Merge pull request #474 from assume-framework/fix_spelling

Add codespell to pre-commit

.pre-commit-config.yaml

@@ -24,3 +24,9 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
       - id: check-illegal-windows-names
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.3.0
+    hooks:
+    -   id: codespell
+        types_or: [python, rst, markdown]
+        files: ^(assume|docs|tests)/

CODE_OF_CONDUCT.md

@@ -11,7 +11,7 @@ SPDX-License-Identifier: AGPL-3.0-or-later
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
+identity and expression, level of experience, education, socioeconomic status,
 nationality, personal appearance, race, caste, color, religion, or sexual
 identity and orientation.
 

Dockerfile

@@ -10,7 +10,7 @@ RUN mkdir /src
 WORKDIR /src
 COPY README.md pyproject.toml .
 #RUN python -m pip install --upgrade pip
-# thats needed to use create the requirements.txt only
+# that's needed to use create the requirements.txt only
 RUN pip install pip-tools
 RUN mkdir assume assume_cli
 RUN touch assume/__init__.py

README.md

@@ -94,7 +94,7 @@ To ease your way into ASSUME we provided some examples and tutorials. The former
 
 ### The Tutorials
 
-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.
+The tutorials work completely 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?
 [![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)

assume/common/base.py

@@ -417,12 +417,12 @@ def get_operation_time(self, start: datetime) -> int:
             # before start of index
             return max_time
         is_off = not arr.iloc[0]
-        runn = 0
+        run = 0
         for val in arr:
             if val == is_off:
                 break
-            runn += 1
-        return (-1) ** is_off * runn
+            run += 1
+        return (-1) ** is_off * run
 
     def get_average_operation_times(self, start: datetime) -> tuple[float, float]:
         """
@@ -448,15 +448,15 @@ def get_average_operation_times(self, start: datetime) -> tuple[float, float]:
 
         op_series = []
         status = arr.iloc[0]
-        runn = 0
+        run = 0
         for val in arr:
             if val == status:
-                runn += 1
+                run += 1
             else:
-                op_series.append(-((-1) ** status) * runn)
-                runn = 1
+                op_series.append(-((-1) ** status) * run)
+                run = 1
                 status = val
-        op_series.append(-((-1) ** status) * runn)
+        op_series.append(-((-1) ** status) * run)
 
         op_times = [operation for operation in op_series if operation > 0]
         if op_times == []:

assume/common/forecasts.py

@@ -183,7 +183,7 @@ def calc_forecast_if_needed(self):
         Calculates the forecasts if they are not already calculated.
 
         This method calculates price forecast and residual load forecast for available markets, if
-        thise don't already exist.
+        these don't already exist.
         """
 
         cols = []
@@ -293,7 +293,7 @@ def calculate_market_price_forecast(self, market_id):
         # calculate infeed of renewables and residual demand_df
         # check if max_power is a series or a float
 
-        # select only those power plant units, which have a bidding strategy for the specifi market_id
+        # select only those power plant units, which have a bidding strategy for the specific market_id
         powerplants_units = self.powerplants_units[
             self.powerplants_units[f"bidding_{market_id}"].notnull()
         ]

assume/common/grid_utils.py

@@ -256,7 +256,7 @@ def read_pypsa_grid(
 ):
     """
     Generates the pypsa grid from a grid dictionary.
-    Does not add the generators, as they are added in different ways, depending on wether redispatch is used.
+    Does not add the generators, as they are added in different ways, depending on whether redispatch is used.
 
     Args:
         network (pypsa.Network): the pypsa network to which the components will be added

assume/common/outputs.py

@@ -91,10 +91,10 @@ def __init__(
             if self.episode == 0:
                 self.del_similar_runs()
 
-        # contruct all timeframe under which hourly values are written to excel and db
+        # construct all timeframe under which hourly values are written to excel and db
         self.start = start
         self.end = end
-        # initalizes dfs for storing and writing asynchron
+        # initializes dfs for storing and writing asynchronous
         self.write_dfs: dict = defaultdict(list)
         self.locks = defaultdict(lambda: Lock())
 
