{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import xarray as xr\n",
    "import pw13python\n",
    "import jmkfigure\n",
    "import pooch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1800, 3600)\n",
      "(1800, 3600)\n",
      "(1800, 3600)\n",
      "(1800, 3600)\n",
      "Figure(504x352.8)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x352.8 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10*0.7, 7*0.7), \n",
    "                        constrained_layout=True, sharex=True, sharey=True)\n",
    "td = ['Aug1320.png', 'Aug2128.png', 'Aug28Sep5.png', 'Sep613.png']\n",
    "tit = ['Aug 13-20', 'Aug 21-28', 'Aug 29 - Sep 5', 'Sep 6 - 13']\n",
    "for n, t in enumerate(td):\n",
    "    ax = axs.flat[n]\n",
    "    ax.set_facecolor('0.75')\n",
    "    with open('chl/'+t, 'rb') as fin:\n",
    "        im_frame = Image.open(fin)\n",
    "        np_frame = np.flipud(np.array(im_frame, dtype='float'))\n",
    "        print(np_frame.shape)\n",
    "        h, w = np.shape(np_frame)\n",
    "        lon = np.linspace(-180, 180, w)\n",
    "        lat = np.linspace(-90, 90, h)\n",
    "        xlims0 = [-128, -123]\n",
    "        ylims0 = [47.5, 49.5]\n",
    "        ilon = (lon>xlims0[0]) & (lon<xlims0[1])\n",
    "        ilat = (lat>ylims0[0]) & (lat<ylims0[1])\n",
    "        frame = np_frame[ilat, :][:, ilon]\n",
    "        lon = lon[ilon]\n",
    "        lat = lat[ilat]\n",
    "        frame[frame>=255] = np.NaN\n",
    "        ax.pcolormesh(lon, lat, frame, vmin=50, vmax=225, rasterized=True)\n",
    "        ax.set_aspect(1./np.cos(48*np.pi/180))\n",
    "        xlims=[-127, -124.]\n",
    "        ylims = [47.5, 49.2]\n",
    "        ylims=[47.8,48.8]\n",
    "        xlims=[-126.-25./60.,-124.-40./60.]\n",
    "        xx = np.zeros(5)\n",
    "        yy = np.zeros(5)\n",
    "        for nn,ii in enumerate([0,0,1,1,0]):\n",
    "            xx[nn] = xlims[ii]\n",
    "        for nn,ii in enumerate([0,1,1,0,0]):\n",
    "            yy[nn] = ylims[ii]\n",
    "        ax.plot(xx,yy,'0.4',linewidth=2, linestyle='--')\n",
    "        ax.set_title(tit[n], loc='left')\n",
    "        pw13python.plotTopo(ax, contourf=False, xlims=xlims0, ylims=ylims0)\n",
    "        if n == 0:\n",
    "            ax.set_ylabel('Lat $[^oN]$')\n",
    "        if n == 3:\n",
    "            ax.set_xlabel('Lon $[^oW]$')\n",
    "jmkfigure.jmkprint('ChlA', 'plotChl.ipynb', dpi=250, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.469436, 0.460652, 0.450127, ...,      nan,      nan,      nan],\n",
      "       [0.621896, 0.691068, 0.871084, ...,      nan,      nan,      nan],\n",
      "       [0.571279, 0.739931, 0.850112, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.157525, 0.156226, 0.155566, ...,      nan,      nan,      nan],\n",
      "       [0.154842, 0.15531 , 0.155706, ...,      nan,      nan,      nan],\n",
      "       [0.156831, 0.157326, 0.15688 , ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_chlorophyll_concentration_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    keywords:       EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n",
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.392838, 0.395278, 0.401084, ...,      nan,      nan,      nan],\n",
      "       [0.430551,      nan, 0.378756, ...,      nan,      nan,      nan],\n",
      "       [0.403222, 0.396138, 0.392171, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.159461, 0.15426 , 0.158314, ...,      nan,      nan,      nan],\n",
      "       [0.159968, 0.154059, 0.151994, ...,      nan,      nan,      nan],\n",
      "       [0.152357, 0.150436, 0.147137, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n",
