test_plot_AMPS_scottglacier_winds.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook testing plots AMPS plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "\n",
    "import gcsfs\n",
    "from tqdm import tqdm\n",
    "import fsspec\n",
    "xr.set_options(display_style=\"html\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### setup runtime parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = 'gs://ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-20161225*'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### list the netcdf files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 8 wrf files.\n",
      "ldeo-glaciology/AMPS/WRF_24/domain_03/wrf-2016122500_f003-cf.nc\n"
     ]
    }
   ],
   "source": [
    "fs = gcsfs.GCSFileSystem(project='ldeo-glaciology', mode='ab', cache_timeout = 0)\n",
    "NCs = fs.glob(pattern)\n",
    "print(f\"Total of {len(NCs)} wrf files.\")\n",
    "print(NCs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## load the first file to inialize the xarray\n",
    "\n",
    "url = 'gs://' + NCs[0]\n",
    "with  fsspec.open(url, mode='rb')  as openfile:  \n",
    "    ds = xr.open_dataset(openfile, chunks={'time': 1})  # these chunk sizes produce chunks of reasonable data volumes and which stretch through all time\n",
    "\n",
    "# ## load the other files, each time concaternating them onto an xarray (AMPS) that grows in the time dimension each iteration. \n",
    "# for i in tqdm(range(1, len(NCs)-1)):  \n",
    "#     url = 'gs://' + NCs[i]\n",
    "#     with  fsspec.open(url, mode='rb')  as openfile:  \n",
    "#         temp = xr.open_dataset(openfile, chunks={'time': 1})  # these chunk sizes produce chunks of reasonable data volumes and which stretch through all time\n",
    "#     ds = xr.concat([ds,temp],'time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (pressure: 5, south_north: 825, time: 1, west_east: 537)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 2016-12-25T03:00:00\n",
       "  * pressure  (pressure) float32 1000.0 850.0 700.0 500.0 300.0\n",
       "    lat       (south_north, west_east) float32 dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;\n",
       "    lon       (south_north, west_east) float32 dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
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       "    year      (time) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    month     (time) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    day       (time) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    hour      (time) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    minute    (time) int32 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "    Z_sfc     (south_north, west_east) float32 dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;\n",
       "    SST       (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    T_sfc     (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    p_sfc     (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    slp       (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    T_2m      (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    Td_2m     (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    r_v_2m    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    q_2m      (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    rh_2m     (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    u_10m_tr  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    v_10m_tr  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    ws_10m    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    wd_10m    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    precip_g  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    Z_p       (time, pressure, south_north, west_east) float32 dask.array&lt;chunksize=(1, 5, 825, 537), meta=np.ndarray&gt;\n",
       "    T_p       (time, pressure, south_north, west_east) float32 dask.array&lt;chunksize=(1, 5, 825, 537), meta=np.ndarray&gt;\n",
       "    Td_p      (time, pressure, south_north, west_east) float32 dask.array&lt;chunksize=(1, 5, 825, 537), meta=np.ndarray&gt;\n",
       "    r_v_p     (time, pressure, south_north, west_east) float32 dask.array&lt;chunksize=(1, 5, 825, 537), meta=np.ndarray&gt;\n",
       "    q_p       (time, pressure, south_north, west_east) float32 dask.array&lt;chunksize=(1, 5, 825, 537), meta=np.ndarray&gt;\n",
       "    SW_d      (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LW_d      (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LW_u      (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LW_d_acc  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LW_u_acc  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    albedo    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    SH        (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LH        (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    SH_acc    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LH_acc    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    MH        (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    u_star    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LWP       (time, south_north, west_east) float64 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    IWP       (time, south_north, west_east) float64 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LandMask  (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    LandUse   (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "    SeaIce    (time, south_north, west_east) float32 dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    title:          wrf-2016122500_f003-cf.nc\n",
