cdec/04_land_surface_temperature.ipynb

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   "id": "3d887eed-ce30-4984-abd3-762deb9af0cc",
   "metadata": {},
   "source": [
    "# Oberflächentemperatur (Land Surface Temperature)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee91c281-afd2-43b5-ba42-fdcef475dc81",
   "metadata": {},
   "source": [
    "## Authentifizierung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dca5e519-d0ff-48f9-b493-80f42101eb35",
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "Visit <a href=\"https://identity.dataspace.copernicus.eu/auth/realms/CDSE/device?user_code=QHKA-VXMK\" title=\"Authenticate at https://identity.dataspace.copernicus.eu/auth/realms/CDSE/device?user_code=QHKA-VXMK\" target=\"_blank\" rel=\"noopener noreferrer\">https://identity.dataspace.copernicus.eu/auth/realms/CDSE/device?user_code=QHKA-VXMK</a> <a href=\"#\" onclick=\"navigator.clipboard.writeText('https://identity.dataspace.copernicus.eu/auth/realms/CDSE/device?user_code=QHKA-VXMK');return false;\" title=\"Copy authentication URL to clipboard\">&#128203;</a> to authenticate."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "✅ Authorized successfully"
      ]
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     "metadata": {},
     "output_type": "display_data"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Authenticated using device code flow.\n"
     ]
    }
   ],
   "source": [
    "import openeo\n",
    "\n",
    "connection = openeo.connect(\"https://openeo.dataspace.copernicus.eu/\").authenticate_oidc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5b84c1-271a-436c-9363-8e6525e6fec7",
   "metadata": {},
   "source": [
    "## Verwaltungsgrenzen von Bremen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "828a58ad-3988-4024-8ffb-0cf9a6e60d39",
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   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bez_lan</th>\n",
       "      <th>sch_lan</th>\n",
       "      <th>repentuncd</th>\n",
       "      <th>unaggid</th>\n",
       "      <th>agglomerat</th>\n",
       "      <th>inhabitant</th>\n",
       "      <th>size</th>\n",
       "      <th>geometry</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Bremen</td>\n",
       "      <td>04000000</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>318.195497</td>\n",
       "      <td>MULTIPOLYGON (((474358.64 5894064.42, 474311.0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   id bez_lan   sch_lan repentuncd unaggid agglomerat  inhabitant        size  \\\n",
       "0   1  Bremen  04000000       None    None       None         NaN  318.195497   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((474358.64 5894064.42, 474311.0...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "gdf = gpd.read_file('Verwaltungsgrenzen_HB_BHV', layer=\"hb_landesflaeche_Teil_I_BRE\")\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1cc183c8-ef90-4870-8f36-c6436c91d419",
   "metadata": {},
   "outputs": [
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[8.492187113405038, 53.21552996082326], [8.491957058900233, 53.21603105426451], [8.491528012983382, 53.21696589931114], [8.490851284654388, 53.21747614073704], [8.490132196948878, 53.21801829753059], [8.489997368748487, 53.2181199548188], [8.489825686842936, 53.218241410225886], [8.489655725748563, 53.21836165022251], [8.48911804462146, 53.218753433132], [8.489063488015722, 53.2187946394678], [8.488998804268967, 53.21884362296153], [8.48849071381447, 53.21922716994375], [8.487316545591165, 53.22012805629163], [8.486194806213618, 53.220974143175724], [8.486031599695547, 53.22109515321036], [8.485562836714399, 53.22144209143969], [8.484996576285914, 53.22188757762144], [8.48436987981969, 53.22233828306809], [8.484063807912252, 53.22258092530432], [8.483935900042741, 53.22268015161794], [8.482785268986172, 53.2235261710974], [8.4827088301362, 53.22359235940707], [8.482499454036793, 53.22422410996038], [8.482344483939638, 53.22469490219535], [8.482295459331933, 53.22484383794733], [8.482263661843758, 53.22494047747444], [8.48219112518265, 53.22516097288353], [8.482022406874178, 53.22567442154769], [8.481963171087129, 53.2258401585111], [8.481897610072421, 