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       [0.44842 ,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.156612,      nan, 0.145368, ...,      nan,      nan,      nan],\n",
      "       [     nan, 0.146218, 0.147702, ...,      nan,      nan,      nan],\n",
      "       [0.14971 , 0.150555, 0.145696, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n",
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.412437, 0.409182, 0.397736, ...,      nan,      nan,      nan],\n",
      "       [0.414744, 0.434285, 0.411811, ...,      nan,      nan,      nan],\n",
      "       [0.448543, 0.449668, 0.436628, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       [0.154634, 0.156808, 0.160454, ...,      nan,      nan,      nan],\n",
      "       [0.150805, 0.155012, 0.161727, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n"
     ]
    }
   ],
   "source": [
    "# 5 Aug = 217\n",
    "%matplotlib qt5\n",
    "import matplotlib.colors as mcolors\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10*0.7, 6*0.7), \n",
    "                        constrained_layout=True, sharex=True, sharey=True)\n",
    "\n",
    "for i in range(4):\n",
    "    ax = axs.flat[i]\n",
    "    yday = 217+ 8 * (i+1) - 8\n",
    "    file_path = pooch.retrieve(\n",
    "        url=f'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A2013{yday}2013{yday+7}.L3m_8D_CHL_chlor_a_4km.nc',\n",
    "        known_hash=None,\n",
    "    )\n",
    "\n",
    "    with xr.open_dataset(file_path, mask_and_scale=True) as ds:\n",
    "        date0 = ds.attrs['time_coverage_start'][:10]\n",
    "        date1 = ds.attrs['time_coverage_end'][5:10]\n",
    "        #print(ds)\n",
    "        xlims0 = [-128, -123]\n",
    "        ylims0 = [47.5, 49.5]\n",
    "\n",
    "        ds = ds.sel(lon=slice(xlims0[0], xlims0[1])).sel(lat=slice(ylims0[1], ylims0[0]))\n",
    "        print(ds.chlor_a)\n",
    "        pc=ax.pcolormesh(ds.lon, ds.lat, ds.chlor_a, norm=mcolors.LogNorm(vmin=1e-1, vmax=10), rasterized=True)\n",
    "        if i == 0:\n",
    "            cb = fig.colorbar(pc, ax=axs, shrink=0.6, extend='both', label='Chl $[mg\\ m^{-3}]$')\n",
    "        ax.set_aspect(1./np.cos(48*np.pi/180))\n",
    "        \n",
    "        # inner box:\n",
    "        ylims=[47.8,48.8]\n",
    "        xlims=[-126.-25./60.,-124.-40./60.]\n",
    "        xx = np.zeros(5)\n",
    "        yy = np.zeros(5)\n",
    "        for nn,ii in enumerate([0,0,1,1,0]):\n",
    "            xx[nn] = xlims[ii]\n",
    "        for nn,ii in enumerate([0,1,1,0,0]):\n",
    "            yy[nn] = ylims[ii]\n",
    "        ax.plot(xx,yy,'0.4',linewidth=2, linestyle='--')\n",
    "        ax.set_title(f'{date0} to {date1}', loc='left', fontsize='medium')\n",
    "        ax.set_facecolor('0.75')\n",
    "        pw13python.plotTopo(ax, contourf=False, xlims=xlims0, ylims=ylims0, \n",
    "                            landcol='sienna')\n",
    "        if i == 0:\n",
    "            ax.set_ylabel('Lat $[^oN]$')\n",
    "        if i == 2:\n",
    "            ax.set_xlabel('Lon $[^oW]$')\n",
    "        if i > 1:\n",
    "            ax.set_xticks(-np.arange(124, 129), labels=np.arange(124, 129))\n",
    "    jmkfigure.jmkprint('ChlA', 'plotChl.ipynb', dpi=250, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20132572013264.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/d26b920992d993a565c525c8bcd72a52-A20132572013264.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: a21a2f9b4b8258cc43e254f5a82528eb60ec1777f2aa11a19a9ce7d7aaec92f4\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.673076, 0.609666, 0.484344, ...,      nan,      nan,      nan],\n",
      "       [0.972121, 0.794606, 0.571255, ...,      nan,      nan,      nan],\n",
