diff --git a/notebooks/0.2.1-MODIS-download.ipynb b/notebooks/0.2.1-MODIS-download.ipynb
index 588f49e..ddf57a0 100644
--- a/notebooks/0.2.1-MODIS-download.ipynb
+++ b/notebooks/0.2.1-MODIS-download.ipynb
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "5ae23a72",
    "metadata": {},
    "outputs": [],
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "id": "d1c63a79",
    "metadata": {},
    "outputs": [],
@@ -118,16 +118,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/lillifreischem/miniconda3/envs/iti/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import earthaccess"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "id": "5201d291",
    "metadata": {},
    "outputs": [
@@ -135,7 +144,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Granules found: 5\n"
+      "Granules found: 3\n"
      ]
     }
    ],
@@ -147,47 +156,37 @@
     "    short_name='MOD021KM',\n",
     "    cloud_hosted=True,\n",
     "    bounding_box=(-10, 20, 10, 50),\n",
-    "    temporal=(\"2019-03-01 10:00\", \"2019-03-01 13:00\"),\n",
+    "    temporal=(\"2018-10-01 08:00\", \"2018-10-01 11:00\"),\n",
     "    count=-1\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[Collection: {'ShortName': 'MOD021KM', 'Version': '6.1'}\n",
-       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -9.072011, 'Latitude': 49.152121}, {'Longitude': 21.956898, 'Latitude': 44.9505}, {'Longitude': 37.925717, 'Latitude': 60.628441}, {'Longitude': -9.837857, 'Latitude': 67.432882}, {'Longitude': -9.072011, 'Latitude': 49.152121}]}}]}}}\n",
-       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2019-03-01T10:35:00.000Z', 'EndingDateTime': '2019-03-01T10:40:00.000Z'}}\n",
-       " Size(MB): 155.911573410034\n",
-       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2019/060/MOD021KM.A2019060.1035.061.2019060192534.hdf'],\n",
-       " Collection: {'ShortName': 'MOD021KM', 'Version': '6.1'}\n",
-       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -10.988818, 'Latitude': 31.324475}, {'Longitude': 13.217814, 'Latitude': 28.048083}, {'Longitude': 21.45928, 'Latitude': 45.174304}, {'Longitude': -9.415287, 'Latitude': 49.608425}, {'Longitude': -10.988818, 'Latitude': 31.324475}]}}]}}}\n",
-       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2019-03-01T10:40:00.000Z', 'EndingDateTime': '2019-03-01T10:45:00.000Z'}}\n",
-       " Size(MB): 154.762685775757\n",
-       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2019/060/MOD021KM.A2019060.1040.061.2019060192541.hdf'],\n",
+       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -7.820944, 'Latitude': 48.615273}, {'Longitude': 22.900313, 'Latitude': 44.459754}, {'Longitude': 38.493195, 'Latitude': 60.213084}, {'Longitude': -8.461157, 'Latitude': 66.899476}, {'Longitude': -7.820944, 'Latitude': 48.615273}]}}]}}}\n",
+       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2018-10-01T10:30:00.000Z', 'EndingDateTime': '2018-10-01T10:35:00.000Z'}}\n",
+       " Size(MB): 167.403575897217\n",
+       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2018/274/MOD021KM.A2018274.1030.061.2018274200208.hdf'],\n",
        " Collection: {'ShortName': 'MOD021KM', 'Version': '6.1'}\n",
-       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -13.763574, 'Latitude': 13.511943}, {'Longitude': 7.574463, 'Latitude': 10.543217}, {'Longitude': 12.859466, 'Latitude': 28.206499}, {'Longitude': -11.152315, 'Latitude': 31.694474}, {'Longitude': -13.763574, 'Latitude': 13.511943}]}}]}}}\n",
-       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2019-03-01T10:45:00.000Z', 'EndingDateTime': '2019-03-01T10:50:00.000Z'}}\n",
-       " Size(MB): 144.453195571899\n",
-       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2019/060/MOD021KM.A2019060.1045.061.2019060192444.hdf'],\n",
+       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -9.762663, 'Latitude': 30.785606}, {'Longitude': 14.294732, 'Latitude': 27.52965}, {'Longitude': 22.400958, 'Latitude': 44.682586}, {'Longitude': -8.148924, 'Latitude': 49.069527}, {'Longitude': -9.762663, 'Latitude': 30.785606}]}}]}}}\n",
+       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2018-10-01T10:35:00.000Z', 'EndingDateTime': '2018-10-01T10:40:00.000Z'}}\n",
+       " Size(MB): 161.66379070282\n",
+       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2018/274/MOD021KM.A2018274.1035.061.2018274200111.hdf'],\n",
        " Collection: {'ShortName': 'MOD021KM', 'Version': '6.1'}\n",
-       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -34.12477, 'Latitude': 45.164849}, {'Longitude': -5.140327, 'Latitude': 41.250024}, {'Longitude': 8.325962, 'Latitude': 57.408571}, {'Longitude': -34.079421, 'Latitude': 63.458085}, {'Longitude': -34.12477, 'Latitude': 45.164849}]}}]}}}\n",
-       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2019-03-01T12:15:00.000Z', 'EndingDateTime': '2019-03-01T12:20:00.000Z'}}\n",
-       " Size(MB): 162.147795677185\n",
-       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2019/060/MOD021KM.A2019060.1215.061.2019061011820.hdf'],\n",
-       " Collection: {'ShortName': 'MOD021KM', 'Version': '6.1'}\n",
-       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -36.263121, 'Latitude': 27.334255}, {'Longitude': -12.968573, 'Latitude': 24.170761}, {'Longitude': -5.627833, 'Latitude': 41.463069}, {'Longitude': -34.414253, 'Latitude': 45.606625}, {'Longitude': -36.263121, 'Latitude': 27.334255}]}}]}}}\n",
-       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2019-03-01T12:20:00.000Z', 'EndingDateTime': '2019-03-01T12:25:00.000Z'}}\n",
-       " Size(MB): 165.607719421387\n",
-       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2019/060/MOD021KM.A2019060.1220.061.2019061011749.hdf']]"
+       " Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -12.580666, 'Latitude': 12.885638}, {'Longitude': 8.697335, 'Latitude': 9.922978}, {'Longitude': 13.946976, 'Latitude': 27.684994}, {'Longitude': -9.932858, 'Latitude': 31.152089}, {'Longitude': -12.580666, 'Latitude': 12.885638}]}}]}}}\n",
+       " Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2018-10-01T10:40:00.000Z', 'EndingDateTime': '2018-10-01T10:45:00.000Z'}}\n",
+       " Size(MB): 153.157649040222\n",
+       " Data: ['https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MOD021KM/2018/274/MOD021KM.A2018274.1040.061.2018274200246.hdf']]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -198,250 +197,2241 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "67e0a7eb",
+   "execution_count": 29,
+   "id": "5d10708d",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Opening 5 granules, approx size: 0.76 GB\n"
+      " Getting 3 granules, approx download size: 0.47 GB\n"
      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13c6ebb901264d85a8252c17cf0bf8f0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "QUEUEING TASKS | : 100%|██████████| 3/3 [00:00<00:00, 365.05it/s]\n",
+      "PROCESSING TASKS | :   0%|          | 0/3 [00:00<?, ?it/s]"
+     ]
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "PROCESSING TASKS | : 100%|██████████| 3/3 [04:11<00:00, 83.91s/it] \n",
+      "COLLECTING RESULTS | : 100%|██████████| 3/3 [00:00<00:00, 7938.75it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "files = earthaccess.download(results, \"./modisdata/earthaccess2/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "391d17b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "ds = xr.open_dataset('./modisdata/earthaccess2/MOD021KM.A2018274.1030.061.2018274200208.hdf', engine='netcdf4')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "ba3b84dc",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ddf3baaf6b6f4d28b6255db32426c101",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/5 [00:00<?, ?it/s]"
+       "['modisdata/earthaccess2/MOD021KM.A2018274.1030.061.2018274200208.hdf',\n",
+       " 'modisdata/earthaccess2/MOD021KM.A2018274.1035.061.2018274200111.hdf',\n",
+       " 'modisdata/earthaccess2/MOD021KM.A2018274.1040.061.2018274200246.hdf']"
       ]
      },
+     "execution_count": 39,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b5dd61a310b643939f2d2407207f9dbb",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
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+       "}\n",
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+       "  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",
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+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
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+       "\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,\n",
+       ".xr-attrs 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><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                            (\n",
+       "                                                        2*nscans:MODIS_SWATH_Type_L1B: 406,\n",
+       "                                                        1KM_geo_dim:MODIS_SWATH_Type_L1B: 271,\n",
+       "                                                        Band_1KM_RefSB:MODIS_SWATH_Type_L1B: 15,\n",
+       "                                                        10*nscans:MODIS_SWATH_Type_L1B: 2030,\n",
+       "                                                        Max_EV_frames:MODIS_SWATH_Type_L1B: 1354,\n",
+       "                                                        ...\n",
+       "                                                        number of scans: 203,\n",
+       "                                                        number of 250m bands: 2,\n",
+       "                                                        detectors per 250m band: 40,\n",
+       "                                                        number of 500m bands: 5,\n",
+       "                                                        detectors per 500m band: 20,\n",
+       "                                                        number of 1km reflective bands: 15)\n",
+       "Coordinates:\n",
+       "  * Band_250M                                          (Band_250M) float32 1....\n",
+       "  * Band_500M                                          (Band_500M) float32 3....\n",
+       "  * Band_1KM_RefSB                                     (Band_1KM_RefSB) float32 ...\n",
+       "  * Band_1KM_Emissive                                  (Band_1KM_Emissive) float32 ...\n",
+       "Dimensions without coordinates: 2*nscans:MODIS_SWATH_Type_L1B,\n",
+       "                                1KM_geo_dim:MODIS_SWATH_Type_L1B,\n",
+       "                                Band_1KM_RefSB:MODIS_SWATH_Type_L1B,\n",
+       "                                10*nscans:MODIS_SWATH_Type_L1B,\n",
+       "                                Max_EV_frames:MODIS_SWATH_Type_L1B,\n",
+       "                                Band_1KM_Emissive:MODIS_SWATH_Type_L1B,\n",
+       "                                ...\n",
+       "                                Band_500M:MODIS_SWATH_Type_L1B,\n",
+       "                                number of emissive bands,\n",
+       "                                detectors per 1km band, number of scans,\n",
+       "                                number of 250m bands, detectors per 250m band,\n",
+       "                                number of 500m bands, detectors per 500m band,\n",
+       "                                number of 1km reflective bands\n",
+       "Data variables: (12/27)\n",
+       "    Latitude                                           (2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    Longitude                                          (2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_RefSB                                       (Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_RefSB_Uncert_Indexes                        (Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_Emissive                                    (Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_Emissive_Uncert_Indexes                     (Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    ...                                                 ...\n",