53.22602355867441], [8.481761258285877, 53.22647350412764], [8.481758449250766, 53.226482840671814], [8.481947040196806, 53.226520156612125], [8.482193434870288, 53.22656637997173], [8.482708375514235, 53.22666297542054], [8.48323480603221, 53.22676220734025], [8.485331958544421, 53.22715750159494], [8.486788874571475, 53.227421683288284], [8.487514151114178, 53.22756359691944], [8.487552418328278, 53.22750766892484], [8.487757826467945, 53.22758010577204], [8.487665476652236, 53.227799629291546], [8.487934393371779, 53.227886559681146], [8.48829771054626, 53.228019119348524], [8.488558037257372, 53.22810681148327], [8.488717094675877, 53.228149644147756], [8.488663210444999, 53.228296835629585], [8.48882814364616, 53.228346983577985], [8.489011224511218, 53.22831984817689], [8.48936491012451, 53.22826910078009], [8.48979299041383, 53.22808440834252], [8.489774464987953, 53.228043670962855], [8.489660583557116, 53.22779322116872], [8.489611992208697, 53.22762158935296], [8.489657611693627, 53.22717385357218], [8.489650646152674, 53.22690297957263], [8.490791426309825, 53.226879794335765], [8.490906827567994, 53.22687868721219], [8.490988012611465, 53.22691696830153], [8.491137894876854, 53.22683391895508], [8.491289633904168, 53.226771103012695], [8.491447382381002, 53.226769978332044], [8.4915522725862, 53.22676718970523], [8.491607221258679, 53.226820010825094], [8.492018077496102, 53.2272153090563], [8.49284401864188, 53.227320314089035], [8.494708405149005, 53.227600065141694], [8.496225756821877, 53.22780092388489], [8.496323836883573, 53.22779145027737], [8.49636882723943, 53.22778714579149], [8.497210417311779, 53.227894424942754], [8.500663052373113, 53.22833449566137], [8.500709437747382, 53.22835579656525], [8.50073099877951, 53.22836570306322], [8.504001806722448, 53.22881439873334], [8.504703586581835, 53.2289106605276], [8.50486473902913, 53.22868003851894], [8.504973801595726, 53.22852363035902], [8.505022238691243, 53.228454255274066], [8.505634343784278, 53.22808877861262]]]], &quot;type&quot;: &quot;MultiPolygon&quot;}, &quot;id&quot;: &quot;0&quot;, &quot;properties&quot;: {&quot;agglomerat&quot;: null, &quot;bez_lan&quot;: &quot;Bremen&quot;, &quot;id&quot;: 1, &quot;inhabitant&quot;: null, &quot;repentuncd&quot;: null, &quot;sch_lan&quot;: &quot;04000000&quot;, &quot;size&quot;: 318.19549689, &quot;unaggid&quot;: null}, &quot;type&quot;: &quot;Feature&quot;}], &quot;type&quot;: &quot;FeatureCollection&quot;});\n",
       "\n",
       "        \n",
       "    \n",
       "    geo_json_816ffbdd05dc348f45252b699186a91e.bindTooltip(\n",
       "    function(layer){\n",
       "    let div = L.DomUtil.create(&#x27;div&#x27;);\n",
       "    \n",
       "    let handleObject = feature =&gt; {\n",
       "        if (feature === null) {\n",
       "            return &#x27;&#x27;;\n",
       "        } else if (typeof(feature)==&#x27;object&#x27;) {\n",
       "            return JSON.stringify(feature);\n",
       "        } else {\n",
       "            return feature;\n",
       "        }\n",
       "    }\n",
       "    let fields = [&quot;id&quot;, &quot;bez_lan&quot;, &quot;sch_lan&quot;, &quot;repentuncd&quot;, &quot;unaggid&quot;, &quot;agglomerat&quot;, &quot;inhabitant&quot;, &quot;size&quot;];\n",
       "    let aliases = [&quot;id&quot;, &quot;bez_lan&quot;, &quot;sch_lan&quot;, &quot;repentuncd&quot;, &quot;unaggid&quot;, &quot;agglomerat&quot;, &quot;inhabitant&quot;, &quot;size&quot;];\n",
       "    let table = &#x27;&lt;table&gt;&#x27; +\n",
       "        String(\n",
       "        fields.map(\n",
       "        (v,i)=&gt;\n",
       "        `&lt;tr&gt;\n",
       "            &lt;th&gt;${aliases[i]}&lt;/th&gt;\n",
       "            \n",
       "            &lt;td&gt;${handleObject(layer.feature.properties[v])}&lt;/td&gt;\n",
       "        &lt;/tr&gt;`).join(&#x27;&#x27;))\n",
       "    +&#x27;&lt;/table&gt;&#x27;;\n",
       "    div.innerHTML=table;\n",
       "    \n",
       "    return div\n",
       "    }\n",
       "    ,{\n",
       "  &quot;sticky&quot;: true,\n",
       "  &quot;className&quot;: &quot;foliumtooltip&quot;,\n",
       "});\n",
       "                     \n",
       "    \n",