       "    Conventions:    CF 1.6, Standard Name Table v19\n",
       "    source:         wrfout_d03_2016122500_f003.nc\n",
       "    notes:          Created with NCL script:  wrfout_to_cf.ncl v2.0\n",
       "    creation_date:  Sat May 27 12:16:11 EDT 2017</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-70e19e4d-f99b-4c67-adc3-0df42cff648e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-70e19e4d-f99b-4c67-adc3-0df42cff648e' 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'>pressure</span>: 5</li><li><span>south_north</span>: 825</li><li><span class='xr-has-index'>time</span>: 1</li><li><span>west_east</span>: 537</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8871d18d-e5eb-448a-b6db-234f149b1b03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8871d18d-e5eb-448a-b6db-234f149b1b03' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2016-12-25T03:00:00</div><input id='attrs-c5bb9ef0-d89e-4bdf-8741-0b982be8e711' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5bb9ef0-d89e-4bdf-8741-0b982be8e711' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6343f731-f352-48e7-8d45-700a3aff317b' class='xr-var-data-in' type='checkbox'><label for='data-6343f731-f352-48e7-8d45-700a3aff317b' 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>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2016-12-25T03:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1000.0 850.0 700.0 500.0 300.0</div><input id='attrs-5f78d0ec-fbcc-4cd8-9b9d-f2b83c9ca412' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f78d0ec-fbcc-4cd8-9b9d-f2b83c9ca412' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8863ed2-c796-4193-8527-7edae63833de' class='xr-var-data-in' type='checkbox'><label for='data-c8863ed2-c796-4193-8527-7edae63833de' 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>long_name :</span></dt><dd>Pressure Levels</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>positive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  850.,  700.,  500.,  300.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;</div><input id='attrs-0859b07e-d73d-4940-b238-9508f81f1297' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0859b07e-d73d-4940-b238-9508f81f1297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01d868e2-9591-4407-beb5-28b5d4e50c1d' class='xr-var-data-in' type='checkbox'><label for='data-01d868e2-9591-4407-beb5-28b5d4e50c1d' 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>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.77 MB </td> <td> 1.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (825, 537) </td> <td> (825, 537) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(825, 537), meta=np.ndarray&gt;</div><input id='attrs-21f8d201-b157-43e4-8fc9-2e5632fd56ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21f8d201-b157-43e4-8fc9-2e5632fd56ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f5199e8-c3fe-4179-ba4e-a4f8f37d85a8' class='xr-var-data-in' type='checkbox'><label for='data-4f5199e8-c3fe-4179-ba4e-a4f8f37d85a8' 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>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><th> Shape </th><td> (825, 537) </td> <td> (825, 537) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-78ede023-45b1-46ce-b5d3-39167433e2be' class='xr-section-summary-in' type='checkbox'  ><label for='section-78ede023-45b1-46ce-b5d3-39167433e2be' class='xr-section-summary' >Data variables: <span>(43)</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>DateTime</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-42d175b7-d62e-485c-8a8c-9d36af82fcde' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42d175b7-d62e-485c-8a8c-9d36af82fcde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b6fb84-333c-4871-b7dd-630a5058c0a3' class='xr-var-data-in' type='checkbox'><label for='data-d8b6fb84-333c-4871-b7dd-630a5058c0a3' 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>long_name :</span></dt><dd>Date and Time</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (1,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>year</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-45f25932-6891-4d34-86d4-1b2ac4005543' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-45f25932-6891-4d34-86d4-1b2ac4005543' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb70402-23a0-4ad9-a119-012bdca3f708' class='xr-var-data-in' type='checkbox'><label for='data-2cb70402-23a0-4ad9-a119-012bdca3f708' 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>long_name :</span></dt><dd>Year</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (1,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c0ca2ebc-ecf1-4a4b-b7a0-1f30c4a6346e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0ca2ebc-ecf1-4a4b-b7a0-1f30c4a6346e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15569644-d16b-4ef9-ac49-b44348aba79f' class='xr-var-data-in' type='checkbox'><label for='data-15569644-d16b-4ef9-ac49-b44348aba79f' 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>long_name :</span></dt><dd>Month</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4 B </td> <td> 4 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (1,) </td> <td> (1,) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> int32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LWP</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;</div><input id='attrs-1852b9e2-744b-4989-8e79-707cce513aef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1852b9e2-744b-4989-8e79-707cce513aef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88c5356b-3dfa-45b8-8b2f-ebd57aae3828' class='xr-var-data-in' type='checkbox'><label for='data-88c5356b-3dfa-45b8-8b2f-ebd57aae3828' 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>long_name :</span></dt><dd>Liquid Water Path</dd><dt><span>standard_name :</span></dt><dd>atmosphere_cloud_liquid_water_content</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>notes :</span></dt><dd>Column integrated cloud water.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>IWP</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;</div><input id='attrs-c6f8c0a3-dbb1-414e-9bcc-0baa7b7843e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6f8c0a3-dbb1-414e-9bcc-0baa7b7843e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4c6a70f-1b5e-46ab-9287-5eadfd4b4930' class='xr-var-data-in' type='checkbox'><label for='data-f4c6a70f-1b5e-46ab-9287-5eadfd4b4930' 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>long_name :</span></dt><dd>Ice Water Path</dd><dt><span>standard_name :</span></dt><dd>atmosphere_cloud_ice_content</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>notes :</span></dt><dd>Column integrated cloud ice.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LandMask</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;</div><input id='attrs-9dc24d43-54cb-4de0-a155-209942053400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dc24d43-54cb-4de0-a155-209942053400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79e44ed9-7d7b-483f-9e81-62d07ca8cfa8' class='xr-var-data-in' type='checkbox'><label for='data-79e44ed9-7d7b-483f-9e81-62d07ca8cfa8' 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>long_name :</span></dt><dd>Land Mask</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd><dt><span>units :</span></dt><dd></dd><dt><span>notes :</span></dt><dd>1=land, 0=water</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SeaIce</span></div><div class='xr-var-dims'>(time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 825, 537), meta=np.ndarray&gt;</div><input id='attrs-dca95ee5-df61-470f-9217-2e5a6d3b6214' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dca95ee5-df61-470f-9217-2e5a6d3b6214' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ea1871d-d574-46b5-bbac-9c8ec7f336f0' class='xr-var-data-in' type='checkbox'><label for='data-7ea1871d-d574-46b5-bbac-9c8ec7f336f0' 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>long_name :</span></dt><dd>Sea Ice</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd></dd><dt><span>notes :</span></dt><dd>1=seaice, 0=water</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.77 MB </td> <td> 1.77 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (1, 825, 537) </td> <td> (1, 825, 537) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a101f171-00d0-4d36-8d48-99f92ca043f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a101f171-00d0-4d36-8d48-99f92ca043f4' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>wrf-2016122500_f003-cf.nc</dd><dt><span>Conventions :</span></dt><dd>CF 1.6, Standard Name Table v19</dd><dt><span>source :</span></dt><dd>wrfout_d03_2016122500_f003.nc</dd><dt><span>notes :</span></dt><dd>Created with NCL script:  wrfout_to_cf.ncl v2.0</dd><dt><span>creation_date :</span></dt><dd>Sat May 27 12:16:11 EDT 2017</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (pressure: 5, south_north: 825, time: 1, west_east: 537)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 2016-12-25T03:00:00\n",
       "  * pressure  (pressure) float32 1000.0 850.0 700.0 500.0 300.0\n",
       "    lat       (south_north, west_east) float32 dask.array<chunksize=(825, 537), meta=np.ndarray>\n",
       "    lon       (south_north, west_east) float32 dask.array<chunksize=(825, 537), meta=np.ndarray>\n",
       "Dimensions without coordinates: south_north, west_east\n",
       "Data variables:\n",
       "    DateTime  (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    year      (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    month     (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    day       (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    hour      (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    minute    (time) int32 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "    Z_sfc     (south_north, west_east) float32 dask.array<chunksize=(825, 537), meta=np.ndarray>\n",
