2ueg9oj5.pyomo.lp'...\n", - "/tmp/tmp2ueg9oj5.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:02<00:00, 24948.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp0x3xdbk0.glpk.raw --wglp /tmp/tmpc99m191o.glpk.glp --cpxlp\n", - " /tmp/tmpowun19s9.pyomo.lp\n", - "Reading problem data from '/tmp/tmpowun19s9.pyomo.lp'...\n", - "/tmp/tmpowun19s9.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 25113.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpbvai91ye.glpk.raw --wglp /tmp/tmpfb_2w9mc.glpk.glp --cpxlp\n", - " /tmp/tmpk5x4i7yq.pyomo.lp\n", - "Reading problem data from '/tmp/tmpk5x4i7yq.pyomo.lp'...\n", - "/tmp/tmpk5x4i7yq.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 23751.99it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpsneoef5m.glpk.raw --wglp /tmp/tmps4qx99k7.glpk.glp --cpxlp\n", - " /tmp/tmpnfa4gfd_.pyomo.lp\n", - "Reading problem data from '/tmp/tmpnfa4gfd_.pyomo.lp'...\n", - "/tmp/tmpnfa4gfd_.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_1 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:03<00:00, 20862.94it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpmpzml_n_.glpk.raw --wglp /tmp/tmphy7mcb3t.glpk.glp --cpxlp\n", - " /tmp/tmp13ub5l10.pyomo.lp\n", - "Reading problem data from '/tmp/tmp13ub5l10.pyomo.lp'...\n", - "/tmp/tmp13ub5l10.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:03, 23250.79it/s] \n", - "Training Episodes: 10%|█ | 1/10 [00:03<00:32, 3.60s/it]INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "\n", - " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:05, 15063.49it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmptw07elbf.glpk.raw --wglp /tmp/tmp0x6dxvb0.glpk.glp --cpxlp\n", - " /tmp/tmpambcx7ro.pyomo.lp\n", - "Reading problem data from '/tmp/tmpambcx7ro.pyomo.lp'...\n", - "/tmp/tmpambcx7ro.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:04, 16453.70it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpbse10e5u.glpk.raw --wglp /tmp/tmpyw23oq3v.glpk.glp --cpxlp\n", - " /tmp/tmpnivr7kob.pyomo.lp\n", - "Reading problem data from '/tmp/tmpnivr7kob.pyomo.lp'...\n", - "/tmp/tmpnivr7kob.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:04, 17221.35it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpolvkucm1.glpk.raw --wglp /tmp/tmpi_t4m2lc.glpk.glp --cpxlp\n", - " /tmp/tmpvus_8u99.pyomo.lp\n", - "Reading problem data from '/tmp/tmpvus_8u99.pyomo.lp'...\n", - "/tmp/tmpvus_8u99.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:03, 18374.44it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpgtsp63b2.glpk.raw --wglp /tmp/tmpd68kmvyd.glpk.glp --cpxlp\n", - " /tmp/tmpnawahz9p.pyomo.lp\n", - "Reading problem data from '/tmp/tmpnawahz9p.pyomo.lp'...\n", - "/tmp/tmpnawahz9p.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:01<00:03, 19350.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpnlu5ux23.glpk.raw --wglp /tmp/tmpyxwz4s_p.glpk.glp --cpxlp\n", - " /tmp/tmpzos92ejh.pyomo.lp\n", - "Reading problem data from '/tmp/tmpzos92ejh.pyomo.lp'...\n", - "/tmp/tmpzos92ejh.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 20968.10it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmplq8gqt0k.glpk.raw --wglp /tmp/tmpxq0098y0.glpk.glp --cpxlp\n", - " /tmp/tmpufnkio4j.pyomo.lp\n", - "Reading problem data from '/tmp/tmpufnkio4j.pyomo.lp'...\n", - "/tmp/tmpufnkio4j.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 22155.97it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp5dt2b0oc.glpk.raw --wglp /tmp/tmp8o6m8qmi.glpk.glp --cpxlp\n", - " /tmp/tmpgygidn7m.pyomo.lp\n", - "Reading problem data from '/tmp/tmpgygidn7m.pyomo.lp'...\n", - "/tmp/tmpgygidn7m.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 20999.51it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp3gzdb_r1.glpk.raw --wglp /tmp/tmp51k72zan.glpk.glp --cpxlp\n", - " /tmp/tmpd3g3oslf.pyomo.lp\n", - "Reading problem data from '/tmp/tmpd3g3oslf.pyomo.lp'...\n", - "/tmp/tmpd3g3oslf.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 20242.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpe9x2tjf9.glpk.raw --wglp /tmp/tmpcozapllo.glpk.glp --cpxlp\n", - " /tmp/tmpr9ot36qy.pyomo.lp\n", - "Reading problem data from '/tmp/tmpr9ot36qy.pyomo.lp'...\n", - "/tmp/tmpr9ot36qy.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:02, 19402.14it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp9t744uu7.glpk.raw --wglp /tmp/tmpstthnk19.glpk.glp --cpxlp\n", - " /tmp/tmprixpurt5.pyomo.lp\n", - "Reading problem data from '/tmp/tmprixpurt5.pyomo.lp'...\n", - "/tmp/tmprixpurt5.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:02<00:02, 19923.94it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpibjig1ky.glpk.raw --wglp /tmp/tmp_0lbb30q.glpk.glp --cpxlp\n", - " /tmp/tmprkynpgr1.pyomo.lp\n", - "Reading problem data from '/tmp/tmprkynpgr1.pyomo.lp'...\n", - "/tmp/tmprkynpgr1.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:02<00:01, 20449.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpr_3fyj2x.glpk.raw --wglp /tmp/tmpfi6fol7b.glpk.glp --cpxlp\n", - " /tmp/tmpde1k0uo2.pyomo.lp\n", - "Reading problem data from '/tmp/tmpde1k0uo2.pyomo.lp'...\n", - "/tmp/tmpde1k0uo2.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 19941.06it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpoiy1c0yc.glpk.raw --wglp /tmp/tmpr8jxzs11.glpk.glp --cpxlp\n", - " /tmp/tmp6k_39sav.pyomo.lp\n", - "Reading problem data from '/tmp/tmp6k_39sav.pyomo.lp'...\n", - "/tmp/tmp6k_39sav.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 18864.41it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp1sj1evtg.glpk.raw --wglp /tmp/tmpf60d7krd.glpk.glp --cpxlp\n", - " /tmp/tmpz2yoze5t.pyomo.lp\n", - "Reading problem data from '/tmp/tmpz2yoze5t.pyomo.lp'...\n", - "/tmp/tmpz2yoze5t.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 18663.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpnnwilo2t.glpk.raw --wglp /tmp/tmpbntwet5y.glpk.glp --cpxlp\n", - " /tmp/tmpwn0651x9.pyomo.lp\n", - "Reading problem data from '/tmp/tmpwn0651x9.pyomo.lp'...\n", - "/tmp/tmpwn0651x9.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:03<00:01, 18298.76it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpd_uy11ih.glpk.raw --wglp /tmp/tmpzpdoz7rf.glpk.glp --cpxlp\n", - " /tmp/tmprayw9lsw.pyomo.lp\n", - "Reading problem data from '/tmp/tmprayw9lsw.pyomo.lp'...\n", - "/tmp/tmprayw9lsw.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:03<00:01, 18869.52it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp14nuv6mg.glpk.raw --wglp /tmp/tmpo3r87q3g.glpk.glp --cpxlp\n", - " /tmp/tmp3vdza3z8.pyomo.lp\n", - "Reading problem data from '/tmp/tmp3vdza3z8.pyomo.lp'...\n", - "/tmp/tmp3vdza3z8.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:03<00:01, 17994.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp7k5tzmso.glpk.raw --wglp /tmp/tmpc8pc38cn.glpk.glp --cpxlp\n", - " /tmp/tmpc7vcg1bb.pyomo.lp\n", - "Reading problem data from '/tmp/tmpc7vcg1bb.pyomo.lp'...\n", - "/tmp/tmpc7vcg1bb.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:03<00:00, 16828.72it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp4l672sh5.glpk.raw --wglp /tmp/tmp6nlohwjm.glpk.glp --cpxlp\n", - " /tmp/tmp7o8efpx4.pyomo.lp\n", - "Reading problem data from '/tmp/tmp7o8efpx4.pyomo.lp'...\n", - "/tmp/tmp7o8efpx4.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 17908.43it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpmzs68lr2.glpk.raw --wglp /tmp/tmp0mj_rh12.glpk.glp --cpxlp\n", - " /tmp/tmpt2fkk745.pyomo.lp\n", - "Reading problem data from '/tmp/tmpt2fkk745.pyomo.lp'...\n", - "/tmp/tmpt2fkk745.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:04<00:00, 18990.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpivomrkn6.glpk.raw --wglp /tmp/tmp627sqbtz.glpk.glp --cpxlp\n", - " /tmp/tmppve9nq1t.pyomo.lp\n", - "Reading problem data from '/tmp/tmppve9nq1t.pyomo.lp'...\n", - "/tmp/tmppve9nq1t.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_2 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:04<00:00, 20269.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpt98xz8ht.glpk.raw --wglp /tmp/tmpgascxfek.glpk.glp --cpxlp\n", - " /tmp/tmpbwkx71rj.pyomo.lp\n", - "Reading problem data from '/tmp/tmpbwkx71rj.pyomo.lp'...\n", - "/tmp/tmpbwkx71rj.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:04, 19574.35it/s] \n", - "Training Episodes: 20%|██ | 2/10 [00:07<00:32, 4.05s/it]INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "\n", - " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:05, 14355.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpt16tq7d0.glpk.raw --wglp /tmp/tmp0zw37ric.glpk.glp --cpxlp\n", - " /tmp/tmpmc211vqy.pyomo.lp\n", - "Reading problem data from '/tmp/tmpmc211vqy.pyomo.lp'...\n", - "/tmp/tmpmc211vqy.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:03, 20554.01it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp8e0esoa2.glpk.raw --wglp /tmp/tmpx7bh3kjd.glpk.glp --cpxlp\n", - " /tmp/tmpdwx9zyjz.pyomo.lp\n", - "Reading problem data from '/tmp/tmpdwx9zyjz.pyomo.lp'...\n", - "/tmp/tmpdwx9zyjz.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 22373.84it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpmeyemsh7.glpk.raw --wglp /tmp/tmpcw2adg8v.glpk.glp --cpxlp\n", - " /tmp/tmprhgc3wes.pyomo.lp\n", - "Reading problem data from '/tmp/tmprhgc3wes.pyomo.lp'...\n", - "/tmp/tmprhgc3wes.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:03, 22644.80it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp3sjdrw0k.glpk.raw --wglp /tmp/tmp_fjd3ap6.glpk.glp --cpxlp\n", - " /tmp/tmpjcy_smhw.pyomo.lp\n", - "Reading problem data from '/tmp/tmpjcy_smhw.pyomo.lp'...\n", - "/tmp/tmpjcy_smhw.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23629.61it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp87k4tn__.glpk.raw --wglp /tmp/tmpth4qqh4l.glpk.glp --cpxlp\n", - " /tmp/tmpdeadx1am.pyomo.lp\n", - "Reading problem data from '/tmp/tmpdeadx1am.pyomo.lp'...\n", - "/tmp/tmpdeadx1am.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:00<00:02, 24455.20it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp2zjwiw40.glpk.raw --wglp /tmp/tmpk20m7gf8.glpk.glp --cpxlp\n", - " /tmp/tmp6smnf8i_.pyomo.lp\n", - "Reading problem data from '/tmp/tmp6smnf8i_.pyomo.lp'...\n", - "/tmp/tmp6smnf8i_.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 24087.81it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp0q5hh2yz.glpk.raw --wglp /tmp/tmpo2vjz_wd.glpk.glp --cpxlp\n", - " /tmp/tmpgv492qod.pyomo.lp\n", - "Reading problem data from '/tmp/tmpgv492qod.pyomo.lp'...\n", - "/tmp/tmpgv492qod.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23952.26it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpkagy58tc.glpk.raw --wglp /tmp/tmp2j6qp_jl.glpk.glp --cpxlp\n", - " /tmp/tmpuaxsiqyo.pyomo.lp\n", - "Reading problem data from '/tmp/tmpuaxsiqyo.pyomo.lp'...\n", - "/tmp/tmpuaxsiqyo.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 23760.02it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp5yhim4_4.glpk.raw --wglp /tmp/tmpbna7142e.glpk.glp --cpxlp\n", - " /tmp/tmpkpy85ovz.pyomo.lp\n", - "Reading problem data from '/tmp/tmpkpy85ovz.pyomo.lp'...\n", - "/tmp/tmpkpy85ovz.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:01, 24322.08it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmprfjtvhm9.glpk.raw --wglp /tmp/tmpw9aexzmn.glpk.glp --cpxlp\n", - " /tmp/tmprfdi8val.pyomo.lp\n", - "Reading problem data from '/tmp/tmprfdi8val.pyomo.lp'...\n", - "/tmp/tmprfdi8val.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:01, 23845.30it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpkrrbnupn.glpk.raw --wglp /tmp/tmpjr7l7_wh.glpk.glp --cpxlp\n", - " /tmp/tmpgc8en5f1.pyomo.lp\n", - "Reading problem data from '/tmp/tmpgc8en5f1.pyomo.lp'...\n", - "/tmp/tmpgc8en5f1.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:01<00:01, 24326.83it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpdxeyze3w.glpk.raw --wglp /tmp/tmpvkf8l8a3.glpk.glp --cpxlp\n", - " /tmp/tmp4gm8ynp1.pyomo.lp\n", - "Reading problem data from '/tmp/tmp4gm8ynp1.pyomo.lp'...\n", - "/tmp/tmp4gm8ynp1.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 24439.56it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpxhb916fg.glpk.raw --wglp /tmp/tmpt3mn5wjy.glpk.glp --cpxlp\n", - " /tmp/tmp_bhh30i8.pyomo.lp\n", - "Reading problem data from '/tmp/tmp_bhh30i8.pyomo.lp'...\n", - "/tmp/tmp_bhh30i8.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 23578.78it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp40s83njh.glpk.raw --wglp /tmp/tmpwygq4gh3.glpk.glp --cpxlp\n", - " /tmp/tmpazkbcvor.pyomo.lp\n", - "Reading problem data from '/tmp/tmpazkbcvor.pyomo.lp'...\n", - "/tmp/tmpazkbcvor.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 23041.29it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpejpk04ty.glpk.raw --wglp /tmp/tmpscq2syqp.glpk.glp --cpxlp\n", - " /tmp/tmp90xyfpnk.pyomo.lp\n", - "Reading problem data from '/tmp/tmp90xyfpnk.pyomo.lp'...\n", - "/tmp/tmp90xyfpnk.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 23733.55it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp22jjnsyh.glpk.raw --wglp /tmp/tmpp6191ieu.glpk.glp --cpxlp\n", - " /tmp/tmpx_injkqi.pyomo.lp\n", - "Reading problem data from '/tmp/tmpx_injkqi.pyomo.lp'...\n", - "/tmp/tmpx_injkqi.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 23844.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmps__k6p1_.glpk.raw --wglp /tmp/tmpi5excchr.glpk.glp --cpxlp\n", - " /tmp/tmpl7vwmgrb.pyomo.lp\n", - "Reading problem data from '/tmp/tmpl7vwmgrb.pyomo.lp'...\n", - "/tmp/tmpl7vwmgrb.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:02<00:00, 23188.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpxzs6sg2y.glpk.raw --wglp /tmp/tmpwft8utsc.glpk.glp --cpxlp\n", - " /tmp/tmpi78di4gj.pyomo.lp\n", - "Reading problem data from '/tmp/tmpi78di4gj.pyomo.lp'...\n", - "/tmp/tmpi78di4gj.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:02<00:00, 23099.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp1_t4ho6s.glpk.raw --wglp /tmp/tmphfck3df2.glpk.glp --cpxlp\n", - " /tmp/tmph6zwnbnm.pyomo.lp\n", - "Reading problem data from '/tmp/tmph6zwnbnm.pyomo.lp'...\n", - "/tmp/tmph6zwnbnm.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 22975.09it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmptgk_g10l.glpk.raw --wglp /tmp/tmp_jq8n4g4.glpk.glp --cpxlp\n", - " /tmp/tmp95cudyvq.pyomo.lp\n", - "Reading problem data from '/tmp/tmp95cudyvq.pyomo.lp'...\n", - "/tmp/tmp95cudyvq.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 16696.03it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpau6n4wbi.glpk.raw --wglp /tmp/tmps7yujjug.glpk.glp --cpxlp\n", - " /tmp/tmpqenz8kjz.pyomo.lp\n", - "Reading problem data from '/tmp/tmpqenz8kjz.pyomo.lp'...\n", - "/tmp/tmpqenz8kjz.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:04<00:00, 11555.84it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp25vtvn5_.glpk.raw --wglp /tmp/tmp_muaqg1h.glpk.glp --cpxlp\n", - " /tmp/tmpi6twmn6s.pyomo.lp\n", - "Reading problem data from '/tmp/tmpi6twmn6s.pyomo.lp'...\n", - "/tmp/tmpi6twmn6s.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:04, 19678.81it/s]\n", - "Training Episodes: 30%|███ | 3/10 [00:12<00:29, 4.19s/it]INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "\n", - " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 00:00:00: 4%|▍ | 3601.0/82800 [00:00<00:04, 17203.09it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpf88z2bg0.glpk.raw --wglp /tmp/tmpn5vl3w_u.glpk.glp --cpxlp\n", - " /tmp/tmp8veys5hk.pyomo.lp\n", - "Reading problem data from '/tmp/tmp8veys5hk.pyomo.lp'...\n", - "/tmp/tmp8veys5hk.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 01:00:00: 9%|▊ | 7201.0/82800 [00:00<00:03, 21178.21it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp9eab_s13.glpk.raw --wglp /tmp/tmpbi8f4t70.glpk.glp --cpxlp\n", - " /tmp/tmpfr_nlni3.pyomo.lp\n", - "Reading problem data from '/tmp/tmpfr_nlni3.pyomo.lp'...\n", - "/tmp/tmpfr_nlni3.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:00<00:03, 22659.63it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp1a1zgm32.glpk.raw --wglp /tmp/tmpw9le5n2d.glpk.glp --cpxlp\n", - " /tmp/tmpza1_7q9r.pyomo.lp\n", - "Reading problem data from '/tmp/tmpza1_7q9r.pyomo.lp'...\n", - "/tmp/tmpza1_7q9r.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 03:00:00: 17%|█▋ | 14401.0/82800 [00:00<00:02, 23171.51it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp_dkto4l0.glpk.raw --wglp /tmp/tmpllvpamz7.glpk.glp --cpxlp\n", - " /tmp/tmpiv_2d8u5.pyomo.lp\n", - "Reading problem data from '/tmp/tmpiv_2d8u5.pyomo.lp'...\n", - "/tmp/tmpiv_2d8u5.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 04:00:00: 22%|██▏ | 18001.0/82800 [00:00<00:02, 23065.75it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp8veh0e3w.glpk.raw --wglp /tmp/tmptblsb08m.glpk.glp --cpxlp\n", - " /tmp/tmp9u3ab1tn.pyomo.lp\n", - "Reading problem data from '/tmp/tmp9u3ab1tn.pyomo.lp'...\n", - "/tmp/tmp9u3ab1tn.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 05:00:00: 26%|██▌ | 21601.0/82800 [00:01<00:02, 22615.46it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpnzkw03m6.glpk.raw --wglp /tmp/tmpwqh28yg0.glpk.glp --cpxlp\n", - " /tmp/tmp1p3ur3wm.pyomo.lp\n", - "Reading problem data from '/tmp/tmp1p3ur3wm.pyomo.lp'...\n", - "/tmp/tmp1p3ur3wm.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:01<00:02, 23336.23it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpdp4qi5fo.glpk.raw --wglp /tmp/tmpfgmya78p.glpk.glp --cpxlp\n", - " /tmp/tmpa9g0a688.pyomo.lp\n", - "Reading problem data from '/tmp/tmpa9g0a688.pyomo.lp'...\n", - "/tmp/tmpa9g0a688.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 07:00:00: 35%|███▍ | 28801.0/82800 [00:01<00:02, 23990.62it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp4pkjmlts.glpk.raw --wglp /tmp/tmptzg2n0ca.glpk.glp --cpxlp\n", - " /tmp/tmpaghu_aj9.pyomo.lp\n", - "Reading problem data from '/tmp/tmpaghu_aj9.pyomo.lp'...\n", - "/tmp/tmpaghu_aj9.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 08:00:00: 39%|███▉ | 32401.0/82800 [00:01<00:02, 23543.97it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpsqv4_zsp.glpk.raw --wglp /tmp/tmpakz0ri0t.glpk.glp --cpxlp\n", - " /tmp/tmpledf0plv.pyomo.lp\n", - "Reading problem data from '/tmp/tmpledf0plv.pyomo.lp'...\n", - "/tmp/tmpledf0plv.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 09:00:00: 43%|████▎ | 36001.0/82800 [00:01<00:02, 15692.21it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpy6i75ktl.glpk.raw --wglp /tmp/tmpfh5yv0u5.glpk.glp --cpxlp\n", - " /tmp/tmpr4_muo_c.pyomo.lp\n", - "Reading problem data from '/tmp/tmpr4_muo_c.pyomo.lp'...\n", - "/tmp/tmpr4_muo_c.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:01<00:02, 18019.01it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpr0_v6zyz.glpk.raw --wglp /tmp/tmpn3ph9rps.glpk.glp --cpxlp\n", - " /tmp/tmp9vu2z9a1.pyomo.lp\n", - "Reading problem data from '/tmp/tmp9vu2z9a1.pyomo.lp'...\n", - "/tmp/tmp9vu2z9a1.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 11:00:00: 52%|█████▏ | 43201.0/82800 [00:02<00:02, 19771.91it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpn1albn02.glpk.raw --wglp /tmp/tmpsvktmfis.glpk.glp --cpxlp\n", - " /tmp/tmp0_kkve0b.pyomo.lp\n", - "Reading problem data from '/tmp/tmp0_kkve0b.pyomo.lp'...\n", - "/tmp/tmp0_kkve0b.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 12:00:00: 57%|█████▋ | 46801.0/82800 [00:02<00:01, 20960.54it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmphhqqze9w.glpk.raw --wglp /tmp/tmpw4ldhuqq.glpk.glp --cpxlp\n", - " /tmp/tmpzfpsmfsl.pyomo.lp\n", - "Reading problem data from '/tmp/tmpzfpsmfsl.pyomo.lp'...\n", - "/tmp/tmpzfpsmfsl.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 13:00:00: 61%|██████ | 50401.0/82800 [00:02<00:01, 22076.42it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpkeg9zxuz.glpk.raw --wglp /tmp/tmp98s139q4.glpk.glp --cpxlp\n", - " /tmp/tmp061b8nni.pyomo.lp\n", - "Reading problem data from '/tmp/tmp061b8nni.pyomo.lp'...\n", - "/tmp/tmp061b8nni.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:02<00:01, 22468.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpx5ybax39.glpk.raw --wglp /tmp/tmpdhvn1d6k.glpk.glp --cpxlp\n", - " /tmp/tmpaiyc4qr8.pyomo.lp\n", - "Reading problem data from '/tmp/tmpaiyc4qr8.pyomo.lp'...\n", - "/tmp/tmpaiyc4qr8.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 15:00:00: 70%|██████▉ | 57601.0/82800 [00:02<00:01, 21997.66it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpd4925kmg.glpk.raw --wglp /tmp/tmpdu2nlsyh.glpk.glp --cpxlp\n", - " /tmp/tmpyhur6zbz.pyomo.lp\n", - "Reading problem data from '/tmp/tmpyhur6zbz.pyomo.lp'...\n", - "/tmp/tmpyhur6zbz.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 16:00:00: 74%|███████▍ | 61201.0/82800 [00:02<00:00, 21782.31it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmphktz2rxf.glpk.raw --wglp /tmp/tmp8rc49vwj.glpk.glp --cpxlp\n", - " /tmp/tmpxlusthcq.pyomo.lp\n", - "Reading problem data from '/tmp/tmpxlusthcq.pyomo.lp'...\n", - "/tmp/tmpxlusthcq.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 17:00:00: 78%|███████▊ | 64801.0/82800 [00:03<00:00, 22494.61it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp_h0elnwc.glpk.raw --wglp /tmp/tmp7vryum3v.glpk.glp --cpxlp\n", - " /tmp/tmpylug0ode.pyomo.lp\n", - "Reading problem data from '/tmp/tmpylug0ode.pyomo.lp'...\n", - "/tmp/tmpylug0ode.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 18:00:00: 83%|████████▎ | 68401.0/82800 [00:03<00:00, 23176.81it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpgnzrqsxy.glpk.raw --wglp /tmp/tmppttqbekb.glpk.glp --cpxlp\n", - " /tmp/tmpcyjqei4n.pyomo.lp\n", - "Reading problem data from '/tmp/tmpcyjqei4n.pyomo.lp'...\n", - "/tmp/tmpcyjqei4n.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 19:00:00: 87%|████████▋ | 72001.0/82800 [00:03<00:00, 23074.36it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpnkm3nfui.glpk.raw --wglp /tmp/tmpr46eydah.glpk.glp --cpxlp\n", - " /tmp/tmp5ehx5m8b.pyomo.lp\n", - "Reading problem data from '/tmp/tmp5ehx5m8b.pyomo.lp'...\n", - "/tmp/tmp5ehx5m8b.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 20:00:00: 91%|█████████▏| 75601.0/82800 [00:03<00:00, 22627.07it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmp6ans92ru.glpk.raw --wglp /tmp/tmpk9egt30z.glpk.glp --cpxlp\n", - " /tmp/tmp_3wuknzb.pyomo.lp\n", - "Reading problem data from '/tmp/tmp_3wuknzb.pyomo.lp'...\n", - "/tmp/tmp_3wuknzb.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "\n", - "tutorial_08_zonal_case_4 2019-01-01 21:00:00: 96%|█████████▌| 79201.0/82800 [00:03<00:00, 21647.41it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write /tmp/tmpfvzvcpv2.glpk.raw --wglp /tmp/tmpds05wu1f.glpk.glp --cpxlp\n", - " /tmp/tmpcdzc3n9q.pyomo.lp\n", - "Reading problem data from '/tmp/tmpcdzc3n9q.pyomo.lp'...\n", - "/tmp/tmpcdzc3n9q.pyomo.lp:24: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/base_market.py\", line 494, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 447, in clear\n", - " instance, results = market_clearing_opt(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/markets/clearing_algorithms/complex_clearing.py\", line 262, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pyomo/opt/base/solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", - "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:03, 21847.46it/s] \n", - "Training Episodes: 40%|████ | 4/10 [00:16<00:24, 4.09s/it]INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "\n", - "Training Episodes: 50%|█████ | 5/10 [00:16<00:13, 2.67s/it]\n", - " 0%| | 1.0/82800 [00:00<3:00:09, 7.66it/s]\u001b[AINFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "\n", - "\n", - "Training Episodes: 50%|█████ | 5/10 [00:16<00:16, 3.32s/it]\n" + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { - "ename": "ValueError", - "evalue": "Time must be > 1546304400.0 but is 1546300799.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_config\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"learning_mode\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m run_learning(\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0minputs_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_learning\u001b[0;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_role\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_inter_episodic_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minter_episodic_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 125\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0;31m# -----------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/assume/world.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m return self.loop.run_until_complete(\n\u001b[0m\u001b[1;32m 687\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_ts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart_ts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_ts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend_ts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 688\u001b[0m )\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m 96\u001b[0m raise RuntimeError(\n\u001b[1;32m 97\u001b[0m 'Event loop stopped before Future completed.')\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.10/asyncio/futures.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__log_traceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception_tb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 202\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.10/asyncio/tasks.py\u001b[0m in \u001b[0;36m__step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0;31m# We use the `send` method directly, because coroutines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;31m# don't have `__iter__` and `__next__` methods.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 233\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoro\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/assume/world.py\u001b[0m in \u001b[0;36masync_run\u001b[0;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[0;31m# allow registration before first opening\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 651\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_ts\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 652\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed_role\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 653\u001b[0m \u001b[0;32mawait\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/mango/util/clock.py\u001b[0m in \u001b[0;36mset_time\u001b[0;34m(self, t)\u001b[0m\n\u001b[1;32m 68\u001b[0m \"\"\"\n\u001b[1;32m 69\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_time\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Time must be > {self._time} but is {t}.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;31m# set time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Time must be > 1546304400.0 but is 1546300799." + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/82800 [00:00 exception=KeyboardInterrupt()>\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/world.py\", line 686, in run\n", - " return self.loop.run_until_complete(\n", - " File \"/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\", line 92, in run_until_complete\n", - " self._run_once()\n", - " File \"/usr/local/lib/python3.10/dist-packages/nest_asyncio.py\", line 133, in _run_once\n", - " handle._run()\n", - " File \"/usr/lib/python3.10/asyncio/events.py\", line 80, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " File \"/usr/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n", - " result = coro.send(None)\n", - " File \"/usr/local/lib/python3.10/dist-packages/mango/util/scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/common/units_operator.py\", line 411, in submit_bids\n", - " orderbook = await self.formulate_bids(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/common/units_operator.py\", line 475, in formulate_bids\n", - " product_bids = unit.calculate_bids(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/common/base.py\", line 109, in calculate_bids\n", - " bids = self.bidding_strategies[market_config.market_id].calculate_bids(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/strategies/naive_strategies.py\", line 42, in calculate_bids\n", - " min_power, max_power = unit.calculate_min_max_power(\n", - " File \"/usr/local/lib/python3.10/dist-packages/assume/units/powerplant.py\", line 349, in calculate_min_max_power\n", - " max_power = max_power.clip(lower=0)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\", line 9098, in clip\n", - " return self._clip_with_scalar(lower, upper, inplace=inplace)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\", line 8838, in _clip_with_scalar\n", - " cond = mask | (self >= lower)\n", - " File \"/usr/local/lib/python3.10/dist-packages/pandas/core/ops/common.py\", line 74, in new_method\n", - " other = item_from_zerodim(other)\n", - "KeyboardInterrupt\n", - "\n", - "tutorial_08_zonal_case_5 2019-01-01 01:00:00: : 1546308001.0it [00:00, 3195660900.48it/s]\u001b[AERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n" + "INFO:assume.common.outputs:tried writing grid data to non postGIS database\n", + "ERROR:mango.agent.core:The check inbox task of Operator South failed!\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 450, in _check_inbox\n", + " self.handle_message(content=content, meta=meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 525, in handle_message\n", + " self._role_context.handle_message(content, meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 401, in handle_message\n", + " self._role_handler.handle_message(content, meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 239, in handle_message\n", + " method(content, meta)\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 211, in handle_market_feedback\n", + " self.write_actual_dispatch(marketconfig.product_type)\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 348, in write_actual_dispatch\n", + " market_dispatch, unit_dispatch_dfs = self.get_actual_dispatch(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 306, in get_actual_dispatch\n", + " market_dispatch = aggregate_step_amount(\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\utils.py\", line 315, in aggregate_step_amount\n", + " groupdata_str = \"_\".join(groupdata)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + "TypeError: sequence item 1: expected str instance, int found\n", + "ERROR:mango.agent.core:The check inbox task of Operator North failed!\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\core.py\", line 450, in _check_inbox\n", + " self.handle_message(content=content, meta=meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 525, in handle_message\n", + " self._role_context.handle_message(content, meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 401, in handle_message\n", + " self._role_handler.handle_message(content, meta)\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\agent\\role.py\", line 239, in handle_message\n", + " method(content, meta)\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 211, in handle_market_feedback\n", + " self.write_actual_dispatch(marketconfig.product_type)\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 348, in write_actual_dispatch\n", + " market_dispatch, unit_dispatch_dfs = self.get_actual_dispatch(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\units_operator.py\", line 306, in get_actual_dispatch\n", + " market_dispatch = aggregate_step_amount(\n", + " ^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\common\\utils.py\", line 315, in aggregate_step_amount\n", + " groupdata_str = \"_\".join(groupdata)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + "TypeError: sequence item 1: expected str instance, int found\n" ] } ], @@ -4324,7 +1387,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "DhYXvzaP_iyK", "metadata": { "colab": { @@ -4337,10 +1400,10 @@ { "data": { "text/plain": [ - "False" + "'False'" ] }, - "execution_count": 11, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } From 956d60f390b77ab5d04cc6fb5ec495572bb10222 Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Mon, 7 Oct 2024 17:23:43 +0200 Subject: [PATCH 17/27] -finilized coupling example --- .../06_advanced_orders_example.ipynb | 2 +- .../notebooks/08_market_zone_coupling.ipynb | 1524 ++++++++--------- 2 files changed, 720 insertions(+), 806 deletions(-) diff --git a/examples/notebooks/06_advanced_orders_example.ipynb b/examples/notebooks/06_advanced_orders_example.ipynb index 7240ca55..a8715825 100644 --- a/examples/notebooks/06_advanced_orders_example.ipynb +++ b/examples/notebooks/06_advanced_orders_example.ipynb @@ -138,7 +138,7 @@ }, "outputs": [], "source": [ - "!pip install 'assume-framework[optimization]'" + "!pip install 'assume-framework'" ] }, { diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index 6b8d1f95..22a5d43d 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -2,10 +2,8 @@ "cells": [ { "cell_type": "markdown", - "id": "d6968cbf", - "metadata": { - "id": "d6968cbf" - }, + "id": "ff81547a", + "metadata": {}, "source": [ "# Market Zone Coupling in the ASSUME Framework\n", "\n", @@ -35,10 +33,8 @@ }, { "cell_type": "markdown", - "id": "b7f48d5e", - "metadata": { - "id": "b7f48d5e" - }, + "id": "76281e67", + "metadata": {}, "source": [ "## 1. Introduction to Market Zone Coupling\n", "\n", @@ -57,10 +53,8 @@ }, { "cell_type": "markdown", - "id": "ad384717", - "metadata": { - "id": "ad384717" - }, + "id": "42ff364e", + "metadata": {}, "source": [ "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", "\n", @@ -72,31 +66,27 @@ { "cell_type": "code", "execution_count": 1, - "id": "d501b239", - "metadata": { - "id": "d501b239", - "vscode": { - "languageId": "shellscript" - } - }, + "id": "0dd1c254", + "metadata": {}, "outputs": [], "source": [ "# Install the ASSUME framework\n", "# !pip install assume-framework\n", "\n", - "# Install Plotly if not already installed\n", - "# !pip install plotly\n", + "# Install the Pyomo library and GLPK solver\n", + "# this is need only in collab\n", + "# when running locally, use pip install assume-framework[optimization]\n", + "# !pip install pyomo\n", + "# !apt-get install -y -qq glpk-utils\n", "\n", - "# Clone the ASSUME repository containing predefined scenarios\n", - "# !git clone https://github.com/assume-framework/assume.git assume-repo" + "# Install Plotly if not already installed\n", + "# !pip install plotly" ] }, { "cell_type": "markdown", - "id": "7935b350", - "metadata": { - "id": "7935b350" - }, + "id": "4266c838", + "metadata": {}, "source": [ "Let's also import some basic libraries that we will use throughout the tutorial." ] @@ -104,17 +94,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "05df5da6", - "metadata": { - "id": "05df5da6", - "outputId": "06e07c72-7a33-43e2-c5d6-da952b29edda" - }, + "id": "a1543685", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/ck/0m6299c93kd95nnbzxsb67v00000gn/T/ipykernel_71588/808789752.py:1: DeprecationWarning: \n", + "/var/folders/ck/0m6299c93kd95nnbzxsb67v00000gn/T/ipykernel_73523/2740924032.py:1: DeprecationWarning: \n", "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", "but was not found to be installed on your system.\n", @@ -140,10 +127,8 @@ }, { "cell_type": "markdown", - "id": "3491bed0", - "metadata": { - "id": "3491bed0" - }, + "id": "902fc3a9", + "metadata": {}, "source": [ "## 3. Understanding the Market Clearing Optimization\n", "\n", @@ -159,43 +144,97 @@ "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." ] }, + { + "cell_type": "markdown", + "id": "4f874cfd", + "metadata": {}, + "source": [ + "### Simplified Market Clearing Optimization Problem\n", + "\n", + "We consider a simplified market clearing optimization model focusing on zone coupling, aiming to minimize the total cost.\n", + "\n", + "#### Sets and Variables:\n", + "- $T$: Set of time periods.\n", + "- $N$: Set of nodes (zones).\n", + "- $L$: Set of lines.\n", + "- $x_o \\in [0, 1]$: Bid acceptance ratio for order $o$.\n", + "- $f_{t, l} \\in \\mathbb{R}$: Power flow on line $l$ at time $t$.\n", + "\n", + "#### Constants:\n", + "- $P_o$: Price of order $o$.\n", + "- $V_o$: Volume of order $o$.\n", + "- $S_l$: Nominal capacity of line $l$.\n", + "\n", + "#### Objective Function:\n", + "Minimize the total cost of accepted orders:\n", + "\n", + "$$\n", + "\\min \\sum_{o \\in O} P_o V_o x_o\n", + "$$\n", + "\n", + "#### Constraints:\n", + "\n", + "1. **Energy Balance for Each Node and Time Period**:\n", + "\n", + "$$\n", + "\\sum_{\\substack{o \\in O \\\\ \\text{node}(o) = n \\\\ \\text{time}(o) = t}} V_o x_o + \\sum_{l \\in L} I_{n, l} f_{t, l} = 0 \\quad \\forall n \\in N, \\, t \\in T\n", + "$$\n", + "\n", + "Where:\n", + "- $I_{n, l}$ is the incidence value for node $n$ and line $l$ (from the incidence matrix).\n", + "\n", + "2. **Transmission Capacity Constraints for Each Line and Time Period**:\n", + "\n", + "$$\n", + "-S_l \\leq f_{t, l} \\leq S_l \\quad \\forall l \\in L, \\, t \\in T\n", + "$$\n", + "\n", + "#### Summary:\n", + "The goal is to minimize the total cost while ensuring energy balance at each node and respecting transmission line capacity limits for each time period.\n", + "\n", + "In actual ASSUME Framework, the optimization problem is more complex and includes additional constraints and variables, and supports also additional bid types such as block and linked orders. However, the simplified model above captures the essence of market clearing with zone coupling.\n" + ] + }, { "cell_type": "code", "execution_count": 3, - "id": "72cdba6b", - "metadata": { - "id": "72cdba6b" - }, + "id": "e2be3fe2", + "metadata": {}, "outputs": [], "source": [ - "# Display a simplified version of the market clearing optimization function\n", "import pyomo.environ as pyo\n", "from pyomo.opt import SolverFactory, TerminationCondition\n", "\n", "\n", - "def simplified_market_clearing_opt(orders, market_products, nodes, incidence_matrix):\n", + "def simplified_market_clearing_opt(orders, incidence_matrix, lines):\n", " \"\"\"\n", " Simplified market clearing optimization focusing on zone coupling.\n", "\n", " Args:\n", " orders (dict): Dictionary of orders with bid_id as keys.\n", - " market_products (list): List of MarketProduct tuples.\n", - " nodes (list): List of market zones.\n", - " incidence_matrix (dict): Transmission capacity between zones.\n", + " lines (DataFrame): DataFrame containing information about the transmission lines.\n", + " incidence_matrix (DataFrame): Incidence matrix describing the network structure.\n", "\n", " Returns:\n", " model (ConcreteModel): The solved Pyomo model.\n", " results (SolverResults): The solver results.\n", " \"\"\"\n", + " nodes = list(incidence_matrix.index)\n", + " line_ids = list(incidence_matrix.columns)\n", "\n", " model = pyo.ConcreteModel()\n", - " # define duals suffix\n", + " # Define dual suffix\n", " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", "\n", " # Define the set of time periods\n", - " model.T = pyo.Set(initialize=[mp[0] for mp in market_products], doc=\"timesteps\")\n", - " # Define the set of zones (nodes)\n", + " model.T = pyo.Set(\n", + " initialize=sorted(set(order[\"time\"] for order in orders.values())),\n", + " doc=\"timesteps\",\n", + " )\n", + " # Define the set of nodes (zones)\n", " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", + " # Define the set of lines\n", + " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", "\n", " # Decision variables for bid acceptance ratios (0 to 1)\n", " model.x = pyo.Var(\n", @@ -205,68 +244,41 @@ " doc=\"bid_acceptance_ratio\",\n", " )\n", "\n", - " # Decision variables for power flows between zones at each time period\n", - " model.flows = pyo.Var(\n", - " model.T, model.nodes, model.nodes, domain=pyo.Reals, doc=\"power_flows\"\n", - " )\n", + " # Decision variables for power flows on each line at each time period\n", + " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", "\n", - " # Energy balance constraints for each zone and time period\n", + " # Energy balance constraint for each node and time period\n", " def energy_balance_rule(model, node, t):\n", - " \"\"\"\n", - " Ensures that for each zone and time period, the total supply minus demand plus imports minus exports equals zero.\n", - " \"\"\"\n", - " # Sum of accepted bid volumes in the zone at time t\n", - " supply = sum(\n", - " orders[o][\"volume\"] * model.x[o]\n", - " for o in orders\n", - " if orders[o][\"node\"] == node and orders[o][\"time\"] == t\n", - " )\n", - " # Sum of power flows into the zone\n", - " imports = sum(\n", - " model.flows[t, other_node, node]\n", - " for other_node in nodes\n", - " if other_node != node\n", - " )\n", - " # Sum of power flows out of the zone\n", - " exports = sum(\n", - " model.flows[t, node, other_node]\n", - " for other_node in nodes\n", - " if other_node != node\n", - " )\n", - " # Energy balance: supply + imports - exports = 0\n", - " return supply + imports - exports == 0\n", + " balance_expr = 0.0\n", + " # Add contributions from orders\n", + " for order_key, order in orders.items():\n", + " if order[\"node\"] == node and order[\"time\"] == t:\n", + " balance_expr += order[\"volume\"] * model.x[order_key]\n", + "\n", + " # Add contributions from line flows based on the incidence matrix\n", + " if incidence_matrix is not None:\n", + " for line in model.lines:\n", + " incidence_value = incidence_matrix.loc[node, line]\n", + " if incidence_value != 0:\n", + " balance_expr += incidence_value * model.flows[t, line]\n", + "\n", + " return balance_expr == 0\n", "\n", - " # Apply the energy balance rule to all zones and time periods\n", " model.energy_balance = pyo.Constraint(\n", " model.nodes, model.T, rule=energy_balance_rule\n", " )\n", "\n", - " def transmission_rule(model, t, node1, node2):\n", + " # Transmission capacity constraints for each line and time period\n", + " def transmission_capacity_rule(model, t, line):\n", " \"\"\"\n", - " Limits the power flow between two zones based on transmission capacity.\n", + " Limits the power flow on each line based on its capacity.\n", " \"\"\"\n", - " capacity = incidence_matrix[node1].get(node2, 0)\n", - " return (-capacity, model.flows[t, node1, node2], capacity)\n", + " capacity = lines.at[line, \"s_nom\"]\n", + " return (-capacity, model.flows[t, line], capacity)\n", "\n", - " # Apply the transmission constraints to all possible flows\n", + " # Apply transmission capacity constraints to all lines and time periods\n", " model.transmission_constraints = pyo.Constraint(\n", - " model.T, model.nodes, model.nodes, rule=transmission_rule\n", - " )\n", - "\n", - " def symmetry_rule(model, t, node1, node2):\n", - " \"\"\"\n", - " Ensures that the flow from node1 to node2 is the negative of the flow from node2 to node1.\n", - " \"\"\"\n", - " if node1 != node2:\n", - " return model.flows[t, node2, node1] == -model.flows[t, node1, node2]\n", - " else:\n", - " return (\n", - " pyo.Constraint.Skip\n", - " ) # No constraint needed for flows within the same node\n", - "\n", - " # Apply the antisymmetry rule to all node pairs and time periods\n", - " model.symmetry_constraints = pyo.Constraint(\n", - " model.T, model.nodes, model.nodes, rule=symmetry_rule\n", + " model.T, model.lines, rule=transmission_capacity_rule\n", " )\n", "\n", " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", @@ -289,20 +301,16 @@ }, { "cell_type": "markdown", - "id": "28637869", - "metadata": { - "id": "28637869" - }, + "id": "8d42c532", + "metadata": {}, "source": [ "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." ] }, { "cell_type": "markdown", - "id": "26e3cfa8", - "metadata": { - "id": "26e3cfa8" - }, + "id": "11addaf0", + "metadata": {}, "source": [ "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", "\n", @@ -311,10 +319,8 @@ }, { "cell_type": "markdown", - "id": "01726994", - "metadata": { - "id": "01726994" - }, + "id": "2a095ffb", + "metadata": {}, "source": [ "### 4.1. Defining Buses and Zones\n", "\n", @@ -324,15 +330,8 @@ { "cell_type": "code", "execution_count": 4, - "id": "ae9695a3", - "metadata": { - "cellView": "form", - "id": "ae9695a3", - "jupyter": { - "source_hidden": true - }, - "outputId": "3be943e1-af78-4833-c6ba-00a1ff4956ce" - }, + "id": "c1731cdc", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -362,33 +361,36 @@ " \n", " \n", " \n", - " name\n", " v_nom\n", " zone_id\n", " x\n", " y\n", " \n", + " \n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " north_1\n", + " north_1\n", " 380.0\n", " DE_1\n", " 10.0\n", " 54.0\n", " \n", " \n", - " 1\n", - " north_2\n", + " north_2\n", " 380.0\n", " DE_1\n", " 9.5\n", " 53.5\n", " \n", " \n", - " 2\n", - " south\n", + " south\n", " 380.0\n", " DE_2\n", " 11.6\n", @@ -399,10 +401,11 @@ "" ], "text/plain": [ - " name v_nom zone_id x y\n", - "0 north_1 380.0 DE_1 10.0 54.0\n", - "1 north_2 380.0 DE_1 9.5 53.5\n", - "2 south 380.0 DE_2 11.6 48.1" + " v_nom zone_id x y\n", + "name \n", + "north_1 380.0 DE_1 10.0 54.0\n", + "north_2 380.0 DE_1 9.5 53.5\n", + "south 380.0 DE_2 11.6 48.1" ] }, "metadata": {}, @@ -419,7 +422,7 @@ " \"x\": [10.0, 9.5, 11.6],\n", " \"y\": [54.0, 53.5, 48.1],\n", " }\n", - ")\n", + ").set_index(\"name\")\n", "\n", "# Display the buses DataFrame\n", "print(\"Buses DataFrame:\")\n", @@ -428,10 +431,8 @@ }, { "cell_type": "markdown", - "id": "02d32661", - "metadata": { - "id": "02d32661" - }, + "id": "50a27c51", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -443,10 +444,8 @@ }, { "cell_type": "markdown", - "id": "01f22792", - "metadata": { - "id": "01f22792" - }, + "id": "1545e3bf", + "metadata": {}, "source": [ "### 4.2. Configuring Transmission Lines\n", "\n", @@ -456,15 +455,8 @@ { "cell_type": "code", "execution_count": 5, - "id": "4a531a25", - "metadata": { - "cellView": "form", - "id": "4a531a25", - "jupyter": { - "source_hidden": true - }, - "outputId": "61f797fe-3fa8-463e-d46c-6c7ec080ccf6" - }, + "id": "64769ec7", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -494,18 +486,24 @@ " \n", " \n", " \n", - " name\n", " bus0\n", " bus1\n", " s_nom\n", " x\n", " r\n", " \n", + " \n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " Line_N1_S\n", + " Line_N1_S\n", " north_1\n", " south\n", " 5000.0\n", @@ -513,8 +511,7 @@ " 0.001\n", " \n", " \n", - " 1\n", - " Line_N2_S\n", + " Line_N2_S\n", " north_2\n", " south\n", " 5000.0\n", @@ -522,8 +519,7 @@ " 0.001\n", " \n", " \n", - " 2\n", - " Line_N1_N2\n", + " Line_N1_N2\n", " north_1\n", " north_2\n", " 5000.0\n", @@ -535,10 +531,11 @@ "" ], "text/plain": [ - " name bus0 bus1 s_nom x r\n", - "0 Line_N1_S north_1 south 5000.0 0.01 0.001\n", - "1 Line_N2_S north_2 south 5000.0 0.01 0.001\n", - "2 Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" ] }, "metadata": {}, @@ -556,7 +553,7 @@ " \"x\": [0.01, 0.01, 0.01],\n", " \"r\": [0.001, 0.001, 0.001],\n", " }\n", - ")\n", + ").set_index(\"name\")\n", "\n", "print(\"Transmission Lines DataFrame:\")\n", "display(lines)" @@ -564,10 +561,8 @@ }, { "cell_type": "markdown", - "id": "13cb21b0", - "metadata": { - "id": "13cb21b0" - }, + "id": "f2290793", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -580,10 +575,8 @@ }, { "cell_type": "markdown", - "id": "8874c322", - "metadata": { - "id": "8874c322" - }, + "id": "c931cf9f", + "metadata": {}, "source": [ "### 4.3. Setting Up Power Plant and Demand Units\n", "\n", @@ -593,15 +586,8 @@ { "cell_type": "code", "execution_count": 6, - "id": "e71b7ce9", - "metadata": { - "cellView": "form", - "id": "e71b7ce9", - "jupyter": { - "source_hidden": true - }, - "outputId": "d34609a8-0758-4aa7-9911-72ffc942a69b" - }, + "id": "8a1f9e35", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -801,15 +787,8 @@ { "cell_type": "code", "execution_count": 7, - "id": "365aa96b", - "metadata": { - "cellView": "form", - "id": "365aa96b", - "jupyter": { - "source_hidden": true - }, - "outputId": "e743edad-038f-43f2-e307-b993fea1d21d" - }, + "id": "16f8a13c", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -920,10 +899,8 @@ }, { "cell_type": "markdown", - "id": "8ce96a59", - "metadata": { - "id": "8ce96a59" - }, + "id": "d847ac5f", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -950,10 +927,8 @@ }, { "cell_type": "markdown", - "id": "e14dc7f7", - "metadata": { - "id": "e14dc7f7" - }, + "id": "8f1d684a", + "metadata": {}, "source": [ "### 4.4. Preparing Demand Data\n", "\n", @@ -963,14 +938,8 @@ { "cell_type": "code", "execution_count": 8, - "id": "c817bc49", - "metadata": { - "cellView": "form", - "id": "c817bc49", - "jupyter": { - "source_hidden": true - } - }, + "id": "a0591f14", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1085,10 +1054,8 @@ }, { "cell_type": "markdown", - "id": "99e7bbbc", - "metadata": { - "id": "99e7bbbc" - }, + "id": "1756e6e3", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -1100,37 +1067,29 @@ }, { "cell_type": "markdown", - "id": "42422a16", - "metadata": { - "id": "42422a16" - }, + "id": "478211c6", + "metadata": {}, "source": [ - "## 5. Mimicking the Market Clearing Process\n", + "## 5. Reproducing the Market Clearing Process\n", "\n", - "With the input files prepared, we can now mimic the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." + "With the input files prepared, we can now reproduce the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." ] }, { "cell_type": "markdown", - "id": "6b68ad0f", - "metadata": { - "id": "6b68ad0f" - }, + "id": "01680700", + "metadata": {}, "source": [ "### 5.1. Calculating the Incidence Matrix\n", "\n", - "The **Incidence Matrix** represents the transmission capacities between different market zones. It is calculated as the sum of the capacities of transmission lines connecting each pair of zones. This matrix is crucial for enforcing transmission constraints during the market clearing process.