Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "830aab49-19a9-47b9-afcc-76e9fbcdcf75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import zipfile\n",
    "import xarray as xr\n",
    "import glob\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "86e1a39a-2a25-40ba-9c8e-ee408598bd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(data.time.data[0]).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4c7777c9-c5ea-44ec-a72b-af1294a67d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1012"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find chloro means\n",
    "monthly_chloro_mean = np.full(12, fill_value=np.nan)\n",
    "chloro_file_list = glob.glob(\"ESACCI*\")\n",
    "for i, chloro_file in enumerate(chloro_file_list):\n",
    "    clear_output(wait=True)\n",
    "    print(i/len(chloro_file_list))\n",
    "    data = xr.open_dataset(chloro_file)\n",
    "    data_month = pd.to_datetime(data.time.data[0]).month\n",
    "    total_chlor_concentration[i] = data.chlor_a.sel(lat=slice(60, 35), lon=slice(-160, -120)).squeeze().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "129cdbf6-c801-42f1-ad20-7d8a30cbb7ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, ..., False, False, False])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MJO_phase_data[:,0]==1974"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f2b5fcf6-0ef6-4bfd-857e-a4332d240eca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9990118577075099\n"
     ]
    }
   ],
   "source": [
    "chloro_file_list = glob.glob(\"ESACCI*\")\n",
    "total_chlor_concentration = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "MJO_phase_data = np.genfromtxt('mjo_phase_data.csv', delimiter=',')\n",
    "daily_MJO_phase = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "data_month = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "for i, chloro_file in enumerate(chloro_file_list):\n",
    "    clear_output(wait=True)\n",
    "    print(i/len(chloro_file_list))\n",
    "    data = xr.open_dataset(chloro_file)\n",
    "    data_time = pd.to_datetime(data.time.data[0])\n",
    "    dayID = np.where((MJO_phase_data[:,0]==data_time.year)&(MJO_phase_data[:,1]==data_time.month)&(MJO_phase_data[:,2]==data_time.day))[0][0]\n",
    "    total_chlor_concentration[i] = data.chlor_a.sel(lat=slice(60, 35), lon=slice(-160, -120)).squeeze().sum()\n",
    "    daily_MJO_phase[i] = MJO_phase_data[dayID][3]\n",
    "    data_month[i] = data_time.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "7cdfe758-fda6-42d4-bb4b-93ccb6fd59cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "june_ID = np.where(data_month==6)\n",
    "july_ID = np.where(data_month==7)\n",
    "august_ID = np.where(data_month==8)\n",
    "\n",
    "june_mean = np.nanmean(total_chlor_concentration[june_ID])\n",
    "july_mean = np.nanmean(total_chlor_concentration[july_ID])\n",
    "august_mean = np.nanmean(total_chlor_concentration[august_ID])\n",
    "\n",
    "chloro_anom = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "for i, total in enumerate(total_chlor_concentration):\n",
    "    anom_month = data_month[i]\n",
    "    if anom_month==6:\n",
    "        chloro_anom[i] = total - june_mean\n",
    "    elif anom_month==7:\n",
    "        chloro_anom[i] = total - july_mean\n",
    "    elif anom_month==8:\n",
    "        chloro_anom[i] = total - august_mean\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "12023d60-b80d-4965-8218-3b736582f399",
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_chlor = np.full(8, fill_value=np.nan)\n",
    "for i, phase in enumerate(np.arange(1,9)):\n",
    "    phaseID = np.where(daily_MJO_phase==phase)\n",
    "    phase_chlor[i]=np.nanmean(chloro_anom[phaseID])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "42b18288-e3c6-4fdc-8b4a-25bd03039ef4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "460"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(chloro_anom>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ad4626d4-c3a1-4724-b550-9d942a5bb39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "552"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(chloro_anom<0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "6a16ea88-adcd-4337-85ea-232f30ac9407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Anomalous Total Chlorophyll (g/m$^3$)')"
      ]
     },
     "execution_count": 131,
     "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": [
    "\n",
    "plt.plot(np.arange(1,9), phase_chlor, 'b+:')\n",
    "plt.title('Pacific Northwest')\n",
    "plt.xlabel('MJO Phase')\n",
    "plt.ylabel('Anomalous Total Chlorophyll (g/m$^3$)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9a719f2a-352e-4cea-9c05-7927fa6a49c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "chloro_file_list = glob.glob(\"ESACCI*\")\n",
    "total_chlor_concentration = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "MJO_phase_data = np.genfromtxt('mjo_phase_data.csv', delimiter=',')\n",
    "daily_MJO_phase = np.full(len(chloro_file_list), fill_value=np.nan)\n",
    "for i, chloro_file in enumerate(chloro_file_list):\n",
    "    clear_output(wait=True)\n",
    "    print(i/len(chloro_file_list))\n",
    "    data = xr.open_dataset(chloro_file)\n",
    "    total_chlor_concentration[i] = data.chlor_a.sel(lat=slice(60, 35), lon=slice(-160, -120)).squeeze().sum()\n",