       "            geo_json_816ffbdd05dc348f45252b699186a91e.addTo(map_c36b334d6670b6e9be17abbdc167e855);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x119d86060>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.explore()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3140ba0c-bedc-4314-876e-01598e2826c8",
   "metadata": {},
   "source": [
    "## Räumliche Ausdehnung\n",
    "\n",
    "Bestimmung der räumlichen Ausdehnung anhand der Vektorgeometrien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fc491aa9-36a4-4c00-9c66-abc98df32b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8.481758449250766, 53.01115244699335, 8.990813245344944, 53.2289106605276]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bounding Box bestimmen\n",
    "bbox = gdf.to_crs('WGS84').total_bounds.tolist()\n",
    "bbox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c86786f-1fd3-4859-9853-8e8b6fb20b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'west': 8.481758449250766,\n",
       " 'south': 53.01115244699335,\n",
       " 'east': 8.990813245344944,\n",
       " 'north': 53.2289106605276}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spatial_extent=dict(zip([\"west\", \"south\", \"east\", \"north\"], bbox))\n",
    "\n",
    "spatial_extent\n",
    "\n",
    "spatial_extent = {'west': 8.721771, 'south': 53.025027, 'east': 8.904419, 'north': 53.114965}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52c35ce-9880-438f-8ed7-eb92e325eb7f",
   "metadata": {},
   "source": [
    "## Zeitliche Ausdehnung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d985ba77-3ba7-418f-b84a-a8c817b9f512",
   "metadata": {},
   "outputs": [],
   "source": [
    "temporal_extent = [\"2024-06-01\", \"2024-08-31\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cca0787b-5f23-4c17-b2a7-e0c9f812fa15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:00:00 Job 'j-2510111146024febaf1f1d58a9a115af': send 'start'\n",
      "0:00:13 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:00:18 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:00:24 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:00:32 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:00:42 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:00:55 Job 'j-2510111146024febaf1f1d58a9a115af': created (progress 0%)\n",
      "0:01:10 Job 'j-2510111146024febaf1f1d58a9a115af': running (progress N/A)\n",
      "0:01:29 Job 'j-2510111146024febaf1f1d58a9a115af': running (progress N/A)\n",
      "0:01:53 Job 'j-2510111146024febaf1f1d58a9a115af': running (progress N/A)\n",
      "0:02:23 Job 'j-2510111146024febaf1f1d58a9a115af': running (progress N/A)\n",
      "0:03:00 Job 'j-2510111146024febaf1f1d58a9a115af': running (progress N/A)\n",
      "0:03:47 Job 'j-2510111146024febaf1f1d58a9a115af': finished (progress 100%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('results/lst.tif')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube = connection.load_collection(\n",
    "    \"SENTINEL3_SLSTR_L2_LST\",\n",
    "    spatial_extent=spatial_extent,\n",
    "    temporal_extent=temporal_extent,\n",
    "    bands=[\"LST\"]\n",
    ")\n",
    "\n",
    "\n",
    "cube = cube.mean_time()\n",
    "\n",
    "lst = cube.save_result(format=\"GTIFF\")\n",
    "job = lst.create_job(title=\"LST\")\n",
    "job.start_and_wait().download_result('results/lst.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c840c746-b6f3-4baa-b198-905387509990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2565447ee8f44fbfb5f467641971172e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[53.1173035, 8.740687], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title',…"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import leafmap\n",
    "\n",
    "m = leafmap.Map()\n",
    "\n",
    "m.add_raster(\n",
    "    'results/lst.tif',\n",
    "    cmap=\"coolwarm\",\n",
    "    layer_name=\"Land Surface Temperature\"\n",
    ")\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1849544-030e-4b5b-8402-dad9f9911c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rasterio as rio\n",
    "\n",
    "src = rio.open('results/lst.tif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1ad30415-08eb-4c28-8e05-86a6dd605157",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Temperatur von Kelvin in Celsius umrechnen\n",