\n", - "\n", - "**Note:** The method of calculating the incidence matrix by simply summing line capacities is a simplified approach. In real-world scenarios, more sophisticated methods are used to accurately represent the network's behavior and constraints. This approach will be extended in future implementations to better reflect real-world complexities." + "The **Incidence Matrix** represents the connection relationships between different nodes in a network. In the context of market zones, it indicates which transmission lines connect which zones. The incidence matrix is a binary matrix where each element denotes whether a particular node is connected to a line or not. This matrix is essential for understanding the structure of the transmission network and for formulating power flow equations during the market clearing process." ] }, { "cell_type": "code", "execution_count": 9, - "id": "70688bfd", - "metadata": { - "id": "70688bfd" - }, + "id": "c9fb8458", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1160,29 +1119,32 @@ " \n", " \n", " \n", - " DE_1\n", - " DE_2\n", + " Line_N1_S\n", + " Line_N2_S\n", + " Line_N1_N2\n", " \n", " \n", " \n", " \n", - " DE_2\n", - " 10000.0\n", - " 0.0\n", + " DE_1\n", + " 1\n", + " 1\n", + " 0\n", " \n", " \n", - " DE_1\n", - " 0.0\n", - " -10000.0\n", + " DE_2\n", + " -1\n", + " -1\n", + " 0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " DE_1 DE_2\n", - "DE_2 10000.0 0.0\n", - "DE_1 0.0 -10000.0" + " Line_N1_S Line_N2_S Line_N1_N2\n", + "DE_1 1 1 0\n", + "DE_2 -1 -1 0" ] }, "metadata": {}, @@ -1190,116 +1152,130 @@ } ], "source": [ - "# Define market products (time periods)\n", - "market_products = []\n", - "for timestamp in demand_df.index:\n", - " market_products.append(\n", - " (\n", - " timestamp, # Start time\n", - " timestamp + pd.Timedelta(hours=1), # End time\n", - " 1, # Only_hours flag (for simplicity)\n", - " )\n", - " )\n", + "# @title Create the incidence matrix\n", + "def create_incidence_matrix(lines, buses, zones_id=None):\n", + " # Determine nodes based on whether we're working with zones or individual buses\n", + " if zones_id:\n", + " nodes = buses[zones_id].unique() # Use zones as nodes\n", + " node_mapping = buses[zones_id].to_dict() # Map bus IDs to zones\n", + " else:\n", + " nodes = buses.index.values # Use buses as nodes\n", + " node_mapping = {bus: bus for bus in nodes} # Identity mapping for buses\n", + "\n", + " # Use the line indices as columns for the incidence matrix\n", + " line_indices = lines.index.values\n", "\n", - "# Define nodes (zones)\n", - "nodes = buses[\"zone_id\"].unique().tolist()\n", + " # Initialize incidence matrix as a DataFrame for easier label-based indexing\n", + " incidence_matrix = pd.DataFrame(0, index=nodes, columns=line_indices)\n", "\n", - "# Calculate the incidence matrix by summing the capacities of transmission lines between zones\n", - "incidence_matrix = {zone: {} for zone in nodes}\n", + " # Fill in the incidence matrix by iterating over lines\n", + " for line_idx, line in lines.iterrows():\n", + " bus0 = line[\"bus0\"]\n", + " bus1 = line[\"bus1\"]\n", "\n", - "for _, line in lines.iterrows():\n", - " # Get zones for each end of the transmission line\n", - " zone0 = buses.loc[buses[\"name\"] == line[\"bus0\"], \"zone_id\"].values[0]\n", - " zone1 = buses.loc[buses[\"name\"] == line[\"bus1\"], \"zone_id\"].values[0]\n", + " # Retrieve mapped nodes (zones or buses)\n", + " node0 = node_mapping.get(bus0)\n", + " node1 = node_mapping.get(bus1)\n", "\n", - " if zone0 != zone1:\n", - " # Add capacity to zone0 -> zone1\n", - " if zone1 not in incidence_matrix[zone0]:\n", - " incidence_matrix[zone0][zone1] = 0\n", - " incidence_matrix[zone0][zone1] += line[\"s_nom\"]\n", + " # Ensure both nodes are valid and part of the defined nodes\n", + " if (\n", + " node0 is not None\n", + " and node1 is not None\n", + " and node0 in nodes\n", + " and node1 in nodes\n", + " ):\n", + " if node0 != node1: # Only create incidence for different nodes\n", + " # Set incidence values: +1 for the \"from\" node and -1 for the \"to\" node\n", + " incidence_matrix.at[node0, line_idx] = (\n", + " 1 # Outgoing from bus0 (or zone0)\n", + " )\n", + " incidence_matrix.at[node1, line_idx] = -1 # Incoming to bus1 (or zone1)\n", "\n", - " # Add capacity to zone1 -> zone0 (assuming bidirectional)\n", - " if zone0 not in incidence_matrix[zone1]:\n", - " incidence_matrix[zone1][zone0] = 0\n", - " incidence_matrix[zone1][zone0] += line[\"s_nom\"]\n", + " # Return the incidence matrix as a DataFrame\n", + " return incidence_matrix\n", "\n", - "# Convert lower triangle values to negative to indicate opposite direction\n", - "for i, zone0 in enumerate(nodes):\n", - " for j, zone1 in enumerate(nodes):\n", - " if i > j and zone1 in incidence_matrix[zone0]:\n", - " incidence_matrix[zone0][zone1] = -incidence_matrix[zone0][zone1]\n", "\n", - "# Display the calculated incidence matrix\n", + "# Calculate the incidence matrix\n", + "incidence_matrix = create_incidence_matrix(lines, buses, \"zone_id\")\n", + "\n", "print(\"Calculated Incidence Matrix between Zones:\")\n", - "display(pd.DataFrame(incidence_matrix).fillna(0))" + "display(incidence_matrix)" ] }, { "cell_type": "markdown", - "id": "cc0dd7b2", - "metadata": { - "id": "cc0dd7b2" - }, + "id": "61e9050c", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", "- **Nodes (Zones):** Extracted from the `buses` DataFrame (`DE_1` and `DE_2`).\n", "- **Transmission Lines:** Iterated over to sum their capacities between different zones.\n", "- **Bidirectional Flow Assumption:** Transmission capacities are added in both directions (`DE_1 -> DE_2` and `DE_2 -> DE_1`).\n", - "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values.\n", - "\n", - "**Sample Output:**\n", - "\n", - "```\n", - "Calculated Incidence Matrix between Zones:\n", - " DE_1 DE_2\n", - "DE_1 0 10000\n", - "DE_2 -10000 0\n", - "```\n", - "\n", - "This output indicates that there is a total transmission capacity of 20,000 MVA from `DE_1` to `DE_2` and vice versa, based on the sum of the capacities of the transmission lines connecting these zones." + "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values." ] }, { "cell_type": "markdown", - "id": "7245c636", - "metadata": { - "id": "7245c636" - }, + "id": "12ccae5f", + "metadata": {}, "source": [ - "### 5.2. Implementing the Simplified Market Clearing Function\n", + "### 5.2. Creating and Mapping Market Orders\n", "\n", - "We will use the `simplified_market_clearing_opt` function defined earlier to perform the market clearing. This function takes in the orders, market products, zones (nodes), and the incidence matrix to determine the optimal bid acceptances and power flows between zones." + "We will construct a dictionary of market orders representing supply and demand bids from power plants and demand units.\n", + "The orders include details such as price, volume, location (node), and time. Once the orders are generated, they will be\n", + "mapped from nodes to corresponding zones using a pre-defined node-to-zone mapping." ] }, { "cell_type": "code", "execution_count": 10, - "id": "03f654fc", - "metadata": { - "cellView": "form", - "id": "03f654fc", - "jupyter": { - "source_hidden": true - } - }, + "id": "4f7366ae", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", - "Sample Supply Order:\n", - "{'price': 5, 'volume': 1000.0, 'node': 'north_1', 'time': Timestamp('2019-01-01 00:00:00')}\n", + "Sample Supply Order:\n" + ] + }, + { + "data": { + "text/plain": [ + "{'price': 5,\n", + " 'volume': 1000.0,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", - "Sample Demand Order:\n", - "{'price': 100, 'volume': -2400, 'node': 'north_1', 'time': Timestamp('2019-01-01 00:00:00')}\n" + "Sample Demand Order:\n" ] + }, + { + "data": { + "text/plain": [ + "{'price': 100,\n", + " 'volume': -2400,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# @title Prepare the orders dictionary based on powerplant_units and demand_units\n", - "\n", + "# @title Construct Orders and Map Nodes to Zones\n", "# Initialize orders dictionary\n", "orders = {}\n", "\n", @@ -1329,65 +1305,23 @@ "\n", "# Display a sample order\n", "print(\"\\nSample Supply Order:\")\n", - "print(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", + "display(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", "\n", "print(\"\\nSample Demand Order:\")\n", - "print(orders[\"demand_north_1_2019-01-01 00:00:00\"])" - ] - }, - { - "cell_type": "markdown", - "id": "d9c9e27c", - "metadata": { - "id": "d9c9e27c" - }, - "source": [ - "**Explanation:**\n", - "\n", - "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume. Units in the north (`DE_1`) are cheaper (`additional_cost` ranging from 5 to 19) compared to southern units (`DE_2`) which are more expensive (`additional_cost` ranging from 20 to 34). This is a simplified representation of the bidding strategy. In actual ASSUME simulations, marginal costs and other factors are considered for more realistic bidding.\n", - "- **Demand Bids:** Each demand unit submits a bid for each time period with zero price and negative volume representing the demand." - ] - }, - { - "cell_type": "markdown", - "id": "40f3fd52", - "metadata": { - "id": "40f3fd52" - }, - "source": [ - "### 5.3. Running the Market Clearing Simulation\n", - "\n", - "We will conduct three simulations:\n", - "\n", - "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", - "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", - "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." - ] - }, - { - "cell_type": "markdown", - "id": "551990cf", - "metadata": { - "id": "551990cf" - }, - "source": [ - "#### Simulation 1: Zero Transmission Capacity Between Zones" + "display(orders[\"demand_north_1_2019-01-01 00:00:00\"])" ] }, { "cell_type": "code", "execution_count": 11, - "id": "123b4e9b", - "metadata": { - "id": "123b4e9b" - }, + "id": "e8b8a17f", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "### Simulation 1: Zero Transmission Capacity Between Zones\n", - "Incidence Matrix for Simulation 1 (Zero Transmission Capacity):\n" + "Mapped Orders:\n" ] }, { @@ -1411,29 +1345,59 @@ " \n", " \n", " \n", - " DE_1\n", - " DE_2\n", + " price\n", + " volume\n", + " node\n", + " time\n", " \n", " \n", " \n", " \n", - " DE_2\n", - " 0.0\n", - " NaN\n", + " Unit 1_2019-01-01 00:00:00\n", + " 5\n", + " 1000.0\n", + " DE_1\n", + " 2019-01-01 00:00:00\n", " \n", " \n", - " DE_1\n", - " NaN\n", - " 0.0\n", + " Unit 1_2019-01-01 01:00:00\n", + " 5\n", + " 1000.0\n", + " DE_1\n", + " 2019-01-01 01:00:00\n", + " \n", + " \n", + " Unit 1_2019-01-01 02:00:00\n", + " 5\n", + " 1000.0\n", + " DE_1\n", + " 2019-01-01 02:00:00\n", + " \n", + " \n", + " Unit 1_2019-01-01 03:00:00\n", + " 5\n", + " 1000.0\n", + " DE_1\n", + " 2019-01-01 03:00:00\n", + " \n", + " \n", + " Unit 1_2019-01-01 04:00:00\n", + " 5\n", + " 1000.0\n", + " DE_1\n", + " 2019-01-01 04:00:00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " DE_1 DE_2\n", - "DE_2 0.0 NaN\n", - "DE_1 NaN 0.0" + " price volume node time\n", + "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", + "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", + "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", + "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", + "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" ] }, "metadata": {}, @@ -1441,41 +1405,78 @@ } ], "source": [ - "print(\"### Simulation 1: Zero Transmission Capacity Between Zones\")\n", + "# Map the orders to zones\n", + "# Create a mapping from node_id to zone_id\n", + "node_mapping = buses[\"zone_id\"].to_dict()\n", "\n", - "# Define nodes (zones)\n", - "nodes_sim1 = nodes.copy()\n", + "# Create a new dictionary with mapped zone IDs\n", + "orders_mapped = {}\n", + "for bid_id, bid in orders.items():\n", + " original_node = bid[\"node\"]\n", + " mapped_zone = node_mapping.get(\n", + " original_node, original_node\n", + " ) # Default to original_node if not found\n", + " orders_mapped[bid_id] = {\n", + " \"price\": bid[\"price\"],\n", + " \"volume\": bid[\"volume\"],\n", + " \"node\": mapped_zone, # Replace bus with zone ID\n", + " \"time\": bid[\"time\"],\n", + " }\n", "\n", - "# Define the incidence matrix as a dictionary with zero transmission capacity\n", - "incidence_matrix_sim1 = {\n", - " \"DE_1\": {\"DE_2\": 0.0}, # Zero capacity from DE_1 to DE_2\n", - " \"DE_2\": {\"DE_1\": 0.0}, # Zero capacity from DE_2 to DE_1\n", - "}\n", + "# Display the mapped orders\n", + "print(\"Mapped Orders:\")\n", + "display(pd.DataFrame(orders_mapped).T.head())" + ] + }, + { + "cell_type": "markdown", + "id": "1a5d589c", + "metadata": {}, + "source": [ + "**Explanation:**\n", "\n", - "# Display the incidence matrix for Simulation 1\n", - "print(\"Incidence Matrix for Simulation 1 (Zero Transmission Capacity):\")\n", - "display(pd.DataFrame(incidence_matrix_sim1))" + "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume.\n", + "- **Demand Bids:** Each demand unit submits a bid for each time period with a high price (set to 100) and a negative volume representing the demand.\n", + "- **Node to Zone Mapping:** After creating the bids, the node information is mapped to corresponding zones for further market clearing steps.\n", + " The mapping uses a pre-defined dictionary (`node_mapping`) to replace each node ID with the corresponding zone ID. In ASSUME, this mapping happens automatically on the market side, but we are simulating it here for educational purposes." ] }, { "cell_type": "markdown", - "id": "80204af5", - "metadata": { - "id": "80204af5" - }, + "id": "f11b487c", + "metadata": {}, "source": [ - "Right now the orders contain the information about their node and not the zone. In ASSUME, the market clearing function will assign the bids to the zones based on the nodes they are connected to. This is done automatically by the framework, but for this simplified example, we will manually assign the bids to the zones." + "### 5.3. Running the Market Clearing Simulation\n", + "\n", + "We will conduct three simulations:\n", + "\n", + "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", + "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", + "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." + ] + }, + { + "cell_type": "markdown", + "id": "07082c73", + "metadata": {}, + "source": [ + "#### Simulation 1: Zero Transmission Capacity Between Zones" ] }, { "cell_type": "code", "execution_count": 12, - "id": "0514d562", - "metadata": { - "cellView": "form", - "id": "0514d562" - }, + "id": "1c7dfee2", + "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Simulation 1: Zero Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 1:\n" + ] + }, { "data": { "text/html": [ @@ -1497,59 +1498,56 @@ " \n", " \n", " \n", - " price\n", - " volume\n", - " node\n", - " time\n", - " \n", - " \n", - " \n", - " \n", - " Unit 1_2019-01-01 00:00:00\n", - " 5\n", - " 1000.0\n", - " DE_1\n", - " 2019-01-01 00:00:00\n", + " bus0\n", + " bus1\n", + " s_nom\n", + " x\n", + " r\n", " \n", " \n", - " Unit 1_2019-01-01 01:00:00\n", - " 5\n", - " 1000.0\n", - " DE_1\n", - " 2019-01-01 01:00:00\n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " Unit 1_2019-01-01 02:00:00\n", - " 5\n", - " 1000.0\n", - " DE_1\n", - " 2019-01-01 02:00:00\n", + " Line_N1_S\n", + " north_1\n", + " south\n", + " 0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", - " Unit 1_2019-01-01 03:00:00\n", - " 5\n", - " 1000.0\n", - " DE_1\n", - " 2019-01-01 03:00:00\n", + " Line_N2_S\n", + " north_2\n", + " south\n", + " 0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", - " Unit 1_2019-01-01 04:00:00\n", - " 5\n", - " 1000.0\n", - " DE_1\n", - " 2019-01-01 04:00:00\n", + " Line_N1_N2\n", + " north_1\n", + " north_2\n", + " 0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", "\n", - "" - ], - "text/plain": [ - " price volume node time\n", - "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", - "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", - "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", - "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", - "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" + "" + ], + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 0 0.01 0.001\n", + "Line_N2_S north_2 south 0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 0 0.01 0.001" ] }, "metadata": {}, @@ -1557,48 +1555,34 @@ } ], "source": [ - "# @title title\n", - "# create a mapping from node_id to zone_id\n", - "node_mapping = buses.set_index(\"name\")[\"zone_id\"].to_dict()\n", + "print(\"### Simulation 1: Zero Transmission Capacity Between Zones\")\n", "\n", - "# Create a new dictionary with mapped zone IDs\n", - "orders_mapped = {}\n", - "for bid_id, bid in orders.items():\n", - " original_node = bid[\"node\"]\n", - " mapped_zone = node_mapping.get(\n", - " original_node, original_node\n", - " ) # Default to original_node if not found\n", - " orders_mapped[bid_id] = {\n", - " \"price\": bid[\"price\"],\n", - " \"volume\": bid[\"volume\"],\n", - " \"node\": mapped_zone, # Replace bus with zone ID\n", - " \"time\": bid[\"time\"],\n", - " }\n", + "lines_sim1 = lines.copy()\n", + "lines_sim1[\"s_nom\"] = 0 # Set transmission capacity to zero for all lines\n", "\n", - "display(pd.DataFrame(orders_mapped).T.head())" + "print(\"Transmission Lines for Simulation 1:\")\n", + "display(lines_sim1)" ] }, { "cell_type": "code", "execution_count": 13, - "id": "543d3534", - "metadata": { - "id": "543d3534" - }, + "id": "c5e4a712", + "metadata": {}, "outputs": [], "source": [ "# Run the simplified market clearing for Simulation 1\n", "model_sim1, results_sim1 = simplified_market_clearing_opt(\n", - " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim1\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim1,\n", ")" ] }, { "cell_type": "markdown", - "id": "11b4a94d", - "metadata": { - "id": "11b4a94d" - }, + "id": "aef7c083", + "metadata": {}, "source": [ "#### Simulation 2: Medium Transmission Capacity Between Zones" ] @@ -1606,17 +1590,15 @@ { "cell_type": "code", "execution_count": 14, - "id": "b7d246fa", - "metadata": { - "id": "b7d246fa" - }, + "id": "86304253", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "### Simulation 2: Medium Transmission Capacity Between Zones\n", - "Incidence Matrix for Simulation 2 (Medium Transmission Capacity):\n" + "Transmission Lines for Simulation 2:\n" ] }, { @@ -1640,29 +1622,56 @@ " \n", " \n", " \n", - " DE_1\n", - " DE_2\n", + " bus0\n", + " bus1\n", + " s_nom\n", + " x\n", + " r\n", + " \n", + " \n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " DE_2\n", + " Line_N1_S\n", + " north_1\n", + " south\n", " 3000.0\n", - " NaN\n", + " 0.01\n", + " 0.001\n", " \n", " \n", - " DE_1\n", - " NaN\n", + " Line_N2_S\n", + " north_2\n", + " south\n", + " 3000.0\n", + " 0.01\n", + " 0.001\n", + " \n", + " \n", + " Line_N1_N2\n", + " north_1\n", + " north_2\n", " 3000.0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", "\n", "" ], "text/plain": [ - " DE_1 DE_2\n", - "DE_2 3000.0 NaN\n", - "DE_1 NaN 3000.0" + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 3000.0 0.01 0.001\n", + "Line_N2_S north_2 south 3000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 3000.0 0.01 0.001" ] }, "metadata": {}, @@ -1672,28 +1681,26 @@ "source": [ "print(\"### Simulation 2: Medium Transmission Capacity Between Zones\")\n", "\n", - "# Define the incidence matrix as a dictionary with medium transmission capacity\n", - "incidence_matrix_sim2 = {\n", - " \"DE_1\": {\"DE_2\": 3000.0}, # Medium transmission capacity from DE_1 to DE_2\n", - " \"DE_2\": {\"DE_1\": 3000.0}, # Medium transmission capacity from DE_2 to DE_1\n", - "}\n", + "# Define the lines for Simulation 2 with medium transmission capacity\n", + "lines_sim2 = lines.copy()\n", + "lines_sim2[\"s_nom\"] = 3000.0 # Set transmission capacity to 3000 MW for all lines\n", "\n", "# Display the incidence matrix for Simulation 2\n", - "print(\"Incidence Matrix for Simulation 2 (Medium Transmission Capacity):\")\n", - "display(pd.DataFrame(incidence_matrix_sim2))\n", + "print(\"Transmission Lines for Simulation 2:\")\n", + "display(lines_sim2)\n", "\n", "# Run the simplified market clearing for Simulation 2\n", "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", - " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim2\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim2,\n", ")" ] }, { "cell_type": "markdown", - "id": "8b6ab5ae", - "metadata": { - "id": "8b6ab5ae" - }, + "id": "5c721991", + "metadata": {}, "source": [ "#### Simulation 3: High Transmission Capacity Between Zones" ] @@ -1701,9 +1708,8 @@ { "cell_type": "code", "execution_count": 15, - "id": "951c9a1b", + "id": "a1c7f344", "metadata": { - "id": "951c9a1b", "lines_to_end_of_cell_marker": 0, "lines_to_next_cell": 1 }, @@ -1713,7 +1719,7 @@ "output_type": "stream", "text": [ "### Simulation 3: High Transmission Capacity Between Zones\n", - "Incidence Matrix for Simulation 3 (High Transmission Capacity):\n" + "Transmission Lines for Simulation 3:\n" ] }, { @@ -1737,29 +1743,56 @@ " \n", " \n", " \n", - " DE_1\n", - " DE_2\n", + " bus0\n", + " bus1\n", + " s_nom\n", + " x\n", + " r\n", + " \n", + " \n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " DE_2\n", - " 10000.0\n", - " NaN\n", + " Line_N1_S\n", + " north_1\n", + " south\n", + " 5000.0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", - " DE_1\n", - " NaN\n", - " 10000.0\n", + " Line_N2_S\n", + " north_2\n", + " south\n", + " 5000.0\n", + " 0.01\n", + " 0.001\n", + " \n", + " \n", + " Line_N1_N2\n", + " north_1\n", + " north_2\n", + " 5000.0\n", + " 0.01\n", + " 0.001\n", " \n", " \n", "\n", "" ], "text/plain": [ - " DE_1 DE_2\n", - "DE_2 10000.0 NaN\n", - "DE_1 NaN 10000.0" + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" ] }, "metadata": {}, @@ -1769,28 +1802,26 @@ "source": [ "print(\"### Simulation 3: High Transmission Capacity Between Zones\")\n", "\n", - "# Define the incidence matrix as a dictionary with high transmission capacity\n", - "incidence_matrix_sim3 = {\n", - " \"DE_1\": {\"DE_2\": 10000.0}, # High transmission capacity from DE_1 to DE_2\n", - " \"DE_2\": {\"DE_1\": 10000.0}, # High transmission capacity from DE_2 to DE_1\n", - "}\n", + "# Define the lines for Simulation 3 with high transmission capacity\n", + "lines_sim3 = lines.copy()\n", + "lines_sim3[\"s_nom\"] = 5000.0 # Set transmission capacity to 5000 MW for all lines\n", "\n", - "# Display the incidence matrix for Simulation 3\n", - "print(\"Incidence Matrix for Simulation 3 (High Transmission Capacity):\")\n", - "display(pd.DataFrame(incidence_matrix_sim3))\n", + "# Display the line capacities for Simulation 3\n", + "print(\"Transmission Lines for Simulation 3:\")\n", + "display(lines_sim3)\n", "\n", "# Run the simplified market clearing for Simulation 3\n", "model_sim3, results_sim3 = simplified_market_clearing_opt(\n", - " orders_mapped, market_products, nodes_sim1, incidence_matrix_sim3\n", + " orders=orders_mapped,\n", + " incidence_matrix=incidence_matrix,\n", + " lines=lines_sim3,\n", ")" ] }, { "cell_type": "markdown", - "id": "2874cc5b", - "metadata": { - "id": "2874cc5b" - }, + "id": "661e6c30", + "metadata": {}, "source": [ "### 5.4. Extracting and Interpreting the Results\n", "\n", @@ -1812,61 +1843,38 @@ { "cell_type": "code", "execution_count": 16, - "id": "508d8c51", - "metadata": { - "id": "508d8c51" - }, + "id": "bdcc49e7", + "metadata": {}, "outputs": [], "source": [ - "# Function to extract accepted bids and calculate clearing prices\n", - "# Function to extract accepted bids, power flows, and market clearing prices using dual variables\n", - "def extract_results(model, orders, nodes):\n", - " # Extract accepted bid ratios\n", - " accepted_bids = {}\n", - " for o in model.x:\n", - " acceptance_ratio = pyo.value(model.x[o])\n", - " if acceptance_ratio > 0:\n", - " accepted_bids[o] = acceptance_ratio\n", - "\n", - " # Extract power flows between zones for each time period\n", - " power_flows = []\n", - " for t in model.T:\n", - " for node1 in nodes:\n", - " for node2 in nodes:\n", - " if node1 != node2:\n", - " flow = pyo.value(model.flows[t, node1, node2])\n", - " if flow != 0:\n", - " power_flows.append(\n", - " {\n", - " \"time\": t,\n", - " \"from_zone\": node1,\n", - " \"to_zone\": node2,\n", - " \"flow_MW\": flow,\n", - " }\n", - " )\n", - "\n", - " # Convert to DataFrame\n", - " power_flows_df = pd.DataFrame(power_flows)\n", + "def extract_results(model, incidence_matrix):\n", + " nodes = list(incidence_matrix.index)\n", + " lines = list(incidence_matrix.columns)\n", "\n", - " # Extract market clearing prices from dual variables of energy balance constraints\n", - " market_clearing_prices = {}\n", - " for node in nodes:\n", - " market_clearing_prices[node] = {\n", - " t: pyo.value(model.dual[model.energy_balance[node, t]]) for t in model.T\n", - " }\n", + " # Extract accepted bid ratios using a dictionary comprehension\n", + " accepted_bids = {\n", + " o: pyo.value(model.x[o]) for o in model.x if pyo.value(model.x[o]) > 0\n", + " }\n", "\n", - " # Convert clearing prices to DataFrame\n", - " clearing_prices = []\n", - " for node in market_clearing_prices:\n", - " for t in market_clearing_prices[node]:\n", - " clearing_prices.append(\n", - " {\n", - " \"zone\": node,\n", - " \"time\": t,\n", - " \"clearing_price\": market_clearing_prices[node][t],\n", - " }\n", - " )\n", + " # Extract power flows on each line for each time period\n", + " power_flows = [\n", + " {\"time\": t, \"line\": line, \"flow_MW\": pyo.value(model.flows[t, line])}\n", + " for t in model.T\n", + " for line in lines\n", + " if pyo.value(model.flows[t, line]) != 0\n", + " ]\n", + " power_flows_df = pd.DataFrame(power_flows)\n", "\n", + " # Extract market clearing prices from dual variables\n", + " clearing_prices = [\n", + " {\n", + " \"zone\": node,\n", + " \"time\": t,\n", + " \"clearing_price\": pyo.value(model.dual[model.energy_balance[node, t]]),\n", + " }\n", + " for node in nodes\n", + " for t in model.T\n", + " ]\n", " clearing_prices_df = pd.DataFrame(clearing_prices)\n", "\n", " return accepted_bids, power_flows_df, clearing_prices_df" @@ -1875,25 +1883,21 @@ { "cell_type": "code", "execution_count": 17, - "id": "ced11135", - "metadata": { - "id": "ced11135" - }, + "id": "512ed95f", + "metadata": {}, "outputs": [], "source": [ "# Extract results for Simulation 1\n", "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", - " model_sim1, orders, nodes_sim1\n", + " model_sim1, incidence_matrix\n", ")" ] }, { "cell_type": "code", "execution_count": 18, - "id": "51373042", - "metadata": { - "id": "51373042" - }, + "id": "7b32b7c3", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1948,10 +1952,8 @@ { "cell_type": "code", "execution_count": 19, - "id": "2bc8fe28", - "metadata": { - "id": "2bc8fe28" - }, + "id": "2d386677", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2116,25 +2118,21 @@ { "cell_type": "code", "execution_count": 20, - "id": "d7bddc51", - "metadata": { - "id": "d7bddc51" - }, + "id": "d8327407", + "metadata": {}, "outputs": [], "source": [ "# Extract results for Simulation 2\n", "accepted_bids_sim2, power_flows_df_sim2, clearing_prices_df_sim2 = extract_results(\n", - " model_sim2, orders, nodes_sim1\n", + " model_sim2, incidence_matrix\n", ")" ] }, { "cell_type": "code", "execution_count": 21, - "id": "a8b84057", - "metadata": { - "id": "a8b84057" - }, + "id": "9b5fc1de", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2165,8 +2163,7 @@ " \n", " \n", " time\n", - " from_zone\n", - " to_zone\n", + " line\n", " flow_MW\n", " \n", " \n", @@ -2174,49 +2171,44 @@ " \n", " 0\n", " 2019-01-01 00:00:00\n", - " DE_1\n", - " DE_2\n", - " 3000.0\n", + " Line_N1_S\n", + " -3000.0\n", " \n", " \n", " 1\n", " 2019-01-01 00:00:00\n", - " DE_2\n", - " DE_1\n", + " Line_N2_S\n", " -3000.0\n", " \n", " \n", " 2\n", - " 2019-01-01 01:00:00\n", - " DE_1\n", - " DE_2\n", - " 3000.0\n", + " 2019-01-01 00:00:00\n", + " Line_N1_N2\n", + " -3000.0\n", " \n", " \n", " 3\n", " 2019-01-01 01:00:00\n", - " DE_2\n", - " DE_1\n", + " Line_N1_S\n", " -3000.0\n", " \n", " \n", " 4\n", - " 2019-01-01 02:00:00\n", - " DE_1\n", - " DE_2\n", - " 3000.0\n", + " 2019-01-01 01:00:00\n", + " Line_N2_S\n", + " -3000.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " time from_zone to_zone flow_MW\n", - "0 2019-01-01 00:00:00 DE_1 DE_2 3000.0\n", - "1 2019-01-01 00:00:00 DE_2 DE_1 -3000.0\n", - "2 2019-01-01 01:00:00 DE_1 DE_2 3000.0\n", - "3 2019-01-01 01:00:00 DE_2 DE_1 -3000.0\n", - "4 2019-01-01 02:00:00 DE_1 DE_2 3000.0" + " time line flow_MW\n", + "0 2019-01-01 00:00:00 Line_N1_S -3000.0\n", + "1 2019-01-01 00:00:00 Line_N2_S -3000.0\n", + "2 2019-01-01 00:00:00 Line_N1_N2 -3000.0\n", + "3 2019-01-01 01:00:00 Line_N1_S -3000.0\n", + "4 2019-01-01 01:00:00 Line_N2_S -3000.0" ] }, "metadata": {}, @@ -2231,10 +2223,8 @@ { "cell_type": "code", "execution_count": 22, - "id": "b9abaaf1", - "metadata": { - "id": "b9abaaf1" - }, + "id": "b7c5d148", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2399,25 +2389,21 @@ { "cell_type": "code", "execution_count": 23, - "id": "c62148ed", - "metadata": { - "id": "c62148ed" - }, + "id": "7f850cf5", + "metadata": {}, "outputs": [], "source": [ "# Extract results for Simulation 3\n", "accepted_bids_sim3, power_flows_df_sim3, clearing_prices_df_sim3 = extract_results(\n", - " model_sim3, orders, nodes_sim1\n", + " model_sim3, incidence_matrix\n", ")" ] }, { "cell_type": "code", "execution_count": 24, - "id": "f94d50ce", - "metadata": { - "id": "f94d50ce" - }, + "id": "3b2528a2", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2448,8 +2434,7 @@ " \n", " \n", " time\n", - " from_zone\n", - " to_zone\n", + " line\n", " flow_MW\n", " \n", " \n", @@ -2457,49 +2442,44 @@ " \n", " 0\n", " 2019-01-01 00:00:00\n", - " DE_1\n", - " DE_2\n", - " 5100.0\n", + " Line_N1_S\n", + " -5000.0\n", " \n", " \n", " 1\n", " 2019-01-01 00:00:00\n", - " DE_2\n", - " DE_1\n", - " -5100.0\n", + " Line_N2_S\n", + " -5000.0\n", " \n", " \n", " 2\n", - " 2019-01-01 01:00:00\n", - " DE_1\n", - " DE_2\n", - " 4700.0\n", + " 2019-01-01 00:00:00\n", + " Line_N1_N2\n", + " -5000.0\n", " \n", " \n", " 3\n", " 2019-01-01 01:00:00\n", - " DE_2\n", - " DE_1\n", - " -4700.0\n", + " Line_N1_S\n", + " -4400.0\n", " \n", " \n", " 4\n", - " 2019-01-01 02:00:00\n", - " DE_1\n", - " DE_2\n", - " 4300.0\n", + " 2019-01-01 01:00:00\n", + " Line_N2_S\n", + " -5000.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " time from_zone to_zone flow_MW\n", - "0 2019-01-01 00:00:00 DE_1 DE_2 5100.0\n", - "1 2019-01-01 00:00:00 DE_2 DE_1 -5100.0\n", - "2 2019-01-01 01:00:00 DE_1 DE_2 4700.0\n", - "3 2019-01-01 01:00:00 DE_2 DE_1 -4700.0\n", - "4 2019-01-01 02:00:00 DE_1 DE_2 4300.0" + " time line flow_MW\n", + "0 2019-01-01 00:00:00 Line_N1_S -5000.0\n", + "1 2019-01-01 00:00:00 Line_N2_S -5000.0\n", + "2 2019-01-01 00:00:00 Line_N1_N2 -5000.0\n", + "3 2019-01-01 01:00:00 Line_N1_S -4400.0\n", + "4 2019-01-01 01:00:00 Line_N2_S -5000.0" ] }, "metadata": {}, @@ -2514,10 +2494,8 @@ { "cell_type": "code", "execution_count": 25, - "id": "3f3881f6", - "metadata": { - "id": "3f3881f6" - }, + "id": "05961462", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -2557,7 +2535,7 @@ " 0\n", " DE_1\n", " 2019-01-01 00:00:00\n", - " 27.0\n", + " 19.0\n", " \n", " \n", " 1\n", @@ -2581,7 +2559,7 @@ " 4\n", " DE_1\n", " 2019-01-01 04:00:00\n", - " 28.0\n", + " 27.0\n", " \n", " \n", "\n", @@ -2589,11 +2567,11 @@ ], "text/plain": [ " zone time clearing_price\n", - "0 DE_1 2019-01-01 00:00:00 27.0\n", + "0 DE_1 2019-01-01 00:00:00 19.0\n", "1 DE_1 2019-01-01 01:00:00 27.0\n", "2 DE_1 2019-01-01 02:00:00 27.0\n", "3 DE_1 2019-01-01 03:00:00 27.0\n", - "4 DE_1 2019-01-01 04:00:00 28.0" + "4 DE_1 2019-01-01 04:00:00 27.0" ] }, "metadata": {}, @@ -2654,7 +2632,7 @@ " 28\n", " DE_2\n", " 2019-01-01 04:00:00\n", - " 28.0\n", + " 27.0\n", " \n", " \n", "\n", @@ -2666,7 +2644,7 @@ "25 DE_2 2019-01-01 01:00:00 27.0\n", "26 DE_2 2019-01-01 02:00:00 27.0\n", "27 DE_2 2019-01-01 03:00:00 27.0\n", - "28 DE_2 2019-01-01 04:00:00 28.0" + "28 DE_2 2019-01-01 04:00:00 27.0" ] }, "metadata": {}, @@ -2681,20 +2659,8 @@ }, { "cell_type": "markdown", - "id": "461c85b5", - "metadata": { - "id": "461c85b5" - }, - "source": [ - "-" - ] - }, - { - "cell_type": "markdown", - "id": "b397c6e9", - "metadata": { - "id": "b397c6e9" - }, + "id": "fb62e2fd", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -2705,10 +2671,8 @@ }, { "cell_type": "markdown", - "id": "83b13999", - "metadata": { - "id": "83b13999" - }, + "id": "3dbd64e0", + "metadata": {}, "source": [ "### 5.5. Comparing Simulations\n", "\n", @@ -2718,11 +2682,8 @@ { "cell_type": "code", "execution_count": 26, - "id": "69e86f18", - "metadata": { - "cellView": "form", - "id": "69e86f18" - }, + "id": "0ffe7033", + "metadata": {}, "outputs": [ { "data": { @@ -2839,7 +2800,7 @@ 29, 29, 29, - 30, + 29, 30, 30, 30, @@ -2885,6 +2846,7 @@ "2019-01-01T23:00:00" ], "y": [ + 19, 27, 27, 27, @@ -2893,13 +2855,12 @@ 28, 28, 28, - 28, 29, 29, 29, 29, 29, - 30, + 29, 30, 30, 30, @@ -3019,7 +2980,7 @@ 29, 29, 29, - 30, + 29, 30, 30, 30, @@ -3069,7 +3030,7 @@ 27, 27, 27, - 28, + 27, 28, 28, 28, @@ -3079,7 +3040,7 @@ 29, 29, 29, - 30, + 29, 30, 30, 30, @@ -3945,7 +3906,7 @@ "fig = go.Figure()\n", "\n", "# Iterate over each zone to plot clearing prices for all three simulations\n", - "for zone in nodes_sim1:\n", + "for zone in incidence_matrix.index:\n", " # Filter data for the current zone and Simulation 1\n", " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", " # Filter data for the current zone and Simulation 2\n", @@ -4008,10 +3969,8 @@ }, { "cell_type": "markdown", - "id": "a7024e65", - "metadata": { - "id": "a7024e65" - }, + "id": "7ee17c77", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -4020,10 +3979,8 @@ }, { "cell_type": "markdown", - "id": "73397059", - "metadata": { - "id": "73397059" - }, + "id": "fb8f157c", + "metadata": {}, "source": [ "## 6. Integrating with ASSUME\n", "\n", @@ -4040,16 +3997,16 @@ "\n", "Additionally, we'll create a new file `fuel_prices.csv`.\n", "\n", + "**Note:** The demand timeseries has been extended to cover 48 hours as ASSUME always requires an additional day of data for the market simulation.\n", + "\n", "#### Create the Inputs Directory and Save CSV Files" ] }, { "cell_type": "code", "execution_count": 27, - "id": "eb54851c", - "metadata": { - "id": "eb54851c" - }, + "id": "531a7a24", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4073,8 +4030,8 @@ "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", "\n", "# Save the DataFrames to CSV files\n", - "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=False)\n", - "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=False)\n", + "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=True)\n", + "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=True)\n", "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", @@ -4084,10 +4041,8 @@ }, { "cell_type": "markdown", - "id": "bc37c1e1", - "metadata": { - "id": "bc37c1e1" - }, + "id": "f788c99a", + "metadata": {}, "source": [ "#### Create `fuel_prices.csv`" ] @@ -4095,10 +4050,8 @@ { "cell_type": "code", "execution_count": 28, - "id": "3e74a43e", - "metadata": { - "id": "3e74a43e" - }, + "id": "2d61a40b", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4109,7 +4062,7 @@ } ], "source": [ - "# Define fuel prices\n", + "# @title Create fuel prices\n", "fuel_prices = {\n", " \"fuel\": [\"uranium\", \"co2\"],\n", " \"price\": [5, 25],\n", @@ -4126,10 +4079,8 @@ }, { "cell_type": "markdown", - "id": "f223540f", - "metadata": { - "id": "f223540f" - }, + "id": "e0e47625", + "metadata": {}, "source": [ "### Step 2: Creating the Configuration YAML File\n", "\n", @@ -4138,10 +4089,8 @@ }, { "cell_type": "markdown", - "id": "0711551b", - "metadata": { - "id": "0711551b" - }, + "id": "44e22a14", + "metadata": {}, "source": [ "#### Create `config.yaml`" ] @@ -4149,10 +4098,8 @@ { "cell_type": "code", "execution_count": 29, - "id": "a1a7164b", - "metadata": { - "id": "a1a7164b" - }, + "id": "821a4002", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4201,10 +4148,8 @@ }, { "cell_type": "markdown", - "id": "129df923", - "metadata": { - "id": "129df923" - }, + "id": "e2e9403a", + "metadata": {}, "source": [ "### Detailed Configuration Explanation\n", "\n", @@ -4244,10 +4189,8 @@ }, { "cell_type": "markdown", - "id": "97bf3b93", - "metadata": { - "id": "97bf3b93" - }, + "id": "6fd79730", + "metadata": {}, "source": [ "### Step 3: Running the Simulation\n", "\n", @@ -4256,10 +4199,8 @@ }, { "cell_type": "markdown", - "id": "3d9088f8", - "metadata": { - "id": "3d9088f8" - }, + "id": "33ff62b1", + "metadata": {}, "source": [ "#### Example Simulation Code" ] @@ -4267,10 +4208,8 @@ { "cell_type": "code", "execution_count": 30, - "id": "2fa30762", - "metadata": { - "id": "2fa30762" - }, + "id": "3a79848a", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4311,7 +4250,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:02, 35130.70it/s] \n" + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:02, 34482.20it/s] \n" ] } ], @@ -4356,10 +4295,8 @@ }, { "cell_type": "markdown", - "id": "2e281f8c", - "metadata": { - "id": "2e281f8c" - }, + "id": "be819122", + "metadata": {}, "source": [ "## 7. Analyzing the Results\n", "\n", @@ -4374,10 +4311,8 @@ }, { "cell_type": "markdown", - "id": "6472a8de", - "metadata": { - "id": "6472a8de" - }, + "id": "5ca43ca3", + "metadata": {}, "source": [ "### 7.1. Locating the Simulation Output Files\n", "\n", @@ -4386,10 +4321,8 @@ }, { "cell_type": "markdown", - "id": "4d2aca20", - "metadata": { - "id": "4d2aca20" - }, + "id": "78707ac9", + "metadata": {}, "source": [ "### 7.2. Loading and Inspecting the Output Data" ] @@ -4397,10 +4330,8 @@ { "cell_type": "code", "execution_count": 31, - "id": "80fdd812", - "metadata": { - "id": "80fdd812" - }, + "id": "6e71a328", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4601,10 +4532,8 @@ }, { "cell_type": "markdown", - "id": "538d2a28", - "metadata": { - "id": "538d2a28" - }, + "id": "870b1c74", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -4628,10 +4557,8 @@ }, { "cell_type": "markdown", - "id": "76b537d2", - "metadata": { - "id": "76b537d2" - }, + "id": "d0fd6e1b", + "metadata": {}, "source": [ "### 7.3. Plotting Clearing Prices\n", "\n", @@ -4640,10 +4567,8 @@ }, { "cell_type": "markdown", - "id": "46af3ed7", - "metadata": { - "id": "46af3ed7" - }, + "id": "934872ad", + "metadata": {}, "source": [ "#### Processing the Market Meta Data" ] @@ -4651,10 +4576,8 @@ { "cell_type": "code", "execution_count": 32, - "id": "f00529e4", - "metadata": { - "id": "f00529e4" - }, + "id": "fd2e3048", + "metadata": {}, "outputs": [ { "name": "stdout", @@ -4770,10 +4693,8 @@ }, { "cell_type": "markdown", - "id": "35d82a8f", - "metadata": { - "id": "35d82a8f" - }, + "id": "bc7fbb63", + "metadata": {}, "source": [ "#### Plotting the market clearing prices" ] @@ -4781,10 +4702,8 @@ { "cell_type": "code", "execution_count": 33, - "id": "a3d56ac7", - "metadata": { - "id": "a3d56ac7" - }, + "id": "87102b35", + "metadata": {}, "outputs": [ { "data": { @@ -5795,10 +5714,8 @@ }, { "cell_type": "markdown", - "id": "9ed0f0a4", - "metadata": { - "id": "9ed0f0a4" - }, + "id": "b34407b1", + "metadata": {}, "source": [ "**Explanation:**\n", "\n", @@ -5814,52 +5731,49 @@ }, { "cell_type": "markdown", - "id": "02eb3518", - "metadata": { - "id": "02eb3518" - }, + "id": "3f448fb4", + "metadata": {}, "source": [ - "## Conclusion\n", + "## **Conclusion**\n", "\n", - "In this tutorial, we successfully integrated our custom market zone coupling simulation with the ASSUME framework. By preparing the necessary input files and configuration, running the simulation, and analyzing the results, we demonstrated how transmission capacities influence market dynamics and clearing prices across different zones.\n", + "Congratulations! You've successfully navigated through the **Market Zone Coupling** process using the **ASSUME Framework**. Here's a quick recap of what you've accomplished:\n", "\n", - "**Key Takeaways:**\n", + "### **Key Achievements:**\n", "\n", - "- **Seamless Integration:** ASSUME allows for straightforward integration of custom market models through well-defined input and configuration files.\n", - "- **Accurate Simulation:** Properly setting up transmission constraints ensures realistic modeling of energy flows and price formations.\n", - "- **Effective Analysis:** Utilizing visualization tools like Plotly and Matplotlib facilitates comprehensive analysis of simulation outcomes.\n", + "1. **Market Setup:**\n", + " - **Defined Zones and Buses:** Established distinct market zones and configured their connections through transmission lines.\n", + " - **Configured Units:** Set up power plant and demand units within each zone, detailing their operational parameters.\n", "\n", - "This setup serves as a foundation for more complex and scalable market simulations, enabling deeper insights into the interplay between supply, demand, and transmission infrastructure in multi-zone electricity markets.\n", + "2. **Market Clearing Optimization:**\n", + " - **Implemented Optimization Model:** Utilized a simplified Pyomo-based model to perform market clearing, accounting for bid acceptances and power flows.\n", + " - **Simulated Transmission Scenarios:** Ran simulations with varying transmission capacities to observe their impact on energy distribution and pricing.\n", "\n", - "Thank you for following along with this tutorial!" + "3. **Result Analysis:**\n", + " - **Extracted Clearing Prices:** Retrieved and interpreted market prices from the optimization results.\n", + " - **Visualized Outcomes:** Created interactive plots to compare how different transmission capacities influence market dynamics across zones.\n", + "\n", + "### **Key Takeaways:**\n", + "\n", + "- **Impact of Transmission Capacity:** Transmission limits play a crucial role in determining energy flows and price convergence between market zones.\n", + "- **ASSUME Framework Efficiency:** ASSUME streamlines complex market simulations, making it easier to model and analyze multi-zone interactions.\n", + "\n", + "### **Next Steps:**\n", + "\n", + "- **Integrate Renewable Sources:** Expand the model to include renewable energy units and assess their impact on market dynamics.\n", + "- **Scale Up Simulations:** Apply the framework to larger, more complex market scenarios to further test its capabilities.\n", + "\n", + "Thank you for participating in this tutorial! With the foundational knowledge gained, you're now equipped to delve deeper into energy market simulations and leverage the ASSUME framework for more advanced analyses." ] } ], "metadata": { - "colab": { - "provenance": [] - }, "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" + "name": "python" } }, "nbformat": 4, From 8999eb53e88f21eed2d730c29af1009722f51bc0 Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Mon, 7 Oct 2024 17:26:53 +0200 Subject: [PATCH 18/27] -uncomment missing parts --- examples/notebooks/08_market_zone_coupling.ipynb | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index 22a5d43d..ab2ff279 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -67,20 +67,24 @@ "cell_type": "code", "execution_count": 1, "id": "0dd1c254", - "metadata": {}, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, "outputs": [], "source": [ "# Install the ASSUME framework\n", - "# !pip install assume-framework\n", + "!pip install assume-framework\n", "\n", "# Install the Pyomo library and GLPK solver\n", "# this is need only in collab\n", "# when running locally, use pip install assume-framework[optimization]\n", - "# !pip install pyomo\n", - "# !apt-get install -y -qq glpk-utils\n", + "!pip install pyomo\n", + "!apt-get install -y -qq glpk-utils\n", "\n", "# Install Plotly if not already installed\n", - "# !pip install plotly" + "!pip install plotly" ] }, { From ab678e5d21ca4f915f41d6f17aa99e9f1b0aa968 Mon Sep 17 00:00:00 2001 From: Nick Harder Date: Mon, 7 Oct 2024 17:39:21 +0200 Subject: [PATCH 19/27] -hide cells -change naming -tested in collab --- .../notebooks/08_market_zone_coupling.ipynb | 14148 ++++++++++------ 1 file changed, 8520 insertions(+), 5628 deletions(-) diff --git a/examples/notebooks/08_market_zone_coupling.ipynb b/examples/notebooks/08_market_zone_coupling.ipynb index ab2ff279..acfeeb70 100644 --- a/examples/notebooks/08_market_zone_coupling.ipynb +++ b/examples/notebooks/08_market_zone_coupling.ipynb @@ -1,5785 +1,8677 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "ff81547a", - "metadata": {}, - "source": [ - "# Market Zone Coupling in the ASSUME Framework\n", - "\n", - "Welcome to the **Market Zone Coupling** tutorial for the ASSUME framework. In this workshop, we will guide you through understanding how market zone coupling is implemented within the ASSUME simulation environment. By the end of this tutorial, you will gain insights into the internal mechanisms of the framework, including how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted.\n", - "\n", - "**We will cover the following topics:**\n", - "\n", - "1. **Introduction to Market Zone Coupling**\n", - "2. **Setting Up the ASSUME Framework for Market Zone Coupling**\n", - "3. **Understanding the Market Clearing Optimization**\n", - "4. **Creating Exemplary Input Files for Market Zone Coupling**\n", - " - 4.1. Defining Buses and Zones\n", - " - 4.2. Configuring Transmission Lines\n", - " - 4.3. Setting Up Power Plant and Demand Units\n", - " - 4.4. Preparing Demand Data\n", - "5. **Understanding the Market Clearing with Zone Coupling**\n", - " - 5.1. Calculating the Incidence Matrix\n", - " - 5.2. Implementing the Simplified Market Clearing Function\n", - " - 5.3. Running the Market Clearing Simulation\n", - " - 5.4. Extracting and Interpreting the Results\n", - " - 5.5. Comparing Simulations\n", - "6. **Integrating with ASSUME**\n", - "7. **Analyzing the Results**\n", - "\n", - "Let's get started!" - ] - }, - { - "cell_type": "markdown", - "id": "76281e67", - "metadata": {}, - "source": [ - "## 1. Introduction to Market Zone Coupling\n", - "\n", - "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. In the ASSUME framework, implementing market zone coupling is straightforward: by properly defining the input data and configuration files, the framework automatically manages the interactions between zones, including transmission constraints and cross-zone trading.\n", - "\n", - "This tutorial aims to provide a deeper understanding of how market zone coupling operates within ASSUME. While the framework handles much of the complexity internally, we will explore the underlying processes, such as the calculation of transmission capacities and the market clearing optimization. This detailed walkthrough is designed to enhance your comprehension of the framework's capabilities and the dynamics of multi-zone electricity markets.\n", - "\n", - "Throughout this tutorial, you will:\n", - "\n", - "- **Define Multiple Market Zones:** Segment the market into distinct zones based on geographical or operational criteria.\n", - "- **Configure Transmission Lines:** Establish connections that allow energy flow between different market zones.\n", - "- **Understand the Market Clearing Process:** Examine how the market clearing algorithm accounts for interactions and constraints across zones.\n", - "\n", - "By the end of this workshop, you will not only know how to set up market zone coupling in ASSUME but also gain insights into the internal mechanisms that drive market interactions and price formations across different zones." - ] - }, - { - "cell_type": "markdown", - "id": "42ff364e", - "metadata": {}, - "source": [ - "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", - "\n", - "Before diving into market zone coupling, ensure that you have the ASSUME framework installed and set up correctly. If you haven't done so already, follow the steps below to install the ASSUME core package and clone the repository containing predefined scenarios.\n", - "\n", - "**Note:** If you already have the ASSUME framework installed and the repository cloned, you can skip executing the following code cells." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "0dd1c254", - "metadata": { - "vscode": { - "languageId": "shellscript" - } - }, - "outputs": [], - "source": [ - "# Install the ASSUME framework\n", - "!pip install assume-framework\n", - "\n", - "# Install the Pyomo library and GLPK solver\n", - "# this is need only in collab\n", - "# when running locally, use pip install assume-framework[optimization]\n", - "!pip install pyomo\n", - "!apt-get install -y -qq glpk-utils\n", - "\n", - "# Install Plotly if not already installed\n", - "!pip install plotly" - ] - }, - { - "cell_type": "markdown", - "id": "4266c838", - "metadata": {}, - "source": [ - "Let's also import some basic libraries that we will use throughout the tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a1543685", - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ck/0m6299c93kd95nnbzxsb67v00000gn/T/ipykernel_73523/2740924032.py:1: DeprecationWarning: \n", - "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n", - "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n", - "but was not found to be installed on your system.\n", - "If this would cause problems for you,\n", - "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n", - " \n", - " import pandas as pd\n" - ] - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "# import plotly for visualization\n", - "import plotly.graph_objects as go\n", - "\n", - "# import yaml for reading and writing YAML files\n", - "import yaml\n", - "\n", - "# Function to display DataFrame in Jupyter\n", - "from IPython.display import display" - ] - }, - { - "cell_type": "markdown", - "id": "902fc3a9", - "metadata": {}, - "source": [ - "## 3. Understanding the Market Clearing Optimization\n", - "\n", - "Market clearing is a crucial component of electricity market simulations. It involves determining the optimal dispatch of supply and demand bids to maximize social welfare while respecting network constraints.\n", - "\n", - "In the context of market zone coupling, the market clearing process must account for:\n", - "\n", - "- **Connection Between Zones:** Transmission lines that allow energy flow between different market zones.\n", - "- **Constraints:** Limits on transmission capacities and ensuring energy balance within and across zones.\n", - "- **Bid Assignment:** Properly assigning bids to their respective zones and considering cross-zone trading.\n", - "- **Price Extraction:** Determining market prices for each zone based on the cleared bids and network constraints.\n", - "\n", - "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." - ] - }, - { - "cell_type": "markdown", - "id": "4f874cfd", - "metadata": {}, - "source": [ - "### Simplified Market Clearing Optimization Problem\n", - "\n", - "We consider a simplified market clearing optimization model focusing on zone coupling, aiming to minimize the total cost.\n", - "\n", - "#### Sets and Variables:\n", - "- $T$: Set of time periods.\n", - "- $N$: Set of nodes (zones).\n", - "- $L$: Set of lines.\n", - "- $x_o \\in [0, 1]$: Bid acceptance ratio for order $o$.\n", - "- $f_{t, l} \\in \\mathbb{R}$: Power flow on line $l$ at time $t$.\n", - "\n", - "#### Constants:\n", - "- $P_o$: Price of order $o$.\n", - "- $V_o$: Volume of order $o$.\n", - "- $S_l$: Nominal capacity of line $l$.\n", - "\n", - "#### Objective Function:\n", - "Minimize the total cost of accepted orders:\n", - "\n", - "$$\n", - "\\min \\sum_{o \\in O} P_o V_o x_o\n", - "$$\n", - "\n", - "#### Constraints:\n", - "\n", - "1. **Energy Balance for Each Node and Time Period**:\n", - "\n", - "$$\n", - "\\sum_{\\substack{o \\in O \\\\ \\text{node}(o) = n \\\\ \\text{time}(o) = t}} V_o x_o + \\sum_{l \\in L} I_{n, l} f_{t, l} = 0 \\quad \\forall n \\in N, \\, t \\in T\n", - "$$\n", - "\n", - "Where:\n", - "- $I_{n, l}$ is the incidence value for node $n$ and line $l$ (from the incidence matrix).\n", - "\n", - "2. **Transmission Capacity Constraints for Each Line and Time Period**:\n", - "\n", - "$$\n", - "-S_l \\leq f_{t, l} \\leq S_l \\quad \\forall l \\in L, \\, t \\in T\n", - "$$\n", - "\n", - "#### Summary:\n", - "The goal is to minimize the total cost while ensuring energy balance at each node and respecting transmission line capacity limits for each time period.\n", - "\n", - "In actual ASSUME Framework, the optimization problem is more complex and includes additional constraints and variables, and supports also additional bid types such as block and linked orders. However, the simplified model above captures the essence of market clearing with zone coupling.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e2be3fe2", - "metadata": {}, - "outputs": [], - "source": [ - "import pyomo.environ as pyo\n", - "from pyomo.opt import SolverFactory, TerminationCondition\n", - "\n", - "\n", - "def simplified_market_clearing_opt(orders, incidence_matrix, lines):\n", - " \"\"\"\n", - " Simplified market clearing optimization focusing on zone coupling.\n", - "\n", - " Args:\n", - " orders (dict): Dictionary of orders with bid_id as keys.\n", - " lines (DataFrame): DataFrame containing information about the transmission lines.\n", - " incidence_matrix (DataFrame): Incidence matrix describing the network structure.\n", - "\n", - " Returns:\n", - " model (ConcreteModel): The solved Pyomo model.\n", - " results (SolverResults): The solver results.