@@ -422,7 +422,7 @@ def write_market_orders(self, market_orders: any, market_id: str):
             market_orders (any): The market orders.
             market_id (str): The id of the market.
         """
-        # check if market results list is empty and skip the funktion and raise a warning
+        # check if market results list is empty and skip the function and raise a warning
         if not market_orders:
             return
 
@@ -585,7 +585,7 @@ def write_flows(self, data: dict[tuple[datetime, str], float]):
         Args:
             data: The records to be put into the table. Formatted like, "(datetime, line), flow" if generated by pyomo or df if it comes from pypsa.
         """
-        # Daten in ein DataFrame umwandeln depending on the data format which differes when different solver are used
+        # Daten in ein DataFrame umwandeln depending on the data format which differs when different solver are used
         # transformation done here to avoid adapting format during clearing
 
         # if data is dataframe

assume/common/units_operator.py

@@ -228,7 +228,7 @@ def handle_registration_feedback(
                     break
             if not found:
                 logger.error(
-                    "Market %s sent registation but is unknown", content["market_id"]
+                    "Market %s sent registration but is unknown", content["market_id"]
                 )
         else:
             logger.error("Market %s did not accept registration", meta["sender_id"])
@@ -386,7 +386,7 @@ async def submit_bids(self, opening: OpeningMessage, meta: MetaDict) -> None:
             meta (MetaDict): The meta data of the market.
 
         Note:
-            This function will accomodate the portfolio optimization in the future.
+            This function will accommodate the portfolio optimization in the future.
         """
 
         products = opening["products"]

assume/common/utils.py

@@ -408,13 +408,13 @@ def separate_orders(orderbook: Orderbook):
 
 def get_products_index(orderbook: Orderbook) -> pd.DatetimeIndex:
     """
-    Creates an index containing all start times of orders in orderbook and all inbetween.
+    Creates an index containing all start times of orders in orderbook and all between.
 
     Args:
         orderbook (Orderbook): The orderbook.
 
     Returns:
-        pd.DatetimeIndex: The index containing all start times of orders in orderbook and all inbetween.
+        pd.DatetimeIndex: The index containing all start times of orders in orderbook and all between.
     """
     if orderbook == []:
         return []
@@ -503,7 +503,7 @@ def adjust_unit_operator_for_learning(
             unit_operator_id = "Operator-RL"
             logger.debug(
                 "Your chosen unit operator %s for the learning unit %s was overwritten with 'Operator-RL', "
-                "since all learning units need to be handeled by one unit operator.",
+                "since all learning units need to be handled by one unit operator.",
                 unit_operator_id,
                 id,
             )
@@ -587,7 +587,7 @@ def rename_study_case(path: str, old_key: str, new_key: str):
 
     Args:
         path (str): The path to the config file.
-        old_key (str): The orginal name of the key without adjustments. E.g. study_case from available_examples: "base".
+        old_key (str): The original name of the key without adjustments. E.g. study_case from available_examples: "base".
         new_key (str): The name of the key with adjustments. E.g. added run number: "base_run_1".
     """
     # Read the YAML file

assume/markets/clearing_algorithms/all_or_nothing.py

@@ -169,7 +169,7 @@ def clear(
                         supply_orders[i]["accepted_volume"] = supply_orders[i]["volume"]
                         demand_orders[i]["accepted_volume"] = demand_orders[i]["volume"]
 
-                        # pay as bid - so the generator gets payed more than he needed to operate
+                        # pay as bid - so the generator gets paid more than he needed to operate
                         supply_orders[i]["accepted_price"] = demand_orders[i]["price"]
                         demand_orders[i]["accepted_price"] = demand_orders[i]["price"]
 

assume/markets/clearing_algorithms/contracts.py

@@ -441,7 +441,7 @@ def swingcontract(
     end: datetime,
 ):
     """
-    The swing contract is used to provide a band in which one price is payed, while the second (higher) price is paid, when the band is left.
+    The swing contract is used to provide a band in which one price is paid, while the second (higher) price is paid, when the band is left.
 