       "    SST       (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    T_sfc     (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    p_sfc     (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    slp       (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    T_2m      (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    Td_2m     (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    r_v_2m    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    q_2m      (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    rh_2m     (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    u_10m_tr  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    v_10m_tr  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    ws_10m    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    wd_10m    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    precip_g  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    Z_p       (time, pressure, south_north, west_east) float32 dask.array<chunksize=(1, 5, 825, 537), meta=np.ndarray>\n",
       "    T_p       (time, pressure, south_north, west_east) float32 dask.array<chunksize=(1, 5, 825, 537), meta=np.ndarray>\n",
       "    Td_p      (time, pressure, south_north, west_east) float32 dask.array<chunksize=(1, 5, 825, 537), meta=np.ndarray>\n",
       "    r_v_p     (time, pressure, south_north, west_east) float32 dask.array<chunksize=(1, 5, 825, 537), meta=np.ndarray>\n",
       "    q_p       (time, pressure, south_north, west_east) float32 dask.array<chunksize=(1, 5, 825, 537), meta=np.ndarray>\n",
       "    SW_d      (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LW_d      (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LW_u      (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LW_d_acc  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LW_u_acc  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    albedo    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    SH        (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LH        (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    SH_acc    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LH_acc    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    MH        (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    u_star    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LWP       (time, south_north, west_east) float64 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    IWP       (time, south_north, west_east) float64 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LandMask  (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    LandUse   (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "    SeaIce    (time, south_north, west_east) float32 dask.array<chunksize=(1, 825, 537), meta=np.ndarray>\n",
       "Attributes:\n",
       "    title:          wrf-2016122500_f003-cf.nc\n",
       "    Conventions:    CF 1.6, Standard Name Table v19\n",
       "    source:         wrfout_d03_2016122500_f003.nc\n",
       "    notes:          Created with NCL script:  wrfout_to_cf.ncl v2.0\n",
       "    creation_date:  Sat May 27 12:16:11 EDT 2017"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((1,), (825,), (537,))\n"
     ]
    }
   ],
   "source": [
    "print(ds.T_sfc.chunks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick overview plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plottime = 0\n",
    "\n",
    "# if plottime == 0:\n",
    "#     plottitle = 'hi'\n",
    "#     plotdate = '23-Dec-2016 1800z'\n",
    "# else:\n",
    "#     plottitle = 'lo'\n",
    "#     plotdate = '27-Dec-2016 1500z'\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5574193150>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['T_sfc'].mean(dim='time').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zoom in to Scott and Amundsen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scott = (-154.2, -85.3)\n",
    "amundsen = (-159.6, -85.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Closest AMPS gridcell for Scott Gl. is -85.33 N, -154.17 E\n"
     ]
    }
   ],
   "source": [
    "def get_closest_AMPS_cell(file_for_latlon, lat, lon):\n",
    "    \"\"\"\n",
    "    SSIA\n",
    "    :param file_for_latlon:\n",
    "    :param lat:\n",
    "    :param lon:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    LAT = file_for_latlon.variables['lat'][:]\n",
    "    LON = file_for_latlon.variables['lon'][:]\n",
    "    a = abs(LON - lon) + abs(LAT - lat)\n",
    "    iii, jjj = np.unravel_index(np.argmin(a.data), a.shape)\n",
    "    return iii, jjj\n",
    "\n",
    "si, sj = get_closest_AMPS_cell(ds, scott[1], scott[0])\n",
    "ai, aj = get_closest_AMPS_cell(ds, amundsen[1], amundsen[0])\n",
    "\n",
    "print(f\"Closest AMPS gridcell for Scott Gl. is {ds['lat'].values[si, sj]:1.2f} N, {ds['lon'].values[si, sj]:1.2f} E\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: The 'extend' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/ipykernel_launcher.py:37: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations. \n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:388: MatplotlibDeprecationWarning: \n",