\n", - " \"\"\"\n", - " nodes = list(incidence_matrix.index)\n", - " line_ids = list(incidence_matrix.columns)\n", - "\n", - " model = pyo.ConcreteModel()\n", - " # Define dual suffix\n", - " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", - "\n", - " # Define the set of time periods\n", - " model.T = pyo.Set(\n", - " initialize=sorted(set(order[\"time\"] for order in orders.values())),\n", - " doc=\"timesteps\",\n", - " )\n", - " # Define the set of nodes (zones)\n", - " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", - " # Define the set of lines\n", - " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", - "\n", - " # Decision variables for bid acceptance ratios (0 to 1)\n", - " model.x = pyo.Var(\n", - " orders.keys(),\n", - " domain=pyo.NonNegativeReals,\n", - " bounds=(0, 1),\n", - " doc=\"bid_acceptance_ratio\",\n", - " )\n", - "\n", - " # Decision variables for power flows on each line at each time period\n", - " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", - "\n", - " # Energy balance constraint for each node and time period\n", - " def energy_balance_rule(model, node, t):\n", - " balance_expr = 0.0\n", - " # Add contributions from orders\n", - " for order_key, order in orders.items():\n", - " if order[\"node\"] == node and order[\"time\"] == t:\n", - " balance_expr += order[\"volume\"] * model.x[order_key]\n", - "\n", - " # Add contributions from line flows based on the incidence matrix\n", - " if incidence_matrix is not None:\n", - " for line in model.lines:\n", - " incidence_value = incidence_matrix.loc[node, line]\n", - " if incidence_value != 0:\n", - " balance_expr += incidence_value * model.flows[t, line]\n", - "\n", - " return balance_expr == 0\n", - "\n", - " model.energy_balance = pyo.Constraint(\n", - " model.nodes, model.T, rule=energy_balance_rule\n", - " )\n", - "\n", - " # Transmission capacity constraints for each line and time period\n", - " def transmission_capacity_rule(model, t, line):\n", - " \"\"\"\n", - " Limits the power flow on each line based on its capacity.\n", - " \"\"\"\n", - " capacity = lines.at[line, \"s_nom\"]\n", - " return (-capacity, model.flows[t, line], capacity)\n", - "\n", - " # Apply transmission capacity constraints to all lines and time periods\n", - " model.transmission_constraints = pyo.Constraint(\n", - " model.T, model.lines, rule=transmission_capacity_rule\n", - " )\n", - "\n", - " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", - " model.objective = pyo.Objective(\n", - " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", - " sense=pyo.minimize,\n", - " doc=\"Total Cost Minimization\",\n", - " )\n", - "\n", - " # Choose the solver (GLPK is used here for simplicity)\n", - " solver = SolverFactory(\"glpk\")\n", - " results = solver.solve(model)\n", - "\n", - " # Check if the solver found an optimal solution\n", - " if results.solver.termination_condition != TerminationCondition.optimal:\n", - " raise Exception(\"Solver did not find an optimal solution.\")\n", - "\n", - " return model, results" - ] - }, - { - "cell_type": "markdown", - "id": "8d42c532", - "metadata": {}, - "source": [ - "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." - ] - }, - { - "cell_type": "markdown", - "id": "11addaf0", - "metadata": {}, - "source": [ - "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", - "\n", - "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand profiles." - ] - }, - { - "cell_type": "markdown", - "id": "2a095ffb", - "metadata": {}, - "source": [ - "### 4.1. Defining Buses and Zones\n", - "\n", - "**Buses** represent nodes in the network where energy can be injected or withdrawn. Each bus is assigned to a **zone**, which groups buses into market areas. This zoning facilitates market coupling by managing interactions between different market regions." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c1731cdc", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "ff81547a", + "metadata": { + "id": "ff81547a" + }, + "source": [ + "# Market Zone Coupling in the ASSUME Framework\n", + "\n", + "Welcome to the **Market Zone Coupling** tutorial for the ASSUME framework. In this workshop, we will guide you through understanding how market zone coupling is implemented within the ASSUME simulation environment. By the end of this tutorial, you will gain insights into the internal mechanisms of the framework, including how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted.\n", + "\n", + "**We will cover the following topics:**\n", + "\n", + "1. **Introduction to Market Zone Coupling**\n", + "2. **Setting Up the ASSUME Framework for Market Zone Coupling**\n", + "3. **Understanding the Market Clearing Optimization**\n", + "4. **Creating Exemplary Input Files for Market Zone Coupling**\n", + " - 4.1. Defining Buses and Zones\n", + " - 4.2. Configuring Transmission Lines\n", + " - 4.3. Setting Up Power Plant and Demand Units\n", + " - 4.4. Preparing Demand Data\n", + "5. **Understanding the Market Clearing with Zone Coupling**\n", + " - 5.1. Calculating the Incidence Matrix\n", + " - 5.2. Implementing the Simplified Market Clearing Function\n", + " - 5.3. Running the Market Clearing Simulation\n", + " - 5.4. Extracting and Interpreting the Results\n", + " - 5.5. Comparing Simulations\n", + "6. **Execution with ASSUME**\n", + "7. **Analyzing the Results**\n", + "\n", + "Let's get started!" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Buses DataFrame:\n" - ] + "cell_type": "markdown", + "id": "76281e67", + "metadata": { + "id": "76281e67" + }, + "source": [ + "## 1. Introduction to Market Zone Coupling\n", + "\n", + "**Market Zone Coupling** is a mechanism that enables different geographical zones within an electricity market to interact and trade energy seamlessly. In the ASSUME framework, implementing market zone coupling is straightforward: by properly defining the input data and configuration files, the framework automatically manages the interactions between zones, including transmission constraints and cross-zone trading.\n", + "\n", + "This tutorial aims to provide a deeper understanding of how market zone coupling operates within ASSUME. While the framework handles much of the complexity internally, we will explore the underlying processes, such as the calculation of transmission capacities and the market clearing optimization. This detailed walkthrough is designed to enhance your comprehension of the framework's capabilities and the dynamics of multi-zone electricity markets.\n", + "\n", + "Throughout this tutorial, you will:\n", + "\n", + "- **Define Multiple Market Zones:** Segment the market into distinct zones based on geographical or operational criteria.\n", + "- **Configure Transmission Lines:** Establish connections that allow energy flow between different market zones.\n", + "- **Understand the Market Clearing Process:** Examine how the market clearing algorithm accounts for interactions and constraints across zones.\n", + "\n", + "By the end of this workshop, you will not only know how to set up market zone coupling in ASSUME but also gain insights into the internal mechanisms that drive market interactions and price formations across different zones." + ] }, { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " v_nom zone_id x y\n", - "name \n", - "north_1 380.0 DE_1 10.0 54.0\n", - "north_2 380.0 DE_1 9.5 53.5\n", - "south 380.0 DE_2 11.6 48.1" + "cell_type": "markdown", + "id": "42ff364e", + "metadata": { + "id": "42ff364e" + }, + "source": [ + "## 2. Setting Up the ASSUME Framework for Market Zone Coupling\n", + "\n", + "Before diving into market zone coupling, ensure that you have the ASSUME framework installed and set up correctly. If you haven't done so already, follow the steps below to install the ASSUME core package and clone the repository containing predefined scenarios.\n", + "\n", + "**Note:** If you already have the ASSUME framework installed and the repository cloned, you can skip executing the following code cells." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Define the buses DataFrame with three nodes and two zones\n", - "buses = pd.DataFrame(\n", - " {\n", - " \"name\": [\"north_1\", \"north_2\", \"south\"],\n", - " \"v_nom\": [380.0, 380.0, 380.0],\n", - " \"zone_id\": [\"DE_1\", \"DE_1\", \"DE_2\"],\n", - " \"x\": [10.0, 9.5, 11.6],\n", - " \"y\": [54.0, 53.5, 48.1],\n", - " }\n", - ").set_index(\"name\")\n", - "\n", - "# Display the buses DataFrame\n", - "print(\"Buses DataFrame:\")\n", - "display(buses)" - ] - }, - { - "cell_type": "markdown", - "id": "50a27c51", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **name:** Identifier for each bus (`north_1`, `north_2`, and `south`).\n", - "- **v_nom:** Nominal voltage level (in kV) for all buses.\n", - "- **zone_id:** Identifier for the market zone to which the bus belongs (`DE_1` for north buses and `DE_2` for the south bus).\n", - "- **x, y:** Geographical coordinates (optional, can be used for mapping or spatial analyses)." - ] - }, - { - "cell_type": "markdown", - "id": "1545e3bf", - "metadata": {}, - "source": [ - "### 4.2. Configuring Transmission Lines\n", - "\n", - "**Transmission Lines** connect buses, allowing energy to flow between them. Each line has a specified capacity and electrical parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "64769ec7", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transmission Lines DataFrame:\n" - ] + "cell_type": "code", + "execution_count": null, + "id": "0dd1c254", + "metadata": { + "id": "0dd1c254" + }, + "outputs": [], + "source": [ + "# Install the ASSUME framework\n", + "!pip install assume-framework\n", + "\n", + "# Install the Pyomo library and GLPK solver\n", + "# this is need only in collab\n", + "# when running locally, use pip install assume-framework[optimization]\n", + "!pip install pyomo\n", + "!apt-get install -y -qq glpk-utils\n", + "\n", + "# Install Plotly if not already installed\n", + "!pip install plotly" + ] }, { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " bus0 bus1 s_nom x r\n", - "name \n", - "Line_N1_S north_1 south 5000.0 0.01 0.001\n", - "Line_N2_S north_2 south 5000.0 0.01 0.001\n", - "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" + "cell_type": "markdown", + "id": "4266c838", + "metadata": { + "id": "4266c838" + }, + "source": [ + "Let's also import some basic libraries that we will use throughout the tutorial." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Define three transmission lines\n", - "lines = pd.DataFrame(\n", - " {\n", - " \"name\": [\"Line_N1_S\", \"Line_N2_S\", \"Line_N1_N2\"],\n", - " \"bus0\": [\"north_1\", \"north_2\", \"north_1\"],\n", - " \"bus1\": [\"south\", \"south\", \"north_2\"],\n", - " \"s_nom\": [5000.0, 5000.0, 5000.0],\n", - " \"x\": [0.01, 0.01, 0.01],\n", - " \"r\": [0.001, 0.001, 0.001],\n", - " }\n", - ").set_index(\"name\")\n", - "\n", - "print(\"Transmission Lines DataFrame:\")\n", - "display(lines)" - ] - }, - { - "cell_type": "markdown", - "id": "f2290793", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **name:** Identifier for each transmission line (`Line_N1_S`, `Line_N2_S`, and `Line_N1_N2`).\n", - "- **bus0, bus1:** The two buses that the line connects.\n", - "- **s_nom:** Nominal apparent power capacity of the line (in MVA).\n", - "- **x:** Reactance of the line (in per unit).\n", - "- **r:** Resistance of the line (in per unit)." - ] - }, - { - "cell_type": "markdown", - "id": "c931cf9f", - "metadata": {}, - "source": [ - "### 4.3. Setting Up Power Plant and Demand Units\n", - "\n", - "**Power Plant Units** represent energy generation sources, while **Demand Units** represent consumption. Each unit is associated with a specific bus (node) and has operational parameters that define its behavior in the market." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "8a1f9e35", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Power Plant Units DataFrame:\n" - ] + "cell_type": "code", + "execution_count": 2, + "id": "a1543685", + "metadata": { + "id": "a1543685" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# import plotly for visualization\n", + "import plotly.graph_objects as go\n", + "\n", + "# import yaml for reading and writing YAML files\n", + "import yaml\n", + "\n", + "# Function to display DataFrame in Jupyter\n", + "from IPython.display import display" + ] }, { - "data": { - "text/html": [ - "
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nametechnologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
0Unit 1nuclearnaive_eomuranium0.01000.00.00.35north_1Operator North
1Unit 2nuclearnaive_eomuranium0.01000.00.00.36north_1Operator North
2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
3Unit 4nuclearnaive_eomuranium0.01000.00.00.38north_1Operator North
4Unit 5nuclearnaive_eomuranium0.01000.00.00.39north_1Operator North
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" - ], - "text/plain": [ - " name technology bidding_zonal fuel_type emission_factor max_power \\\n", - "0 Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", - "1 Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", - "2 Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", - "3 Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", - "4 Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "0 0.0 0.3 5 north_1 Operator North \n", - "1 0.0 0.3 6 north_1 Operator North \n", - "2 0.0 0.3 7 north_1 Operator North \n", - "3 0.0 0.3 8 north_1 Operator North \n", - "4 0.0 0.3 9 north_1 Operator North " + "cell_type": "markdown", + "id": "902fc3a9", + "metadata": { + "id": "902fc3a9" + }, + "source": [ + "## 3. Understanding the Market Clearing Optimization\n", + "\n", + "Market clearing is a crucial component of electricity market simulations. It involves determining the optimal dispatch of supply and demand bids to maximize social welfare while respecting network constraints.\n", + "\n", + "In the context of market zone coupling, the market clearing process must account for:\n", + "\n", + "- **Connection Between Zones:** Transmission lines that allow energy flow between different market zones.\n", + "- **Constraints:** Limits on transmission capacities and ensuring energy balance within and across zones.\n", + "- **Bid Assignment:** Properly assigning bids to their respective zones and considering cross-zone trading.\n", + "- **Price Extraction:** Determining market prices for each zone based on the cleared bids and network constraints.\n", + "\n", + "The ASSUME framework uses Pyomo to formulate and solve the market clearing optimization problem. Below is a simplified version of the market clearing function, highlighting key components related to zone coupling." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Create the power plant units DataFrame\n", - "\n", - "# Define the total number of units\n", - "num_units = 30 # Reduced for simplicity\n", - "\n", - "# Generate the 'name' column: Unit 1 to Unit 30\n", - "names = [f\"Unit {i}\" for i in range(1, num_units + 1)]\n", - "\n", - "# All other columns with constant values\n", - "technology = [\"nuclear\"] * num_units\n", - "bidding_zonal = [\"naive_eom\"] * num_units\n", - "fuel_type = [\"uranium\"] * num_units\n", - "emission_factor = [0.0] * num_units\n", - "max_power = [1000.0] * num_units\n", - "min_power = [0.0] * num_units\n", - "efficiency = [0.3] * num_units\n", - "\n", - "# Generate 'additional_cost':\n", - "# - North units (1-15): 5 to 19\n", - "# - South units (16-30): 20 to 34\n", - "additional_cost = list(range(5, 5 + num_units))\n", - "\n", - "# Initialize 'node' and 'unit_operator' lists\n", - "node = []\n", - "unit_operator = []\n", - "\n", - "for i in range(1, num_units + 1):\n", - " if 1 <= i <= 8:\n", - " node.append(\"north_1\") # All north units connected to 'north_1'\n", - " unit_operator.append(\"Operator North\")\n", - " elif 9 <= i <= 15:\n", - " node.append(\"north_2\")\n", - " unit_operator.append(\"Operator North\")\n", - " else:\n", - " node.append(\"south\") # All south units connected to 'south'\n", - " unit_operator.append(\"Operator South\")\n", - "\n", - "# Create the DataFrame\n", - "powerplant_units = pd.DataFrame(\n", - " {\n", - " \"name\": names,\n", - " \"technology\": technology,\n", - " \"bidding_zonal\": bidding_zonal, # bidding strategy used to bid on the zonal market. Should be same name as in config file\n", - " \"fuel_type\": fuel_type,\n", - " \"emission_factor\": emission_factor,\n", - " \"max_power\": max_power,\n", - " \"min_power\": min_power,\n", - " \"efficiency\": efficiency,\n", - " \"additional_cost\": additional_cost,\n", - " \"node\": node,\n", - " \"unit_operator\": unit_operator,\n", - " }\n", - ")\n", - "\n", - "print(\"Power Plant Units DataFrame:\")\n", - "display(powerplant_units.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "16f8a13c", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand Units DataFrame:\n" - ] + "cell_type": "markdown", + "id": "4f874cfd", + "metadata": { + "id": "4f874cfd" + }, + "source": [ + "### Simplified Market Clearing Optimization Problem\n", + "\n", + "We consider a simplified market clearing optimization model focusing on zone coupling, aiming to minimize the total cost.\n", + "\n", + "#### Sets and Variables:\n", + "- $T$: Set of time periods.\n", + "- $N$: Set of nodes (zones).\n", + "- $L$: Set of lines.\n", + "- $x_o \\in [0, 1]$: Bid acceptance ratio for order $o$.\n", + "- $f_{t, l} \\in \\mathbb{R}$: Power flow on line $l$ at time $t$.\n", + "\n", + "#### Constants:\n", + "- $P_o$: Price of order $o$.\n", + "- $V_o$: Volume of order $o$.\n", + "- $S_l$: Nominal capacity of line $l$.\n", + "\n", + "#### Objective Function:\n", + "Minimize the total cost of accepted orders:\n", + "\n", + "$$\n", + "\\min \\sum_{o \\in O} P_o V_o x_o\n", + "$$\n", + "\n", + "#### Constraints:\n", + "\n", + "1. **Energy Balance for Each Node and Time Period**:\n", + "\n", + "$$\n", + "\\sum_{\\substack{o \\in O \\\\ \\text{node}(o) = n \\\\ \\text{time}(o) = t}} V_o x_o + \\sum_{l \\in L} I_{n, l} f_{t, l} = 0 \\quad \\forall n \\in N, \\, t \\in T\n", + "$$\n", + "\n", + "Where:\n", + "- $I_{n, l}$ is the incidence value for node $n$ and line $l$ (from the incidence matrix).\n", + "\n", + "2. **Transmission Capacity Constraints for Each Line and Time Period**:\n", + "\n", + "$$\n", + "-S_l \\leq f_{t, l} \\leq S_l \\quad \\forall l \\in L, \\, t \\in T\n", + "$$\n", + "\n", + "#### Summary:\n", + "The goal is to minimize the total cost while ensuring energy balance at each node and respecting transmission line capacity limits for each time period.\n", + "\n", + "In actual ASSUME Framework, the optimization problem is more complex and includes additional constraints and variables, and supports also additional bid types such as block and linked orders. However, the simplified model above captures the essence of market clearing with zone coupling.\n" + ] }, { - "data": { - "text/html": [ - "
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nametechnologybidding_zonalmax_powermin_powerunit_operatornode
0demand_north_1inflex_demandnaive_eom1000000eom_denorth_1
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" - ], - "text/plain": [ - " name technology bidding_zonal max_power min_power \\\n", - "0 demand_north_1 inflex_demand naive_eom 100000 0 \n", - "1 demand_north_2 inflex_demand naive_eom 100000 0 \n", - "2 demand_south inflex_demand naive_eom 100000 0 \n", - "\n", - " unit_operator node \n", - "0 eom_de north_1 \n", - "1 eom_de north_2 \n", - "2 eom_de south " + "cell_type": "code", + "execution_count": 3, + "id": "e2be3fe2", + "metadata": { + "id": "e2be3fe2" + }, + "outputs": [], + "source": [ + "import pyomo.environ as pyo\n", + "from pyomo.opt import SolverFactory, TerminationCondition\n", + "\n", + "\n", + "def simplified_market_clearing_opt(orders, incidence_matrix, lines):\n", + " \"\"\"\n", + " Simplified market clearing optimization focusing on zone coupling.\n", + "\n", + " Args:\n", + " orders (dict): Dictionary of orders with bid_id as keys.\n", + " lines (DataFrame): DataFrame containing information about the transmission lines.\n", + " incidence_matrix (DataFrame): Incidence matrix describing the network structure.\n", + "\n", + " Returns:\n", + " model (ConcreteModel): The solved Pyomo model.\n", + " results (SolverResults): The solver results.\n", + " \"\"\"\n", + " nodes = list(incidence_matrix.index)\n", + " line_ids = list(incidence_matrix.columns)\n", + "\n", + " model = pyo.ConcreteModel()\n", + " # Define dual suffix\n", + " model.dual = pyo.Suffix(direction=pyo.Suffix.IMPORT)\n", + "\n", + " # Define the set of time periods\n", + " model.T = pyo.Set(\n", + " initialize=sorted(set(order[\"time\"] for order in orders.values())),\n", + " doc=\"timesteps\",\n", + " )\n", + " # Define the set of nodes (zones)\n", + " model.nodes = pyo.Set(initialize=nodes, doc=\"nodes\")\n", + " # Define the set of lines\n", + " model.lines = pyo.Set(initialize=line_ids, doc=\"lines\")\n", + "\n", + " # Decision variables for bid acceptance ratios (0 to 1)\n", + " model.x = pyo.Var(\n", + " orders.keys(),\n", + " domain=pyo.NonNegativeReals,\n", + " bounds=(0, 1),\n", + " doc=\"bid_acceptance_ratio\",\n", + " )\n", + "\n", + " # Decision variables for power flows on each line at each time period\n", + " model.flows = pyo.Var(model.T, model.lines, domain=pyo.Reals, doc=\"power_flows\")\n", + "\n", + " # Energy balance constraint for each node and time period\n", + " def energy_balance_rule(model, node, t):\n", + " balance_expr = 0.0\n", + " # Add contributions from orders\n", + " for order_key, order in orders.items():\n", + " if order[\"node\"] == node and order[\"time\"] == t:\n", + " balance_expr += order[\"volume\"] * model.x[order_key]\n", + "\n", + " # Add contributions from line flows based on the incidence matrix\n", + " if incidence_matrix is not None:\n", + " for line in model.lines:\n", + " incidence_value = incidence_matrix.loc[node, line]\n", + " if incidence_value != 0:\n", + " balance_expr += incidence_value * model.flows[t, line]\n", + "\n", + " return balance_expr == 0\n", + "\n", + " model.energy_balance = pyo.Constraint(\n", + " model.nodes, model.T, rule=energy_balance_rule\n", + " )\n", + "\n", + " # Transmission capacity constraints for each line and time period\n", + " def transmission_capacity_rule(model, t, line):\n", + " \"\"\"\n", + " Limits the power flow on each line based on its capacity.\n", + " \"\"\"\n", + " capacity = lines.at[line, \"s_nom\"]\n", + " return (-capacity, model.flows[t, line], capacity)\n", + "\n", + " # Apply transmission capacity constraints to all lines and time periods\n", + " model.transmission_constraints = pyo.Constraint(\n", + " model.T, model.lines, rule=transmission_capacity_rule\n", + " )\n", + "\n", + " # Objective: Minimize total cost (sum of bid prices multiplied by accepted volumes)\n", + " model.objective = pyo.Objective(\n", + " expr=sum(orders[o][\"price\"] * orders[o][\"volume\"] * model.x[o] for o in orders),\n", + " sense=pyo.minimize,\n", + " doc=\"Total Cost Minimization\",\n", + " )\n", + "\n", + " # Choose the solver (GLPK is used here for simplicity)\n", + " solver = SolverFactory(\"glpk\")\n", + " results = solver.solve(model)\n", + "\n", + " # Check if the solver found an optimal solution\n", + " if results.solver.termination_condition != TerminationCondition.optimal:\n", + " raise Exception(\"Solver did not find an optimal solution.\")\n", + "\n", + " return model, results" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Define the demand units\n", - "demand_units = pd.DataFrame(\n", - " {\n", - " \"name\": [\"demand_north_1\", \"demand_north_2\", \"demand_south\"],\n", - " \"technology\": [\"inflex_demand\"] * 3,\n", - " \"bidding_zonal\": [\"naive_eom\"] * 3,\n", - " \"max_power\": [100000, 100000, 100000],\n", - " \"min_power\": [0, 0, 0],\n", - " \"unit_operator\": [\"eom_de\"] * 3,\n", - " \"node\": [\"north_1\", \"north_2\", \"south\"],\n", - " }\n", - ")\n", - "\n", - "# Display the demand_units DataFrame\n", - "print(\"Demand Units DataFrame:\")\n", - "display(demand_units)" - ] - }, - { - "cell_type": "markdown", - "id": "d847ac5f", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **Power Plant Units:**\n", - " - **name:** Identifier for each power plant unit (`Unit 1` to `Unit 30`).\n", - " - **technology:** Type of technology (`nuclear` for all units).\n", - " - **bidding_nodal:** Bidding strategy used (`naive_eom` for all units).\n", - " - **fuel_type:** Type of fuel used (`uranium` for all units).\n", - " - **emission_factor:** Emissions per unit of energy produced (`0.0` for all units).\n", - " - **max_power, min_power:** Operational power limits (`1000.0` MW max, `0.0` MW min for all units).\n", - " - **efficiency:** Conversion efficiency (`0.3` for all units).\n", - " - **additional_cost:** Additional operational costs (`5` to `34`, with southern units being more expensive).\n", - " - **node:** The bus (zone) to which the unit is connected (`north_1` for units `1-15`, `south` for units `16-30`).\n", - " - **unit_operator:** Operator responsible for the unit (`Operator North` for northern units, `Operator South` for southern units).\n", - "\n", - "- **Demand Units:**\n", - " - **name:** Identifier for each demand unit (`demand_north_1`, `demand_north_2`, and `demand_south`).\n", - " - **technology:** Type of demand (`inflex_demand` for all units).\n", - " - **bidding_zonal:** Bidding strategy used (`naive_eom` for all units).\n", - " - **max_power, min_power:** Operational power limits (`100000` MW max, `0` MW min for all units).\n", - " - **unit_operator:** Operator responsible for the unit (`eom_de` for all units).\n", - " - **node:** The bus (zone) to which the unit is connected (`north_1`, `north_2`, and `south`)." - ] - }, - { - "cell_type": "markdown", - "id": "8f1d684a", - "metadata": {}, - "source": [ - "### 4.4. Preparing Demand Data\n", - "\n", - "**Demand Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a0591f14", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Demand DataFrame:\n" - ] + "cell_type": "markdown", + "id": "8d42c532", + "metadata": { + "id": "8d42c532" + }, + "source": [ + "The above function is a simplified representation focusing on the essential aspects of market zone coupling. In the following sections, we will delve deeper into creating input files and mimicking the market clearing process using this function to understand the inner workings of the ASSUME framework." + ] }, { - "data": { - "text/html": [ - "
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demand_north_1demand_north_2demand_south
datetime
2019-01-01 00:00:002400240017400
2019-01-01 01:00:002800280016800
2019-01-01 02:00:003200320016200
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" - ], - "text/plain": [ - " demand_north_1 demand_north_2 demand_south\n", - "datetime \n", - "2019-01-01 00:00:00 2400 2400 17400\n", - "2019-01-01 01:00:00 2800 2800 16800\n", - "2019-01-01 02:00:00 3200 3200 16200\n", - "2019-01-01 03:00:00 3600 3600 15600\n", - "2019-01-01 04:00:00 4000 4000 15000" + "cell_type": "markdown", + "id": "11addaf0", + "metadata": { + "id": "11addaf0" + }, + "source": [ + "## 4. Creating Exemplary Input Files for Market Zone Coupling\n", + "\n", + "To implement market zone coupling, users need to prepare specific input files that define the network's structure, units, and demand profiles. Below, we will guide you through creating the necessary DataFrames for buses, transmission lines, power plant units, demand units, and demand profiles." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title Define the demand DataFrame\n", - "\n", - "# the demand for the north_1 and north_2 zones increases by 400 MW per hour\n", - "# while the demand for the south zone decreases by 600 MW per hour\n", - "# the demand starts at 2400 MW for the north zones and 17400 MW for the south zone\n", - "demand_df = pd.DataFrame(\n", - " {\n", - " \"datetime\": pd.date_range(start=\"2019-01-01\", periods=24, freq=\"h\"),\n", - " \"demand_north_1\": [2400 + i * 400 for i in range(24)],\n", - " \"demand_north_2\": [2400 + i * 400 for i in range(24)],\n", - " \"demand_south\": [17400 - i * 600 for i in range(24)],\n", - " }\n", - ")\n", - "\n", - "# Convert the 'datetime' column to datetime objects and set as index\n", - "demand_df.set_index(\"datetime\", inplace=True)\n", - "\n", - "# Display the demand_df DataFrame\n", - "print(\"Demand DataFrame:\")\n", - "display(demand_df.head())" - ] - }, - { - "cell_type": "markdown", - "id": "1756e6e3", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **datetime:** Timestamp for each demand value.\n", - "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", - "\n", - "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." - ] - }, - { - "cell_type": "markdown", - "id": "478211c6", - "metadata": {}, - "source": [ - "## 5. Reproducing the Market Clearing Process\n", - "\n", - "With the input files prepared, we can now reproduce the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." - ] - }, - { - "cell_type": "markdown", - "id": "01680700", - "metadata": {}, - "source": [ - "### 5.1. Calculating the Incidence Matrix\n", - "\n", - "The **Incidence Matrix** represents the connection relationships between different nodes in a network. In the context of market zones, it indicates which transmission lines connect which zones. The incidence matrix is a binary matrix where each element denotes whether a particular node is connected to a line or not. This matrix is essential for understanding the structure of the transmission network and for formulating power flow equations during the market clearing process." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "c9fb8458", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculated Incidence Matrix between Zones:\n" - ] + "cell_type": "markdown", + "id": "2a095ffb", + "metadata": { + "id": "2a095ffb" + }, + "source": [ + "### 4.1. Defining Buses and Zones\n", + "\n", + "**Buses** represent nodes in the network where energy can be injected or withdrawn. Each bus is assigned to a **zone**, which groups buses into market areas. This zoning facilitates market coupling by managing interactions between different market regions." + ] }, { - "data": { - "text/html": [ - "
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v_nomzone_idxy
name
north_1380.0DE_110.054.0
north_2380.0DE_19.553.5
south380.0DE_211.648.1
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Creating and Mapping Market Orders\n", - "\n", - "We will construct a dictionary of market orders representing supply and demand bids from power plants and demand units.\n", - "The orders include details such as price, volume, location (node), and time. Once the orders are generated, they will be\n", - "mapped from nodes to corresponding zones using a pre-defined node-to-zone mapping." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4f7366ae", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Supply Order:\n" - ] + "cell_type": "markdown", + "id": "50a27c51", + "metadata": { + "id": "50a27c51" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **name:** Identifier for each bus (`north_1`, `north_2`, and `south`).\n", + "- **v_nom:** Nominal voltage level (in kV) for all buses.\n", + "- **zone_id:** Identifier for the market zone to which the bus belongs (`DE_1` for north buses and `DE_2` for the south bus).\n", + "- **x, y:** Geographical coordinates (optional, can be used for mapping or spatial analyses)." + ] }, { - "data": { - "text/plain": [ - "{'price': 5,\n", - " 'volume': 1000.0,\n", - " 'node': 'north_1',\n", - " 'time': Timestamp('2019-01-01 00:00:00')}" + "cell_type": "markdown", + "id": "1545e3bf", + "metadata": { + "id": "1545e3bf" + }, + "source": [ + "### 4.2. Configuring Transmission Lines\n", + "\n", + "**Transmission Lines** connect buses, allowing energy to flow between them. Each line has a specified capacity and electrical parameters." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Sample Demand Order:\n" - ] + "cell_type": "code", + "execution_count": 5, + "id": "64769ec7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 192 + }, + "cellView": "form", + "id": "64769ec7", + "outputId": "a47490cb-d06c-4152-8be6-64985a8dcbd0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Transmission Lines DataFrame:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 5000.0 0.01 0.001\n", + "Line_N2_S north_2 south 5000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 5000.0 0.01 0.001" + ], + "text/html": [ + "\n", + "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south5000.00.010.001
Line_N2_Snorth_2south5000.00.010.001
Line_N1_N2north_1north_25000.00.010.001
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pricevolumenodetime
Unit 1_2019-01-01 00:00:0051000.0DE_12019-01-01 00:00:00
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2Unit 3nuclearnaive_eomuranium0.01000.00.00.37north_1Operator North
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Should be same name as in config file\n", + " \"fuel_type\": fuel_type,\n", + " \"emission_factor\": emission_factor,\n", + " \"max_power\": max_power,\n", + " \"min_power\": min_power,\n", + " \"efficiency\": efficiency,\n", + " \"additional_cost\": additional_cost,\n", + " \"node\": node,\n", + " \"unit_operator\": unit_operator,\n", + " }\n", + ")\n", + "\n", + "print(\"Power Plant Units DataFrame:\")\n", + "display(powerplant_units.head())" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Map the orders to zones\n", - "# Create a mapping from node_id to zone_id\n", - "node_mapping = buses[\"zone_id\"].to_dict()\n", - "\n", - "# Create a new dictionary with mapped zone IDs\n", - "orders_mapped = {}\n", - "for bid_id, bid in orders.items():\n", - " original_node = bid[\"node\"]\n", - " mapped_zone = node_mapping.get(\n", - " original_node, original_node\n", - " ) # Default to original_node if not found\n", - " orders_mapped[bid_id] = {\n", - " \"price\": bid[\"price\"],\n", - " \"volume\": bid[\"volume\"],\n", - " \"node\": mapped_zone, # Replace bus with zone ID\n", - " \"time\": bid[\"time\"],\n", - " }\n", - "\n", - "# Display the mapped orders\n", - "print(\"Mapped Orders:\")\n", - "display(pd.DataFrame(orders_mapped).T.head())" - ] - }, - { - "cell_type": "markdown", - "id": "1a5d589c", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **Power Plant Bids:** Each power plant unit submits a bid for each time period with its `additional_cost` as the bid price and `max_power` as the bid volume.\n", - "- **Demand Bids:** Each demand unit submits a bid for each time period with a high price (set to 100) and a negative volume representing the demand.\n", - "- **Node to Zone Mapping:** After creating the bids, the node information is mapped to corresponding zones for further market clearing steps.\n", - " The mapping uses a pre-defined dictionary (`node_mapping`) to replace each node ID with the corresponding zone ID. In ASSUME, this mapping happens automatically on the market side, but we are simulating it here for educational purposes." - ] - }, - { - "cell_type": "markdown", - "id": "f11b487c", - "metadata": {}, - "source": [ - "### 5.3. Running the Market Clearing Simulation\n", - "\n", - "We will conduct three simulations:\n", - "\n", - "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", - "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", - "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." - ] - }, - { - "cell_type": "markdown", - "id": "07082c73", - "metadata": {}, - "source": [ - "#### Simulation 1: Zero Transmission Capacity Between Zones" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1c7dfee2", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 1: Zero Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 1:\n" - ] + "cell_type": "markdown", + "source": [ + "- **Power Plant Units:**\n", + " - **name:** Identifier for each power plant unit (`Unit 1` to `Unit 30`).\n", + " - **technology:** Type of technology (`nuclear` for all units).\n", + " - **bidding_nodal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **fuel_type:** Type of fuel used (`uranium` for all units).\n", + " - **emission_factor:** Emissions per unit of energy produced (`0.0` for all units).\n", + " - **max_power, min_power:** Operational power limits (`1000.0` MW max, `0.0` MW min for all units).\n", + " - **efficiency:** Conversion efficiency (`0.3` for all units).\n", + " - **additional_cost:** Additional operational costs (`5` to `34`, with southern units being more expensive).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1` for units `1-15`, `south` for units `16-30`).\n", + " - **unit_operator:** Operator responsible for the unit (`Operator North` for northern units, `Operator South` for southern units)." + ], + "metadata": { + "id": "Uwp8L0rombac" + }, + "id": "Uwp8L0rombac" }, { - "data": { - "text/html": [ - "
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bus0bus1s_nomxr
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" - ], - "text/plain": [ - " bus0 bus1 s_nom x r\n", - "name \n", - "Line_N1_S north_1 south 3000.0 0.01 0.001\n", - "Line_N2_S north_2 south 3000.0 0.01 0.001\n", - "Line_N1_N2 north_1 north_2 3000.0 0.01 0.001" + "cell_type": "markdown", + "id": "d847ac5f", + "metadata": { + "id": "d847ac5f" + }, + "source": [ + "- **Demand Units:**\n", + " - **name:** Identifier for each demand unit (`demand_north_1`, `demand_north_2`, and `demand_south`).\n", + " - **technology:** Type of demand (`inflex_demand` for all units).\n", + " - **bidding_zonal:** Bidding strategy used (`naive_eom` for all units).\n", + " - **max_power, min_power:** Operational power limits (`100000` MW max, `0` MW min for all units).\n", + " - **unit_operator:** Operator responsible for the unit (`eom_de` for all units).\n", + " - **node:** The bus (zone) to which the unit is connected (`north_1`, `north_2`, and `south`)." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"### Simulation 2: Medium Transmission Capacity Between Zones\")\n", - "\n", - "# Define the lines for Simulation 2 with medium transmission capacity\n", - "lines_sim2 = lines.copy()\n", - "lines_sim2[\"s_nom\"] = 3000.0 # Set transmission capacity to 3000 MW for all lines\n", - "\n", - "# Display the incidence matrix for Simulation 2\n", - "print(\"Transmission Lines for Simulation 2:\")\n", - "display(lines_sim2)\n", - "\n", - "# Run the simplified market clearing for Simulation 2\n", - "model_sim2, results_sim2 = simplified_market_clearing_opt(\n", - " orders=orders_mapped,\n", - " incidence_matrix=incidence_matrix,\n", - " lines=lines_sim2,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "5c721991", - "metadata": {}, - "source": [ - "#### Simulation 3: High Transmission Capacity Between Zones" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a1c7f344", - "metadata": { - "lines_to_end_of_cell_marker": 0, - "lines_to_next_cell": 1 - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Simulation 3: High Transmission Capacity Between Zones\n", - "Transmission Lines for Simulation 3:\n" - ] + "cell_type": "markdown", + "id": "8f1d684a", + "metadata": { + "id": "8f1d684a" + }, + "source": [ + "### 4.4. Preparing Demand Data\n", + "\n", + "**Demand Data** provides the expected electricity demand for each demand unit over time. This data is essential for simulating how demand varies and affects market dynamics." + ] }, { - "data": { - "text/html": [ - "
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Extracting and Interpreting the Results\n", - "\n", - "After running all three simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones.\n", - "\n", - "#### Extracting Clearing Prices\n", - "\n", - "The **clearing prices** for each market zone and time period are extracted using the dual variables associated with the energy balance constraints in the optimization model. Specifically, the dual variable of the energy balance constraint for a given zone and time period represents the marginal price of electricity in that zone at that time.\n", - "\n", - "In the `extract_results` function, the following steps are performed to obtain the clearing prices:\n", - "\n", - "1. **Energy Balance Constraints:** For each zone and time period, the energy balance equation ensures that the total supply plus imports minus exports equals the demand.\n", - "2. **Dual Variables:** The dual variable (`model.dual[model.energy_balance[node, t]]`) associated with each energy balance constraint captures the sensitivity of the objective function (total cost) to a marginal increase in demand or supply.\n", - "3. **Clearing Price Interpretation:** The value of the dual variable corresponds to the clearing price in the respective zone and time period, reflecting the cost of supplying an additional unit of electricity.\n", - "\n", - "This method leverages the duality in optimization to efficiently extract market prices resulting from the optimal dispatch of bids under the given constraints." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bdcc49e7", - "metadata": {}, - "outputs": [], - "source": [ - "def extract_results(model, incidence_matrix):\n", - " nodes = list(incidence_matrix.index)\n", - " lines = list(incidence_matrix.columns)\n", - "\n", - " # Extract accepted bid ratios using a dictionary comprehension\n", - " accepted_bids = {\n", - " o: pyo.value(model.x[o]) for o in model.x if pyo.value(model.x[o]) > 0\n", - " }\n", - "\n", - " # Extract power flows on each line for each time period\n", - " power_flows = [\n", - " {\"time\": t, \"line\": line, \"flow_MW\": pyo.value(model.flows[t, line])}\n", - " for t in model.T\n", - " for line in lines\n", - " if pyo.value(model.flows[t, line]) != 0\n", - " ]\n", - " power_flows_df = pd.DataFrame(power_flows)\n", - "\n", - " # Extract market clearing prices from dual variables\n", - " clearing_prices = [\n", - " {\n", - " \"zone\": node,\n", - " \"time\": t,\n", - " \"clearing_price\": pyo.value(model.dual[model.energy_balance[node, t]]),\n", - " }\n", - " for node in nodes\n", - " for t in model.T\n", - " ]\n", - " clearing_prices_df = pd.DataFrame(clearing_prices)\n", - "\n", - " return accepted_bids, power_flows_df, clearing_prices_df" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "512ed95f", - "metadata": {}, - "outputs": [], - "source": [ - "# Extract results for Simulation 1\n", - "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", - " model_sim1, incidence_matrix\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "7b32b7c3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation 1: Power Flows Between Zones\n" - ] }, { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: []\n", - "Index: []" + "cell_type": "markdown", + "id": "1756e6e3", + "metadata": { + "id": "1756e6e3" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **datetime:** Timestamp for each demand value.\n", + "- **demand_north_1, demand_north_2, demand_south:** Demand values for each respective demand unit.\n", + "\n", + "**Note:** The demand timeseries has been designed to be fulfillable by the defined power plants in both zones." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Simulation 1: Power Flows Between Zones\")\n", - "display(power_flows_df_sim1.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "2d386677", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation 1: Clearing Prices per Zone and Time\n" - ] + "cell_type": "markdown", + "id": "478211c6", + "metadata": { + "id": "478211c6" + }, + "source": [ + "## 5. Reproducing the Market Clearing Process\n", + "\n", + "With the input files prepared, we can now reproduce the market clearing process using the simplified market clearing function. This will help us understand how different market zones interact, how constraints are managed, how bids are assigned, and how market prices are extracted." + ] }, { - "data": { - "text/html": [ - "
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zonetimeclearing_price
0DE_12019-01-01 00:00:009.0
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" - ], - "text/plain": [ - " zone time clearing_price\n", - "0 DE_1 2019-01-01 00:00:00 9.0\n", - "1 DE_1 2019-01-01 01:00:00 10.0\n", - "2 DE_1 2019-01-01 02:00:00 11.0\n", - "3 DE_1 2019-01-01 03:00:00 12.0\n", - "4 DE_1 2019-01-01 04:00:00 13.0" + "cell_type": "markdown", + "id": "01680700", + "metadata": { + "id": "01680700" + }, + "source": [ + "### 5.1. Calculating the Incidence Matrix\n", + "\n", + "The **Incidence Matrix** represents the connection relationships between different nodes in a network. In the context of market zones, it indicates which transmission lines connect which zones. The incidence matrix is a binary matrix where each element denotes whether a particular node is connected to a line or not. This matrix is essential for understanding the structure of the transmission network and for formulating power flow equations during the market clearing process." ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "
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zonetimeclearing_price
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Line_N1_SLine_N2_SLine_N1_N2
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timelineflow_MW
02019-01-01 00:00:00Line_N1_S-3000.0
12019-01-01 00:00:00Line_N2_S-3000.0
22019-01-01 00:00:00Line_N1_N2-3000.0
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42019-01-01 01:00:00Line_N2_S-3000.0
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" - ], - "text/plain": [ - " time line flow_MW\n", - "0 2019-01-01 00:00:00 Line_N1_S -3000.0\n", - "1 2019-01-01 00:00:00 Line_N2_S -3000.0\n", - "2 2019-01-01 00:00:00 Line_N1_N2 -3000.0\n", - "3 2019-01-01 01:00:00 Line_N1_S -3000.0\n", - "4 2019-01-01 01:00:00 Line_N2_S -3000.0" + "cell_type": "markdown", + "id": "61e9050c", + "metadata": { + "id": "61e9050c" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Nodes (Zones):** Extracted from the `buses` DataFrame (`DE_1` and `DE_2`).\n", + "- **Transmission Lines:** Iterated over to sum their capacities between different zones.\n", + "- **Bidirectional Flow Assumption:** Transmission capacities are added in both directions (`DE_1 -> DE_2` and `DE_2 -> DE_1`).\n", + "- **Lower Triangle Negative Values:** To indicate the opposite direction of power flow, capacities in the lower triangle of the matrix are converted to negative values." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Simulation 2: Power Flows Between Zones\")\n", - "display(power_flows_df_sim2.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "b7c5d148", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation 2: Clearing Prices per Zone and Time\n" - ] + "cell_type": "markdown", + "id": "12ccae5f", + "metadata": { + "id": "12ccae5f" + }, + "source": [ + "### 5.2. Creating and Mapping Market Orders\n", + "\n", + "We will construct a dictionary of market orders representing supply and demand bids from power plants and demand units.\n", + "The orders include details such as price, volume, location (node), and time. Once the orders are generated, they will be\n", + "mapped from nodes to corresponding zones using a pre-defined node-to-zone mapping." + ] }, { - "data": { - "text/html": [ - "
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zonetimeclearing_price
0DE_12019-01-01 00:00:0015.0
1DE_12019-01-01 01:00:0016.0
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3DE_12019-01-01 03:00:0018.0
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" + "cell_type": "code", + "execution_count": 11, + "id": "4f7366ae", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 225 + }, + "cellView": "form", + "id": "4f7366ae", + "outputId": "1c291cb1-8e7b-4e36-cce9-ddd00735225d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Sample Supply Order:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "{'price': 5,\n", + " 'volume': 1000.0,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Sample Demand Order:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "{'price': 100,\n", + " 'volume': -2400,\n", + " 'node': 'north_1',\n", + " 'time': Timestamp('2019-01-01 00:00:00')}" + ] + }, + "metadata": {} + } ], - "text/plain": [ - " zone time clearing_price\n", - "0 DE_1 2019-01-01 00:00:00 15.0\n", - "1 DE_1 2019-01-01 01:00:00 16.0\n", - "2 DE_1 2019-01-01 02:00:00 17.0\n", - "3 DE_1 2019-01-01 03:00:00 18.0\n", - "4 DE_1 2019-01-01 04:00:00 19.0" + "source": [ + "# @title Construct Orders and Map Nodes to Zones\n", + "# Initialize orders dictionary\n", + "orders = {}\n", + "\n", + "# Add power plant bids\n", + "for _, row in powerplant_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": row[\"additional_cost\"], # Assuming additional_cost as bid price\n", + " \"volume\": row[\"max_power\"], # Assuming max_power as bid volume\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Add demand bids\n", + "for _, row in demand_units.iterrows():\n", + " bid_id = row[\"name\"]\n", + " for timestamp in demand_df.index:\n", + " orders[f\"{bid_id}_{timestamp}\"] = {\n", + " \"price\": 100, # Demand bids with high price\n", + " \"volume\": -demand_df.loc[\n", + " timestamp, row[\"name\"]\n", + " ], # Negative volume for demand\n", + " \"node\": row[\"node\"],\n", + " \"time\": timestamp,\n", + " }\n", + "\n", + "# Display a sample order\n", + "print(\"\\nSample Supply Order:\")\n", + "display(orders[\"Unit 1_2019-01-01 00:00:00\"])\n", + "\n", + "print(\"\\nSample Demand Order:\")\n", + "display(orders[\"demand_north_1_2019-01-01 00:00:00\"])" ] - }, - "metadata": {}, - "output_type": "display_data" }, { - "data": { - "text/html": [ - "
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zonetimeclearing_price
24DE_22019-01-01 00:00:0031.0
25DE_22019-01-01 01:00:0030.0
26DE_22019-01-01 02:00:0030.0
27DE_22019-01-01 03:00:0029.0
28DE_22019-01-01 04:00:0029.0
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" + "cell_type": "code", + "execution_count": 12, + "id": "e8b8a17f", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 224 + }, + "cellView": "form", + "id": "e8b8a17f", + "outputId": "ae3db259-f2e7-4b60-91b1-ca130140fb30" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mapped Orders:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " price volume node time\n", + "Unit 1_2019-01-01 00:00:00 5 1000.0 DE_1 2019-01-01 00:00:00\n", + "Unit 1_2019-01-01 01:00:00 5 1000.0 DE_1 2019-01-01 01:00:00\n", + "Unit 1_2019-01-01 02:00:00 5 1000.0 DE_1 2019-01-01 02:00:00\n", + "Unit 1_2019-01-01 03:00:00 5 1000.0 DE_1 2019-01-01 03:00:00\n", + "Unit 1_2019-01-01 04:00:00 5 1000.0 DE_1 2019-01-01 04:00:00" + ], + "text/html": [ + "\n", + "
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pricevolumenodetime
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Unit 1_2019-01-01 04:00:0051000.0DE_12019-01-01 04:00:00
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timelineflow_MW
02019-01-01 00:00:00Line_N1_S-5000.0
12019-01-01 00:00:00Line_N2_S-5000.0
22019-01-01 00:00:00Line_N1_N2-5000.0
32019-01-01 01:00:00Line_N1_S-4400.0
42019-01-01 01:00:00Line_N2_S-5000.0
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" - ], - "text/plain": [ - " time line flow_MW\n", - "0 2019-01-01 00:00:00 Line_N1_S -5000.0\n", - "1 2019-01-01 00:00:00 Line_N2_S -5000.0\n", - "2 2019-01-01 00:00:00 Line_N1_N2 -5000.0\n", - "3 2019-01-01 01:00:00 Line_N1_S -4400.0\n", - "4 2019-01-01 01:00:00 Line_N2_S -5000.0" + "cell_type": "markdown", + "id": "f11b487c", + "metadata": { + "id": "f11b487c" + }, + "source": [ + "### 5.3. Running the Market Clearing Simulation\n", + "\n", + "We will conduct three simulations:\n", + "\n", + "1. **Simulation 1:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **zero**.\n", + "2. **Simulation 2:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **medium**.\n", + "3. **Simulation 3:** Transmission capacities between `DE_1` (north) and `DE_2` (south) are **high**." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Simulation 3: Power Flows Between Zones\")\n", - "display(power_flows_df_sim3.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "05961462", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation 3: Clearing Prices per Zone and Time\n" - ] + "cell_type": "markdown", + "id": "07082c73", + "metadata": { + "id": "07082c73" + }, + "source": [ + "#### Simulation 1: Zero Transmission Capacity Between Zones" + ] }, { - "data": { - "text/html": [ - "
