notebooks/scratch/sat-utils.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Landsat Development"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sat Utils\n",
    "\n",
    "- https://github.com/sat-utils/sat-stac\n",
    "- https://github.com/sat-utils/sat-search\n",
    "- https://github.com/sat-utils/sat-search/blob/master/tutorial-1.ipynb\n",
    "- https://github.com/sat-utils/sat-stac/blob/master/tutorial-1.ipynb\n",
    "- https://github.com/sat-utils/sat-stac/blob/master/tutorial-2.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241 items\n"
     ]
    }
   ],
   "source": [
    "from satsearch import Search\n",
    "\n",
    "# if you want to specify collection\n",
    "query = {\n",
    "#     \"collection\": {\n",
    "#         \"eq\": \"landsat-8-l1\"\n",
    "#     }\n",
    "}\n",
    "search = Search(bbox=[-110, 39.5, -105, 40.5],\n",
    "                time='2020-02-12T00:00:00Z/2020-03-18T12:31:12Z',\n",
    "                query=query)\n",
    "\n",
    "print('%s items' % search.found())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241 items\n",
      "2 collections\n",
      "[sentinel-2-l1c, landsat-8-l1]\n"
     ]
    }
   ],
   "source": [
    "items = search.items()\n",
    "print('%s items' % len(items))\n",
    "print('%s collections' % len(items._collections))\n",
    "print(items._collections)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PODPAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from podpac.datalib.satutils import SatUtils\n",
    "import podpac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = [38, 38.5, 40]         # lat dimension\n",
    "lon = [-110, -110.5, -111]  # lon dimension\n",
    "time = ['2020-02-05', '2020-02-06']\n",
    "\n",
    "# Create PODPAC coordinates\n",
    "c = podpac.Coordinates([lat, lon, time], dims=['lat', 'lon', 'time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# search\n",
    "node = SatUtils(collection=\"landsat-8-l1\", asset=\"B3\")\n",
    "s = node.search(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<SatUtilsSource(source='https://landsat-pds.s3.amazonaws.com/c1/L8/038/032/LC08_L1TP_038032_20200205_20200211_01_T1/LC08_L1TP_038032_20200205_20200211_01_T1_B3.TIF', interpolation='nearest')>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "src = node.sources\n",
    "src"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INTERNAL SEARCH CONVIENCE METHODS\n",
    "\n",
    "# items\n",
    "# items = node._search.items()\n",
    "\n",
    "# summary\n",
    "# print(items.summary())\n",
    "\n",
    "# save to json\n",
    "# node.items.save(\"test.json\")\n",
    "\n",
    "# item\n",
    "# item = items[0]\n",
    "\n",
    "# datetime\n",
    "# item.datetime\n",
    "\n",
    "# properties\n",
    "# item.properties \n",
    "\n",
    "# assets to download\n",
    "# item.assets\n",
    "\n",
    "# date of item\n",
    "# item.properties[\"datetime\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:podpac.core.data.file_source:Downloading: https://landsat-pds.s3.amazonaws.com/c1/L8/038/032/LC08_L1TP_038032_20200205_20200211_01_T1/LC08_L1TP_038032_20200205_20200211_01_T1_B3.TIF\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.UnitsDataArray (lat: 3, lon: 3, time: 2)>\n",
       "array([[[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [   nan,    nan]],\n",
       "\n",
       "       [[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [   nan,    nan]],\n",
       "\n",
       "       [[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [20323., 20323.]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 38.0 38.5 40.0\n",
       "  * lon      (lon) float64 -110.0 -110.5 -111.0\n",
       "  * time     (time) datetime64[ns] 2020-02-05 2020-02-06\n",
       "Attributes:\n",
       "    layer_style:  <podpac.core.style.Style object at 0x7fd300435ad0>\n",
       "    crs:          EPSG:4326"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = node.eval(c)\n",
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fd43abbc690>"
      ]
     },
     "execution_count": 31,
     "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": [
    "output[:,:,1].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Landsat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import podpac\n",
    "from podpac.datalib.satutils import Landsat8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = [38, 38.5, 40]         # lat dimension\n",
    "lon = [-110, -110.5, -111]  # lon dimension\n",
    "time = ['2020-02-05', '2020-02-06']\n",
    "\n",
    "# Create PODPAC coordinates\n",
    "c = podpac.Coordinates([lat, lon, time], dims=['lat', 'lon', 'time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "node = Landsat8(asset=\"B1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:podpac.core.data.file_source:Loading AWS resource: s3://landsat-pds/c1/L8/038/032/LC08_L1TP_038032_20200205_20200211_01_T1/LC08_L1TP_038032_20200205_20200211_01_T1_B1.TIF\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.UnitsDataArray (lat: 3, lon: 3, time: 2)>\n",
       "array([[[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [   nan,    nan]],\n",
       "\n",
       "       [[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [   nan,    nan]],\n",
       "\n",
       "       [[   nan,    nan],\n",
       "        [   nan,    nan],\n",
       "        [22060., 22060.]]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 38.0 38.5 40.0\n",
       "  * lon      (lon) float64 -110.0 -110.5 -111.0\n",
       "  * time     (time) datetime64[ns] 2020-02-05 2020-02-06\n",
       "Attributes:\n",
       "    layer_style:  <podpac.core.style.Style object at 0x7f8e40c181d0>\n",
       "    crs:          EPSG:4326"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = node.eval(c)\n",
    "output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentinel 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import podpac\n",
    "from podpac.datalib.satutils import Sentinel2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat = [38, 38.5, 40]         # lat dimension\n",
    "lon = [-110, -110.5, -111]  # lon dimension\n",
    "time = ['2020-02-24', '2020-02-25']\n",
    "\n",
    "# Create PODPAC coordinates\n",