      "The 'inframe' parameter of draw() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. Use Axes.redraw_in_frame() instead. If any parameter follows 'inframe', they should be passed as keyword, not positionally.\n",
      "  inframe=inframe)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## THIS NEEDS cartopy 0.18 but can't install it for some reason...\n",
    "\n",
    "import shapely.geometry as sgeom\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "\n",
    "box = sgeom.box(minx=155, maxx=165, miny=-83, maxy=-85.5)\n",
    "x0, y0, x1, y1 = box.bounds\n",
    "myproj = ccrs.SouthPolarStereo(central_longitude=180)\n",
    "# myproj = ccrs.Orthographic()\n",
    "\n",
    "plt.figure(figsize=(4, 5), facecolor='white', dpi=144)\n",
    "ax = plt.axes(projection=myproj)\n",
    "ax.set_extent([x0, x1, y0, y1], ccrs.PlateCarree())  \n",
    "ax.coastlines('50m')\n",
    "# ax.stock_img()\n",
    "\n",
    "# pcm1 = ax.pcolormesh(ds.lon, ds.lat, ds['SH'].mean(dim='time'),\\\n",
    "#                     vmin=-30, vmax=30,\\\n",
    "#                     transform=ccrs.PlateCarree())#, cmap=plt.get_cmap(\"BuPu\")\n",
    "## Alternative\n",
    "kwargs = dict(x='lon', y='lat', ax=ax, transform=ccrs.PlateCarree())\n",
    "pcm1 = ds['SH'].mean(dim='time').plot.contourf(\n",
    "    levels=31, robust=True,  **kwargs)\n",
    "\n",
    "\n",
    "# c = plt.colorbar(orientation='vertical', shrink=0.4, pad=0.10)\n",
    "cb1 = plt.colorbar(pcm1, ax=ax, label=r'Surface Heat Flux $[W {m^{-2}}]$', \\\n",
    "                   orientation='horizontal', extend='both', \\\n",
    "                  shrink=0.9, pad=0.01)\n",
    "ax.plot(ds.lon[si, sj], ds.lat[si, sj], 'ko', markersize=3, transform=ccrs.PlateCarree())\n",
    "ax.plot(ds.lon[ai, aj], ds.lat[ai, aj], 'ko', markersize=3, transform=ccrs.PlateCarree())\n",
    "# ax.plot(LON[ssj, ssi], LAT[ssj, ssi], 'ko', markersize=2, transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.gridlines(color=\"black\", linestyle=\"dotted\")\n",
    "# plt.suptitle('LH')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figs/mapplot_TNB_Fsfc_mean.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Plotting of arrays of cftime.datetime objects or arrays indexed by cftime.datetime objects requires the optional `nc-time-axis` (v1.2.0 or later) package.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-640962a08237>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     ds_map[var].plot.contourf(\n\u001b[0;32m---> 31\u001b[0;31m         \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m31\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     )\n\u001b[1;32m     33\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\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/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mplotmethod\u001b[0;34m(_PlotMethods_obj, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, colors, center, robust, extend, levels, infer_intervals, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m    842\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"_PlotMethods_obj\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"newplotfunc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"kwargs\"\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[1;32m    843\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mallargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 844\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnewplotfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mallargs\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    845\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    846\u001b[0m     \u001b[0;31m# Add to class _PlotMethods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mnewplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0myplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylab_extra\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_resolve_intervals_2dplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m         \u001b[0m_ensure_plottable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myplt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzval\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    717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m         cmap_params, cbar_kwargs = _process_cmap_cbar_kwargs(\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/utils.py\u001b[0m in \u001b[0;36m_ensure_plottable\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    606\u001b[0m         ):\n\u001b[1;32m    607\u001b[0m             raise ImportError(\n\u001b[0;32m--> 608\u001b[0;31m                 \u001b[0;34m\"Plotting of arrays of cftime.datetime \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m                 \u001b[0;34m\"objects or arrays indexed by \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m                 \u001b[0;34m\"cftime.datetime objects requires the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: Plotting of arrays of cftime.datetime objects or arrays indexed by cftime.datetime objects requires the optional `nc-time-axis` (v1.2.0 or later) package."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:388: MatplotlibDeprecationWarning: \n",