+       "    Noise in Thermal Detectors                         (number of emissive bands, detectors per 1km band) uint8 ...\n",
+       "    Change in relative responses of thermal detectors  (number of emissive bands, detectors per 1km band) uint8 ...\n",
+       "    DC Restore Change for Thermal Bands                (number of scans, number of emissive bands, detectors per 1km band) int8 ...\n",
+       "    DC Restore Change for Reflective 250m Bands        (number of scans, number of 250m bands, detectors per 250m band) int8 ...\n",
+       "    DC Restore Change for Reflective 500m Bands        (number of scans, number of 500m bands, detectors per 500m band) int8 ...\n",
+       "    DC Restore Change for Reflective 1km Bands         (number of scans, number of 1km reflective bands, detectors per 1km band) int8 ...\n",
+       "Attributes: (12/58)\n",
+       "    HDFEOSVersion:                                               HDFEOS_V2.19\n",
+       "    StructMetadata.0:                                            GROUP=SwathS...\n",
+       "    HDFEOS_FractionalOffset_10*nscans_MODIS_SWATH_Type_L1B:      0.0\n",
+       "    HDFEOS_FractionalOffset_Max_EV_frames_MODIS_SWATH_Type_L1B:  0.0\n",
+       "    CoreMetadata.0:                                              \\nGROUP     ...\n",
+       "    ArchiveMetadata.0:                                           \\nGROUP     ...\n",
+       "    ...                                                          ...\n",
+       "    Detector Quality Flag:                                       [ 0  0  0  0...\n",
+       "    Detector Quality Flag2:                                      [0 0 0 0 0 0...\n",
+       "    Earth-Sun Distance:                                          1.0011935\n",
+       "    Solar Irradiance on RSB Detectors over pi:                   [511.46   51...\n",
+       "    identifier_product_doi:                                      10.5067/MODI...\n",
+       "    identifier_product_doi_authority:                            http://dx.do...</pre><div class='xr-wrap' style='display:none'><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-6c19d71e-5d86-4ca6-9195-2df176d61987' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c19d71e-5d86-4ca6-9195-2df176d61987' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>2*nscans:MODIS_SWATH_Type_L1B</span>: 406</li><li><span>1KM_geo_dim:MODIS_SWATH_Type_L1B</span>: 271</li><li><span>Band_1KM_RefSB:MODIS_SWATH_Type_L1B</span>: 15</li><li><span>10*nscans:MODIS_SWATH_Type_L1B</span>: 2030</li><li><span>Max_EV_frames:MODIS_SWATH_Type_L1B</span>: 1354</li><li><span>Band_1KM_Emissive:MODIS_SWATH_Type_L1B</span>: 16</li><li><span>Band_250M:MODIS_SWATH_Type_L1B</span>: 2</li><li><span>Band_500M:MODIS_SWATH_Type_L1B</span>: 5</li><li><span class='xr-has-index'>Band_250M</span>: 2</li><li><span class='xr-has-index'>Band_500M</span>: 5</li><li><span class='xr-has-index'>Band_1KM_RefSB</span>: 15</li><li><span class='xr-has-index'>Band_1KM_Emissive</span>: 16</li><li><span>number of emissive bands</span>: 16</li><li><span>detectors per 1km band</span>: 10</li><li><span>number of scans</span>: 203</li><li><span>number of 250m bands</span>: 2</li><li><span>detectors per 250m band</span>: 40</li><li><span>number of 500m bands</span>: 5</li><li><span>detectors per 500m band</span>: 20</li><li><span>number of 1km reflective bands</span>: 15</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a5b8e6ca-ad59-42ff-a44e-4bb87f4dc3b2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5b8e6ca-ad59-42ff-a44e-4bb87f4dc3b2' 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'>Band_250M</span></div><div class='xr-var-dims'>(Band_250M)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0</div><input id='attrs-2347ce13-16fa-4bfa-a236-cac77a6a126d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2347ce13-16fa-4bfa-a236-cac77a6a126d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90500c59-9855-414d-ae5b-64230875fd23' class='xr-var-data-in' type='checkbox'><label for='data-90500c59-9855-414d-ae5b-64230875fd23' 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>250M Band Numbers for Subsetting</dd></dl></div><div class='xr-var-data'><pre>array([1., 2.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Band_500M</span></div><div class='xr-var-dims'>(Band_500M)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.0 4.0 5.0 6.0 7.0</div><input id='attrs-56075ee1-5415-4dc3-9860-e4afad078ddd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56075ee1-5415-4dc3-9860-e4afad078ddd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1ab7984-9592-4fa6-8b3b-80a0242892c8' class='xr-var-data-in' type='checkbox'><label for='data-e1ab7984-9592-4fa6-8b3b-80a0242892c8' 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>500M Band Numbers for Subsetting</dd></dl></div><div class='xr-var-data'><pre>array([3., 4., 5., 6., 7.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Band_1KM_RefSB</span></div><div class='xr-var-dims'>(Band_1KM_RefSB)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>8.0 9.0 10.0 ... 18.0 19.0 26.0</div><input id='attrs-5e452b73-9801-4ba0-897e-fa70598001e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e452b73-9801-4ba0-897e-fa70598001e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7f2b159-3c93-4331-9d9f-fc8b0573cccd' class='xr-var-data-in' type='checkbox'><label for='data-c7f2b159-3c93-4331-9d9f-fc8b0573cccd' 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>1KM Reflective Solar Band Numbers for Subsetting</dd></dl></div><div class='xr-var-data'><pre>array([ 8. ,  9. , 10. , 11. , 12. , 13. , 13.5, 14. , 14.5, 15. , 16. , 17. ,\n",
+       "       18. , 19. , 26. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Band_1KM_Emissive</span></div><div class='xr-var-dims'>(Band_1KM_Emissive)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 21.0 22.0 ... 34.0 35.0 36.0</div><input id='attrs-67dfef5b-7a71-4355-ab9a-27d04bda9c10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67dfef5b-7a71-4355-ab9a-27d04bda9c10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb423972-d570-491e-8496-a53e9091870f' class='xr-var-data-in' type='checkbox'><label for='data-bb423972-d570-491e-8496-a53e9091870f' 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>1KM Emissive Band Numbers for Subsetting</dd></dl></div><div class='xr-var-data'><pre>array([20., 21., 22., 23., 24., 25., 27., 28., 29., 30., 31., 32., 33., 34.,\n",
+       "       35., 36.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de8d01bd-f3d3-4850-8e79-63f3ef353985' class='xr-section-summary-in' type='checkbox'  ><label for='section-de8d01bd-f3d3-4850-8e79-63f3ef353985' class='xr-section-summary' >Data variables: <span>(27)</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>Latitude</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-43291b75-a86e-4203-bf85-99970aaebb21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43291b75-a86e-4203-bf85-99970aaebb21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da6a958-ef40-4014-bbc5-14c985de64fc' class='xr-var-data-in' type='checkbox'><label for='data-8da6a958-ef40-4014-bbc5-14c985de64fc' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-90.  90.]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Longitude</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-449b7e9c-df0f-4f25-8265-093c61829947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-449b7e9c-df0f-4f25-8265-093c61829947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4feedaa5-a6cd-43a9-b65e-5e07dd8e2baa' class='xr-var-data-in' type='checkbox'><label for='data-4feedaa5-a6cd-43a9-b65e-5e07dd8e2baa' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-180.  180.]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_1KM_RefSB</span></div><div class='xr-var-dims'>(Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10858bfe-25f8-4694-87c3-f143f768a26a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10858bfe-25f8-4694-87c3-f143f768a26a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d46bad42-717f-40d1-afe7-08aac2ed3b7a' class='xr-var-data-in' type='checkbox'><label for='data-d46bad42-717f-40d1-afe7-08aac2ed3b7a' 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>Earth View 1KM Reflective Solar Bands Scaled Integers</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[    0 32767]</dd><dt><span>band_names :</span></dt><dd>8,9,10,11,12,13lo,13hi,14lo,14hi,15,16,17,18,19,26</dd><dt><span>radiance_scales :</span></dt><dd>[0.01487274 0.00995487 0.00658661 0.0040879  0.00407722 0.00114868\n",
+       " 0.00084735 0.00152917 0.00084248 0.00105768 0.00093799 0.00811724\n",
+       " 0.00905797 0.00753956 0.00327654]</dd><dt><span>radiance_offsets :</span></dt><dd>[316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722\n",
+       " 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722]</dd><dt><span>radiance_units :</span></dt><dd>Watts/m^2/micrometer/steradian</dd><dt><span>reflectance_scales :</span></dt><dd>[2.6828411e-05 1.6466722e-05 1.0470717e-05 6.8322238e-06 6.7853684e-06\n",
+       " 2.3364810e-06 1.7235611e-06 3.1930131e-06 1.7591535e-06 2.5723873e-06\n",
+       " 3.0357983e-06 2.7344862e-05 3.2647880e-05 2.7190577e-05 2.8272805e-05]</dd><dt><span>reflectance_offsets :</span></dt><dd>[316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722\n",
+       " 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722]</dd><dt><span>reflectance_units :</span></dt><dd>none</dd><dt><span>corrected_counts_scales :</span></dt><dd>[0.12619403 0.12619403 0.12619403 0.12619403 0.12619403 0.12619403\n",
+       " 0.12619403 0.12619403 0.12619403 0.12619403 0.12619403 0.12619403\n",
+       " 0.12619403 0.12619403 0.12619403]</dd><dt><span>corrected_counts_offsets :</span></dt><dd>[316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722\n",
+       " 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722 316.9722]</dd><dt><span>corrected_counts_units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[41229300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_1KM_RefSB_Uncert_Indexes</span></div><div class='xr-var-dims'>(Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2efd3abc-5b67-4b68-b760-88ba27569879' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2efd3abc-5b67-4b68-b760-88ba27569879' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c857d73-39fb-47cd-9007-123d3063446b' class='xr-var-data-in' type='checkbox'><label for='data-3c857d73-39fb-47cd-9007-123d3063446b' 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>Earth View 1KM Reflective Solar Bands Uncertainty Indexes</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 15]</dd><dt><span>specified_uncertainty :</span></dt><dd>[1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5]</dd><dt><span>scaling_factor :</span></dt><dd>[7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 7. 5.]</dd><dt><span>uncertainty_units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><pre>[41229300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_1KM_Emissive</span></div><div class='xr-var-dims'>(Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d205683-0cf7-4577-85ea-3dd6e89a9a8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d205683-0cf7-4577-85ea-3dd6e89a9a8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cefb7c4-4bcc-47e9-a49e-0600374f72aa' class='xr-var-data-in' type='checkbox'><label for='data-0cefb7c4-4bcc-47e9-a49e-0600374f72aa' 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>Earth View 1KM Emissive Bands Scaled Integers</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[    0 32767]</dd><dt><span>band_names :</span></dt><dd>20,21,22,23,24,25,27,28,29,30,31,32,33,34,35,36</dd><dt><span>radiance_scales :</span></dt><dd>[6.2623985e-05 3.1495101e-03 6.9215974e-05 7.9103978e-05 3.1556141e-05\n",
+       " 5.6398207e-05 1.1755730e-04 1.9244973e-04 5.3248694e-04 4.0632344e-04\n",
+       " 8.4002200e-04 7.2969758e-04 2.6226387e-04 2.0069582e-04 1.7670827e-04\n",