    "lst = src.read() - 272.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f95d4a2d-0060-43c0-9cd8-2f5e9e2c4ec2",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32m~/cdec-hackathon/game/.venv/lib/python3.12/site-packages/IPython/core/formatters.py:984\u001b[39m, in \u001b[36mIPythonDisplayFormatter.__call__\u001b[39m\u001b[34m(self, obj)\u001b[39m\n\u001b[32m    982\u001b[39m method = get_real_method(obj, \u001b[38;5;28mself\u001b[39m.print_method)\n\u001b[32m    983\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m method \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\u001b[32m--> \u001b[39m\u001b[32m984\u001b[39m     \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    985\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/cdec-hackathon/game/.venv/lib/python3.12/site-packages/plotly/basedatatypes.py:850\u001b[39m, in \u001b[36mBaseFigure._ipython_display_\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    847\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplotly\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mio\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpio\u001b[39;00m\n\u001b[32m    849\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m pio.renderers.render_on_display \u001b[38;5;129;01mand\u001b[39;00m pio.renderers.default:\n\u001b[32m--> \u001b[39m\u001b[32m850\u001b[39m     \u001b[43mpio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    851\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    852\u001b[39m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mrepr\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/cdec-hackathon/game/.venv/lib/python3.12/site-packages/plotly/io/_renderers.py:415\u001b[39m, in \u001b[36mshow\u001b[39m\u001b[34m(fig, renderer, validate, **kwargs)\u001b[39m\n\u001b[32m    410\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    411\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    412\u001b[39m     )\n\u001b[32m    414\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m nbformat \u001b[38;5;129;01mor\u001b[39;00m Version(nbformat.__version__) < Version(\u001b[33m\"\u001b[39m\u001b[33m4.2.0\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m415\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    416\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    417\u001b[39m     )\n\u001b[32m    419\u001b[39m display_jupyter_version_warnings()\n\u001b[32m    421\u001b[39m ipython_display.display(bundle, raw=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[31mValueError\u001b[39m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "coloraxis": "coloraxis",
         "hovertemplate": "x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>",
         "name": "0",
         "type": "heatmap",
         "xaxis": "x",
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      ],
      "text/plain": [
       "Figure({\n",
       "    'data': [{'coloraxis': 'coloraxis',\n",
       "              'hovertemplate': 'x: %{x}<br>y: %{y}<br>color: %{z}<extra></extra>',\n",
       "              'name': '0',\n",
       "              'type': 'heatmap',\n",
       "              'xaxis': 'x',\n",
       "              'yaxis': 'y',\n",
       "              'z': {'bdata': ('gIXtQEA3+UCgiQxBwH4QQeB4DkFAJw' ... 'FAhAlBICoJQYDzBUHAw/9AwED/QA=='),\n",
       "                    'dtype': 'f4',\n",
       "                    'shape': '25, 58'}}],\n",
       "    'layout': {'coloraxis': {'colorscale': [[0.0, 'rgb(3, 35, 51)'],\n",
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       "               'margin': {'t': 60},\n",
       "               'template': '...',\n",
       "               'xaxis': {'anchor': 'y', 'constrain': 'domain', 'domain': [0.0, 1.0], 'scaleanchor': 'y'},\n",
       "               'yaxis': {'anchor': 'x', 'autorange': 'reversed', 'constrain': 'domain', 'domain': [0.0, 1.0]}}\n",
       "})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.imshow(\n",
    "    lst[0],\n",
    "    color_continuous_scale='thermal',\n",
    "    #zmin=5,\n",
    "    #zmax=12\n",
    ")"
   ]
  }
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