      "       [0.744753, 0.66272 , 0.568138, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.176722,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan, 0.247191, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_chlorophyll_concentration_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    keywords:       EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20132652013272.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/3e05531909f52a4706b2b59e34808a60-A20132652013272.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 65b4d2155039738f4cb17dfd9d6831e058c9f1b22fe0fd2a531f228661d3a177\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.800815, 0.8181  , 0.805542, ...,      nan,      nan,      nan],\n",
      "       [0.722924, 0.762622, 0.774006, ...,      nan,      nan,      nan],\n",
      "       [0.769045, 0.801255, 0.821273, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.289011, 0.289011, 0.285166, ...,      nan,      nan,      nan],\n",
      "       [0.27449 , 0.264079, 0.260804, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20132732013280.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/f546ddf963d7a977c61bbd7962c74be1-A20132732013280.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 238fa534cc364874eead1ac0a0f8c065e2a3f59a3aa3ed01627493fb6621e506\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[0.641046, 0.662972, 0.706347, ...,      nan,      nan,      nan],\n",
      "       [0.599374, 0.629812, 0.629813, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan, 0.628836, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.466052, 0.466052, 0.46038 , ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan],\n",
      "       [     nan,      nan,      nan, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_of_chlorophyll_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20132812013288.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/e2e9b70281c8da0043cfcd7cda969893-A20132812013288.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 228203f751825a739d6ac0a2d27ffd63cf2bafd60dfdbeaa266433f14df49882\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'chlor_a' (lat: 48, lon: 120)>\n",
      "array([[1.286208, 1.266805, 1.246251, ...,      nan,      nan,      nan],\n",
      "       [1.221878, 1.177327, 1.120961, ...,      nan,      nan,      nan],\n",
      "       [1.300217, 1.007789, 0.981614, ...,      nan,      nan,      nan],\n",
      "       ...,\n",
      "       [0.545388, 0.55362 , 0.544273, ...,      nan,      nan,      nan],\n",
      "       [0.561782, 0.575202, 0.575796, ...,      nan,      nan,      nan],\n",
      "       [0.571038, 0.571828, 0.565527, ...,      nan,      nan,      nan]],\n",
      "      dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float32 49.48 49.44 49.4 49.35 ... 47.65 47.6 47.56 47.52\n",
      "  * lon      (lon) float32 -128.0 -127.9 -127.9 -127.9 ... -123.1 -123.1 -123.0\n",
      "Attributes:\n",
      "    long_name:      Chlorophyll Concentration, OCI Algorithm\n",
      "    units:          mg m^-3\n",
      "    standard_name:  mass_concentration_chlorophyll_concentration_in_sea_water\n",
      "    valid_min:      0.001\n",
      "    valid_max:      100.0\n",
      "    reference:      Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a alg...\n",
      "    keywords:       EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL\n",
      "    display_scale:  log\n",
      "    display_min:    0.01\n",
      "    display_max:    20.0\n",
      "Figure(1400x838)\n"
     ]
    }
   ],
   "source": [
    "# Earlier # 5 Aug = 217\n",
    "%matplotlib qt5\n",
    "import matplotlib.colors as mcolors\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10*0.7, 6*0.7), \n",
    "                        constrained_layout=True, sharex=True, sharey=True)\n",
    "\n",
    "for i in range(4):\n",