+       " 1.1833857e-04]</dd><dt><span>radiance_offsets :</span></dt><dd>[2730.5835 2730.5835 2730.5835 2730.5835 1077.4448 1560.3334 2730.5833\n",
+       " 2317.4883 2730.5835 1560.3333 1577.3397 1658.2213 2501.2976 2501.2976\n",
+       " 2501.2979 2501.2979]</dd><dt><span>radiance_units :</span></dt><dd>Watts/m^2/micrometer/steradian</dd></dl></div><div class='xr-var-data'><pre>[43977920 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_1KM_Emissive_Uncert_Indexes</span></div><div class='xr-var-dims'>(Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3c841511-4c5a-46fb-9a53-37fed22a6bb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c841511-4c5a-46fb-9a53-37fed22a6bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59d671ad-b4f2-4af7-9c0e-a66771a0ade4' class='xr-var-data-in' type='checkbox'><label for='data-59d671ad-b4f2-4af7-9c0e-a66771a0ade4' 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>Earth View 1KM Emissive Bands Uncertainty Indexes</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 15]</dd><dt><span>specified_uncertainty :</span></dt><dd>[0.5625 2.5    0.5    0.5    0.5    0.5    0.5    0.5    0.5    0.5\n",
+       " 0.375  0.375  0.5    0.5    0.5    0.5   ]</dd><dt><span>scaling_factor :</span></dt><dd>[5. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.]</dd><dt><span>uncertainty_units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><pre>[43977920 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_250_Aggr1km_RefSB</span></div><div class='xr-var-dims'>(Band_250M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d6e9360-3185-48cb-b26c-0615e21be642' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d6e9360-3185-48cb-b26c-0615e21be642' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4690e946-2b01-47e1-b774-6102a2f496af' class='xr-var-data-in' type='checkbox'><label for='data-4690e946-2b01-47e1-b774-6102a2f496af' 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>Earth View 250M Aggregated 1km Reflective Solar Bands Scaled Integers</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[    0 32767]</dd><dt><span>band_names :</span></dt><dd>1,2</dd><dt><span>radiance_scales :</span></dt><dd>[0.02907379 0.01079062]</dd><dt><span>radiance_offsets :</span></dt><dd>[-0. -0.]</dd><dt><span>radiance_units :</span></dt><dd>Watts/m^2/micrometer/steradian</dd><dt><span>reflectance_scales :</span></dt><dd>[5.7002773e-05 3.4247674e-05]</dd><dt><span>reflectance_offsets :</span></dt><dd>[-0. -0.]</dd><dt><span>reflectance_units :</span></dt><dd>none</dd><dt><span>corrected_counts_scales :</span></dt><dd>[0.1249733 0.1249733]</dd><dt><span>corrected_counts_offsets :</span></dt><dd>[-0. -0.]</dd><dt><span>corrected_counts_units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[5497240 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_250_Aggr1km_RefSB_Uncert_Indexes</span></div><div class='xr-var-dims'>(Band_250M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cf9214b2-1831-4b92-8370-3fafbb9e0fb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf9214b2-1831-4b92-8370-3fafbb9e0fb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e3349d3-31e4-45c0-984d-bd1609432a13' class='xr-var-data-in' type='checkbox'><label for='data-7e3349d3-31e4-45c0-984d-bd1609432a13' 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>Earth View 250M Aggregated 1km Reflective Solar Bands Uncertainty Indexes</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 15]</dd><dt><span>specified_uncertainty :</span></dt><dd>[1.5 1.5]</dd><dt><span>scaling_factor :</span></dt><dd>[7. 7.]</dd><dt><span>uncertainty_units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><pre>[5497240 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_250_Aggr1km_RefSB_Samples_Used</span></div><div class='xr-var-dims'>(Band_250M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-586504fc-0eec-43a0-b65b-323ee0a43eb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-586504fc-0eec-43a0-b65b-323ee0a43eb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2baf376c-4e94-4d1b-a1b6-1c8496b8885b' class='xr-var-data-in' type='checkbox'><label for='data-2baf376c-4e94-4d1b-a1b6-1c8496b8885b' 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>Earth View 250M Aggregated 1km Reflective Solar Bands Number of Samples Used in Aggregation</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 28]</dd></dl></div><div class='xr-var-data'><pre>[5497240 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_500_Aggr1km_RefSB</span></div><div class='xr-var-dims'>(Band_500M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9e0bf73-2e3c-4c85-8924-b126746cdee9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9e0bf73-2e3c-4c85-8924-b126746cdee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a926fdb6-7177-401b-8e5e-302d10dd3faa' class='xr-var-data-in' type='checkbox'><label for='data-a926fdb6-7177-401b-8e5e-302d10dd3faa' 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>Earth View 500M Aggregated 1km Reflective Solar Bands Scaled Integers</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[    0 32767]</dd><dt><span>band_names :</span></dt><dd>3,4,5,6,7</dd><dt><span>radiance_scales :</span></dt><dd>[0.03958831 0.03012581 0.00673914 0.00286493 0.00092178]</dd><dt><span>radiance_offsets :</span></dt><dd>[-0. -0. -0. -0. -0.]</dd><dt><span>radiance_units :</span></dt><dd>Watts/m^2/micrometer/steradian</dd><dt><span>reflectance_scales :</span></dt><dd>[5.9708342e-05 5.0842536e-05 4.4740111e-05 3.7555455e-05 3.2137144e-05]</dd><dt><span>reflectance_offsets :</span></dt><dd>[-0. -0. -0. -0. -0.]</dd><dt><span>reflectance_units :</span></dt><dd>none</dd><dt><span>corrected_counts_scales :</span></dt><dd>[0.1249733 0.1249733 0.1249733 0.1249733 0.1249733]</dd><dt><span>corrected_counts_offsets :</span></dt><dd>[-0. -0. -0. -0. -0.]</dd><dt><span>corrected_counts_units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[13743100 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_500_Aggr1km_RefSB_Uncert_Indexes</span></div><div class='xr-var-dims'>(Band_500M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7bc4544d-7b80-46bd-b9a4-3d863db641cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bc4544d-7b80-46bd-b9a4-3d863db641cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13fdad18-254d-43dd-81a2-bb150da97e92' class='xr-var-data-in' type='checkbox'><label for='data-13fdad18-254d-43dd-81a2-bb150da97e92' 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>Earth View 500M Aggregated 1km Reflective Solar Bands Uncertainty Indexes</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 15]</dd><dt><span>specified_uncertainty :</span></dt><dd>[1.5 1.5 1.5 1.5 1.5]</dd><dt><span>scaling_factor :</span></dt><dd>[7. 7. 5. 5. 5.]</dd><dt><span>uncertainty_units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><pre>[13743100 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_500_Aggr1km_RefSB_Samples_Used</span></div><div class='xr-var-dims'>(Band_500M:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-264b600e-af63-47cc-aa3c-d32a65ccfcbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-264b600e-af63-47cc-aa3c-d32a65ccfcbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03abeeb5-668b-4d47-93a0-281199b7efba' class='xr-var-data-in' type='checkbox'><label for='data-03abeeb5-668b-4d47-93a0-281199b7efba' 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>Earth View 500M Aggregated 1km Reflective Solar Bands Number of Samples Used in Aggregation</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[0 6]</dd></dl></div><div class='xr-var-data'><pre>[13743100 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Height</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a59ca0b-a670-4b63-9d22-0d3b464b6a5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a59ca0b-a670-4b63-9d22-0d3b464b6a5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1faa2854-e006-47d0-9987-3e083a3c0d39' class='xr-var-data-in' type='checkbox'><label for='data-1faa2854-e006-47d0-9987-3e083a3c0d39' 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>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[ -400 10000]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SensorZenith</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd813da9-3599-4412-abb8-114aba5965a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd813da9-3599-4412-abb8-114aba5965a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5aa64ec5-39c9-4618-9f9f-a802f59ec4e3' class='xr-var-data-in' type='checkbox'><label for='data-5aa64ec5-39c9-4618-9f9f-a802f59ec4e3' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[    0 18000]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SensorAzimuth</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cb5bbe1-8e7e-4baa-a23c-cdb21e7ba4fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9cb5bbe1-8e7e-4baa-a23c-cdb21e7ba4fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77db6e50-38fe-4c7d-bf0a-cf6d2b50e14f' class='xr-var-data-in' type='checkbox'><label for='data-77db6e50-38fe-4c7d-bf0a-cf6d2b50e14f' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-18000  18000]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Range</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d395b474-106e-4c48-9233-f08f2f663b2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d395b474-106e-4c48-9233-f08f2f663b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93a8d4a1-99d1-4a0f-b885-1dd397195a47' class='xr-var-data-in' type='checkbox'><label for='data-93a8d4a1-99d1-4a0f-b885-1dd397195a47' 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>units :</span></dt><dd>meters</dd><dt><span>valid_range :</span></dt><dd>[27000 65535]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarZenith</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0109e295-21c2-4c16-a020-ca365885eea3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0109e295-21c2-4c16-a020-ca365885eea3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82aabb50-d538-4024-bd08-80d54e2bda66' class='xr-var-data-in' type='checkbox'><label for='data-82aabb50-d538-4024-bd08-80d54e2bda66' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[    0 18000]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SolarAzimuth</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-538c0a0f-1204-4b64-ac5f-fc14dd08e841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-538c0a0f-1204-4b64-ac5f-fc14dd08e841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1759f6ab-f5e8-4b83-bae9-ebb32ee0a628' class='xr-var-data-in' type='checkbox'><label for='data-1759f6ab-f5e8-4b83-bae9-ebb32ee0a628' 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>units :</span></dt><dd>degrees</dd><dt><span>valid_range :</span></dt><dd>[-18000  18000]</dd><dt><span>line_numbers :</span></dt><dd>3,8</dd><dt><span>frame_numbers :</span></dt><dd>3,8,13,...