notebooks/test_pi_calc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook tests the PI code to ensure it runs smoothly and provides reasonable output. A full QC of the code is provided in \"verify_pi\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(sys.path[0]+'/..')\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# load PI calculation module\n",
    "from tcpyPI import pi, cape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change default figure font settings\n",
    "font = {'family' : 'sans-serif',\n",
    "        'weight' : 1,\n",
    "        'size'   : 16}\n",
    "\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data and Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data location\n",
    "dat_loc='../data/sample_data.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 73, lon: 144, month: 12, p: 31)\n",
       "Coordinates:\n",
       "  * p        (p) float64 1e+03 975.0 950.0 925.0 900.0 ... 40.0 30.0 20.0 10.0\n",
       "  * lat      (lat) float64 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5\n",
       "  * month    (month) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0\n",
       "Data variables:\n",
       "    lsm      (lat, lon) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 1.0 1.0 1.0 1.0 1.0\n",
       "    sst      (month, lat, lon) float64 ...\n",
       "    msl      (month, lat, lon) float64 ...\n",
       "    t        (month, p, lat, lon) float64 ...\n",
       "    q        (month, p, lat, lon) float64 ...\n",
       "    Vmax     (month, lat, lon) float64 ...\n",
       "    To       (month, lat, lon) float64 ...\n",
       "    Pmin     (month, lat, lon) float64 ...\n",
       "    LNB      (month, lat, lon) float64 ...\n",
       "    PI_flag  (month, lat, lon) float64 ...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5ef652ad-9170-491d-be80-9487a8ff58e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5ef652ad-9170-491d-be80-9487a8ff58e7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span class='xr-has-index'>month</span>: 12</li><li><span class='xr-has-index'>p</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b81491a2-4306-44dd-b15a-b83f2cdca101' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b81491a2-4306-44dd-b15a-b83f2cdca101' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p</span></div><div class='xr-var-dims'>(p)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03 975.0 950.0 ... 20.0 10.0</div><input id='attrs-c4aa7e38-0c85-4dcf-a602-d6c2351d3927' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4aa7e38-0c85-4dcf-a602-d6c2351d3927' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9bf23d6-4c9f-4c53-8d66-1b7c3d6ad232' class='xr-var-data-in' type='checkbox'><label for='data-a9bf23d6-4c9f-4c53-8d66-1b7c3d6ad232' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Atmospheric Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  975.,  950.,  925.,  900.,  875.,  850.,  825.,  800.,  775.,\n",
       "        750.,  725.,  700.,  650.,  600.,  550.,  500.,  450.,  400.,  350.,\n",
       "        300.,  250.,  200.,  150.,  100.,   70.,   50.,   40.,   30.,   20.,\n",
       "         10.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-fa79f21a-c70a-4e85-b063-e105e3758d28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa79f21a-c70a-4e85-b063-e105e3758d28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2febb013-cd87-41d9-a945-4ec4d1b7fafd' class='xr-var-data-in' type='checkbox'><label for='data-2febb013-cd87-41d9-a945-4ec4d1b7fafd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -177.5 ... 175.0 177.5</div><input id='attrs-72672e0d-8396-439b-95da-ac8a5005ad07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72672e0d-8396-439b-95da-ac8a5005ad07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5346270f-cf61-4db5-916d-c0b972e1f78f' class='xr-var-data-in' type='checkbox'><label for='data-5346270f-cf61-4db5-916d-c0b972e1f78f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees</dd></dl></div><div class='xr-var-data'><pre>array([-180. , -177.5, -175. , -172.5, -170. , -167.5, -165. , -162.5, -160. ,\n",
       "       -157.5, -155. , -152.5, -150. , -147.5, -145. , -142.5, -140. , -137.5,\n",
       "       -135. , -132.5, -130. , -127.5, -125. , -122.5, -120. , -117.5, -115. ,\n",