      "The 'inframe' parameter of draw() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. Use Axes.redraw_in_frame() instead. If any parameter follows 'inframe', they should be passed as keyword, not positionally.\n",
      "  inframe=inframe)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_map = ds.sel(\n",
    "  time='2016-12-25',\n",
    "  method='nearest'\n",
    ").sel(south_north=slice(-90,-50), west_east=slice(-150,-170)).mean(dim='time').squeeze()\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import matplotlib.path as mpath\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "fig, axarr = plt.subplots(nrows=2,\n",
    "                          figsize=[18,8],\n",
    "                          subplot_kw={'projection':ccrs.PlateCarree(200)}\n",
    "                         )\n",
    "\n",
    "for ax, var, cmap in zip(\n",
    "  axarr.flat,\n",
    "  ['SH', 'T_sfc'],\n",
    "  ['RdBu', 'RdBu']  # [cmo.haline, cmo.matter]\n",
    "):\n",
    "    \n",
    "    if var == 'SH':\n",
    "        vmin = -20\n",
    "        vmax = 20\n",
    "    else:\n",
    "        vmin = None\n",
    "        vmax = None\n",
    "    \n",
    "    kwargs = dict(ax=ax, transform=ccrs.PlateCarree()) ## x='west_east', y='south_north', \n",
    "    \n",
    "    ds_map[var].plot.contourf(\n",
    "        levels=31, robust=True, cmap=cmap, vmin=vmin, vmax=vmax,  **kwargs\n",
    "    )\n",
    "ax.set_title('')\n",
    "ax.add_feature(cfeature.LAND, edgecolor='0.3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bot method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.5, -79.6 | ['2016-12-25T03:00Z']\n"
     ]
    }
   ],
   "source": [
    "central_lon = ds.lon.mean().values.item()\n",
    "central_lat = ds.lat.mean().values.item()\n",
    "\n",
    "lon_min = ds.lon.min().values.item()\n",
    "lon_max = ds.lon.max().values.item()\n",
    "lon_range = lon_max - lon_min\n",
    "\n",
    "lat_min = ds.lat.min().values.item()\n",
    "lat_max = ds.lat.max().values.item()\n",
    "lat_range = lat_max - lat_min\n",
    "\n",
    "proj = ccrs.Orthographic(central_longitude=central_lon,\n",
    "                         central_latitude=central_lat)\n",
    "\n",
    "date_str = np.datetime_as_string(ds.time.values, timezone='UTC', unit='m')\n",
    "\n",
    "location = f'{central_lon:3.1f}, {central_lat:3.1f} | {date_str}'\n",
    "print(location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.041872760334066 2.4167491041336264\n"
     ]
    }
   ],
   "source": [
    "# tiles near equator are square\n",
    "# towards pole, both dimensions contract\n",
    "scale_lon = 2 + 0.75 * abs(np.deg2rad(central_lat))\n",
    "scale_lat = 2 + 0.3 * abs(np.deg2rad(central_lat))\n",
    "\n",
    "print(scale_lon, scale_lat)\n",
    "\n",
    "extent = [central_lon - lon_range/scale_lon, central_lon + lon_range/scale_lon,\n",
    "          central_lat - lat_range/scale_lat, central_lat + lat_range/scale_lat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "_pcolorargs() got an unexpected keyword argument 'allmatch'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-962a6baee6a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m plot(ds.SST.mean('time'), center=False, robust=True, # cmap=cmocean.cm.thermal,\n\u001b[0;32m---> 26\u001b[0;31m      cbar_kwargs={'shrink': 0.5})\n\u001b[0m",
      "\u001b[0;32m<ipython-input-19-962a6baee6a7>\u001b[0m in \u001b[0;36mplot\u001b[0;34m(da, clip_extent, **kwargs)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0mgl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgridlines\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[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0mda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mydim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     17\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'LLC4320 {da.long_name} | {location}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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--> 446\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_da\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    447\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m     \u001b[0;31m# we can't use functools.wraps here since that also modifies the name / qualname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, rtol, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    198\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mplotfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    201\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mnewplotfunc\u001b[0;34m(darray, x, y, figsize, size, aspect, ax, row, col, col_wrap, xincrease, yincrease, add_colorbar, add_labels, vmin, vmax, cmap, center, robust, extend, levels, infer_intervals, colors, subplot_kws, cbar_ax, cbar_kwargs, xscale, yscale, xticks, yticks, xlim, ylim, norm, **kwargs)\u001b[0m\n\u001b[1;32m    755\u001b[0m             \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"vmax\"\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[1;32m    756\u001b[0m             \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"norm\"\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--> 757\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    758\u001b[0m         )\n\u001b[1;32m    759\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/xarray/plot/plot.