</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gflags</span></div><div class='xr-var-dims'>(2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58fa18db-58fb-43b4-bb85-c469cff902f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58fa18db-58fb-43b4-bb85-c469cff902f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c84828-3cb2-4a98-82b7-be12d767dfab' class='xr-var-data-in' type='checkbox'><label for='data-13c84828-3cb2-4a98-82b7-be12d767dfab' 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>Bit 7(MSB) :</span></dt><dd>1 = invalid input data</dd><dt><span>Bit 6 :</span></dt><dd>1 = no ellipsoid intersection</dd><dt><span>Bit 5 :</span></dt><dd>1 = no valid terrain data</dd><dt><span>Bit 4 :</span></dt><dd>1 = DEM missing or of inferior quality</dd><dt><span>Bit 3 :</span></dt><dd>1 = invalid sensor range</dd></dl></div><div class='xr-var-data'><pre>[110026 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_Band26</span></div><div class='xr-var-dims'>(10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8fe5c105-bbb9-4b3c-83fa-9293cc58d6ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8fe5c105-bbb9-4b3c-83fa-9293cc58d6ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b991780d-4f00-4e5a-8169-8bedb2f70dc6' class='xr-var-data-in' type='checkbox'><label for='data-b991780d-4f00-4e5a-8169-8bedb2f70dc6' 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>Earth View Band 26 Scaled Integers</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[    0 32767]</dd><dt><span>radiance_scales :</span></dt><dd>0.0032765407</dd><dt><span>radiance_offsets :</span></dt><dd>316.9722</dd><dt><span>radiance_units :</span></dt><dd>Watts/m^2/micrometer/steradian</dd><dt><span>reflectance_scales :</span></dt><dd>2.8272805e-05</dd><dt><span>reflectance_offsets :</span></dt><dd>316.9722</dd><dt><span>reflectance_units :</span></dt><dd>none</dd><dt><span>corrected_counts_scales :</span></dt><dd>0.12619403</dd><dt><span>corrected_counts_offsets :</span></dt><dd>316.9722</dd><dt><span>corrected_counts_units :</span></dt><dd>counts</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EV_Band26_Uncert_Indexes</span></div><div class='xr-var-dims'>(10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-54ae1582-a02e-4e73-b5d8-3ede85dbbc22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54ae1582-a02e-4e73-b5d8-3ede85dbbc22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-686a1f91-1f3a-440b-ac7a-aa9bfe3afcc6' class='xr-var-data-in' type='checkbox'><label for='data-686a1f91-1f3a-440b-ac7a-aa9bfe3afcc6' 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>Earth View Band 26 Uncertainty Indexes</dd><dt><span>units :</span></dt><dd>none</dd><dt><span>valid_range :</span></dt><dd>[ 0 15]</dd><dt><span>specified_uncertainty :</span></dt><dd>1.5</dd><dt><span>scaling_factor :</span></dt><dd>5.0</dd><dt><span>uncertainty_units :</span></dt><dd>percent</dd></dl></div><div class='xr-var-data'><pre>[2748620 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Noise in Thermal Detectors</span></div><div class='xr-var-dims'>(number of emissive bands, detectors per 1km band)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91bb7515-7db1-4ed3-976f-ed6f30d1b957' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91bb7515-7db1-4ed3-976f-ed6f30d1b957' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da45d377-4d24-4b28-9788-b7d45ec713aa' class='xr-var-data-in' type='checkbox'><label for='data-da45d377-4d24-4b28-9788-b7d45ec713aa' 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'></dl></div><div class='xr-var-data'><pre>array([[ 34,  29,  28,  29,  28,  26,  28,  29,  29,  26],\n",
+       "       [ 29,  29,  30,  21,  26,  28,  28,  31,  37,  37],\n",
+       "       [ 30,  29,  30,  29,  29,  29,  31,  28,  32,  26],\n",
+       "       [ 30,  31,  30,  29,  29,  30,  30,  29,  30,  30],\n",
+       "       [ 29,  27,  28,  31,  31,  28,  31,  30,  27,  29],\n",
+       "       [ 30,  29,  30,  28,  29,  28,  28,  28,  28,  30],\n",
+       "       [ 87,  47,  38,  42,  37,  34,  44,  55,  89,  90],\n",
+       "       [ 63,  46,  59,  46,  46,  40,  63,  57,  55,  57],\n",
+       "       [ 43,  39,  47,  45,  45,  51,  37,  50,  44,  41],\n",
+       "       [ 45,  44,  51,  61,  59,  51,  59,  68,  55,  56],\n",
+       "       [ 29,  25,  30,  31,  28,  26,  28,  19,  26,  33],\n",
+       "       [ 27,  30,  36,  30,  27,  23,  21,  23,  27,  26],\n",
+       "       [ 57,  27,  27,  28,  28,  28,  26,  27,  27,  28],\n",
+       "       [ 30,  30,  29,  29,  36,  46,  51,  53,  32,  28],\n",
+       "       [ 30,  29,  20,  29,  28,  29,  28,  28,  28,  29],\n",
+       "       [ 29,  28,  29,  28,  28,  27, 255,  27,  28,  27]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Change in relative responses of thermal detectors</span></div><div class='xr-var-dims'>(number of emissive bands, detectors per 1km band)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae5da5b6-a940-40d3-a7bb-5fda0ee84306' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae5da5b6-a940-40d3-a7bb-5fda0ee84306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5780629-1988-47c6-aa17-1159eabac512' class='xr-var-data-in' type='checkbox'><label for='data-a5780629-1988-47c6-aa17-1159eabac512' 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'></dl></div><div class='xr-var-data'><pre>array([[ 30,  30,  30,  30,  30,  30,  30,  30,  30,  30],\n",
+       "       [ 29,  29,  29,  29,  29,  29,  29,  29,  29,  30],\n",
+       "       [ 30,  30,  30,  30,  30,  30,  30,  28,  29,  29],\n",
+       "       [ 29,  30,  30,  30,  29,  30,  30,  30,  29,  27],\n",
+       "       [ 29,  29,  29,  29,  29,  29,  29,  29,  29,  28],\n",
+       "       [ 29,  29,  28,  28,  29,  29,  28,  28,  28,  28],\n",
+       "       [ 28,  28,  28,  28,  27,  28,  29,  29,  29,  29],\n",
+       "       [ 31,  30,  30,  30,  30,  29,  30,  30,  29,  29],\n",
+       "       [ 30,  30,  30,  30,  30,  30,  29,  30,  30,  29],\n",
+       "       [ 27,  27,  27,  28,  28,  27,  27,  27,  26,  26],\n",
+       "       [ 29,  29,  29,  29,  29,  29,  29,  28,  28,  28],\n",
+       "       [ 29,  29,  29,  29,  29,  29,  29,  29,  29,  29],\n",
+       "       [ 29,  28,  29,  29,  29,  29,  29,  29,  28,  28],\n",
+       "       [ 29,  29,  29,  29,  29,  29,  29,  29,  29,  28],\n",
+       "       [ 30,  29,  29,  29,  29,  29,  29,  29,  29,  29],\n",
+       "       [ 29,  29,  29,  29,  28,  28, 255,  28,  28,  28]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DC Restore Change for Thermal Bands</span></div><div class='xr-var-dims'>(number of scans, number of emissive bands, detectors per 1km band)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ce32381-12a8-4ec6-8b80-ae7c72b6bb10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ce32381-12a8-4ec6-8b80-ae7c72b6bb10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1be96e6-81bf-4e7b-8bb9-de190ed3077d' class='xr-var-data-in' type='checkbox'><label for='data-c1be96e6-81bf-4e7b-8bb9-de190ed3077d' 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'></dl></div><div class='xr-var-data'><pre>array([[[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DC Restore Change for Reflective 250m Bands</span></div><div class='xr-var-dims'>(number of scans, number of 250m bands, detectors per 250m band)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86907616-492a-4fcd-bd04-f20b4ee58e32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86907616-492a-4fcd-bd04-f20b4ee58e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdb63997-04da-4f03-8291-ad2f6197e446' class='xr-var-data-in' type='checkbox'><label for='data-cdb63997-04da-4f03-8291-ad2f6197e446' 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'></dl></div><div class='xr-var-data'><pre>array([[[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DC Restore Change for Reflective 500m Bands</span></div><div class='xr-var-dims'>(number of scans, number of 500m bands, detectors per 500m band)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29e030c0-f1eb-47d2-a245-09282d1c0492' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29e030c0-f1eb-47d2-a245-09282d1c0492' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b971bd-adbb-4bfd-9e81-f8bd1b4f3d73' class='xr-var-data-in' type='checkbox'><label for='data-15b971bd-adbb-4bfd-9e81-f8bd1b4f3d73' 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'></dl></div><div class='xr-var-data'><pre>array([[[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]]], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DC Restore Change for Reflective 1km Bands</span></div><div class='xr-var-dims'>(number of scans, number of 1km reflective bands, detectors per 1km band)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70127bd9-d3c9-4b90-b5ea-c12b2ee32323' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70127bd9-d3c9-4b90-b5ea-c12b2ee32323' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a892302-54b7-4278-8a17-250ea03dd05a' class='xr-var-data-in' type='checkbox'><label for='data-5a892302-54b7-4278-8a17-250ea03dd05a' 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'></dl></div><div class='xr-var-data'><pre>array([[[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]],\n",
+       "\n",
+       "       [[0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        ...,\n",
+       "        [0, 0, ..., 0, 0],\n",
+       "        [0, 0, ..., 0, 0]]], dtype=int8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd818a21-f263-4651-9c2b-d70c248c111d' class='xr-section-summary-in' type='checkbox'  ><label for='section-bd818a21-f263-4651-9c2b-d70c248c111d' class='xr-section-summary' >Attributes: <span>(58)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>HDFEOSVersion :</span></dt><dd>HDFEOS_V2.19</dd><dt><span>StructMetadata.0 :</span></dt><dd>GROUP=SwathStructure\n",
+       "\tGROUP=SWATH_1\n",
+       "\t\tSwathName=&quot;MODIS_SWATH_Type_L1B&quot;\n",
+       "\t\tGROUP=Dimension\n",
+       "\t\t\tOBJECT=Dimension_1\n",
+       "\t\t\t\tDimensionName=&quot;Band_250M&quot;\n",
+       "\t\t\t\tSize=2\n",
+       "\t\t\tEND_OBJECT=Dimension_1\n",
+       "\t\t\tOBJECT=Dimension_2\n",
+       "\t\t\t\tDimensionName=&quot;Band_500M&quot;\n",
+       "\t\t\t\tSize=5\n",
+       "\t\t\tEND_OBJECT=Dimension_2\n",
+       "\t\t\tOBJECT=Dimension_3\n",
+       "\t\t\t\tDimensionName=&quot;Band_1KM_RefSB&quot;\n",
+       "\t\t\t\tSize=15\n",
+       "\t\t\tEND_OBJECT=Dimension_3\n",
+       "\t\t\tOBJECT=Dimension_4\n",
+       "\t\t\t\tDimensionName=&quot;Band_1KM_Emissive&quot;\n",
+       "\t\t\t\tSize=16\n",
+       "\t\t\tEND_OBJECT=Dimension_4\n",
+       "\t\t\tOBJECT=Dimension_5\n",
+       "\t\t\t\tDimensionName=&quot;10*nscans&quot;\n",
+       "\t\t\t\tSize=2030\n",
+       "\t\t\tEND_OBJECT=Dimension_5\n",
+       "\t\t\tOBJECT=Dimension_6\n",
+       "\t\t\t\tDimensionName=&quot;Max_EV_frames&quot;\n",
+       "\t\t\t\tSize=1354\n",
+       "\t\t\tEND_OBJECT=Dimension_6\n",
+       "\t\t\tOBJECT=Dimension_7\n",
+       "\t\t\t\tDimensionName=&quot;2*nscans&quot;\n",
+       "\t\t\t\tSize=406\n",
+       "\t\t\tEND_OBJECT=Dimension_7\n",
+       "\t\t\tOBJECT=Dimension_8\n",
+       "\t\t\t\tDimensionName=&quot;1KM_geo_dim&quot;\n",
+       "\t\t\t\tSize=271\n",
+       "\t\t\tEND_OBJECT=Dimension_8\n",
+       "\t\tEND_GROUP=Dimension\n",
+       "\t\tGROUP=DimensionMap\n",
+       "\t\t\tOBJECT=DimensionMap_1\n",
+       "\t\t\t\tGeoDimension=&quot;2*nscans&quot;\n",
+       "\t\t\t\tDataDimension=&quot;10*nscans&quot;\n",
+       "\t\t\t\tOffset=2\n",
+       "\t\t\t\tIncrement=5\n",
+       "\t\t\tEND_OBJECT=DimensionMap_1\n",
+       "\t\t\tOBJECT=DimensionMap_2\n",
+       "\t\t\t\tGeoDimension=&quot;1KM_geo_dim&quot;\n",
+       "\t\t\t\tDataDimension=&quot;Max_EV_frames&quot;\n",
+       "\t\t\t\tOffset=2\n",
+       "\t\t\t\tIncrement=5\n",
+       "\t\t\tEND_OBJECT=DimensionMap_2\n",
+       "\t\tEND_GROUP=DimensionMap\n",
+       "\t\tGROUP=IndexDimensionMap\n",
+       "\t\tEND_GROUP=IndexDimensionMap\n",
+       "\t\tGROUP=GeoField\n",
+       "\t\t\tOBJECT=GeoField_1\n",
+       "\t\t\t\tGeoFieldName=&quot;Latitude&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=GeoField_1\n",
+       "\t\t\tOBJECT=GeoField_2\n",
+       "\t\t\t\tGeoFieldName=&quot;Longitude&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=GeoField_2\n",
+       "\t\tEND_GROUP=GeoField\n",
+       "\t\tGROUP=DataField\n",
+       "\t\t\tOBJECT=DataField_1\n",
+       "\t\t\t\tDataFieldName=&quot;EV_1KM_RefSB&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_RefSB&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_1\n",
+       "\t\t\tOBJECT=DataField_2\n",
+       "\t\t\t\tDataFieldName=&quot;EV_1KM_RefSB_Uncert_Indexes&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_RefSB&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_2\n",
+       "\t\t\tOBJECT=DataField_3\n",
+       "\t\t\t\tDataFieldName=&quot;EV_1KM_Emissive&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_Emissive&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_3\n",
+       "\t\t\tOBJECT=DataField_4\n",