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zonetimeclearing_price
0DE_12019-01-01 00:00:0019.0
1DE_12019-01-01 01:00:0027.0
2DE_12019-01-01 02:00:0027.0
3DE_12019-01-01 03:00:0027.0
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" + "cell_type": "code", + "execution_count": 36, + "id": "1c7dfee2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 + }, + "id": "1c7dfee2", + "outputId": "86090b82-98e1-4d3b-bb1b-74b3c1c37e43" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "### Simulation 1: Zero Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 1:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 0 0.01 0.001\n", + "Line_N2_S north_2 south 0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 0 0.01 0.001" + ], + "text/html": [ + "\n", + "
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bus0bus1s_nomxr
name
Line_N1_Snorth_1south00.010.001
Line_N2_Snorth_2south00.010.001
Line_N1_N2north_1north_200.010.001
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zonetimeclearing_price
24DE_22019-01-01 00:00:0027.0
25DE_22019-01-01 01:00:0027.0
26DE_22019-01-01 02:00:0027.0
27DE_22019-01-01 03:00:0027.0
28DE_22019-01-01 04:00:0027.0
\n", - "
" - ], - "text/plain": [ - " zone time clearing_price\n", - "24 DE_2 2019-01-01 00:00:00 27.0\n", - "25 DE_2 2019-01-01 01:00:00 27.0\n", - "26 DE_2 2019-01-01 02:00:00 27.0\n", - "27 DE_2 2019-01-01 03:00:00 27.0\n", - "28 DE_2 2019-01-01 04:00:00 27.0" + "cell_type": "markdown", + "id": "aef7c083", + "metadata": { + "id": "aef7c083" + }, + "source": [ + "#### Simulation 2: Medium Transmission Capacity Between Zones" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "print(\"Simulation 3: Clearing Prices per Zone and Time\")\n", - "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_1\"].head())\n", - "display(clearing_prices_df_sim3.loc[clearing_prices_df_sim3[\"zone\"] == \"DE_2\"].head())" - ] - }, - { - "cell_type": "markdown", - "id": "fb62e2fd", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **Accepted Bids:** Shows which bids were accepted in each simulation and the ratio at which they were accepted.\n", - "- **Power Flows:** Indicates the amount of energy transmitted between zones. In Simulation 1, with zero transmission capacity, there should be no power flows between `DE_1` and `DE_2`. In Simulation 2 and 3, with medium and high transmission capacities, power flows can occur between zones.\n", - "- **Clearing Prices:** Represents the average bid price in each zone at each time period. Comparing prices across simulations can reveal the impact of transmission capacity on market prices." - ] - }, - { - "cell_type": "markdown", - "id": "3dbd64e0", - "metadata": {}, - "source": [ - "### 5.5. Comparing Simulations\n", - "\n", - "To better understand the impact of transmission capacity, let's compare the key results from all three simulations." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "0ffe7033", - "metadata": {}, - "outputs": [ + }, { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "dash": "dash" - }, - "mode": "lines", - "name": "DE_1 - Sim1 (Zero Capacity)", - "type": "scatter", - "x": [ - "2019-01-01T00:00:00", - "2019-01-01T01:00:00", - "2019-01-01T02:00:00", - "2019-01-01T03:00:00", - "2019-01-01T04:00:00", - "2019-01-01T05:00:00", - "2019-01-01T06:00:00", - "2019-01-01T07:00:00", - "2019-01-01T08:00:00", - "2019-01-01T09:00:00", - "2019-01-01T10:00:00", - "2019-01-01T11:00:00", - "2019-01-01T12:00:00", - "2019-01-01T13:00:00", - "2019-01-01T14:00:00", - "2019-01-01T15:00:00", - "2019-01-01T16:00:00", - "2019-01-01T17:00:00", - "2019-01-01T18:00:00", - "2019-01-01T19:00:00", - "2019-01-01T20:00:00", - "2019-01-01T21:00:00", - "2019-01-01T22:00:00", - "2019-01-01T23:00:00" - ], - "y": [ - 9, - 10, - 11, - 12, - 13, - 13, - 14, - 15, - 16, - 17, - 17, - 18, - 19, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100 - ] + "cell_type": "code", + "execution_count": 15, + "id": "86304253", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 }, + "id": "86304253", + "outputId": "3fa73e8b-d0e3-4fe8-d88c-1a896fb3e1ff" + }, + "outputs": [ { - "line": { - "dash": "dot" - }, - "mode": "lines", - "name": "DE_1 - Sim2 (Medium Capacity)", - "type": "scatter", - "x": [ - "2019-01-01T00:00:00", - "2019-01-01T01:00:00", - "2019-01-01T02:00:00", - "2019-01-01T03:00:00", - "2019-01-01T04:00:00", - "2019-01-01T05:00:00", - "2019-01-01T06:00:00", - "2019-01-01T07:00:00", - "2019-01-01T08:00:00", - "2019-01-01T09:00:00", - "2019-01-01T10:00:00", - "2019-01-01T11:00:00", - "2019-01-01T12:00:00", - "2019-01-01T13:00:00", - "2019-01-01T14:00:00", - "2019-01-01T15:00:00", - "2019-01-01T16:00:00", - "2019-01-01T17:00:00", - "2019-01-01T18:00:00", - "2019-01-01T19:00:00", - "2019-01-01T20:00:00", - "2019-01-01T21:00:00", - "2019-01-01T22:00:00", - "2019-01-01T23:00:00" - ], - "y": [ - 15, - 16, - 17, - 18, - 19, - 19, - 28, - 28, - 28, - 29, - 29, - 29, - 29, - 29, - 29, - 30, - 30, - 30, - 30, - 31, - 31, - 100, - 100, - 100 - ] + "output_type": "stream", + "name": "stdout", + "text": [ + "### Simulation 2: Medium Transmission Capacity Between Zones\n", + "Transmission Lines for Simulation 2:\n" + ] }, { - "line": { - "dash": "solid" - }, - "mode": "lines", - "name": "DE_1 - Sim3 (High Capacity)", - "type": "scatter", - "x": [ - "2019-01-01T00:00:00", - "2019-01-01T01:00:00", - "2019-01-01T02:00:00", - "2019-01-01T03:00:00", - "2019-01-01T04:00:00", - "2019-01-01T05:00:00", - "2019-01-01T06:00:00", - "2019-01-01T07:00:00", - "2019-01-01T08:00:00", - "2019-01-01T09:00:00", - "2019-01-01T10:00:00", - "2019-01-01T11:00:00", - "2019-01-01T12:00:00", - "2019-01-01T13:00:00", - "2019-01-01T14:00:00", - "2019-01-01T15:00:00", - "2019-01-01T16:00:00", - "2019-01-01T17:00:00", - "2019-01-01T18:00:00", - "2019-01-01T19:00:00", - "2019-01-01T20:00:00", - "2019-01-01T21:00:00", - "2019-01-01T22:00:00", - "2019-01-01T23:00:00" - ], - "y": [ - 19, - 27, - 27, - 27, - 27, - 28, - 28, - 28, - 28, - 29, - 29, - 29, - 29, - 29, - 29, - 30, - 30, - 30, - 30, - 31, - 31, - 31, - 31, - 31 - ] + "output_type": "display_data", + "data": { + "text/plain": [ + " bus0 bus1 s_nom x r\n", + "name \n", + "Line_N1_S north_1 south 3000.0 0.01 0.001\n", + "Line_N2_S north_2 south 3000.0 0.01 0.001\n", + "Line_N1_N2 north_1 north_2 3000.0 0.01 0.001" + ], + "text/html": [ + "\n", + "
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Extracting and Interpreting the Results\n", + "\n", + "After running all three simulations, we can extract the results to understand how the presence or absence of transmission capacity affects bid acceptances and power flows between zones.\n", + "\n", + "#### Extracting Clearing Prices\n", + "\n", + "The **clearing prices** for each market zone and time period are extracted using the dual variables associated with the energy balance constraints in the optimization model. Specifically, the dual variable of the energy balance constraint for a given zone and time period represents the marginal price of electricity in that zone at that time.\n", + "\n", + "In the `extract_results` function, the following steps are performed to obtain the clearing prices:\n", + "\n", + "1. **Energy Balance Constraints:** For each zone and time period, the energy balance equation ensures that the total supply plus imports minus exports equals the demand.\n", + "2. **Dual Variables:** The dual variable (`model.dual[model.energy_balance[node, t]]`) associated with each energy balance constraint captures the sensitivity of the objective function (total cost) to a marginal increase in demand or supply.\n", + "3. **Clearing Price Interpretation:** The value of the dual variable corresponds to the clearing price in the respective zone and time period, reflecting the cost of supplying an additional unit of electricity.\n", + "\n", + "This method leverages the duality in optimization to efficiently extract market prices resulting from the optimal dispatch of bids under the given constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bdcc49e7", + "metadata": { + "cellView": "form", + "id": "bdcc49e7" + }, + "outputs": [], + "source": [ + "# @title Function to extract market clearing results from the optimization model\n", + "def extract_results(model, incidence_matrix):\n", + " nodes = list(incidence_matrix.index)\n", + " lines = list(incidence_matrix.columns)\n", + "\n", + " # Extract accepted bid ratios using a dictionary comprehension\n", + " accepted_bids = {\n", + " o: pyo.value(model.x[o]) for o in model.x if pyo.value(model.x[o]) > 0\n", + " }\n", + "\n", + " # Extract power flows on each line for each time period\n", + " power_flows = [\n", + " {\"time\": t, \"line\": line, \"flow_MW\": pyo.value(model.flows[t, line])}\n", + " for t in model.T\n", + " for line in lines\n", + " if pyo.value(model.flows[t, line]) != 0\n", + " ]\n", + " power_flows_df = pd.DataFrame(power_flows)\n", + "\n", + " # Extract market clearing prices from dual variables\n", + " clearing_prices = [\n", + " {\n", + " \"zone\": node,\n", + " \"time\": t,\n", + " \"clearing_price\": pyo.value(model.dual[model.energy_balance[node, t]]),\n", + " }\n", + " for node in nodes\n", + " for t in model.T\n", + " ]\n", + " clearing_prices_df = pd.DataFrame(clearing_prices)\n", + "\n", + " return accepted_bids, power_flows_df, clearing_prices_df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "512ed95f", + "metadata": { + "id": "512ed95f" + }, + "outputs": [], + "source": [ + "# Extract results for Simulation 1\n", + "accepted_bids_sim1, power_flows_df_sim1, clearing_prices_df_sim1 = extract_results(\n", + " model_sim1, incidence_matrix\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "7b32b7c3", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 70 }, + "id": "7b32b7c3", + "outputId": "7d56dd2f-8ab9-4a95-df0b-dbd6aac660e4" + }, + "outputs": [ { - 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zonetimeclearing_price
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zonetimeclearing_price
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timelineflow_MW
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zonetimeclearing_price
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zonetimeclearing_price
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27DE_22019-01-01 03:00:0027.0
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\n", + "\n", + "" + ] + }, + "metadata": {} + } + ], + "source": [ + "# @title Plot the market clearing prices for each zone and simulation run\n", + "# Initialize the Plotly figure\n", + "fig = go.Figure()\n", + "\n", + "# Iterate over each zone to plot clearing prices for all three simulations\n", + "for zone in incidence_matrix.index:\n", + " # Filter data for the current zone and Simulation 1\n", + " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 2\n", + " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", + " # Filter data for the current zone and Simulation 3\n", + " zone_prices_sim3 = clearing_prices_df_sim3[clearing_prices_df_sim3[\"zone\"] == zone]\n", + "\n", + " # Add trace for Simulation 1\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim1[\"time\"],\n", + " y=zone_prices_sim1[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim1 (Zero Capacity)\",\n", + " line=dict(dash=\"dash\"), # Dashed line for Simulation 1\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 2\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim2[\"time\"],\n", + " y=zone_prices_sim2[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim2 (Medium Capacity)\",\n", + " line=dict(dash=\"dot\"), # Dotted line for Simulation 2\n", + " )\n", + " )\n", + "\n", + " # Add trace for Simulation 3\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=zone_prices_sim3[\"time\"],\n", + " y=zone_prices_sim3[\"clearing_price\"],\n", + " mode=\"lines\",\n", + " name=f\"{zone} - Sim3 (High Capacity)\",\n", + " line=dict(dash=\"solid\"), # Solid line for Simulation 3\n", + " )\n", + " )\n", + "\n", + "# Update layout for better aesthetics and interactivity\n", + "fig.update_layout(\n", + " title=\"Clearing Prices per Zone Over Time: Sim1, Sim2, & Sim3\",\n", + " xaxis_title=\"Time\",\n", + " yaxis_title=\"Clearing Price\",\n", + " legend_title=\"Simulations\",\n", + " xaxis=dict(\n", + " tickangle=45,\n", + " type=\"date\", # Ensure the x-axis is treated as dates\n", + " ),\n", + " hovermode=\"x unified\", # Unified hover for better comparison\n", + " template=\"plotly_white\", # Clean white background\n", + " width=1000,\n", + " height=600,\n", + ")\n", + "\n", + "# Display the interactive plot\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7ee17c77", + "metadata": { + "id": "7ee17c77" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across all three simulations. The dashed lines represent Simulation 1 (no transmission capacity), dotted lines represent Simulation 2 (medium transmission capacity), and solid lines represent Simulation 3 (high transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." + ] + }, + { + "cell_type": "markdown", + "id": "fb8f157c", + "metadata": { + "id": "fb8f157c" + }, + "source": [ + "## 6. Execution with ASSUME\n", + "\n", + "In a real-world scenario, the ASSUME framework handles the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we'll integrate our prepared data and configuration into ASSUME to execute the simulation seamlessly.\n", + "\n", + "### Step 1: Saving Input Files\n", + "\n", + "We will save the generated input DataFrames to the `inputs/tutorial_08` folder. The required files are:\n", + "- `demand_units.csv`\n", + "- `demand_df.csv`\n", + "- `powerplant_units.csv`\n", + "- `buses.csv`\n", + "- `lines.csv`\n", + "\n", + "Additionally, we'll create a new file `fuel_prices.csv`.\n", + "\n", + "**Note:** The demand timeseries has been extended to cover 48 hours as ASSUME always requires an additional day of data for the market simulation.\n", + "\n", + "#### Create the Inputs Directory and Save CSV Files" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "531a7a24", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - "yaxis": { - "title": { - "text": "Clearing Price" - } + "id": "531a7a24", + "outputId": "abc151f4-2f50-4ebd-b405-49f0340cd96d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Input CSV files have been saved to 'inputs/tutorial_08'.\n" + ] } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# @title title\n", - "# Initialize the Plotly figure\n", - "fig = go.Figure()\n", - "\n", - "# Iterate over each zone to plot clearing prices for all three simulations\n", - "for zone in incidence_matrix.index:\n", - " # Filter data for the current zone and Simulation 1\n", - " zone_prices_sim1 = clearing_prices_df_sim1[clearing_prices_df_sim1[\"zone\"] == zone]\n", - " # Filter data for the current zone and Simulation 2\n", - " zone_prices_sim2 = clearing_prices_df_sim2[clearing_prices_df_sim2[\"zone\"] == zone]\n", - " # Filter data for the current zone and Simulation 3\n", - " zone_prices_sim3 = clearing_prices_df_sim3[clearing_prices_df_sim3[\"zone\"] == zone]\n", - "\n", - " # Add trace for Simulation 1\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=zone_prices_sim1[\"time\"],\n", - " y=zone_prices_sim1[\"clearing_price\"],\n", - " mode=\"lines\",\n", - " name=f\"{zone} - Sim1 (Zero Capacity)\",\n", - " line=dict(dash=\"dash\"), # Dashed line for Simulation 1\n", - " )\n", - " )\n", - "\n", - " # Add trace for Simulation 2\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=zone_prices_sim2[\"time\"],\n", - " y=zone_prices_sim2[\"clearing_price\"],\n", - " mode=\"lines\",\n", - " name=f\"{zone} - Sim2 (Medium Capacity)\",\n", - " line=dict(dash=\"dot\"), # Dotted line for Simulation 2\n", - " )\n", - " )\n", - "\n", - " # Add trace for Simulation 3\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=zone_prices_sim3[\"time\"],\n", - " y=zone_prices_sim3[\"clearing_price\"],\n", - " mode=\"lines\",\n", - " name=f\"{zone} - Sim3 (High Capacity)\",\n", - " line=dict(dash=\"solid\"), # Solid line for Simulation 3\n", - " )\n", - " )\n", - "\n", - "# Update layout for better aesthetics and interactivity\n", - "fig.update_layout(\n", - " title=\"Clearing Prices per Zone Over Time: Sim1, Sim2, & Sim3\",\n", - " xaxis_title=\"Time\",\n", - " yaxis_title=\"Clearing Price\",\n", - " legend_title=\"Simulations\",\n", - " xaxis=dict(\n", - " tickangle=45,\n", - " type=\"date\", # Ensure the x-axis is treated as dates\n", - " ),\n", - " hovermode=\"x unified\", # Unified hover for better comparison\n", - " template=\"plotly_white\", # Clean white background\n", - " width=1000,\n", - " height=600,\n", - ")\n", - "\n", - "# Display the interactive plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "7ee17c77", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **Clearing Prices Plot:** Shows how market prices vary over time for each zone across all three simulations. The dashed lines represent Simulation 1 (no transmission capacity), dotted lines represent Simulation 2 (medium transmission capacity), and solid lines represent Simulation 3 (high transmission capacity). This visualization helps in observing how the presence of transmission capacity affects price convergence or divergence between zones." - ] - }, - { - "cell_type": "markdown", - "id": "fb8f157c", - "metadata": {}, - "source": [ - "## 6. Integrating with ASSUME\n", - "\n", - "In a real-world scenario, the ASSUME framework handles the reading of CSV files and the configuration of the simulation through configuration files. For the purpose of this tutorial, we'll integrate our prepared data and configuration into ASSUME to execute the simulation seamlessly.\n", - "\n", - "### Step 1: Saving Input Files\n", - "\n", - "We will save the generated input DataFrames to the `inputs/tutorial_08` folder. The required files are:\n", - "- `demand_units.csv`\n", - "- `demand_df.csv`\n", - "- `powerplant_units.csv`\n", - "- `buses.csv`\n", - "- `lines.csv`\n", - "\n", - "Additionally, we'll create a new file `fuel_prices.csv`.\n", - "\n", - "**Note:** The demand timeseries has been extended to cover 48 hours as ASSUME always requires an additional day of data for the market simulation.\n", - "\n", - "#### Create the Inputs Directory and Save CSV Files" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "531a7a24", - "metadata": {}, - "outputs": [ + ], + "source": [ + "import os\n", + "\n", + "# Define the input directory\n", + "input_dir = \"inputs/tutorial_08\"\n", + "\n", + "# Create the directory if it doesn't exist\n", + "os.makedirs(input_dir, exist_ok=True)\n", + "\n", + "# extend demand_df for another day with the same demand profile\n", + "demand_df = pd.concat([demand_df, demand_df])\n", + "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", + "\n", + "# Save the DataFrames to CSV files\n", + "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=True)\n", + "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=True)\n", + "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", + "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", + "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", + "\n", + "print(\"Input CSV files have been saved to 'inputs/tutorial_08'.\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input CSV files have been saved to 'inputs/tutorial_08'.\n" - ] - } - ], - "source": [ - "import os\n", - "\n", - "# Define the input directory\n", - "input_dir = \"inputs/tutorial_08\"\n", - "\n", - "# Create the directory if it doesn't exist\n", - "os.makedirs(input_dir, exist_ok=True)\n", - "\n", - "# extend demand_df for another day with the same demand profile\n", - "demand_df = pd.concat([demand_df, demand_df])\n", - "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=48, freq=\"h\")\n", - "\n", - "# Save the DataFrames to CSV files\n", - "buses.to_csv(os.path.join(input_dir, \"buses.csv\"), index=True)\n", - "lines.to_csv(os.path.join(input_dir, \"lines.csv\"), index=True)\n", - "powerplant_units.to_csv(os.path.join(input_dir, \"powerplant_units.csv\"), index=False)\n", - "demand_units.to_csv(os.path.join(input_dir, \"demand_units.csv\"), index=False)\n", - "demand_df.to_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", - "\n", - "print(\"Input CSV files have been saved to 'inputs/tutorial_08'.\")" - ] - }, - { - "cell_type": "markdown", - "id": "f788c99a", - "metadata": {}, - "source": [ - "#### Create `fuel_prices.csv`" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "2d61a40b", - "metadata": {}, - "outputs": [ + "cell_type": "code", + "execution_count": 29, + "id": "2d61a40b", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "cellView": "form", + "id": "2d61a40b", + "outputId": "8ce46e76-c462-4c8e-db62-8f787b354403" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" + ] + } + ], + "source": [ + "# @title Create fuel prices\n", + "fuel_prices = {\n", + " \"fuel\": [\"uranium\", \"co2\"],\n", + " \"price\": [5, 25],\n", + "}\n", + "\n", + "# Convert to DataFrame and save as CSV\n", + "fuel_prices_df = pd.DataFrame(fuel_prices).T\n", + "fuel_prices_df.to_csv(\n", + " os.path.join(input_dir, \"fuel_prices_df.csv\"), index=True, header=False\n", + ")\n", + "\n", + "print(\"Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\")" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\n" - ] - } - ], - "source": [ - "# @title Create fuel prices\n", - "fuel_prices = {\n", - " \"fuel\": [\"uranium\", \"co2\"],\n", - " \"price\": [5, 25],\n", - "}\n", - "\n", - "# Convert to DataFrame and save as CSV\n", - "fuel_prices_df = pd.DataFrame(fuel_prices).T\n", - "fuel_prices_df.to_csv(\n", - " os.path.join(input_dir, \"fuel_prices_df.csv\"), index=True, header=False\n", - ")\n", - "\n", - "print(\"Fuel Prices CSV file has been saved to 'inputs/tutorial_08/fuel_prices.csv'.\")" - ] - }, - { - "cell_type": "markdown", - "id": "e0e47625", - "metadata": {}, - "source": [ - "### Step 2: Creating the Configuration YAML File\n", - "\n", - "The configuration file defines the simulation parameters, including market settings and network configurations. Below is the YAML configuration tailored for our tutorial." - ] - }, - { - "cell_type": "markdown", - "id": "44e22a14", - "metadata": {}, - "source": [ - "#### Create `config.yaml`" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "821a4002", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "e0e47625", + "metadata": { + "id": "e0e47625" + }, + "source": [ + "### Step 2: Creating the Configuration YAML File\n", + "\n", + "The configuration file defines the simulation parameters, including market settings and network configurations. Below is the YAML configuration tailored for our tutorial." + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to 'inputs/tutorial_08/config.yaml'.\n" - ] - } - ], - "source": [ - "config = {\n", - " \"zonal_case\": {\n", - " \"start_date\": \"2019-01-01 00:00\",\n", - " \"end_date\": \"2019-01-01 23:00\",\n", - " \"time_step\": \"1h\",\n", - " \"save_frequency_hours\": 24,\n", - " \"markets_config\": {\n", - " \"zonal\": {\n", - " \"operator\": \"EOM_operator\",\n", - " \"product_type\": \"energy\",\n", - " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", - " \"opening_frequency\": \"1h\",\n", - " \"opening_duration\": \"1h\",\n", - " \"volume_unit\": \"MWh\",\n", - " \"maximum_bid_volume\": 100000,\n", - " \"maximum_bid_price\": 3000,\n", - " \"minimum_bid_price\": -500,\n", - " \"price_unit\": \"EUR/MWh\",\n", - " \"market_mechanism\": \"pay_as_clear_complex\",\n", - " \"additional_fields\": [\"bid_type\", \"node\"],\n", - " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", - " }\n", - " },\n", - " }\n", - "}\n", - "\n", - "# Define the path for the config file\n", - "config_path = os.path.join(input_dir, \"config.yaml\")\n", - "\n", - "# Save the configuration to a YAML file\n", - "with open(config_path, \"w\") as file:\n", - " yaml.dump(config, file, sort_keys=False)\n", - "\n", - "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" - ] - }, - { - "cell_type": "markdown", - "id": "e2e9403a", - "metadata": {}, - "source": [ - "### Detailed Configuration Explanation\n", - "\n", - "The `config.yaml` file plays a key role in defining the simulation parameters. Below is a detailed explanation of each configuration parameter:\n", - "\n", - "- **zonal_case:**\n", - " - **start_date:** The start date and time for the simulation (`2019-01-01 00:00`).\n", - " - **end_date:** The end date and time for the simulation (`2019-01-02 00:00`).\n", - " - **time_step:** The simulation time step (`1h`), indicating hourly intervals.\n", - " - **save_frequency_hours:** How frequently the simulation results are saved (`24` hours).\n", - "\n", - "- **markets_config:**\n", - " - **zonal:** The name of the market. Remember, that our power plant units had a column named bidding_zonal. This is how a particluar bidding strategy is assigned to a particluar market.\n", - " - **operator:** The market operator (`EOM_operator`).\n", - " - **product_type:** Type of market product (`energy`).\n", - " - **products:** List defining the market products:\n", - " - **duration:** Duration of the product (`1h`).\n", - " - **count:** Number of products (`1`).\n", - " - **first_delivery:** When the first delivery occurs (`1h`).\n", - " - **opening_frequency:** Frequency of market openings (`1h`).\n", - " - **opening_duration:** Duration of market openings (`1h`).\n", - " - **volume_unit:** Unit of volume measurement (`MWh`).\n", - " - **maximum_bid_volume:** Maximum volume allowed per bid (`100000` MWh).\n", - " - **maximum_bid_price:** Maximum price allowed per bid (`3000` EUR/MWh).\n", - " - **minimum_bid_price:** Minimum price allowed per bid (`-500` EUR/MWh).\n", - " - **price_unit:** Unit of price measurement (`EUR/MWh`).\n", - " - **market_mechanism:** The market clearing mechanism (`pay_as_clear_complex`).\n", - " - **additional_fields:** Additional fields required for bids:\n", - " - **bid_type:** Type of bid (e.g., supply or demand).\n", - " - **node:** The market zone associated with the bid.\n", - " - **param_dict:**\n", - " - **network_path:** Path to the network files (`.` indicates current directory).\n", - " - **zones_identifier:** Identifier used for market zones (`zone_id`).\n", - "\n", - "This configuration ensures that the simulation accurately represents the zonal market dynamics, including bid restrictions and market operations." - ] - }, - { - "cell_type": "markdown", - "id": "6fd79730", - "metadata": {}, - "source": [ - "### Step 3: Running the Simulation\n", - "\n", - "With the input files and configuration in place, we can now run the simulation using ASSUME's built-in functions." - ] - }, - { - "cell_type": "markdown", - "id": "33ff62b1", - "metadata": {}, - "source": [ - "#### Example Simulation Code" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "3a79848a", - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "id": "44e22a14", + "metadata": { + "id": "44e22a14" + }, + "source": [ + "#### Create `config.yaml`" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] + "cell_type": "code", + "execution_count": 30, + "id": "821a4002", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "821a4002", + "outputId": "ac8bf62b-8e38-4199-a45a-5c5397342bef" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Configuration YAML file has been saved to 'inputs/tutorial_08/config.yaml'.\n" + ] + } + ], + "source": [ + "config = {\n", + " \"zonal_case\": {\n", + " \"start_date\": \"2019-01-01 00:00\",\n", + " \"end_date\": \"2019-01-01 23:00\",\n", + " \"time_step\": \"1h\",\n", + " \"save_frequency_hours\": 24,\n", + " \"markets_config\": {\n", + " \"zonal\": {\n", + " \"operator\": \"EOM_operator\",\n", + " \"product_type\": \"energy\",\n", + " \"products\": [{\"duration\": \"1h\", \"count\": 1, \"first_delivery\": \"1h\"}],\n", + " \"opening_frequency\": \"1h\",\n", + " \"opening_duration\": \"1h\",\n", + " \"volume_unit\": \"MWh\",\n", + " \"maximum_bid_volume\": 100000,\n", + " \"maximum_bid_price\": 3000,\n", + " \"minimum_bid_price\": -500,\n", + " \"price_unit\": \"EUR/MWh\",\n", + " \"market_mechanism\": \"pay_as_clear_complex\",\n", + " \"additional_fields\": [\"bid_type\", \"node\"],\n", + " \"param_dict\": {\"network_path\": \".\", \"zones_identifier\": \"zone_id\"},\n", + " }\n", + " },\n", + " }\n", + "}\n", + "\n", + "# Define the path for the config file\n", + "config_path = os.path.join(input_dir, \"config.yaml\")\n", + "\n", + "# Save the configuration to a YAML file\n", + "with open(config_path, \"w\") as file:\n", + " yaml.dump(config, file, sort_keys=False)\n", + "\n", + "print(f\"Configuration YAML file has been saved to '{config_path}'.\")" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/82800 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
supply_volumedemand_volumedemand_volume_energysupply_volume_energypricemax_pricemin_pricenodeproduct_startproduct_endonly_hoursmarket_idsimulation
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2019-01-01 01:00:0015000560056001500043.66743.66743.667DE_12019-01-01 01:00:002019-01-01 02:00:00NaNzonaltutorial_08_zonal_case
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2019-01-01 02:00:0015000640064001500043.66743.66743.667DE_12019-01-01 02:00:002019-01-01 03:00:00NaNzonaltutorial_08_zonal_case
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2019-01-01 03:00:0015000720072001500043.66743.66743.667DE_12019-01-01 03:00:002019-01-01 04:00:00NaNzonaltutorial_08_zonal_case
\n", - "" - ], - "text/plain": [ - " supply_volume demand_volume demand_volume_energy \\\n", - "time \n", - "2019-01-01 01:00:00 15000 5600 5600 \n", - "2019-01-01 01:00:00 7400 16800 16800 \n", - "2019-01-01 02:00:00 15000 6400 6400 \n", - "2019-01-01 02:00:00 7600 16200 16200 \n", - "2019-01-01 03:00:00 15000 7200 7200 \n", - "\n", - " supply_volume_energy price max_price min_price node \\\n", - "time \n", - "2019-01-01 01:00:00 15000 43.667 43.667 43.667 DE_1 \n", - "2019-01-01 01:00:00 7400 43.667 43.667 43.667 DE_2 \n", - "2019-01-01 02:00:00 15000 43.667 43.667 43.667 DE_1 \n", - "2019-01-01 02:00:00 7600 43.667 43.667 43.667 DE_2 \n", - "2019-01-01 03:00:00 15000 43.667 43.667 43.667 DE_1 \n", - "\n", - " product_start product_end only_hours \\\n", - "time \n", - "2019-01-01 01:00:00 2019-01-01 01:00:00 2019-01-01 02:00:00 NaN \n", - "2019-01-01 01:00:00 2019-01-01 01:00:00 2019-01-01 02:00:00 NaN \n", - "2019-01-01 02:00:00 2019-01-01 02:00:00 2019-01-01 03:00:00 NaN \n", - "2019-01-01 02:00:00 2019-01-01 02:00:00 2019-01-01 03:00:00 NaN \n", - "2019-01-01 03:00:00 2019-01-01 03:00:00 2019-01-01 04:00:00 NaN \n", - "\n", - " market_id simulation \n", - "time \n", - "2019-01-01 01:00:00 zonal tutorial_08_zonal_case \n", - "2019-01-01 01:00:00 zonal tutorial_08_zonal_case \n", - "2019-01-01 02:00:00 zonal tutorial_08_zonal_case \n", - "2019-01-01 02:00:00 zonal tutorial_08_zonal_case \n", - "2019-01-01 03:00:00 zonal tutorial_08_zonal_case " + "cell_type": "markdown", + "id": "be819122", + "metadata": { + "id": "be819122" + }, + "source": [ + "## 7. Analyzing the Results\n", + "\n", + "After running the simulation, you can analyze the results using the methods demonstrated in section 5. This integration with ASSUME allows for more extensive and scalable simulations, leveraging the framework's capabilities for handling complex market scenarios.\n", + "\n", + "In this section, we will:\n", + "\n", + "1. **Locate the Simulation Output Files:** Understand where the simulation results are saved.\n", + "2. **Load and Inspect the Output Data:** Read the output CSV files and examine their structure.\n", + "3. **Plot Clearing Prices:** Visualize the market clearing prices to compare with our previous manual simulations." ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define the path to the simulation output\n", - "output_dir = \"outputs/tutorial_08_zonal_case\"\n", - "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", - "\n", - "# Load the market_meta.csv file\n", - "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", - "# drop the first column\n", - "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", - "\n", - "# Display a sample of the data\n", - "print(\"Sample of market_meta.csv:\")\n", - "display(market_meta.head())" - ] - }, - { - "cell_type": "markdown", - "id": "870b1c74", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **market_meta.csv:** This file contains the market outcomes for each zone and time period, including supply and demand volumes, clearing prices, and other relevant metrics.\n", - "- **Columns:**\n", - " - `supply_volume`: Total volume supplied in the zone.\n", - " - `demand_volume`: Total volume demanded in the zone.\n", - " - `demand_volume_energy`: Energy demand volume (same as `demand_volume` for energy markets).\n", - " - `supply_volume_energy`: Energy supply volume (same as `supply_volume` for energy markets).\n", - " - `price`: Clearing price in the zone for the time period.\n", - " - `max_price`: Maximum bid price accepted.\n", - " - `min_price`: Minimum bid price accepted.\n", - " - `node`: Identifier for the market zone (`DE_1` or `DE_2`).\n", - " - `product_start`: Start time of the market product.\n", - " - `product_end`: End time of the market product.\n", - " - `only_hours`: Indicator flag (not used in this context).\n", - " - `market_id`: Identifier for the market (`zonal`).\n", - " - `time`: Timestamp for the market product.\n", - " - `simulation`: Identifier for the simulation case (`tutorial_08_zonal_case`)." - ] - }, - { - "cell_type": "markdown", - "id": "d0fd6e1b", - "metadata": {}, - "source": [ - "### 7.3. Plotting Clearing Prices\n", - "\n", - "To verify that the simulation results align with our previous manual demonstrations, we'll plot the clearing prices for each zone over time. This will help us observe how transmission capacities influence price convergence or divergence between zones." - ] - }, - { - "cell_type": "markdown", - "id": "934872ad", - "metadata": {}, - "source": [ - "#### Processing the Market Meta Data" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "fd2e3048", - "metadata": {}, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample of Processed Clearing Prices:\n" - ] + "cell_type": "markdown", + "id": "5ca43ca3", + "metadata": { + "id": "5ca43ca3" + }, + "source": [ + "### 7.1. Locating the Simulation Output Files\n", + "\n", + "The simulation outputs are saved in the `outputs/tutorial_08_zonal_case` directory. Specifically, the key output file we'll work with is `market_meta.csv`, which contains detailed information about the market outcomes for each zone and time period." + ] }, { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " DE_1_price DE_2_price\n", - "time \n", - "2019-01-01 01:00:00 43.667 43.667\n", - "2019-01-01 02:00:00 43.667 43.667\n", - "2019-01-01 03:00:00 43.667 43.667\n", - "2019-01-01 04:00:00 43.667 43.667\n", - "2019-01-01 05:00:00 44.667 44.667" + "cell_type": "markdown", + "id": "78707ac9", + "metadata": { + "id": "78707ac9" + }, + "source": [ + "### 7.2. Loading and Inspecting the Output Data" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Extract unique zones\n", - "zones = market_meta[\"node\"].unique()\n", - "\n", - "# Initialize an empty DataFrame to store clearing prices per zone and time\n", - "clearing_prices_df = pd.DataFrame()\n", - "\n", - "# Populate the DataFrame with clearing prices for each zone\n", - "for zone in zones:\n", - " zone_data = market_meta[market_meta[\"node\"] == zone][[\"price\"]]\n", - " zone_data = zone_data.rename(columns={\"price\": f\"{zone}_price\"})\n", - " clearing_prices_df = (\n", - " pd.merge(\n", - " clearing_prices_df,\n", - " zone_data,\n", - " left_index=True,\n", - " right_index=True,\n", - " how=\"outer\",\n", - " )\n", - " if not clearing_prices_df.empty\n", - " else zone_data\n", - " )\n", - "\n", - "# Sort the DataFrame by time\n", - "clearing_prices_df = clearing_prices_df.sort_index()\n", - "\n", - "# Display a sample of the processed clearing prices\n", - 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"#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" + "metadata": {} + } + ], + "source": [ + "# Define the path to the simulation output\n", + "output_dir = \"outputs/tutorial_08_zonal_case\"\n", + "market_meta_path = os.path.join(output_dir, \"market_meta.csv\")\n", + "\n", + "# Load the market_meta.csv file\n", + "market_meta = pd.read_csv(market_meta_path, index_col=\"time\", parse_dates=True)\n", + "# drop the first column\n", + "market_meta = market_meta.drop(columns=market_meta.columns[0])\n", + "\n", + "# Display a sample of the data\n", + "print(\"Sample of market_meta.csv:\")\n", + "display(market_meta.head())" + ] + }, + { + "cell_type": "markdown", + "id": "870b1c74", + "metadata": { + "id": "870b1c74" + }, + "source": [ + "**Explanation:**\n", + "\n", + "- **market_meta.csv:** This file contains the market outcomes for each zone and time period, including supply and demand volumes, clearing prices, and other relevant metrics.\n", + "- **Columns:**\n", + " - `supply_volume`: Total volume supplied in the zone.\n", + " - `demand_volume`: Total volume demanded in the zone.\n", + " - `demand_volume_energy`: Energy demand volume (same as `demand_volume` for energy markets).\n", + " - `supply_volume_energy`: Energy supply volume (same as `supply_volume` for energy markets).\n", + " - `price`: Clearing price in the zone for the time period.\n", + " - `max_price`: Maximum bid price accepted.\n", + " - `min_price`: Minimum bid price accepted.\n", + " - `node`: Identifier for the market zone (`DE_1` or `DE_2`).\n", + " - `product_start`: Start time of the market product.\n", + " - `product_end`: End time of the market product.\n", + " - `only_hours`: Indicator flag (not used in this context).\n", + " - `market_id`: Identifier for the market (`zonal`).\n", + " - `time`: Timestamp for the market product.\n", + " - `simulation`: Identifier for the simulation case (`tutorial_08_zonal_case`)." + ] + }, + { + "cell_type": "markdown", + "id": "d0fd6e1b", + "metadata": { + "id": "d0fd6e1b" + }, + "source": [ + "### 7.3. Plotting Clearing Prices\n", + "\n", + "To verify that the simulation results align with our previous manual demonstrations, we'll plot the clearing prices for each zone over time. This will help us observe how transmission capacities influence price convergence or divergence between zones." + ] + }, + { + "cell_type": "markdown", + "id": "934872ad", + "metadata": { + "id": "934872ad" + }, + "source": [ + "#### Processing the Market Meta Data" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "fd2e3048", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "id": "fd2e3048", + "outputId": "7d9d0dc5-7042-488f-93d9-655bf4139807" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Sample of Processed Clearing Prices:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " DE_1_price DE_2_price\n", + "time \n", + "2019-01-01 01:00:00 43.667 43.667\n", + "2019-01-01 02:00:00 43.667 43.667\n", + "2019-01-01 03:00:00 43.667 43.667\n", + "2019-01-01 04:00:00 44.667 44.667\n", + "2019-01-01 05:00:00 44.667 44.667" ], - [ - 0.8888888888888888, - "#fdca26" + "text/html": [ + "\n", + "
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For instance, higher transmission capacity might lead to more price convergence between zones, while zero capacity could result in divergent price levels due to isolated supply and demand dynamics." + ] + }, + { + "cell_type": "markdown", + "id": "3f448fb4", + "metadata": { + "id": "3f448fb4" + }, + "source": [ + "## **Conclusion**\n", + "\n", + "Congratulations! You've successfully navigated through the **Market Zone Coupling** process using the **ASSUME Framework**. Here's a quick recap of what you've accomplished:\n", + "\n", + "### **Key Achievements:**\n", + "\n", + "1. **Market Setup:**\n", + " - **Defined Zones and Buses:** Established distinct market zones and configured their connections through transmission lines.\n", + " - **Configured Units:** Set up power plant and demand units within each zone, detailing their operational parameters.\n", + "\n", + "2. **Market Clearing Optimization:**\n", + " - **Implemented Optimization Model:** Utilized a simplified Pyomo-based model to perform market clearing, accounting for bid acceptances and power flows.\n", + " - **Simulated Transmission Scenarios:** Ran simulations with varying transmission capacities to observe their impact on energy distribution and pricing.\n", + "\n", + "3. **Result Analysis:**\n", + " - **Extracted Clearing Prices:** Retrieved and interpreted market prices from the optimization results.\n", + " - **Visualized Outcomes:** Created interactive plots to compare how different transmission capacities influence market dynamics across zones.\n", + "\n", + "### **Key Takeaways:**\n", + "\n", + "- **Impact of Transmission Capacity:** Transmission limits play a crucial role in determining energy flows and price convergence between market zones.\n", + "- **ASSUME Framework Efficiency:** ASSUME streamlines complex market simulations, making it easier to model and analyze multi-zone interactions.\n", + "\n", + "### **Next Steps:**\n", + "\n", + "- **Integrate Renewable Sources:** Expand the model to include renewable energy units and assess their impact on market dynamics.\n", + "- **Scale Up Simulations:** Apply the framework to larger, more complex market scenarios to further test its capabilities.\n", + "\n", + "Thank you for participating in this tutorial! With the foundational knowledge gained, you're now equipped to delve deeper into energy market simulations and leverage the ASSUME framework for more advanced analyses." + ] + } + ], + "metadata": { + "jupytext": { + "cell_metadata_filter": "-all", + "main_language": "python", + "notebook_metadata_filter": "-all" + }, + "language_info": { + "name": "python" + }, + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" } - ], - "source": [ - "# Initialize the Plotly figure\n", - "fig = go.Figure()\n", - "\n", - "# Iterate over each zone to plot clearing prices\n", - "for zone in zones:\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=clearing_prices_df.index,\n", - " y=clearing_prices_df[f\"{zone}_price\"],\n", - " mode=\"lines\",\n", - " name=f\"{zone} - Simulation\",\n", - " line=dict(width=2),\n", - " )\n", - " )\n", - "\n", - "# Update layout for better aesthetics and interactivity\n", - "fig.update_layout(\n", - " title=\"Clearing Prices per Zone Over Time: Simulation Results\",\n", - " xaxis_title=\"Time\",\n", - " yaxis_title=\"Clearing Price (EUR/MWh)\",\n", - " legend_title=\"Market Zones\",\n", - " xaxis=dict(\n", - " tickangle=45,\n", - " type=\"date\", # Ensure the x-axis is treated as dates\n", - " ),\n", - " hovermode=\"x unified\", # Unified hover for better comparison\n", - " template=\"plotly_white\", # Clean white background\n", - " width=1000,\n", - " height=600,\n", - ")\n", - "\n", - "# Display the interactive plot\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b34407b1", - "metadata": {}, - "source": [ - "**Explanation:**\n", - "\n", - "- **Plot Details:**\n", - " - **Lines:** Each zone's clearing price over time is represented by a distinct line.\n", - " - **Interactivity:** The Plotly plot allows for interactive exploration of the data, such as zooming and hovering for specific values.\n", - " - **Aesthetics:** The clean white template and clear labels enhance readability.\n", - "\n", - "- **Interpretation:**\n", - " - **Price Trends:** Observing how clearing prices fluctuate over time within each zone.\n", - " - **Impact of Transmission Capacity:** Comparing price levels between zones can reveal the effects of transmission capacities on market equilibrium. For instance, higher transmission capacity might lead to more price convergence between zones, while zero capacity could result in divergent price levels due to isolated supply and demand dynamics." - ] - }, - { - "cell_type": "markdown", - "id": "3f448fb4", - "metadata": {}, - "source": [ - "## **Conclusion**\n", - "\n", - "Congratulations! You've successfully navigated through the **Market Zone Coupling** process using the **ASSUME Framework**. Here's a quick recap of what you've accomplished:\n", - "\n", - "### **Key Achievements:**\n", - "\n", - "1. **Market Setup:**\n", - " - **Defined Zones and Buses:** Established distinct market zones and configured their connections through transmission lines.\n", - " - **Configured Units:** Set up power plant and demand units within each zone, detailing their operational parameters.\n", - "\n", - "2. **Market Clearing Optimization:**\n", - " - **Implemented Optimization Model:** Utilized a simplified Pyomo-based model to perform market clearing, accounting for bid acceptances and power flows.\n", - " - **Simulated Transmission Scenarios:** Ran simulations with varying transmission capacities to observe their impact on energy distribution and pricing.\n", - "\n", - "3. **Result Analysis:**\n", - " - **Extracted Clearing Prices:** Retrieved and interpreted market prices from the optimization results.\n", - " - **Visualized Outcomes:** Created interactive plots to compare how different transmission capacities influence market dynamics across zones.\n", - "\n", - "### **Key Takeaways:**\n", - "\n", - "- **Impact of Transmission Capacity:** Transmission limits play a crucial role in determining energy flows and price convergence between market zones.\n", - "- **ASSUME Framework Efficiency:** ASSUME streamlines complex market simulations, making it easier to model and analyze multi-zone interactions.\n", - "\n", - "### **Next Steps:**\n", - "\n", - "- **Integrate Renewable Sources:** Expand the model to include renewable energy units and assess their impact on market dynamics.\n", - "- **Scale Up Simulations:** Apply the framework to larger, more complex market scenarios to further test its capabilities.\n", - "\n", - "Thank you for participating in this tutorial! With the foundational knowledge gained, you're now equipped to delve deeper into energy market simulations and leverage the ASSUME framework for more advanced analyses." - ] - } - ], - "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 + "nbformat": 4, + "nbformat_minor": 5 } From a02eb6f6ff7ec3dd4f86b26da822559e7e1b3875 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 10:48:56 +0200 Subject: [PATCH 20/27] - simple path fixes for new xRL tut --- .../notebooks/09_example_Sim_and_xRL.ipynb | 2114 ++++++++++++----- 1 file changed, 1520 insertions(+), 594 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index b16964f3..baaac466 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e62e00c9", + "id": "3ba8dd1e", "metadata": { "id": "e62e00c9" }, @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "fb3aa803", + "id": "3a2ed19f", "metadata": { "id": "fb3aa803" }, @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "0d793362", + "id": "131db756", "metadata": { "id": "0d793362" }, @@ -32,7 +32,7 @@ }, { "cell_type": "markdown", - "id": "87bdf688", + "id": "667dc923", "metadata": { "id": "87bdf688" }, @@ -52,7 +52,7 @@ " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", "\n", " 3.2. [Creating a SHAP Explainer](#32-creating-a-shap-explainer)\n", - " \n", + "\n", "4. [Visualizing SHAP Values](#visualizing-shap-values)\n", "5. [Conclusion](#conclusion)\n", "6. [Additional Resources](#additional-resources)" @@ -60,7 +60,7 @@ }, { "cell_type": "markdown", - "id": "5e8c7fec", + "id": "ed057a2a", "metadata": { "id": "5e8c7fec" }, @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "06e91420", + "id": "3a3562cf", "metadata": { "id": "06e91420" }, @@ -80,7 +80,7 @@ }, { "cell_type": "markdown", - "id": "47b1e7ab", + "id": "4c610270", "metadata": { "id": "47b1e7ab" }, @@ -90,7 +90,7 @@ }, { "cell_type": "markdown", - "id": "ec0717c1", + "id": "9978b51b", "metadata": { "id": "ec0717c1" }, @@ -100,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "0d59bb0a", + "id": "23bed9c3", "metadata": { "id": "0d59bb0a" }, @@ -140,7 +140,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ee220130", + "id": "02dea28f", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -162,7 +162,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "Hn_DvrqR7oK2", + "id": "544d38a4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -173,99 +173,30 @@ "languageId": "shellscript" } }, - 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"!pip install nbconvert" + "#!pip install nbconvert" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "75cd480a", + "execution_count": 3, + "id": "973fc56c", "metadata": { - "id": "75cd480a" + "id": "75cd480a", + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "import pandas as pd\n", - "\n", - "# import plotly for visualization\n", - "import plotly.graph_objects as go\n", - "\n", - "# import yaml for reading and writing YAML files\n", - "import yaml\n", - "\n", "# Function to display DataFrame in Jupyter" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "bfd1daf2", + "execution_count": 4, + "id": "a578e164", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -285,6 +216,14 @@ "source": [ "import importlib.util\n", "\n", + "import pandas as pd\n", + "\n", + "# import plotly for visualization\n", + "import plotly.graph_objects as go\n", + "\n", + "# import yaml for reading and writing YAML files\n", + "import yaml\n", + "\n", "# Check if 'google.colab' is available\n", "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", @@ -298,62 +237,43 @@ }, { "cell_type": "markdown", - "id": "636ea9ae", + "id": "73087adf", "metadata": { "id": "636ea9ae" }, "source": [ - "Load the created example files from the tutorial before:" + "**Load the created example files from the tutorial before:**\n", + "\n", + "we need to get the results form the market zone splitting tutorial, for which we are defining the RL Agent here. If you are working in colab execute the follwoing cells. If you are not working in colab but on your local machine simply open the respective tuttorial notebook and let it run." ] }, { "cell_type": "code", "execution_count": 5, - "id": "988d3e15", + "id": "116b9e37", "metadata": { "vscode": { "languageId": "shellscript" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[WinError 2] Das System kann die angegebene Datei nicht finden: 'content'\n", - "c:\\Users\\tg3533\\Documents\\Code\\assume\\examples\\notebooks\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[NbConvertApp] Converting notebook 08_market_zone_coupling.ipynb to notebook\n", - "C:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\zmq\\_future.py:724: RuntimeWarning: Proactor event loop does not implement add_reader family of methods required for zmq. Registering an additional selector thread for add_reader support via tornado. Use `asyncio.set_event_loop_policy(WindowsSelectorEventLoopPolicy())` to avoid this warning.\n", - " self._get_loop()\n", - "[NbConvertApp] Writing 188324 bytes to output.ipynb\n", - "Der Befehl \"cp\" ist entweder falsch geschrieben oder\n", - "konnte nicht gefunden werden.\n" - ] - } - ], + "outputs": [], "source": [ - "#if used locally\n", + "# if used locally\n", "#%cd assume/examples/notebooks/\n", "\n", "# if used in colab\n", "#%cd assume-repo/examples/notebooks/\n", "\n", "#!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", - " \n", + "\n", "#%cd content\n", "#!cp -r assume-repo/examples/notebooks/inputs ." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "233f315b", + "execution_count": 6, + "id": "9871a2a5", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -384,9 +304,6 @@ "demand_units = pd.read_csv(os.path.join(input_dir, \"demand_units.csv\"))\n", "demand_df = pd.read_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", "\n", - "# extend demand_df for another day with the same demand profile\n", - "demand_df = pd.concat([demand_df, demand_df])\n", - "demand_df.index = pd.date_range(start=\"2019-01-01\", periods=96, freq=\"h\")\n", "\n", "# Read the fuel prices DataFrame from CSV file\n", "fuel_prices_df = pd.read_csv(os.path.join(input_dir, \"fuel_prices_df.csv\"))\n", @@ -397,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "6985289b", + "id": "bcfa21b7", "metadata": { "id": "6985289b" }, @@ -409,8 +326,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "b205256f", + "execution_count": 7, + "id": "ac7d12fb", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -441,11 +358,6 @@ " \n", " \n", " \n", - " Unnamed: 0.3\n", - " Unnamed: 0.2\n", - " Unnamed: 0.1\n", - " Unnamed: 0\n", - " name\n", " technology\n", " bidding_zonal\n", " fuel_type\n", @@ -457,15 +369,23 @@ " node\n", " unit_operator\n", " \n", + " \n", + " name\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " Unit 1\n", + " Unit 1\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -478,12 +398,7 @@ " Operator North\n", " \n", " \n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " 1\n", - " Unit 2\n", + " Unit 2\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -496,12 +411,7 @@ " Operator North\n", " \n", " \n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " 2\n", - " Unit 3\n", + " Unit 3\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -514,12 +424,7 @@ " Operator North\n", " \n", " \n", - " 3\n", - " 3\n", - " 3\n", - " 3\n", - " 3\n", - " Unit 4\n", + " Unit 4\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -532,12 +437,7 @@ " Operator North\n", " \n", " \n", - " 4\n", - " 4\n", - " 4\n", - " 4\n", - " 4\n", - " Unit 5\n", + " Unit 5\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -550,12 +450,7 @@ " Operator North\n", " \n", " \n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " 5\n", - " Unit 6\n", + " Unit 6\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -568,12 +463,7 @@ " Operator North\n", " \n", " \n", - " 6\n", - " 6\n", - " 6\n", - " 6\n", - " 6\n", - " Unit 7\n", + " Unit 7\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -586,12 +476,7 @@ " Operator North\n", " \n", " \n", - " 7\n", - " 7\n", - " 7\n", - " 7\n", - " 7\n", - " Unit 8\n", + " Unit 8\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -604,12 +489,7 @@ " Operator North\n", " \n", " \n", - " 8\n", - " 8\n", - " 8\n", - " 8\n", - " 8\n", - " Unit 9\n", + " Unit 9\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -622,12 +502,7 @@ " Operator North\n", " \n", " \n", - " 9\n", - " 9\n", - " 9\n", - " 9\n", - " 9\n", - " Unit 10\n", + " Unit 10\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -640,12 +515,7 @@ " Operator North\n", " \n", " \n", - " 10\n", - " 10\n", - " 10\n", - " 10\n", - " 10\n", - " Unit 11\n", + " Unit 11\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -658,12 +528,7 @@ " Operator North\n", " \n", " \n", - " 11\n", - " 11\n", - " 11\n", - " 11\n", - " 11\n", - " Unit 12\n", + " Unit 12\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -676,12 +541,7 @@ " Operator North\n", " \n", " \n", - " 12\n", - " 12\n", - " 12\n", - " 12\n", - " 12\n", - " Unit 13\n", + " Unit 13\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -694,12 +554,7 @@ " Operator North\n", " \n", " \n", - " 13\n", - " 13\n", - " 13\n", - " 13\n", - " 13\n", - " Unit 14\n", + " Unit 14\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -712,12 +567,7 @@ " Operator North\n", " \n", " \n", - " 14\n", - " 14\n", - " 14\n", - " 14\n", - " 14\n", - " Unit 15\n", + " Unit 15\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -730,12 +580,7 @@ " Operator North\n", " \n", " \n", - " 15\n", - " 15\n", - " 15\n", - " 15\n", - " 15\n", - " Unit 16\n", + " Unit 16\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -748,12 +593,7 @@ " Operator South\n", " \n", " \n", - " 16\n", - " 16\n", - " 16\n", - " 16\n", - " 16\n", - " Unit 17\n", + " Unit 17\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -766,12 +606,7 @@ " Operator South\n", " \n", " \n", - " 17\n", - " 17\n", - " 17\n", - " 17\n", - " 17\n", - " Unit 18\n", + " Unit 18\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -784,12 +619,7 @@ " Operator South\n", " \n", " \n", - " 18\n", - " 18\n", - " 18\n", - " 18\n", - " 18\n", - " Unit 19\n", + " Unit 19\n", " nuclear\n", " naive_eom\n", " uranium\n", @@ -802,12 +632,7 @@ " Operator South\n", " \n", " \n", - " 19\n", - " 19\n", - " 19\n", - " 19\n", - " 19\n", - " Unit 20\n", + " Unit 20\n", " nuclear\n", " pp_learning\n", " uranium\n", @@ -824,74 +649,54 @@ "" ], "text/plain": [ - " Unnamed: 0.3 Unnamed: 0.2 Unnamed: 0.1 Unnamed: 0 name technology \\\n", - "0 0 0 0 0 Unit 1 nuclear \n", - "1 1 1 1 1 Unit 2 nuclear \n", - "2 2 2 2 2 Unit 3 nuclear \n", - "3 3 3 3 3 Unit 4 nuclear \n", - "4 4 4 4 4 Unit 5 nuclear \n", - "5 5 5 5 5 Unit 6 nuclear \n", - "6 6 6 6 6 Unit 7 nuclear \n", - "7 7 7 7 7 Unit 8 nuclear \n", - "8 8 8 8 8 Unit 9 nuclear \n", - "9 9 9 9 9 Unit 10 nuclear \n", - "10 10 10 10 10 Unit 11 nuclear \n", - "11 11 11 11 11 Unit 12 nuclear \n", - "12 12 12 12 12 Unit 13 nuclear \n", - "13 13 13 13 13 Unit 14 nuclear \n", - "14 14 14 14 14 Unit 15 nuclear \n", - "15 15 15 15 15 Unit 16 nuclear \n", - "16 16 16 16 16 Unit 17 nuclear \n", - "17 17 17 17 17 Unit 18 nuclear \n", - "18 18 18 18 18 Unit 19 nuclear \n", - "19 19 19 19 19 Unit 20 nuclear \n", - "\n", - " bidding_zonal fuel_type emission_factor max_power min_power efficiency \\\n", - "0 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "1 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "2 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "3 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "4 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "5 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "6 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "7 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "8 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "9 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "10 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "11 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "12 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "13 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "14 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "15 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "16 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "17 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "18 naive_eom uranium 0.0 1000.0 0.0 0.3 \n", - "19 pp_learning uranium 0.0 5000.0 0.0 0.3 \n", + " technology bidding_zonal fuel_type emission_factor max_power \\\n", + "name \n", + "Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 6 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 7 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 8 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 9 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 10 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", + "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", "\n", - " additional_cost node unit_operator \n", - "0 5 north_1 Operator North \n", - "1 6 north_1 Operator North \n", - "2 7 north_1 Operator North \n", - "3 8 north_1 Operator North \n", - "4 9 north_1 Operator North \n", - "5 10 north_1 Operator North \n", - "6 11 north_1 Operator North \n", - "7 12 north_1 Operator North \n", - "8 13 north_2 Operator North \n", - "9 14 north_2 Operator North \n", - "10 15 north_2 Operator North \n", - "11 16 north_2 Operator North \n", - "12 17 north_2 Operator North \n", - "13 18 north_2 Operator North \n", - "14 19 north_2 Operator North \n", - "15 20 south Operator South \n", - "16 21 south Operator South \n", - "17 22 south Operator South \n", - "18 23 south Operator South \n", - "19 24 south Operator-RL " + " min_power efficiency additional_cost node unit_operator \n", + "name \n", + "Unit 1 0.0 0.3 5 north_1 Operator North \n", + "Unit 2 0.0 0.3 6 north_1 Operator North \n", + "Unit 3 0.0 0.3 7 north_1 Operator North \n", + "Unit 4 0.0 0.3 8 north_1 Operator North \n", + "Unit 5 0.0 0.3 9 north_1 Operator North \n", + "Unit 6 0.0 0.3 10 north_1 Operator North \n", + "Unit 7 0.0 0.3 11 north_1 Operator North \n", + "Unit 8 0.0 0.3 12 north_1 Operator North \n", + "Unit 9 0.0 0.3 13 north_2 Operator North \n", + "Unit 10 0.0 0.3 14 north_2 Operator North \n", + "Unit 11 0.0 0.3 15 north_2 Operator North \n", + "Unit 12 0.0 0.3 16 north_2 Operator North \n", + "Unit 13 0.0 0.3 17 north_2 Operator North \n", + "Unit 14 0.0 0.3 18 north_2 Operator North \n", + "Unit 15 0.0 0.3 19 north_2 Operator North \n", + "Unit 16 0.0 0.3 20 south Operator South \n", + "Unit 17 0.0 0.3 21 south Operator South \n", + "Unit 18 0.0 0.3 22 south Operator South \n", + "Unit 19 0.0 0.3 23 south Operator South \n", + "Unit 20 0.0 0.3 24 south Operator-RL " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -907,13 +712,16 @@ "# assig specific RL unit operator to plant\n", "powerplant_units.loc[19, \"unit_operator\"] = \"Operator-RL\"\n", "\n", + "# make name column to index\n", + "powerplant_units.set_index(\"name\", inplace=True, drop=True)\n", + "\n", "powerplant_units" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "QBTGrw62_5I7", + "execution_count": 8, + "id": "9316ac03", "metadata": { "id": "QBTGrw62_5I7" }, @@ -925,7 +733,7 @@ }, { "cell_type": "markdown", - "id": "cce0e8b4", + "id": "8f995ab9", "metadata": { "id": "cce0e8b4" }, @@ -935,8 +743,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "9c555ce9", + "execution_count": 9, + "id": "03cc1a12", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -980,7 +788,6 @@ " },\n", " \"learning_config\": {\n", " \"continue_learning\": False,\n", - " \"trained_policies_save_path\": \"null\",\n", " \"max_bid_price\": 100,\n", " \"algorithm\": \"matd3\",\n", " \"learning_rate\": 0.001,\n", @@ -1011,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "3f0f38fb", + "id": "59a880d8", "metadata": { "id": "3f0f38fb" }, @@ -1021,8 +828,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "201251c6", + "execution_count": 10, + "id": "823db62a", "metadata": { "cellView": "form", "id": "201251c6" @@ -1232,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "dcacfe26", + "id": "d52cb7d4", "metadata": { "id": "dcacfe26" }, @@ -1244,8 +1051,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "bfadf522", + "execution_count": 11, + "id": "9092a097", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1269,6 +1076,33 @@ "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", + "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", + "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", + "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", + "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801it [00:03, 21334.78it/s]\n", + "Training Episodes: 10%|█ | 1/10 [00:03<00:35, 3.94s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "INFO:assume.scenario.loader_csv:Adding markets\n", "INFO:assume.scenario.loader_csv:Read units from file\n", "INFO:assume.scenario.loader_csv:Adding power_plant units\n", @@ -1280,161 +1114,1287 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:05, 15043.53it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzodwb_u6.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmphiz0tfsu.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpwjttcr5q.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3dv4zbp4.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:02, 28219.50it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9q5q22kw.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohfetpqf.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcvn6kp04.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5c23xr6s.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 04:00:00: 22%|██▏ | 18001/82800 [00:00<00:01, 32720.76it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpisxw9ix8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpga9w7msy.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppen1aa2y.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3rqljc6n.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:00<00:01, 34611.39it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpscthkcqt.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3jg09edi.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6uk22o84.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1f6_ql3l.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:00<00:01, 35574.53it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpq15d8y1v.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpb7_9oq3e.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4kvnq040.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgb6uksuj.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 37188.77it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_wfe8xgm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpwezb1qq8.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0a9fbozj.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjk44ceob.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:01<00:01, 35811.70it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpiqujlpl7.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmid8tbjp.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp31ujp0qm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbmvl8_91.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:01<00:00, 32012.85it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptpjdcbtd.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpv6i_y9hq.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:01<00:00, 32390.30it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp29cgs_0f.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg662dy1b.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:01<00:00, 32580.89it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgkj26dx8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp885496ex.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvvyzn7z9.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6m_69yna.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 34332.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpocvop3u1.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9ojfuaoc.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthb_s5k8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8sa7td7n.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:02<00:00, 34505.65it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp53yqi16u.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkc57k0tt.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:02<00:00, 34451.35it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", + "ERROR:pyomo.opt:Solver log:\n", + "GLPSOL--GLPK LP/MIP Solver 5.0\n", + "Parameter(s) specified in the command line:\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8mgt8nwl.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_t4nf7ek.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp:27: constraints section missing\n", + "CPLEX LP file processing error\n", + "\n", + "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", + "ERROR:asyncio:Task exception was never retrieved\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", + " result = coro.send(None)\n", + " ^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", + " return await self._coro\n", + " ^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", + " raise e\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", + " ) = self.clear(self.all_orders, market_products)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", + " instance, results = market_clearing_opt(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", + " results = solver.solve(instance, options=options)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", + " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:02, 33917.07it/s] \n" + ] + } + ], + "source": [ + "# import the main World class and the load_scenario_folder functions from assume\n", + "from assume import World\n", + "from assume.scenario.loader_csv import load_scenario_folder\n", + "\n", + "# Define paths for input and output data\n", + "csv_path = \"outputs\"\n", + "\n", + "# Define the data format and database URI\n", + "# Use \"local_db\" for SQLite database or \"timescale\" for TimescaleDB in Docker\n", + "\n", + "# Create directories if they don't exist\n", + "os.makedirs(csv_path, exist_ok=True)\n", + "os.makedirs(\"local_db\", exist_ok=True)\n", + "\n", + "data_format = \"local_db\" # \"local_db\" or \"timescale\"\n", + "\n", + "if data_format == \"local_db\":\n", + " db_uri = \"sqlite:///local_db/assume_db.db\"\n", + "elif data_format == \"timescale\":\n", + " db_uri = \"postgresql://assume:assume@localhost:5432/assume\"\n", + "\n", + "# Create the World instance\n", + "world = World(database_uri=db_uri, export_csv_path=csv_path)\n", + "\n", + "# Load the scenario by providing the world instance\n", + "# The path to the inputs folder and the scenario name (subfolder in inputs)\n", + "# and the study case name (which config to use for the simulation)\n", + "load_scenario_folder(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + ")\n", + "\n", + "# run learning if learning mode is enabled\n", + "# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\n", + "\n", + "if world.learning_config.get(\"learning_mode\", False):\n", + " run_learning(\n", + " world,\n", + " inputs_path=inputs_path,\n", + " scenario=\"tutorial_08\",\n", + " study_case=\"zonal_case\",\n", + " )\n", + "\n", + "# Run the simulation\n", + "world.run()" + ] + }, + { + "cell_type": "markdown", + "id": "32d1f8e9", + "metadata": { + "id": "2194f71b" + }, + "source": [ + "**Generate same plotly figure**\n", + "\n", + "We use the same code described in the amarket zone tutprial to generate the plotly curve displaying the market price." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "96bfa2ee", + "metadata": { + "id": "bdb21cbe" + }, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'outputs/tutorial_08_zonal_case\\\\market_meta.csv'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[12], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m market_meta_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarket_meta.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Load the market_meta.csv file\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarket_meta_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# drop the first column\u001b[39;00m\n\u001b[0;32m 11\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m market_meta\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39mmarket_meta\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m0\u001b[39m])\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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869\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m 870\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[0;32m 872\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[1;32m--> 873\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\n\u001b[0;32m 874\u001b[0m handle,\n\u001b[0;32m 875\u001b[0m ioargs\u001b[38;5;241m.\u001b[39mmode,\n\u001b[0;32m 876\u001b[0m encoding\u001b[38;5;241m=\u001b[39mioargs\u001b[38;5;241m.\u001b[39mencoding,\n\u001b[0;32m 877\u001b[0m errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m 878\u001b[0m newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 879\u001b[0m )\n\u001b[0;32m 880\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 881\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[0;32m 882\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'outputs/tutorial_08_zonal_case\\\\market_meta.csv'" + ] + } + ], + "source": [ + "# @ title make makret price plotly figure\n", + "\n", "\n", "# Define the path to the simulation output\n", "output_dir = \"outputs/tutorial_08_zonal_case\"\n", @@ -1509,7 +2469,7 @@ }, { "cell_type": "markdown", - "id": "e77c41cf", + "id": "f82620fb", "metadata": { "id": "e77c41cf" }, @@ -1521,7 +2481,7 @@ }, { "cell_type": "markdown", - "id": "cbf18570", + "id": "79dcba85", "metadata": { "id": "cbf18570" }, @@ -1531,7 +2491,7 @@ }, { "cell_type": "markdown", - "id": "a5ac592c", + "id": "27a60e46", "metadata": { "id": "a5ac592c" }, @@ -1544,7 +2504,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ae266ecb", + "id": "b9f4d746", "metadata": { "id": "ae266ecb", "vscode": { @@ -1553,14 +2513,14 @@ }, "outputs": [], "source": [ - "!pip install matplotlib\n", - "!pip install shap==0.42.1\n", - "!pip install scikit-learn==1.3.0" + "#!pip install matplotlib\n", + "#!pip install shap==0.42.1\n", + "#!pip install scikit-learn==1.3.0" ] }, { "cell_type": "markdown", - "id": "5069ff93", + "id": "0423e316", "metadata": { "id": "5069ff93" }, @@ -1614,7 +2574,7 @@ }, { "cell_type": "markdown", - "id": "21d49573", + "id": "39959bf3", "metadata": { "id": "21d49573" }, @@ -1624,7 +2584,7 @@ }, { "cell_type": "markdown", - "id": "d16712fc", + "id": "1a6567e4", "metadata": { "id": "d16712fc" }, @@ -1634,7 +2594,7 @@ }, { "cell_type": "markdown", - "id": "2c0c1262", + "id": "f5fa5479", "metadata": { "id": "2c0c1262" }, @@ -1645,7 +2605,7 @@ }, { "cell_type": "markdown", - "id": "2fb6fc14", + "id": "df893c68", "metadata": { "id": "2fb6fc14" }, @@ -1655,7 +2615,7 @@ }, { "cell_type": "markdown", - "id": "e0b69db6", + "id": "d2145dcf", "metadata": { "id": "e0b69db6" }, @@ -1667,7 +2627,7 @@ }, { "cell_type": "markdown", - "id": "f870b3e8", + "id": "e3de2358", "metadata": { "id": "f870b3e8" }, @@ -1677,7 +2637,7 @@ }, { "cell_type": "markdown", - "id": "aaa7c3d3", + "id": "b6de4111", "metadata": { "id": "aaa7c3d3" }, @@ -1688,7 +2648,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b6ee4f28", + "id": "0a23dfa1", "metadata": { "id": "b6ee4f28" }, @@ -1703,9 +2663,10 @@ }, { "cell_type": "markdown", - "id": "aa268f14", + "id": "b44ce6d3", "metadata": { - "id": "aa268f14" + "id": "aa268f14", + "lines_to_next_cell": 0 }, "source": [ "the simulation common.py contains utility functions and class definitions\n", @@ -1714,9 +2675,10 @@ }, { "cell_type": "markdown", - "id": "f866385a", + "id": "efc3087c", "metadata": { - "id": "f866385a" + "id": "f866385a", + "lines_to_next_cell": 0 }, "source": [ "**Define the Actor Neural Network Class**" @@ -1724,9 +2686,10 @@ }, { "cell_type": "markdown", - "id": "ff7cf03a", + "id": "1a7c97b7", "metadata": { - "id": "ff7cf03a" + "id": "ff7cf03a", + "lines_to_next_cell": 0 }, "source": [ "We define the actor neural network class that will be used to predict actions based on observations." @@ -1735,10 +2698,9 @@ { "cell_type": "code", "execution_count": null, - "id": "7216677a", + "id": "168757a6", "metadata": { - "id": "7216677a", - "lines_to_next_cell": 1 + "id": "7216677a" }, "outputs": [], "source": [ @@ -1748,55 +2710,25 @@ { "cell_type": "code", "execution_count": null, - "id": "5a4d6e17", + "id": "2f4d6257", "metadata": { "id": "5a4d6e17" }, "outputs": [], "source": [ - "def load_config(file_path):\n", - " \"\"\"\n", - " Load the configuration file.\n", - " \"\"\"\n", - " with open(file_path) as file:\n", - " config = yaml.safe_load(file)\n", - " return config\n", + "# which actor is the RL actor\n", + "ACTOR_NUM = len(powerplant_units) # 20\n", "\n", - "\n", - "# Some Variable definitions:\n", - "\n", - "EPISODES = 3\n", - "NUMBER_OF_AGENTS = 1\n", - "SIM_TIMESPAN_DAYS = 31\n", - "ACTOR_NUM = 1\n", - "EXAMPLE = 1\n", - "\n", - "SIM_TIMESPAN_HOURS = SIM_TIMESPAN_DAYS * 24\n", - "\n", - "# actor 1-5 are the default non-rl actors, so we just skip those\n", - "ACTOR_NUM_ADJ = ACTOR_NUM + 6 # 6 #9\n", - "\n", - "\n", - "# Get the current working directory\n", - "current_dir = os.getcwd()\n", - "# Go up one level\n", - "one_level_up = os.path.dirname(current_dir)\n", - "# Go up two levels\n", - "two_levels_up = os.path.dirname(one_level_up)\n", - "\n", - "# Paths\n", - "path = os.path.join(\n", - " two_levels_up,\n", - " f\"assume/examples/output/{EXAMPLE}/{EPISODES}_episodes_{SIM_TIMESPAN_DAYS}_simDays_{NUMBER_OF_AGENTS}_rlAgents\",\n", - ")\n", - "actor_path = os.path.join(path, f\"actor_pp_{ACTOR_NUM_ADJ}.pt\")\n", - "\n", - "# DEFINTIONS" + "# Path to actor we want to analyse\n", + "actor_path = os.path.join(\n", + " input_dir,\n", + " \"learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt\",\n", + ")" ] }, { "cell_type": "markdown", - "id": "ddfe95d9", + "id": "bd023be3", "metadata": { "id": "ddfe95d9" }, @@ -1807,7 +2739,7 @@ { "cell_type": "code", "execution_count": null, - "id": "44862f06", + "id": "f38c74cb", "metadata": { "id": "44862f06" }, @@ -1816,10 +2748,12 @@ "# @title Load observations function\n", "\n", "\n", - "def load_observations(path, ACTOR_NUM, feature_names):\n", + "def load_observations(path, feature_names):\n", " # Load observations\n", " obs_path = f\"{path}/buffer_obs.json\"\n", "\n", + " print(obs_path)\n", + "\n", " with open(obs_path) as file:\n", " json_data = json.load(file)\n", "\n", @@ -1833,17 +2767,12 @@ " # filter only first 700 observations\n", " input_data = input_data[:700]\n", "\n", - " if NUMBER_OF_AGENTS == 1:\n", - " return pd.DataFrame(input_data, columns=feature_names), input_data\n", - " else:\n", - " return pd.DataFrame(\n", - " input_data[:, ACTOR_NUM], columns=feature_names\n", - " ), input_data[:, ACTOR_NUM]" + " return pd.DataFrame(input_data, columns=feature_names), input_data" ] }, { "cell_type": "markdown", - "id": "444b8679", + "id": "0e5eb3d3", "metadata": { "id": "444b8679" }, @@ -1853,7 +2782,7 @@ }, { "cell_type": "markdown", - "id": "7f218d15", + "id": "105cdba0", "metadata": { "id": "7f218d15" }, @@ -1864,21 +2793,21 @@ { "cell_type": "code", "execution_count": null, - "id": "8fa78b12", + "id": "47473ec8", "metadata": { "id": "8fa78b12" }, "outputs": [], "source": [ "path = (\n", - " inputs_path + \"/example_02a/learned_strategies/base/buffer_obs\"\n", + " input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", ") # Replace with your data path" ] }, { "cell_type": "code", "execution_count": null, - "id": "8f9189ca", + "id": "76fdf5d0", "metadata": { "id": "8f9189ca" }, @@ -1893,7 +2822,7 @@ }, { "cell_type": "markdown", - "id": "6c5f1986", + "id": "6126c1a9", "metadata": { "id": "6c5f1986" }, @@ -1903,7 +2832,7 @@ }, { "cell_type": "markdown", - "id": "36ae8f9e", + "id": "4d38d028", "metadata": { "id": "36ae8f9e" }, @@ -1914,20 +2843,20 @@ { "cell_type": "code", "execution_count": null, - "id": "d522969d", + "id": "1819ff26", "metadata": { "id": "d522969d" }, "outputs": [], "source": [ - "df_obs, input_data = load_observations(path, ACTOR_NUM, feature_names)\n", + "df_obs, input_data = load_observations(path, feature_names)\n", "\n", "df_obs" ] }, { "cell_type": "markdown", - "id": "5d8b9dcf", + "id": "509b19bf", "metadata": { "id": "5d8b9dcf" }, @@ -1937,7 +2866,7 @@ }, { "cell_type": "markdown", - "id": "b1b50488", + "id": "bf2b101a", "metadata": { "id": "b1b50488" }, @@ -1948,7 +2877,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4da4de57", + "id": "c27178fe", "metadata": { "id": "4da4de57" }, @@ -1963,18 +2892,12 @@ { "cell_type": "code", "execution_count": null, - "id": "37adecfa", + "id": "35a088d5", "metadata": { "id": "37adecfa" }, "outputs": [], "source": [ - "ACTOR_NUM = 1 # Replace with the actor number or identifier\n", - "actor_path = (\n", - " inputs_path\n", - " + \"/example_02a/learned_strategies/base/last_policies/actors/actor_pp_6.pt\"\n", - ") # Path to the trained actor model\n", - "\n", "# Load the trained model parameters\n", "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", "model.load_state_dict(model_state[\"actor\"])" @@ -1982,7 +2905,7 @@ }, { "cell_type": "markdown", - "id": "d4a63712", + "id": "fe43c9c6", "metadata": { "id": "d4a63712" }, @@ -1993,7 +2916,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e6460cfb", + "id": "4ee77fed", "metadata": { "id": "e6460cfb" }, @@ -2010,7 +2933,7 @@ { "cell_type": "code", "execution_count": null, - "id": "be91cd97", + "id": "9cf0d9b7", "metadata": { "id": "be91cd97", "lines_to_next_cell": 2 @@ -2026,9 +2949,10 @@ { "cell_type": "code", "execution_count": null, - "id": "b21c93c7", + "id": "f86348b7", "metadata": { - "id": "b21c93c7" + "id": "b21c93c7", + "lines_to_next_cell": 2 }, "outputs": [], "source": [ @@ -2044,7 +2968,7 @@ }, { "cell_type": "markdown", - "id": "ddd1ab1e", + "id": "633c5100", "metadata": { "id": "ddd1ab1e" }, @@ -2054,9 +2978,10 @@ }, { "cell_type": "markdown", - "id": "ae7b108b", + "id": "c278a8c6", "metadata": { - "id": "ae7b108b" + "id": "ae7b108b", + "lines_to_next_cell": 2 }, "source": [ "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." @@ -2065,10 +2990,9 @@ { "cell_type": "code", "execution_count": null, - "id": "6d9be211", + "id": "77b37ebc", "metadata": { - "id": "6d9be211", - "lines_to_next_cell": 1 + "id": "6d9be211" }, "outputs": [], "source": [ @@ -2083,7 +3007,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f0f7857b", + "id": "ce3b3daa", "metadata": { "id": "f0f7857b" }, @@ -2097,7 +3021,7 @@ { "cell_type": "code", "execution_count": null, - "id": "84bb96cf", + "id": "1eac5396", "metadata": { "id": "84bb96cf" }, @@ -2110,7 +3034,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2a7929e4", + "id": "c38544fd", "metadata": { "id": "2a7929e4" }, @@ -2123,7 +3047,7 @@ { "cell_type": "code", "execution_count": null, - "id": "44fa277f", + "id": "60ed8910", "metadata": { "id": "44fa277f" }, @@ -2134,7 +3058,7 @@ }, { "cell_type": "markdown", - "id": "c1f3d550", + "id": "02908679", "metadata": { "id": "c1f3d550" }, @@ -2144,7 +3068,7 @@ }, { "cell_type": "markdown", - "id": "3a0f0cbe", + "id": "dd158aba", "metadata": { "id": "3a0f0cbe" }, @@ -2155,7 +3079,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a6af4983", + "id": "2f5af8e0", "metadata": { "id": "a6af4983" }, @@ -2168,7 +3092,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2e318a5b", + "id": "4020bf97", "metadata": { "id": "2e318a5b" }, @@ -2203,7 +3127,7 @@ }, { "cell_type": "markdown", - "id": "9a888f8b", + "id": "bd527a94", "metadata": { "id": "9a888f8b" }, @@ -2213,7 +3137,7 @@ }, { "cell_type": "markdown", - "id": "c6c4ce8c", + "id": "3244d847", "metadata": { "id": "c6c4ce8c" }, @@ -2224,7 +3148,7 @@ }, { "cell_type": "markdown", - "id": "86545200", + "id": "37dc7965", "metadata": { "id": "86545200" }, @@ -2234,7 +3158,7 @@ }, { "cell_type": "markdown", - "id": "06f3977c", + "id": "294524a4", "metadata": { "id": "06f3977c" }, @@ -2244,7 +3168,7 @@ }, { "cell_type": "markdown", - "id": "dadd0a0c", + "id": "ba535ae6", "metadata": { "id": "dadd0a0c" }, @@ -2254,7 +3178,7 @@ }, { "cell_type": "markdown", - "id": "37633c16", + "id": "1282f757", "metadata": { "id": "37633c16" }, @@ -2264,7 +3188,7 @@ }, { "cell_type": "markdown", - "id": "8735d66f", + "id": "731058ed", "metadata": { "id": "8735d66f" }, @@ -2274,7 +3198,7 @@ }, { "cell_type": "markdown", - "id": "d6b0332f", + "id": "9b8fb14b", "metadata": { "id": "d6b0332f" }, @@ -2287,7 +3211,7 @@ }, { "cell_type": "markdown", - "id": "a8cdea5f", + "id": "3c17d271", "metadata": { "id": "a8cdea5f" }, @@ -2297,11 +3221,13 @@ } ], "metadata": { - "colab": { - "provenance": [] + "jupytext": { + "cell_metadata_filter": "vscode,id,outputId,cellView,colab,-all", + "main_language": "python", + "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "assume-framework", "language": "python", "name": "python3" }, From aeda37729e3550e81fcf63ee0875748704b0218b Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 13:35:33 +0200 Subject: [PATCH 21/27] - revert tensor check in uit operator since it is done now in the market during the order book check --- assume/reinforcement_learning/learning_unit_operator.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/assume/reinforcement_learning/learning_unit_operator.py b/assume/reinforcement_learning/learning_unit_operator.py index 2c52d9e1..bdd78bd1 100644 --- a/assume/reinforcement_learning/learning_unit_operator.py +++ b/assume/reinforcement_learning/learning_unit_operator.py @@ -276,11 +276,6 @@ async def formulate_bids( ) for i, order in enumerate(product_bids): order["agent_id"] = (self.context.addr, self.context.aid) - # check for tensors and convert to numpy - if isinstance(order["volume"], th.Tensor): - order["volume"] = np.float64(order["volume"].cpu().numpy()) - if isinstance(order["price"], th.Tensor): - order["price"] = np.float64(order["price"].cpu().numpy()) if market.volume_tick: order["volume"] = round(order["volume"] / market.volume_tick) From 2346d43efefd5c95f37c5c99b5527fe337ca1e3b Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 14:13:36 +0200 Subject: [PATCH 22/27] - add config explanations and further clarification --- .../notebooks/09_example_Sim_and_xRL.ipynb | 680 +++++++++++------- 1 file changed, 413 insertions(+), 267 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index baaac466..4ff4493f 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -105,7 +105,7 @@ "id": "0d59bb0a" }, "source": [ - "### 1.1 Multi-Agent Deep Reinforcement Learning \n", + "### 1.1 Run an the simulation MADRL Simulation \n", "\n", "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", "\n", @@ -131,15 +131,14 @@ "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", "\n", - "\n", - "#### Run an the simulation MADRL Simulation\n", + "#### 1.1.1 Install Assume and needed Packages\n", "\n", "Similar to the other tutorial, we can run Assume in the following way." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 19, "id": "02dea28f", "metadata": { "colab": { @@ -161,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 20, "id": "544d38a4", "metadata": { "colab": { @@ -181,21 +180,16 @@ ] }, { - "cell_type": "code", - "execution_count": 3, - "id": "973fc56c", - "metadata": { - "id": "75cd480a", - "lines_to_next_cell": 0 - }, - "outputs": [], + "cell_type": "markdown", + "id": "5ca60145", + "metadata": {}, "source": [ - "# Function to display DataFrame in Jupyter" + "Define paths to use depending on colab or local usage." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 22, "id": "a578e164", "metadata": { "colab": { @@ -242,14 +236,14 @@ "id": "636ea9ae" }, "source": [ - "**Load the created example files from the tutorial before:**\n", + "#### 1.1.2 Create and Load example files from market splitting tutorial\n", "\n", - "we need to get the results form the market zone splitting tutorial, for which we are defining the RL Agent here. If you are working in colab execute the follwoing cells. If you are not working in colab but on your local machine simply open the respective tuttorial notebook and let it run." + "We need to get the results form the market zone splitting tutorial, for which we are defining the RL Agent here. If you are working in colab execute the follwoing cells. If you are not working in colab but on your local machine simply open the respective tuttorial notebook and let it run." ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 23, "id": "116b9e37", "metadata": { "vscode": { @@ -272,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 24, "id": "9871a2a5", "metadata": { "colab": { @@ -298,16 +292,7 @@ "\n", "\n", "# Read the DataFrames from CSV files\n", - "buses = pd.read_csv(os.path.join(input_dir, \"buses.csv\"))\n", - "lines = pd.read_csv(os.path.join(input_dir, \"lines.csv\"))\n", "powerplant_units = pd.read_csv(os.path.join(input_dir, \"powerplant_units.csv\"))\n", - "demand_units = pd.read_csv(os.path.join(input_dir, \"demand_units.csv\"))\n", - "demand_df = pd.read_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", - "\n", - "\n", - "# Read the fuel prices DataFrame from CSV file\n", - "fuel_prices_df = pd.read_csv(os.path.join(input_dir, \"fuel_prices_df.csv\"))\n", - "\n", "\n", "print(\"Input CSV files have been read from 'inputs/tutorial_08'.\")" ] @@ -319,14 +304,18 @@ "id": "6985289b" }, "source": [ - "**Let's make this a leanring example**\n", + "#### 1.1.3 Let's make this a leanring example\n", + "\n", + "The next cells show how we can transform any configured example in Assume into a learning example.\n", + "\n", + "**Define a learning power plan**\n", "\n", "We place a learning nuclear power plant in the south zone that has a 5 times hihger maximal power, to generate a scenario where it has a price impact." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "id": "ac7d12fb", "metadata": { "colab": { @@ -696,7 +685,7 @@ "Unit 20 0.0 0.3 24 south Operator-RL " ] }, - "execution_count": 7, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -715,12 +704,12 @@ "# make name column to index\n", "powerplant_units.set_index(\"name\", inplace=True, drop=True)\n", "\n", - "powerplant_units" + "powerplant_units.tail(10)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 26, "id": "9316ac03", "metadata": { "id": "QBTGrw62_5I7" @@ -738,12 +727,58 @@ "id": "cce0e8b4" }, "source": [ - "Change the yaml to configure the learning specific hyperparameters. For a deep dive explanation of the different parameters we refer again to the solely RL tutorial. **TODO** Erklärung einfügen" + "**Configure Learning Hyperparameters in YAML**\n", + "\n", + "Change the yaml to configure the learning specific hyperparameters. In the following we provide a brief description of the hyper parameters:\n", + "\n", + "- **continue_learning** (`False`): \n", + " - Indicates whether the agent should continue training from a previously saved state or start fresh.\n", + "\n", + "- **max_bid_price** (`100`): \n", + " - The maximum bid price allowed during the agent's interaction with the environment, which is used to scale the output of the actor.\n", + "\n", + "- **algorithm** (`\"matd3\"`): \n", + " - The type of reinforcement learning algorithm used. In this case, `MATD3`, which stands for Multi-Agent Twin Delayed Deep Deterministic Policy Gradient.\n", + "\n", + "- **learning_rate** (`0.001`): \n", + " - The learning rate for the algorithm's optimizer. This determines how big the steps of the models parameter update are during training.\n", + "\n", + "- **training_episodes** (`50`): \n", + " - The total number of training episodes the agent will go through.\n", + "\n", + "- **episodes_collecting_initial_experience** (`3`): \n", + " - The number of episodes dedicated to collecting initial experience before training begins. During this period the agent follows a random policy around some base value.\n", + "\n", + "- **train_freq** (`\"24h\"`): \n", + " - The frequency at which the model is trained. In this case, training occurs every 24 hours.\n", + "\n", + "- **gradient_steps** (`-1`): \n", + " - The number of gradient steps to be taken at each training interval. A value of `-1` typically means to perform as many gradient steps as the number of collected experience samples allows.\n", + "\n", + "- **batch_size** (`256`): \n", + " - The number of samples in each mini-batch used for training the model.\n", + "\n", + "- **gamma** (`0.99`): \n", + " - The discount factor for future rewards, representing how much importance the agent places on long-term rewards versus immediate rewards.\n", + "\n", + "- **device** (`\"cpu\"`): \n", + " - The computational device used for training. In this case, training is performed on a CPU.\n", + "\n", + "- **noise_sigma** (`0.1`): \n", + " - The standard deviation (sigma) of the noise added to the actions for exploration purposes.\n", + "\n", + "- **noise_scale** (`1`): \n", + "- **noise_dt** (`1`): \n", + " - A scaling factor applied to the noise added for exploration, influencing the amount of exploration.\n", + " - it is used for decay, but since both values are 1 no decay is applied. \n", + "\n", + "- **validation_episodes_interval** (`5`): \n", + " - The interval at which the model is validated during training, i.e., validation occurs every 5 episodes.\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 27, "id": "03cc1a12", "metadata": { "colab": { @@ -828,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 28, "id": "823db62a", "metadata": { "cellView": "form", @@ -1046,12 +1081,12 @@ "source": [ "**Run the example case**\n", "\n", - "Now we run the example case similar to before in the market zone tutorial." + "Now we run the example case similar to before in the market zone tutorial. the only difference is that we call the run_learning function, whcih itterates multiple times over the simulation horizon. " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 29, "id": "9092a097", "metadata": { "colab": { @@ -1095,8 +1130,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801it [00:03, 21334.78it/s]\n", - "Training Episodes: 10%|█ | 1/10 [00:03<00:35, 3.94s/it]" + "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801it [00:07, 11523.88it/s]\n", + "Training Episodes: 10%|█ | 1/10 [00:07<01:06, 7.34s/it]" ] }, { @@ -1114,8 +1149,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801it [00:03, 22022.34it/s]\n", - "Training Episodes: 20%|██ | 2/10 [00:07<00:31, 3.88s/it]" + "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801it [00:06, 13007.33it/s]\n", + "Training Episodes: 20%|██ | 2/10 [00:13<00:54, 6.85s/it]" ] }, { @@ -1133,8 +1168,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801it [00:03, 22761.24it/s]\n", - "Training Episodes: 30%|███ | 3/10 [00:11<00:26, 3.80s/it]" + "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801it [00:05, 16083.00it/s]\n", + "Training Episodes: 30%|███ | 3/10 [00:19<00:42, 6.13s/it]" ] }, { @@ -1152,8 +1187,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801it [00:03, 22272.16it/s]\n", - "Training Episodes: 40%|████ | 4/10 [00:15<00:22, 3.80s/it]" + "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801it [00:04, 16996.09it/s]\n", + "Training Episodes: 40%|████ | 4/10 [00:24<00:34, 5.67s/it]" ] }, { @@ -1171,8 +1206,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_5 2019-01-01 23:00:00: : 82801it [00:03, 22871.70it/s]\n", - "Training Episodes: 50%|█████ | 5/10 [00:18<00:18, 3.76s/it]" + "tutorial_08_zonal_case_5 2019-01-01 23:00:00: : 82801it [00:04, 16731.95it/s]\n", + "Training Episodes: 50%|█████ | 5/10 [00:29<00:27, 5.46s/it]" ] }, { @@ -1190,8 +1225,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801it [00:04, 20158.42it/s]\n", - "Training Episodes: 60%|██████ | 6/10 [00:23<00:15, 3.90s/it]" + "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801it [00:05, 14364.50it/s]\n", + "Training Episodes: 60%|██████ | 6/10 [00:35<00:22, 5.60s/it]" ] }, { @@ -1209,8 +1244,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801it [00:04, 19840.53it/s]\n", - "Training Episodes: 70%|███████ | 7/10 [00:27<00:12, 4.02s/it]" + "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801it [00:05, 15810.60it/s]\n", + "Training Episodes: 70%|███████ | 7/10 [00:40<00:16, 5.53s/it]" ] }, { @@ -1228,8 +1263,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_8 2019-01-01 23:00:00: : 82801it [00:04, 19951.23it/s]\n", - "Training Episodes: 80%|████████ | 8/10 [00:31<00:08, 4.09s/it]" + "tutorial_08_zonal_case_8 2019-01-01 23:00:00: : 82801it [00:04, 16575.24it/s]\n", + "Training Episodes: 80%|████████ | 8/10 [00:45<00:10, 5.39s/it]" ] }, { @@ -1247,8 +1282,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_9 2019-01-01 23:00:00: : 82801it [00:03, 21970.90it/s]\n", - "Training Episodes: 90%|█████████ | 9/10 [00:35<00:04, 4.01s/it]" + "tutorial_08_zonal_case_9 2019-01-01 23:00:00: : 82801it [00:05, 16490.33it/s]\n", + "Training Episodes: 90%|█████████ | 9/10 [00:50<00:05, 5.32s/it]" ] }, { @@ -1266,45 +1301,50 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_10 2019-01-01 23:00:00: : 82801it [00:03, 21372.28it/s]" + "tutorial_08_zonal_case_10 2019-01-01 23:00:00: : 82801it [00:05, 15885.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + "tutorial_08_zonal_case_eval_1 2019-01-01 23:00:00: : 82801it [00:05, 16500.24it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 1, metric='avg_reward', value=4437.80\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_1 2019-01-01 23:00:00: : 82801it [00:03, 21520.87it/s]" + "Training Episodes: 100%|██████████| 10/10 [01:23<00:00, 8.38s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 1, metric='avg_reward', value=4437.80\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { @@ -1312,51 +1352,40 @@ "output_type": "stream", "text": [ "\n", - "Training Episodes: 100%|██████████| 10/10 [01:02<00:00, 6.27s/it]" + " 0%| | 0/82800 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1370,8 +1399,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1384,7 +1413,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:05, 15043.53it/s]" + "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:04, 17411.89it/s]" ] }, { @@ -1395,15 +1424,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzodwb_u6.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmphiz0tfsu.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6wwf6xyg.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxs_dl8jj.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_z8zood_.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1417,27 +1446,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 01:00:00: 9%|▊ | 7201/82800 [00:00<00:03, 18953.42it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpwjttcr5q.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3dv4zbp4.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5z5lpxxw.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcwl0x8cr.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpodtn80a_.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1451,8 +1493,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1465,7 +1507,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:02, 28219.50it/s]" + "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:03, 23493.66it/s]" ] }, { @@ -1476,15 +1518,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9q5q22kw.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohfetpqf.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc1bc7p68.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcog2f74o.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuam41zek.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1498,27 +1540,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 03:00:00: 17%|█▋ | 14401/82800 [00:00<00:02, 26294.36it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcvn6kp04.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5c23xr6s.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpm93ulvjn.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8j_p18wt.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp70khcqmt.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1532,8 +1587,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1546,7 +1601,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 04:00:00: 22%|██▏ | 18001/82800 [00:00<00:01, 32720.76it/s]" + "tutorial_08_zonal_case 2019-01-01 04:00:00: 22%|██▏ | 18001/82800 [00:00<00:02, 25111.33it/s]" ] }, { @@ -1557,15 +1612,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpisxw9ix8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpga9w7msy.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthu0lqjt.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzl9g5igo.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5i5tz7lx.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1579,27 +1634,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 05:00:00: 26%|██▌ | 21601/82800 [00:00<00:02, 27019.03it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppen1aa2y.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3rqljc6n.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmprnbu0mo4.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpnlmjovxb.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppw2vajtv.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1613,8 +1681,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1627,7 +1695,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:00<00:01, 34611.39it/s]" + "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:01<00:02, 28085.52it/s]" ] }, { @@ -1638,15 +1706,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpscthkcqt.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp3jg09edi.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpj89p1lfm.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpi6696nvu.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpx0i9nr4g.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1660,27 +1728,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 07:00:00: 35%|███▍ | 28801/82800 [00:01<00:01, 27564.75it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6uk22o84.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1f6_ql3l.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp88uo7gl9.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0n_1tbx7.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_fhn46hq.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1694,8 +1775,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1708,7 +1789,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:00<00:01, 35574.53it/s]" + "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:01<00:01, 27640.87it/s]" ] }, { @@ -1719,15 +1800,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpq15d8y1v.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpb7_9oq3e.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxtjtkez5.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvddxwz19.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl5uky8em.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1741,27 +1822,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 09:00:00: 43%|████▎ | 36001/82800 [00:01<00:01, 27693.43it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4kvnq040.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgb6uksuj.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjixxyriz.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppyb9hsrq.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpprjsr9rz.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1775,8 +1869,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1789,7 +1883,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 37188.77it/s]" + "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 27827.46it/s]" ] }, { @@ -1800,15 +1894,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_wfe8xgm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpwezb1qq8.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_sdlf6mq.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpesoeqw3j.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4nnc2imb.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1822,27 +1916,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 11:00:00: 52%|█████▏ | 43201/82800 [00:01<00:01, 28298.97it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0a9fbozj.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjk44ceob.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuy11rdv7.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpw8r2_7lp.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpyz5krcz_.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1856,8 +1963,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1870,7 +1977,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:01<00:01, 35811.70it/s]" + "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:01<00:01, 28852.64it/s]" ] }, { @@ -1881,15 +1988,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpiqujlpl7.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmid8tbjp.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpz5h9o2t6.