From c98d8f12bcd02b00b9f7a6d1d7128c46f6f7199d Mon Sep 17 00:00:00 2001 From: mathvansoest Date: Wed, 27 Nov 2024 16:28:51 +0100 Subject: [PATCH 1/9] Recoded the computation of the LAU_NUTS_CM.parquet file. --- notebooks/99_LAU_NUTS.ipynb | 418 +++++++----------------------------- 1 file changed, 79 insertions(+), 339 deletions(-) diff --git a/notebooks/99_LAU_NUTS.ipynb b/notebooks/99_LAU_NUTS.ipynb index 96c606721d..b90ea030eb 100644 --- a/notebooks/99_LAU_NUTS.ipynb +++ b/notebooks/99_LAU_NUTS.ipynb @@ -6482,7 +6482,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -6491,7 +6491,7 @@ "167957" ] }, - "execution_count": 14, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -6504,9 +6504,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "CRSError", + "evalue": "Invalid projection: ESPG:4326: (Internal Proj Error: proj_create: crs not found)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mCRSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[66], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Convert shapes to a GeoDataFrame\u001b[39;00m\n\u001b[0;32m 2\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m gpd\u001b[38;5;241m.\u001b[39mGeoDataFrame\u001b[38;5;241m.\u001b[39mfrom_features(fpgnzd)\n\u001b[1;32m----> 3\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m \u001b[43mshapes_gdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_crs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mESPG:4326\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m shapes_gdf\u001b[38;5;241m.\u001b[39mto_crs(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mESPG:3035\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\geopandas\\geodataframe.py:1339\u001b[0m, in \u001b[0;36mGeoDataFrame.set_crs\u001b[1;34m(self, crs, epsg, inplace, allow_override)\u001b[0m\n\u001b[0;32m 1337\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1338\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m-> 1339\u001b[0m df\u001b[38;5;241m.\u001b[39mgeometry \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_crs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1340\u001b[0m \u001b[43m \u001b[49m\u001b[43mcrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepsg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_override\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 1341\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df\n", + "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\geopandas\\geoseries.py:1037\u001b[0m, in \u001b[0;36mGeoSeries.set_crs\u001b[1;34m(self, crs, epsg, inplace, allow_override)\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;124;03mSet the Coordinate Reference System (CRS) of a ``GeoSeries``.\u001b[39;00m\n\u001b[0;32m 968\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1034\u001b[0m \n\u001b[0;32m 1035\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m crs \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[1;32m-> 1037\u001b[0m crs \u001b[38;5;241m=\u001b[39m \u001b[43mCRS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_user_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m epsg \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[0;32m 1039\u001b[0m crs \u001b[38;5;241m=\u001b[39m CRS\u001b[38;5;241m.\u001b[39mfrom_epsg(epsg)\n", + "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\crs\\crs.py:501\u001b[0m, in \u001b[0;36mCRS.from_user_input\u001b[1;34m(cls, value, **kwargs)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mcls\u001b[39m):\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m--> 501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\crs\\crs.py:348\u001b[0m, in \u001b[0;36mCRS.__init__\u001b[1;34m(self, projparams, **kwargs)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mcrs \u001b[38;5;241m=\u001b[39m projparams\n\u001b[0;32m 347\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 348\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mcrs \u001b[38;5;241m=\u001b[39m \u001b[43m_CRS\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msrs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\_crs.pyx:2378\u001b[0m, in \u001b[0;36mpyproj._crs._CRS.__init__\u001b[1;34m()\u001b[0m\n", + "\u001b[1;31mCRSError\u001b[0m: Invalid projection: ESPG:4326: (Internal Proj Error: proj_create: crs not found)" + ] + } + ], "source": [ "# Convert shapes to a GeoDataFrame\n", "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" @@ -6514,22 +6531,19 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")" + ] + }, + { + "cell_type": "code", + "execution_count": 72, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\soest\\AppData\\Local\\Temp\\ipykernel_62380\\1320939396.py:3: UserWarning: CRS mismatch between the CRS of left geometries and the CRS of right geometries.\n", - "Use `to_crs()` to reproject one of the input geometries to match the CRS of the other.\n", - "\n", - "Left CRS: EPSG:4326\n", - "Right CRS: None\n", - "\n", - " joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n" - ] - }, { "data": { "text/html": [ @@ -6581,7 +6595,7 @@ " 67.719735\n", " 2020\n", " DE_03361001\n", - " POLYGON ((9.07578 52.98510, 9.07958 52.99275, ...\n", + " POLYGON ((4258927.295 3320011.519, 4259193.583...\n", " 29151\n", " 1.0\n", " \n", @@ -6598,7 +6612,7 @@ " 38.448908\n", " 2020\n", " DE_03361002\n", - " POLYGON ((9.16004 52.96257, 9.17682 52.95673, ...\n", + " POLYGON ((4264556.862 3317435.819, 4265677.354...\n", " 29151\n", " 1.0\n", " \n", @@ -6615,7 +6629,7 @@ " 83.462333\n", " 2020\n", " DE_03361003\n", - " POLYGON ((9.32560 52.76771, 9.32330 52.77258, ...\n", + " POLYGON ((4275479.269 3295636.790, 4275328.930...\n", " 29151\n", " 1.0\n", " \n", @@ -6632,7 +6646,7 @@ " 21.349247\n", " 2020\n", " DE_03361004\n", - " POLYGON ((8.95213 52.88891, 8.95141 52.89676, ...\n", + " POLYGON ((4250468.027 3309420.666, 4250432.180...\n", " 29151\n", " 1.0\n", " \n", @@ -6649,7 +6663,7 @@ " 174.871744\n", " 2020\n", " DE_03361005\n", - " POLYGON ((9.27815 52.90163, 9.30342 52.91093, ...\n", + " POLYGON ((4272425.673 3310568.711, 4274136.202...\n", " 29151\n", " 1.0\n", " \n", @@ -6683,7 +6697,7 @@ " 18.356042\n", " 2020\n", " FR_97614\n", - " POLYGON ((45.13836 -12.86943, 45.13394 -12.867...\n", + " POLYGON ((8727685.023 -2785490.190, 8727139.84...\n", " 165557\n", " 1.0\n", " \n", @@ -6700,7 +6714,7 @@ " 11.016715\n", " 2020\n", " FR_97616\n", - " POLYGON ((45.13226 -12.88889, 45.11965 -12.877...\n", + " POLYGON ((8727114.505 -2787670.745, 8725517.75...\n", " 165557\n", " 1.0\n", " \n", @@ -6717,7 +6731,7 @@ " 34.284037\n", " 2020\n", " FR_97617\n", - " POLYGON ((45.17306 -12.76405, 45.17253 -12.772...\n", + " POLYGON ((8730964.436 -2773602.954, 8730972.75...\n", " 165557\n", " 1.0\n", " \n", @@ -6734,8 +6748,8 @@ " 6.372824\n", " 2020\n", " FR_97608\n", - " MULTIPOLYGON (((45.29645 -12.78407, 45.27135 -...\n", - " 165549\n", + " MULTIPOLYGON (((8745852.102 -2769772.861, 8742...\n", + " 165548\n", " 1.0\n", " \n", " \n", @@ -6751,7 +6765,7 @@ " 4.367692\n", " 2020\n", " FR_97615\n", - " POLYGON ((45.29645 -12.78407, 45.29559 -12.796...\n", + " POLYGON ((8745852.102 -2769772.861, 8745849.32...\n", " 165549\n", " 1.0\n", " \n", @@ -6788,29 +6802,29 @@ "97668 2619.690480 4.367692 2020 FR_97615 \n", "\n", " geometry index_right value \n", - "1192 POLYGON ((9.07578 52.98510, 9.07958 52.99275, ... 29151 1.0 \n", - "1193 POLYGON ((9.16004 52.96257, 9.17682 52.95673, ... 29151 1.0 \n", - "1195 POLYGON ((9.32560 52.76771, 9.32330 52.77258, ... 29151 1.0 \n", - "1196 POLYGON ((8.95213 52.88891, 8.95141 52.89676, ... 29151 1.0 \n", - "1197 POLYGON ((9.27815 52.90163, 9.30342 52.91093, ... 29151 1.0 \n", + "1192 POLYGON ((4258927.295 3320011.519, 4259193.583... 29151 1.0 \n", + "1193 POLYGON ((4264556.862 3317435.819, 4265677.354... 29151 1.0 \n", + "1195 POLYGON ((4275479.269 3295636.790, 4275328.930... 29151 1.0 \n", + "1196 POLYGON ((4250468.027 3309420.666, 4250432.180... 29151 1.0 \n", + "1197 POLYGON ((4272425.673 3310568.711, 4274136.202... 29151 1.0 \n", "... ... ... ... \n", - "97667 POLYGON ((45.13836 -12.86943, 45.13394 -12.867... 165557 1.0 \n", - "97669 POLYGON ((45.13226 -12.88889, 45.11965 -12.877... 165557 1.0 \n", - "97670 POLYGON ((45.17306 -12.76405, 45.17253 -12.772... 165557 1.0 \n", - "97660 MULTIPOLYGON (((45.29645 -12.78407, 45.27135 -... 165549 1.0 \n", - "97668 POLYGON ((45.29645 -12.78407, 45.29559 -12.796... 165549 1.0 \n", + "97667 POLYGON ((8727685.023 -2785490.190, 8727139.84... 165557 1.0 \n", + "97669 POLYGON ((8727114.505 -2787670.745, 8725517.75... 165557 1.0 \n", + "97670 POLYGON ((8730964.436 -2773602.954, 8730972.75... 165557 1.0 \n", + "97660 MULTIPOLYGON (((8745852.102 -2769772.861, 8742... 165548 1.0 \n", + "97668 POLYGON ((8745852.102 -2769772.861, 8745849.32... 165549 1.0 \n", "\n", "[11965 rows x 14 columns]" ] }, - "execution_count": 16, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Perform a spatial join to find intersections\n", - "LAU_NUTS_reproj = LAU_NUTS_data.to_crs(\"EPSG:4326\")\n", + "LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", "joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", "\n", "# remove columns added by shapes_gdf\n", @@ -6818,322 +6832,48 @@ "\n", "# Remove duplicate rows based on the index of gdf1\n", "LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", - "LAU_NUTS_CLEANCM" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write geodataframe to parquet\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "out_file= ds_dir.joinpath('LAU_NUTS_CFHP.parquet')\n", "\n", - "# altered_lau_data.to_parquet(out_file_altered)\n", - "LAU_NUTS_data.to_parquet(out_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'P:\\\\11207608-coclico\\\\FASTTRACK_DATA\\\\XX_NUTS\\\\NUTS_RG_01M_2021_3035.parquet'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nuts_parquet_file" + "# Remove extra columns resulting from the spatial join\n", + "LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "LAU_NUTS_CLEANCM\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 80, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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0CZ_579475CZ579475Libňatov363.062.2401585.8322472020CZ_579475b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00...
1CZ_579483CZ579483Libotov178.041.7747594.2609462020CZ_579483b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x08\\x00...
2CZ_579491CZ579491Vranov181.037.4088734.8384242020CZ_579491b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\n\\x00\\x...
3CZ_579505CZ579505Malá Úpa142.05.31851026.6992072020CZ_579505b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x17\\x00...
4AT_41004AT41004Eggendorf im Traunkreis1041.0116.4817478.9370232020AT_41004b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00...
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98608SK_599816SK599816Košice - mestská časť Nad jazerom24443.06686.2115513.6557322020SK_599816b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\t\\x00\\x...
98609SK_599824SK599824Košice - mestská časť Juh22692.02324.8819559.7604962020SK_599824b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x10\\x00...
98610SK_599841SK599841Košice - mestská časť Šaca5969.0124.67277747.8773332020SK_599841b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x1d\\x00...
98611SK_599859SK599859Košice - mestská časť Poľov1219.094.04951112.9612582020SK_599859b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0e\\x00...
98612SK_599875SK599875Košice - mestská časť Sídlisko Ťahanovce21936.02655.8225238.2595882020SK_599875b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x12\\x00...