+       "\t\t\t\tDataFieldName=&quot;EV_1KM_Emissive_Uncert_Indexes&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_Emissive&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_4\n",
+       "\t\t\tOBJECT=DataField_5\n",
+       "\t\t\t\tDataFieldName=&quot;EV_250_Aggr1km_RefSB&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;Band_250M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_5\n",
+       "\t\t\tOBJECT=DataField_6\n",
+       "\t\t\t\tDataFieldName=&quot;EV_250_Aggr1km_RefSB_Uncert_Indexes&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;Band_250M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_6\n",
+       "\t\t\tOBJECT=DataField_7\n",
+       "\t\t\t\tDataFieldName=&quot;EV_250_Aggr1km_RefSB_Samples_Used&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT8\n",
+       "\t\t\t\tDimList=(&quot;Band_250M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_7\n",
+       "\t\t\tOBJECT=DataField_8\n",
+       "\t\t\t\tDataFieldName=&quot;EV_500_Aggr1km_RefSB&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;Band_500M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_8\n",
+       "\t\t\tOBJECT=DataField_9\n",
+       "\t\t\t\tDataFieldName=&quot;EV_500_Aggr1km_RefSB_Uncert_Indexes&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;Band_500M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_9\n",
+       "\t\t\tOBJECT=DataField_10\n",
+       "\t\t\t\tDataFieldName=&quot;EV_500_Aggr1km_RefSB_Samples_Used&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT8\n",
+       "\t\t\t\tDimList=(&quot;Band_500M&quot;,&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_10\n",
+       "\t\t\tOBJECT=DataField_11\n",
+       "\t\t\t\tDataFieldName=&quot;Height&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_11\n",
+       "\t\t\tOBJECT=DataField_12\n",
+       "\t\t\t\tDataFieldName=&quot;SensorZenith&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_12\n",
+       "\t\t\tOBJECT=DataField_13\n",
+       "\t\t\t\tDataFieldName=&quot;SensorAzimuth&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_13\n",
+       "\t\t\tOBJECT=DataField_14\n",
+       "\t\t\t\tDataFieldName=&quot;Range&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_14\n",
+       "\t\t\tOBJECT=DataField_15\n",
+       "\t\t\t\tDataFieldName=&quot;SolarZenith&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_15\n",
+       "\t\t\tOBJECT=DataField_16\n",
+       "\t\t\t\tDataFieldName=&quot;SolarAzimuth&quot;\n",
+       "\t\t\t\tDataType=DFNT_INT16\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_16\n",
+       "\t\t\tOBJECT=DataField_17\n",
+       "\t\t\t\tDataFieldName=&quot;gflags&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;2*nscans&quot;,&quot;1KM_geo_dim&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_17\n",
+       "\t\t\tOBJECT=DataField_18\n",
+       "\t\t\t\tDataFieldName=&quot;Band_250M&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;Band_250M&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_18\n",
+       "\t\t\tOBJECT=DataField_19\n",
+       "\t\t\t\tDataFieldName=&quot;Band_500M&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;Band_500M&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_19\n",
+       "\t\t\tOBJECT=DataField_20\n",
+       "\t\t\t\tDataFieldName=&quot;Band_1KM_RefSB&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_RefSB&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_20\n",
+       "\t\t\tOBJECT=DataField_21\n",
+       "\t\t\t\tDataFieldName=&quot;Band_1KM_Emissive&quot;\n",
+       "\t\t\t\tDataType=DFNT_FLOAT32\n",
+       "\t\t\t\tDimList=(&quot;Band_1KM_Emissive&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_21\n",
+       "\t\t\tOBJECT=DataField_22\n",
+       "\t\t\t\tDataFieldName=&quot;EV_Band26&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT16\n",
+       "\t\t\t\tDimList=(&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_22\n",
+       "\t\t\tOBJECT=DataField_23\n",
+       "\t\t\t\tDataFieldName=&quot;EV_Band26_Uncert_Indexes&quot;\n",
+       "\t\t\t\tDataType=DFNT_UINT8\n",
+       "\t\t\t\tDimList=(&quot;10*nscans&quot;,&quot;Max_EV_frames&quot;)\n",
+       "\t\t\tEND_OBJECT=DataField_23\n",
+       "\t\tEND_GROUP=DataField\n",
+       "\t\tGROUP=MergedFields\n",
+       "\t\tEND_GROUP=MergedFields\n",
+       "\tEND_GROUP=SWATH_1\n",
+       "END_GROUP=SwathStructure\n",
+       "GROUP=GridStructure\n",
+       "END_GROUP=GridStructure\n",
+       "GROUP=PointStructure\n",
+       "END_GROUP=PointStructure\n",
+       "END\n",
+       "</dd><dt><span>HDFEOS_FractionalOffset_10*nscans_MODIS_SWATH_Type_L1B :</span></dt><dd>0.0</dd><dt><span>HDFEOS_FractionalOffset_Max_EV_frames_MODIS_SWATH_Type_L1B :</span></dt><dd>0.0</dd><dt><span>CoreMetadata.0 :</span></dt><dd>\n",
+       "GROUP                  = INVENTORYMETADATA\n",
+       "  GROUPTYPE            = MASTERGROUP\n",
+       "\n",
+       "  GROUP                  = ECSDATAGRANULE\n",
+       "\n",
+       "    OBJECT                 = REPROCESSINGPLANNED\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;further update is anticipated&quot;\n",
+       "    END_OBJECT             = REPROCESSINGPLANNED\n",
+       "\n",
+       "    OBJECT                 = REPROCESSINGACTUAL\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;processed once&quot;\n",
+       "    END_OBJECT             = REPROCESSINGACTUAL\n",
+       "\n",
+       "    OBJECT                 = LOCALGRANULEID\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;MOD021KM.A2018274.1030.061.2018274200208.hdf&quot;\n",
+       "    END_OBJECT             = LOCALGRANULEID\n",
+       "\n",
+       "    OBJECT                 = DAYNIGHTFLAG\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;Day&quot;\n",
+       "    END_OBJECT             = DAYNIGHTFLAG\n",
+       "\n",
+       "    OBJECT                 = PRODUCTIONDATETIME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;2018-10-01T20:02:08.000Z&quot;\n",
+       "    END_OBJECT             = PRODUCTIONDATETIME\n",
+       "\n",
+       "  END_GROUP              = ECSDATAGRANULE\n",
+       "\n",
+       "  GROUP                  = MEASUREDPARAMETER\n",
+       "\n",
+       "    OBJECT                 = MEASUREDPARAMETERCONTAINER\n",
+       "      CLASS                = &quot;1&quot;\n",
+       "\n",
+       "      OBJECT                 = PARAMETERNAME\n",
+       "        CLASS                = &quot;1&quot;\n",
+       "        NUM_VAL              = 1\n",
+       "        VALUE                = &quot;EV_1KM_RefSB&quot;\n",
+       "      END_OBJECT             = PARAMETERNAME\n",
+       "\n",
+       "      GROUP                  = QAFLAGS\n",
+       "        CLASS                = &quot;1&quot;\n",
+       "\n",
+       "        OBJECT                 = AUTOMATICQUALITYFLAG\n",
+       "          NUM_VAL              = 1\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "          VALUE                = &quot;Suspect&quot;\n",
+       "        END_OBJECT             = AUTOMATICQUALITYFLAG\n",
+       "\n",
+       "        OBJECT                 = AUTOMATICQUALITYFLAGEXPLANATION\n",
+       "          NUM_VAL              = 1\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "          VALUE                = &quot;not being investigated&quot;\n",
+       "        END_OBJECT             = AUTOMATICQUALITYFLAGEXPLANATION\n",
+       "\n",
+       "        OBJECT                 = SCIENCEQUALITYFLAG\n",
+       "          NUM_VAL              = 1\n",
+       "          VALUE                = &quot;Not Investigated&quot;\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "        END_OBJECT             = SCIENCEQUALITYFLAG\n",
+       "\n",
+       "      END_GROUP              = QAFLAGS\n",
+       "\n",
+       "      GROUP                  = QASTATS\n",
+       "        CLASS                = &quot;1&quot;\n",
+       "\n",
+       "        OBJECT                 = QAPERCENTINTERPOLATEDDATA\n",
+       "          NUM_VAL              = 1\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "          VALUE                = 0\n",
+       "        END_OBJECT             = QAPERCENTINTERPOLATEDDATA\n",
+       "\n",
+       "        OBJECT                 = QAPERCENTMISSINGDATA\n",
+       "          NUM_VAL              = 1\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "          VALUE                = 0\n",
+       "        END_OBJECT             = QAPERCENTMISSINGDATA\n",
+       "\n",
+       "        OBJECT                 = QAPERCENTOUTOFBOUNDSDATA\n",
+       "          NUM_VAL              = 1\n",
+       "          CLASS                = &quot;1&quot;\n",
+       "          VALUE                = 29\n",
+       "        END_OBJECT             = QAPERCENTOUTOFBOUNDSDATA\n",
+       "\n",
+       "      END_GROUP              = QASTATS\n",
+       "\n",
+       "    END_OBJECT             = MEASUREDPARAMETERCONTAINER\n",
+       "\n",
+       "    OBJECT                 = MEASUREDPARAMETERCONTAINER\n",
+       "      CLASS                = &quot;2&quot;\n",
+       "\n",
+       "      OBJECT                 = PARAMETERNAME\n",
+       "        CLASS                = &quot;2&quot;\n",
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+       "    OBJECT                 = EASTBOUNDINGCOORDINATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = 38.4914035946138\n",
+       "    END_OBJECT             = EASTBOUNDINGCOORDINATE\n",
+       "\n",
+       "    OBJECT                 = WESTBOUNDINGCOORDINATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = -7.8287560027392\n",
+       "    END_OBJECT             = WESTBOUNDINGCOORDINATE\n",
+       "\n",
+       "  END_GROUP              = BOUNDINGRECTANGLE\n",
+       "\n",
+       "  GROUP                  = ALGORITHMPACKAGE\n",
+       "\n",
+       "    OBJECT                 = ALGORITHMPACKAGEACCEPTANCEDATE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;1999-12-14&quot;\n",
+       "    END_OBJECT             = ALGORITHMPACKAGEACCEPTANCEDATE\n",
+       "\n",
+       "    OBJECT                 = ALGORITHMPACKAGEMATURITYCODE\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;OPERATIONAL&quot;\n",
+       "    END_OBJECT             = ALGORITHMPACKAGEMATURITYCODE\n",
+       "\n",
+       "    OBJECT                 = ALGORITHMPACKAGENAME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;MODIS Level 1B Algorithm Package&quot;\n",
+       "    END_OBJECT             = ALGORITHMPACKAGENAME\n",
+       "\n",
+       "    OBJECT                 = ALGORITHMPACKAGEVERSION\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;6.2.2.18_Terra&quot;\n",
+       "    END_OBJECT             = ALGORITHMPACKAGEVERSION\n",
+       "\n",
+       "  END_GROUP              = ALGORITHMPACKAGE\n",
+       "\n",
+       "  GROUP                  = PROJECT\n",
+       "\n",
+       "    OBJECT                 = INSTRUMENTNAME\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;Moderate Resolution Imaging SpectroRadiometer&quot;\n",
+       "    END_OBJECT             = INSTRUMENTNAME\n",
+       "\n",
+       "    OBJECT                 = PROCESSINGCENTER\n",
+       "      NUM_VAL              = 1\n",
+       "      VALUE                = &quot;MODAPS&quot;\n",
+       "    END_OBJECT             = PROCESSINGCENTER\n",
+       "\n",
+       "  END_GROUP              = PROJECT\n",
+       "\n",
+       "  OBJECT                 = DESCRREVISION\n",
+       "    NUM_VAL              = 1\n",
+       "    VALUE                = &quot;6.1&quot;\n",
+       "  END_OBJECT             = DESCRREVISION\n",
+       "\n",
+       "  OBJECT                 = PRODUCTIONHISTORY\n",
+       "    NUM_VAL              = 1\n",
+       "    VALUE                = &quot;PGE02:6.2.2.18;PGE01:6.1.4&quot;\n",
+       "  END_OBJECT             = PRODUCTIONHISTORY\n",
+       "\n",
+       "  OBJECT                 = LONGNAME\n",
+       "    NUM_VAL              = 1\n",
+       "    VALUE                = &quot;MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km&quot;\n",
+       "  END_OBJECT             = LONGNAME\n",
