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Further re-reading, more work on dNBR (e.g. Potsdam-Mittelmark size calculation)

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heyarne 2021-02-25 14:04:44 +00:00
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true-color-image/01 Download and plot single true-color image.ipynb
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{ {
"cells": [ "cells": [
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"import rasterio"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},

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}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -33,13 +33,13 @@
"\n", "\n",
"This can be done by running the Docker container like this:\n", "This can be done by running the Docker container like this:\n",
"\n", "\n",
"```\n", "``` bash\n",
"docker run -it --name=eratosthenes --net=host -v \"$(pwd)\":/home/jovyan -e SCIHUB_USERNAME=YOUR_USERNAME_HERE -e SCIHUB_PASSWORD=YOUR_PASSWORD_HERE eratosthenes:latest\n", "docker run -it --name=eratosthenes --net=host -v \"$(pwd)\":/home/jovyan -e SCIHUB_USERNAME=YOUR_USERNAME_HERE -e SCIHUB_PASSWORD=YOUR_PASSWORD_HERE eratosthenes:latest\n",
"```\n", "```\n",
"\n", "\n",
"The important part is\n", "The environment variables are defined via the `-e` flags:\n",
"\n", "\n",
"```\n", "``` bash\n",
"-e SCIHUB_USERNAME=YOUR_USERNAME_HERE -e SCIHUB_PASSWORD=YOUR_PASSWORD_HERE\n", "-e SCIHUB_USERNAME=YOUR_USERNAME_HERE -e SCIHUB_PASSWORD=YOUR_PASSWORD_HERE\n",
"```\n", "```\n",
"\n", "\n",
@ -48,7 +48,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -67,7 +67,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -78,7 +78,6 @@
"\u001b[0;32mdef\u001b[0m \u001b[0msearch_osm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;32mdef\u001b[0m \u001b[0msearch_osm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0;34m'''\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0;34m'''\u001b[0m\n",
"\u001b[0;34m Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.\u001b[0m\n", "\u001b[0;34m Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.\u001b[0m\n",
"\u001b[0;34m This allows us to fetch detailed geometries for virtually any place on earth.\u001b[0m\n",
"\u001b[0;34m '''\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m '''\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0murlescaped_place\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0murlescaped_place\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murllib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
"\u001b[0;34m\u001b[0m \u001b[0msearch_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'https://nominatim.openstreetmap.org/search/?q={}'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\n", "\u001b[0;34m\u001b[0m \u001b[0msearch_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'https://nominatim.openstreetmap.org/search/?q={}'\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\n",
@ -99,7 +98,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -242,7 +241,7 @@
"4 LINESTRING (9.41929 54.40283, 9.42072 54.40343... " "4 LINESTRING (9.41929 54.40283, 9.42072 54.40343... "
] ]
}, },
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -258,7 +257,7 @@
"source": [ "source": [
"OpenStreetMap contains geoinformation at vastly different scales and of very different types.\n", "OpenStreetMap contains geoinformation at vastly different scales and of very different types.\n",
"We can use it to query outlines of parks or entire countries.\n", "We can use it to query outlines of parks or entire countries.\n",
"You can consult the [OpenStreetMap wiki](https://wiki.openstreetmap.org/wiki/Main_Page) for more detailed information about this.\n", "The [OpenStreetMap wiki](https://wiki.openstreetmap.org/wiki/Main_Page) contains exhaustive information about the architectural design of OpenStreetMap.\n",
"Using the `search_osm` function we are very flexible in the type of query information we can retrieve.\n", "Using the `search_osm` function we are very flexible in the type of query information we can retrieve.\n",
"\n", "\n",
"The first result is the city's centroid.\n", "The first result is the city's centroid.\n",
@ -267,7 +266,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -276,7 +275,7 @@
"<AxesSubplot:>" "<AxesSubplot:>"
] ]
}, },
"execution_count": 7, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
@ -307,7 +306,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 6,
"metadata": {}, "metadata": {},
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"source": [ "source": [
@ -328,7 +327,7 @@
}, },
{ {
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@ -338,7 +337,7 @@
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{ {
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"execution_count": 10, "execution_count": 8,
"metadata": {}, "metadata": {},
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"source": [ "source": [
@ -357,7 +356,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -366,10 +365,10 @@
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"13.061432364 52.311332164 0.7266411720000008 0.39108917200000093\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,105.0137535)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.014532823440000016\" opacity=\"0.6\" d=\"M 13.6477328,52.3382448 L 13.6474433,52.338271 L 13.131305,52.3872254 L 13.1310825,52.3872512 L 13.1307559,52.3873763 L 13.1298482,52.3878739 L 13.1267317,52.3895836 L 13.0907635,52.4115602 L 13.090275,52.4122142 L 13.088345,52.4196325 L 13.1284297,52.5860175 L 13.1289619,52.5873048 L 13.2827678,52.6607411 L 13.4794873,52.6755087 L 13.4884252,52.6707899 L 13.523022,52.6450365 L 13.7564462,52.4461766 L 13.7611609,52.4377097 L 13.76095,52.4373221 L 13.7390343,52.4073353 L 13.7342706,52.4021367 L 13.7341193,52.4019835 L 13.6922028,52.3672074 L 13.6513711,52.3392255 L 13.6509462,52.3389451 L 13.6506935,52.3388565 L 13.6488471,52.3383381 L 13.6484243,52.3382725 L 13.6477328,52.3382448 z\" /></g></svg>" "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"13.061432364 52.311332164 0.7266411720000008 0.39108917200000093\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,105.0137535)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.014532823440000016\" opacity=\"0.6\" d=\"M 13.6477328,52.3382448 L 13.6474433,52.338271 L 13.131305,52.3872254 L 13.1310825,52.3872512 L 13.1307559,52.3873763 L 13.1298482,52.3878739 L 13.1267317,52.3895836 L 13.0907635,52.4115602 L 13.090275,52.4122142 L 13.088345,52.4196325 L 13.1284297,52.5860175 L 13.1289619,52.5873048 L 13.2827678,52.6607411 L 13.4794873,52.6755087 L 13.4884252,52.6707899 L 13.523022,52.6450365 L 13.7564462,52.4461766 L 13.7611609,52.4377097 L 13.76095,52.4373221 L 13.7390343,52.4073353 L 13.7342706,52.4021367 L 13.7341193,52.4019835 L 13.6922028,52.3672074 L 13.6513711,52.3392255 L 13.6509462,52.3389451 L 13.6506935,52.3388565 L 13.6488471,52.3383381 L 13.6484243,52.3382725 L 13.6477328,52.3382448 z\" /></g></svg>"