       "       -112.5, -110. , -107.5, -105. , -102.5, -100. ,  -97.5,  -95. ,  -92.5,\n",
       "        -90. ,  -87.5,  -85. ,  -82.5,  -80. ,  -77.5,  -75. ,  -72.5,  -70. ,\n",
       "        -67.5,  -65. ,  -62.5,  -60. ,  -57.5,  -55. ,  -52.5,  -50. ,  -47.5,\n",
       "        -45. ,  -42.5,  -40. ,  -37.5,  -35. ,  -32.5,  -30. ,  -27.5,  -25. ,\n",
       "        -22.5,  -20. ,  -17.5,  -15. ,  -12.5,  -10. ,   -7.5,   -5. ,   -2.5,\n",
       "          0. ,    2.5,    5. ,    7.5,   10. ,   12.5,   15. ,   17.5,   20. ,\n",
       "         22.5,   25. ,   27.5,   30. ,   32.5,   35. ,   37.5,   40. ,   42.5,\n",
       "         45. ,   47.5,   50. ,   52.5,   55. ,   57.5,   60. ,   62.5,   65. ,\n",
       "         67.5,   70. ,   72.5,   75. ,   77.5,   80. ,   82.5,   85. ,   87.5,\n",
       "         90. ,   92.5,   95. ,   97.5,  100. ,  102.5,  105. ,  107.5,  110. ,\n",
       "        112.5,  115. ,  117.5,  120. ,  122.5,  125. ,  127.5,  130. ,  132.5,\n",
       "        135. ,  137.5,  140. ,  142.5,  145. ,  147.5,  150. ,  152.5,  155. ,\n",
       "        157.5,  160. ,  162.5,  165. ,  167.5,  170. ,  172.5,  175. ,  177.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0</div><input id='attrs-c3d77fe3-8a06-47d5-923a-99476cec35c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3d77fe3-8a06-47d5-923a-99476cec35c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31250d98-2455-4c73-96cc-2e8cc426b39c' class='xr-var-data-in' type='checkbox'><label for='data-31250d98-2455-4c73-96cc-2e8cc426b39c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Month</dd><dt><span>units :</span></dt><dd>Month Number</dd></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f418566-78c8-4b9f-8d5b-756a942a04eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f418566-78c8-4b9f-8d5b-756a942a04eb' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lsm</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3744d41b-062a-487f-b167-e47536eacdd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3744d41b-062a-487f-b167-e47536eacdd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b71e4fea-77cb-47f9-b95f-d5853563d4f9' class='xr-var-data-in' type='checkbox'><label for='data-b71e4fea-77cb-47f9-b95f-d5853563d4f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ERA-I Land-sea Mask</dd><dt><span>units :</span></dt><dd>0=Ocean, 1=Land</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.],\n",
       "       [1., 1., 1., ..., 1., 1., 1.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae1110a1-ed36-4642-8141-475f9c64a1e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1110a1-ed36-4642-8141-475f9c64a1e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43eba800-879c-4f40-b2b5-ab6457c56bd9' class='xr-var-data-in' type='checkbox'><label for='data-43eba800-879c-4f40-b2b5-ab6457c56bd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>msl</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0f4731f-2d2f-464b-b4a1-1d45a85047a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0f4731f-2d2f-464b-b4a1-1d45a85047a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8845542-7e31-439f-a604-9dd66e3b8c89' class='xr-var-data-in' type='checkbox'><label for='data-e8845542-7e31-439f-a604-9dd66e3b8c89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Mean Sea Level Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(month, p, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0826cb6-ff8f-43f0-a998-84712864c12e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0826cb6-ff8f-43f0-a998-84712864c12e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e316eb3-8935-40b8-acd4-7de003967ff6' class='xr-var-data-in' type='checkbox'><label for='data-0e316eb3-8935-40b8-acd4-7de003967ff6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Atmospheric Temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><pre>[3910464 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(month, p, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-621f34ba-f26d-4243-9e73-cda2b2a4416f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-621f34ba-f26d-4243-9e73-cda2b2a4416f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33c3934b-b8b4-4d80-ac71-abb037dbacf8' class='xr-var-data-in' type='checkbox'><label for='data-33c3934b-b8b4-4d80-ac71-abb037dbacf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Specific