     Args:
         contract (dict): the contract which is executed

assume/markets/clearing_algorithms/redispatch.py

@@ -180,7 +180,7 @@ def clear(
         # run linear powerflow
         redispatch_network.lpf()
 
-        # check lines for congestion where power flow is larget than s_nom
+        # check lines for congestion where power flow is larger than s_nom
         line_loading = (
             redispatch_network.lines_t.p0.abs() / redispatch_network.lines.s_nom
         )

assume/reinforcement_learning/algorithms/matd3.py

@@ -403,7 +403,7 @@ def update_policy(self):
                 actor_target = self.learning_role.rl_strats[u_id].actor_target
 
                 if i % 100 == 0:
-                    # only update target netwroks every 100 steps, to have delayed network update
+                    # only update target networks every 100 steps, to have delayed network update
                     transitions = self.learning_role.buffer.sample(self.batch_size)
                     states = transitions.observations
                     actions = transitions.actions

assume/reinforcement_learning/buffer.py

@@ -147,7 +147,7 @@ def add(
 
     def sample(self, batch_size: int) -> ReplayBufferSamples:
         """
-        Samples a randome batch of experiences from the replay buffer.
+        Samples a random batch of experiences from the replay buffer.
 
         Args:
             batch_size (int): The number of experiences to sample.

assume/reinforcement_learning/learning_role.py

@@ -71,8 +71,8 @@ def __init__(
 
         self.learning_rate = learning_config.get("learning_rate", 1e-4)
 
-        # if we do not have initital experience collected we will get an error as no samples are avaiable on the
-        # buffer from which we can draw exprience to adapt the strategy, hence we set it to minium one episode
+        # if we do not have initial experience collected we will get an error as no samples are available on the
+        # buffer from which we can draw experience to adapt the strategy, hence we set it to minimum one episode
 
         self.episodes_collecting_initial_experience = max(
             learning_config.get("episodes_collecting_initial_experience", 5), 1
@@ -312,7 +312,7 @@ def compare_and_save_policies(self, metrics: dict) -> bool:
                         f"New best policy saved, episode: {self.eval_episodes_done + 1}, {metric=}, value={value:.2f}"
                     )
 
-            # if we do not see any improvment in the last x evaluation runs we stop the training
+            # if we do not see any improvement in the last x evaluation runs we stop the training
             if len(self.rl_eval[metric]) >= self.early_stopping_steps:
                 self.avg_rewards.append(
                     sum(self.rl_eval[metric][-self.early_stopping_steps :])

assume/scenario/loader_amiris.py

@@ -458,7 +458,7 @@ def load_amiris(
     """
     Loads an Amiris scenario.
     Markups and markdowns are handled by linearly interpolating the agents volume.
-    This mimicks the behavior of the way it is done in AMIRIS.
+    This mimics the behavior of the way it is done in AMIRIS.
 
     Args:
         world (World): the ASSUME world

assume/scenario/loader_csv.py

@@ -548,7 +548,7 @@ def setup_world(
         scenario_data (dict): A dictionary containing the configuration and loaded files for the scenario and study case.
         study_case (str): The specific study case within the scenario to be loaded.
         perform_evaluation (bool, optional): A flag indicating whether evaluation should be performed. Defaults to False.
-        terminate_learning (bool, optional): An automatically set flag indicating that we terminated the learning process now, either because we reach the end of the episode itteration or because we triggered an early stopping.
+        terminate_learning (bool, optional): An automatically set flag indicating that we terminated the learning process now, either because we reach the end of the episode iteration or because we triggered an early stopping.
         episode (int, optional): The episode number for learning. Defaults to 0.
         eval_episode (int, optional): The episode number for evaluation. Defaults to 0.
 
@@ -695,7 +695,7 @@ def setup_world(
         units[op].extend(op_units)
 
     # if distributed_role is true - there is a manager available
-    # and we cann add each units_operator as a separate process
+    # and we can add each units_operator as a separate process
     if world.distributed_role is True:
         logger.info("Adding unit operators and units - with subprocesses")
         for op, op_units in units.items():
@@ -742,7 +742,7 @@ def load_scenario_folder(
         scenario (str): The name of the scenario to be loaded.
         study_case (str): The specific study case within the scenario to be loaded.
         perform_evaluation (bool, optional): A flag indicating whether evaluation should be performed. Defaults to False.
-        terminate_learning (bool, optional): An automatically set flag indicating that we terminated the learning process now, either because we reach the end of the episode itteration or because we triggered an early stopping.
+        terminate_learning (bool, optional): An automatically set flag indicating that we terminated the learning process now, either because we reach the end of the episode iteration or because we triggered an early stopping.
         episode (int, optional): The episode number for learning. Defaults to 0.
         eval_episode (int, optional): The episode number for evaluation. Defaults to 0.
 