\n", - "

98613 rows × 10 columns

\n", - "
" - ], - "text/plain": [ - " GISCO_ID CNTR_CODE LAU_ID LAU_NAME \\\n", - "0 CZ_579475 CZ 579475 Libňatov \n", - "1 CZ_579483 CZ 579483 Libotov \n", - "2 CZ_579491 CZ 579491 Vranov \n", - "3 CZ_579505 CZ 579505 Malá Úpa \n", - "4 AT_41004 AT 41004 Eggendorf im Traunkreis \n", - "... ... ... ... ... \n", - "98608 SK_599816 SK 599816 Košice - mestská časť Nad jazerom \n", - "98609 SK_599824 SK 599824 Košice - mestská časť Juh \n", - "98610 SK_599841 SK 599841 Košice - mestská časť Šaca \n", - "98611 SK_599859 SK 599859 Košice - mestská časť Poľov \n", - "98612 SK_599875 SK 599875 Košice - mestská časť Sídlisko Ťahanovce \n", - "\n", - " POP_2020 POP_DENS_2 AREA_KM2 YEAR FID \\\n", - "0 363.0 62.240158 5.832247 2020 CZ_579475 \n", - "1 178.0 41.774759 4.260946 2020 CZ_579483 \n", - "2 181.0 37.408873 4.838424 2020 CZ_579491 \n", - "3 142.0 5.318510 26.699207 2020 CZ_579505 \n", - "4 1041.0 116.481747 8.937023 2020 AT_41004 \n", - "... ... ... ... ... ... \n", - "98608 24443.0 6686.211551 3.655732 2020 SK_599816 \n", - "98609 22692.0 2324.881955 9.760496 2020 SK_599824 \n", - "98610 5969.0 124.672777 47.877333 2020 SK_599841 \n", - "98611 1219.0 94.049511 12.961258 2020 SK_599859 \n", - "98612 21936.0 2655.822523 8.259588 2020 SK_599875 \n", - "\n", - " geometry \n", - "0 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00... \n", - "1 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x08\\x00... \n", - "2 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\n\\x00\\x... \n", - "3 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x17\\x00... \n", - "4 b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0b\\x00... \n", - "... ... \n", - "98608 b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\t\\x00\\x... \n", - "98609 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x10\\x00... \n", - "98610 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x1d\\x00... \n", - "98611 b'\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x0e\\x00... \n", - "98612 b\"\\x01\\x03\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x12\\x00... \n", - "\n", - "[98613 rows x 10 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Hell yeah!\n" + ] } ], "source": [ - "# Test new parquet file\n", - "test_parquet = pd.read_parquet(lau_parquet_file)\n", - "test_parquet" + "# Check if its the same as original parquet is the same\n", + "test_parq = gpd.read_parquet(r\"p:\\11207608-coclico\\FULLTRACK_DATA\\WP4\\LAU_stats\\LAU_2020_NUTS_2021_01M_3035_CM_original.parquet\")\n", + "\n", + "if test_parq.equals(LAU_NUTS_CLEANCM):\n", + " print('Hell yeah!')" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 79, "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "import shapely\n", - "shapely.Polygon(LAU.geometry[0])" + "# Write geodataframe to parquet\n", + "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", + "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", + "out_file= ds_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", + "\n", + "# altered_lau_data.to_parquet(out_file_altered)\n", + "LAU_NUTS_CLEANCM.to_parquet(out_file)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 336bc48fc9f62aae2c3117955f4dff53146e6665 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 10:20:05 +0100 Subject: [PATCH 2/9] update to LAU NUTS --- notebooks/99_LAU_NUTS.ipynb | 125 +- notebooks/99_LAU_NUTS2.ipynb | 6889 ++++++++++++++++++++++++++++++++++ 2 files changed, 6946 insertions(+), 68 deletions(-) create mode 100644 notebooks/99_LAU_NUTS2.ipynb diff --git a/notebooks/99_LAU_NUTS.ipynb b/notebooks/99_LAU_NUTS.ipynb index b90ea030eb..863b1e2f8b 100644 --- a/notebooks/99_LAU_NUTS.ipynb +++ b/notebooks/99_LAU_NUTS.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -863,7 +863,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -6452,7 +6452,7 @@ "# Retrieve total bounds in CRS of coastal mask\n", "totalbbox = LAU_NUTS_data.to_crs(\"EPSG:4326\").total_bounds\n", "\n", - "# Polygonized, empty vecotr for storing the Coastal Mask Polygons\n", + "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", "pgnzd = []\n", "# read tif files in the coastal mask folder\n", "for idx, file in enumerate(os.listdir(folder_mask)):\n", @@ -6482,48 +6482,51 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "167957" + "1008" ] }, - "execution_count": 64, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# flatten the list\n", - "fpgnzd = list(itertools.chain(*pgnzd))\n", - "len(fpgnzd)" + "len(pgnzd)" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 84, "metadata": {}, "outputs": [ { - "ename": "CRSError", - "evalue": "Invalid projection: ESPG:4326: (Internal Proj Error: proj_create: crs not found)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mCRSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[66], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m# Convert shapes to a GeoDataFrame\u001b[39;00m\n\u001b[0;32m 2\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m gpd\u001b[38;5;241m.\u001b[39mGeoDataFrame\u001b[38;5;241m.\u001b[39mfrom_features(fpgnzd)\n\u001b[1;32m----> 3\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m \u001b[43mshapes_gdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_crs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mESPG:4326\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m shapes_gdf \u001b[38;5;241m=\u001b[39m shapes_gdf\u001b[38;5;241m.\u001b[39mto_crs(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mESPG:3035\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\geopandas\\geodataframe.py:1339\u001b[0m, in \u001b[0;36mGeoDataFrame.set_crs\u001b[1;34m(self, crs, epsg, inplace, allow_override)\u001b[0m\n\u001b[0;32m 1337\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1338\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m-> 1339\u001b[0m df\u001b[38;5;241m.\u001b[39mgeometry \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_crs\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1340\u001b[0m \u001b[43m \u001b[49m\u001b[43mcrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepsg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepsg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_override\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 1341\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1342\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m df\n", - "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\geopandas\\geoseries.py:1037\u001b[0m, in \u001b[0;36mGeoSeries.set_crs\u001b[1;34m(self, crs, epsg, inplace, allow_override)\u001b[0m\n\u001b[0;32m 966\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 967\u001b[0m \u001b[38;5;124;03mSet the Coordinate Reference System (CRS) of a ``GeoSeries``.\u001b[39;00m\n\u001b[0;32m 968\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1034\u001b[0m \n\u001b[0;32m 1035\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1036\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m crs \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[1;32m-> 1037\u001b[0m crs \u001b[38;5;241m=\u001b[39m \u001b[43mCRS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_user_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m epsg \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[0;32m 1039\u001b[0m crs \u001b[38;5;241m=\u001b[39m CRS\u001b[38;5;241m.\u001b[39mfrom_epsg(epsg)\n", - "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\crs\\crs.py:501\u001b[0m, in \u001b[0;36mCRS.from_user_input\u001b[1;34m(cls, value, **kwargs)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mcls\u001b[39m):\n\u001b[0;32m 500\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m value\n\u001b[1;32m--> 501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\crs\\crs.py:348\u001b[0m, in \u001b[0;36mCRS.__init__\u001b[1;34m(self, projparams, **kwargs)\u001b[0m\n\u001b[0;32m 346\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mcrs \u001b[38;5;241m=\u001b[39m projparams\n\u001b[0;32m 347\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 348\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mcrs \u001b[38;5;241m=\u001b[39m \u001b[43m_CRS\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msrs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pyproj\\_crs.pyx:2378\u001b[0m, in \u001b[0;36mpyproj._crs._CRS.__init__\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mCRSError\u001b[0m: Invalid projection: ESPG:4326: (Internal Proj Error: proj_create: crs not found)" - ] + "data": { + "text/plain": [ + "167957" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" } ], + "source": [ + "# flatten the list\n", + "fpgnzd = list(itertools.chain(*pgnzd))\n", + "len(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], "source": [ "# Convert shapes to a GeoDataFrame\n", "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" @@ -6531,17 +6534,20 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", - "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")" + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", + "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", + "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", + "shapes_gdf.to_parquet(ds_dir.joinpath('CoastalMask_3035_polygonized.parquet'))" ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -6577,8 +6583,6 @@ " YEAR\n", " FID\n", " geometry\n", - " index_right\n", - " value\n", " \n", " \n", " \n", @@ -6596,8 +6600,6 @@ " 2020\n", " DE_03361001\n", " POLYGON ((4258927.295 3320011.519, 4259193.583...\n", - " 29151\n", - " 1.0\n", " \n", " \n", " 1193\n", @@ -6613,8 +6615,6 @@ " 2020\n", " DE_03361002\n", " POLYGON ((4264556.862 3317435.819, 4265677.354...\n", - " 29151\n", - " 1.0\n", " \n", " \n", " 1195\n", @@ -6630,8 +6630,6 @@ " 2020\n", " DE_03361003\n", " POLYGON ((4275479.269 3295636.790, 4275328.930...\n", - " 29151\n", - " 1.0\n", " \n", " \n", " 1196\n", @@ -6647,8 +6645,6 @@ " 2020\n", " DE_03361004\n", " POLYGON ((4250468.027 3309420.666, 4250432.180...\n", - " 29151\n", - " 1.0\n", " \n", " \n", " 1197\n", @@ -6664,8 +6660,6 @@ " 2020\n", " DE_03361005\n", " POLYGON ((4272425.673 3310568.711, 4274136.202...\n", - " 29151\n", - " 1.0\n", " \n", " \n", " ...\n", @@ -6681,8 +6675,6 @@ " ...\n", " ...\n", " ...\n", - " ...\n", - " ...\n", " \n", " \n", " 97667\n", @@ -6698,8 +6690,6 @@ " 2020\n", " FR_97614\n", " POLYGON ((8727685.023 -2785490.190, 8727139.84...\n", - " 165557\n", - " 1.0\n", " \n", " \n", " 97669\n", @@ -6715,8 +6705,6 @@ " 2020\n", " FR_97616\n", " POLYGON ((8727114.505 -2787670.745, 8725517.75...\n", - " 165557\n", - " 1.0\n", " \n", " \n", " 97670\n", @@ -6732,8 +6720,6 @@ " 2020\n", " FR_97617\n", " POLYGON ((8730964.436 -2773602.954, 8730972.75...\n", - " 165557\n", - " 1.0\n", " \n", " \n", " 97660\n", @@ -6749,8 +6735,6 @@ " 2020\n", " FR_97608\n", " MULTIPOLYGON (((8745852.102 -2769772.861, 8742...\n", - " 165548\n", - " 1.0\n", " \n", " \n", " 97668\n", @@ -6766,12 +6750,10 @@ " 2020\n", " FR_97615\n", " POLYGON ((8745852.102 -2769772.861, 8745849.32...\n", - " 165549\n", - " 1.0\n", " \n", " \n", "\n", - "

11965 rows × 14 columns

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11965 rows × 12 columns

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NUTS_IDLEVL_CODECNTR_CODENAME_LATNNUTS_NAMEMOUNT_TYPEURBN_TYPECOAST_TYPEgeometry
0AL0ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
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3DK0DKDanmarkDanmark0.000MULTIPOLYGON (((4650502.736 3591342.844, 46503...
4CY0CYb'4bfd70726f73'??????0.000MULTIPOLYGON (((6527040.718 1762367.593, 65267...
..............................
2005NO0B13NOJan MayenJan Mayen3.031POLYGON ((3623747.621 5400386.841, 3624031.138...
2006EE0093EEKesk-EestiKesk-Eesti4.031MULTIPOLYGON (((5216227.688 4159212.769, 52172...
2007NO01NONorgeNorge0.000MULTIPOLYGON (((4961367.759 5413266.131, 49622...
2008NO0B2NOJan Mayen and SvalbardJan Mayen and SvalbardNaN00MULTIPOLYGON (((4744650.828 6379141.635, 47446...
2009NO0B23NOSvalbardSvalbard3.031MULTIPOLYGON (((4760536.161 6404487.632, 47607...
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4AT_41004ATAT31AT31241004Eggendorf im Traunkreis1041.0116.4817478.9370232020AT_41004POLYGON ((4631543.792 2788412.230, 4630476.583...
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98608SK_599816SKSK04SK042599816Košice - mestská časť Nad jazerom24443.06686.2115513.6557322020SK_599816POLYGON ((5149120.507 2905279.350, 5150154.014...
98609SK_599824SKSK04SK042599824Košice - mestská časť Juh22692.02324.8819559.7604962020SK_599824POLYGON ((5144451.048 2904986.166, 5144591.047...
98610SK_599841SKSK04SK042599841Košice - mestská časť Šaca5969.0124.67277747.8773332020SK_599841POLYGON ((5141041.890 2900350.394, 5144604.564...
98611SK_599859SKSK04SK042599859Košice - mestská časť Poľov1219.094.04951112.9612582020SK_599859POLYGON ((5141041.890 2900350.394, 5141299.765...
98612SK_599875SKSK04SK042599875Košice - mestská časť Sídlisko Ťahanovce21936.02655.8225238.2595882020SK_599875POLYGON ((5147879.755 2916088.650, 5147433.909...