+       "\n",
+       "  OBJECT                 = PROCESSINGENVIRONMENT\n",
+       "    NUM_VAL              = 1\n",
+       "    VALUE                = &quot;Linux minion7152 3.10.0-862.3.3.el7.x86_64 &quot;\n",
+       "  END_OBJECT             = PROCESSINGENVIRONMENT\n",
+       "\n",
+       "END_GROUP              = ARCHIVEDMETADATA\n",
+       "\n",
+       "END\n",
+       "</dd><dt><span>Number of Scans :</span></dt><dd>203</dd><dt><span>Number of Day mode scans :</span></dt><dd>203</dd><dt><span>Number of Night mode scans :</span></dt><dd>0</dd><dt><span>Incomplete Scans :</span></dt><dd>0</dd><dt><span>Max Earth View Frames :</span></dt><dd>1354</dd><dt><span>%Valid EV Observations :</span></dt><dd>[100.00001   99.99495  100.00001  100.00001   93.86191   98.632416\n",
+       "  97.3279    99.9964    97.41114   86.93374   75.25158   75.67583\n",
+       "  43.518276  33.599586  52.466732  35.26479   27.57751   28.886095\n",
+       "  99.998436 100.00001  100.00001  100.00001   99.98552   99.99924\n",
+       "  99.99804  100.00001  100.00001  100.00001   99.891396 100.00001\n",
+       " 100.00001  100.00001  100.00001  100.00001  100.00001  100.00001\n",
+       " 100.00001   90.      ]</dd><dt><span>%Saturated EV Observations :</span></dt><dd>[0.0000000e+00 5.0502615e-03 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 1.8190947e-05 2.7286420e-05 3.6018074e-03 2.4565055e+00 1.3066266e+01\n",
+       " 2.3608574e+01 2.3482367e+01 5.6481724e+01 6.6400414e+01 4.7529415e+01\n",
+       " 6.4735138e+01 7.2422493e+01 7.1113907e+01 1.5644215e-03 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00]</dd><dt><span>% L1A EV All Scan Data are Missing :</span></dt><dd>0.0</dd><dt><span>% L1A EV RSB DN Not in Day Mode :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% L1A EV DN Missing Within Scan :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% Dead Detector EV Data :</span></dt><dd>[  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 100.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.\n",
+       "   0.   0.   0.   0.   0.   0.   0.   0.   0.   0. 100.   0.   0.   0.]</dd><dt><span>% Dead Subframe EV Data :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% Sector Rotation EV Data :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% Saturated EV Data :</span></dt><dd>[0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 5.4572843e-04 2.7286420e-03 3.3653250e-03 3.0924610e-03 4.6386914e-03\n",
+       " 5.9120576e-03 7.1854237e-03 5.5482388e-03 3.0015062e-03 4.4567818e-03\n",
+       " 6.2758764e-03 8.0040162e-03 3.6381893e-03 2.1829137e-03 3.9110538e-03\n",
+       " 6.0030124e-03 6.4577861e-03 9.9140657e-03 6.0030124e-03 7.3673334e-03\n",
+       " 7.5492430e-03 9.3683377e-03 8.0949711e-03 8.4587904e-03 7.7311522e-03\n",
+       " 8.6407000e-03 7.0944694e-03 6.2758764e-03 7.5492430e-03 2.9105514e-03\n",
+       " 3.2743705e-03 2.9105514e-03 3.0015062e-03 2.4557777e-03 2.2738683e-03\n",
+       " 1.6371852e-03 2.8195968e-03 4.0020081e-03 3.2743705e-03 2.4557777e-03\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 1.8190946e-04 0.0000000e+00 1.8190946e-04 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 1.8190946e-04 0.0000000e+00 3.6381892e-04 0.0000000e+00 0.0000000e+00\n",
+       " 2.9105514e-03 5.4572839e-03 3.2743705e-03 3.6381892e-04 3.6381893e-03\n",
+       " 4.7296463e-03 4.7296463e-03 4.0020081e-03 3.6381893e-03 3.2743705e-03\n",
+       " 2.3015187e+00 2.3302603e+00 2.4237618e+00 2.4947064e+00 2.5802038e+00\n",
+       " 2.5918460e+00 2.4797900e+00 2.4441357e+00 2.4088452e+00 2.5099869e+00\n",
+       " 1.2851904e+01 1.2709287e+01 1.2743850e+01 1.2861363e+01 1.2998159e+01\n",
+       " 1.3126587e+01 1.3181160e+01 1.3311407e+01 1.3483857e+01 1.3395085e+01\n",
+       " 2.3614759e+01 2.3819954e+01 2.3464502e+01 2.3486694e+01 2.3583107e+01\n",
+       " 2.3577286e+01 2.3536539e+01 2.3695528e+01 2.3715174e+01 2.3592203e+01\n",
+       " 2.3719175e+01 2.3586018e+01 2.3468504e+01 2.3503431e+01 2.3488878e+01\n",
+       " 2.3408838e+01 2.3498701e+01 2.3463774e+01 2.3418661e+01 2.3267675e+01\n",
+       " 5.6633873e+01 5.6552380e+01 5.6666256e+01 5.6738289e+01 5.6666981e+01\n",
+       " 5.6542919e+01 5.6140171e+01 5.6264961e+01 5.6243134e+01 5.6368286e+01\n",
+       " 6.5396454e+01 6.5754814e+01 6.6216141e+01 6.6616341e+01 6.6679642e+01\n",
+       " 6.6533386e+01 6.6413689e+01 6.6537392e+01 6.6802979e+01 6.7053284e+01\n",
+       " 4.7994629e+01 4.7689022e+01 4.7756329e+01 4.7807991e+01 4.7344120e+01\n",
+       " 4.7540947e+01 4.7386688e+01 4.7379414e+01 4.7129105e+01 4.7265900e+01\n",
+       " 6.4906387e+01 6.4834351e+01 6.4992981e+01 6.4712112e+01 6.4668091e+01\n",
+       " 6.5139236e+01 6.4719749e+01 6.4595322e+01 6.4369026e+01 6.4414139e+01\n",
+       " 7.1916451e+01 7.2211876e+01 7.2289734e+01 7.2393417e+01 7.2417068e+01\n",
+       " 7.2486191e+01 7.2560776e+01 7.2325752e+01 7.2798714e+01 7.2824905e+01\n",
+       " 7.0561592e+01 7.0716576e+01 7.0959976e+01 7.0883209e+01 7.1141884e+01\n",
+       " 7.1330338e+01 7.1168808e+01 7.1486420e+01 7.1474052e+01 7.1416199e+01\n",
+       " 2.5467325e-03 4.3658274e-03 7.2763785e-04 3.6381892e-04 1.0914569e-03\n",
+       " 3.6381892e-04 3.6381892e-04 1.4552757e-03 7.2763785e-04 3.6381893e-03\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00]</dd><dt><span>% TEB EV Data With Moon in SVP :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% EV Data Where Cannot Compute BG DN :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% RSB EV Data With dn** Below Scale :</span></dt><dd>[0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 1.8669369  1.758155   0.         0.         0.         1.5133048\n",
+       " 1.1949633  1.137298   1.0501634  1.2346195  0.99267995 0.87571216\n",
+       " 1.6244515  1.2449884  1.5298586  1.5413189  1.2897382  1.5047551\n",
+       " 0.80640465 1.5964375  1.7350525  1.7934455  0.9663031  0.94247293\n",
+       " 1.3730527  1.3841492  1.2766407  1.0163282  1.1913251  1.2080607\n",
+       " 1.1032809  2.5745647  1.6344565  1.5103943  1.4940225  1.5720617\n",
+       " 1.4394497  1.3852406  0.91100264 0.839876   2.8807182  3.6785731\n",
+       " 2.5987587  2.46742    3.2785544  3.2556338  3.1232038  2.923831\n",
+       " 3.164679   3.050258   2.9927745  2.3335347  2.72082    2.61222\n",
+       " 2.5852973  2.4739687  1.9253298  2.0828633  1.7310505  1.5618746\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.         0.         0.\n",
+       " 0.         0.         0.         0.        ]</dd><dt><span>% EV Data Where Nadir Door Closed :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
+       " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]</dd><dt><span>% EV Data Not Calibrated :</span></dt><dd>[0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 5.4572843e-04 2.7286420e-03 3.3653250e-03 3.0924610e-03 4.6386914e-03\n",
+       " 5.9120576e-03 7.1854237e-03 5.5482388e-03 3.0015062e-03 4.4567818e-03\n",
+       " 6.2758764e-03 8.0040162e-03 3.6381893e-03 2.1829137e-03 3.9110538e-03\n",
+       " 6.0030124e-03 6.4577861e-03 9.9140657e-03 6.0030124e-03 7.3673334e-03\n",
+       " 7.5492430e-03 9.3683377e-03 8.0949711e-03 8.4587904e-03 7.7311522e-03\n",
+       " 8.6407000e-03 7.0944694e-03 6.2758764e-03 7.5492430e-03 2.9105514e-03\n",
+       " 3.2743705e-03 2.9105514e-03 3.0015062e-03 2.4557777e-03 2.2738683e-03\n",
+       " 1.6371852e-03 2.8195968e-03 4.0020081e-03 3.2743705e-03 2.4557777e-03\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 1.8669369e+00 1.7581550e+00 0.0000000e+00 1.0000000e+02 0.0000000e+00\n",
+       " 1.5133048e+00 1.1949633e+00 1.1372980e+00 1.0501634e+00 1.2346195e+00\n",
+       " 9.9267995e-01 8.7571216e-01 1.6244515e+00 1.2449884e+00 1.5298586e+00\n",
+       " 1.5413189e+00 1.2897382e+00 1.5047551e+00 8.0640465e-01 1.5964375e+00\n",
+       " 1.7350525e+00 1.7934455e+00 9.6630311e-01 9.4247293e-01 1.3730527e+00\n",
+       " 1.3841492e+00 1.2766407e+00 1.0163282e+00 1.1913251e+00 1.2080607e+00\n",
+       " 1.1032809e+00 2.5745647e+00 1.6344565e+00 1.5103943e+00 1.4940225e+00\n",
+       " 1.5722436e+00 1.4394497e+00 1.3854225e+00 9.1100264e-01 8.3987600e-01\n",
+       " 2.8807182e+00 3.6785731e+00 2.5987587e+00 2.4674201e+00 3.2785544e+00\n",
+       " 3.2556338e+00 3.1232038e+00 2.9238310e+00 3.1646791e+00 3.0502579e+00\n",
+       " 2.9927745e+00 2.3335347e+00 2.7208200e+00 2.6122200e+00 2.5852973e+00\n",
+       " 2.4741507e+00 1.9253298e+00 2.0832272e+00 1.7310505e+00 1.5618746e+00\n",
+       " 2.9105514e-03 5.4572839e-03 3.2743705e-03 3.6381892e-04 3.6381893e-03\n",
+       " 4.7296463e-03 4.7296463e-03 4.0020081e-03 3.6381893e-03 3.2743705e-03\n",
+       " 2.4652371e+00 2.4888854e+00 2.5729277e+00 2.6205878e+00 2.6958983e+00\n",
+       " 2.7082682e+00 2.6104009e+00 2.5718360e+00 2.5361819e+00 2.6184049e+00\n",
+       " 1.2851904e+01 1.2709287e+01 1.2743850e+01 1.2861363e+01 1.2998159e+01\n",
+       " 1.3126587e+01 1.3181160e+01 1.3311407e+01 1.3483857e+01 1.3395085e+01\n",
+       " 2.4769520e+01 2.4879395e+01 2.4656374e+01 2.4658192e+01 2.4666924e+01\n",
+       " 2.4728773e+01 2.4716768e+01 2.4800810e+01 2.4860111e+01 2.4747328e+01\n",
+       " 2.4455908e+01 2.4418436e+01 2.4301649e+01 2.4293282e+01 2.4282003e+01\n",
+       " 2.4294374e+01 2.4355131e+01 2.4344580e+01 2.4326025e+01 2.4170311e+01\n",
+       " 5.6633873e+01 5.6552380e+01 5.6666256e+01 5.6738289e+01 5.6666981e+01\n",
+       " 5.6542919e+01 5.6140171e+01 5.6264961e+01 5.6243134e+01 5.6368286e+01\n",
+       " 6.5396454e+01 6.5754814e+01 6.6216141e+01 6.6616341e+01 6.6679642e+01\n",
+       " 6.6533386e+01 6.6413689e+01 6.6537392e+01 6.6802979e+01 6.7053284e+01\n",
+       " 4.7994629e+01 4.7689022e+01 4.7756329e+01 4.7807991e+01 4.7344120e+01\n",
+       " 4.7540947e+01 4.7388870e+01 4.7383778e+01 4.7160755e+01 4.7266262e+01\n",
+       " 6.4906387e+01 6.4834351e+01 6.4992981e+01 6.4712112e+01 6.4668091e+01\n",
+       " 6.5139236e+01 6.4719749e+01 6.4595322e+01 6.4369751e+01 6.4414139e+01\n",
+       " 7.1916451e+01 7.2211876e+01 7.2289734e+01 7.2393417e+01 7.2417068e+01\n",
+       " 7.2486191e+01 7.2560776e+01 7.2325752e+01 7.2798714e+01 7.2824905e+01\n",
+       " 7.0561592e+01 7.0716576e+01 7.0959976e+01 7.0883209e+01 7.1141884e+01\n",
+       " 7.1330338e+01 7.1168808e+01 7.1486420e+01 7.1474052e+01 7.1416199e+01\n",
+       " 2.5467325e-03 4.3658274e-03 7.2763785e-04 3.6381892e-04 1.0914569e-03\n",
+       " 3.6381892e-04 3.6381892e-04 1.4552757e-03 7.2763785e-04 3.6381893e-03\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
+       " 0.0000000e+00 1.0000000e+02 0.0000000e+00 0.0000000e+00 0.0000000e+00]</dd><dt><span>Bit QA Flags Last Value :</span></dt><dd>827592</dd><dt><span>Bit QA Flags Change :</span></dt><dd>8</dd><dt><span>Granule Average QA Values :</span></dt><dd>[290.0246    290.0113    290.04468   290.02982   290.01608   289.9758\n",
+       " 290.04715   290.02512   290.024     290.00238   289.99057   290.0013\n",
+       " 270.4201    270.61353    83.32663    83.0199    278.3056    278.31595\n",
+       " 278.4609    276.3617    277.41763   276.3409    275.41498   269.73215\n",
+       " 266.58603   268.04044   268.10645    82.62998    82.759865  125.82615\n",
+       " 125.8335    186.49095     6.5530667   0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.          0.          0.          0.          0.\n",