    "    ax = axs.flat[i]\n",
    "    yday = 217+ 8 * (i+1) + 32\n",
    "    file_path = pooch.retrieve(\n",
    "        url=f'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A2013{yday}2013{yday+7}.L3m_8D_CHL_chlor_a_4km.nc',\n",
    "        known_hash=None,\n",
    "    )\n",
    "\n",
    "    with xr.open_dataset(file_path, mask_and_scale=True) as ds:\n",
    "        date0 = ds.attrs['time_coverage_start'][:10]\n",
    "        date1 = ds.attrs['time_coverage_end'][5:10]\n",
    "        #print(ds)\n",
    "        xlims0 = [-128, -123]\n",
    "        ylims0 = [47.5, 49.5]\n",
    "\n",
    "        ds = ds.sel(lon=slice(xlims0[0], xlims0[1])).sel(lat=slice(ylims0[1], ylims0[0]))\n",
    "        print(ds.chlor_a)\n",
    "        pc=ax.pcolormesh(ds.lon, ds.lat, ds.chlor_a, norm=mcolors.LogNorm(vmin=1e-1, vmax=10), rasterized=True)\n",
    "        if i == 0:\n",
    "            cb = fig.colorbar(pc, ax=axs, shrink=0.6, extend='both', label='Chl $[mg\\ m^{-3}]$')\n",
    "        ax.set_aspect(1./np.cos(48*np.pi/180))\n",
    "        \n",
    "        # inner box:\n",
    "        ylims=[47.8,48.8]\n",
    "        xlims=[-126.-25./60.,-124.-40./60.]\n",
    "        xx = np.zeros(5)\n",
    "        yy = np.zeros(5)\n",
    "        for nn,ii in enumerate([0,0,1,1,0]):\n",
    "            xx[nn] = xlims[ii]\n",
    "        for nn,ii in enumerate([0,1,1,0,0]):\n",
    "            yy[nn] = ylims[ii]\n",
    "        ax.plot(xx,yy,'0.4',linewidth=2, linestyle='--')\n",
    "        ax.set_title(f'{date0} to {date1}', loc='left', fontsize='medium')\n",
    "        ax.set_facecolor('0.75')\n",
    "        pw13python.plotTopo(ax, contourf=False, xlims=xlims0, ylims=ylims0, \n",
    "                            landcol='sienna')\n",
    "        if i == 0:\n",
    "            ax.set_ylabel('Lat $[^oN]$')\n",
    "        if i == 2:\n",
    "            ax.set_xlabel('Lon $[^oW]$')\n",
    "        if i > 1:\n",
    "            ax.set_xticks(-np.arange(124, 129), labels=np.arange(124, 129))\n",
    "    jmkfigure.jmkprint('ChlAAfter', 'plotChl.ipynb', dpi=250, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20131612013168.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/033ce795084efa8be2b6ae1e082c1ddd-A20131612013168.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 1711f980a22a675c13c7ff6b84c20c278d5748d80e73fadd563b7f67044e2cf5\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20131692013176.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/2b0de44cbdfcb78b1b0e85baf038d596-A20131692013176.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 258d08d090dcfd064f1f71da7297b90e903d497ee2c144abda44a8f5d12da718\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20131772013184.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/730bdf48af87e6edc78bc001c71ea481-A20131772013184.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: 6236ba7a142affedcf4ea39636d6d90977bc9319b27a6b84ffc714207e1a1074\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1400x838)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data from 'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A20131852013192.L3m_8D_CHL_chlor_a_4km.nc' to file '/Users/jklymak/Library/Caches/pooch/ac2f38d675f8c262c9ce36103e559fa9-A20131852013192.L3m_8D_CHL_chlor_a_4km.nc'.\n",
      "SHA256 hash of downloaded file: f7feda4dd2cabc5a00930c0c39277908f2c09ca8764659b5484045b153d57a3d\n",
      "Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(1400x838)\n"
     ]
    }
   ],
   "source": [
    "# Earlier # 5 Aug = 217\n",
    "%matplotlib qt5\n",
    "import matplotlib.colors as mcolors\n",
    "fig, axs = plt.subplots(2, 2, figsize=(10*0.7, 6*0.7), \n",
    "                        constrained_layout=True, sharex=True, sharey=True)\n",
    "\n",