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mestská časť Poľov 1219.0 94.049511 \n", + "98612 Košice - mestská časť Sídlisko Ťahanovce 21936.0 2655.822523 \n", + "\n", + " AREA_KM2 YEAR FID \\\n", + "0 5.832247 2020 CZ_579475 \n", + "1 4.260946 2020 CZ_579483 \n", + "2 4.838424 2020 CZ_579491 \n", + "3 26.699207 2020 CZ_579505 \n", + "4 8.937023 2020 AT_41004 \n", + "... ... ... ... \n", + "98608 3.655732 2020 SK_599816 \n", + "98609 9.760496 2020 SK_599824 \n", + "98610 47.877333 2020 SK_599841 \n", + "98611 12.961258 2020 SK_599859 \n", + "98612 8.259588 2020 SK_599875 \n", + "\n", + " geometry \n", + "0 POLYGON ((4747705.585 3057545.260, 4746836.524... \n", + "1 POLYGON ((4731662.418 3049054.578, 4732297.093... \n", + "2 POLYGON ((4540137.623 2963864.496, 4538706.113... \n", + "3 POLYGON ((4730399.261 3087673.334, 4730914.253... \n", + "4 POLYGON ((4631543.792 2788412.230, 4630476.583... \n", + "... ... \n", + "98608 POLYGON ((5149120.507 2905279.350, 5150154.014... \n", + "98609 POLYGON ((5144451.048 2904986.166, 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"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-33.23_y44.04.tif\n", + "4539\n", + "4540\n", + "4541\n", + "4542\n", + "4543\n", + "4544\n", + "4545\n", + "4546\n", + "4547\n", + "4548\n", + "4549\n", + "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x7.73_y-3.75.tif\n", + "4550\n", + "4551\n", + "4552\n", + "4553\n", + "4554\n", + "4555\n", + "4556\n", + "4557\n", + "4558\n", + "4559\n", + "4560\n", + "4561\n" + ] + } + ], + "source": [ + "# After LAU and NUTS are match we will only select the LAU's that overlap with the Coastal Mask\n", + "\n", + "# masking the data with the Coastal Mask (takes abou 45 min)\n", + "folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", + "\n", + "# Retrieve total bounds in CRS of coastal mask\n", + "totalbbox = LAU_NUTS_data.to_crs(\"EPSG:4326\").total_bounds\n", + "\n", + "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", + "pgnzd = []\n", + "# read tif files in the coastal mask folder\n", + "for idx, file in enumerate(os.listdir(folder_mask)):\n", + " print(idx)\n", + " if file.endswith(\".tif\"):\n", + " img = xr.open_dataset(os.path.join(folder_mask, file), engine=\"rasterio\", mask_and_scale=False) \n", + " if geometry.box(*img.rio.bounds()).intersects(geometry.box(*totalbbox)): # only do the ones in the LAU_NUTS bbox --> speed up to 17 min\n", + " print(os.path.join(folder_mask, file))\n", + "\n", + " # get the data\n", + " data_array = img.band_data.values\n", + " mask = data_array == 1\n", + " \n", + " # Split the string into a list of values and correct the gdal affine transformation\n", + " geotrans = list(map(float, img.spatial_ref.GeoTransform.split(' ')))\n", + " afn = Affine.from_gdal(*geotrans)\n", + "\n", + " # Extract shapes\n", + " results = (\n", + " {'properties': {'value': v}, 'geometry': s}\n", + " for i, (s, v) in enumerate(\n", + " shapes(data_array, mask=mask, transform=afn))\n", + " )\n", + "\n", + " pgnzd.append(list(results))" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1008" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "167957" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# flatten the list\n", + "fpgnzd = list(itertools.chain(*pgnzd))\n", + "len(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert shapes to a GeoDataFrame\n", + "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", + "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", + "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", + "shapes_gdf.to_parquet(ds_dir.joinpath('CoastalMask_3035_polygonized.parquet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GISCO_IDCNTR_CODEnuts_2nuts_3LAU_IDLAU_NAMEPOP_2020POP_DENS_2AREA_KM2YEARFIDgeometry
1192DE_03361001DEDE93DE93B03361001Achim, Stadt31923.0471.39877167.7197352020DE_03361001POLYGON ((4258927.295 3320011.519, 4259193.583...
1193DE_03361002DEDE93DE93B03361002Blender2885.075.03464038.4489082020DE_03361002POLYGON ((4264556.862 3317435.819, 4265677.354...
1195DE_03361003DEDE93DE93B03361003Dörverden9009.0107.94090883.4623332020DE_03361003POLYGON ((4275479.269 3295636.790, 4275328.930...
1196DE_03361004DEDE93DE93B03361004Emtinghausen1464.068.57384621.3492472020DE_03361004POLYGON ((4250468.027 3309420.666, 4250432.180...
1197DE_03361005DEDE93DE93B03361005Kirchlinteln9911.056.675823174.8717442020DE_03361005POLYGON ((4272425.673 3310568.711, 4274136.202...
.......................................
97667FR_97614FRFRY5FRY5097614Ouangani10203.0555.83878618.3560422020FR_97614POLYGON ((8727685.023 -2785490.190, 8727139.84...
97669FR_97616FRFRY5FRY5097616Sada11156.01012.64308611.0167152020FR_97616POLYGON ((8727114.505 -2787670.745, 8725517.75...
97670FR_97617FRFRY5FRY5097617Tsingoni13934.0406.42821334.2840372020FR_97617POLYGON ((8730964.436 -2773602.954, 8730972.75...
97660FR_97608FRFRY5FRY5097608Dzaoudzi17831.02797.9745666.3728242020FR_97608MULTIPOLYGON (((8745852.102 -2769772.861, 8742...
97668FR_97615FRFRY5FRY5097615Pamandzi11442.02619.6904804.3676922020FR_97615POLYGON ((8745852.102 -2769772.861, 8745849.32...
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11965 rows × 12 columns

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" + ], + "text/plain": [ + " GISCO_ID CNTR_CODE nuts_2 nuts_3 LAU_ID LAU_NAME POP_2020 \\\n", + "1192 DE_03361001 DE DE93 DE93B 03361001 Achim, Stadt 31923.0 \n", + "1193 DE_03361002 DE DE93 DE93B 03361002 Blender 2885.0 \n", + "1195 DE_03361003 DE DE93 DE93B 03361003 Dörverden 9009.0 \n", + "1196 DE_03361004 DE DE93 DE93B 03361004 Emtinghausen 1464.0 \n", + "1197 DE_03361005 DE DE93 DE93B 03361005 Kirchlinteln 9911.0 \n", + "... ... ... ... ... ... ... ... \n", + "97667 FR_97614 FR FRY5 FRY50 97614 Ouangani 10203.0 \n", + "97669 FR_97616 FR FRY5 FRY50 97616 Sada 11156.0 \n", + "97670 FR_97617 FR FRY5 FRY50 97617 Tsingoni 13934.0 \n", + "97660 FR_97608 FR FRY5 FRY50 97608 Dzaoudzi 17831.0 \n", + "97668 FR_97615 FR FRY5 FRY50 97615 Pamandzi 11442.0 \n", + "\n", + " POP_DENS_2 AREA_KM2 YEAR FID \\\n", + "1192 471.398771 67.719735 2020 DE_03361001 \n", + "1193 75.034640 38.448908 2020 DE_03361002 \n", + "1195 107.940908 83.462333 2020 DE_03361003 \n", + "1196 68.573846 21.349247 2020 DE_03361004 \n", + "1197 56.675823 174.871744 2020 DE_03361005 \n", + "... ... ... ... ... \n", + "97667 555.838786 18.356042 2020 FR_97614 \n", + "97669 1012.643086 11.016715 2020 FR_97616 \n", + "97670 406.428213 34.284037 2020 FR_97617 \n", + "97660 2797.974566 6.372824 2020 FR_97608 \n", + "97668 2619.690480 4.367692 2020 FR_97615 \n", + "\n", + " geometry \n", + "1192 POLYGON ((4258927.295 3320011.519, 4259193.583... \n", + "1193 POLYGON ((4264556.862 3317435.819, 4265677.354... \n", + "1195 POLYGON ((4275479.269 3295636.790, 4275328.930... \n", + "1196 POLYGON ((4250468.027 3309420.666, 4250432.180... \n", + "1197 POLYGON ((4272425.673 3310568.711, 4274136.202... \n", + "... ... \n", + "97667 POLYGON ((8727685.023 -2785490.190, 8727139.84... \n", + "97669 POLYGON ((8727114.505 -2787670.745, 8725517.75... \n", + "97670 POLYGON ((8730964.436 -2773602.954, 8730972.75... \n", + "97660 MULTIPOLYGON (((8745852.102 -2769772.861, 8742... \n", + "97668 POLYGON ((8745852.102 -2769772.861, 8745849.32... \n", + "\n", + "[11965 rows x 12 columns]" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Perform a spatial join to find intersections\n", + "LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", + "joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", + "\n", + "# remove columns added by shapes_gdf\n", + "joined_gdf.drop(columns=['index_right', 'value'])\n", + "\n", + "# Remove duplicate rows based on the index of gdf1\n", + "LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", + "\n", + "# Remove extra columns resulting from the spatial join\n", + "LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "LAU_NUTS_CLEANCM\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hell yeah!\n" + ] + } + ], + "source": [ + "# Check if its the same as original parquet is the same\n", + "test_parq = gpd.read_parquet(r\"p:\\11207608-coclico\\FULLTRACK_DATA\\WP4\\LAU_stats\\LAU_2020_NUTS_2021_01M_3035_CM_original.parquet\")\n", + "\n", + "if test_parq.equals(LAU_NUTS_CLEANCM):\n", + " print('Hell yeah!')" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "# Write geodataframe to parquet\n", + "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", + "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", + "out_file= ds_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", + "\n", + "# altered_lau_data.to_parquet(out_file_altered)\n", + "LAU_NUTS_CLEANCM.to_parquet(out_file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 637827e74eeae4fc27e522c1e6953dea838486d5 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 10:35:41 +0100 Subject: [PATCH 3/9] Update 99_LAU_NUTS.ipynb --- notebooks/99_LAU_NUTS.ipynb | 6191 ++++++++++++++++++++++++++++++++++- 1 file changed, 6174 insertions(+), 17 deletions(-) diff --git a/notebooks/99_LAU_NUTS.ipynb b/notebooks/99_LAU_NUTS.ipynb index 863b1e2f8b..955de50368 100644 --- a/notebooks/99_LAU_NUTS.ipynb +++ b/notebooks/99_LAU_NUTS.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -305,7 +305,7 @@ "[98613 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -532,7 +532,7 @@ "[2010 rows x 9 columns]" ] }, - "execution_count": 9, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -543,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -557,7 +557,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +587,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -852,7 +852,7 @@ "[98613 rows x 12 columns]" ] }, - "execution_count": 8, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -6446,7 +6446,7 @@ "source": [ "# After LAU and NUTS are match we will only select the LAU's that overlap with the Coastal Mask\n", "\n", - "# masking the data with the Coastal Mask (takes abou 45 min)\n", + "# masking the data with the Coastal Mask (takes about 45 min)\n", "folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", "\n", "# Retrieve total bounds in CRS of coastal mask\n", @@ -6823,7 +6823,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -6857,12 +6857,6169 @@ "LAU_NUTS_CLEANCM.to_parquet(out_file)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work on masking the NUTS data " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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NUTS_IDLEVL_CODECNTR_CODENAME_LATNNUTS_NAMEMOUNT_TYPEURBN_TYPECOAST_TYPEgeometry
0AL0ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
1CZ0CZ?esko?esko0.000POLYGON ((4624843.654 3112209.741, 4625546.618...
2DE0DEDeutschlandDeutschland0.000MULTIPOLYGON (((4355225.365 2715902.993, 43541...
3DK0DKDanmarkDanmark0.000MULTIPOLYGON (((4650502.736 3591342.844, 46503...
4CY0CYb'4bfd70726f73'??????0.000MULTIPOLYGON (((6527040.718 1762367.593, 65267...
..............................
2005NO0B13NOJan MayenJan Mayen3.031POLYGON ((3623747.621 5400386.841, 3624031.138...
2006EE0093EEKesk-EestiKesk-Eesti4.031MULTIPOLYGON (((5216227.688 4159212.769, 52172...
2007NO01NONorgeNorge0.000MULTIPOLYGON (((4961367.759 5413266.131, 49622...
2008NO0B2NOJan Mayen and SvalbardJan Mayen and SvalbardNaN00MULTIPOLYGON (((4744650.828 6379141.635, 47446...
2009NO0B23NOSvalbardSvalbard3.031MULTIPOLYGON (((4760536.161 6404487.632, 47607...