Humidity</dd><dt><span>units :</span></dt><dd>g/kg</dd></dl></div><div class='xr-var-data'><pre>[3910464 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Vmax</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-018864a0-e5e5-4628-906f-98f3a7644700' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-018864a0-e5e5-4628-906f-98f3a7644700' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5895aeca-ad7c-4668-8b4f-b096f35a060c' class='xr-var-data-in' type='checkbox'><label for='data-5895aeca-ad7c-4668-8b4f-b096f35a060c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Maximum Potential Intensity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>To</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e26e0703-b4a4-4b8e-a094-dfe2ff095172' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e26e0703-b4a4-4b8e-a094-dfe2ff095172' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53c05fd1-85b4-414d-8a55-74a359fa2ac0' class='xr-var-data-in' type='checkbox'><label for='data-53c05fd1-85b4-414d-8a55-74a359fa2ac0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Outflow Temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Pmin</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd6aaf37-95d6-4cb1-a499-252584fc1778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd6aaf37-95d6-4cb1-a499-252584fc1778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bd1783f-a48b-4fc4-b72f-ccec9529e522' class='xr-var-data-in' type='checkbox'><label for='data-3bd1783f-a48b-4fc4-b72f-ccec9529e522' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Minimum Central Pressure</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LNB</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-168af28d-1645-4a41-ae76-b548e12c6c21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-168af28d-1645-4a41-ae76-b548e12c6c21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d443ac9a-d7da-4c75-967b-065a1ddfb551' class='xr-var-data-in' type='checkbox'><label for='data-d443ac9a-d7da-4c75-967b-065a1ddfb551' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Level of Neutral Bouyancy for a Parcel Reversibly Lifted from Sea Level</dd><dt><span>units :</span></dt><dd>hPa</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PI_flag</span></div><div class='xr-var-dims'>(month, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8a2e9507-4e65-48a1-9e90-09d244256447' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a2e9507-4e65-48a1-9e90-09d244256447' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-032e2c37-bec8-47dd-9c27-26b391f05e96' class='xr-var-data-in' type='checkbox'><label for='data-032e2c37-bec8-47dd-9c27-26b391f05e96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Flag for BE02 algorithm</dd></dl></div><div class='xr-var-data'><pre>[126144 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a92bdf8-e8c3-4733-887c-7a0e8ffa345c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a92bdf8-e8c3-4733-887c-7a0e8ffa345c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 73, lon: 144, month: 12, p: 31)\n",
       "Coordinates:\n",
       "  * p        (p) float64 1e+03 975.0 950.0 925.0 900.0 ... 40.0 30.0 20.0 10.0\n",
       "  * lat      (lat) float64 90.0 87.5 85.0 82.5 80.0 ... -82.5 -85.0 -87.5 -90.0\n",
       "  * lon      (lon) float64 -180.0 -177.5 -175.0 -172.5 ... 172.5 175.0 177.5\n",
       "  * month    (month) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0\n",
       "Data variables:\n",
       "    lsm      (lat, lon) float64 ...\n",
       "    sst      (month, lat, lon) float64 ...\n",
       "    msl      (month, lat, lon) float64 ...\n",
       "    t        (month, p, lat, lon) float64 ...\n",
       "    q        (month, p, lat, lon) float64 ...\n",
       "    Vmax     (month, lat, lon) float64 ...\n",
       "    To       (month, lat, lon) float64 ...\n",
       "    Pmin     (month, lat, lon) float64 ...\n",
       "    LNB      (month, lat, lon) float64 ...\n",
       "    PI_flag  (month, lat, lon) float64 ..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load and view netcdf file\n",
    "ds = xr.open_dataset(dat_loc)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check Data Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# view the land-sea mask as a sanity check of our data loading\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.contour(ds.lon,ds.lat,ds.lsm,[0.1,],colors='k',linewidths=2.5)\n",