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0zjthvfm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5jy2gw_v.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1903,27 +2010,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 13:00:00: 61%|██████ | 50401/82800 [00:01<00:01, 28930.58it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp31ujp0qm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbmvl8_91.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp883g3uge.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpe4qawphc.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4gg0rke2.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1937,8 +2057,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1951,7 +2071,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:01<00:00, 32012.85it/s]" + "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:02<00:00, 29114.14it/s]" ] }, { @@ -1962,15 +2082,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptpjdcbtd.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpv6i_y9hq.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp2buno1ex.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc7c_l2s1.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjbk4fuau.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -1984,8 +2104,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -1998,7 +2118,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:01<00:00, 32390.30it/s]" + "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:02<00:00, 28945.96it/s]" ] }, { @@ -2009,15 +2129,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp29cgs_0f.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg662dy1b.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp7y5zb_vx.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmkesy780.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpv2_uz8yt.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2031,8 +2151,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -2045,7 +2165,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:01<00:00, 32580.89it/s]" + "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:02<00:00, 28717.01it/s]" ] }, { @@ -2056,15 +2176,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgkj26dx8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp885496ex.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp80xs7oil.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8rznhtvc.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmc89_zkz.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2078,27 +2198,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 17:00:00: 78%|███████▊ | 64801/82800 [00:02<00:00, 27699.07it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvvyzn7z9.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6m_69yna.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg756ah2x.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg9y0nlzo.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6uzjy52q.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2112,8 +2245,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -2126,7 +2259,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 34332.05it/s]" + "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 25430.62it/s]" ] }, { @@ -2137,15 +2270,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpocvop3u1.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9ojfuaoc.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp338vf917.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmplkij6hnz.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1lj4pmbk.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2159,27 +2292,40 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n", + "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case 2019-01-01 19:00:00: 87%|████████▋ | 72001/82800 [00:02<00:00, 26133.69it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpthb_s5k8.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8sa7td7n.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp52zz_tor.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4hxle4nq.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvtv21un6.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2193,8 +2339,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -2207,7 +2353,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:02<00:00, 34505.65it/s]" + "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:02<00:00, 26373.97it/s]" ] }, { @@ -2218,15 +2364,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp53yqi16u.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkc57k0tt.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9hwnjfn4.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmph55l26tw.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptdnf49x_.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2240,8 +2386,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -2254,7 +2400,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:02<00:00, 34451.35it/s]" + "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:02<00:00, 26761.79it/s]" ] }, { @@ -2265,15 +2411,15 @@ "ERROR:pyomo.opt:Solver log:\n", "GLPSOL--GLPK LP/MIP Solver 5.0\n", "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8mgt8nwl.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_t4nf7ek.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuagvdnmp.pyomo.lp:27: constraints section missing\n", + " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp3a_9tkp.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptc_xsyyn.glpk.glp\n", + " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp\n", + "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp'...\n", + "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp:27: constraints section missing\n", "CPLEX LP file processing error\n", "\n", "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", + "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", "Traceback (most recent call last):\n", " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", " result = coro.send(None)\n", @@ -2287,8 +2433,8 @@ " ) = self.clear(self.all_orders, market_products)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " instance, results = market_clearing_opt(\n", - " ^^^^^^^^^^^^^^^^^^^^\n", + " while True:\n", + " \n", " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", " results = solver.solve(instance, options=options)\n", " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", @@ -2301,7 +2447,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:02, 33917.07it/s] \n" + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:03, 26947.63it/s] \n" ] } ], @@ -2362,14 +2508,14 @@ "id": "2194f71b" }, "source": [ - "**Generate same plotly figure**\n", + "**Compare the results**\n", "\n", - "We use the same code described in the amarket zone tutprial to generate the plotly curve displaying the market price." + "We use the same code described in the market zone tutorial to generate the plotly curve displaying the market price." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 30, "id": "96bfa2ee", "metadata": { "id": "bdb21cbe" @@ -2382,7 +2528,7 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[12], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m market_meta_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarket_meta.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Load the market_meta.csv file\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarket_meta_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# drop the first column\u001b[39;00m\n\u001b[0;32m 11\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m market_meta\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39mmarket_meta\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m0\u001b[39m])\n", + "Cell \u001b[1;32mIn[30], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m market_meta_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarket_meta.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Load the market_meta.csv file\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarket_meta_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# drop the first column\u001b[39;00m\n\u001b[0;32m 11\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m market_meta\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39mmarket_meta\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m0\u001b[39m])\n", "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", From 49ff583b563ca252393a8b2ba2a36c6653048bd2 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 14:47:20 +0200 Subject: [PATCH 23/27] - fix buffer writing --- .../notebooks/09_example_Sim_and_xRL.ipynb | 4039 ++++++++++++----- 1 file changed, 2926 insertions(+), 1113 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 4ff4493f..ee71534a 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "02dea28f", "metadata": { "colab": { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "id": "544d38a4", "metadata": { "colab": { @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "id": "a578e164", "metadata": { "colab": { @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "id": "116b9e37", "metadata": { "vscode": { @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 12, "id": "9871a2a5", "metadata": { "colab": { @@ -293,6 +293,7 @@ "\n", "# Read the DataFrames from CSV files\n", "powerplant_units = pd.read_csv(os.path.join(input_dir, \"powerplant_units.csv\"))\n", + "demand_df = pd.read_csv(os.path.join(input_dir, \"demand_df.csv\"))\n", "\n", "print(\"Input CSV files have been read from 'inputs/tutorial_08'.\")" ] @@ -315,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 7, "id": "ac7d12fb", "metadata": { "colab": { @@ -374,136 +375,6 @@ " \n", " \n", " \n", - " Unit 1\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 5\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 2\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 6\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 3\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 7\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 4\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 8\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 5\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 9\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 6\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 10\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 7\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 11\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 8\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 12\n", - " north_1\n", - " Operator North\n", - " \n", - " \n", - " Unit 9\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 13\n", - " north_2\n", - " Operator North\n", - " \n", - " \n", - " Unit 10\n", - " nuclear\n", - " naive_eom\n", - " uranium\n", - " 0.0\n", - " 1000.0\n", - " 0.0\n", - " 0.3\n", - " 14\n", - " north_2\n", - " Operator North\n", - " \n", - " \n", " Unit 11\n", " nuclear\n", " naive_eom\n", @@ -640,16 +511,6 @@ "text/plain": [ " technology bidding_zonal fuel_type emission_factor max_power \\\n", "name \n", - "Unit 1 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 2 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 3 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 4 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 5 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 6 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 7 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 8 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 9 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 10 nuclear naive_eom uranium 0.0 1000.0 \n", "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", @@ -663,16 +524,6 @@ "\n", " min_power efficiency additional_cost node unit_operator \n", "name \n", - "Unit 1 0.0 0.3 5 north_1 Operator North \n", - "Unit 2 0.0 0.3 6 north_1 Operator North \n", - "Unit 3 0.0 0.3 7 north_1 Operator North \n", - "Unit 4 0.0 0.3 8 north_1 Operator North \n", - "Unit 5 0.0 0.3 9 north_1 Operator North \n", - "Unit 6 0.0 0.3 10 north_1 Operator North \n", - "Unit 7 0.0 0.3 11 north_1 Operator North \n", - "Unit 8 0.0 0.3 12 north_1 Operator North \n", - "Unit 9 0.0 0.3 13 north_2 Operator North \n", - "Unit 10 0.0 0.3 14 north_2 Operator North \n", "Unit 11 0.0 0.3 15 north_2 Operator North \n", "Unit 12 0.0 0.3 16 north_2 Operator North \n", "Unit 13 0.0 0.3 17 north_2 Operator North \n", @@ -685,7 +536,7 @@ "Unit 20 0.0 0.3 24 south Operator-RL " ] }, - "execution_count": 25, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -704,21 +555,21 @@ "# make name column to index\n", "powerplant_units.set_index(\"name\", inplace=True, drop=True)\n", "\n", + "# store power plant units to csv again\n", + "powerplant_units.to_csv(input_dir + \"/powerplant_units.csv\")\n", + "\n", "powerplant_units.tail(10)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "id": "9316ac03", "metadata": { "id": "QBTGrw62_5I7" }, "outputs": [], - "source": [ - "# store power plant units to csv again\n", - "powerplant_units.to_csv(input_dir + \"/powerplant_units.csv\")" - ] + "source": [] }, { "cell_type": "markdown", @@ -778,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "id": "03cc1a12", "metadata": { "colab": { @@ -802,7 +653,7 @@ " \"start_date\": \"2019-01-01 00:00\",\n", " \"end_date\": \"2019-01-01 23:00\",\n", " \"time_step\": \"1h\",\n", - " \"save_frequency_hours\": 24,\n", + " \"save_frequency_hours\": 4,\n", " \"learning_mode\": \"True\",\n", " \"markets_config\": {\n", " \"zonal\": {\n", @@ -828,7 +679,7 @@ " \"learning_rate\": 0.001,\n", " \"training_episodes\": 10,\n", " \"episodes_collecting_initial_experience\": 3,\n", - " \"train_freq\": \"24h\",\n", + " \"train_freq\": \"4h\",\n", " \"gradient_steps\": -1,\n", " \"batch_size\": 256,\n", " \"gamma\": 0.99,\n", @@ -836,7 +687,7 @@ " \"noise_sigma\": 0.1,\n", " \"noise_scale\": 1,\n", " \"noise_dt\": 1,\n", - " \"validation_episodes_interval\": 5,\n", + " \"validation_episodes_interval\": 1,\n", " },\n", " }\n", "}\n", @@ -863,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 10, "id": "823db62a", "metadata": { "cellView": "form", @@ -1039,6 +890,8 @@ "\n", " eval_episode += 1\n", "\n", + " \n", + "\n", " world.reset()\n", "\n", " # if at end of simulation save last policies\n", @@ -1086,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "id": "9092a097", "metadata": { "colab": { @@ -1130,109 +983,252 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801it [00:07, 11523.88it/s]\n", - "Training Episodes: 10%|█ | 1/10 [00:07<01:06, 7.34s/it]" + "Training Episodes: 0%| | 0/10 [00:00 exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.72 0.40666667]]\n", + "\n", + " [[2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.76 0.40666667]]\n", + "\n", + " [[2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.8 0.40666667]]\n", + "\n", + " [[2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.84 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 00:00:00: 4%|▍ | 3601/82800 [00:00<00:04, 17411.89it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpxs_dl8jj.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_z8zood_.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp0v8gtp3.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.88 0.40666667]]\n", + "\n", + " [[2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.92 0.40666667]]\n", + "\n", + " [[2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.96 0.40666667]]\n", + "\n", + " [[2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 01:00:00: 9%|▊ | 7201/82800 [00:00<00:03, 18953.42it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcwl0x8cr.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpodtn80a_.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpohqackq8.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 02:00:00: 13%|█▎ | 10801/82800 [00:00<00:03, 23493.66it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpcog2f74o.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpuam41zek.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl9efbzv9.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 03:00:00: 17%|█▋ | 14401/82800 [00:00<00:02, 26294.36it/s]" + "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:05, 16034.77it/s]\n", + "Training Episodes: 30%|███ | 3/10 [00:27<01:02, 8.97s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8j_p18wt.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp70khcqmt.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzkdmj_es.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "[[[2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.0, 0.40666666626930237]], [[2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 1.0, 0.40666666626930237]], [[2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.7200000286102295, 0.40666666626930237]], [[2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.7599999904632568, 0.40666666626930237]], [[2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.800000011920929, 0.40666666626930237]], [[2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.8399999737739563, 0.40666666626930237]], [[2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.8799999952316284, 0.40666666626930237]], [[2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 1.0, 0.40666666626930237]], [[2.4000000953674316, 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[00:00<00:02, 25111.33it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpzl9g5igo.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5i5tz7lx.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9qsd6yb3.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0. 0.40666667]]\n", + "\n", + " [[2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.68 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 05:00:00: 26%|██▌ | 21601/82800 [00:00<00:02, 27019.03it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpnlmjovxb.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppw2vajtv.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpsbd_7huf.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.72 0.40666667]]\n", + "\n", + " [[2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.76 0.40666667]]\n", + "\n", + " [[2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.8 0.40666667]]\n", + "\n", + " [[2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 06:00:00: 30%|███ | 25201/82800 [00:01<00:02, 28085.52it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpi6696nvu.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpx0i9nr4g.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpalzdeplr.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.92 0.40666667]]\n", + "\n", + " [[2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.96 0.40666667]]\n", + "\n", + " [[2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 07:00:00: 35%|███▍ | 28801/82800 [00:01<00:01, 27564.75it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0n_1tbx7.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_fhn46hq.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1mirpjz_.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", - "text": [ - "tutorial_08_zonal_case 2019-01-01 08:00:00: 39%|███▉ | 32401/82800 [00:01<00:01, 27640.87it/s]" - ] + "text": [] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvddxwz19.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpl5uky8em.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpib349hvz.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 09:00:00: 43%|████▎ | 36001/82800 [00:01<00:01, 27693.43it/s]" + "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:04, 16637.54it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppyb9hsrq.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpprjsr9rz.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9f2xi9b7.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" - ] - }, - { + "[[[2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 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2019-01-01 23:00:00: : 82801.0it [00:05, 15744.05it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 2, metric='avg_reward', value=4460.81\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Training Episodes: 50%|█████ | 5/10 [00:56<01:02, 12.51s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " We are adding stuff to the buffer\n", + "[[[2.24 2.26 2.28 2.3 2.32 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"\n", + " [[2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" + ] + }, + { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 10:00:00: 48%|████▊ | 39601/82800 [00:01<00:01, 27827.46it/s]" + "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801.0it [00:06, 12378.33it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpesoeqw3j.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4nnc2imb.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptzq87vcd.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "[[[2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.0, 0.40666666626930237]], [[2.259999990463257, 2.2799999713897705, 2.299999952316284, 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Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpw8r2_7lp.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpyz5krcz_.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp9eukynh5.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 3, metric='avg_reward', value=4470.85\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 12:00:00: 57%|█████▋ | 46801/82800 [00:01<00:01, 28852.64it/s]" + "Training Episodes: 60%|██████ | 6/10 [01:11<00:53, 13.32s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp0zjthvfm.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp5jy2gw_v.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpfei33xt1.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 13:00:00: 61%|██████ | 50401/82800 [00:01<00:01, 28930.58it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpe4qawphc.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4gg0rke2.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpgd0dzwgt.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0. 0.40666667]]\n", + "\n", + " [[2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.68 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 14:00:00: 65%|██████▌ | 54001/82800 [00:02<00:00, 29114.14it/s]" + "tutorial_08_zonal_case_7 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:01<00:07, 9945.75it/s]\u001b[A" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpc7c_l2s1.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpjbk4fuau.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp_ifyw8wu.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.72 0.40666667]]\n", + "\n", + " [[2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.76 0.40666667]]\n", + "\n", + " [[2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.8 0.40666667]]\n", + "\n", + " [[2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.84 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 15:00:00: 70%|██████▉ | 57601/82800 [00:02<00:00, 28945.96it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmkesy780.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpv2_uz8yt.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpssw9dxd7.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.88 0.40666667]]\n", + "\n", + " [[2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.92 0.40666667]]\n", + "\n", + " [[2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.96 0.40666667]]\n", + "\n", + " [[2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 16:00:00: 74%|███████▍ | 61201/82800 [00:02<00:00, 28717.01it/s]" + "tutorial_08_zonal_case_7 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:03<00:04, 9982.14it/s] \u001b[A" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp8rznhtvc.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpmc89_zkz.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmppbs52yjm.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 17:00:00: 78%|███████▊ | 64801/82800 [00:02<00:00, 27699.07it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpg9y0nlzo.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp6uzjy52q.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpkltjr_3b.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 18:00:00: 83%|████████▎ | 68401/82800 [00:02<00:00, 25430.62it/s]" + "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801.0it [00:07, 10727.90it/s]0:06<00:01, 9494.97it/s] \u001b[A\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmplkij6hnz.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp1lj4pmbk.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp25gr6tjt.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "[[[2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.0, 0.40666666626930237]], [[2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 1.0, 0.40666666626930237]], [[2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.7200000286102295, 0.40666666626930237]], [[2.299999952316284, 2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.7599999904632568, 0.40666666626930237]], [[2.319999933242798, 2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.800000011920929, 0.40666666626930237]], [[2.3399999141693115, 2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.40666666626930237, 0.8399999737739563, 0.40666666626930237]], [[2.359999895095825, 2.380000114440918, 2.4000000953674316, 2.4200000762939453, 2.440000057220459, 2.4600000381469727, 2.4800000190734863, 2.5, 2.5199999809265137, 2.5399999618530273, 2.559999942779541, 2.5799999237060547, 2.5999999046325684, 2.619999885559082, 2.640000104904175, 2.6600000858306885, 2.680000066757202, 2.2200000286102295, 2.240000009536743, 2.259999990463257, 2.2799999713897705, 2.299999952316284, 2.319999933242798, 2.3399999141693115, 0.40666666626930237, 0.40666666626930237, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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"INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 19:00:00: 87%|████████▋ | 72001/82800 [00:02<00:00, 26133.69it/s]" + "tutorial_08_zonal_case_eval_4 2019-01-01 23:00:00: : 82801.0it [00:05, 15931.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp4hxle4nq.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpvtv21un6.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpbdzops3l.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 4, metric='avg_reward', value=4473.54\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 20:00:00: 91%|█████████▏| 75601/82800 [00:02<00:00, 26373.97it/s]" + "\n", + "Training Episodes: 70%|███████ | 7/10 [01:28<00:43, 14.35s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmph55l26tw.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptdnf49x_.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmp84wgsiao.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 21:00:00: 96%|█████████▌| 79201/82800 [00:02<00:00, 26761.79it/s]" + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "ERROR:pyomo.opt:Solver (glpk) returned non-zero return code (1)\n", - "ERROR:pyomo.opt:Solver log:\n", - "GLPSOL--GLPK LP/MIP Solver 5.0\n", - "Parameter(s) specified in the command line:\n", - " --write C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmpp3a_9tkp.glpk.raw --wglp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptc_xsyyn.glpk.glp\n", - " --cpxlp C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp\n", - "Reading problem data from 'C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp'...\n", - "C:\\Users\\tg3533\\AppData\\Local\\Temp\\tmptngumm57.pyomo.lp:27: constraints section missing\n", - "CPLEX LP file processing error\n", - "\n", - "ERROR:assume.markets.base_market:clearing failed: Solver (glpk) did not exit normally\n", - "ERROR:asyncio:Task exception was never retrieved\n", - "future: exception=ApplicationError('Solver (glpk) did not exit normally')>\n", - "Traceback (most recent call last):\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py\", line 277, in __step\n", - " result = coro.send(None)\n", - " ^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\scheduling.py\", line 194, in run\n", - " return await self._coro\n", - " ^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 629, in clear_market\n", - " raise e\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\base_market.py\", line 626, in clear_market\n", - " ) = self.clear(self.all_orders, market_products)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 449, in clear\n", - " while True:\n", - " \n", - " File \"C:\\Users\\tg3533\\Documents\\Code\\assume\\assume\\markets\\clearing_algorithms\\complex_clearing.py\", line 252, in market_clearing_opt\n", - " results = solver.solve(instance, options=options)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"c:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pyomo\\opt\\base\\solvers.py\", line 628, in solve\n", - " raise ApplicationError(\"Solver (%s) did not exit normally\" % self.name)\n", - "pyomo.common.errors.ApplicationError: Solver (glpk) did not exit normally\n" + " We are adding stuff to the buffer\n", + "[[[2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0. 0.40666667]]\n", + "\n", + " [[2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.68 0.40666667]]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801it [00:03, 26947.63it/s] \n" + "tutorial_08_zonal_case_8 2019-01-01 02:00:00: 13%|█▎ | 10801.0/82800 [00:01<00:07, 9580.65it/s]\u001b[A\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " We are adding stuff to the buffer\n", + "[[[2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.72 0.40666667]]\n", + "\n", + " [[2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.76 0.40666667]]\n", + "\n", + " [[2.32 2.34 2.36 2.38 2.4 2.42\n", + " 2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.8 0.40666667]]\n", + "\n", + " [[2.34 2.36 2.38 2.4 2.42 2.44\n", + " 2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.84 0.40666667]]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_8 2019-01-01 06:00:00: 30%|███ | 25201.0/82800 [00:02<00:05, 9924.24it/s] \u001b[A\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " We are adding stuff to the buffer\n", + "[[[2.36 2.38 2.4 2.42 2.44 2.46\n", + " 2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.88 0.40666667]]\n", + "\n", + " [[2.38 2.4 2.42 2.44 2.46 2.48\n", + " 2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.92 0.40666667]]\n", + "\n", + " [[2.4 2.42 2.44 2.46 2.48 2.5\n", + " 2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.96 0.40666667]]\n", + "\n", + " [[2.42 2.44 2.46 2.48 2.5 2.52\n", + " 2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 1. 0.40666667]]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_8 2019-01-01 10:00:00: 48%|████▊ | 39601.0/82800 [00:03<00:04, 10323.32it/s]\u001b[A\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " We are adding stuff to the buffer\n", + "[[[2.44 2.46 2.48 2.5 2.52 2.54\n", + " 2.56 2.58 2.6 2.62 2.64 2.66\n", + " 2.68 2.22 2.24 2.26 2.28 2.3\n", + " 2.32 2.34 2.36 2.38 2.4 2.42\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.46 2.48 2.5 2.52 2.54 2.56\n", + " 2.58 2.6 2.62 2.64 2.66 2.68\n", + " 2.22 2.24 2.26 2.28 2.3 2.32\n", + " 2.34 2.36 2.38 2.4 2.42 2.44\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.48 2.5 2.52 2.54 2.56 2.58\n", + " 2.6 2.62 2.64 2.66 2.68 2.22\n", + " 2.24 2.26 2.28 2.3 2.32 2.34\n", + " 2.36 2.38 2.4 2.42 2.44 2.46\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.5 2.52 2.54 2.56 2.58 2.6\n", + " 2.62 2.64 2.66 2.68 2.22 2.24\n", + " 2.26 2.28 2.3 2.32 2.34 2.36\n", + " 2.38 2.4 2.42 2.44 2.46 2.48\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_8 2019-01-01 14:00:00: 65%|██████▌ | 54001.0/82800 [00:05<00:02, 9956.87it/s] \u001b[A" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " We are adding stuff to the buffer\n", + "[[[2.52 2.54 2.56 2.58 2.6 2.62\n", + " 2.64 2.66 2.68 2.22 2.24 2.26\n", + " 2.28 2.3 2.32 2.34 2.36 2.38\n", + " 2.4 2.42 2.44 2.46 2.48 2.5\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0. 0. 0. 0. 0. 0.\n", + " 1. 0.40666667]]\n", + "\n", + " [[2.54 2.56 2.58 2.6 2.62 2.64\n", + " 2.66 2.68 2.22 2.24 2.26 2.28\n", + " 2.3 2.32 2.34 2.36 2.38 2.4\n", + " 2.42 2.44 2.46 2.48 2.5 2.52\n", + " 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0.40666667 0.40666667 0.40666667 0.40666667\n", + " 0.40666667 0.40666667 0.40666667 0.40666667 0.40666667 0.\n", + " 0. 0. 0. 0. 0. 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"INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Training Episodes: 70%|███████ | 7/10 [01:37<00:41, 13.99s/it]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Time must be > 1546383600.0 but is 1546300799.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[14], line 39\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 39\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtutorial_08\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzonal_case\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;66;03m# Run the simulation\u001b[39;00m\n\u001b[0;32m 47\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", + "Cell \u001b[1;32mIn[10], line 152\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 142\u001b[0m setup_world(\n\u001b[0;32m 143\u001b[0m world\u001b[38;5;241m=\u001b[39mworld,\n\u001b[0;32m 144\u001b[0m scenario_data\u001b[38;5;241m=\u001b[39mscenario_data,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 147\u001b[0m eval_episode\u001b[38;5;241m=\u001b[39meval_episode,\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 150\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 152\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m total_rewards \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39moutput_role\u001b[38;5;241m.\u001b[39mget_sum_reward()\n\u001b[0;32m 155\u001b[0m avg_reward \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(total_rewards)\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", + "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime must be > \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# set time\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time \u001b[38;5;241m=\u001b[39m t\n", + "\u001b[1;31mValueError\u001b[0m: Time must be > 1546383600.0 but is 1546300799." + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_eval_5 2019-01-01 23:00:00: : 1546466401.0it [00:10, 154384521.99it/s]\n" ] } ], @@ -2515,27 +3277,981 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "id": "96bfa2ee", "metadata": { "id": "bdb21cbe" }, "outputs": [ { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'outputs/tutorial_08_zonal_case\\\\market_meta.csv'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[30], line 9\u001b[0m\n\u001b[0;32m 6\u001b[0m market_meta_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarket_meta.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m# Load the market_meta.csv file\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmarket_meta_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparse_dates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# drop the first column\u001b[39;00m\n\u001b[0;32m 11\u001b[0m market_meta \u001b[38;5;241m=\u001b[39m market_meta\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39mmarket_meta\u001b[38;5;241m.\u001b[39mcolumns[\u001b[38;5;241m0\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[0;32m 1013\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m 1014\u001b[0m dialect,\n\u001b[0;32m 1015\u001b[0m delimiter,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1022\u001b[0m dtype_backend\u001b[38;5;241m=\u001b[39mdtype_backend,\n\u001b[0;32m 1023\u001b[0m )\n\u001b[0;32m 1024\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m-> 1026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 617\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m 619\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> 620\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 622\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[0;32m 623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 1617\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 1620\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m 1878\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m 1879\u001b[0m mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 1880\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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"metadata": { "id": "b6ee4f28" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -2843,7 +4567,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "168757a6", "metadata": { "id": "7216677a" @@ -2855,7 +4579,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "2f4d6257", "metadata": { "id": "5a4d6e17" @@ -2884,7 +4608,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "f38c74cb", "metadata": { "id": "44862f06" @@ -2907,9 +4631,11 @@ " input_data = np.array(json_data)\n", " input_data = np.squeeze(input_data)\n", "\n", + " print(len(input_data))\n", " # filter out arrays where all value are 0\n", " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", "\n", + " print(len(input_data))\n", " # filter only first 700 observations\n", " input_data = input_data[:700]\n", "\n", @@ -2938,7 +4664,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "47473ec8", "metadata": { "id": "8fa78b12" @@ -2952,7 +4678,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "76fdf5d0", "metadata": { "id": "8f9189ca" @@ -2988,12 +4714,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "1819ff26", "metadata": { "id": "d522969d" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../inputs\\tutorial_08/learned_strategies/zonal_case/buffer_obs/buffer_obs.json\n", + "500000\n", + "0\n" + ] + }, + { + "data": { + "text/html": [ + "
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price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [price forecast t+1, price forecast t+2, price forecast t+3, price forecast t+4, price forecast t+5, price forecast t+6, price forecast t+7, price forecast t+8, price forecast t+9, price forecast t+10, price forecast t+11, price forecast t+12, price forecast t+13, price forecast t+14, price forecast t+15, price forecast t+16, price forecast t+17, price forecast t+18, price forecast t+19, price forecast t+20, price forecast t+21, price forecast t+22, price forecast t+23, price forecast t+24, residual load forecast t+1, residual load forecast t+2, residual load forecast t+3, residual load forecast t+4, residual load forecast t+5, residual load forecast t+6, residual load forecast t+7, residual load forecast t+8, residual load forecast t+9, residual load forecast t+10, residual load forecast t+11, residual load forecast t+12, residual load forecast t+13, residual load forecast t+14, residual load forecast t+15, residual load forecast t+16, residual load forecast t+17, residual load forecast t+18, residual load forecast t+19, residual load forecast t+20, residual load forecast t+21, residual load forecast t+22, residual load forecast t+23, residual load forecast t+24, total capacity t-1, marginal costs t-1]\n", + "Index: []\n", + "\n", + "[0 rows x 50 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df_obs, input_data = load_observations(path, feature_names)\n", "\n", @@ -3022,7 +4820,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "c27178fe", "metadata": { "id": "4da4de57" @@ -3037,12 +4835,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "35a088d5", "metadata": { "id": "37adecfa" }, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '../inputs\\\\tutorial_08\\\\learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[21], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Load the trained model parameters\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m model_state \u001b[38;5;241m=\u001b[39m \u001b[43mth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactor_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(model_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactor\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:997\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[0;32m 994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pickle_load_args\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m 995\u001b[0m pickle_load_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 997\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_file_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_file:\n\u001b[0;32m 998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[0;32m 999\u001b[0m \u001b[38;5;66;03m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[0;32m 1000\u001b[0m \u001b[38;5;66;03m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[0;32m 1001\u001b[0m \u001b[38;5;66;03m# reset back to the original position.\u001b[39;00m\n\u001b[0;32m 1002\u001b[0m orig_position \u001b[38;5;241m=\u001b[39m opened_file\u001b[38;5;241m.\u001b[39mtell()\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:444\u001b[0m, in \u001b[0;36m_open_file_like\u001b[1;34m(name_or_buffer, mode)\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_file_like\u001b[39m(name_or_buffer, mode):\n\u001b[0;32m 443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_path(name_or_buffer):\n\u001b[1;32m--> 444\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_open_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 445\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m mode:\n", + "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:425\u001b[0m, in \u001b[0;36m_open_file.__init__\u001b[1;34m(self, name, mode)\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, mode):\n\u001b[1;32m--> 425\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../inputs\\\\tutorial_08\\\\learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt'" + ] + } + ], "source": [ "# Load the trained model parameters\n", "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", From 04a67c9952ce490bb9a4c8b0882531287d2755d1 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 17:56:03 +0200 Subject: [PATCH 24/27] - finished debugging xRL Script ith zonal example (wuhu) --- .../notebooks/09_example_Sim_and_xRL.ipynb | 4921 +++++++++-------- 1 file changed, 2563 insertions(+), 2358 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index ee71534a..e9ec75e9 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "02dea28f", "metadata": { "colab": { @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "544d38a4", "metadata": { "colab": { @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "a578e164", "metadata": { "colab": { @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "116b9e37", "metadata": { "vscode": { @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "9871a2a5", "metadata": { "colab": { @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "ac7d12fb", "metadata": { "colab": { @@ -536,7 +536,7 @@ "Unit 20 0.0 0.3 24 south Operator-RL " ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "id": "03cc1a12", "metadata": { "colab": { @@ -714,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "823db62a", "metadata": { "cellView": "form", @@ -939,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "id": "9092a097", "metadata": { "colab": { @@ -958,7 +958,13 @@ "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from ../inputs\n", "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", @@ -983,252 +989,70 @@ "name": "stderr", "output_type": "stream", "text": [ - "Training Episodes: 0%| | 0/10 [00:00 1546383600.0 but is 1546300799.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[14], line 39\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[38;5;66;03m# run learning if learning mode is enabled\u001b[39;00m\n\u001b[0;32m 36\u001b[0m \u001b[38;5;66;03m# needed as we simulate the modelling horizon multiple times to train reinforcement learning run_learning( world, inputs_path=input_path, scenario=scenario, study_case=study_case, )\u001b[39;00m\n\u001b[0;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m world\u001b[38;5;241m.\u001b[39mlearning_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlearning_mode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m---> 39\u001b[0m \u001b[43mrun_learning\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 40\u001b[0m \u001b[43m \u001b[49m\u001b[43mworld\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 41\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mscenario\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtutorial_08\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mstudy_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzonal_case\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;66;03m# Run the simulation\u001b[39;00m\n\u001b[0;32m 47\u001b[0m world\u001b[38;5;241m.\u001b[39mrun()\n", - "Cell \u001b[1;32mIn[10], line 152\u001b[0m, in \u001b[0;36mrun_learning\u001b[1;34m(world, inputs_path, scenario, study_case, verbose)\u001b[0m\n\u001b[0;32m 142\u001b[0m setup_world(\n\u001b[0;32m 143\u001b[0m world\u001b[38;5;241m=\u001b[39mworld,\n\u001b[0;32m 144\u001b[0m scenario_data\u001b[38;5;241m=\u001b[39mscenario_data,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 147\u001b[0m eval_episode\u001b[38;5;241m=\u001b[39meval_episode,\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 150\u001b[0m world\u001b[38;5;241m.\u001b[39mlearning_role\u001b[38;5;241m.\u001b[39mload_inter_episodic_data(inter_episodic_data)\n\u001b[1;32m--> 152\u001b[0m \u001b[43mworld\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m total_rewards \u001b[38;5;241m=\u001b[39m world\u001b[38;5;241m.\u001b[39moutput_role\u001b[38;5;241m.\u001b[39mget_sum_reward()\n\u001b[0;32m 155\u001b[0m avg_reward \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(total_rewards)\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:686\u001b[0m, in \u001b[0;36mWorld.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 683\u001b[0m end_ts \u001b[38;5;241m=\u001b[39m datetime2timestamp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mend)\n\u001b[0;32m 685\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 686\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_until_complete\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 687\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43masync_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_ts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_ts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mend_ts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 688\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[0;32m 690\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\nest_asyncio.py:98\u001b[0m, in \u001b[0;36m_patch_loop..run_until_complete\u001b[1;34m(self, future)\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m f\u001b[38;5;241m.\u001b[39mdone():\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 97\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEvent loop stopped before Future completed.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\futures.py:203\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__log_traceback \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 203\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\u001b[38;5;241m.\u001b[39mwith_traceback(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception_tb)\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_result\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\asyncio\\tasks.py:277\u001b[0m, in \u001b[0;36mTask.__step\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 274\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 275\u001b[0m \u001b[38;5;66;03m# We use the `send` method directly, because coroutines\u001b[39;00m\n\u001b[0;32m 276\u001b[0m \u001b[38;5;66;03m# don't have `__iter__` and `__next__` methods.\u001b[39;00m\n\u001b[1;32m--> 277\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39msend(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m 278\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 279\u001b[0m result \u001b[38;5;241m=\u001b[39m coro\u001b[38;5;241m.\u001b[39mthrow(exc)\n", - "File \u001b[1;32m~\\Documents\\Code\\assume\\assume\\world.py:651\u001b[0m, in \u001b[0;36mWorld.async_run\u001b[1;34m(self, start_ts, end_ts)\u001b[0m\n\u001b[0;32m 648\u001b[0m pbar \u001b[38;5;241m=\u001b[39m tqdm(total\u001b[38;5;241m=\u001b[39mend_ts \u001b[38;5;241m-\u001b[39m start_ts)\n\u001b[0;32m 650\u001b[0m \u001b[38;5;66;03m# allow registration before first opening\u001b[39;00m\n\u001b[1;32m--> 651\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_time\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_ts\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistributed_role \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock_manager\u001b[38;5;241m.\u001b[39mbroadcast(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclock\u001b[38;5;241m.\u001b[39mtime)\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\mango\\util\\clock.py:70\u001b[0m, in \u001b[0;36mExternalClock.set_time\u001b[1;34m(self, t)\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;124;03mNew time is set\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m t \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time:\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTime must be > \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m but is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 71\u001b[0m \u001b[38;5;66;03m# set time\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time \u001b[38;5;241m=\u001b[39m t\n", - "\u001b[1;31mValueError\u001b[0m: Time must be > 1546383600.0 but is 1546300799." - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_eval_5 2019-01-01 23:00:00: : 1546466401.0it [00:10, 154384521.99it/s]\n" + "tutorial_08_zonal_case 2019-01-01 23:00:00: : 82801.0it [00:05, 14990.57it/s] \n" ] } ], @@ -3277,7 +1487,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "96bfa2ee", "metadata": { "id": "bdb21cbe" @@ -3323,14 +1533,14 @@ "2019-01-01T23:00:00" ], "y": [ - 38.667, - 38.667, - 38.667, - 38.667, - 39.667, - 39.667, - 39.667, - 39.667, + 98.173, + 98.187, + 98.186, + 98.184, + 98.189, + 98.192, + 98.192, + 98.193, 3000, 3000, 3000, @@ -3381,14 +1591,14 @@ "2019-01-01T23:00:00" ], "y": [ - 38.667, - 38.667, - 38.667, - 38.667, - 39.667, - 39.667, - 39.667, - 39.667, + 98.173, + 98.187, + 98.186, + 98.184, + 98.189, + 98.192, + 98.192, + 98.193, 3000, 3000, 3000, @@ -4365,7 +2575,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "b9f4d746", "metadata": { "id": "ae266ecb", @@ -4418,586 +2628,2503 @@ "- $f(S)$ is the model’s prediction when using only the features in subset $S$.\n", "\n", "\n", - "The `shap` library is a popular tool for computing Shapley values for machine learning models, including neural networks.\n", + "The `shap` library is a popular tool for computing Shapley values for machine learning models, including neural networks.\n", + "\n", + "\n", + "\n", + "Why Use SHAP in RL?\n", + "- Model-Agnostic: Applicable to any machine learning model, including neural networks.\n", + "- Local Explanations: Provides explanations for individual predictions (actions).\n", + "- Consistency: Ensures that features contributing more to the prediction have higher Shapley values.\n", + "\n", + "\n", + "Properties of SHAP:\n", + "1. Local Accuracy: The sum of Shapley values equals the difference between the model output and the expected output.\n", + "2. Missingness: Features not present in the model have zero Shapley value.\n", + "3. Consistency: If a model changes so that a feature contributes more to the prediction, the Shapley value of that feature should not decrease." + ] + }, + { + "cell_type": "markdown", + "id": "39959bf3", + "metadata": { + "id": "21d49573" + }, + "source": [ + "## 3. Calculating SHAP values " + ] + }, + { + "cell_type": "markdown", + "id": "1a6567e4", + "metadata": { + "id": "d16712fc" + }, + "source": [ + "We will work with:" + ] + }, + { + "cell_type": "markdown", + "id": "f5fa5479", + "metadata": { + "id": "2c0c1262" + }, + "source": [ + "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", + "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." + ] + }, + { + "cell_type": "markdown", + "id": "df893c68", + "metadata": { + "id": "2fb6fc14" + }, + "source": [ + "Our goal is to:" + ] + }, + { + "cell_type": "markdown", + "id": "d2145dcf", + "metadata": { + "id": "e0b69db6" + }, + "source": [ + "- Load the observations and the trained actor model.\n", + "- Use the model to predict actions.\n", + "- Apply SHAP to explain the model's predictions." + ] + }, + { + "cell_type": "markdown", + "id": "e3de2358", + "metadata": { + "id": "f870b3e8" + }, + "source": [ + "### 3.1. Loading and Preparing Data " + ] + }, + { + "cell_type": "markdown", + "id": "b6de4111", + "metadata": { + "id": "aaa7c3d3" + }, + "source": [ + "First, let's load the necessary libraries and the data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0a23dfa1", + "metadata": { + "id": "b6ee4f28" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import shap\n", + "import torch as th\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "markdown", + "id": "b44ce6d3", + "metadata": { + "id": "aa268f14", + "lines_to_next_cell": 0 + }, + "source": [ + "the simulation common.py contains utility functions and class definitions\n", + "from common import load_observations, Actor" + ] + }, + { + "cell_type": "markdown", + "id": "efc3087c", + "metadata": { + "id": "f866385a", + "lines_to_next_cell": 0 + }, + "source": [ + "**Define the Actor Neural Network Class**" + ] + }, + { + "cell_type": "markdown", + "id": "1a7c97b7", + "metadata": { + "id": "ff7cf03a", + "lines_to_next_cell": 0 + }, + "source": [ + "We define the actor neural network class that will be used to predict actions based on observations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "168757a6", + "metadata": { + "id": "7216677a" + }, + "outputs": [], + "source": [ + "from assume.reinforcement_learning.neural_network_architecture import MLPActor" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "2f4d6257", + "metadata": { + "id": "5a4d6e17" + }, + "outputs": [], + "source": [ + "# which actor is the RL actor\n", + "ACTOR_NUM = len(powerplant_units) # 20\n", + "\n", + "# Path to actor we want to analyse\n", + "actor_path = os.path.join(\n", + " input_dir,\n", + " f\"learned_strategies/zonal_case/avg_reward_eval_policies/actors/actor_Unit {ACTOR_NUM}.pt\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bd023be3", + "metadata": { + "id": "ddfe95d9" + }, + "source": [ + "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f38c74cb", + "metadata": { + "id": "44862f06" + }, + "outputs": [], + "source": [ + "# @title Load observations function\n", + "\n", + "\n", + "def load_observations(path, feature_names):\n", + " # Load observations\n", + " obs_path = f\"{path}/buffer_obs.json\"\n", + "\n", + " print(obs_path)\n", "\n", + " with open(obs_path) as file:\n", + " json_data = json.load(file)\n", "\n", + " # Convert the list of lists into a 2D numpy array\n", + " input_data = np.array(json_data)\n", + " input_data = np.squeeze(input_data)\n", "\n", - "Why Use SHAP in RL?\n", - "- Model-Agnostic: Applicable to any machine learning model, including neural networks.\n", - "- Local Explanations: Provides explanations for individual predictions (actions).\n", - "- Consistency: Ensures that features contributing more to the prediction have higher Shapley values.\n", + " print(len(input_data))\n", + " # filter out arrays where all value are 0\n", + " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", "\n", + " print(len(input_data))\n", + " # filter only first 700 observations\n", + " input_data = input_data[:700]\n", "\n", - "Properties of SHAP:\n", - "1. Local Accuracy: The sum of Shapley values equals the difference between the model output and the expected output.\n", - "2. Missingness: Features not present in the model have zero Shapley value.\n", - "3. Consistency: If a model changes so that a feature contributes more to the prediction, the Shapley value of that feature should not decrease." - ] - }, - { - "cell_type": "markdown", - "id": "39959bf3", - "metadata": { - "id": "21d49573" - }, - "source": [ - "## 3. Calculating SHAP values " + " return pd.DataFrame(input_data, columns=feature_names), input_data" ] }, { "cell_type": "markdown", - "id": "1a6567e4", + "id": "0e5eb3d3", "metadata": { - "id": "d16712fc" + "id": "444b8679" }, "source": [ - "We will work with:" + "**Define Paths and Parameters**" ] }, { "cell_type": "markdown", - "id": "f5fa5479", + "id": "105cdba0", "metadata": { - "id": "2c0c1262" + "id": "7f218d15" }, "source": [ - "- **Observations (`input_data`)**: These are the inputs to our actor neural network, representing the state of the environment.\n", - "- **Trained Actor Model**: A neural network representing the decision making of one RL power plant that outputs actions based on the observations." + "Adjust the following paths and parameters according to your data and model." ] }, { - "cell_type": "markdown", - "id": "df893c68", + "cell_type": "code", + "execution_count": 33, + "id": "47473ec8", "metadata": { - "id": "2fb6fc14" + "id": "8fa78b12" }, + "outputs": [], "source": [ - "Our goal is to:" + "path = (\n", + " input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", + ") # Replace with your data path" ] }, { - "cell_type": "markdown", - "id": "d2145dcf", + "cell_type": "code", + "execution_count": 34, + "id": "76fdf5d0", "metadata": { - "id": "e0b69db6" + "id": "8f9189ca" }, + "outputs": [], "source": [ - "- Load the observations and the trained actor model.\n", - "- Use the model to predict actions.\n", - "- Apply SHAP to explain the model's predictions." + "# Define feature names (replace with actual feature names)\n", + "# make columns names\n", + "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", + "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", + "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" ] }, { "cell_type": "markdown", - "id": "e3de2358", + "id": "6126c1a9", "metadata": { - "id": "f870b3e8" + "id": "6c5f1986" }, "source": [ - "### 3.1. Loading and Preparing Data " + "**Load Observations and Input Data**" ] }, { "cell_type": "markdown", - "id": "b6de4111", + "id": "4d38d028", "metadata": { - "id": "aaa7c3d3" + "id": "36ae8f9e" }, "source": [ - "First, let's load the necessary libraries and the data." + "Load the observations and input data using the utility function." ] }, { "cell_type": "code", - "execution_count": 13, - "id": "0a23dfa1", + "execution_count": 35, + "id": "1819ff26", "metadata": { - "id": "b6ee4f28" + "id": "d522969d" }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" + "../inputs\\tutorial_08/learned_strategies/zonal_case/buffer_obs/buffer_obs.json\n", + "500000\n", + "180\n" ] + }, + { + "data": { + "text/html": [ + "