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2010 rows × 9 columns

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" + ], + "text/plain": [ + " NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN \\\n", + "0 AL 0 AL b'5368716970eb726961' \n", + "1 CZ 0 CZ ?esko \n", + "2 DE 0 DE Deutschland \n", + "3 DK 0 DK Danmark \n", + "4 CY 0 CY b'4bfd70726f73' \n", + "... ... ... ... ... \n", + "2005 NO0B1 3 NO Jan Mayen \n", + "2006 EE009 3 EE Kesk-Eesti \n", + "2007 NO0 1 NO Norge \n", + "2008 NO0B 2 NO Jan Mayen and Svalbard \n", + "2009 NO0B2 3 NO Svalbard \n", + "\n", + " NUTS_NAME MOUNT_TYPE URBN_TYPE COAST_TYPE \\\n", + "0 b'5368716970eb726961' 0.0 0 0 \n", + "1 ?esko 0.0 0 0 \n", + "2 Deutschland 0.0 0 0 \n", + "3 Danmark 0.0 0 0 \n", + "4 ?????? 0.0 0 0 \n", + "... ... ... ... ... \n", + "2005 Jan Mayen 3.0 3 1 \n", + "2006 Kesk-Eesti 4.0 3 1 \n", + "2007 Norge 0.0 0 0 \n", + "2008 Jan Mayen and Svalbard NaN 0 0 \n", + "2009 Svalbard 3.0 3 1 \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", + "1 POLYGON ((4624843.654 3112209.741, 4625546.618... \n", + "2 MULTIPOLYGON (((4355225.365 2715902.993, 43541... \n", + "3 MULTIPOLYGON (((4650502.736 3591342.844, 46503... \n", + "4 MULTIPOLYGON (((6527040.718 1762367.593, 65267... \n", + "... ... \n", + "2005 POLYGON ((3623747.621 5400386.841, 3624031.138... \n", + "2006 MULTIPOLYGON (((5216227.688 4159212.769, 52172... \n", + "2007 MULTIPOLYGON (((4961367.759 5413266.131, 49622... \n", + "2008 MULTIPOLYGON (((4744650.828 6379141.635, 47446... \n", + "2009 MULTIPOLYGON (((4760536.161 6404487.632, 47607... \n", + "\n", + "[2010 rows x 9 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# shapefile\n", + "NUTS\n", + "\n", + "# open parquet file with geopandas\n", + "NUTS_parq = gpd.read_parquet(nuts_parquet_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + 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"folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", + "\n", + "# Retrieve total bounds in CRS of coastal mask\n", + "totalbbox = NUTS.to_crs(\"EPSG:4326\").total_bounds\n", + "\n", + "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", + "pgnzd = []\n", + "# read tif files in the coastal mask folder\n", + "for idx, file in enumerate(os.listdir(folder_mask)):\n", + " print(idx)\n", + " if file.endswith(\".tif\"):\n", + " img = xr.open_dataset(os.path.join(folder_mask, file), engine=\"rasterio\", mask_and_scale=False) \n", + " if geometry.box(*img.rio.bounds()).intersects(geometry.box(*totalbbox)): # only do the ones in the NUTS bbox --> speed up to 17 min\n", + " print(os.path.join(folder_mask, file))\n", + "\n", + " # get the data\n", + " data_array = img.band_data.values\n", + " mask = data_array == 1\n", + " \n", + " # Split the string into a list of values and correct the gdal affine transformation\n", + " geotrans = list(map(float, img.spatial_ref.GeoTransform.split(' ')))\n", + " afn = Affine.from_gdal(*geotrans)\n", + "\n", + " # Extract shapes\n", + " results = (\n", + " {'properties': {'value': v}, 'geometry': s}\n", + " for i, (s, v) in enumerate(\n", + " shapes(data_array, mask=mask, transform=afn))\n", + " )\n", + "\n", + " pgnzd.append(list(results))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1116" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(pgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "170926" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# flatten the list\n", + "fpgnzd = list(itertools.chain(*pgnzd))\n", + "len(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert shapes to a GeoDataFrame\n", + "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " geometry value\n", + "0 POLYGON ((55.44542 74.41708, 55.44542 74.41625... 1.0\n", + "1 POLYGON ((55.12125 74.22875, 55.12125 74.22708... 1.0\n", + "2 POLYGON ((55.27875 74.29292, 55.27875 74.29208... 1.0\n", + "3 POLYGON ((55.32542 74.16208, 55.32542 74.16125... 1.0\n", + "4 POLYGON ((55.44792 74.13875, 55.44792 74.13792... 1.0\n", + "... ... ...\n", + "170921 POLYGON ((11.12875 -3.66458, 11.12875 -3.66542... 1.0\n", + "170922 POLYGON ((11.13042 -3.66458, 11.13042 -3.66542... 1.0\n", + "170923 POLYGON ((11.13625 -3.73208, 11.13625 -3.73292... 1.0\n", + "170924 POLYGON ((9.30208 -0.33375, 9.30208 -0.33875, ... 1.0\n", + "170925 POLYGON ((10.66292 -3.38625, 10.66292 -3.39375... 1.0\n", + "\n", + "[170926 rows x 2 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shapes_gdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Perform a spatial join to find intersections\n", + "# LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", + "# joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", + "\n", + "# # remove columns added by shapes_gdf\n", + "# joined_gdf.drop(columns=['index_right', 'value'])\n", + "\n", + "# # Remove duplicate rows based on the index of gdf1\n", + "# LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", + "\n", + "# # Remove extra columns resulting from the spatial join\n", + "# LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "# LAU_NUTS_CLEANCM" + ] } ], "metadata": { From 23e1c0a50e976993aea4bacdcdfc408382ccc747 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:20:39 +0100 Subject: [PATCH 4/9] update LAU and NUTS mask NUTS area with CM as well --- notebooks/99_LAU_NUTS.ipynb | 839 +++-- notebooks/99_LAU_NUTS2.ipynb | 6889 ---------------------------------- 2 files changed, 508 insertions(+), 7220 deletions(-) delete mode 100644 notebooks/99_LAU_NUTS2.ipynb diff --git a/notebooks/99_LAU_NUTS.ipynb b/notebooks/99_LAU_NUTS.ipynb index 955de50368..cf6eeb92ff 100644 --- a/notebooks/99_LAU_NUTS.ipynb +++ b/notebooks/99_LAU_NUTS.ipynb @@ -9,9 +9,24 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_14636\\3768201458.py:12: DeprecationWarning: Shapely 2.0 is installed, but because PyGEOS is also installed, GeoPandas still uses PyGEOS by default. However, starting with version 0.14, the default will switch to Shapely. To force to use Shapely 2.0 now, you can either uninstall PyGEOS or set the environment variable USE_PYGEOS=0. You can do this before starting the Python process, or in your code before importing geopandas:\n", + "\n", + "import os\n", + "os.environ['USE_PYGEOS'] = '0'\n", + "import geopandas\n", + "\n", + "In the next release, GeoPandas will switch to using Shapely by default, even if PyGEOS is installed. If you only have PyGEOS installed to get speed-ups, this switch should be smooth. However, if you are using PyGEOS directly (calling PyGEOS functions on geometries from GeoPandas), this will then stop working and you are encouraged to migrate from PyGEOS to Shapely 2.0 (https://shapely.readthedocs.io/en/latest/migration_pygeos.html).\n", + " import geopandas as gpd\n" + ] + } + ], "source": [ "# Load software\n", "import os\n", @@ -43,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -67,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -77,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -305,7 +320,7 @@ "[98613 rows x 10 columns]" ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -316,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -532,7 +547,7 @@ "[2010 rows x 9 columns]" ] }, - "execution_count": 26, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -543,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -555,9 +570,16 @@ "NUTS.to_parquet(nuts_parquet_file)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work on masking the LAU data " + ] + }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -587,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -852,13 +874,14 @@ "[98613 rows x 12 columns]" ] }, - "execution_count": 29, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "LAU_NUTS_data" + "LAU_NUTS_data\n", + "LAU_NUTS_data.to_parquet(LAU_dir.join_path('LAU_2020_NUTS_2021_01M_3035.parquet'))" ] }, { @@ -6540,9 +6563,7 @@ "source": [ "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "shapes_gdf.to_parquet(ds_dir.joinpath('CoastalMask_3035_polygonized.parquet'))" + "shapes_gdf.to_parquet(LAU_dir.joinpath('CoastalMask_3035_polygonized_LAU.parquet'))" ] }, { @@ -6821,27 +6842,6 @@ "LAU_NUTS_CLEANCM\n" ] }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hell yeah!\n" - ] - } - ], - "source": [ - "# Check if its the same as original parquet is the same\n", - "test_parq = gpd.read_parquet(r\"p:\\11207608-coclico\\FULLTRACK_DATA\\WP4\\LAU_stats\\LAU_2020_NUTS_2021_01M_3035_CM_original.parquet\")\n", - "\n", - "if test_parq.equals(LAU_NUTS_CLEANCM):\n", - " print('Hell yeah!')" - ] - }, { "cell_type": "code", "execution_count": 89, @@ -6849,9 +6849,7 @@ "outputs": [], "source": [ "# Write geodataframe to parquet\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "out_file= ds_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", + "out_file= LAU_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", "\n", "# altered_lau_data.to_parquet(out_file_altered)\n", "LAU_NUTS_CLEANCM.to_parquet(out_file)" @@ -6866,227 +6864,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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4CY0CYb'4bfd70726f73'??????0.000MULTIPOLYGON (((6527040.718 1762367.593, 65267...
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2005NO0B13NOJan MayenJan Mayen3.031POLYGON ((3623747.621 5400386.841, 3624031.138...
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2009NO0B23NOSvalbardSvalbard3.031MULTIPOLYGON (((4760536.161 6404487.632, 47607...
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2010 rows × 9 columns

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" - ], - "text/plain": [ - " NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN \\\n", - "0 AL 0 AL b'5368716970eb726961' \n", - "1 CZ 0 CZ ?esko \n", - "2 DE 0 DE Deutschland \n", - "3 DK 0 DK Danmark \n", - "4 CY 0 CY b'4bfd70726f73' \n", - "... ... ... ... ... \n", - "2005 NO0B1 3 NO Jan Mayen \n", - "2006 EE009 3 EE Kesk-Eesti \n", - "2007 NO0 1 NO Norge \n", - "2008 NO0B 2 NO Jan Mayen and Svalbard \n", - "2009 NO0B2 3 NO Svalbard \n", - "\n", - " NUTS_NAME MOUNT_TYPE URBN_TYPE COAST_TYPE \\\n", - "0 b'5368716970eb726961' 0.0 0 0 \n", - "1 ?esko 0.0 0 0 \n", - "2 Deutschland 0.0 0 0 \n", - "3 Danmark 0.0 0 0 \n", - "4 ?????? 0.0 0 0 \n", - "... ... ... ... ... \n", - "2005 Jan Mayen 3.0 3 1 \n", - "2006 Kesk-Eesti 4.0 3 1 \n", - "2007 Norge 0.0 0 0 \n", - "2008 Jan Mayen and Svalbard NaN 0 0 \n", - "2009 Svalbard 3.0 3 1 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", - "1 POLYGON ((4624843.654 3112209.741, 4625546.618... \n", - "2 MULTIPOLYGON (((4355225.365 2715902.993, 43541... \n", - "3 MULTIPOLYGON (((4650502.736 3591342.844, 46503... \n", - "4 MULTIPOLYGON (((6527040.718 1762367.593, 65267... \n", - "... ... \n", - "2005 POLYGON ((3623747.621 5400386.841, 3624031.138... \n", - "2006 MULTIPOLYGON (((5216227.688 4159212.769, 52172... \n", - "2007 MULTIPOLYGON (((4961367.759 5413266.131, 49622... \n", - "2008 MULTIPOLYGON (((4744650.828 6379141.635, 47446... \n", - "2009 MULTIPOLYGON (((4760536.161 6404487.632, 47607... \n", - "\n", - "[2010 rows x 9 columns]" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# shapefile\n", "NUTS\n", @@ -7097,7 +6877,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -12822,7 +12602,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -12831,7 +12611,7 @@ "1116" ] }, - "execution_count": 41, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -12842,7 +12622,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -12851,7 +12631,7 @@ "170926" ] }, - "execution_count": 42, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -12864,7 +12644,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -12874,11 +12654,22 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 18, "metadata": {}, - "outputs": [ - { - "data": { + "outputs": [], + "source": [ + "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", + "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", + "shapes_gdf.to_parquet(NUTS_dir.joinpath('CoastalMask_3035_polygonized_NUTS.parquet'))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { "text/html": [ "
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NUTS_IDLEVL_CODECNTR_CODENAME_LATNNUTS_NAMEMOUNT_TYPEURBN_TYPECOAST_TYPEgeometry
0AL0ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
49AL01ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
153AL032ALJugJug0.000MULTIPOLYGON (((5211962.696 2061668.541, 52149...
363AL0353ALb'566c6f72eb'b'566c6f72eb'4.021MULTIPOLYGON (((5117808.521 1998738.510, 51189...
10EL0ELb'456c6ce16461'??????0.000MULTIPOLYGON (((6083864.252 1676459.388, 60836...
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328PT202PTb'52656769e36f20417574f36e6f6d6120646f732041e7...b'52656769e36f20417574f36e6f6d6120646f732041e7...0.000MULTIPOLYGON (((1282493.513 2380255.965, 12840...
1825PT2003PTb'52656769e36f20417574f36e6f6d6120646f732041e7...b'52656769e36f20417574f36e6f6d6120646f732041e7...2.021MULTIPOLYGON (((1282493.513 2380255.965, 12840...
230PT31PTb'52656769e36f20417574f36e6f6d61206461204d6164...b'52656769e36f20417574f36e6f6d61206461204d6164...0.000MULTIPOLYGON (((1887078.357 1537180.631, 18868...
329PT302PTb'52656769e36f20417574f36e6f6d61206461204d6164...b'52656769e36f20417574f36e6f6d61206461204d6164...0.000MULTIPOLYGON (((1887078.357 1537180.631, 18868...
1826PT3003PTb'52656769e36f20417574f36e6f6d61206461204d6164...b'52656769e36f20417574f36e6f6d61206461204d6164...3.011MULTIPOLYGON (((1887078.357 1537180.631, 18868...