    "    daily_MJO_phase[i] = MJO_phase_data[i+1][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c9b0c04a-1294-44b5-b921-e334f128eb43",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'chlor_a'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a287ef53-9850-4754-abb5-18083921a51e' class='xr-array-in' type='checkbox' ><label for='section-a287ef53-9850-4754-abb5-18083921a51e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.59844214</span></div><pre class='xr-array-data'>array(0.59844214, dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-eef9df68-a040-4163-83e9-2f6c81ed4cb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eef9df68-a040-4163-83e9-2f6c81ed4cb2' class='xr-section-summary' >Coordinates: <span>(1)</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>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-06-01</div><input id='attrs-7275c4b7-c90c-4fd9-996b-9b88842f6e06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7275c4b7-c90c-4fd9-996b-9b88842f6e06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf98b925-8d74-4f0f-bf8d-0e2e545ddd5a' class='xr-var-data-in' type='checkbox'><label for='data-bf98b925-8d74-4f0f-bf8d-0e2e545ddd5a' 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><pre class='xr-var-data'>array(&#x27;2010-06-01T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-b91fbf15-ded5-4918-8d67-caf3e3a87976' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b91fbf15-ded5-4918-8d67-caf3e3a87976' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'chlor_a' ()>\n",
       "array(0.59844214, dtype=float32)\n",
       "Coordinates:\n",
       "    time     datetime64[ns] 2010-06-01"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.chlor_a.sel(lat=slice(60, 35), lon=slice(-160, -120)).squeeze().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b7168dd2-02c0-4c89-ba99-9187dab969c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f6d6134de90>"
      ]
     },
     "execution_count": 40,
     "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": [
    "xr.plot.contourf(data.chlor_a.sel(lat=slice(60,35), lon=slice(-160, -120)).squeeze(), vmin=0, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3af4aa80-9025-4fa6-973c-6305d0e6ea35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100617-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100629-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100625-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100621-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100604-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100607-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100619-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100620-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100623-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100616-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100602-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100624-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100606-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100626-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100622-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100612-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100601-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100628-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100608-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100605-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100609-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100610-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100611-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100627-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100613-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100615-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100603-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100618-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100630-fv5.0.1.nc\n",
      "ESACCI-OC-L3S-CHLOR_A-MERGED-1D_DAILY_4km_GEO_PML_OCx-20100614-fv5.0.1.nc\n"
     ]
    }
   ],
   "source": [
    "with zipfile.ZipFile('/home/disk/p/pangulo/ResearchDerby_2022/chloro_2010_06.zip', 'r') as zipObj:\n",
    "    listOfiles = zipObj.namelist()\n",
    "    data = zipObj.read(listOfiles[0])\n",
    "    for elem in listOfiles:\n",
    "        print(elem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76de3185-165c-4c8a-ba87-0cf397ea11b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "687c24fa-6e5e-49ca-a1d1-11addd5b9860",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "embedded null byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-fc78e6fba6d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfile2read\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetcdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNetCDFFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/scipy/io/netcdf.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, mode, mmap, version, maskandscale)\u001b[0m\n\u001b[1;32m    247\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0momode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'r+'\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'a'\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'%sb'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0momode\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    250\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmmap\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m                 \u001b[0;31m# Mmapped files on PyPy cannot be usually closed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: embedded null byte"
     ]
    }
   ],
   "source": [
    "file2read = netcdf.NetCDFFile(data,'r')"
   ]
  }
 ],
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