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[0;34m(x, y, z, ax, infer_intervals, **kwargs)\u001b[0m\n\u001b[1;32m    985\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_infer_interval_breaks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m     \u001b[0mprimitive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    988\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m     \u001b[0;31m# by default, pcolormesh picks \"round\" values for bounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1457\u001b[0m                              ' consider using PlateCarree/RotatedPole.')\n\u001b[1;32m   1458\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transform'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1459\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pcolormesh_patched\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1460\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\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[1;32m   1461\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/srv/conda/envs/notebook/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py\u001b[0m in \u001b[0;36m_pcolormesh_patched\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1489\u001b[0m         \u001b[0mallmatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mshading\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'gouraud'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1491\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pcolorargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pcolormesh'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallmatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallmatch\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   1492\u001b[0m         \u001b[0mNy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: _pcolorargs() got an unexpected keyword argument 'allmatch'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/cartopy/mpl/geoaxes.py:388: MatplotlibDeprecationWarning: \n",
      "The 'inframe' parameter of draw() was deprecated in Matplotlib 3.3 and will be removed two minor releases later. Use Axes.redraw_in_frame() instead. If any parameter follows 'inframe', they should be passed as keyword, not positionally.\n",
      "  inframe=inframe)\n"
     ]
    },
    {
     "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": [
    "import cmocean\n",
    "plt.rcParams['font.size'] = 16\n",
    "\n",
    "def plot(da, clip_extent=True, **kwargs):\n",
    "    xdim = 'XC' if 'i' in da.dims else 'lon'\n",
    "    ydim = 'YC' if 'j' in da.dims else 'lat'\n",
    "    \n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "    ax = fig.add_axes([0, 0.02, 1, 0.91],\n",
    "                      projection=ccrs.Orthographic(central_lon, central_lat))\n",
    "    ax.background_patch.set_facecolor('0.6')\n",
    "    if clip_extent:\n",
    "        ax.set_extent(extent, crs=ccrs.PlateCarree())\n",
    "    gl = ax.gridlines()\n",
    "    \n",
    "    da.plot(ax=ax, x=xdim, y=ydim, transform=ccrs.PlateCarree(), **kwargs)\n",
    "    ax.set_title(f'LLC4320 {da.long_name} | {location}')\n",
    "    \n",
    "#     logo_axis = fig.add_axes([0.81, 0.017, 0.12, 0.05])\n",
    "#     fig.text(0.815, 0.1, f'© Ryan Abernathey\\n{year} CC BY',\n",
    "#              fontdict={'size': 11})\n",
    "#     logo_axis.imshow(logo_img, interpolation='hanning')\n",
    "#     logo_axis.axis('off')\n",
    "\n",
    "plot(ds.SST.mean('time'), center=False, robust=True, # cmap=cmocean.cm.thermal,\n",
    "     cbar_kwargs={'shrink': 0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLOT timeseries of 10m wind speeds "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(825, 537)\n",
      "(825, 537)\n",
      "(825, 537)\n"
     ]
    }
   ],
   "source": [
    "print(ds.lon.shape)\n",
    "print(ds.lat.shape)\n",
    "print(ds['ws_10m'].isel(time=0).shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get indices for Scott and Amundsen Glaciers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "method 1 - mininmum distance search\n",
    "\n",
    "method 2 - xarray labeled coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # rotate winds to earth-relative using the correct formulas\n",
    "# ds['ue'] = ds['U10'] * ds['COSALPHA'] - ds['V10'] * ds['SINALPHA']\n",
    "# ds['ve'] = ds['V10'] * ds['COSALPHA'] + ds['U10'] * ds['SINALPHA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Crude method using indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5533ab7e50>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(ds['ws_10m'].isel(south_north=ai, west_east=aj), label='Amundsen')\n",
    "plt.plot(ds['ws_10m'].isel(south_north=si, west_east=sj), label='Scott')\n",