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921 rows × 9 columns

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" + ], + "text/plain": [ + " NUTS_ID LEVL_CODE CNTR_CODE \\\n", + "0 AL 0 AL \n", + "49 AL0 1 AL \n", + "153 AL03 2 AL \n", + "363 AL035 3 AL \n", + "10 EL 0 EL \n", + "... ... ... ... \n", + "328 PT20 2 PT \n", + "1825 PT200 3 PT \n", + "230 PT3 1 PT \n", + "329 PT30 2 PT \n", + "1826 PT300 3 PT \n", + "\n", + " NAME_LATN \\\n", + "0 b'5368716970eb726961' \n", + "49 b'5368716970eb726961' \n", + "153 Jug \n", + "363 b'566c6f72eb' \n", + "10 b'456c6ce16461' \n", + "... ... \n", + "328 b'52656769e36f20417574f36e6f6d6120646f732041e7... \n", + "1825 b'52656769e36f20417574f36e6f6d6120646f732041e7... \n", + "230 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "329 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "1826 b'52656769e36f20417574f36e6f6d61206461204d6164... \n", + "\n", + " NUTS_NAME MOUNT_TYPE \\\n", + "0 b'5368716970eb726961' 0.0 \n", + "49 b'5368716970eb726961' 0.0 \n", + "153 Jug 0.0 \n", + "363 b'566c6f72eb' 4.0 \n", + "10 ?????? 0.0 \n", + "... ... ... \n", + "328 b'52656769e36f20417574f36e6f6d6120646f732041e7... 0.0 \n", + "1825 b'52656769e36f20417574f36e6f6d6120646f732041e7... 2.0 \n", + "230 b'52656769e36f20417574f36e6f6d61206461204d6164... 0.0 \n", + "329 b'52656769e36f20417574f36e6f6d61206461204d6164... 0.0 \n", + "1826 b'52656769e36f20417574f36e6f6d61206461204d6164... 3.0 \n", + "\n", + " URBN_TYPE COAST_TYPE geometry \n", + "0 0 0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", + "49 0 0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", + "153 0 0 MULTIPOLYGON (((5211962.696 2061668.541, 52149... \n", + "363 2 1 MULTIPOLYGON (((5117808.521 1998738.510, 51189... \n", + "10 0 0 MULTIPOLYGON (((6083864.252 1676459.388, 60836... \n", + "... ... ... ... \n", + "328 0 0 MULTIPOLYGON (((1282493.513 2380255.965, 12840... \n", + "1825 2 1 MULTIPOLYGON (((1282493.513 2380255.965, 12840... \n", + "230 0 0 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "329 0 0 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "1826 1 1 MULTIPOLYGON (((1887078.357 1537180.631, 18868... \n", + "\n", + "[921 rows x 9 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", - "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")" + "# Perform a spatial join to find intersections\n", + "NUTS_reproj = NUTS.set_crs(\"EPSG:3035\")\n", + "joined_gdf = gpd.sjoin(NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", + "\n", + "# remove columns added by shapes_gdf\n", + "joined_gdf.drop(columns=['index_right', 'value'])\n", + "\n", + "# Remove duplicate rows based on the index of gdf1\n", + "NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", + "\n", + "# Remove extra columns resulting from the spatial join\n", + "NUTS_CLEANCM = NUTS_CLEANCM[NUTS_CLEANCM.columns[:-2]]\n", + "\n", + "NUTS_CLEANCM" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1514" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# testern = NUTS[NUTS.LEVL_CODE == 3]\n", + "# len(testern)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "610" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# tester = NUTS_CLEANCM[NUTS_CLEANCM.LEVL_CODE == 3]\n", + "# len(tester)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "# Perform a spatial join to find intersections\n", - "# LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", - "# joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", - "\n", - "# # remove columns added by shapes_gdf\n", - "# joined_gdf.drop(columns=['index_right', 'value'])\n", - "\n", - "# # Remove duplicate rows based on the index of gdf1\n", - "# LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", - "\n", - "# # Remove extra columns resulting from the spatial join\n", - "# LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", + "# Write geodataframe to parquet\n", + "out_file= NUTS_dir.joinpath('NUTS_RG_01M_2021_3035_CM.parquet')\n", "\n", - "# LAU_NUTS_CLEANCM" + "# altered_lau_data.to_parquet(out_file_altered)\n", + "NUTS_CLEANCM.to_parquet(out_file)" ] } ], diff --git a/notebooks/99_LAU_NUTS2.ipynb b/notebooks/99_LAU_NUTS2.ipynb deleted file mode 100644 index 863b1e2f8b..0000000000 --- a/notebooks/99_LAU_NUTS2.ipynb +++ /dev/null @@ -1,6889 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Notebook to process LAU and NUTS shapefile to parquet" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# Load software\n", - "import os\n", - "import pathlib\n", - "import sys\n", - "import pandas as pd\n", - "from shapely import Polygon, geometry\n", - "from affine import Affine\n", - "from rasterio.features import shapes\n", - "import json\n", - "import itertools\n", - "import numpy as np\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import xarray as xr\n", - "from dotenv import load_dotenv\n", - "import math\n", - "from pathlib import Path\n", - "from typing import Any, Dict, List, Optional, Tuple, Union\n", - "import rioxarray as rio\n", - "\n", - "# Import custom functionality\n", - "from coclicodata.drive_config import p_drive\n", - "\n", - "# Define (local and) remote drives\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FASTTRACK_DATA\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Set path to geopackage\n", - "LAU_dir = coclico_data_dir.joinpath('XX_LAU')\n", - "NUTS_dir = coclico_data_dir.joinpath('XX_NUTS')\n", - "\n", - "LAU_file = LAU_dir.joinpath('LAU_RG_01M_2020_3035.shp')\n", - "NUTS_file = NUTS_dir.joinpath('NUTS_RG_01M_2021_3035.shp')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "LAU = gpd.read_file(LAU_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Load data\n", - "NUTS = gpd.read_file(NUTS_file) " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GISCO_IDCNTR_CODELAU_IDLAU_NAMEPOP_2020POP_DENS_2AREA_KM2YEARFIDgeometry
0CZ_579475CZ579475Libňatov363.062.2401585.8322472020CZ_579475POLYGON ((4747705.585 3057545.260, 4746836.524...
1CZ_579483CZ579483Libotov178.041.7747594.2609462020CZ_579483POLYGON ((4731662.418 3049054.578, 4732297.093...
2CZ_579491CZ579491Vranov181.037.4088734.8384242020CZ_579491POLYGON ((4540137.623 2963864.496, 4538706.113...
3CZ_579505CZ579505Malá Úpa142.05.31851026.6992072020CZ_579505POLYGON ((4730399.261 3087673.334, 4730914.253...
4AT_41004AT41004Eggendorf im Traunkreis1041.0116.4817478.9370232020AT_41004POLYGON ((4631543.792 2788412.230, 4630476.583...
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98608SK_599816SK599816Košice - mestská časť Nad jazerom24443.06686.2115513.6557322020SK_599816POLYGON ((5149120.507 2905279.350, 5150154.014...
98609SK_599824SK599824Košice - mestská časť Juh22692.02324.8819559.7604962020SK_599824POLYGON ((5144451.048 2904986.166, 5144591.047...
98610SK_599841SK599841Košice - mestská časť Šaca5969.0124.67277747.8773332020SK_599841POLYGON ((5141041.890 2900350.394, 5144604.564...
98611SK_599859SK599859Košice - mestská časť Poľov1219.094.04951112.9612582020SK_599859POLYGON ((5141041.890 2900350.394, 5141299.765...
98612SK_599875SK599875Košice - mestská časť Sídlisko Ťahanovce21936.02655.8225238.2595882020SK_599875POLYGON ((5147879.755 2916088.650, 5147433.909...
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98613 rows × 10 columns

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" - ], - "text/plain": [ - " GISCO_ID CNTR_CODE LAU_ID LAU_NAME \\\n", - "0 CZ_579475 CZ 579475 Libňatov \n", - "1 CZ_579483 CZ 579483 Libotov \n", - "2 CZ_579491 CZ 579491 Vranov \n", - "3 CZ_579505 CZ 579505 Malá Úpa \n", - "4 AT_41004 AT 41004 Eggendorf im Traunkreis \n", - "... ... ... ... ... \n", - "98608 SK_599816 SK 599816 Košice - mestská časť Nad jazerom \n", - "98609 SK_599824 SK 599824 Košice - mestská časť Juh \n", - "98610 SK_599841 SK 599841 Košice - mestská časť Šaca \n", - "98611 SK_599859 SK 599859 Košice - mestská časť Poľov \n", - "98612 SK_599875 SK 599875 Košice - mestská časť Sídlisko Ťahanovce \n", - "\n", - " POP_2020 POP_DENS_2 AREA_KM2 YEAR FID \\\n", - "0 363.0 62.240158 5.832247 2020 CZ_579475 \n", - "1 178.0 41.774759 4.260946 2020 CZ_579483 \n", - "2 181.0 37.408873 4.838424 2020 CZ_579491 \n", - "3 142.0 5.318510 26.699207 2020 CZ_579505 \n", - "4 1041.0 116.481747 8.937023 2020 AT_41004 \n", - "... ... ... ... ... ... \n", - "98608 24443.0 6686.211551 3.655732 2020 SK_599816 \n", - "98609 22692.0 2324.881955 9.760496 2020 SK_599824 \n", - "98610 5969.0 124.672777 47.877333 2020 SK_599841 \n", - "98611 1219.0 94.049511 12.961258 2020 SK_599859 \n", - "98612 21936.0 2655.822523 8.259588 2020 SK_599875 \n", - "\n", - " geometry \n", - "0 POLYGON ((4747705.585 3057545.260, 4746836.524... \n", - "1 POLYGON ((4731662.418 3049054.578, 4732297.093... \n", - "2 POLYGON ((4540137.623 2963864.496, 4538706.113... \n", - "3 POLYGON ((4730399.261 3087673.334, 4730914.253... \n", - "4 POLYGON ((4631543.792 2788412.230, 4630476.583... \n", - "... ... \n", - "98608 POLYGON ((5149120.507 2905279.350, 5150154.014... \n", - "98609 POLYGON ((5144451.048 2904986.166, 5144591.047... \n", - "98610 POLYGON ((5141041.890 2900350.394, 5144604.564... \n", - "98611 POLYGON ((5141041.890 2900350.394, 5141299.765... \n", - "98612 POLYGON ((5147879.755 2916088.650, 5147433.909... \n", - "\n", - "[98613 rows x 10 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LAU" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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NUTS_IDLEVL_CODECNTR_CODENAME_LATNNUTS_NAMEMOUNT_TYPEURBN_TYPECOAST_TYPEgeometry
0AL0ALb'5368716970eb726961'b'5368716970eb726961'0.000MULTIPOLYGON (((5121233.536 2221719.441, 51208...
1CZ0CZ?esko?esko0.000POLYGON ((4624843.654 3112209.741, 4625546.618...
2DE0DEDeutschlandDeutschland0.000MULTIPOLYGON (((4355225.365 2715902.993, 43541...
3DK0DKDanmarkDanmark0.000MULTIPOLYGON (((4650502.736 3591342.844, 46503...
4CY0CYb'4bfd70726f73'??????0.000MULTIPOLYGON (((6527040.718 1762367.593, 65267...
..............................
2005NO0B13NOJan MayenJan Mayen3.031POLYGON ((3623747.621 5400386.841, 3624031.138...
2006EE0093EEKesk-EestiKesk-Eesti4.031MULTIPOLYGON (((5216227.688 4159212.769, 52172...
2007NO01NONorgeNorge0.000MULTIPOLYGON (((4961367.759 5413266.131, 49622...
2008NO0B2NOJan Mayen and SvalbardJan Mayen and SvalbardNaN00MULTIPOLYGON (((4744650.828 6379141.635, 47446...
2009NO0B23NOSvalbardSvalbard3.031MULTIPOLYGON (((4760536.161 6404487.632, 47607...