+       "   0.          0.       ]</dd><dt><span>Electronics Redundancy Vector :</span></dt><dd>[22405973     1048]</dd><dt><span>Electronics Configuration Change :</span></dt><dd>[0 0]</dd><dt><span>Reflective LUT Serial Number and Date of Last Change :</span></dt><dd>R529 2018:09:05:16:00</dd><dt><span>Emissive LUT Serial Number and Date of Last Change :</span></dt><dd>E521 2018:09:05:16:00</dd><dt><span>QA LUT Serial Number and Date of Last Change :</span></dt><dd>Q517 2018:09:05:16:00</dd><dt><span>Focal Plane Set Point State :</span></dt><dd>1</dd><dt><span>Doors and Screens Configuration :</span></dt><dd>-32</dd><dt><span>Reflective Bands With Bad Data :</span></dt><dd>[0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0]</dd><dt><span>Emissive Bands With Bad Data :</span></dt><dd>[0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 1]</dd><dt><span>Noise in Black Body Thermistors :</span></dt><dd>[1 1 1 2 1 2 4 2 2 2 2 4]</dd><dt><span>Noise in Average BB Temperature :</span></dt><dd>2</dd><dt><span>Noise in LWIR FPA Temperature :</span></dt><dd>124</dd><dt><span>Noise in MWIR FPA Temperature :</span></dt><dd>8</dd><dt><span>Noise in Scan Mirror Thermistor #1 :</span></dt><dd>8</dd><dt><span>Noise in Scan Mirror Thermistor #2 :</span></dt><dd>2</dd><dt><span>Noise in Scan Mirror Thermistor Average :</span></dt><dd>1</dd><dt><span>Noise in Instrument Temperature :</span></dt><dd>0</dd><dt><span>Noise in Cavity Temperature :</span></dt><dd>0</dd><dt><span>Noise in Temperature of NIR FPA :</span></dt><dd>0</dd><dt><span>Noise in Temperature of Vis FPA :</span></dt><dd>0</dd><dt><span>Dead Detector List :</span></dt><dd>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 1 0 0 0]</dd><dt><span>Noisy Detector List :</span></dt><dd>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+       " 1 1 1 1 1 0 1 1 1]</dd><dt><span>Dead Subframe List :</span></dt><dd>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0]</dd><dt><span>Noisy Subframe List :</span></dt><dd>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0]</dd><dt><span>Detector Quality Flag :</span></dt><dd>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       " 64 64 64 70 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64\n",
+       " 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64 64\n",
+       " 64 64 64 64 64 64 64 64 64 64 64 64  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 12  8  8  8  8  8\n",
+       "  8  8  8  8  0  0  0  0  0  0  4  4  0  0  0  0  0  0  0  0  0  0  0  4\n",
+       "  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 64 64 64 64\n",
+       " 64 64 64 64 64 64  1  0  0  0  0  0  0  0  1  1  0  4  4  0  0  0  0  4\n",
+       "  0  4  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  5  0  0  0  0\n",
+       "  0  0  0  0  0  0  0  0 64 64 64 64 64 64 64 64 64 64 69 64 64 64 64 64\n",
+       " 64 64 64 64 64 64 64 64 64 65 65 65 64 64 64 64 64 64 64 64 64 64 64 64\n",
+       " 65 65 65 65 65 65 66 65 65 65]</dd><dt><span>Detector Quality Flag2 :</span></dt><dd>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]</dd><dt><span>Earth-Sun Distance :</span></dt><dd>1.0011935</dd><dt><span>Solar Irradiance on RSB Detectors over pi :</span></dt><dd>[511.46   511.46   511.46   511.46   511.46   511.46   511.46   511.588\n",
+       " 511.62   511.588  511.588  511.588  511.556  511.556  511.556  511.492\n",
+       " 511.492  511.556  511.588  511.588  511.588  511.62   511.62   511.62\n",
+       " 511.62   511.62   511.556  511.492  511.46   511.365  511.301  511.206\n",
+       " 511.11   510.983  510.824  510.378  509.614  509.614  509.614  509.614\n",
+       " 315.763  315.763  315.763  315.763  315.763  315.763  315.763  315.795\n",
+       " 315.795  315.795  315.795  315.795  315.795  315.763  315.795  315.795\n",
+       " 315.795  315.795  315.795  315.795  315.795  315.795  315.795  315.795\n",
+       " 315.795  315.795  315.763  315.763  315.763  315.732  315.732  315.7\n",
+       " 315.763  315.795  315.859  315.954  316.082  316.273  316.273  316.273\n",
+       " 664.599  664.599  664.599  664.599  664.631  664.631  664.631  664.631\n",
+       " 664.663  664.663  664.631  664.631  664.599  664.663  664.599  664.599\n",
+       " 664.567  664.567  664.567  664.567  593.934  593.934  593.934  593.934\n",
+       " 593.966  593.934  593.934  593.934  593.934  593.934  593.934  593.934\n",
+       " 593.934  593.934  593.934  593.934  593.966  593.998  593.998  593.998\n",
+       " 150.974  150.974  150.974  150.974  150.974  150.974  151.006  151.006\n",
+       " 151.006  151.006  151.006  151.006  151.006  150.974  151.006  151.006\n",
+       " 150.974  150.974  150.974  150.974   76.458   76.458   76.458   76.458\n",
+       "  76.458   76.458   76.458   76.458   76.4899  76.4899  76.4899  76.4899\n",
+       "  76.4899  76.4899  76.458   76.458   76.458   76.458   76.458   76.458\n",
+       "  28.7752  28.7752  28.7752  28.7752  28.7434  28.7434  28.7434  28.7434\n",
+       "  28.7434  28.7752  28.7434  28.7434  28.7434  28.7434  28.7434  28.7434\n",
+       "  28.7434  28.7434  28.7434  28.7434 555.228  555.228  555.164  555.101\n",
+       " 555.164  555.323  555.642  556.151  556.756  557.138  606.094  606.094\n",
+       " 606.094  606.03   605.935  605.903  605.935  606.03   605.903  605.871\n",
+       " 630.572  630.572  630.572  630.572  630.572  630.572  630.604  630.54\n",
+       " 630.54   630.413  599.728  599.728  599.728  599.759  599.759  599.759\n",
+       " 599.759  599.759  599.791  599.791  602.242  602.242  602.306  602.306\n",
+       " 602.306  602.306  602.306  602.338  602.401  602.433  492.807  492.807\n",
+       " 492.712  492.68   492.648  492.648  492.712  492.839  493.094  493.062\n",
+       " 492.807  492.807  492.712  492.68   492.648  492.648  492.712  492.839\n",
+       " 493.094  493.062  480.075  480.075  480.075  480.043  480.043  480.043\n",
+       " 480.043  480.043  480.043  480.075  480.075  480.075  480.075  480.043\n",
+       " 480.043  480.043  480.043  480.043  480.043  480.075  412.084  412.084\n",
+       " 412.116  412.179  412.179  412.179  412.211  412.148  412.148  412.148\n",
+       " 309.684  309.684  309.684  309.716  309.684  309.716  309.716  309.716\n",
+       " 309.747  309.811  297.556  297.556  297.556  297.556  297.556  297.556\n",
+       " 297.556  297.556  297.556  297.556  278.107  278.107  278.107  278.107\n",
+       " 278.107  278.107  278.107  278.107  278.107  278.107  277.948  277.948\n",
+       " 277.948  277.948  277.948  277.948  277.948  277.948  277.948  277.948\n",
+       " 116.151  116.151  116.151  116.151  116.151  116.183  116.183  116.183\n",
+       " 116.183  116.183 ]</dd><dt><span>identifier_product_doi :</span></dt><dd>10.5067/MODIS/MOD021KM.061</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>http://dx.doi.org</dd></dl></div></li></ul></div></div>"
+      ],
       "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/5 [00:00<?, ?it/s]"
+       "<xarray.Dataset>\n",
+       "Dimensions:                                            (\n",
+       "                                                        2*nscans:MODIS_SWATH_Type_L1B: 406,\n",
+       "                                                        1KM_geo_dim:MODIS_SWATH_Type_L1B: 271,\n",
+       "                                                        Band_1KM_RefSB:MODIS_SWATH_Type_L1B: 15,\n",
+       "                                                        10*nscans:MODIS_SWATH_Type_L1B: 2030,\n",
+       "                                                        Max_EV_frames:MODIS_SWATH_Type_L1B: 1354,\n",
+       "                                                        ...\n",
+       "                                                        number of scans: 203,\n",
+       "                                                        number of 250m bands: 2,\n",
+       "                                                        detectors per 250m band: 40,\n",
+       "                                                        number of 500m bands: 5,\n",
+       "                                                        detectors per 500m band: 20,\n",
+       "                                                        number of 1km reflective bands: 15)\n",
+       "Coordinates:\n",
+       "  * Band_250M                                          (Band_250M) float32 1....\n",
+       "  * Band_500M                                          (Band_500M) float32 3....\n",
+       "  * Band_1KM_RefSB                                     (Band_1KM_RefSB) float32 ...\n",
+       "  * Band_1KM_Emissive                                  (Band_1KM_Emissive) float32 ...\n",
+       "Dimensions without coordinates: 2*nscans:MODIS_SWATH_Type_L1B,\n",
+       "                                1KM_geo_dim:MODIS_SWATH_Type_L1B,\n",
+       "                                Band_1KM_RefSB:MODIS_SWATH_Type_L1B,\n",
+       "                                10*nscans:MODIS_SWATH_Type_L1B,\n",
+       "                                Max_EV_frames:MODIS_SWATH_Type_L1B,\n",
+       "                                Band_1KM_Emissive:MODIS_SWATH_Type_L1B,\n",
+       "                                ...\n",
+       "                                Band_500M:MODIS_SWATH_Type_L1B,\n",
+       "                                number of emissive bands,\n",
+       "                                detectors per 1km band, number of scans,\n",
+       "                                number of 250m bands, detectors per 250m band,\n",
+       "                                number of 500m bands, detectors per 500m band,\n",
+       "                                number of 1km reflective bands\n",
+       "Data variables: (12/27)\n",
+       "    Latitude                                           (2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    Longitude                                          (2*nscans:MODIS_SWATH_Type_L1B, 1KM_geo_dim:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_RefSB                                       (Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_RefSB_Uncert_Indexes                        (Band_1KM_RefSB:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_Emissive                                    (Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    EV_1KM_Emissive_Uncert_Indexes                     (Band_1KM_Emissive:MODIS_SWATH_Type_L1B, 10*nscans:MODIS_SWATH_Type_L1B, Max_EV_frames:MODIS_SWATH_Type_L1B) float32 ...\n",
+       "    ...                                                 ...\n",
+       "    Noise in Thermal Detectors                         (number of emissive bands, detectors per 1km band) uint8 ...\n",
+       "    Change in relative responses of thermal detectors  (number of emissive bands, detectors per 1km band) uint8 ...\n",
+       "    DC Restore Change for Thermal Bands                (number of scans, number of emissive bands, detectors per 1km band) int8 ...\n",
+       "    DC Restore Change for Reflective 250m Bands        (number of scans, number of 250m bands, detectors per 250m band) int8 ...\n",
+       "    DC Restore Change for Reflective 500m Bands        (number of scans, number of 500m bands, detectors per 500m band) int8 ...\n",
+       "    DC Restore Change for Reflective 1km Bands         (number of scans, number of 1km reflective bands, detectors per 1km band) int8 ...\n",
+       "Attributes: (12/58)\n",
+       "    HDFEOSVersion:                                               HDFEOS_V2.19\n",
+       "    StructMetadata.0:                                            GROUP=SwathS...\n",
+       "    HDFEOS_FractionalOffset_10*nscans_MODIS_SWATH_Type_L1B:      0.0\n",
+       "    HDFEOS_FractionalOffset_Max_EV_frames_MODIS_SWATH_Type_L1B:  0.0\n",
+       "    CoreMetadata.0:                                              \\nGROUP     ...\n",
+       "    ArchiveMetadata.0:                                           \\nGROUP     ...\n",
+       "    ...                                                          ...\n",
+       "    Detector Quality Flag:                                       [ 0  0  0  0...\n",
+       "    Detector Quality Flag2:                                      [0 0 0 0 0 0...\n",
+       "    Earth-Sun Distance:                                          1.0011935\n",
+       "    Solar Irradiance on RSB Detectors over pi:                   [511.46   51...\n",
+       "    identifier_product_doi:                                      10.5067/MODI...\n",
+       "    identifier_product_doi_authority:                            http://dx.do..."