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"<shapely.geometry.polygon.Polygon at 0x7f9f50db5850>" "<shapely.geometry.polygon.Polygon at 0x7eff2c992f70>"
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} }
@ -381,14 +380,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"metadata": {},
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"source": []
},
{
"cell_type": "code",
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"metadata": {}, "metadata": {},
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@ -408,7 +400,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 11,
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{ {
@ -437,7 +429,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -463,7 +455,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
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@ -473,7 +465,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -482,7 +474,7 @@
"Text(0.5, 1.0, 'Area of interest, its convex hull and products returned from the API')" "Text(0.5, 1.0, 'Area of interest, its convex hull and products returned from the API')"
] ]
}, },
"execution_count": 19, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
@ -530,7 +522,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 15,
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@ -542,7 +534,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -755,7 +747,7 @@
"[4 rows x 46 columns]" "[4 rows x 46 columns]"
] ]
}, },
"execution_count": 22, "execution_count": 16,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -777,7 +769,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -838,7 +830,7 @@
"11 S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2... 16.512980" "11 S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_2... 16.512980"
] ]
}, },
"execution_count": 23, "execution_count": 17,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -851,20 +843,20 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Let's download all of these `input/raster/true_color_image`.\n", "These files will all be downloaded to `input/true_color_image`.\n",
"Repeated calls to `download` or `download_all` will skip the download process if a product has previously been downloaded." "Repeated calls to `download` or `download_all` will skip the download process if a product has previously been downloaded."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from pathlib import Path\n", "from pathlib import Path\n",
"from tqdm.notebook import tqdm\n", "from tqdm.notebook import tqdm\n",
"\n", "\n",
"dst_path = Path('input/raster/true_color_image')\n", "dst_path = Path('input/true_color_image')\n",
"\n", "\n",
"# we make sure to create the directory at dst_path so the notebook can run\n", "# we make sure to create the directory at dst_path so the notebook can run\n",
"# without interruptions\n", "# without interruptions\n",
@ -873,18 +865,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 19,
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" 'md5': '86D9DB1D0C6C087D66AE28E4A106EF04',\n",
" 'date': datetime.datetime(2020, 6, 15, 10, 15, 59, 24000),\n",
" 'footprint': 'POLYGON((12.004760925069467 53.21197782662703,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.071595116334858 52.22621087043558,12.004760925069467 53.21197782662703))',\n",
" 'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('b64d4450-d999-41dc-9ecb-6d263f322dd3')/$value\",\n",
" 'Online': True,\n",
" 'Creation Date': datetime.datetime(2020, 6, 16, 1, 22, 2, 573000),\n",
" 'Ingestion Date': datetime.datetime(2020, 6, 16, 1, 21, 9, 378000),\n",
" 'path': 'input/true_color_image/S2B_MSIL2A_20200615T101559_N0214_R065_T33UUU_20200615T141117.zip',\n",
" 'downloaded_bytes': 1176239030},\n",
" '10190cf1-5690-48a3-b71e-861629703a3f': {'id': '10190cf1-5690-48a3-b71e-861629703a3f',\n",
" 'title': 'S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324',\n",
" 'size': 862966648,\n",
" 'md5': 'A93C8F7AA12C2396CF57CFD02ADF21A2',\n",
" 'date': datetime.datetime(2020, 6, 2, 10, 5, 59, 24000),\n",
" 'footprint': 'POLYGON((12.358321903656456 52.23137459287384,12.415699786858166 52.36633012201262,12.47808001229512 52.51194199175493,12.5407946185535 52.65754848189568,12.603698138951865 52.80313660845604,12.667166015291048 52.94869104851679,12.731188091140115 53.09422646557383,12.789415151489028 53.226293171356296,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.358321903656456 52.23137459287384))',\n",
" 'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('10190cf1-5690-48a3-b71e-861629703a3f')/$value\",\n",
" 'Online': True,\n",
" 'Creation Date': datetime.datetime(2020, 6, 2, 18, 42, 4, 900000),\n",
" 'Ingestion Date': datetime.datetime(2020, 6, 2, 18, 40, 43, 331000),\n",
" 'path': 'input/true_color_image/S2B_MSIL2A_20200602T100559_N0214_R022_T33UUU_20200602T135324.zip',\n",
" 'downloaded_bytes': 862966648},\n",
" 'e385d397-4d1b-4fa2-a1ce-917f420c86dd': {'id': 'e385d397-4d1b-4fa2-a1ce-917f420c86dd',\n",
" 'title': 'S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_20200531T134031',\n",
" 'size': 1162711104,\n",
" 'md5': '7E74A83DA5EA1176CFB68517E53A343D',\n",
" 'date': datetime.datetime(2020, 5, 31, 10, 20, 31, 24000),\n",
" 'footprint': 'POLYGON((12.004760925069467 53.21197782662703,13.648341514492438 53.24196354764364,13.678544065009483 52.25515074878083,12.071595116334858 52.22621087043558,12.004760925069467 53.21197782662703))',\n",
" 'url': \"https://scihub.copernicus.eu/apihub/odata/v1/Products('e385d397-4d1b-4fa2-a1ce-917f420c86dd')/$value\",\n",
" 'Online': True,\n",
" 'Creation Date': datetime.datetime(2020, 5, 31, 20, 22, 5, 475000),\n",
" 'Ingestion Date': datetime.datetime(2020, 5, 31, 20, 21, 27, 279000),\n",
" 'path': 'input/true_color_image/S2A_MSIL2A_20200531T102031_N0214_R065_T33UUU_20200531T134031.zip',\n",
" 'downloaded_bytes': 1162711104}}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
} }
], ],
"source": [ "source": [
@ -1041,14 +1099,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 23, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Downloaded 0.00GB\n" "Downloaded 3.78GB\n"
] ]
} }
], ],