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2010 rows × 9 columns

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" - ], - "text/plain": [ - " NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN \\\n", - "0 AL 0 AL b'5368716970eb726961' \n", - "1 CZ 0 CZ ?esko \n", - "2 DE 0 DE Deutschland \n", - "3 DK 0 DK Danmark \n", - "4 CY 0 CY b'4bfd70726f73' \n", - "... ... ... ... ... \n", - "2005 NO0B1 3 NO Jan Mayen \n", - "2006 EE009 3 EE Kesk-Eesti \n", - "2007 NO0 1 NO Norge \n", - "2008 NO0B 2 NO Jan Mayen and Svalbard \n", - "2009 NO0B2 3 NO Svalbard \n", - "\n", - " NUTS_NAME MOUNT_TYPE URBN_TYPE COAST_TYPE \\\n", - "0 b'5368716970eb726961' 0.0 0 0 \n", - "1 ?esko 0.0 0 0 \n", - "2 Deutschland 0.0 0 0 \n", - "3 Danmark 0.0 0 0 \n", - "4 ?????? 0.0 0 0 \n", - "... ... ... ... ... \n", - "2005 Jan Mayen 3.0 3 1 \n", - "2006 Kesk-Eesti 4.0 3 1 \n", - "2007 Norge 0.0 0 0 \n", - "2008 Jan Mayen and Svalbard NaN 0 0 \n", - "2009 Svalbard 3.0 3 1 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((5121233.536 2221719.441, 51208... \n", - "1 POLYGON ((4624843.654 3112209.741, 4625546.618... \n", - "2 MULTIPOLYGON (((4355225.365 2715902.993, 43541... \n", - "3 MULTIPOLYGON (((4650502.736 3591342.844, 46503... \n", - "4 MULTIPOLYGON (((6527040.718 1762367.593, 65267... \n", - "... ... \n", - "2005 POLYGON ((3623747.621 5400386.841, 3624031.138... \n", - "2006 MULTIPOLYGON (((5216227.688 4159212.769, 52172... \n", - "2007 MULTIPOLYGON (((4961367.759 5413266.131, 49622... \n", - "2008 MULTIPOLYGON (((4744650.828 6379141.635, 47446... \n", - "2009 MULTIPOLYGON (((4760536.161 6404487.632, 47607... \n", - "\n", - "[2010 rows x 9 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "NUTS" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Write data as parquet file\n", - "lau_parquet_file = str(LAU_file).replace('.shp','.parquet')\n", - "LAU.to_parquet(lau_parquet_file)\n", - "\n", - "nuts_parquet_file = str(NUTS_file).replace('.shp','.parquet')\n", - "NUTS.to_parquet(nuts_parquet_file)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Match LAU and NUTS using this work: https://edjnet.github.io/lau_centres/lau_nuts.html\n", - "\n", - "LAU_NUTS_match = pd.read_csv(r'p:\\11207608-coclico\\FASTTRACK_DATA\\XX_NUTS\\lau_2020_nuts_2021_concordance_by_geo.csv')\n", - "# Drop unneeded columns\n", - "LAU_NUTS_match = LAU_NUTS_match.drop(columns=['gisco_id','country','lau_id','lau_name','population','area_km2','year'])\n", - "LAU_NUTS_match = LAU_NUTS_match.rename(columns={'fid': 'FID'})\n", - "\n", - "# Merge the two dataframes\n", - "LAU_NUTS_data = LAU.merge(LAU_NUTS_match,on='FID')\n", - "\n", - "# Reorder columns\n", - "# Retrieve column names\n", - "cols = LAU_NUTS_data.columns\n", - "\n", - "# Move columns\n", - "cols = cols.insert([2],cols[-2:])\n", - "\n", - "# Drop the old ones\n", - "cols = cols[:-2]\n", - "\n", - "# Store in final form\n", - "LAU_NUTS_data = LAU_NUTS_data[cols]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GISCO_IDCNTR_CODEnuts_2nuts_3LAU_IDLAU_NAMEPOP_2020POP_DENS_2AREA_KM2YEARFIDgeometry
0CZ_579475CZCZ05CZ052579475Libňatov363.062.2401585.8322472020CZ_579475POLYGON ((4747705.585 3057545.260, 4746836.524...
1CZ_579483CZCZ05CZ052579483Libotov178.041.7747594.2609462020CZ_579483POLYGON ((4731662.418 3049054.578, 4732297.093...
2CZ_579491CZCZ03CZ032579491Vranov181.037.4088734.8384242020CZ_579491POLYGON ((4540137.623 2963864.496, 4538706.113...
3CZ_579505CZCZ05CZ052579505Malá Úpa142.05.31851026.6992072020CZ_579505POLYGON ((4730399.261 3087673.334, 4730914.253...
4AT_41004ATAT31AT31241004Eggendorf im Traunkreis1041.0116.4817478.9370232020AT_41004POLYGON ((4631543.792 2788412.230, 4630476.583...
.......................................
98608SK_599816SKSK04SK042599816Košice - mestská časť Nad jazerom24443.06686.2115513.6557322020SK_599816POLYGON ((5149120.507 2905279.350, 5150154.014...
98609SK_599824SKSK04SK042599824Košice - mestská časť Juh22692.02324.8819559.7604962020SK_599824POLYGON ((5144451.048 2904986.166, 5144591.047...
98610SK_599841SKSK04SK042599841Košice - mestská časť Šaca5969.0124.67277747.8773332020SK_599841POLYGON ((5141041.890 2900350.394, 5144604.564...
98611SK_599859SKSK04SK042599859Košice - mestská časť Poľov1219.094.04951112.9612582020SK_599859POLYGON ((5141041.890 2900350.394, 5141299.765...
98612SK_599875SKSK04SK042599875Košice - mestská časť Sídlisko Ťahanovce21936.02655.8225238.2595882020SK_599875POLYGON ((5147879.755 2916088.650, 5147433.909...
\n", - "

98613 rows × 12 columns

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" - ], - "text/plain": [ - " GISCO_ID CNTR_CODE nuts_2 nuts_3 LAU_ID \\\n", - "0 CZ_579475 CZ CZ05 CZ052 579475 \n", - "1 CZ_579483 CZ CZ05 CZ052 579483 \n", - "2 CZ_579491 CZ CZ03 CZ032 579491 \n", - "3 CZ_579505 CZ CZ05 CZ052 579505 \n", - "4 AT_41004 AT AT31 AT312 41004 \n", - "... ... ... ... ... ... \n", - "98608 SK_599816 SK SK04 SK042 599816 \n", - "98609 SK_599824 SK SK04 SK042 599824 \n", - "98610 SK_599841 SK SK04 SK042 599841 \n", - "98611 SK_599859 SK SK04 SK042 599859 \n", - "98612 SK_599875 SK SK04 SK042 599875 \n", - "\n", - " LAU_NAME POP_2020 POP_DENS_2 \\\n", - "0 Libňatov 363.0 62.240158 \n", - "1 Libotov 178.0 41.774759 \n", - "2 Vranov 181.0 37.408873 \n", - "3 Malá Úpa 142.0 5.318510 \n", - "4 Eggendorf im Traunkreis 1041.0 116.481747 \n", - "... ... ... ... \n", - "98608 Košice - mestská časť Nad jazerom 24443.0 6686.211551 \n", - "98609 Košice - mestská časť Juh 22692.0 2324.881955 \n", - "98610 Košice - mestská časť Šaca 5969.0 124.672777 \n", - "98611 Košice - mestská časť Poľov 1219.0 94.049511 \n", - "98612 Košice - mestská časť Sídlisko Ťahanovce 21936.0 2655.822523 \n", - "\n", - " AREA_KM2 YEAR FID \\\n", - "0 5.832247 2020 CZ_579475 \n", - "1 4.260946 2020 CZ_579483 \n", - "2 4.838424 2020 CZ_579491 \n", - "3 26.699207 2020 CZ_579505 \n", - "4 8.937023 2020 AT_41004 \n", - "... ... ... ... \n", - "98608 3.655732 2020 SK_599816 \n", - "98609 9.760496 2020 SK_599824 \n", - "98610 47.877333 2020 SK_599841 \n", - "98611 12.961258 2020 SK_599859 \n", - "98612 8.259588 2020 SK_599875 \n", - "\n", - " geometry \n", - "0 POLYGON ((4747705.585 3057545.260, 4746836.524... \n", - "1 POLYGON ((4731662.418 3049054.578, 4732297.093... \n", - "2 POLYGON ((4540137.623 2963864.496, 4538706.113... \n", - "3 POLYGON ((4730399.261 3087673.334, 4730914.253... \n", - "4 POLYGON ((4631543.792 2788412.230, 4630476.583... \n", - "... ... \n", - "98608 POLYGON ((5149120.507 2905279.350, 5150154.014... \n", - "98609 POLYGON ((5144451.048 2904986.166, 5144591.047... \n", - "98610 POLYGON ((5141041.890 2900350.394, 5144604.564... \n", - "98611 POLYGON ((5141041.890 2900350.394, 5141299.765... \n", - "98612 POLYGON ((5147879.755 2916088.650, 5147433.909... \n", - "\n", - "[98613 rows x 12 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "LAU_NUTS_data" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n", - "3\n", - "4\n", - "5\n", - "6\n", - "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-63.95_y-3.75.tif\n", - "7\n", - "8\n", - "9\n", - "10\n", - "11\n", - "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-26.4_y13.32.tif\n", - "12\n", - "13\n", - "14\n", - "15\n", - "16\n", - "17\n", - "18\n", - "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-5.92_y-20.81.tif\n", - "19\n", - "20\n", - "21\n", - "p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\\B01_x-33.23_y33.8.tif\n", - 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"\n", - "# masking the data with the Coastal Mask (takes abou 45 min)\n", - "folder_mask = r\"p:\\11207608-coclico\\FASTTRACK_DATA\\19_coastal_mask\\cogs\"\n", - "\n", - "# Retrieve total bounds in CRS of coastal mask\n", - "totalbbox = LAU_NUTS_data.to_crs(\"EPSG:4326\").total_bounds\n", - "\n", - "# Polygonized, empty vector for storing the Coastal Mask Polygons\n", - "pgnzd = []\n", - "# read tif files in the coastal mask folder\n", - "for idx, file in enumerate(os.listdir(folder_mask)):\n", - " print(idx)\n", - " if file.endswith(\".tif\"):\n", - " img = xr.open_dataset(os.path.join(folder_mask, file), engine=\"rasterio\", mask_and_scale=False) \n", - " if geometry.box(*img.rio.bounds()).intersects(geometry.box(*totalbbox)): # only do the ones in the LAU_NUTS bbox --> speed up to 17 min\n", - " print(os.path.join(folder_mask, file))\n", - "\n", - " # get the data\n", - " data_array = img.band_data.values\n", - " mask = data_array == 1\n", - " \n", - " # Split the string into a list of values and correct the gdal affine transformation\n", - " geotrans = list(map(float, img.spatial_ref.GeoTransform.split(' ')))\n", - " afn = Affine.from_gdal(*geotrans)\n", - "\n", - " # Extract shapes\n", - " results = (\n", - " {'properties': {'value': v}, 'geometry': s}\n", - " for i, (s, v) in enumerate(\n", - " shapes(data_array, mask=mask, transform=afn))\n", - " )\n", - "\n", - " pgnzd.append(list(results))" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1008" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(pgnzd)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "167957" - ] - }, - "execution_count": 84, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# flatten the list\n", - "fpgnzd = list(itertools.chain(*pgnzd))\n", - "len(fpgnzd)" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [], - "source": [ - "# Convert shapes to a GeoDataFrame\n", - "shapes_gdf = gpd.GeoDataFrame.from_features(fpgnzd)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "shapes_gdf = shapes_gdf.set_crs(\"EPSG:4326\")\n", - "shapes_gdf = shapes_gdf.to_crs(\"EPSG:3035\")\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "shapes_gdf.to_parquet(ds_dir.joinpath('CoastalMask_3035_polygonized.parquet'))" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GISCO_IDCNTR_CODEnuts_2nuts_3LAU_IDLAU_NAMEPOP_2020POP_DENS_2AREA_KM2YEARFIDgeometry
1192DE_03361001DEDE93DE93B03361001Achim, Stadt31923.0471.39877167.7197352020DE_03361001POLYGON ((4258927.295 3320011.519, 4259193.583...
1193DE_03361002DEDE93DE93B03361002Blender2885.075.03464038.4489082020DE_03361002POLYGON ((4264556.862 3317435.819, 4265677.354...
1195DE_03361003DEDE93DE93B03361003Dörverden9009.0107.94090883.4623332020DE_03361003POLYGON ((4275479.269 3295636.790, 4275328.930...
1196DE_03361004DEDE93DE93B03361004Emtinghausen1464.068.57384621.3492472020DE_03361004POLYGON ((4250468.027 3309420.666, 4250432.180...
1197DE_03361005DEDE93DE93B03361005Kirchlinteln9911.056.675823174.8717442020DE_03361005POLYGON ((4272425.673 3310568.711, 4274136.202...
.......................................
97667FR_97614FRFRY5FRY5097614Ouangani10203.0555.83878618.3560422020FR_97614POLYGON ((8727685.023 -2785490.190, 8727139.84...
97669FR_97616FRFRY5FRY5097616Sada11156.01012.64308611.0167152020FR_97616POLYGON ((8727114.505 -2787670.745, 8725517.75...
97670FR_97617FRFRY5FRY5097617Tsingoni13934.0406.42821334.2840372020FR_97617POLYGON ((8730964.436 -2773602.954, 8730972.75...
97660FR_97608FRFRY5FRY5097608Dzaoudzi17831.02797.9745666.3728242020FR_97608MULTIPOLYGON (((8745852.102 -2769772.861, 8742...
97668FR_97615FRFRY5FRY5097615Pamandzi11442.02619.6904804.3676922020FR_97615POLYGON ((8745852.102 -2769772.861, 8745849.32...