@@ -880,7 +880,7 @@ def run_learning(
     world.learning_role.initialize_policy(actors_and_critics=actors_and_critics)
     world.output_role.del_similar_runs()
 
-    # check if we already stored policies for this simualtion
+    # check if we already stored policies for this simulation
     save_path = world.learning_config["trained_policies_save_path"]
 
     if Path(save_path).is_dir():
@@ -939,7 +939,7 @@ def run_learning(
             )
 
         # -----------------------------------------
-        # Give the newly initliazed learning role the needed information across episodes
+        # Give the newly initialized learning role the needed information across episodes
         world.learning_role.load_inter_episodic_data(inter_episodic_data)
 
         world.run()

assume/scenario/oeds/infrastructure.py

@@ -440,9 +440,9 @@ def get_wind_turbines_in_area(self, area=520, wind_type="on_shore"):
         # If the response Dataframe is not empty set technical parameter
         if df.empty:
             return df
-        # all WEA with nan set hight to mean value
+        # all WEA with nan set height to mean value
         df["height"] = df["height"].fillna(df["height"].mean())
-        # all WEA with nan set hight to mean diameter
+        # all WEA with nan set height to mean diameter
         df["diameter"] = df["diameter"].fillna(df["diameter"].mean())
         # all WEA with na are on shore and not allocated to a sea cluster
         df["nordicSea"] = df["nordicSea"].astype(float).fillna(0)

assume/strategies/learning_advanced_orders.py

@@ -47,7 +47,7 @@ class RLAdvancedOrderStrategy(RLStrategy):
         If SB and linked orders (LB) are allowed, the strategy will use SB for the inflexible power and LB for the flexible power.
         If SB and block orders (BB) are allowed, the strategy will use BB for the inflexible power and SB for the flexible power.
         If all three order types (SB, BB, LB) are allowed, the strategy will use BB for the inflexible power
-        and LB for the flexible power, exept the inflexible power is 0,
+        and LB for the flexible power, except the inflexible power is 0,
         then it will use SB for the flexible power (as for VREs).
     """
 
@@ -133,7 +133,7 @@ def calculate_bids(
             bid_quantity_inflex = min_power[start]
 
             # 3.1 formulate the bids for Pmax - Pmin
-            # Pmin, the minium run capacity is the inflexible part of the bid, which should always be accepted
+            # Pmin, the minimum run capacity is the inflexible part of the bid, which should always be accepted
 
             if op_time <= -unit.min_down_time or op_time > 0:
                 bid_quantity_flex = max_power[start] - bid_quantity_inflex
@@ -317,7 +317,7 @@ def create_observation(
 
         current_costs = unit.calculate_marginal_cost(start, current_volume)
 
-        # scale unit outpus
+        # scale unit outputs
         scaled_max_power = current_volume / scaling_factor_total_capacity
         scaled_marginal_cost = current_costs / scaling_factor_marginal_cost
 
@@ -342,7 +342,7 @@ def create_observation(
             ]
         )
 
-        # transfer arry to GPU for NN processing
+        # transfer array to GPU for NN processing
         observation = (
             th.tensor(observation, dtype=self.float_type)
             .to(self.device, non_blocking=True)
@@ -445,7 +445,7 @@ def calculate_reward(
 
         # ---------------------------
         # 4.1 Calculate Reward
-        # The straight forward implemntation would be reward = profit, yet we would like to give the agent more guidance
+        # The straight forward implementation would be reward = profit, yet we would like to give the agent more guidance
         # in the learning process, so we add a regret term to the reward, which is the opportunity cost
         # define the reward and scale it