2
true-color-image/01b Plot Downloaded products.ipynb
View file

@ -75,7 +75,7 @@
"source": [ "source": [
"from pathlib import Path\n", "from pathlib import Path\n",
"\n", "\n",
"raster_files = Path('input/raster/true_color_image')\n", "raster_files = Path('input/true_color_image')\n",
"product = list(raster_files.glob('S2B_MSIL2A_20200602T100559*.zip'))[0]\n", "product = list(raster_files.glob('S2B_MSIL2A_20200602T100559*.zip'))[0]\n",
"product" "product"
] ]

9
true-color-image/01d Brandenburg Mosaic.ipynb
View file

@ -2396,7 +2396,11 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# we create the directory structure we need for downloading our mosaic tiles\n", "# we create the directory structure we need for downloading our mosaic tiles\n",
"! mkdir -p input/raster/brandenburg_mosaic" "from pathlib import Path\n",
"from tqdm.notebook import tqdm\n",
"\n",
"output_dir = Path('input/brandenburg_mosaic')\n",
"output_dir.mkdir(exist_ok=True, parents=True)"
] ]
}, },
{ {
@ -2423,7 +2427,8 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"products_subset = {idx: products[idx] for idx in to_download.index}\n", "products_subset = {idx: products[idx] for idx in to_download.index}\n",
"downloaded, _, _ = api.download_all(products_subset, 'input/raster/brandenburg_mosaic')\n", "api._tqdm = tqdm # ← monkey-patch for nicer progress bar\n",
"downloaded, _, _ = api.download_all(products_subset, output_dir)\n",
"downloaded_paths = [d['path'] for d in downloaded.values()]" "downloaded_paths = [d['path'] for d in downloaded.values()]"
] ]
}, },