    "plt.ylabel('10 m Windspeed (m s$^{-1}$)')\n",
    "plt.legend()\n",
    "# plt.savefig('figs/lineplot_WHAFT_ScottAmundsen_WS10_' + plottitle + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better? method using labeled coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT timeseries of other surface terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'ws_10m' ()>\n",
      "dask.array<nanarg_agg-aggregate, shape=(), dtype=int64, chunksize=(), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "    lat      float32 dask.array<chunksize=(), meta=np.ndarray>\n",
      "    lon      float32 dask.array<chunksize=(), meta=np.ndarray>\n",
      "2016-12-25T03:00:00.000000000\n",
      "<xarray.DataArray 'ws_10m' ()>\n",
      "dask.array<nanarg_agg-aggregate, shape=(), dtype=int64, chunksize=(), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "    lat      float32 dask.array<chunksize=(), meta=np.ndarray>\n",
      "    lon      float32 dask.array<chunksize=(), meta=np.ndarray>\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 4 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-6502a4b16003>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ws_10m'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msouth_north\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwest_east\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\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----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\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[0;31mIndexError\u001b[0m: index 4 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "print(ds['ws_10m'].isel(south_north=si, west_east=sj).argmax())\n",
    "print(ds['time'].values[0])\n",
    "print(ds['ws_10m'].isel(south_north=si, west_east=sj).argmin())\n",
    "print(ds['time'].values[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(8,10), sharex=True, sharey=False)\n",
    "ln01 = axes[0].plot(ds['time'], ds['ws_10m'].isel(south_north=ai, west_east=aj), \n",
    "                     marker='.', color='blue', label='Amundsen')\n",
    "ln02 = axes[0].plot(ds['time'], ds['ws_10m'].isel(south_north=si, west_east=sj), \n",
    "                    marker='.', color='orange', label='Scott')\n",
    "ln11 = axes[1].plot(ds['time'], ds['SH'].isel(south_north=ai, west_east=aj), \n",
    "                   marker='.', color='blue')\n",
    "ln12 = axes[1].plot(ds['time'], ds['SH'].isel(south_north=si, west_east=sj), \n",
    "                   marker='.', color='orange')\n",
    "# ln13 = axes[1].plot(ds['time'], ds['LW_d'].isel(south_north=ai, west_east=aj), \n",
    "#                    color='blue')\n",
    "# ln14 = axes[1].plot(ds['time'], ds['LW_d'].isel(south_north=si, west_east=sj), \n",
    "#                     color='orange')\n",
    "ln21 = axes[2].plot(ds['time'], ds['T_sfc'].isel(south_north=ai, west_east=aj), \n",
    "                   color='blue')\n",
    "ln21 = axes[2].plot(ds['time'], ds['T_sfc'].isel(south_north=si, west_east=sj), \n",
    "                    color='orange')\n",
    "\n",
    "axes[0].set_ylabel('10 m Windspeed (m s$^{-1}$)')\n",
    "axes[1].set_ylabel('Sensible Heat Flux (W / m s$^{-2}$)')\n",
    "axes[2].set_ylabel('T_sfc ($^{o}C$)')\n",
    "axes[0].legend()\n",
    "# plt.xlim(pd.Timestamp('2017-02-10'), pd.Timestamp('2017-02-17'))\n",
    "fig.autofmt_xdate()\n",
    "# plt.savefig('figs/lineplot_WHAFT_ScottAmundsen_WS10_' + plottitle + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Currently can't do this with 2D coordinates\n",
    "# ds = ds.set_index(south_north='lat')\n",
    "# ds = ds.set_index(west_east='lon')\n",
    "# ds.sel(west_east=scott[1], south_north=scott[0], method='nearest')[u'T_sfc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT vertical profile of winds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n",
      "/srv/conda/envs/notebook/lib/python3.7/site-packages/dask/array/numpy_compat.py:41: RuntimeWarning: invalid value encountered in true_divide\n",
      "  x = np.divide(x1, x2, out)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8,10), sharex=False, sharey=True)\n",
    "ln01 = axes[0].plot(ds['T_p'].isel(south_north=ai, west_east=aj).mean('time'), ds['pressure'],\n",
    "                    marker='.', color='blue', label='Amundsen')\n",
    "ln02 = axes[0].plot(ds['T_p'].isel(south_north=si, west_east=sj).mean('time'), ds['pressure'],\n",
    "                    marker='.', color='orange', label='Scott')\n",
    "ln11 = axes[1].plot(ds['q_p'].isel(south_north=ai, west_east=aj).mean('time'), ds['pressure'], \n",
    "                   marker='.', color='blue')\n",
    "ln12 = axes[1].plot(ds['q_p'].isel(south_north=si, west_east=sj).mean('time'), ds['pressure'], \n",
    "                   marker='.', color='orange')\n",
    "\n",
    "axes[0].set_ylabel('T ($^{o}C$)')\n",
    "axes[1].set_ylabel('Specific Humidity (kg / kg$^{-1}$)')\n",
    "axes[0].legend()\n",
    "plt.ylim([900, 300])\n",
    "fig.autofmt_xdate()\n",
    "# plt.savefig('figs/lineplot_WHAFT_ScottAmundsen_WS10_' + plottitle + '.pdf')"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}