    "for i in range(4):\n",
    "    ax = axs.flat[i]\n",
    "    yday = 217+ 8 * (i+1) - 64\n",
    "    file_path = pooch.retrieve(\n",
    "        url=f'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A2013{yday}2013{yday+7}.L3m_8D_CHL_chlor_a_4km.nc',\n",
    "        known_hash=None,\n",
    "    )\n",
    "\n",
    "    with xr.open_dataset(file_path, mask_and_scale=True) as ds:\n",
    "        date0 = ds.attrs['time_coverage_start'][:10]\n",
    "        date1 = ds.attrs['time_coverage_end'][5:10]\n",
    "        #print(ds)\n",
    "        xlims0 = [-128, -123]\n",
    "        ylims0 = [47.5, 49.5]\n",
    "\n",
    "        ds = ds.sel(lon=slice(xlims0[0], xlims0[1])).sel(lat=slice(ylims0[1], ylims0[0]))\n",
    "        pc=ax.pcolormesh(ds.lon, ds.lat, ds.chlor_a, norm=mcolors.LogNorm(vmin=1e-1, vmax=10), rasterized=True)\n",
    "        if i == 0:\n",
    "            cb = fig.colorbar(pc, ax=axs, shrink=0.6, extend='both', label='Chl $[mg\\ m^{-3}]$')\n",
    "        ax.set_aspect(1./np.cos(48*np.pi/180))\n",
    "        \n",
    "        # inner box:\n",
    "        ylims=[47.8,48.8]\n",
    "        xlims=[-126.-25./60.,-124.-40./60.]\n",
    "        xx = np.zeros(5)\n",
    "        yy = np.zeros(5)\n",
    "        for nn,ii in enumerate([0,0,1,1,0]):\n",
    "            xx[nn] = xlims[ii]\n",
    "        for nn,ii in enumerate([0,1,1,0,0]):\n",
    "            yy[nn] = ylims[ii]\n",
    "        ax.plot(xx,yy,'0.4',linewidth=2, linestyle='--')\n",
    "        ax.set_title(f'{date0} to {date1}', loc='left', fontsize='medium')\n",
    "        ax.set_facecolor('0.75')\n",
    "        pw13python.plotTopo(ax, contourf=False, xlims=xlims0, ylims=ylims0, \n",
    "                            landcol='sienna')\n",
    "        if i == 0:\n",
    "            ax.set_ylabel('Lat $[^oN]$')\n",
    "        if i == 2:\n",
    "            ax.set_xlabel('Lon $[^oW]$')\n",
    "        if i > 1:\n",
    "            ax.set_xticks(-np.arange(124, 129), labels=np.arange(124, 129))\n",
    "    jmkfigure.jmkprint('ChlAJune', 'plotChl.ipynb', dpi=250, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:   (lat: 360, lon: 480, rgb: 3, eightbitcolor: 256)\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 54.98 54.94 54.9 54.85 ... 40.15 40.1 40.06 40.02\n",
      "  * lon       (lon) float32 -140.0 -139.9 -139.9 -139.9 ... -120.1 -120.1 -120.0\n",
      "Dimensions without coordinates: rgb, eightbitcolor\n",
      "Data variables:\n",
      "    sst       (lat, lon) float32 ...\n",
      "    qual_sst  (lat, lon) float32 ...\n",
      "    palette   (rgb, eightbitcolor) uint8 ...\n",
      "Attributes: (12/59)\n",
      "    product_name:                     AQUA_MODIS.20130813_20130820.L3m.8D.NSS...\n",
      "    instrument:                       MODIS\n",
      "    title:                            MODISA Level-3 Standard Mapped Image\n",
      "    project:                          Ocean Biology Processing Group (NASA/GS...\n",
      "    platform:                         Aqua\n",
      "    temporal_range:                   8-day\n",
      "    ...                               ...\n",
      "    publisher_url:                    https://oceandata.sci.gsfc.nasa.gov\n",
      "    processing_level:                 L3 Mapped\n",
      "    cdm_data_type:                    grid\n",
      "    data_bins:                        15456452\n",
      "    data_minimum:                     -1.765\n",
      "    data_maximum:                     35.32\n"
     ]
    }
   ],
   "source": [
    "# 5 Aug = 217\n",
    "%matplotlib qt5\n",
    "yday = 217+24\n",
    "file_path = pooch.retrieve(\n",
    "        url=f'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/AQUA_MODIS.20130813_20130820.L3m.8D.NSST.sst.4km.nc',\n",