    "c = podpac.Coordinates([lat, lon, time], dims=['lat', 'lon', 'time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "node = Sentinel2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find assets\n",
    "s = node.search(c)\n",
    "node.list_assets()\n",
    "s.found()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from podpac import settings\n",
    "settings[\"AWS_REGION_NAME\"] = \"us-east-1\"\n",
    "settings[\"AWS_REQUESTER_PAYS\"] = False\n",
    "settings.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:podpac.core.data.file_source:Loading AWS resource: s3://sentinel-s2-l1c/tiles/12/S/WJ/2020/2/24/0/B02.jp2\n",
      "/Users/marcus/computing/creare/podpac/podpac/podpac/core/cache/ram_cache_store.py:89: UserWarning: Warning: Process is using more RAM than the specified limit in settings.RAM_CACHE_MAX_BYTES. No longer caching. Consider increasing this limit or try clearing the cache (e.g. podpac.utils.clear_cache(mode='RAM') to clear ALL cached results in RAM)\n",
      "  UserWarning,\n",
      "INFO:podpac.core.data.file_source:Loading AWS resource: s3://sentinel-s2-l1c/tiles/12/T/WK/2020/2/24/0/B02.jp2\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-1aa1a7bad332>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msettings\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"AWS_REQUESTER_PAYS\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mnode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSentinel2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"B02\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\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      6\u001b[0m     \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/node.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 984\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\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    985\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_output\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_coordinates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/datalib/satutils.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;31m# run normal eval once self.data is prepared\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSatUtils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\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    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[0;31m##########\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/node.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 984\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\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    985\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_output\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_coordinates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/compositor/compositor.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_requested_coordinates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteroutputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomposite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\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    242\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/compositor/ordered_compositor.py\u001b[0m in \u001b[0;36mcomposite\u001b[0;34m(self, coordinates, data_arrays, result)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUnitsDataArray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_arrays\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     52\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\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     53\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/compositor/compositor.py\u001b[0m in \u001b[0;36miteroutputs\u001b[0;34m(self, coordinates)\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_multi_threaded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0msrc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msources\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoordinates\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    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mnode_eval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/node.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    982\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 984\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoordinates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput\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    985\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_output\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache_coordinates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/data/datasource.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, coordinates, output)\u001b[0m\n\u001b[1;32m    448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    449\u001b[0m         \u001b[0;31m# get data from data source\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 450\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_requested_source_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_data\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[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0;31m# if not provided, create output using the evaluated coordinates, or\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/computing/creare/podpac/podpac/podpac/core/data/datasource.py\u001b[0m in \u001b[0;36m_get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# fill nan_vals in data array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mnan_val\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan_vals\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m             \u001b[0mudata_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mudata_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnan_val\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mudata_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# get true color image\n",
    "with settings:\n",
    "    settings[\"AWS_REQUESTER_PAYS\"] = True\n",
    "    node = Sentinel2(asset=\"B02\")\n",
    "    output = node.eval(c)\n",
    "    output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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