notebooks/clock_pypi.ipynb

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   "source": [
    "# Goal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code estimates the time it takes to make large numbers of pyPI runs. I randomly draw N samples from the sample dataset and then calculate their associated potential intensities with the **pi.py** module and using \"apply_ufunc\" in xarray. These calculations were performed on laptop with a 3.1 GHz Intel Core processor.\n",
    "\n",
    "The size of the sample data is important because pyPI calculations are profile dependant. There are 31 vertical levels in our sample dataset, and about 126,000 points. Running the whole dataset takes about 15 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import what we need\n",
    "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",
    "import time\n",
    "import random\n",
    "# load PI calculation module\n",
    "from tcpyPI import pi"
   ]
  },
  {
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data location\n",
    "dat_loc='../data/sample_data.nc'\n",
    "# load= netcdf file\n",
    "ds = xr.open_dataset(dat_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "</style><div class='xr-wrap'><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-7197b729-7261-4cc9-86d7-47e51bfa5a12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7197b729-7261-4cc9-86d7-47e51bfa5a12' 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-25504739-a1e5-44cf-86dd-67a1b3446f6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25504739-a1e5-44cf-86dd-67a1b3446f6b' 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-765b9b0f-050e-4a78-bf64-825015f7866f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-765b9b0f-050e-4a78-bf64-825015f7866f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f19ee39-54c2-40e2-948a-f015e519c141' class='xr-var-data-in' type='checkbox'><label for='data-1f19ee39-54c2-40e2-948a-f015e519c141' 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><pre class='xr-var-data'>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></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-520373eb-da18-496f-83af-1e19731437ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-520373eb-da18-496f-83af-1e19731437ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b767c1ea-1d0d-4acf-8ef8-f18e2c891989' class='xr-var-data-in' type='checkbox'><label for='data-b767c1ea-1d0d-4acf-8ef8-f18e2c891989' 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><pre class='xr-var-data'>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></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-5748e668-43be-4ee3-b4b0-1d09ac219366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5748e668-43be-4ee3-b4b0-1d09ac219366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f31480c-92f3-45d8-bb74-9d22999ce084' class='xr-var-data-in' type='checkbox'><label for='data-7f31480c-92f3-45d8-bb74-9d22999ce084' 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><pre class='xr-var-data'>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></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-9331d11e-7dbc-4613-9b93-7223901c3629' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9331d11e-7dbc-4613-9b93-7223901c3629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-118aa93a-42ef-49f9-8bf7-71096c329c04' class='xr-var-data-in' type='checkbox'><label for='data-118aa93a-42ef-49f9-8bf7-71096c329c04' 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><pre class='xr-var-data'>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-d3366c3a-e609-46ae-8a56-6d3ca8c40106' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3366c3a-e609-46ae-8a56-6d3ca8c40106' 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-aba99044-a080-44c7-a13a-146e09f17567' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aba99044-a080-44c7-a13a-146e09f17567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e55d9e11-1f98-475b-9242-cebc865597ad' class='xr-var-data-in' type='checkbox'><label for='data-e55d9e11-1f98-475b-9242-cebc865597ad' 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><pre class='xr-var-data'>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></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-2e74f90f-d8b2-488c-8a5a-30241f3458de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e74f90f-d8b2-488c-8a5a-30241f3458de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1b7a2cb-682c-4719-816a-8c8d378c3a04' class='xr-var-data-in' type='checkbox'><label for='data-d1b7a2cb-682c-4719-816a-8c8d378c3a04' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-78e38921-eb60-4ed0-926f-6d07afaaea33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78e38921-eb60-4ed0-926f-6d07afaaea33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99b6450b-4939-40a9-aabc-b5cbf54ceead' class='xr-var-data-in' type='checkbox'><label for='data-99b6450b-4939-40a9-aabc-b5cbf54ceead' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-8b8e2937-4516-4395-a497-99d6be4caa5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b8e2937-4516-4395-a497-99d6be4caa5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790ecb72-8f08-4cf0-8386-83b7cd55fb81' class='xr-var-data-in' type='checkbox'><label for='data-790ecb72-8f08-4cf0-8386-83b7cd55fb81' 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><pre class='xr-var-data'>[3910464 values with dtype=float64]</pre></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-db3eff75-b3f4-455f-a011-e5150e5391ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db3eff75-b3f4-455f-a011-e5150e5391ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe1fca7-0bd7-4901-b62b-ba2d001a83db' class='xr-var-data-in' type='checkbox'><label for='data-dbe1fca7-0bd7-4901-b62b-ba2d001a83db' 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><pre class='xr-var-data'>[3910464 values with dtype=float64]</pre></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-778501d5-252a-4da2-af62-e9778adc74e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-778501d5-252a-4da2-af62-e9778adc74e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0518ee14-433e-4245-a4bf-e94277d628c1' class='xr-var-data-in' type='checkbox'><label