       ]
      },
+     "execution_count": 41,
      "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "Error: None is not a valid NetCDF 3 file\n            If this is a NetCDF4 file, you may need to install the\n            netcdf4 library, e.g.,\n\n            $ pip install netcdf4\n            ",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/scipy_.py:109\u001b[0m, in \u001b[0;36m_open_scipy_netcdf\u001b[0;34m(filename, mode, mmap, version)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m scipy\u001b[38;5;241m.\u001b[39mio\u001b[38;5;241m.\u001b[39mnetcdf_file(filename, mode\u001b[38;5;241m=\u001b[39mmode, mmap\u001b[38;5;241m=\u001b[39mmmap, version\u001b[38;5;241m=\u001b[39mversion)\n\u001b[1;32m    110\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# netcdf3 message is obscure in this case\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/scipy/io/_netcdf.py:278\u001b[0m, in \u001b[0;36mnetcdf_file.__init__\u001b[0;34m(self, filename, mode, mmap, version, maskandscale)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mra\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 278\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read()\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/scipy/io/_netcdf.py:599\u001b[0m, in \u001b[0;36mnetcdf_file._read\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    598\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m magic \u001b[38;5;241m==\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCDF\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 599\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not a valid NetCDF 3 file\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m    600\u001b[0m                     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilename)\n\u001b[1;32m    601\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mversion_byte\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m frombuffer(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;241m1\u001b[39m), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m>b\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n",
-      "\u001b[0;31mTypeError\u001b[0m: Error: None is not a valid NetCDF 3 file",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[17], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mxr\u001b[39;00m\n\u001b[1;32m      3\u001b[0m files \u001b[38;5;241m=\u001b[39m earthaccess\u001b[38;5;241m.\u001b[39mopen(results)\n\u001b[0;32m----> 5\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_mfdataset(files, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscipy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/api.py:1038\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[0;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[1;32m   1035\u001b[0m     open_ \u001b[38;5;241m=\u001b[39m open_dataset\n\u001b[1;32m   1036\u001b[0m     getattr_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m\n\u001b[0;32m-> 1038\u001b[0m datasets \u001b[38;5;241m=\u001b[39m [open_(p, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mopen_kwargs) \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m paths]\n\u001b[1;32m   1039\u001b[0m closers \u001b[38;5;241m=\u001b[39m [getattr_(ds, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_close\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[1;32m   1040\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preprocess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/api.py:1038\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1035\u001b[0m     open_ \u001b[38;5;241m=\u001b[39m open_dataset\n\u001b[1;32m   1036\u001b[0m     getattr_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m\n\u001b[0;32m-> 1038\u001b[0m datasets \u001b[38;5;241m=\u001b[39m [open_(p, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mopen_kwargs) \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m paths]\n\u001b[1;32m   1039\u001b[0m closers \u001b[38;5;241m=\u001b[39m [getattr_(ds, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_close\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[1;32m   1040\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preprocess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    555\u001b[0m     decode_cf,\n\u001b[1;32m    556\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    562\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[1;32m    567\u001b[0m     filename_or_obj,\n\u001b[1;32m    568\u001b[0m     drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecoders,\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    571\u001b[0m )\n\u001b[1;32m    572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    573\u001b[0m     backend_ds,\n\u001b[1;32m    574\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    585\u001b[0m )\n\u001b[1;32m    586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/scipy_.py:309\u001b[0m, in \u001b[0;36mScipyBackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, mode, format, group, mmap, lock)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    293\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    306\u001b[0m     lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    307\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    308\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 309\u001b[0m     store \u001b[38;5;241m=\u001b[39m ScipyDataStore(\n\u001b[1;32m    310\u001b[0m         filename_or_obj, mode\u001b[38;5;241m=\u001b[39mmode, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m, group\u001b[38;5;241m=\u001b[39mgroup, mmap\u001b[38;5;241m=\u001b[39mmmap, lock\u001b[38;5;241m=\u001b[39mlock\n\u001b[1;32m    311\u001b[0m     )\n\u001b[1;32m    313\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/scipy_.py:161\u001b[0m, in \u001b[0;36mScipyDataStore.__init__\u001b[0;34m(self, filename_or_obj, mode, format, group, mmap, lock)\u001b[0m\n\u001b[1;32m    153\u001b[0m     manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    154\u001b[0m         _open_scipy_netcdf,\n\u001b[1;32m    155\u001b[0m         filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    158\u001b[0m         kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(mmap\u001b[38;5;241m=\u001b[39mmmap, version\u001b[38;5;241m=\u001b[39mversion),\n\u001b[1;32m    159\u001b[0m     )\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 161\u001b[0m     scipy_dataset \u001b[38;5;241m=\u001b[39m _open_scipy_netcdf(\n\u001b[1;32m    162\u001b[0m         filename_or_obj, mode\u001b[38;5;241m=\u001b[39mmode, mmap\u001b[38;5;241m=\u001b[39mmmap, version\u001b[38;5;241m=\u001b[39mversion\n\u001b[1;32m    163\u001b[0m     )\n\u001b[1;32m    164\u001b[0m     manager \u001b[38;5;241m=\u001b[39m DummyFileManager(scipy_dataset)\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager \u001b[38;5;241m=\u001b[39m manager\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/scipy_.py:120\u001b[0m, in \u001b[0;36m_open_scipy_netcdf\u001b[0;34m(filename, mode, mmap, version)\u001b[0m\n\u001b[1;32m    113\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;124m    If this is a NetCDF4 file, you may need to install the\u001b[39m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;124m    netcdf4 library, e.g.,\u001b[39m\n\u001b[1;32m    116\u001b[0m \n\u001b[1;32m    117\u001b[0m \u001b[38;5;124m    $ pip install netcdf4\u001b[39m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;124m    \u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\n\u001b[1;32m    119\u001b[0m     errmsg \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m msg\n\u001b[0;32m--> 120\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(errmsg)\n\u001b[1;32m    121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
-      "\u001b[0;31mTypeError\u001b[0m: Error: None is not a valid NetCDF 3 file\n            If this is a NetCDF4 file, you may need to install the\n            netcdf4 library, e.g.,\n\n            $ pip install netcdf4\n            "
-     ]
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "import xarray as xr\n",
     "\n",
-    "files = earthaccess.open(results)\n",
-    "\n",
-    "ds = xr.open_mfdataset(files, engine='scipy')\n"
+    "ds = xr.open_dataset(files[0], engine='netcdf4')\n",
+    "ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 49,
+   "id": "72aa293c",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Getting 5 granules, approx download size: 0.76 GB\n"
-     ]
-    },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f119822ba6bc4c4493f8dfb0e00096d2",
-       "version_major": 2,
-       "version_minor": 0
-      },
       "text/plain": [
-       "QUEUEING TASKS | :   0%|          | 0/5 [00:00<?, ?it/s]"
+       "<matplotlib.collections.QuadMesh at 0x152499120>"
       ]
      },
+     "execution_count": 49,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ac52a2b8de32494fb065109410d4c3d8",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "image/png": 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",
       "text/plain": [
-       "PROCESSING TASKS | :   0%|          | 0/5 [00:00<?, ?it/s]"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15846326c4c549a7bc2564fe7cc09d0d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "COLLECTING RESULTS | :   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "files = earthaccess.download(results, \"./data-tmp\")"
+    "ds.EV_250_Aggr1km_RefSB[0].plot(cmap='Blues_r') # band 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 52,
+   "id": "553a85aa",
    "metadata": {},
    "outputs": [
     {
-     "ename": "OSError",
-     "evalue": "[Errno -128] NetCDF: Attempt to use feature that was not turned on when netCDF was built.: '/home/anna.jungbluth/rs_tools/notebooks/data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_key]\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key]\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('/home/anna.jungbluth/rs_tools/notebooks/data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '5da846f8-ae5a-4181-9e84-7bb0c1977f9b']",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf\u001b[39m\u001b[38;5;124m'\u001b[39m, engine\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnetcdf4\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/api.py:566\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    554\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m    555\u001b[0m     decode_cf,\n\u001b[1;32m    556\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    562\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m    563\u001b[0m )\n\u001b[1;32m    565\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 566\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[1;32m    567\u001b[0m     filename_or_obj,\n\u001b[1;32m    568\u001b[0m     drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[1;32m    569\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecoders,\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    571\u001b[0m )\n\u001b[1;32m    572\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m    573\u001b[0m     backend_ds,\n\u001b[1;32m    574\u001b[0m     filename_or_obj,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    585\u001b[0m )\n\u001b[1;32m    586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:590\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m    570\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    571\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    587\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    588\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m    589\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 590\u001b[0m     store \u001b[38;5;241m=\u001b[39m NetCDF4DataStore\u001b[38;5;241m.\u001b[39mopen(\n\u001b[1;32m    591\u001b[0m         filename_or_obj,\n\u001b[1;32m    592\u001b[0m         mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[1;32m    593\u001b[0m         \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m,\n\u001b[1;32m    594\u001b[0m         group\u001b[38;5;241m=\u001b[39mgroup,\n\u001b[1;32m    595\u001b[0m         clobber\u001b[38;5;241m=\u001b[39mclobber,\n\u001b[1;32m    596\u001b[0m         diskless\u001b[38;5;241m=\u001b[39mdiskless,\n\u001b[1;32m    597\u001b[0m         persist\u001b[38;5;241m=\u001b[39mpersist,\n\u001b[1;32m    598\u001b[0m         lock\u001b[38;5;241m=\u001b[39mlock,\n\u001b[1;32m    599\u001b[0m         autoclose\u001b[38;5;241m=\u001b[39mautoclose,\n\u001b[1;32m    600\u001b[0m     )\n\u001b[1;32m    602\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m    603\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:391\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m    385\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m    386\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m    387\u001b[0m )\n\u001b[1;32m    388\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m    389\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m    390\u001b[0m )\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(manager, group\u001b[38;5;241m=\u001b[39mgroup, mode\u001b[38;5;241m=\u001b[39mmode, lock\u001b[38;5;241m=\u001b[39mlock, autoclose\u001b[38;5;241m=\u001b[39mautoclose)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:338\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m    336\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m    337\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 338\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[1;32m    339\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:400\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    399\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 400\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire()\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:394\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    393\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 394\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m    395\u001b[0m         ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode)\n\u001b[1;32m    396\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire_with_cache_info(needs_lock)\n\u001b[1;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opener(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2469\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2028\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mOSError\u001b[0m: [Errno -128] NetCDF: Attempt to use feature that was not turned on when netCDF was built.: '/home/anna.jungbluth/rs_tools/notebooks/data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf'"
-     ]
-    }
-   ],
-   "source": [
-    "ds = xr.open_dataset('./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf', engine='netcdf4')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "7f2ed171",
-   "metadata": {},
-   "outputs": [
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x152628340>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
     {
-     "ename": "RasterioIOError",
-     "evalue": "'./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf' not recognized as a supported file format.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_key]\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key]\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
-      "\u001b[0;31mKeyError\u001b[0m: [<function open at 0x7f779f93f740>, ('./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf',), 'r', (('sharing', False),), '170e9519-8377-486b-9f19-16cd3607aca6']",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m                      Traceback (most recent call last)",
-      "File \u001b[0;32mrasterio/_base.pyx:310\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mrasterio/_base.pyx:221\u001b[0m, in \u001b[0;36mrasterio._base.open_dataset\u001b[0;34m()\u001b[0m\n",
-      "File \u001b[0;32mrasterio/_err.pyx:221\u001b[0m, in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: './data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf' not recognized as a supported file format.",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mRasterioIOError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[30], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrioxarray\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mrxr\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m modis \u001b[38;5;241m=\u001b[39m  rxr\u001b[38;5;241m.\u001b[39mopen_rasterio(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf\u001b[39m\u001b[38;5;124m'\u001b[39m, masked\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/rioxarray/_io.py:1124\u001b[0m, in \u001b[0;36mopen_rasterio\u001b[0;34m(filename, parse_coordinates, chunks, cache, lock, masked, mask_and_scale, variable, group, default_name, decode_times, decode_timedelta, band_as_variable, **open_kwargs)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1123\u001b[0m         manager \u001b[38;5;241m=\u001b[39m URIManager(file_opener, filename, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, kwargs\u001b[38;5;241m=\u001b[39mopen_kwargs)\n\u001b[0;32m-> 1124\u001b[0m     riods \u001b[38;5;241m=\u001b[39m manager\u001b[38;5;241m.\u001b[39macquire()\n\u001b[1;32m   1125\u001b[0m     captured_warnings \u001b[38;5;241m=\u001b[39m rio_warnings\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;66;03m# raise the NotGeoreferencedWarning if applicable\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:193\u001b[0m, in \u001b[0;36mCachingFileManager.acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    179\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Acquire a file object from the manager.\u001b[39;00m\n\u001b[1;32m    180\u001b[0m \n\u001b[1;32m    181\u001b[0m \u001b[38;5;124;03m    A new file is only opened if it has expired from the\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[38;5;124;03m        An open file object, as returned by ``opener(*args, **kwargs)``.\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 193\u001b[0m     file, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire_with_cache_info(needs_lock)\n\u001b[1;32m    194\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m file\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opener(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/rasterio/env.py:451\u001b[0m, in \u001b[0;36mensure_env_with_credentials.<locals>.wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    448\u001b[0m     session \u001b[38;5;241m=\u001b[39m DummySession()\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m env_ctor(session\u001b[38;5;241m=\u001b[39msession):\n\u001b[0;32m--> 451\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m f(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/rasterio/__init__.py:304\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m path \u001b[38;5;241m=\u001b[39m _parse_path(raw_dataset_path)\n\u001b[1;32m    303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 304\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m DatasetReader(path, driver\u001b[38;5;241m=\u001b[39mdriver, sharing\u001b[38;5;241m=\u001b[39msharing, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    306\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m get_writer_for_path(path, driver\u001b[38;5;241m=\u001b[39mdriver)(\n\u001b[1;32m    307\u001b[0m         path, mode, driver\u001b[38;5;241m=\u001b[39mdriver, sharing\u001b[38;5;241m=\u001b[39msharing, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    308\u001b[0m     )\n",
-      "File \u001b[0;32mrasterio/_base.pyx:312\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mRasterioIOError\u001b[0m: './data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf' not recognized as a supported file format."
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "import rioxarray as rxr\n",
-    "\n",
-    "modis =  rxr.open_rasterio('./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf', masked=True)\n"
+    "ds.EV_500_Aggr1km_RefSB[0].plot(cmap='Blues_r') # band 3"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "id": "41c60b75",
+   "execution_count": 45,
+   "id": "9c9bcb8a",
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "did not find a match in any of xarray's currently installed IO backends ['netcdf4', 'scipy']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:\nhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\nhttps://docs.xarray.dev/en/stable/user-guide/io.html",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[34], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m modis \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/api.py:547\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mupdate(backend_kwargs)\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 547\u001b[0m     engine \u001b[38;5;241m=\u001b[39m plugins\u001b[38;5;241m.\u001b[39mguess_engine(filename_or_obj)\n\u001b[1;32m    549\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_array_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    550\u001b[0m     from_array_kwargs \u001b[38;5;241m=\u001b[39m {}\n",
-      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/xarray/backends/plugins.py:197\u001b[0m, in \u001b[0;36mguess_engine\u001b[0;34m(store_spec)\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    191\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound the following matches with the input file in xarray\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms IO \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    192\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbackends: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcompatible_engines\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. But their dependencies may not be installed, see:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    193\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://docs.xarray.dev/en/stable/user-guide/io.html \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    194\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    195\u001b[0m     )\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_msg)\n",
-      "\u001b[0;31mValueError\u001b[0m: did not find a match in any of xarray's currently installed IO backends ['netcdf4', 'scipy']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:\nhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\nhttps://docs.xarray.dev/en/stable/user-guide/io.html"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x15230d4b0>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "modis = xr.open_dataset('./data-tmp/MOD021KM.A2019060.1035.061.2019060192534.hdf')"
+    "ds.EV_1KM_Emissive[5].plot(cmap='Blues') # band 25"
    ]
   }
  ],
@@ -461,7 +2451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,