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Neural Network Class**" + "We initialize and load the trained actor neural network." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "c27178fe", + "metadata": { + "id": "4da4de57" + }, + "outputs": [], + "source": [ + "# Initialize the model\n", + "obs_dim = len(feature_names)\n", + "act_dim = 2 # Adjust if your model outputs a different number of actions\n", + "model = MLPActor(obs_dim=obs_dim, act_dim=act_dim, float_type=th.float)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "35a088d5", + "metadata": { + "id": "37adecfa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load the trained model parameters\n", + "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", + "model.load_state_dict(model_state[\"actor\"])" ] }, { "cell_type": "markdown", - "id": "1a7c97b7", + "id": "fe43c9c6", "metadata": { - "id": "ff7cf03a", - "lines_to_next_cell": 0 + "id": "d4a63712" }, "source": [ - "We define the actor neural network class that will be used to predict actions based on observations." + "Get the actions base on observation tensor we just loaded." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "168757a6", + "execution_count": 41, + "id": "4ee77fed", "metadata": { - "id": "7216677a" + "id": "e6460cfb" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([0.9817, 0.9805], grad_fn=),\n", + " tensor([0.9819, 0.9806], grad_fn=),\n", + " tensor([0.9819, 0.9807], grad_fn=),\n", + " tensor([0.9818, 0.9806], grad_fn=),\n", + " tensor([0.9819, 0.9807], grad_fn=),\n", + " tensor([0.9819, 0.9807], grad_fn=),\n", + " tensor([0.9820, 0.9807], grad_fn=),\n", + " tensor([0.9819, 0.9807], grad_fn=),\n", + " tensor([0.9820, 0.9808], grad_fn=),\n", + " tensor([0.9820, 0.9808], grad_fn=),\n", + " tensor([0.9820, 0.9808], grad_fn=),\n", + " tensor([0.9820, 0.9808], 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"id": "5a4d6e17" + "id": "be91cd97", + "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "# which actor is the RL actor\n", - "ACTOR_NUM = len(powerplant_units) # 20\n", - "\n", - "# Path to actor we want to analyse\n", - "actor_path = os.path.join(\n", - " input_dir,\n", - " \"learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt\",\n", + "# Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " input_data, predictions, test_size=0.15, random_state=42\n", ")" ] }, - { - "cell_type": "markdown", - "id": "bd023be3", - "metadata": { - "id": "ddfe95d9" - }, - "source": [ - "We define a utility function to load observations and input data from a specified path. Analyzing the shap values for all observations and all parameters would make this notebook quite lengthy, so we’re filtering the observation data frame to include only 700 observations." - ] - }, { "cell_type": "code", - "execution_count": 22, - "id": "f38c74cb", + "execution_count": 43, + "id": "f86348b7", "metadata": { - "id": "44862f06" + "id": "b21c93c7", + "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" + ] + } + ], "source": [ - "# @title Load observations function\n", - "\n", - "\n", - "def load_observations(path, feature_names):\n", - " # Load observations\n", - " obs_path = f\"{path}/buffer_obs.json\"\n", - "\n", - " print(obs_path)\n", - "\n", - " with open(obs_path) as file:\n", - " json_data = json.load(file)\n", - "\n", - " # Convert the list of lists into a 2D numpy array\n", - " input_data = np.array(json_data)\n", - " input_data = np.squeeze(input_data)\n", - "\n", - " print(len(input_data))\n", - " # filter out arrays where all value are 0\n", - " input_data = input_data[~np.all(input_data == 0, axis=1)]\n", - "\n", - " print(len(input_data))\n", - " # filter only first 700 observations\n", - " input_data = input_data[:700]\n", + "# Convert data to tensors\n", + "y_train = th.stack(y_train)\n", + "y_test = th.stack(y_test)\n", "\n", - " return pd.DataFrame(input_data, columns=feature_names), input_data" + "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", + "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", + "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", + "y_test_tensor = th.tensor(y_test, dtype=th.float32)" ] }, { "cell_type": "markdown", - "id": "0e5eb3d3", + "id": "633c5100", "metadata": { - "id": "444b8679" + "id": "ddd1ab1e" }, "source": [ - "**Define Paths and Parameters**" + "## 3.2. Creating a SHAP Explainer " ] }, { "cell_type": "markdown", - "id": "105cdba0", + "id": "c278a8c6", "metadata": { - "id": "7f218d15" + "id": "ae7b108b", + "lines_to_next_cell": 2 }, "source": [ - "Adjust the following paths and parameters according to your data and model." + "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." ] }, { "cell_type": "code", - "execution_count": 17, - "id": "47473ec8", + "execution_count": 44, + "id": "77b37ebc", "metadata": { - "id": "8fa78b12" + "id": "6d9be211" }, "outputs": [], "source": [ - "path = (\n", - " input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", - ") # Replace with your data path" + "# Define a prediction function for SHAP\n", + "def model_predict(X):\n", + " X_tensor = th.tensor(X, dtype=th.float32)\n", + " model.eval()\n", + " with th.no_grad():\n", + " return model(X_tensor).numpy()" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "76fdf5d0", + "execution_count": 45, + "id": "ce3b3daa", "metadata": { - "id": "8f9189ca" + "id": "f0f7857b" }, "outputs": [], "source": [ - "# Define feature names (replace with actual feature names)\n", - "# make columns names\n", - "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", - "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", - "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" - ] - }, - { - "cell_type": "markdown", - "id": "6126c1a9", - "metadata": { - "id": "6c5f1986" - }, - "source": [ - "**Load Observations and Input Data**" + "# Use a subset of training data for the background dataset\n", + "background_size = 100 # Adjust the size as needed\n", + "background = X_train[:background_size]" ] }, { - "cell_type": "markdown", - "id": "4d38d028", + "cell_type": "code", + "execution_count": 46, + "id": "1eac5396", "metadata": { - "id": "36ae8f9e" + "id": "84bb96cf" }, + "outputs": [], "source": [ - "Load the observations and input data using the utility function." + "# Create the SHAP Kernel Explainer\n", + "explainer = shap.KernelExplainer(model_predict, background)" ] }, { "cell_type": "code", - "execution_count": 23, - "id": "1819ff26", + "execution_count": 47, + "id": "c38544fd", "metadata": { - "id": "d522969d" + "id": "2a7929e4" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/27 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
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\n", - "" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [price forecast t+1, price forecast t+2, price forecast t+3, price forecast t+4, price forecast t+5, price forecast t+6, price forecast t+7, price forecast t+8, price forecast t+9, price forecast t+10, price forecast t+11, price forecast t+12, price forecast t+13, price forecast t+14, price forecast t+15, price forecast t+16, price forecast t+17, price forecast t+18, price forecast t+19, price forecast t+20, price forecast t+21, price forecast t+22, price forecast t+23, price forecast t+24, residual load forecast t+1, residual load forecast t+2, residual load forecast t+3, residual load forecast t+4, residual load forecast t+5, residual load forecast t+6, residual load forecast t+7, residual load forecast t+8, residual load forecast t+9, residual load forecast t+10, residual load forecast t+11, residual load forecast t+12, residual load forecast t+13, residual load forecast t+14, residual load forecast t+15, residual load forecast t+16, residual load forecast t+17, residual load forecast t+18, residual load forecast t+19, residual load forecast t+20, residual load forecast t+21, residual load forecast t+22, residual load forecast t+23, residual load forecast t+24, total capacity t-1, marginal costs t-1]\n", - "Index: []\n", - "\n", - "[0 rows x 50 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_obs, input_data = load_observations(path, feature_names)\n", - "\n", - "df_obs" - ] - }, - { - "cell_type": "markdown", - "id": "509b19bf", - "metadata": { - "id": "5d8b9dcf" - }, - "source": [ - "**Load the Trained Actor Model**" - ] - }, - { - "cell_type": "markdown", - "id": "bf2b101a", - "metadata": { - "id": "b1b50488" - }, - "source": [ - "We initialize and load the trained actor neural network." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "c27178fe", - "metadata": { - "id": "4da4de57" - }, - "outputs": [], - "source": [ - "# Initialize the model\n", - "obs_dim = len(feature_names)\n", - "act_dim = 2 # Adjust if your model outputs a different number of actions\n", - "model = MLPActor(obs_dim=obs_dim, act_dim=act_dim, float_type=th.float)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "35a088d5", - "metadata": { - "id": "37adecfa" - }, - "outputs": [ + "name": "stderr", + "output_type": "stream", + "text": [ + " 37%|███▋ | 10/27 [00:15<00:22, 1.32s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [-3.17708670e-05 -2.64823923e-05 -3.16427512e-05 -2.68370703e-05\n", + " -3.32147749e-05 -3.06224358e-05 -2.28378868e-05 -2.40040321e-05\n", + " -1.24336474e-05 -7.34867284e-06 0.00000000e+00 9.55680114e-06\n", + " 2.11486637e-05 2.44649569e-05 3.59179205e-05 3.95665233e-05\n", + " 5.16796840e-05 6.26730589e-05 5.80524365e-05 5.67502791e-05\n", + " -5.46251467e-05 -4.92127886e-05 -3.58558458e-05 -2.35060547e-05\n", + " 3.50611652e-05 4.14175693e-05 4.56681116e-05 6.13252217e-05\n", + " 5.84130754e-05 -1.91542921e-05 -1.55284156e-05 -1.21360012e-05\n", + " -9.51639367e-06 -1.81051726e-05 -1.49846235e-05 -2.91964478e-05\n", + " -4.08589761e-05 -3.17165084e-05 -4.22840296e-05 -4.22819367e-05\n", + " -3.45260485e-05 -4.10210425e-05 -4.80262216e-05 -4.65657606e-05\n", + " 4.01227811e-05 4.63281100e-05 2.94364667e-05 3.68527894e-05\n", + " -2.23238666e-05]\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [-3.34260392e-05 -2.90359853e-05 -3.33053131e-05 -2.94900972e-05\n", + " -3.49166149e-05 -3.20489188e-05 -2.47577896e-05 -2.54890280e-05\n", + " -1.27465092e-05 -7.88170431e-06 0.00000000e+00 1.03697559e-05\n", + " 2.29365949e-05 2.61668137e-05 3.78773386e-05 4.13032778e-05\n", + " 5.61335883e-05 6.63862389e-05 6.42385675e-05 5.98274156e-05\n", + " -5.84059429e-05 -5.21078235e-05 -3.73849421e-05 -2.51767975e-05\n", + " 3.63503349e-05 4.31242270e-05 4.59107781e-05 6.34474758e-05\n", + " 6.32324548e-05 -2.06913377e-05 -1.62846063e-05 -1.27542799e-05\n", + " -1.01668740e-05 -1.89847947e-05 -1.57340290e-05 -3.17066926e-05\n", + " -4.29125412e-05 -3.61921930e-05 -4.27130720e-05 -4.73525866e-05\n", + " -3.80946372e-05 -4.56683790e-05 -5.11233530e-05 -4.97346580e-05\n", + " 4.32161832e-05 4.97823671e-05 3.25573266e-05 3.94185385e-05\n", + " 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"text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.00000000000001\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 6.40440656e-06 5.49981207e-06 6.46499801e-06 5.88617867e-06\n", + " 0.00000000e+00 6.28989807e-06 9.09252250e-06 1.62297947e-05\n", + " 1.60108273e-05 2.98257515e-05 3.33707337e-05 4.81163868e-05\n", + " 5.57133365e-05 5.20797366e-05 -6.26550257e-05 -4.95905733e-05\n", + " -4.47234229e-05 -4.34625332e-05 -3.03905439e-05 -1.75585962e-05\n", + " -1.82290448e-05 -1.17339651e-05 -4.45300846e-06 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0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.00000000000001\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 5.04736476e-05 4.17906055e-05 5.00108497e-05 4.24306910e-05\n", + " 5.24408562e-05 4.84511613e-05 4.60908364e-05 -9.65771085e-05\n", + " -6.39084069e-05 -7.49208698e-05 -5.70938579e-05 -6.17315270e-05\n", + " -4.29878684e-05 -2.61887897e-05 -2.50065982e-05 -1.49044184e-05\n", + " -7.56874228e-06 0.00000000e+00 0.00000000e+00 1.09800807e-05\n", + " 2.30910926e-05 3.12407671e-05 3.12331281e-05 3.75080417e-05\n", + " -2.99353654e-05 -2.61562436e-05 -2.44056954e-05 -2.61952297e-05\n", + " -1.61422576e-05 -1.91562621e-05 -1.53191965e-05 7.34406125e-05\n", + " 8.59173464e-05 9.45679257e-05 5.29178016e-05 7.84912299e-05\n", + " 7.18835252e-05 4.23596885e-05 4.53392734e-05 4.03229991e-05\n", + " -3.42133773e-05 -4.02395281e-05 -4.74144496e-05 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"INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 2.52256022e-05 2.09966961e-05 2.50344367e-05 2.11856664e-05\n", + " 2.63440682e-05 2.43012775e-05 2.49423080e-05 3.61380365e-05\n", + " 3.01396379e-05 4.81958242e-05 4.93150715e-05 -8.76476553e-05\n", + " -6.62099544e-05 -4.46268650e-05 -4.68342399e-05 -3.49892897e-05\n", + " -2.87464974e-05 -2.60961998e-05 -1.58440348e-05 -5.41693814e-06\n", + " 0.00000000e+00 6.72163302e-06 1.05368292e-05 1.87202646e-05\n", + " -2.96927830e-05 -2.64578224e-05 -2.45678563e-05 -2.62804386e-05\n", + " -1.64628777e-05 -1.89502115e-05 -1.53565167e-05 -1.20915299e-05\n", + " -9.49697066e-06 -1.79207640e-05 -1.48662015e-05 7.86387996e-05\n", + " 7.22826919e-05 4.19331962e-05 4.56963998e-05 4.05394278e-05\n", + " 2.82115795e-05 3.34284887e-05 4.06033796e-05 3.62999283e-05\n", + " -5.05939665e-05 -4.97566386e-05 -2.80598882e-05 -3.98292868e-05\n", + " 3.18620445e-05]\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 2.65333822e-05 2.30140642e-05 2.63389729e-05 2.32870327e-05\n", + " 2.76931393e-05 2.54292824e-05 2.70538036e-05 3.83855991e-05\n", + " 3.09193455e-05 5.17153422e-05 5.30883584e-05 -9.50058353e-05\n", + " -7.17666640e-05 -4.77171089e-05 -4.93965313e-05 -3.65174415e-05\n", + " -3.12367010e-05 -2.76429241e-05 -1.75386370e-05 -5.71335735e-06\n", + " 0.00000000e+00 7.10785822e-06 1.09757439e-05 2.00536797e-05\n", + " -3.07813697e-05 -2.75637310e-05 -2.46867416e-05 -2.71886799e-05\n", + " -1.78233831e-05 -2.04562592e-05 -1.60950342e-05 -1.27028789e-05\n", + " -1.01379518e-05 -1.87787930e-05 -1.55978247e-05 8.53655120e-05\n", + " 7.58995546e-05 4.78566672e-05 4.61671451e-05 4.53993902e-05\n", + " 3.11203755e-05 3.72189022e-05 4.32364973e-05 3.87602719e-05\n", + " -5.44313753e-05 -5.34190059e-05 -3.10241419e-05 -4.26006498e-05\n", + " 3.37050048e-05]\n" + ] + }, + { + "name": "stderr", + "output_type": 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"text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 1.26542506e-05 1.05182165e-05 1.25498520e-05 1.06432844e-05\n", + " 1.32090742e-05 1.21963720e-05 1.43855521e-05 2.28308023e-05\n", + " 2.07246523e-05 3.58716347e-05 3.87086764e-05 5.46859842e-05\n", + " 6.15961017e-05 -5.37750377e-05 -5.77655855e-05 -4.48229824e-05\n", + " -3.93964986e-05 -3.77906606e-05 -2.55889587e-05 -1.35699292e-05\n", + " -1.25083602e-05 -5.56553245e-06 0.00000000e+00 9.37588127e-06\n", 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"INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [-1.24711777e-05 -1.04931097e-05 -1.24508175e-05 -1.04191954e-05\n", + " -1.32963343e-05 -1.21219894e-05 -6.73969358e-06 -3.58565597e-06\n", + " 0.00000000e+00 1.13761970e-05 1.74986464e-05 2.91143561e-05\n", + " 3.87770430e-05 3.85118929e-05 5.21063779e-05 5.41436805e-05\n", + " 6.77689849e-05 -6.13990127e-05 -4.54050463e-05 -2.99552628e-05\n", + " -3.62749943e-05 -3.02928041e-05 -2.03707891e-05 -9.26914638e-06\n", + " 3.48838790e-05 4.13671909e-05 -2.42926078e-05 -2.63759680e-05\n", + " -1.63212011e-05 -1.87484348e-05 -1.54574934e-05 -1.18095087e-05\n", + " -9.40406550e-06 -1.81035347e-05 -1.49701629e-05 -2.84924574e-05\n", + " -3.98709871e-05 -3.14955187e-05 -4.17989029e-05 -4.21262813e-05\n", + " -3.40396536e-05 3.33576903e-05 4.06183668e-05 3.66956344e-05\n", + " 4.01870556e-05 4.63309277e-05 2.95737132e-05 3.68287417e-05\n", + " 0.00000000e+00]\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [-1.31100302e-05 -1.14941557e-05 -1.30975897e-05 -1.14582279e-05\n", + " -1.39715299e-05 -1.26819099e-05 -7.30436588e-06 -3.79942637e-06\n", + " 0.00000000e+00 1.22119595e-05 1.88415947e-05 3.15701731e-05\n", + " 4.20351921e-05 4.11735635e-05 5.49468558e-05 5.64906361e-05\n", + " 7.36027472e-05 -6.50353760e-05 -5.02499603e-05 -3.15710689e-05\n", + " -3.87806463e-05 -3.20625715e-05 -2.12308352e-05 -9.92490724e-06\n", + " 3.61589815e-05 4.30811850e-05 -2.44070552e-05 -2.72910893e-05\n", + " -1.76858400e-05 -2.02532303e-05 -1.62042047e-05 -1.24047874e-05\n", + " -1.00422807e-05 -1.89755247e-05 -1.57088652e-05 -3.09539871e-05\n", + " -4.18569425e-05 -3.59449271e-05 -4.22024119e-05 -4.71584343e-05\n", + " -3.75574674e-05 3.71691332e-05 4.32597829e-05 3.91955170e-05\n", + " 4.32560206e-05 4.97568501e-05 3.26802217e-05 3.93741999e-05\n", + " 0.00000000e+00]\n" + ] + }, { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: '../inputs\\\\tutorial_08\\\\learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[21], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Load the trained model parameters\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m model_state \u001b[38;5;241m=\u001b[39m \u001b[43mth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactor_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_location\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(model_state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mactor\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:997\u001b[0m, in \u001b[0;36mload\u001b[1;34m(f, map_location, pickle_module, weights_only, mmap, **pickle_load_args)\u001b[0m\n\u001b[0;32m 994\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pickle_load_args\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m 995\u001b[0m pickle_load_args[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mencoding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 997\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_file_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_file:\n\u001b[0;32m 998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_zipfile(opened_file):\n\u001b[0;32m 999\u001b[0m \u001b[38;5;66;03m# The zipfile reader is going to advance the current file position.\u001b[39;00m\n\u001b[0;32m 1000\u001b[0m \u001b[38;5;66;03m# If we want to actually tail call to torch.jit.load, we need to\u001b[39;00m\n\u001b[0;32m 1001\u001b[0m \u001b[38;5;66;03m# reset back to the original position.\u001b[39;00m\n\u001b[0;32m 1002\u001b[0m orig_position \u001b[38;5;241m=\u001b[39m opened_file\u001b[38;5;241m.\u001b[39mtell()\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:444\u001b[0m, in \u001b[0;36m_open_file_like\u001b[1;34m(name_or_buffer, mode)\u001b[0m\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_open_file_like\u001b[39m(name_or_buffer, mode):\n\u001b[0;32m 443\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_path(name_or_buffer):\n\u001b[1;32m--> 444\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_open_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 445\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m mode:\n", - "File \u001b[1;32mc:\\Users\\tg3533\\AppData\\Local\\miniconda3\\envs\\assume-framework\\Lib\\site-packages\\torch\\serialization.py:425\u001b[0m, in \u001b[0;36m_open_file.__init__\u001b[1;34m(self, name, mode)\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, mode):\n\u001b[1;32m--> 425\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m)\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../inputs\\\\tutorial_08\\\\learned_strategies/zonal_case/avg_reward_eval_policies/actor_pp_{ACTOR_NUM}.pt'" + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 27/27 [00:38<00:00, 1.44s/it]\n" ] } ], - "source": [ - "# Load the trained model parameters\n", - "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", - "model.load_state_dict(model_state[\"actor\"])" - ] - }, - { - "cell_type": "markdown", - "id": "fe43c9c6", - "metadata": { - "id": "d4a63712" - }, - "source": [ - "Get the actions base on observation tensor we just loaded." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ee77fed", - "metadata": { - "id": "e6460cfb" - }, - "outputs": [], - "source": [ - "predictions = []\n", - "for obs in input_data:\n", - " obs_tensor = th.tensor(obs, dtype=th.float)\n", - " prediction = model(obs_tensor)\n", - " predictions.append(prediction)\n", - "predictions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cf0d9b7", - "metadata": { - "id": "be91cd97", - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " input_data, predictions, test_size=0.15, random_state=42\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f86348b7", - "metadata": { - "id": "b21c93c7", - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [ - "# Convert data to tensors\n", - "y_train = th.stack(y_train)\n", - "y_test = th.stack(y_test)\n", - "\n", - "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", - "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", - "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", - "y_test_tensor = th.tensor(y_test, dtype=th.float32)" - ] - }, - { - "cell_type": "markdown", - "id": "633c5100", - "metadata": { - "id": "ddd1ab1e" - }, - "source": [ - "## 3.2. Creating a SHAP Explainer " - ] - }, - { - "cell_type": "markdown", - "id": "c278a8c6", - "metadata": { - "id": "ae7b108b", - "lines_to_next_cell": 2 - }, - "source": [ - "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77b37ebc", - "metadata": { - "id": "6d9be211" - }, - "outputs": [], - "source": [ - "# Define a prediction function for SHAP\n", - "def model_predict(X):\n", - " X_tensor = th.tensor(X, dtype=th.float32)\n", - " model.eval()\n", - " with th.no_grad():\n", - " return model(X_tensor).numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce3b3daa", - "metadata": { - "id": "f0f7857b" - }, - "outputs": [], - "source": [ - "# Use a subset of training data for the background dataset\n", - "background_size = 100 # Adjust the size as needed\n", - "background = X_train[:background_size]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1eac5396", - "metadata": { - "id": "84bb96cf" - }, - "outputs": [], - "source": [ - "# Create the SHAP Kernel Explainer\n", - "explainer = shap.KernelExplainer(model_predict, background)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c38544fd", - "metadata": { - "id": "2a7929e4" - }, - "outputs": [], "source": [ "# Calculate SHAP values for the test set\n", "shap_values = explainer.shap_values(X_test)" @@ -5005,12 +5132,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "60ed8910", "metadata": { "id": "44fa277f" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[-4.44507299e-05, -3.70878677e-05, -4.41125138e-05, ...,\n", + " 3.66767070e-05, -4.03680515e-05, 0.00000000e+00],\n", + " [-1.25899770e-05, -1.04533859e-05, -1.24064115e-05, ...,\n", + " 3.66173281e-05, 3.20080367e-05, 0.00000000e+00],\n", + " [ 6.49159712e-06, 0.00000000e+00, 6.44950511e-06, ...,\n", + " -3.98000386e-05, 3.21382656e-05, 0.00000000e+00],\n", + " ...,\n", + " [ 6.60218013e-06, 5.15955777e-06, 6.46386764e-06, ...,\n", + " -3.99887359e-05, 3.20920592e-05, 0.00000000e+00],\n", + " [ 1.26542506e-05, 1.05182165e-05, 1.25498520e-05, ...,\n", + " -3.98064763e-05, 3.18891411e-05, 0.00000000e+00],\n", + " [-1.24711777e-05, -1.04931097e-05, -1.24508175e-05, ...,\n", + " 3.68287417e-05, 0.00000000e+00, 0.00000000e+00]]),\n", + " array([[-4.67654224e-05, -4.06610081e-05, -4.64230037e-05, ...,\n", + " 3.92361459e-05, -4.26883905e-05, 0.00000000e+00],\n", + " [-1.32381226e-05, -1.14537804e-05, -1.30501587e-05, ...,\n", + " 3.91520122e-05, 3.38670409e-05, 0.00000000e+00],\n", + " [ 6.68053868e-06, 5.78421920e-06, 6.63772552e-06, ...,\n", + " -4.26702344e-05, 3.38169457e-05, 0.00000000e+00],\n", + " ...,\n", + " [ 6.68128704e-06, 5.78555670e-06, 6.63398078e-06, ...,\n", + " -4.26684923e-05, 3.38145231e-05, 0.00000000e+00],\n", + " [ 1.33078696e-05, 1.15247753e-05, 1.32020865e-05, ...,\n", + " -4.25510263e-05, 3.37302594e-05, 0.00000000e+00],\n", + " [-1.31100302e-05, -1.14941557e-05, -1.30975897e-05, ...,\n", + " 3.93741999e-05, 0.00000000e+00, 0.00000000e+00]])]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "shap_values" ] @@ -5037,25 +5200,67 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "2f5af8e0", - "metadata": { - "id": "a6af4983" - }, - "outputs": [], - "source": [ - "print(shap_values[0].shape)\n", - "print(X_test.shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "4020bf97", "metadata": { "id": "2e318a5b" }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", From f406399acd7eb7b0d0b328782798c8901221a70d Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 18:03:24 +0200 Subject: [PATCH 25/27] - some xAI cleaning --- .../notebooks/09_example_Sim_and_xRL.ipynb | 154 ++++-------------- 1 file changed, 30 insertions(+), 124 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index e9ec75e9..7f0ad666 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -2741,71 +2741,6 @@ "from sklearn.model_selection import train_test_split" ] }, - { - "cell_type": "markdown", - "id": "b44ce6d3", - "metadata": { - "id": "aa268f14", - "lines_to_next_cell": 0 - }, - "source": [ - "the simulation common.py contains utility functions and class definitions\n", - "from common import load_observations, Actor" - ] - }, - { - "cell_type": "markdown", - "id": "efc3087c", - "metadata": { - "id": "f866385a", - "lines_to_next_cell": 0 - }, - "source": [ - "**Define the Actor Neural Network Class**" - ] - }, - { - "cell_type": "markdown", - "id": "1a7c97b7", - "metadata": { - "id": "ff7cf03a", - "lines_to_next_cell": 0 - }, - "source": [ - "We define the actor neural network class that will be used to predict actions based on observations." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "168757a6", - "metadata": { - "id": "7216677a" - }, - "outputs": [], - "source": [ - "from assume.reinforcement_learning.neural_network_architecture import MLPActor" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "2f4d6257", - "metadata": { - "id": "5a4d6e17" - }, - "outputs": [], - "source": [ - "# which actor is the RL actor\n", - "ACTOR_NUM = len(powerplant_units) # 20\n", - "\n", - "# Path to actor we want to analyse\n", - "actor_path = os.path.join(\n", - " input_dir,\n", - " f\"learned_strategies/zonal_case/avg_reward_eval_policies/actors/actor_Unit {ACTOR_NUM}.pt\",\n", - ")" - ] - }, { "cell_type": "markdown", "id": "bd023be3", @@ -2852,56 +2787,6 @@ " return pd.DataFrame(input_data, columns=feature_names), input_data" ] }, - { - "cell_type": "markdown", - "id": "0e5eb3d3", - "metadata": { - "id": "444b8679" - }, - "source": [ - "**Define Paths and Parameters**" - ] - }, - { - "cell_type": "markdown", - "id": "105cdba0", - "metadata": { - "id": "7f218d15" - }, - "source": [ - "Adjust the following paths and parameters according to your data and model." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "47473ec8", - "metadata": { - "id": "8fa78b12" - }, - "outputs": [], - "source": [ - "path = (\n", - " input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", - ") # Replace with your data path" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "76fdf5d0", - "metadata": { - "id": "8f9189ca" - }, - "outputs": [], - "source": [ - "# Define feature names (replace with actual feature names)\n", - "# make columns names\n", - "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", - "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", - "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]" - ] - }, { "cell_type": "markdown", "id": "6126c1a9", @@ -3367,6 +3252,17 @@ } ], "source": [ + "# path to extra loggedobservation values \n", + "path = (\n", + " input_dir + \"/learned_strategies/zonal_case/buffer_obs\"\n", + ") \n", + "\n", + "# Define feature names (replace with actual feature names)\n", + "# make columns names\n", + "names_1 = [\"price forecast t+\" + str(x) for x in range(1, 25)]\n", + "names_2 = [\"residual load forecast t+\" + str(x) for x in range(1, 25)]\n", + "feature_names = names_1 + names_2 + [\"total capacity t-1\"] + [\"marginal costs t-1\"]\n", + "\n", "df_obs, input_data = load_observations(path, feature_names)\n", "\n", "df_obs" @@ -3389,18 +3285,20 @@ "id": "b1b50488" }, "source": [ - "We initialize and load the trained actor neural network." + "We initialize and load the trained actor neural network. Therefore, we define the actor neural network class that will be used to predict actions based on observations." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 51, "id": "c27178fe", "metadata": { "id": "4da4de57" }, "outputs": [], "source": [ + "from assume.reinforcement_learning.neural_network_architecture import MLPActor\n", + "\n", "# Initialize the model\n", "obs_dim = len(feature_names)\n", "act_dim = 2 # Adjust if your model outputs a different number of actions\n", @@ -3409,7 +3307,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 52, "id": "35a088d5", "metadata": { "id": "37adecfa" @@ -3421,12 +3319,21 @@ "" ] }, - "execution_count": 40, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# which actor is the RL actor\n", + "ACTOR_NUM = len(powerplant_units) # 20\n", + "\n", + "# Path to actor we want to analyse\n", + "actor_path = os.path.join(\n", + " input_dir,\n", + " f\"learned_strategies/zonal_case/avg_reward_eval_policies/actors/actor_Unit {ACTOR_NUM}.pt\",\n", + ")\n", + "\n", "# Load the trained model parameters\n", "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", "model.load_state_dict(model_state[\"actor\"])" @@ -3641,12 +3548,11 @@ } ], "source": [ - "predictions = []\n", + "actions = []\n", "for obs in input_data:\n", " obs_tensor = th.tensor(obs, dtype=th.float)\n", - " prediction = model(obs_tensor)\n", - " predictions.append(prediction)\n", - "predictions" + " action = model(obs_tensor)\n", + " actions.append(action)" ] }, { @@ -3661,7 +3567,7 @@ "source": [ "# Split the data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(\n", - " input_data, predictions, test_size=0.15, random_state=42\n", + " input_data, actions, test_size=0.15, random_state=42\n", ")" ] }, From 23d6b69326d76a1fbf4decfb0dd06def658f2998 Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 20:56:31 +0200 Subject: [PATCH 26/27] - added description and checked differenc ebetween price plots with and woithout RL --- .../notebooks/09_example_Sim_and_xRL.ipynb | 7532 +++++++++++++---- 1 file changed, 6104 insertions(+), 1428 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 7f0ad666..3de54175 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 53, "id": "03cc1a12", "metadata": { "colab": { @@ -677,7 +677,7 @@ " \"max_bid_price\": 100,\n", " \"algorithm\": \"matd3\",\n", " \"learning_rate\": 0.001,\n", - " \"training_episodes\": 10,\n", + " \"training_episodes\": 50,\n", " \"episodes_collecting_initial_experience\": 3,\n", " \"train_freq\": \"4h\",\n", " \"gradient_steps\": -1,\n", @@ -687,7 +687,7 @@ " \"noise_sigma\": 0.1,\n", " \"noise_scale\": 1,\n", " \"noise_dt\": 1,\n", - " \"validation_episodes_interval\": 1,\n", + " \"validation_episodes_interval\": 3,\n", " },\n", " }\n", "}\n", @@ -939,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 54, "id": "9092a097", "metadata": { "colab": { @@ -958,13 +958,7 @@ "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from ../inputs\n", "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", @@ -989,8 +983,46 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:07, 11607.99it/s]\n", - "Training Episodes: 10%|█ | 1/10 [00:07<01:05, 7.23s/it]" + "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:06, 12013.00it/s]\n", + "Training Episodes: 2%|▏ | 1/50 [00:06<05:42, 7.00s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12345.61it/s]\n", + "Training Episodes: 4%|▍ | 2/50 [00:13<05:31, 6.91s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:05, 15499.94it/s]\n", + "Training Episodes: 6%|▌ | 3/50 [00:19<04:54, 6.26s/it]" ] }, { @@ -1008,8 +1040,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12522.58it/s]\n", - "Training Episodes: 20%|██ | 2/10 [00:14<00:55, 6.96s/it]" + "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:07, 11198.80it/s]\n", + "Training Episodes: 8%|▊ | 4/50 [00:26<05:10, 6.75s/it]" ] }, { @@ -1027,8 +1059,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_3 2019-01-01 23:00:00: : 82801.0it [00:06, 12138.86it/s]\n", - "Training Episodes: 30%|███ | 3/10 [00:20<00:48, 6.97s/it]" + "tutorial_08_zonal_case_5 2019-01-01 23:00:00: : 82801.0it [00:06, 13713.80it/s]\n", + "Training Episodes: 10%|█ | 5/50 [00:32<04:53, 6.52s/it]" ] }, { @@ -1046,7 +1078,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_4 2019-01-01 23:00:00: : 82801.0it [00:06, 12230.64it/s]" + "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801.0it [00:05, 14446.60it/s]" ] }, { @@ -1065,14 +1097,14 @@ "output_type": "stream", "text": [ "\n", - "tutorial_08_zonal_case_eval_1 2019-01-01 23:00:00: : 82801.0it [00:06, 11999.91it/s]" + "tutorial_08_zonal_case_eval_1 2019-01-01 23:00:00: : 82801.0it [00:05, 15472.29it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 1, metric='avg_reward', value=4437.90\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 1, metric='avg_reward', value=4469.33\n" ] }, { @@ -1080,7 +1112,7 @@ "output_type": "stream", "text": [ "\n", - "Training Episodes: 40%|████ | 4/10 [00:34<00:58, 9.73s/it]" + "Training Episodes: 12%|█▏ | 6/50 [00:44<05:58, 8.14s/it]" ] }, { @@ -1098,7 +1130,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_5 2019-01-01 23:00:00: : 82801.0it [00:06, 11935.11it/s]\n" + "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801.0it [00:06, 12253.09it/s]\n", + "Training Episodes: 14%|█▍ | 7/50 [00:51<05:32, 7.72s/it]" ] }, { @@ -1116,22 +1149,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12750.66it/s]" + "tutorial_08_zonal_case_8 2019-01-01 23:00:00: : 82801.0it [00:05, 14559.37it/s]\n", + "Training Episodes: 16%|█▌ | 8/50 [00:56<04:58, 7.11s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 2, metric='avg_reward', value=4463.07\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n", - "Training Episodes: 50%|█████ | 5/10 [00:48<00:55, 11.17s/it]" + "tutorial_08_zonal_case_9 2019-01-01 23:00:00: : 82801.0it [00:05, 13944.09it/s]" ] }, { @@ -1149,7 +1186,23 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_6 2019-01-01 23:00:00: : 82801.0it [00:06, 12780.81it/s]\n" + "\n", + "tutorial_08_zonal_case_eval_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12881.54it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 2, metric='avg_reward', value=4474.74\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Training Episodes: 18%|█▊ | 9/50 [01:09<06:02, 8.84s/it]" ] }, { @@ -1167,22 +1220,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_3 2019-01-01 23:00:00: : 82801.0it [00:05, 15020.90it/s]" + "tutorial_08_zonal_case_10 2019-01-01 23:00:00: : 82801.0it [00:07, 11479.97it/s]\n", + "Training Episodes: 20%|██ | 10/50 [01:16<05:35, 8.38s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 3, metric='avg_reward', value=4471.19\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n", - "Training Episodes: 60%|██████ | 6/10 [01:00<00:46, 11.55s/it]" + "tutorial_08_zonal_case_11 2019-01-01 23:00:00: : 82801.0it [00:06, 13599.80it/s]\n", + "Training Episodes: 22%|██▏ | 11/50 [01:23<05:00, 7.72s/it]" ] }, { @@ -1200,7 +1258,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_7 2019-01-01 23:00:00: : 82801.0it [00:07, 11748.34it/s]\n" + "tutorial_08_zonal_case_12 2019-01-01 23:00:00: : 82801.0it [00:06, 11938.13it/s]" ] }, { @@ -1218,14 +1276,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_4 2019-01-01 23:00:00: : 82801.0it [00:06, 13672.43it/s]" + "\n", + "tutorial_08_zonal_case_eval_3 2019-01-01 23:00:00: : 82801.0it [00:06, 12805.93it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 4, metric='avg_reward', value=4473.71\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 3, metric='avg_reward', value=4475.47\n" ] }, { @@ -1233,7 +1292,26 @@ "output_type": "stream", "text": [ "\n", - "Training Episodes: 70%|███████ | 7/10 [01:14<00:36, 12.16s/it]" + "Training Episodes: 24%|██▍ | 12/50 [01:36<06:02, 9.53s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_13 2019-01-01 23:00:00: : 82801.0it [00:05, 14116.94it/s]\n", + "Training Episodes: 26%|██▌ | 13/50 [01:42<05:13, 8.46s/it]" ] }, { @@ -1251,7 +1329,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_8 2019-01-01 23:00:00: : 82801.0it [00:06, 11865.91it/s]\n" + "tutorial_08_zonal_case_14 2019-01-01 23:00:00: : 82801.0it [00:07, 11604.06it/s]\n", + "Training Episodes: 28%|██▊ | 14/50 [01:50<04:51, 8.10s/it]" ] }, { @@ -1269,14 +1348,45 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_5 2019-01-01 23:00:00: : 82801.0it [00:06, 13445.87it/s]" + "tutorial_08_zonal_case_15 2019-01-01 23:00:00: : 82801.0it [00:06, 13097.26it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_eval_4 2019-01-01 23:00:00: : 82801.0it [00:05, 13963.28it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 5, metric='avg_reward', value=4474.55\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 4, metric='avg_reward', value=4475.63\n" ] }, { @@ -1284,7 +1394,45 @@ "output_type": "stream", "text": [ "\n", - "Training Episodes: 80%|████████ | 8/10 [01:27<00:25, 12.57s/it]" + "Training Episodes: 30%|███ | 15/50 [02:02<05:29, 9.43s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_16 2019-01-01 23:00:00: : 82801.0it [00:07, 11624.53it/s]\n", + "Training Episodes: 32%|███▏ | 16/50 [02:09<04:57, 8.76s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "tutorial_08_zonal_case_17 2019-01-01 23:00:00: : 82801.0it [00:05, 14092.96it/s]\n", + "Training Episodes: 34%|███▍ | 17/50 [02:15<04:21, 7.93s/it]" ] }, { @@ -1302,7 +1450,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_9 2019-01-01 23:00:00: : 82801.0it [00:07, 11202.30it/s]" + "tutorial_08_zonal_case_18 2019-01-01 23:00:00: : 82801.0it [00:06, 12444.20it/s]" ] }, { @@ -1321,14 +1469,14 @@ "output_type": "stream", "text": [ "\n", - "tutorial_08_zonal_case_eval_6 2019-01-01 23:00:00: : 82801.0it [00:06, 12927.91it/s]" + "tutorial_08_zonal_case_eval_5 2019-01-01 23:00:00: : 82801.0it [00:05, 15771.05it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 6, metric='avg_reward', value=4474.91\n" + "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 5, metric='avg_reward', value=4475.69\n" ] }, { @@ -1336,7 +1484,7 @@ "output_type": "stream", "text": [ "\n", - "Training Episodes: 90%|█████████ | 9/10 [01:41<00:13, 13.05s/it]" + "Training Episodes: 36%|███▌ | 18/50 [02:27<04:53, 9.19s/it]" ] }, { @@ -1354,7 +1502,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_10 2019-01-01 23:00:00: : 82801.0it [00:06, 12173.56it/s]\n" + "tutorial_08_zonal_case_19 2019-01-01 23:00:00: : 82801.0it [00:05, 13997.61it/s]\n", + "Training Episodes: 38%|███▊ | 19/50 [02:33<04:15, 8.24s/it]" ] }, { @@ -1372,21 +1521,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "tutorial_08_zonal_case_eval_7 2019-01-01 23:00:00: : 82801.0it [00:05, 14857.60it/s]\n" + "tutorial_08_zonal_case_20 2019-01-01 23:00:00: : 82801.0it [00:06, 11920.26it/s]\n", + "Training Episodes: 40%|████ | 20/50 [02:41<03:56, 7.90s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "INFO:assume.reinforcement_learning.learning_role:New best policy saved, episode: 7, metric='avg_reward', value=4475.08\n" + "INFO:assume.scenario.loader_csv:Adding markets\n", + "INFO:assume.scenario.loader_csv:Read units from file\n", + "INFO:assume.scenario.loader_csv:Adding power_plant units\n", + "INFO:assume.scenario.loader_csv:Adding demand units\n", + "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Training Episodes: 100%|██████████| 10/10 [02:19<00:00, 13.93s/it]" + "tutorial_08_zonal_case_21 2019-01-01 23:00:00: : 82801.0it [00:06, 13709.91it/s]" ] }, { @@ -1405,125 +1559,1009 @@ "output_type": "stream", "text": [ "\n", - " 0%| | 0/82800 [00:000.406667\n", " 0.406667\n", " 0.406667\n", - " 1.00\n", + " 0.72\n", " 0.406667\n", " \n", " \n", @@ -2986,7 +4016,7 @@ " 0.406667\n", " 0.406667\n", " 0.406667\n", - " 0.80\n", + " 1.00\n", " 0.406667\n", " \n", " \n", @@ -3014,7 +4044,9 @@ " ...\n", " \n", " \n", - " 175\n", + " 695\n", + " 2.46\n", + " 2.48\n", " 2.50\n", " 2.52\n", " 2.54\n", @@ -3023,14 +4055,12 @@ " 2.60\n", " 2.62\n", " 2.64\n", - " 2.66\n", - " 2.68\n", " ...\n", " 0.406667\n", " 0.406667\n", " 0.406667\n", - " 0.000000\n", - " 0.000000\n", + " 0.406667\n", + " 0.406667\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3038,7 +4068,9 @@ " 0.406667\n", " \n", " \n", - " 176\n", + " 696\n", + " 2.48\n", + " 2.50\n", " 2.52\n", " 2.54\n", " 2.56\n", @@ -3047,13 +4079,11 @@ " 2.62\n", " 2.64\n", " 2.66\n", - " 2.68\n", - " 2.22\n", " ...\n", " 0.406667\n", " 0.406667\n", - " 0.000000\n", - " 0.000000\n", + " 0.406667\n", + " 0.406667\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3062,7 +4092,9 @@ " 0.406667\n", " \n", " \n", - " 177\n", + " 697\n", + " 2.50\n", + " 2.52\n", " 2.54\n", " 2.56\n", " 2.58\n", @@ -3071,12 +4103,10 @@ " 2.64\n", " 2.66\n", " 2.68\n", - " 2.22\n", - " 2.24\n", " ...\n", " 0.406667\n", - " 0.000000\n", - " 0.000000\n", + " 0.406667\n", + " 0.406667\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3086,7 +4116,9 @@ " 0.406667\n", " \n", " \n", - " 178\n", + " 698\n", + " 2.52\n", + " 2.54\n", " 2.56\n", " 2.58\n", " 2.60\n", @@ -3095,11 +4127,9 @@ " 2.66\n", " 2.68\n", " 2.22\n", - " 2.24\n", - " 2.26\n", " ...\n", - " 0.000000\n", - " 0.000000\n", + " 0.406667\n", + " 0.406667\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3110,7 +4140,9 @@ " 0.406667\n", " \n", " \n", - " 179\n", + " 699\n", + " 2.54\n", + " 2.56\n", " 2.58\n", " 2.60\n", " 2.62\n", @@ -3119,10 +4151,8 @@ " 2.68\n", " 2.22\n", " 2.24\n", - " 2.26\n", - " 2.28\n", " ...\n", - " 0.000000\n", + " 0.406667\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", @@ -3135,7 +4165,7 @@ " \n", " \n", "\n", - "

180 rows × 50 columns

\n", + "

700 rows × 50 columns