for='data-0518ee14-433e-4245-a4bf-e94277d628c1' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-fb97ef3e-bea0-4aea-8fb3-8173b850de69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb97ef3e-bea0-4aea-8fb3-8173b850de69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78cc1b1a-7936-4286-9458-accb197bc025' class='xr-var-data-in' type='checkbox'><label for='data-78cc1b1a-7936-4286-9458-accb197bc025' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-1c0806c3-5722-4a8e-9d8d-c7ce210091b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c0806c3-5722-4a8e-9d8d-c7ce210091b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e2a84e9-7d6c-4019-8dcf-560a46f2e56a' class='xr-var-data-in' type='checkbox'><label for='data-9e2a84e9-7d6c-4019-8dcf-560a46f2e56a' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-37f8b391-fbf4-4eb6-9798-730af0519f7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37f8b391-fbf4-4eb6-9798-730af0519f7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f3f4831-23c9-4894-8508-dfbc2a3f4324' class='xr-var-data-in' type='checkbox'><label for='data-1f3f4831-23c9-4894-8508-dfbc2a3f4324' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></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-d403dffd-f811-46ae-9cb3-1b2a2a3be178' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d403dffd-f811-46ae-9cb3-1b2a2a3be178' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea35812c-9380-4075-b3fb-824d79d0dbd4' class='xr-var-data-in' type='checkbox'><label for='data-ea35812c-9380-4075-b3fb-824d79d0dbd4' 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><pre class='xr-var-data'>[126144 values with dtype=float64]</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b1f3f2d9-67bc-4139-a0b7-8f0962fe7902' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1f3f2d9-67bc-4139-a0b7-8f0962fe7902' 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": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Sample Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample from the test data randomly to create data sets with a scaling number of points. From these we will test 10 times to capture the variability in the runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the random seed\n",
    "np.random.seed(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126144   31\n"
     ]
    }
   ],
   "source": [
    "ngrid=ds.lat.size*ds.lon.size*ds.month.size\n",
    "nlev=ds.p.size\n",
    "print(ngrid,' ',nlev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     1,    216,   1331,   4096,   9261,  17576,  29791,  46656,\n",
       "        68921,  97336, 132651, 175616, 226981, 287496, 357911, 438976,\n",
       "       531441, 636056, 753571, 884736])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define the number of samples in each dataset\n",
    "nsamps=np.arange(0,100)+1\n",
    "nsamps=nsamps[::5]\n",
    "ncube=nsamps**3\n",
    "ncube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time the Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   Time Elapsed=3.7192490100860596\n",
      "1   Time Elapsed=5.793249845504761\n",
      "2   Time Elapsed=9.140596151351929\n",
      "3   Time Elapsed=20.804136991500854\n",
      "4   Time Elapsed=42.195549964904785\n",
      "5   Time Elapsed=83.61440587043762\n",
      "6   Time Elapsed=114.07612872123718\n",
      "7   Time Elapsed=197.2674698829651\n",
      "8   Time Elapsed=278.9141421318054\n",
      "9   Time Elapsed=390.67475390434265\n",
      "10   Time Elapsed=540.4342930316925\n",
      "11   Time Elapsed=755.1462428569794\n",
      "12   Time Elapsed=1037.4361078739166\n",
      "13   Time Elapsed=1446.1667609214783\n",
      "14   Time Elapsed=1881.5437519550323\n",
      "15   Time Elapsed=2250.4643280506134\n",
      "16   Time Elapsed=2846.8729298114777\n",
      "17   Time Elapsed=3551.042883872986\n",
      "18   Time Elapsed=4295.858358860016\n",
      "19   Time Elapsed=5184.055063962936\n"
     ]
    }
   ],
   "source": [
    "# create the time elapsed array\n",
    "nruns=10\n",
    "time_elapsed=np.zeros((nruns,len(nsamps)),dtype='float32')\n",
    "from xarray import apply_ufunc\n",
    "\n",
    "time0=time.time()\n",
    "\n",
    "for j in range(len(nsamps)):\n",
    "    rlat,rlon,rmon=np.random.randint(0,ds.lat.size,nsamps[j]),np.random.randint(0,ds.lon.size,nsamps[j]),np.random.randint(0,ds.month.size,nsamps[j])\n",
    "    ds0=ds.isel(lat=rlat,lon=rlon,month=rmon)\n",
    "    \n",
    "    for i in range(nruns):\n",
    "        start_time = time.time()\n",
    "        result = apply_ufunc(\n",
    "            pi,\n",
    "            ds0['sst'], ds0['msl'], ds0['p'], ds0['t'], ds0['q'],\n",
    "            kwargs=dict(CKCD=0.9, ascent_flag=0, diss_flag=1, miss_handle=1),\n",
    "            input_core_dims=[\n",
    "                [], [], ['p', ], ['p', ], ['p', ],\n",
    "            ],\n",
    "            output_core_dims=[\n",
    "                [], [], [], [], []\n",
    "            ],\n",
    "            vectorize=True\n",
    "        )\n",
    "        time_elapsed[i,j]=time.time()-start_time\n",
    "        del result\n",
    "    del rlat, rlon, rmon, ds0\n",
    "    print(str(j)+'   Time Elapsed='+str(time.time()-time0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit a curve\n",
    "z = np.polyfit(ncube,np.mean(time_elapsed,axis=0), 1)\n",
    "p = np.poly1d(z)\n",
    "x = np.linspace(0, 1e6, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared is 0.9802794696381533\n"
     ]
    }
   ],
   "source": [
    "print('R-squared is '+str(np.ma.corrcoef(ncube,np.mean(time_elapsed,axis=0))[0,1]**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.errorbar(ncube,np.mean(time_elapsed,axis=0), yerr=2*np.std(time_elapsed,axis=0),color='k',label='2$\\sigma$')\n",
    "plt.plot(ncube,np.mean(time_elapsed,axis=0),'b',lw=4,label='Mean Runtime')\n",
    "plt.plot(x,p(x),'--k',label='Fit='+str(round(z[0]*100000,2))+'s per 100k runs')\n",
    "plt.ylabel('Time per pyPI run (s)')\n",
    "plt.xlabel('Number of samples')\n",
    "plt.grid()\n",
    "plt.xticks(np.arange(0,1e6+1,1e5),labels=['0','100k','200k','300k','400k','500k','600k','700k','800k','900k','1Mil.'])\n",
    "plt.xlim([0,1e6])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": 4
}