    "        known_hash=None,\n",
    "    )\n",
    "with xr.open_dataset(file_path, mask_and_scale=True) as ds:\n",
    "    #print(ds)\n",
    "    ds = ds.sel(lon=slice(-140, -120)).sel(lat=slice(55, 40))\n",
    "    print(ds)\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    pc = ax.imshow(ds.sst)\n",
    "    #ax.set_xlim([2000, 2050])\n",
    "    #ax.set_ylim([2000, 2050])\n",
    "    \n",
    "    fig.colorbar(pc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'long_name': 'Chlorophyll Concentration, OCI Algorithm',\n",
       " 'units': 'mg m^-3',\n",
       " 'standard_name': 'mass_concentration_chlorophyll_concentration_in_sea_water',\n",
       " 'valid_min': 0.001,\n",
       " 'valid_max': 100.0,\n",
       " 'reference': 'Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.',\n",
       " 'keywords': 'EARTH SCIENCE > OCEANS > OCEAN CHEMISTRY > CHLOROPHYLL',\n",
       " 'display_scale': 'log',\n",
       " 'display_min': 0.01,\n",
       " 'display_max': 20.0}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ds.chlor_a.attrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Larger scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(600x838)\n"
     ]
    }
   ],
   "source": [
    "# Earlier # 5 Aug = 217\n",
    "%matplotlib qt5\n",
    "import matplotlib.colors as mcolors\n",
    "fig, axs = plt.subplots(1, 1, figsize=(3, 6*0.7), \n",
    "                        constrained_layout=True, sharex=True, sharey=True, squeeze=False)\n",
    "\n",
    "for i in range(1):\n",
    "    ax = axs.flat[i]\n",
    "    yday = 217+ 8 * (i+1)\n",
    "    file_path = pooch.retrieve(\n",
    "        url=f'https://oceandata.sci.gsfc.nasa.gov/cgi/getfile/A2013{yday}2013{yday+7}.L3m_8D_CHL_chlor_a_4km.nc',\n",
    "        known_hash=None,\n",
    "    )\n",
    "\n",
    "    with xr.open_dataset(file_path, mask_and_scale=True) as ds:\n",
    "        date0 = ds.attrs['time_coverage_start'][:10]\n",
    "        date1 = ds.attrs['time_coverage_end'][5:10]\n",
    "        #print(ds)\n",
    "        xlims0 = [-130, -123.5]\n",
    "        ylims0 = [40.5, 52]\n",
    "\n",
    "        ds = ds.sel(lon=slice(xlims0[0], xlims0[1])).sel(lat=slice(ylims0[1], ylims0[0]))\n",
    "        pc=ax.pcolormesh(ds.lon, ds.lat, ds.chlor_a, norm=mcolors.LogNorm(vmin=1e-1, vmax=10), rasterized=True)\n",
    "        if i == 0:\n",
    "            cb = fig.colorbar(pc, ax=axs, shrink=0.6, extend='both', label='Chl $[mg\\ m^{-3}]$')\n",
    "        ax.set_aspect(1./np.cos(48*np.pi/180))\n",
    "        \n",
    "        # inner box:\n",
    "        ylims=[47.8,48.8]\n",
    "        xlims=[-126.-25./60.,-124.-40./60.]\n",
    "        xx = np.zeros(5)\n",
    "        yy = np.zeros(5)\n",
    "        for nn,ii in enumerate([0,0,1,1,0]):\n",
    "            xx[nn] = xlims[ii]\n",
    "        for nn,ii in enumerate([0,1,1,0,0]):\n",
    "            yy[nn] = ylims[ii]\n",
    "        ax.plot(xx,yy,'0.4',linewidth=2, linestyle='--')\n",
    "        ax.set_title(f'{date0} to {date1}', loc='left', fontsize='medium')\n",
    "        ax.set_facecolor('0.75')\n",
    "        if i == 0:\n",
    "            ax.set_ylabel('Lat $[^oN]$')\n",
    "        if i == 2:\n",
    "            ax.set_xlabel('Lon $[^oW]$')\n",
    "        if i > 1:\n",
    "            ax.set_xticks(-np.arange(124, 129), labels=np.arange(124, 129))\n",
    "jmkfigure.jmkprint('ChlANorthAmerica', 'plotChl.ipynb', dpi=250, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'10.5067/AQUA/MODIS/L3M/CHL/2018'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ds.chlor_a.attrs['reference'])\n",
    "display(ds.attrs['identifier_product_doi'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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