\n", "" ], "text/plain": [ @@ -3146,11 +4176,11 @@ "3 2.30 2.32 2.34 \n", "4 2.32 2.34 2.36 \n", ".. ... ... ... \n", - "175 2.50 2.52 2.54 \n", - "176 2.52 2.54 2.56 \n", - "177 2.54 2.56 2.58 \n", - "178 2.56 2.58 2.60 \n", - "179 2.58 2.60 2.62 \n", + "695 2.46 2.48 2.50 \n", + "696 2.48 2.50 2.52 \n", + "697 2.50 2.52 2.54 \n", + "698 2.52 2.54 2.56 \n", + "699 2.54 2.56 2.58 \n", "\n", " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", "0 2.30 2.32 2.34 \n", @@ -3159,11 +4189,11 @@ "3 2.36 2.38 2.40 \n", "4 2.38 2.40 2.42 \n", ".. ... ... ... \n", - "175 2.56 2.58 2.60 \n", - "176 2.58 2.60 2.62 \n", - "177 2.60 2.62 2.64 \n", - "178 2.62 2.64 2.66 \n", - "179 2.64 2.66 2.68 \n", + "695 2.52 2.54 2.56 \n", + "696 2.54 2.56 2.58 \n", + "697 2.56 2.58 2.60 \n", + "698 2.58 2.60 2.62 \n", + "699 2.60 2.62 2.64 \n", "\n", " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", "0 2.36 2.38 2.40 \n", @@ -3172,11 +4202,11 @@ "3 2.42 2.44 2.46 \n", "4 2.44 2.46 2.48 \n", ".. ... ... ... \n", - "175 2.62 2.64 2.66 \n", - "176 2.64 2.66 2.68 \n", - "177 2.66 2.68 2.22 \n", - "178 2.68 2.22 2.24 \n", - "179 2.22 2.24 2.26 \n", + "695 2.58 2.60 2.62 \n", + "696 2.60 2.62 2.64 \n", + "697 2.62 2.64 2.66 \n", + "698 2.64 2.66 2.68 \n", + "699 2.66 2.68 2.22 \n", "\n", " price forecast t+10 ... residual load forecast t+17 \\\n", "0 2.42 ... 0.000000 \n", @@ -3185,11 +4215,11 @@ "3 2.48 ... 0.000000 \n", "4 2.50 ... 0.000000 \n", ".. ... ... ... \n", - "175 2.68 ... 0.406667 \n", - "176 2.22 ... 0.406667 \n", - "177 2.24 ... 0.406667 \n", - "178 2.26 ... 0.000000 \n", - "179 2.28 ... 0.000000 \n", + "695 2.64 ... 0.406667 \n", + "696 2.66 ... 0.406667 \n", + "697 2.68 ... 0.406667 \n", + "698 2.22 ... 0.406667 \n", + "699 2.24 ... 0.406667 \n", "\n", " residual load forecast t+18 residual load forecast t+19 \\\n", "0 0.000000 0.000000 \n", @@ -3198,11 +4228,11 @@ "3 0.000000 0.000000 \n", "4 0.000000 0.000000 \n", ".. ... ... \n", - "175 0.406667 0.406667 \n", - "176 0.406667 0.000000 \n", - "177 0.000000 0.000000 \n", - "178 0.000000 0.000000 \n", - "179 0.000000 0.000000 \n", + "695 0.406667 0.406667 \n", + "696 0.406667 0.406667 \n", + "697 0.406667 0.406667 \n", + "698 0.406667 0.000000 \n", + "699 0.000000 0.000000 \n", "\n", " residual load forecast t+20 residual load forecast t+21 \\\n", "0 0.000000 0.000000 \n", @@ -3211,11 +4241,11 @@ "3 0.000000 0.406667 \n", "4 0.406667 0.406667 \n", ".. ... ... \n", - "175 0.000000 0.000000 \n", - "176 0.000000 0.000000 \n", - "177 0.000000 0.000000 \n", - "178 0.000000 0.000000 \n", - "179 0.000000 0.000000 \n", + "695 0.406667 0.406667 \n", + "696 0.406667 0.000000 \n", + "697 0.000000 0.000000 \n", + "698 0.000000 0.000000 \n", + "699 0.000000 0.000000 \n", "\n", " residual load forecast t+22 residual load forecast t+23 \\\n", "0 0.000000 0.000000 \n", @@ -3224,29 +4254,29 @@ "3 0.406667 0.406667 \n", "4 0.406667 0.406667 \n", ".. ... ... \n", - "175 0.000000 0.000000 \n", - "176 0.000000 0.000000 \n", - "177 0.000000 0.000000 \n", - "178 0.000000 0.000000 \n", - "179 0.000000 0.000000 \n", + "695 0.000000 0.000000 \n", + "696 0.000000 0.000000 \n", + "697 0.000000 0.000000 \n", + "698 0.000000 0.000000 \n", + "699 0.000000 0.000000 \n", "\n", " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", "0 0.406667 0.00 0.406667 \n", "1 0.406667 0.68 0.406667 \n", - "2 0.406667 1.00 0.406667 \n", + "2 0.406667 0.72 0.406667 \n", "3 0.406667 0.76 0.406667 \n", - "4 0.406667 0.80 0.406667 \n", + "4 0.406667 1.00 0.406667 \n", ".. ... ... ... \n", - "175 0.000000 1.00 0.406667 \n", - "176 0.000000 1.00 0.406667 \n", - "177 0.000000 1.00 0.406667 \n", - "178 0.000000 1.00 0.406667 \n", - "179 0.000000 1.00 0.406667 \n", + "695 0.000000 1.00 0.406667 \n", + "696 0.000000 1.00 0.406667 \n", + "697 0.000000 1.00 0.406667 \n", + "698 0.000000 1.00 0.406667 \n", + "699 0.000000 1.00 0.406667 \n", "\n", - "[180 rows x 50 columns]" + "[700 rows x 50 columns]" ] }, - "execution_count": 35, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" } @@ -3290,7 +4320,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 62, "id": "c27178fe", "metadata": { "id": "4da4de57" @@ -3307,377 +4337,3771 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 63, "id": "35a088d5", "metadata": { "id": "37adecfa" }, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# which actor is the RL actor\n", - "ACTOR_NUM = len(powerplant_units) # 20\n", - "\n", - "# Path to actor we want to analyse\n", - "actor_path = os.path.join(\n", - " input_dir,\n", - " f\"learned_strategies/zonal_case/avg_reward_eval_policies/actors/actor_Unit {ACTOR_NUM}.pt\",\n", - ")\n", - "\n", - "# Load the trained model parameters\n", - "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", - "model.load_state_dict(model_state[\"actor\"])" - ] - }, - { - "cell_type": "markdown", - "id": "fe43c9c6", - "metadata": { - "id": "d4a63712" - }, - "source": [ - "Get the actions base on observation tensor we just loaded." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "4ee77fed", - "metadata": { - "id": "e6460cfb" - }, - "outputs": [ + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# which actor is the RL actor\n", + "ACTOR_NUM = len(powerplant_units) # 20\n", + "\n", + "# Path to actor we want to analyse\n", + "actor_path = os.path.join(\n", + " input_dir,\n", + " f\"learned_strategies/zonal_case/avg_reward_eval_policies/actors/actor_Unit {ACTOR_NUM}.pt\",\n", + ")\n", + "\n", + "# Load the trained model parameters\n", + "model_state = th.load(actor_path, map_location=th.device(\"cpu\"))\n", + "model.load_state_dict(model_state[\"actor\"])" + ] + }, + { + "cell_type": "markdown", + "id": "fe43c9c6", + "metadata": { + "id": "d4a63712" + }, + "source": [ + "Get the actions base on observation tensor we just loaded." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "4ee77fed", + "metadata": { + "id": "e6460cfb" + }, + "outputs": [], + "source": [ + "actions = []\n", + "for obs in input_data:\n", + " obs_tensor = th.tensor(obs, dtype=th.float)\n", + " action = model(obs_tensor)\n", + " actions.append(action)" + ] + }, + { + "cell_type": "markdown", + "id": "633c5100", + "metadata": { + "id": "ddd1ab1e" + }, + "source": [ + "## 3.2. Creating a SHAP Explainer \n", + "\n", + "In the next step we create the Shap explainer. In this example we facilitat the Kenrel Shap method. You can easly swithc it out for Deep Shap. The SHAP Kernel Explainer is a model-agnostic method for computing SHAP values, which can be applied to any machine learning model, including black-box models like neural networks, decision trees, or ensemble models. It uses a simplified linear approximation based on the Kernel SHAP method to estimate the SHAP values, allowing you to interpret how each feature contributes to a particular model’s prediction. Basically the SHAP Kernel Explainer builds a weighted linear regression model around each prediction, using different combinations (coalitions) of input features to simulate their presence or absence. This results in SHAP values that represent the marginal contribution of each feature.\n", + "\n", + "As we fit a linear regression, we split the observatoin and action data into test and train data sets." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "e5738928", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" + ] + } + ], + "source": [ + "# @ Title Split the data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " input_data, actions, test_size=0.15, random_state=42\n", + ")\n", + "\n", + "# Convert data to tensors\n", + "y_train = th.stack(y_train)\n", + "y_test = th.stack(y_test)\n", + "\n", + "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", + "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", + "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", + "y_test_tensor = th.tensor(y_test, dtype=th.float32)" + ] + }, + { + "cell_type": "markdown", + "id": "c278a8c6", + "metadata": { + "id": "ae7b108b", + "lines_to_next_cell": 2 + }, + "source": [ + "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "77b37ebc", + "metadata": { + "id": "6d9be211" + }, + "outputs": [], + "source": [ + "# @ Title Define a prediction function for generating actions for SHAP Explainer\n", + "def model_predict(X):\n", + " X_tensor = th.tensor(X, dtype=th.float32)\n", + " model.eval()\n", + " with th.no_grad():\n", + " return model(X_tensor).numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "1eac5396", + "metadata": { + "id": "84bb96cf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:shap:Using 595 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n" + ] + } + ], + "source": [ + "# Create the SHAP Kernel Explainer\n", + "explainer = shap.KernelExplainer(model_predict, X_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c38544fd", + "metadata": { + "id": "2a7929e4" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/105 [00:00),\n", - " tensor([0.9819, 0.9806], grad_fn=),\n", - " tensor([0.9819, 0.9807], grad_fn=),\n", - " tensor([0.9818, 0.9806], grad_fn=),\n", - " tensor([0.9819, 0.9807], grad_fn=),\n", - " tensor([0.9819, 0.9807], grad_fn=),\n", - " tensor([0.9820, 0.9807], grad_fn=),\n", - " tensor([0.9819, 0.9807], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - " tensor([0.9820, 0.9808], grad_fn=),\n", - 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"outputs": [ + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [-7.08871540e-06 -6.60973510e-06 -7.03551215e-06 -6.60045136e-06\n", + " -7.97565113e-06 -7.72578028e-06 -6.16150331e-06 -3.49100038e-06\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.64941321e-06\n", + " 6.65001201e-06 8.06412755e-06 9.07020034e-06 1.19488695e-05\n", + " 1.15058489e-05 1.30915366e-05 -8.97219726e-06 -6.40358028e-06\n", + " 7.79700799e-06 8.59588381e-06 9.11471863e-06 8.53495261e-06\n", + " 7.65965502e-06 1.29213789e-05 1.09182862e-05 0.00000000e+00\n", + " 0.00000000e+00 -3.88455239e-06 -4.12810965e-06 -5.57878664e-06\n", + " -7.17007358e-06 -6.88044350e-06 -8.88134329e-06 -8.95269960e-06\n", + " -9.08045839e-06 -8.49139563e-06 -8.23327821e-06 -1.01394660e-05\n", + " -8.75959261e-06 -8.07884894e-06 7.81292559e-06 8.56877562e-06\n", + " 0.00000000e+00]\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [ 1.72487005e-05 1.61709439e-05 1.75454024e-05 1.47112825e-05\n", + " 1.90359899e-05 1.86765286e-05 1.46925727e-05 8.91740032e-06\n", + " 7.99686499e-06 5.75022636e-06 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 -1.02304865e-05 -1.57618412e-05\n", + " -1.64844273e-05 -2.02406704e-05 -2.19710692e-05 -3.06357256e-05\n", + " -2.85858214e-05 -3.30018818e-05 2.03853421e-05 1.48116122e-05\n", + " -2.03804931e-05 -2.16667207e-05 -2.33724497e-05 -2.20164458e-05\n", + " -2.02428310e-05 -3.15608390e-05 -2.85867713e-05 5.00757446e-06\n", + " 6.82220169e-06 9.77218230e-06 9.99109795e-06 1.22702507e-05\n", + " 1.73503750e-05 1.61175580e-05 2.16297748e-05 2.32121626e-05\n", + " 2.17380927e-05 2.07723316e-05 1.97472717e-05 2.40736116e-05\n", + " 2.13037060e-05 1.91649673e-05 -1.86408301e-05 -2.00378635e-05\n", + " 0.00000000e+00]\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" + " 65%|██████▍ | 68/105 [07:25<03:58, 6.43s/it]" ] - } - ], - "source": [ - "# Convert data to tensors\n", - "y_train = th.stack(y_train)\n", - "y_test = th.stack(y_test)\n", - "\n", - "X_train_tensor = th.tensor(X_train, dtype=th.float32)\n", - "y_train_tensor = th.tensor(y_train, dtype=th.float32)\n", - "X_test_tensor = th.tensor(X_test, dtype=th.float32)\n", - "y_test_tensor = th.tensor(y_test, dtype=th.float32)" - ] - }, - { - "cell_type": "markdown", - "id": "633c5100", - "metadata": { - "id": "ddd1ab1e" - }, - "source": [ - "## 3.2. Creating a SHAP Explainer " - ] - }, - { - "cell_type": "markdown", - "id": "c278a8c6", - "metadata": { - "id": "ae7b108b", - "lines_to_next_cell": 2 - }, - "source": [ - "We define a prediction function compatible with SHAP and create a Kernel SHAP explainer." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "77b37ebc", - "metadata": { - "id": "6d9be211" - }, - "outputs": [], - "source": [ - "# Define a prediction function for SHAP\n", - "def model_predict(X):\n", - " X_tensor = th.tensor(X, dtype=th.float32)\n", - " model.eval()\n", - " with th.no_grad():\n", - " return model(X_tensor).numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "ce3b3daa", - "metadata": { - "id": "f0f7857b" - }, - "outputs": [], - "source": [ - "# Use a subset of training data for the background dataset\n", - "background_size = 100 # Adjust the size as needed\n", - "background = X_train[:background_size]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "1eac5396", - "metadata": { - "id": "84bb96cf" - }, - "outputs": [], - "source": [ - "# Create the SHAP Kernel Explainer\n", - "explainer = shap.KernelExplainer(model_predict, background)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c38544fd", - "metadata": { - "id": "2a7929e4" - }, - "outputs": [ + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.00000000000001\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 6.06359874e-06 0.00000000e+00 7.77248247e-06\n", + " 6.90868368e-06 8.22820850e-06 -1.12478854e-05 -1.10785106e-05\n", + " -8.70052606e-06 -7.09680497e-06 -5.89480208e-06 -5.42807445e-06\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " -7.04067314e-06 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " -7.52003433e-06 -6.70610633e-06 8.64504095e-06 7.81055485e-06\n", + " 7.47638359e-06 8.92929091e-06 7.00548497e-06 8.10196449e-06\n", + " 6.23736588e-06 5.75997601e-06 6.55434315e-06 -9.71501205e-06\n", + " 0.00000000e+00]\n", + "INFO:shap:np.sum(w_aug) = 49.00000000000001\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 -4.45996604e-06 -6.29712313e-06\n", + " -1.07594978e-05 -1.53549641e-05 -1.45376443e-05 -2.13967608e-05\n", + " -2.03244378e-05 -2.45967091e-05 2.60894661e-05 2.56258191e-05\n", + " 1.96674838e-05 1.71781396e-05 1.31997262e-05 1.27793164e-05\n", + " 7.23506792e-06 4.80164456e-06 0.00000000e+00 0.00000000e+00\n", + " 1.55319536e-05 1.28550999e-05 1.06897713e-05 7.95943896e-06\n", + " 5.28043341e-06 5.43335002e-06 5.38047072e-06 0.00000000e+00\n", + " 7.28677076e-06 9.97986379e-06 1.04095020e-05 1.23230914e-05\n", + " 1.70765130e-05 1.62324520e-05 -2.20614308e-05 -2.31496596e-05\n", + " -2.19470037e-05 -2.08094359e-05 -1.88246768e-05 -2.36064293e-05\n", + " -2.03849923e-05 -1.83117999e-05 -1.82009684e-05 2.00169406e-05\n", + " -1.50092752e-05]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 66%|██████▌ | 69/105 [07:32<03:52, 6.45s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000002\n", + "INFO:shap:phi = [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 4.26663296e-06\n", + " 5.23026564e-06 5.63953744e-06 7.43932427e-06 1.15530720e-05\n", + " 9.93578379e-06 -1.04574417e-05 -7.97716409e-06 -9.88196126e-06\n", + " -6.56655486e-06 -5.85590327e-06 0.00000000e+00 0.00000000e+00\n", + " 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"INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.00000000000001\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0000000000000004\n", + "INFO:shap:phi = [ 6.65614130e-06 5.86627424e-06 6.83182574e-06 0.00000000e+00\n", + " 7.17321440e-06 7.17415191e-06 9.14512353e-06 -1.13297866e-05\n", + " -1.12918105e-05 -1.22346528e-05 -8.69146342e-06 -9.99454903e-06\n", + " -6.49567668e-06 -5.86794670e-06 -4.99356909e-06 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 2.95566675e-06 4.36371586e-06 5.45268928e-06 5.51607857e-06\n", + " -6.45114606e-06 0.00000000e+00 -4.91110815e-06 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.11152661e-05\n", + " 1.33081936e-05 1.47821886e-05 1.01284305e-05 9.05769715e-06\n", + " 1.05169543e-05 8.25378892e-06 8.66815922e-06 8.33545715e-06\n", + " -8.81977684e-06 -8.66842748e-06 -8.52516040e-06 -1.00651839e-05\n", + " 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2.15931368e-05 1.94625301e-05 1.84130177e-05 1.98701989e-05\n", + " -1.52667173e-05]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 68%|██████▊ | 71/105 [07:53<04:52, 8.61s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:shap:num_full_subsets = 1\n", + "INFO:shap:remaining_weight_vector = [0.15162364 0.10327987 0.07918123 0.06478465 0.05524272 0.04847831\n", + " 0.04345312 0.03959062 0.03654519 0.03409718 0.0321005 0.03045432\n", + " 0.02908698 0.02794632 0.0269936 0.02619967 0.02554233 0.0250046\n", + " 0.02457349 0.02423915 0.02399431 0.02383382 0.02375437]\n", + "INFO:shap:num_paired_subset_sizes = 24\n", + "INFO:shap:weight_left = 0.7710518569800939\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [-6.16229884e-06 -5.47730986e-06 -6.10950563e-06 -5.19349835e-06\n", + " -6.91541276e-06 -6.61723958e-06 -4.83976660e-06 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 0.00000000e+00 0.00000000e+00 7.63455030e-06\n", + " 7.25438719e-06 8.99470072e-06 9.42091316e-06 1.31297342e-05\n", + " 1.18638624e-05 -8.81280554e-06 -7.35728774e-06 -5.13845240e-06\n", + " 7.96415938e-06 9.36405793e-06 8.89784336e-06 8.94263984e-06\n", + " 7.93395211e-06 1.31911248e-05 -2.21253773e-06 0.00000000e+00\n", + " 0.00000000e+00 -4.04170232e-06 -3.79773402e-06 -5.24540819e-06\n", + " -7.21961580e-06 -6.50261520e-06 -9.29290174e-06 -9.52911789e-06\n", + " -9.57806058e-06 -8.67632620e-06 -7.65273696e-06 -9.54481129e-06\n", + " -8.90484366e-06 8.26067315e-06 7.68351641e-06 8.17503441e-06\n", + " -7.86525406e-06]\n", + "INFO:shap:np.sum(w_aug) = 49.0\n", + "INFO:shap:np.sum(self.kernelWeights) = 1.0\n", + "INFO:shap:phi = [ 1.49574514e-05 1.41937030e-05 1.51984773e-05 1.29675076e-05\n", + " 1.66274289e-05 1.59803600e-05 1.20869931e-05 7.37696135e-06\n", + " 5.34144269e-06 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", + " 0.00000000e+00 -9.72545747e-06 -1.25542063e-05 -1.79675869e-05\n", + " -1.83816771e-05 -2.26701327e-05 -2.41101566e-05 -3.32572728e-05\n", + " -3.05325914e-05 2.10117302e-05 1.77615192e-05 1.31421256e-05\n", + " -2.04154286e-05 -2.20369651e-05 -2.31540199e-05 -2.22665412e-05\n", + " -2.05731124e-05 -3.20925343e-05 5.29934905e-06 5.83442809e-06\n", + " 7.11754701e-06 9.93385212e-06 1.04224384e-05 1.29618230e-05\n", + " 1.70951426e-05 1.62490595e-05 2.22719937e-05 2.35208965e-05\n", + " 2.21201560e-05 2.10470410e-05 1.94062272e-05 2.40342424e-05\n", + " 2.18203507e-05 -1.86217084e-05 -1.84147454e-05 -1.98296202e-05\n", + " 1.88924576e-05]\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/27 [00:00" ] @@ -5138,7 +9814,7 @@ }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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", 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" ] From 7b39a5742170c066d22ab21a0c97211e47d418ca Mon Sep 17 00:00:00 2001 From: kim-mskw Date: Tue, 8 Oct 2024 21:28:11 +0200 Subject: [PATCH 27/27] - some proof reading --- .../notebooks/09_example_Sim_and_xRL.ipynb | 8992 +---------------- 1 file changed, 252 insertions(+), 8740 deletions(-) diff --git a/examples/notebooks/09_example_Sim_and_xRL.ipynb b/examples/notebooks/09_example_Sim_and_xRL.ipynb index 3de54175..b0502123 100644 --- a/examples/notebooks/09_example_Sim_and_xRL.ipynb +++ b/examples/notebooks/09_example_Sim_and_xRL.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3ba8dd1e", + "id": "3efad297", "metadata": { "id": "e62e00c9" }, @@ -12,17 +12,17 @@ }, { "cell_type": "markdown", - "id": "3a2ed19f", + "id": "20db7561", "metadata": { "id": "fb3aa803" }, "source": [ - "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we'll explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. We'll work through a practical example involving an the simulation simulation in a reinforcement learning setting, and demonstrate how to compute and visualize feature attributions for the agent's actions." + "Welcome to this tutorial on **Explainable Reinforcement Learning (XRL)**! In this guide, we will explore how to interpret and explain the decisions made by reinforcement learning agents using the SHAP (SHapley Additive exPlanations) library. Through a practical example involving a simulation in a reinforcement learning setting, we'll demonstrate how to compute and visualize feature attributions for the agent's actions." ] }, { "cell_type": "markdown", - "id": "131db756", + "id": "046a32c0", "metadata": { "id": "0d793362" }, @@ -32,7 +32,7 @@ }, { "cell_type": "markdown", - "id": "667dc923", + "id": "f45c2689", "metadata": { "id": "87bdf688" }, @@ -47,7 +47,7 @@ "\n", " 2.2 Introduction to SHAP Values\n", "\n", - "3. [Calculating SHAP values](#3-calculating-shap-values)\n", + "3. [Calculating SHAP Values](#3-calculating-shap-values)\n", "\n", " 3.1. [Loading and Preparing Data](#loading-and-preparing-data)\n", "\n", @@ -60,7 +60,7 @@ }, { "cell_type": "markdown", - "id": "ed057a2a", + "id": "5adc5d28", "metadata": { "id": "5e8c7fec" }, @@ -70,76 +70,93 @@ }, { "cell_type": "markdown", - "id": "3a3562cf", + "id": "a5b2ef10", "metadata": { "id": "06e91420" }, "source": [ - "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as black boxes due to their complex architectures and non-linear computations. This opacity poses challenges in understanding and trusting the decisions made by RL agents, especially in critical applications." + "Reinforcement Learning (RL) has achieved remarkable success in various domains, such as game playing, robotics, and autonomous systems. However, RL models, particularly those using deep neural networks, are often seen as **black boxes** due to their complex architectures and non-linear computations. This opacity makes it challenging to understand and trust the decisions made by RL agents, especially in critical applications where transparency is essential." ] }, { "cell_type": "markdown", - "id": "4c610270", + "id": "d1fcbef6", "metadata": { "id": "47b1e7ab" }, "source": [ - "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into the agent's decision-making process. By leveraging explainability techniques, we can interpret the actions of an RL agent, understand the influence of input features, and potentially improve the model's performance and fairness." + "**Explainable Reinforcement Learning (XRL)** aims to bridge this gap by providing insights into an agent's decision-making process. By leveraging explainability techniques, we can:\n", + "- Interpret the actions of an RL agent.\n", + "- Understand the influence of input features on decisions.\n", + "- Potentially improve the model's performance, fairness, and transparency." ] }, { "cell_type": "markdown", - "id": "9978b51b", + "id": "5ced3235", "metadata": { "id": "ec0717c1" }, "source": [ - "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network within an RL framework to explain the agent's actions." + "In this tutorial, we will demonstrate how to apply SHAP values to a trained actor neural network in an RL framework to explain the agent's actions." ] }, { "cell_type": "markdown", - "id": "23bed9c3", + "id": "49f01746", "metadata": { - "id": "0d59bb0a" + "id": "0d59bb0a", + "lines_to_next_cell": 0 }, "source": [ - "### 1.1 Run an the simulation MADRL Simulation \n", + "### 1.1 Running a MADRL Simulation \n", "\n", - "In ASSUME, we implement RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. Key aspects include:\n", + "In this tutorial, we will simulate RL agents using a Multi-Agent Deep Reinforcement Learning (MADRL) approach. The agents operate in a market-splitting environment where they interact and learn optimal strategies over time. Here’s a breakdown of the key components:\n", "\n", + "- **Observations**: Each agent receives observations, including market forecasts, unit-specific information, and past actions.\n", + "- **Actions**: The agents decide on bidding strategies, such as bid prices for both inflexible and flexible capacities.\n", + "- **Rewards**: The agents are rewarded based on profits and opportunity costs, helping them learn optimal bidding strategies.\n", + "- **Algorithm**: We utilize a multi-agent version of the TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithm, which ensures stable learning even in non-stationary environments.\n", "\n", - "- **Observations**: Each agent receives observations comprising market forecasts, unit-specific information, and past actions.\n", - "- **Actions**: Agents decide on bidding strategies, such as bid prices for inflexible and flexible capacities.\n", - "- **Rewards**: Agents receive rewards based on profits and opportunity costs, guiding them to learn optimal bidding strategies.\n", - "- **Algorithm**: We utilize a multi-agent version of the TD3 algorithm, ensuring stable learning in a non-stationary environment.\n", + "For a more detailed explanation of the RL configurations, refer to the [Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial).\n", "\n", - "For a deep dive into the RL configurations we refer to one of the other tutorials, such as\n", - "[Deep Reinforcement Learning Tutorial](https://example.com/deep-rl-tutorial)\n", + "### Key Aspects of the Simulation\n", "\n", - "Agents need observations to make informed decisions. Observations include:\n", + "Agents require **observations** to make informed decisions, which include:\n", "\n", - "- **Residual Load Forecast**: Forecasted net demand over the next 24 hours.\n", + "- **Residual Load Forecast**: Forecasted net demand (electricity demand minus renewable generation) over the next 24 hours.\n", "- **Price Forecast**: Forecasted market prices over the next 24 hours.\n", - "- **Marginal Cost**: Current marginal cost of the unit.\n", - "- **Previous Output**: Dispatched capacity from the previous time step.\n", + "- **Marginal Cost**: The current marginal cost of operating the agent's power-generating unit.\n", + "- **Previous Output**: The agent’s dispatched capacity (energy production) from the previous time step.\n", "\n", + "### Agent Actions\n", "\n", - "Agents choose actions based on the observations. The action space is two-dimensional, corresponding to:\n", + "The action space for the agents is two-dimensional and consists of:\n", "\n", - "- Bid Price for Inflexible Capacity (p_inflex): The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", - "- Bid Price for Flexible Capacity (p_flex): The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n", + "- **Bid Price for Inflexible Capacity (p_inflex)**: The price at which the agent offers its minimum power output (must-run capacity) to the market.\n", + "- **Bid Price for Flexible Capacity (p_flex)**: The price for the additional capacity above the minimum output that the agent can flexibly adjust.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f3b17e49", + "metadata": { + "id": "e62e00c9" + }, + "source": [ + "#### 1.1.1 Install Assume and Required Packages\n", "\n", - "#### 1.1.1 Install Assume and needed Packages\n", + "In this section, we will install the necessary packages to run the **Assume framework** along with other dependencies.\n", + "The process is similar to the other tutorial on Assume.\n", "\n", - "Similar to the other tutorial, we can run Assume in the following way." + "The following commands will install Assume and its dependencies for reinforcement learning, along with additional libraries such as Plotly for visualization.\n", + "Make sure to install these before running the main code." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "02dea28f", + "execution_count": null, + "id": "647079b9", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -153,15 +170,25 @@ }, "outputs": [], "source": [ - "#!pip install 'assume-framework[learning]'\n", - "#!pip install plotly\n", - "#!git clone https://github.com/assume-framework/assume.git assume-repo" + "!pip install 'assume-framework[learning]'\n", + "!pip install plotly\n", + "!git clone https://github.com/assume-framework/assume.git assume-repo" + ] + }, + { + "cell_type": "markdown", + "id": "a3ca95d2", + "metadata": { + "id": "e62e00c9" + }, + "source": [ + "You will also need to install additional optimization libraries like Pyomo and GLPK. These libraries are crucial for modeling and solving optimization problems in the simulation." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "544d38a4", + "execution_count": null, + "id": "7c8f0fb4", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -174,51 +201,41 @@ }, "outputs": [], "source": [ - "#!pip install pyomo\n", - "#!apt-get install -y -qq glpk-utils\n", - "#!pip install nbconvert" + "!pip install pyomo\n", + "!apt-get install -y -qq glpk-utils\n", + "!pip install nbconvert" ] }, { "cell_type": "markdown", - "id": "5ca60145", + "id": "e28add86", "metadata": {}, "source": [ - "Define paths to use depending on colab or local usage." + "Define paths to differentiate between Colab or local usage.\n", + "If you're running this on Google Colab, the paths might differ slightly from your local environment.\n", + "You can configure the paths accordingly based on where you're executing the code." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "a578e164", + "execution_count": null, + "id": "b7c91474", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bfd1daf2", - "outputId": "1edeb31f-bc3a-493e-b518-01f4188c44b6" + "id": "e62e00c9" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../inputs\n" - ] - } - ], + "outputs": [], "source": [ "import importlib.util\n", "\n", "import pandas as pd\n", "\n", - "# import plotly for visualization\n", + "#import plotly for visualization\n", "import plotly.graph_objects as go\n", "\n", - "# import yaml for reading and writing YAML files\n", + "#import yaml for reading and writing YAML files\n", "import yaml\n", "\n", - "# Check if 'google.colab' is available\n", + "#Check if 'google.colab' is available\n", "IN_COLAB = importlib.util.find_spec(\"google.colab\") is not None\n", "\n", "colab_inputs_path = \"assume-repo/examples/inputs\"\n", @@ -231,43 +248,52 @@ }, { "cell_type": "markdown", - "id": "73087adf", + "id": "d95724ea", "metadata": { "id": "636ea9ae" }, "source": [ - "#### 1.1.2 Create and Load example files from market splitting tutorial\n", + "#### 1.1.2 Create and Load Example Files from Market Splitting Tutorial\n", + "\n", + "To define the RL Agent, we need to obtain the results from the **Market Zone Splitting** tutorial.\n", + "This tutorial provides essential data that the RL agent will use for decision-making.\n", "\n", - "We need to get the results form the market zone splitting tutorial, for which we are defining the RL Agent here. If you are working in colab execute the follwoing cells. If you are not working in colab but on your local machine simply open the respective tuttorial notebook and let it run." + "If you are working in **Google Colab**, execute the following cells to download and run the necessary notebook automatically. \n", + "If you are working on your **local machine**, simply open the respective tutorial notebook and execute it manually." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "116b9e37", + "execution_count": null, + "id": "85fdfe19", "metadata": { + "lines_to_next_cell": 2, "vscode": { "languageId": "shellscript" } }, "outputs": [], "source": [ - "# if used locally\n", - "#%cd assume/examples/notebooks/\n", + "# For local execution:\n", + "%cd assume/examples/notebooks/\n", "\n", - "# if used in colab\n", - "#%cd assume-repo/examples/notebooks/\n", + "# For execution in Google Colab:\n", + "%cd assume-repo/examples/notebooks/\n", "\n", - "#!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", + "# Execute the Market Zone Splitting tutorial:\n", + "!jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=60 --output output.ipynb 08_market_zone_coupling.ipynb\n", "\n", - "#%cd content\n", - "#!cp -r assume-repo/examples/notebooks/inputs ." + "# Return to content folder (for Colab):\n", + "%cd content\n", + "\n", + "# Copy inputs directory to the working folder (for Colab):\n", + "!cp -r assume-repo/examples/notebooks/inputs ." ] }, { "cell_type": "code", - "execution_count": 5, - "id": "9871a2a5", + "execution_count": null, + "id": "1ca7eab9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -275,15 +301,7 @@ "id": "233f315b", "outputId": "f98da7d4-0080-4546-c642-838f722965b0" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input CSV files have been read from 'inputs/tutorial_08'.\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -300,24 +318,24 @@ }, { "cell_type": "markdown", - "id": "bcfa21b7", + "id": "a9fc51a8", "metadata": { "id": "6985289b" }, "source": [ - "#### 1.1.3 Let's make this a leanring example\n", + "#### 1.1.3 Transform the Scenario into a Learning Example\n", "\n", - "The next cells show how we can transform any configured example in Assume into a learning example.\n", + "The following cells show how we can convert any pre-configured scenario in Assume into a learning example.\n", "\n", - "**Define a learning power plan**\n", + "**Define a Learning Power Plant**\n", "\n", - "We place a learning nuclear power plant in the south zone that has a 5 times hihger maximal power, to generate a scenario where it has a price impact." + "In this example, we place a learning nuclear power plant in the southern zone. This plant has five times the maximum power of a typical plant, which allows us to create a scenario where its actions have a noticeable impact on market prices." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "ac7d12fb", + "execution_count": null, + "id": "8c4153fa", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -326,311 +344,86 @@ "id": "b205256f", "outputId": "b9bb887b-f534-4a50-dd5b-229be1012600" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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technologybidding_zonalfuel_typeemission_factormax_powermin_powerefficiencyadditional_costnodeunit_operator
name
Unit 11nuclearnaive_eomuranium0.01000.00.00.315north_2Operator North
Unit 12nuclearnaive_eomuranium0.01000.00.00.316north_2Operator North
Unit 13nuclearnaive_eomuranium0.01000.00.00.317north_2Operator North
Unit 14nuclearnaive_eomuranium0.01000.00.00.318north_2Operator North
Unit 15nuclearnaive_eomuranium0.01000.00.00.319north_2Operator North
Unit 16nuclearnaive_eomuranium0.01000.00.00.320southOperator South
Unit 17nuclearnaive_eomuranium0.01000.00.00.321southOperator South
Unit 18nuclearnaive_eomuranium0.01000.00.00.322southOperator South
Unit 19nuclearnaive_eomuranium0.01000.00.00.323southOperator South
Unit 20nuclearpp_learninguranium0.05000.00.00.324southOperator-RL
\n", - "
" - ], - "text/plain": [ - " technology bidding_zonal fuel_type emission_factor max_power \\\n", - "name \n", - "Unit 11 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 12 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 13 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 14 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 15 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 16 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 17 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 18 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 19 nuclear naive_eom uranium 0.0 1000.0 \n", - "Unit 20 nuclear pp_learning uranium 0.0 5000.0 \n", - "\n", - " min_power efficiency additional_cost node unit_operator \n", - "name \n", - "Unit 11 0.0 0.3 15 north_2 Operator North \n", - "Unit 12 0.0 0.3 16 north_2 Operator North \n", - "Unit 13 0.0 0.3 17 north_2 Operator North \n", - "Unit 14 0.0 0.3 18 north_2 Operator North \n", - "Unit 15 0.0 0.3 19 north_2 Operator North \n", - "Unit 16 0.0 0.3 20 south Operator South \n", - "Unit 17 0.0 0.3 21 south Operator South \n", - "Unit 18 0.0 0.3 22 south Operator South \n", - "Unit 19 0.0 0.3 23 south Operator South \n", - "Unit 20 0.0 0.3 24 south Operator-RL " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "# create scarcity in south germany\n", + "# Create scarcity in southern Germany by limiting the number of power plants\n", "powerplant_units = powerplant_units[:20]\n", "\n", - "# assign RL power plant and give it market power\n", + "# Assign the RL-controlled power plant and give it market power\n", "powerplant_units.loc[19, \"bidding_zonal\"] = \"pp_learning\"\n", - "powerplant_units.loc[19, \"max_power\"] = 5000\n", + "powerplant_units.loc[19, \"max_power\"] = 5000 # Set maximum power to 5000 MW\n", "\n", - "# assig specific RL unit operator to plant\n", + "# Assign a specific RL unit operator to the plant\n", "powerplant_units.loc[19, \"unit_operator\"] = \"Operator-RL\"\n", "\n", - "# make name column to index\n", + "# Set the 'name' column as the index\n", "powerplant_units.set_index(\"name\", inplace=True, drop=True)\n", "\n", - "# store power plant units to csv again\n", + "# Save the updated power plant units to a CSV file\n", "powerplant_units.to_csv(input_dir + \"/powerplant_units.csv\")\n", "\n", + "# Show the last 10 entries\n", "powerplant_units.tail(10)" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9316ac03", - "metadata": { - "id": "QBTGrw62_5I7" - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", - "id": "8f995ab9", + "id": "c8a22a61", "metadata": { "id": "cce0e8b4" }, "source": [ "**Configure Learning Hyperparameters in YAML**\n", "\n", - "Change the yaml to configure the learning specific hyperparameters. In the following we provide a brief description of the hyper parameters:\n", + "The following YAML configuration contains the learning-specific hyperparameters that will guide the RL agent's training process. Below is a brief description of these hyperparameters:\n", "\n", "- **continue_learning** (`False`): \n", - " - Indicates whether the agent should continue training from a previously saved state or start fresh.\n", + " - Whether to continue training from a previously saved state or start fresh.\n", "\n", "- **max_bid_price** (`100`): \n", - " - The maximum bid price allowed during the agent's interaction with the environment, which is used to scale the output of the actor.\n", + " - The maximum allowable bid price for the agent, used to scale the actor's output.\n", "\n", "- **algorithm** (`\"matd3\"`): \n", - " - The type of reinforcement learning algorithm used. In this case, `MATD3`, which stands for Multi-Agent Twin Delayed Deep Deterministic Policy Gradient.\n", + " - The learning algorithm to be used, in this case `MATD3` (Multi-Agent Twin Delayed Deep Deterministic Policy Gradient).\n", "\n", "- **learning_rate** (`0.001`): \n", - " - The learning rate for the algorithm's optimizer. This determines how big the steps of the models parameter update are during training.\n", + " - The rate at which the model’s parameters are updated during training.\n", "\n", "- **training_episodes** (`50`): \n", - " - The total number of training episodes the agent will go through.\n", + " - The total number of episodes for training the agent.\n", "\n", "- **episodes_collecting_initial_experience** (`3`): \n", - " - The number of episodes dedicated to collecting initial experience before training begins. During this period the agent follows a random policy around some base value.\n", + " - Number of episodes dedicated to collecting initial experience before actual training begins, during which the agent follows a random policy.\n", "\n", - "- **train_freq** (`\"24h\"`): \n", - " - The frequency at which the model is trained. In this case, training occurs every 24 hours.\n", + "- **train_freq** (`\"4h\"`): \n", + " - Frequency of model training, in this case, every 4 hours.\n", "\n", "- **gradient_steps** (`-1`): \n", - " - The number of gradient steps to be taken at each training interval. A value of `-1` typically means to perform as many gradient steps as the number of collected experience samples allows.\n", + " - The number of gradient updates to perform at each training step. A value of `-1` typically means that all collected experience will be used for training.\n", "\n", "- **batch_size** (`256`): \n", - " - The number of samples in each mini-batch used for training the model.\n", + " - The size of the mini-batch used for training.\n", "\n", "- **gamma** (`0.99`): \n", - " - The discount factor for future rewards, representing how much importance the agent places on long-term rewards versus immediate rewards.\n", + " - The discount factor for future rewards, balancing short-term vs. long-term reward importance.\n", "\n", "- **device** (`\"cpu\"`): \n", - " - The computational device used for training. In this case, training is performed on a CPU.\n", + " - The computational device for training. In this case, the CPU is used.\n", "\n", "- **noise_sigma** (`0.1`): \n", - " - The standard deviation (sigma) of the noise added to the actions for exploration purposes.\n", + " - The standard deviation of the exploration noise added to actions.\n", "\n", - "- **noise_scale** (`1`): \n", - "- **noise_dt** (`1`): \n", - " - A scaling factor applied to the noise added for exploration, influencing the amount of exploration.\n", - " - it is used for decay, but since both values are 1 no decay is applied. \n", + "- **noise_scale** (`1`) and **noise_dt** (`1`): \n", + " - Parameters controlling the scale and time step of the exploration noise. Since both are set to 1, no decay is applied.\n", "\n", - "- **validation_episodes_interval** (`5`): \n", - " - The interval at which the model is validated during training, i.e., validation occurs every 5 episodes.\n" + "- **validation_episodes_interval** (`3`): \n", + " - The interval (in episodes) at which validation is performed during training." ] }, { "cell_type": "code", - "execution_count": 53, - "id": "03cc1a12", + "execution_count": null, + "id": "f6c64dc2", "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -638,16 +431,9 @@ "id": "9c555ce9", "outputId": "473126ae-3c3e-4698-e3a5-347cc00e5108" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration YAML file has been saved to '../inputs\\tutorial_08\\config.yaml'.\n" - ] - } - ], + "outputs": [], "source": [ + "# YAML configuration for the RL training\n", "config = {\n", " \"zonal_case\": {\n", " \"start_date\": \"2019-01-01 00:00\",\n", @@ -692,7 +478,7 @@ " }\n", "}\n", "\n", - "# Define the path for the config file\n", + "# Define the path for the configuration file\n", "config_path = os.path.join(input_dir, \"config.yaml\")\n", "\n", "# Save the configuration to a YAML file\n", @@ -704,18 +490,19 @@ }, { "cell_type": "markdown", - "id": "59a880d8", + "id": "052cbdb4", "metadata": { "id": "3f0f38fb" }, "source": [ - "For XRL, we need enhanced logging of the learning process, which is not currently a feature of ASSUME itself. Therefore, we are overriding some functions to enable this logging specifically for the purpose of this tutorial." + "In order to make this setup compatible with XRL, we need to enhance the logging of the learning process. \n", + "ASSUME does not have this feature natively, so we will override some functions to enable this logging for the purpose of this tutorial." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "823db62a", + "execution_count": null, + "id": "a01977d5", "metadata": { "cellView": "form", "id": "201251c6" @@ -927,20 +714,21 @@ }, { "cell_type": "markdown", - "id": "d52cb7d4", + "id": "29fa6b82", "metadata": { "id": "dcacfe26" }, "source": [ - "**Run the example case**\n", + "**Run the Example Case**\n", "\n", - "Now we run the example case similar to before in the market zone tutorial. the only difference is that we call the run_learning function, whcih itterates multiple times over the simulation horizon. " + "Now we run the example case as done previously in the market zone tutorial. \n", + "The main difference here is that we call the `run_learning()` function, which iterates multiple times over the simulation horizon for reinforcement learning." ] }, { "cell_type": "code", - "execution_count": 54, - "id": "9092a097", + "execution_count": null, + "id": "0c1c9334", "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -949,1546 +737,36 @@ "id": "bfadf522", "outputId": "7c91ab13-a3c2-4e89-d8ac-d20be95391f6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.world:connected to db\n", - "INFO:assume.scenario.loader_csv:Starting Scenario tutorial_08/zonal_case from ../inputs\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n", - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n", - "INFO:assume.scenario.loader_csv:storage_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:industrial_dsm_units not found. Returning None\n", - "INFO:assume.scenario.loader_csv:forecasts_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:cross_border_flows not found. Returning None\n", - "INFO:assume.scenario.loader_csv:availability_df not found. Returning None\n", - "INFO:assume.scenario.loader_csv:electricity_prices not found. Returning None\n", - "INFO:assume.scenario.loader_csv:price_forecasts not found. Returning None\n", - "INFO:assume.scenario.loader_csv:temperature not found. Returning None\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_1 2019-01-01 23:00:00: : 82801.0it [00:06, 12013.00it/s]\n", - "Training Episodes: 2%|▏ | 1/50 [00:06<05:42, 7.00s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - "INFO:assume.scenario.loader_csv:Adding power_plant units\n", - "INFO:assume.scenario.loader_csv:Adding demand units\n", - "INFO:assume.scenario.loader_csv:Adding unit operators and units\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "tutorial_08_zonal_case_2 2019-01-01 23:00:00: : 82801.0it [00:06, 12345.61it/s]\n", - "Training Episodes: 4%|▍ | 2/50 [00:13<05:31, 6.91s/it]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO:assume.scenario.loader_csv:Adding markets\n", - "INFO:assume.scenario.loader_csv:Read units from file\n", - 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price forecast t+1price forecast t+2price forecast t+3price forecast t+4price forecast t+5price forecast t+6price forecast t+7price forecast t+8price forecast t+9price forecast t+10...residual load forecast t+17residual load forecast t+18residual load forecast t+19residual load forecast t+20residual load forecast t+21residual load forecast t+22residual load forecast t+23residual load forecast t+24total capacity t-1marginal costs t-1
02.242.262.282.302.322.342.362.382.402.42...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.4066670.000.406667
12.262.282.302.322.342.362.382.402.422.44...0.0000000.0000000.0000000.0000000.0000000.0000000.4066670.4066670.680.406667
22.282.302.322.342.362.382.402.422.442.46...0.0000000.0000000.0000000.0000000.0000000.4066670.4066670.4066670.720.406667
32.302.322.342.362.382.402.422.442.462.48...0.0000000.0000000.0000000.0000000.4066670.4066670.4066670.4066670.760.406667
42.322.342.362.382.402.422.442.462.482.50...0.0000000.0000000.0000000.4066670.4066670.4066670.4066670.4066671.000.406667
..................................................................
6952.462.482.502.522.542.562.582.602.622.64...0.4066670.4066670.4066670.4066670.4066670.0000000.0000000.0000001.000.406667
6962.482.502.522.542.562.582.602.622.642.66...0.4066670.4066670.4066670.4066670.0000000.0000000.0000000.0000001.000.406667
6972.502.522.542.562.582.602.622.642.662.68...0.4066670.4066670.4066670.0000000.0000000.0000000.0000000.0000001.000.406667
6982.522.542.562.582.602.622.642.662.682.22...0.4066670.4066670.0000000.0000000.0000000.0000000.0000000.0000001.000.406667
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\n", - "" - ], - "text/plain": [ - " price forecast t+1 price forecast t+2 price forecast t+3 \\\n", - "0 2.24 2.26 2.28 \n", - "1 2.26 2.28 2.30 \n", - "2 2.28 2.30 2.32 \n", - "3 2.30 2.32 2.34 \n", - "4 2.32 2.34 2.36 \n", - ".. ... ... ... \n", - "695 2.46 2.48 2.50 \n", - "696 2.48 2.50 2.52 \n", - "697 2.50 2.52 2.54 \n", - "698 2.52 2.54 2.56 \n", - "699 2.54 2.56 2.58 \n", - "\n", - " price forecast t+4 price forecast t+5 price forecast t+6 \\\n", - "0 2.30 2.32 2.34 \n", - "1 2.32 2.34 2.36 \n", - "2 2.34 2.36 2.38 \n", - "3 2.36 2.38 2.40 \n", - "4 2.38 2.40 2.42 \n", - ".. ... ... ... \n", - "695 2.52 2.54 2.56 \n", - "696 2.54 2.56 2.58 \n", - "697 2.56 2.58 2.60 \n", - "698 2.58 2.60 2.62 \n", - "699 2.60 2.62 2.64 \n", - "\n", - " price forecast t+7 price forecast t+8 price forecast t+9 \\\n", - "0 2.36 2.38 2.40 \n", - "1 2.38 2.40 2.42 \n", - "2 2.40 2.42 2.44 \n", - "3 2.42 2.44 2.46 \n", - "4 2.44 2.46 2.48 \n", - ".. ... ... ... \n", - "695 2.58 2.60 2.62 \n", - "696 2.60 2.62 2.64 \n", - "697 2.62 2.64 2.66 \n", - "698 2.64 2.66 2.68 \n", - "699 2.66 2.68 2.22 \n", - "\n", - " price forecast t+10 ... residual load forecast t+17 \\\n", - "0 2.42 ... 0.000000 \n", - "1 2.44 ... 0.000000 \n", - "2 2.46 ... 0.000000 \n", - "3 2.48 ... 0.000000 \n", - "4 2.50 ... 0.000000 \n", - ".. ... ... ... \n", - "695 2.64 ... 0.406667 \n", - "696 2.66 ... 0.406667 \n", - "697 2.68 ... 0.406667 \n", - "698 2.22 ... 0.406667 \n", - "699 2.24 ... 0.406667 \n", - "\n", - " residual load forecast t+18 residual load forecast t+19 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.000000 \n", - "4 0.000000 0.000000 \n", - ".. ... ... \n", - "695 0.406667 0.406667 \n", - "696 0.406667 0.406667 \n", - "697 0.406667 0.406667 \n", - "698 0.406667 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+20 residual load forecast t+21 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.000000 \n", - "2 0.000000 0.000000 \n", - "3 0.000000 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "695 0.406667 0.406667 \n", - "696 0.406667 0.000000 \n", - "697 0.000000 0.000000 \n", - "698 0.000000 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+22 residual load forecast t+23 \\\n", - "0 0.000000 0.000000 \n", - "1 0.000000 0.406667 \n", - "2 0.406667 0.406667 \n", - "3 0.406667 0.406667 \n", - "4 0.406667 0.406667 \n", - ".. ... ... \n", - "695 0.000000 0.000000 \n", - "696 0.000000 0.000000 \n", - "697 0.000000 0.000000 \n", - "698 0.000000 0.000000 \n", - "699 0.000000 0.000000 \n", - "\n", - " residual load forecast t+24 total capacity t-1 marginal costs t-1 \n", - "0 0.406667 0.00 0.406667 \n", - "1 0.406667 0.68 0.406667 \n", - "2 0.406667 0.72 0.406667 \n", - "3 0.406667 0.76 0.406667 \n", - "4 0.406667 1.00 0.406667 \n", - ".. ... ... ... \n", - "695 0.000000 1.00 0.406667 \n", - "696 0.000000 1.00 0.406667 \n", - "697 0.000000 1.00 0.406667 \n", - "698 0.000000 1.00 0.406667 \n", - "699 0.000000 1.00 0.406667 \n", - "\n", - "[700 rows x 50 columns]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# path to extra loggedobservation values \n", "path = (\n", @@ -4300,7 +1167,7 @@ }, { "cell_type": "markdown", - "id": "509b19bf", + "id": "8add0715", "metadata": { "id": "5d8b9dcf" }, @@ -4310,7 +1177,7 @@ }, { "cell_type": "markdown", - "id": "bf2b101a", + "id": "d7a9e67d", "metadata": { "id": "b1b50488" }, @@ -4320,8 +1187,8 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "c27178fe", + "execution_count": null, + "id": "cca85e13", "metadata": { "id": "4da4de57" }, @@ -4337,23 +1204,12 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "35a088d5", + "execution_count": null, + "id": "1cd3b7e6", "metadata": { "id": "37adecfa" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# which actor is the RL actor\n", "ACTOR_NUM = len(powerplant_units) # 20\n", @@ -4371,7 +1227,7 @@ }, { "cell_type": "markdown", - "id": "fe43c9c6", + "id": "85d43a1b", "metadata": { "id": "d4a63712" }, @@ -4381,8 +1237,8 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "4ee77fed", + "execution_count": null, + "id": "c507d331", "metadata": { "id": "e6460cfb" }, @@ -4397,7 +1253,7 @@ }, { "cell_type": "markdown", - "id": "633c5100", + "id": "579665bc", "metadata": { "id": "ddd1ab1e" }, @@ -4411,19 +1267,10 @@ }, { "cell_type": "code", - "execution_count": 65, - "id": "e5738928", + "execution_count": null, + "id": "b0758eb5", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", - "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n" - ] - } - ], + "outputs": [], "source": [ "# @ Title Split the data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(\n", @@ -4442,7 +1289,7 @@ }, { "cell_type": "markdown", - "id": "c278a8c6", + "id": "abbaa45f", "metadata": { "id": "ae7b108b", "lines_to_next_cell": 2 @@ -4453,8 +1300,8 @@ }, { "cell_type": "code", - "execution_count": 66, - "id": "77b37ebc", + "execution_count": null, + "id": "40e12192", "metadata": { "id": "6d9be211" }, @@ -4470,20 +1317,12 @@ }, { "cell_type": "code", - "execution_count": 67, - "id": "1eac5396", + "execution_count": null, + "id": "56a32f41", "metadata": { "id": "84bb96cf" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:shap:Using 595 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n" - ] - } - ], + "outputs": [], "source": [ "# Create the SHAP Kernel Explainer\n", "explainer = shap.KernelExplainer(model_predict, X_train)" @@ -4491,5270 +1330,12 @@ }, { "cell_type": "code", - "execution_count": 68, - "id": "c38544fd", + "execution_count": null, + "id": "4279910b", "metadata": { "id": "2a7929e4" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/105 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Summary plot for the first output dimension\n", "shap.summary_plot(shap_values[0], X_test, feature_names=feature_names, show=False)\n", @@ -9873,7 +1399,7 @@ }, { "cell_type": "markdown", - "id": "bd527a94", + "id": "7ee7ebfb", "metadata": { "id": "9a888f8b" }, @@ -9883,7 +1409,7 @@ }, { "cell_type": "markdown", - "id": "3244d847", + "id": "555afa84", "metadata": { "id": "c6c4ce8c" }, @@ -9894,7 +1420,7 @@ }, { "cell_type": "markdown", - "id": "37dc7965", + "id": "5dc307ed", "metadata": { "id": "86545200" }, @@ -9904,7 +1430,7 @@ }, { "cell_type": "markdown", - "id": "294524a4", + "id": "740b57e2", "metadata": { "id": "06f3977c" }, @@ -9914,7 +1440,7 @@ }, { "cell_type": "markdown", - "id": "ba535ae6", + "id": "0f79634a", "metadata": { "id": "dadd0a0c" }, @@ -9924,7 +1450,7 @@ }, { "cell_type": "markdown", - "id": "1282f757", + "id": "d0035820", "metadata": { "id": "37633c16" }, @@ -9934,7 +1460,7 @@ }, { "cell_type": "markdown", - "id": "731058ed", + "id": "80c10373", "metadata": { "id": "8735d66f" }, @@ -9944,7 +1470,7 @@ }, { "cell_type": "markdown", - "id": "9b8fb14b", + "id": "80754bea", "metadata": { "id": "d6b0332f" }, @@ -9957,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "3c17d271", + "id": "0b873097", "metadata": { "id": "a8cdea5f" }, @@ -9968,26 +1494,12 @@ ], "metadata": { "jupytext": { - "cell_metadata_filter": "vscode,id,outputId,cellView,colab,-all", + "cell_metadata_filter": "vscode,outputId,colab,cellView,id,-all", "main_language": "python", "notebook_metadata_filter": "-all" }, - "kernelspec": { - "display_name": "assume-framework", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "name": "python" } }, "nbformat": 4,