1
true-color-image/03b Forest Fires 2018 and 2019.ipynb
View file

@ -8,6 +8,7 @@
"- Instead of focusing on all fires in an entire year, we focus on several firest that were covered well\n", "- Instead of focusing on all fires in an entire year, we focus on several firest that were covered well\n",
"- → Jueterbog / Luckenwalde\n", "- → Jueterbog / Luckenwalde\n",
" - 2018/08/24: https://www.dw.com/en/forest-fires-near-berlin-could-burn-for-days/a-45203042\n", " - 2018/08/24: https://www.dw.com/en/forest-fires-near-berlin-could-burn-for-days/a-45203042\n",
" - Article says \"fires began on thursday\", i.e. 2018/08/23\n",
" - 2019\n", " - 2019\n",
" - Fire in June (\"einer der größten Brände der letzten Jahrzehnte\") https://web.archive.org/web/20190622070809/https://www.rbb24.de/panorama/beitrag/2019/06/waldbrand-jueterbog-sonntag-lage-unter-kontrolle.html\n", " - Fire in June (\"einer der größten Brände der letzten Jahrzehnte\") https://web.archive.org/web/20190622070809/https://www.rbb24.de/panorama/beitrag/2019/06/waldbrand-jueterbog-sonntag-lage-unter-kontrolle.html\n",
" - Another fire in July https://web.archive.org/web/20190730000919/https://www.rbb24.de/panorama/beitrag/2019/07/loescharbeiten-jueterbog-truppenuebungsplatz-brandenburg.html" " - Another fire in July https://web.archive.org/web/20190730000919/https://www.rbb24.de/panorama/beitrag/2019/07/loescharbeiten-jueterbog-truppenuebungsplatz-brandenburg.html"

610
true-color-image/03c dNBR.ipynb
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21
true-color-image/sentinel_helpers.py
View file

@ -1,3 +1,4 @@
from dateutil.parser import parse as parse_datetime
import urllib.parse import urllib.parse
from pathlib import Path from pathlib import Path
@ -22,7 +23,6 @@ import warnings
def search_osm(place): def search_osm(place):
''' '''
Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string. Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.
This allows us to fetch detailed geometries for virtually any place on earth.
''' '''
urlescaped_place = urllib.parse.quote(place) urlescaped_place = urllib.parse.quote(place)
search_url = ('https://nominatim.openstreetmap.org/search/?q={}' + search_url = ('https://nominatim.openstreetmap.org/search/?q={}' +
@ -90,15 +90,15 @@ def scihub_band_paths(p, bands, resolution=None):
rasters = p.glob('**/*.jp2') rasters = p.glob('**/*.jp2')
# take only the paths that contain one of the given bands # take only the paths that contain one of the given bands
rasters = [raster for band in bands for raster in rasters if band in raster] rasters = [Path(raster) for band in bands for raster in rasters if band in raster]
# if a resolution is given, further discard the bands we don't need # if a resolution is given, further discard the bands we don't need
if resolution: if resolution:
rasters = [raster for raster in rasters if resolution in raster] rasters = [raster for raster in rasters if resolution in raster.name]
if p.suffix == '.zip': if p.suffix == '.zip':
# we have to reformat the paths to # we have to reformat the paths to
rasters = [f'zip+file://{p}!/{r}' for r in rasters] rasters = [Path(f'zip+file://{p}!/{r.name}') for r in rasters]
return rasters return rasters
@ -179,6 +179,18 @@ def scihub_normalize_range(v):
''' '''
return np.clip(v, 0, 2000) / 2000 return np.clip(v, 0, 2000) / 2000
def scihub_band_date(band):
'''
Given a string, `pathlib.Path` or `rasterio.DataSetReader`, returns the
datetime encoded in the filename.
'''
if type(band) is r.DatasetReader:
file_name = band.name
else:
file_name = Path(band).name
return parse_datetime(file_name.split('_')[-3])
# TODO: This is documented somewhere in the python docs, we should link to it here # TODO: This is documented somewhere in the python docs, we should link to it here
@ -215,3 +227,4 @@ def geodataframe_on_map(geodataframe):
folium.GeoJson(geodataframe.to_json()).add_to(m) folium.GeoJson(geodataframe.to_json()).add_to(m)
m.fit_bounds([[miny, minx], [maxy, maxx]]) m.fit_bounds([[miny, minx], [maxy, maxx]])
return m return m