    "plt.xlabel(ds.lon.standard_name)\n",
    "plt.ylabel(ds.lat.standard_name)\n",
    "plt.title(ds.lsm.standard_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test CAPE calculation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grab one profile from the NA and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# choose a location and month\n",
    "want_lat=10\n",
    "want_lon=-180\n",
    "want_mon=9\n",
    "# find the index of the profile\n",
    "xi=np.where(ds.lon==want_lon)[0][0]\n",
    "yi=np.where(ds.lat==want_lat)[0][0]\n",
    "moni=int(want_mon)\n",
    "# moni,yi,xi=0,19,76\n",
    "# get the profile and input values\n",
    "p1=np.asarray(ds.p)\n",
    "t1=np.asarray(ds.t.isel(lat=yi,lon=xi,month=moni))\n",
    "q1=np.asarray(ds.q.isel(lat=yi,lon=xi,month=moni))\n",
    "sst1=np.asarray(ds.sst.isel(lat=yi,lon=xi,month=moni))\n",
    "msl1=np.asarray(ds.msl.isel(lat=yi,lon=xi,month=moni))\n",
    "# get the output values\n",
    "to1=np.asarray(ds.To.isel(lat=yi,lon=xi,month=moni))\n",
    "pi1=np.asarray(ds.Vmax.isel(lat=yi,lon=xi,month=moni))\n",
    "pmin1=np.asarray(ds.Pmin.isel(lat=yi,lon=xi,month=moni))\n",
    "lnb1=np.asarray(ds.LNB.isel(lat=yi,lon=xi,month=moni))\n",
    "flag1=np.asarray(ds.PI_flag.isel(lat=yi,lon=xi,month=moni))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(t1,p1,lw=4)\n",
    "plt.ylim([0,1000])\n",
    "plt.gca().invert_yaxis()\n",
    "#plt.yscale('log')\n",
    "plt.ylabel(ds.p.standard_name)\n",
    "plt.xlabel('Temperature (C)')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initial parcel characteristics\n",
    "TP=t1[0]+273.15;\n",
    "PP=np.min([p1[0],1000.0])\n",
    "ES0=6.112*np.exp(17.67*t1[0]/(243.5+t1[0]))\n",
    "RP=0.622*ES0/(PP-ES0)\n",
    "# convert to the proper units for CAPE module\n",
    "t1k=t1+273.15\n",
    "q1gg=q1*0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "(CAPED,TOB,LNB,IFLAG)=cape(TP,RP,PP,t1k,q1gg,p1,ascent_flag=0,ptop=50,miss_handle=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "(VMAX,PMIN,IFL,TO,LNB)=pi(sst1,msl1,p1,t1,q1,CKCD=0.9,ascent_flag=0,diss_flag=1,V_reduc=0.8,miss_handle=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PI module run time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Module Runtime = 0.0007244467735290528\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "nruns=100\n",
    "# time the loop\n",
    "time_check=np.zeros((nruns,))\n",
    "for i in range(nruns):\n",
    "    start = time.time()\n",
    "    check=pi(sst1,msl1,p1,t1,q1,CKCD=0.9,ascent_flag=0,diss_flag=1,V_reduc=0.8,miss_handle=0)\n",
    "    end = time.time()\n",
    "    time_check[i,]=end - start\n",
    "    \n",
    "print('Average Module Runtime = '+str(np.mean(time_check)))\n",
    "plt.figure()\n",
    "plt.hist(time_check)\n",
    "plt.ylabel('Number of runs (out of 100)')\n",
    "plt.xlabel('Time elapsed (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare output with MATLAB calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------PYTHON PI-------\n",
      "VMAX_check = 80.93490863320275\n",
      "PMIN_check = 884.2852067162651\n",
      "To_check = 196.1764039256962\n",
      "LNB_check = 83.7545053531184\n",
      "IFL_check = 1\n",
      "-------MATLAB PI-------\n",
      "VMAX = 80.94025179690918\n",
      "PMIN = 884.267888061597\n",
      "To = 196.17747790897812\n",
      "LNB = 83.75044975211635\n",
      "Flag = 1.0\n"
     ]
    }
   ],
   "source": [
    "print('-------PYTHON PI-------')\n",
    "print('VMAX_check = '+str(VMAX))\n",
    "print('PMIN_check = '+str(PMIN))\n",
    "print('To_check = '+str(TO))\n",
    "print('LNB_check = '+str(LNB))\n",
    "print('IFL_check = '+str(IFL))\n",
    "\n",
    "print('-------MATLAB PI-------')\n",
    "print('VMAX = '+str(pi1))\n",
    "print('PMIN = '+str(pmin1))\n",
    "print('To = '+str(to1))\n",
    "print('LNB = '+str(lnb1))\n",
    "print('Flag = '+str(flag1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python code appears to be reproducing the MATLAB CAPE and PI outputs. A full quality control is performed in the verify_pi.ipynb notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}