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11965 rows × 12 columns

\n", - "
" - ], - "text/plain": [ - " GISCO_ID CNTR_CODE nuts_2 nuts_3 LAU_ID LAU_NAME POP_2020 \\\n", - "1192 DE_03361001 DE DE93 DE93B 03361001 Achim, Stadt 31923.0 \n", - "1193 DE_03361002 DE DE93 DE93B 03361002 Blender 2885.0 \n", - "1195 DE_03361003 DE DE93 DE93B 03361003 Dörverden 9009.0 \n", - "1196 DE_03361004 DE DE93 DE93B 03361004 Emtinghausen 1464.0 \n", - "1197 DE_03361005 DE DE93 DE93B 03361005 Kirchlinteln 9911.0 \n", - "... ... ... ... ... ... ... ... \n", - "97667 FR_97614 FR FRY5 FRY50 97614 Ouangani 10203.0 \n", - "97669 FR_97616 FR FRY5 FRY50 97616 Sada 11156.0 \n", - "97670 FR_97617 FR FRY5 FRY50 97617 Tsingoni 13934.0 \n", - "97660 FR_97608 FR FRY5 FRY50 97608 Dzaoudzi 17831.0 \n", - "97668 FR_97615 FR FRY5 FRY50 97615 Pamandzi 11442.0 \n", - "\n", - " POP_DENS_2 AREA_KM2 YEAR FID \\\n", - "1192 471.398771 67.719735 2020 DE_03361001 \n", - "1193 75.034640 38.448908 2020 DE_03361002 \n", - "1195 107.940908 83.462333 2020 DE_03361003 \n", - "1196 68.573846 21.349247 2020 DE_03361004 \n", - "1197 56.675823 174.871744 2020 DE_03361005 \n", - "... ... ... ... ... \n", - "97667 555.838786 18.356042 2020 FR_97614 \n", - "97669 1012.643086 11.016715 2020 FR_97616 \n", - "97670 406.428213 34.284037 2020 FR_97617 \n", - "97660 2797.974566 6.372824 2020 FR_97608 \n", - "97668 2619.690480 4.367692 2020 FR_97615 \n", - "\n", - " geometry \n", - "1192 POLYGON ((4258927.295 3320011.519, 4259193.583... \n", - "1193 POLYGON ((4264556.862 3317435.819, 4265677.354... \n", - "1195 POLYGON ((4275479.269 3295636.790, 4275328.930... \n", - "1196 POLYGON ((4250468.027 3309420.666, 4250432.180... \n", - "1197 POLYGON ((4272425.673 3310568.711, 4274136.202... \n", - "... ... \n", - "97667 POLYGON ((8727685.023 -2785490.190, 8727139.84... \n", - "97669 POLYGON ((8727114.505 -2787670.745, 8725517.75... \n", - "97670 POLYGON ((8730964.436 -2773602.954, 8730972.75... \n", - "97660 MULTIPOLYGON (((8745852.102 -2769772.861, 8742... \n", - "97668 POLYGON ((8745852.102 -2769772.861, 8745849.32... \n", - "\n", - "[11965 rows x 12 columns]" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Perform a spatial join to find intersections\n", - "LAU_NUTS_reproj = LAU_NUTS_data.set_crs(\"EPSG:3035\")\n", - "joined_gdf = gpd.sjoin(LAU_NUTS_reproj, shapes_gdf, how='inner', predicate='intersects')\n", - "\n", - "# remove columns added by shapes_gdf\n", - "joined_gdf.drop(columns=['index_right', 'value'])\n", - "\n", - "# Remove duplicate rows based on the index of gdf1\n", - "LAU_NUTS_CLEANCM = joined_gdf[~joined_gdf.index.duplicated(keep='first')]\n", - "\n", - "# Remove extra columns resulting from the spatial join\n", - "LAU_NUTS_CLEANCM = LAU_NUTS_CLEANCM[LAU_NUTS_CLEANCM.columns[:-2]]\n", - "\n", - "LAU_NUTS_CLEANCM\n" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hell yeah!\n" - ] - } - ], - "source": [ - "# Check if its the same as original parquet is the same\n", - "test_parq = gpd.read_parquet(r\"p:\\11207608-coclico\\FULLTRACK_DATA\\WP4\\LAU_stats\\LAU_2020_NUTS_2021_01M_3035_CM_original.parquet\")\n", - "\n", - "if test_parq.equals(LAU_NUTS_CLEANCM):\n", - " print('Hell yeah!')" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [], - "source": [ - "# Write geodataframe to parquet\n", - "coclico_data_dir = p_drive.joinpath(\"11207608-coclico\", \"FULLTRACK_DATA\")\n", - "ds_dir = coclico_data_dir.joinpath('WP4','LAU_stats')\n", - "out_file= ds_dir.joinpath('LAU_2020_NUTS_2021_01M_3035_CM.parquet')\n", - "\n", - "# altered_lau_data.to_parquet(out_file_altered)\n", - "LAU_NUTS_CLEANCM.to_parquet(out_file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 4691acd5ab972b8249ca73a34316d16925968a6d Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:45:30 +0100 Subject: [PATCH 5/9] Create 99_LAU_CM_stacs.py Create script to make coastal masked LAU --- scripts/create_stacs/99_LAU_CM_stacs.py | 534 ++++++++++++++++++++++++ 1 file changed, 534 insertions(+) create mode 100644 scripts/create_stacs/99_LAU_CM_stacs.py diff --git a/scripts/create_stacs/99_LAU_CM_stacs.py b/scripts/create_stacs/99_LAU_CM_stacs.py new file mode 100644 index 0000000000..4b963dcb17 --- /dev/null +++ b/scripts/create_stacs/99_LAU_CM_stacs.py @@ -0,0 +1,534 @@ +# %% +# ## Load software +import sys + +branch = "dev" +sys.path.insert(0, "../src") + +from coastmonitor.io.drive_config import configure_instance + +is_local_instance = configure_instance(branch=branch) + +import dataclasses +import datetime +import logging +import os +import cv2 +import pathlib +import re +import json +import pyarrow +import gcsfs +import geopandas as gpd +import pandas as pd +import numpy as np +from typing import Any + +import fsspec +import pystac +import stac_geoparquet +from typing import List +from posixpath import join as urljoin +from dotenv import load_dotenv +from pystac.stac_io import DefaultStacIO + +from coclicodata.etl.cloud_utils import ( + load_google_credentials, + dir_to_google_cloud, + file_to_google_cloud, +) +from coclicodata.drive_config import p_drive +from coclicodata.coclico_stac.reshape_im import reshape_aspectratio_image + +from coastmonitor import stac_table +from coastmonitor.stac.layouts import ParquetLayout + +# %% +# ## Define variables +# hard-coded input params at project level +GCS_PROTOCOL = "https://storage.googleapis.com" +GCS_PROJECT = "coclico-11207608-002" +BUCKET_NAME = "coclico-data-public" +BUCKET_PROJ = "coclico" +PROJ_NAME = "LAU_CM" + +# hard-coded STAC templates +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent + +# hard-coded input params which differ per dataset +DATASET_DIR = "XX_LAU" +# CF_FILE = "Global_merit_coastal_mask_landwards.tif" +COLLECTION_ID = "LAU_CM" # name of stac collection +MAX_FILE_SIZE = 500 # max file size in MB + +# define local directories +home = pathlib.Path().home() +tmp_dir = home.joinpath("data", "tmp") +coclico_data_dir = p_drive.joinpath( + "11207608-coclico", "FASTTRACK_DATA" +) # remote p drive +cred_data_dir = p_drive.joinpath("11207608-coclico", "FASTTRACK_DATA") + +# use local or remote data dir +use_local_data = False + +if use_local_data: + ds_dir = tmp_dir +else: + ds_dir = coclico_data_dir + +if not ds_dir.exists(): + raise FileNotFoundError(f"Data dir does not exist, {str(ds_dir)}") + +# # directory to export result +# cog_dirs = ds_dir.joinpath("cogs") +ds_path = ds_dir.joinpath("XX_LAU") +ds_fp = ds_path.joinpath("LAU_2020_NUTS_2021_01M_3035_CM.parquet") # file directory + +# # load metadata template +metadata_fp = ds_path.joinpath("metadata", ds_fp.name).with_suffix(".json") +with open(metadata_fp, "r") as f: + metadata = json.load(f) + +# # extend keywords +metadata["KEYWORDS"].extend(["Full-Track", "Background Layers"]) + +# # data output configurations +HREF_PREFIX = urljoin( + GCS_PROTOCOL, BUCKET_NAME, BUCKET_PROJ, PROJ_NAME +) # cloud export directory +TMP_DIR = pathlib.Path.home() / "tmp" + +PARQUET_MEDIA_TYPE = "application/vnd.apache.parquet" + +# CONTAINER_NAME = "transects" +# PREFIX = f"gcts-{TRANSECT_LENGTH}m.parquet" +# BASE_URL = f"gs://{CONTAINER_NAME}/{PREFIX}" +GEOPARQUET_STAC_ITEMS_HREF = ( + f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{COLLECTION_ID}/{ds_fp.name}" +) + + +# %% +# %% +def read_parquet_schema_df(uri: str) -> List: # pd.DataFrame: + """Return a Pandas dataframe corresponding to the schema of a local URI of a parquet file. + + The returned dataframe has the columns: column, pa_dtype + """ + # Ref: https://stackoverflow.com/a/64288036/ + # Ref: https://stackoverflow.com/questions/41567081/get-schema-of-parquet-file-in-python + schema = pyarrow.parquet.read_schema(uri, memory_map=True) + # schema = pd.DataFrame(({"name": name, "type": str(pa_dtype)} for name, pa_dtype in zip(schema.names, schema.types))) + schema = [ + { + "name": name, + "type": str(pa_dtype), + "description": "", + } # TODO: add column descriptions once received from the VU + for name, pa_dtype in zip(schema.names, schema.types) + ] + # schema = schema.reindex(columns=["name", "type"], fill_value=pd.NA) # Ensures columns in case the parquet file has an empty dataframe. + return schema + + +def partition_dataframe(df: pd.DataFrame, batch_size: int) -> list[pd.DataFrame]: + """ + Splits a DataFrame into partitions approximately equal to or smaller than the specified batch size. + + Args: + df (pd.DataFrame): The DataFrame to be partitioned. + batch_size (int): The maximum number of rows each partition should have. + + Returns: + List[pd.DataFrame]: A list of DataFrames, each with a size up to the specified batch size. + """ + n_rows = len(df) + if n_rows <= batch_size: + return [df] + + num_partitions = (n_rows + batch_size - 1) // batch_size + partition_size = (n_rows + num_partitions - 1) // num_partitions + + partitions = [ + df.iloc[i : i + partition_size] for i in range(0, n_rows, partition_size) + ] + return partitions + + +@dataclasses.dataclass +class PathParts: + """ + Parses a path into its component parts, supporting variations with and without hive partitioning, + and with and without geographical bounds. + """ + + path: str + container: str | None = None + prefix: str | None = None + name: str | None = None + stac_item_id: str | None = None + + def __post_init__(self) -> None: + # Strip any protocol pattern like "xyz://" + stripped_path = re.sub(r"^\w+://", "", self.path) + split = stripped_path.rstrip("/").split("/") + + # Extract container + self.container = split[0] + + # Determine if there is hive partitioning and extract it + hive_partition_info = [ + "_".join(part.split("=")) for part in split[1:-1] if "=" in part + ] + + # Extract name, which is the filename with the .parquet extension + self.name = split[-1] + + # Construct the stac_item_id + # Include hive partitioning info if present, then add the file name, replacing ".parquet" and ensuring underscores + parts_to_join = hive_partition_info + [self.name.replace(".parquet", "")] + self.stac_item_id = "_".join(parts_to_join) + + +def create_collection( + description: str | None = None, extra_fields: dict[str, Any] | None = None +) -> pystac.Collection: + + providers = [ + pystac.Provider( + name=metadata["PROVIDERS"]["name"], + roles=[ + pystac.provider.ProviderRole.PRODUCER, + pystac.provider.ProviderRole.LICENSOR, + ], + url=metadata["PROVIDERS"]["url"], + ), + pystac.Provider( + name="Deltares", + roles=[ + pystac.provider.ProviderRole.PROCESSOR, + pystac.provider.ProviderRole.HOST, + ], + url="https://deltares.nl", + ), + ] + + start_datetime = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + + extent = pystac.Extent( + pystac.SpatialExtent([metadata["SPATIAL_EXTENT"]]), + pystac.TemporalExtent([[start_datetime, None]]), + ) + + # double check, this is hard-coded! + # links = [ + # pystac.Link( + # pystac.RelType.LICENSE, + # target="https://creativecommons.org/publicdomain/zero/1.0/", + # media_type="text/html", + # title="CC License", + # ) + # ] + + if "Creative Commons" in metadata["LICENSE"] and "4.0" in metadata["LICENSE"]: + metadata["LICENSE"] = "CC-BY-4.0" + + collection = pystac.Collection( + id=COLLECTION_ID, + title=metadata["TITLE"], + description=metadata["DESCRIPTION"], + license=metadata["LICENSE"], + providers=providers, + extent=extent, + catalog_type=pystac.CatalogType.RELATIVE_PUBLISHED, + ) + + collection.add_asset( + "thumbnail", + pystac.Asset( + f"https://storage.googleapis.com/coclico-data-public/coclico/assets/thumbnails/{COLLECTION_ID}.jpeg", + title="Thumbnail", + media_type=pystac.MediaType.JPEG, + ), + ) + # collection.links = links + collection.keywords = metadata["KEYWORDS"] + + pystac.extensions.item_assets.ItemAssetsExtension.add_to(collection) + + collection.extra_fields["item_assets"] = { + "data": { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + "roles": ["data"], + "type": stac_table.PARQUET_MEDIA_TYPE, + **ASSET_EXTRA_FIELDS, + } + } + + if extra_fields: + collection.extra_fields.update(extra_fields) + + pystac.extensions.scientific.ScientificExtension.add_to(collection) + collection.extra_fields["sci:citation"] = metadata["CITATION"] + + collection.stac_extensions.append(stac_table.SCHEMA_URI) + + pystac.extensions.version.VersionExtension.add_to(collection) + collection.extra_fields["version"] = "1" + + return collection + + +def create_item( + asset_href: str, + storage_options: dict[str, Any] | None = None, + asset_extra_fields: dict[str, Any] | None = None, +) -> pystac.Item: + """Create a STAC Item + + For + + Args: + asset_href (str): The HREF pointing to an asset associated with the item + + Returns: + Item: STAC Item object + """ + + parts = PathParts(asset_href) + + properties = { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + } + + dt = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + # shape = shapely.box(*bbox) + # geometry = shapely.geometry.mapping(shape) + template = pystac.Item( + id=parts.stac_item_id, + properties=properties, + geometry=None, + bbox=None, + datetime=dt, + stac_extensions=[], + ) + + item = stac_table.generate( + uri=asset_href, + template=template, + infer_bbox=True, + infer_geometry=None, + datetime_column=None, + infer_datetime=stac_table.InferDatetimeOptions.no, + count_rows=True, + asset_key="data", + asset_extra_fields=asset_extra_fields, + proj=True, + storage_options=storage_options, + validate=False, + ) + assert isinstance(item, pystac.Item) + + item.common_metadata.created = datetime.datetime.utcnow() + + # add descriptions to item properties + if "table:columns" in ASSET_EXTRA_FIELDS and "table:columns" in item.properties: + source_lookup = { + col["name"]: col for col in ASSET_EXTRA_FIELDS["table:columns"] + } + + for target_col in item.properties["table:columns"]: + source_col = source_lookup.get(target_col["name"]) + if source_col: + target_col.setdefault("description", source_col.get("description")) + + # TODO: make configurable upstream + item.assets["data"].title = metadata["TITLE_ABBREVIATION"] + item.assets["data"].description = metadata["SHORT_DESCRIPTION"] + + return item + + +# %% +# ## Do the work +if __name__ == "__main__": + log = logging.getLogger() + log.setLevel(logging.ERROR) + + # loading credentials + load_google_credentials( + google_token_fp=cred_data_dir.joinpath("google_credentials_new.json") + ) + + # %% test if file is multi-indexed, if we need to write to the cloud and whether we need to split files + dum = gpd.read_parquet(ds_fp) # read parquet file + split = "N" # value to determine if we need to split the files + for file in os.listdir(ds_path): + if os.path.getsize(ds_path.joinpath(file)) / 10**6 < MAX_FILE_SIZE: + split = "Y" # change slit to Yes + break + + # bucket content + uri = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{PROJ_NAME}" + # storage_options = {"account_name": "coclico", "credential": sas_token} + # fs, token, [root] = fsspec.get_fs_token_paths(uri, storage_options=storage_options) + fs = gcsfs.GCSFileSystem( + gcs_project=GCS_PROJECT, token=os.environ["GOOGLE_APPLICATION_CREDENTIALS"] + ) + paths = fs.glob(uri + "/*.parquet") + uris = ["gs://" + p for p in paths] + + # TODO: build something in for assessing size of parquet data, do this in both the if and elif statements + # if ( + # dum.index.nlevels > 1 or split == "Y" + # ) and paths == []: # if multi-indexed or split and there is nothing in the cloud + # files = os.listdir(ds_path) # list all files in the directory + # files_clean = [k for k in files if ".parquet" in k] # only select parquet files + + # for file in files_clean: + # print(file) + # file_size = os.path.getsize(ds_path.joinpath(file)) / 10**6 + + # if file_size < MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + + # # write to the cloud, single file + # dspd.to_parquet( + # f"{uri}/{file}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif file_size > MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + + # batch_size = int( + # np.ceil(len(dspd) / np.ceil(file_size / MAX_FILE_SIZE)) + # ) # calc batch size (max number of rows per partition) + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + # splitted_dspd = partition_dataframe(dspd, batch_size) # calc partitions + + # # write to the cloud, all split files + # for idx, split_dspd in enumerate(splitted_dspd): + # file_name = ( + # file.split(".")[0] + # + "_{:02d}.".format(idx + 1) + # + file.split(".")[1] + # ) # add zero-padded index (+1 to start at 1) to file name + # split_dspd.to_parquet( + # f"{uri}/{file_name}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif ( + # dum.index.nlevels == 1 and split == "N" and paths == [] + # ): # if not multi-indexed and no need to split and cloud file does not exist + + # upload directory to the cloud (files already parquet) + file_to_google_cloud( + file_path=str(ds_fp), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + file_name=ds_fp.name, + ) + + # %% get descriptions + COLUMN_DESCRIPTIONS = read_parquet_schema_df( + uris[0] + ) # select first file of the cloud directory + + ASSET_EXTRA_FIELDS = { + "table:storage_options": {"account_name": "coclico"}, + "table:columns": COLUMN_DESCRIPTIONS, + } + + # %% add to STAC + catalog = pystac.Catalog.from_file(str(STAC_DIR / "catalog.json")) + + stac_io = DefaultStacIO() + layout = ParquetLayout() + + collection = create_collection(extra_fields={"base_url": uri}) + + for uri in uris: + print(uri) + item = create_item(uri) + collection.add_item(item) + + collection.update_extent_from_items() + + items = list(collection.get_all_items()) + items_as_json = [i.to_dict() for i in items] + item_extents = stac_geoparquet.to_geodataframe(items_as_json) + + with fsspec.open(GEOPARQUET_STAC_ITEMS_HREF, mode="wb") as f: + item_extents.to_parquet(f) + + collection.add_asset( + "geoparquet-stac-items", + pystac.Asset( + GEOPARQUET_STAC_ITEMS_HREF, + title="GeoParquet STAC items", + description="Snapshot of the collection's STAC items exported to GeoParquet format.", + media_type=PARQUET_MEDIA_TYPE, + roles=["data"], + ), + ) + + # Set thumbnail directory + THUMB_DIR = pathlib.Path(__file__).parent.parent.joinpath("thumbnails") + THUMB_FILE = THUMB_DIR.joinpath(COLLECTION_ID + ".png") + + # Make sure image is reshaped to desired aspect ratio (default = 16/9) + cropped_im = reshape_aspectratio_image(str(THUMB_FILE)) + + # Overwrite image with cropped version + cv2.imwrite(str(THUMB_FILE), cropped_im) + + # Upload thumbnail to cloud + THUMB_URL = file_to_google_cloud( + str(THUMB_FILE), + GCS_PROJECT, + BUCKET_NAME, + BUCKET_PROJ, + "assets/thumbnails", + THUMB_FILE.name, + return_URL=True, + ) + + # Add thumbnail + collection.add_asset( + "thumbnail", + pystac.Asset( + THUMB_URL, # noqa: E501 + title="Thumbnail", + media_type=pystac.MediaType.PNG, + ), + ) + + if catalog.get_child(collection.id): + catalog.remove_child(collection.id) + print(f"Removed child: {collection.id}.") + + catalog.add_child(collection) + + collection.normalize_hrefs(str(STAC_DIR / collection.id), layout) + + collection.validate_all() + + catalog.save( + catalog_type=pystac.CatalogType.SELF_CONTAINED, + dest_href=str(STAC_DIR), + stac_io=stac_io, + ) + +# %% From 01fe538fb9a4771b436150b2479cb98cfce55200 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:45:53 +0100 Subject: [PATCH 6/9] Add LAU CM to catalog Add LAU CM to catalog --- current/LAU_CM/collection.json | 178 ++++++++++++++++++ .../items/LAU_2020_NUTS_2021_01M_3035_CM.json | 155 +++++++++++++++ current/catalog.json | 6 + scripts/thumbnails/LAU_CM.png | Bin 0 -> 195642 bytes scripts/thumbnails/NUTS _CM.png | Bin 0 -> 195642 bytes 5 files changed, 339 insertions(+) create mode 100644 current/LAU_CM/collection.json create mode 100644 current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json create mode 100644 scripts/thumbnails/LAU_CM.png create mode 100644 scripts/thumbnails/NUTS _CM.png diff --git a/current/LAU_CM/collection.json b/current/LAU_CM/collection.json new file mode 100644 index 0000000000..e51143539d --- /dev/null +++ b/current/LAU_CM/collection.json @@ -0,0 +1,178 @@ +{ + "type": "Collection", + "id": "LAU_CM", + "stac_version": "1.0.0", + "description": "The LAUs are a administrative to help ensure that data are readily available and policies can be implemented effectively using these units, a subdivision of the NUTS 3 regions covering the EU's whole economic territory and appropriate for implementing local level typologies included in Tercet, namely the coastal area and the degree of urbanisation (DEGURBA) classification, including city and functional urban areas (FUA) definitions.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "item", + "href": "./items/LAU_2020_NUTS_2021_01M_3035_CM.json", + "type": "application/json" + }, + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + } + ], + "stac_extensions": [ + "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", + "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/version/v1.2.0/schema.json" + ], + "item_assets": { + "data": { + "title": "LAU", + "description": "A system of local administrative units (LAUs) compatible with NUTS, clipped only to the coastal area", + "roles": [ + "data" + ], + "type": "application/vnd.apache.parquet", + "table:storage_options": { + "account_name": "coclico" + }, + "table:columns": [ + { + "name": "GISCO_ID", + "type": "string", + "description": "" + }, + { + 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z#&x&e?$?@tC?z8JZz1U-mn~lG+p}lJ^y$(F-$ByhaJ>5JtIIxn=k8ml_3fGCa+n}Q zAR=hQ2oW(25aB=||47N}uY5dy?BGFtI~#;j5eWCptdTg zM$GtGr(V6qFaU@M0Iju0WYv;|n?87@Wp?t0!pbo-uBT=99x*};10o)g*Pi-)w!3!X z#0!e{9lrOWKlSU=>F!&m#CY8x1tJ0wK_im*iiqHENZP=z{YN*h&mT8tP+D?4EiD2O zv285^r4&vh&=3sGTe#L~n&U6MK!rjO96D06Ipqd8%{{w2Ql7kwV+b;kB N002ovPDHLkV1kdlkDCAh literal 0 HcmV?d00001 From eddb023a25b520623ffd7dd94a64fd4ca96d0158 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:58:58 +0100 Subject: [PATCH 7/9] Create 99_NUTS_CM_stacs.py Script to add coastal masked CM to STAC --- scripts/create_stacs/99_NUTS_CM_stacs.py | 534 +++++++++++++++++++++++ 1 file changed, 534 insertions(+) create mode 100644 scripts/create_stacs/99_NUTS_CM_stacs.py diff --git a/scripts/create_stacs/99_NUTS_CM_stacs.py b/scripts/create_stacs/99_NUTS_CM_stacs.py new file mode 100644 index 0000000000..f4c54ea509 --- /dev/null +++ b/scripts/create_stacs/99_NUTS_CM_stacs.py @@ -0,0 +1,534 @@ +# %% +# ## Load software +import sys + +branch = "dev" +sys.path.insert(0, "../src") + +from coastmonitor.io.drive_config import configure_instance + +is_local_instance = configure_instance(branch=branch) + +import dataclasses +import datetime +import logging +import os +import cv2 +import pathlib +import re +import json +import pyarrow +import gcsfs +import geopandas as gpd +import pandas as pd +import numpy as np +from typing import Any + +import fsspec +import pystac +import stac_geoparquet +from typing import List +from posixpath import join as urljoin +from dotenv import load_dotenv +from pystac.stac_io import DefaultStacIO + +from coclicodata.etl.cloud_utils import ( + load_google_credentials, + dir_to_google_cloud, + file_to_google_cloud, +) +from coclicodata.drive_config import p_drive +from coclicodata.coclico_stac.reshape_im import reshape_aspectratio_image + +from coastmonitor import stac_table +from coastmonitor.stac.layouts import ParquetLayout + +# %% +# ## Define variables +# hard-coded input params at project level +GCS_PROTOCOL = "https://storage.googleapis.com" +GCS_PROJECT = "coclico-11207608-002" +BUCKET_NAME = "coclico-data-public" +BUCKET_PROJ = "coclico" +PROJ_NAME = "NUTS_CM" + +# hard-coded STAC templates +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent + +# hard-coded input params which differ per dataset +DATASET_DIR = "XX_NUTS" +# CF_FILE = "Global_merit_coastal_mask_landwards.tif" +COLLECTION_ID = "NUTS_CM" # name of stac collection +MAX_FILE_SIZE = 500 # max file size in MB + +# define local directories +home = pathlib.Path().home() +tmp_dir = home.joinpath("data", "tmp") +coclico_data_dir = p_drive.joinpath( + "11207608-coclico", "FASTTRACK_DATA" +) # remote p drive +cred_data_dir = p_drive.joinpath("11207608-coclico", "FASTTRACK_DATA") + +# use local or remote data dir +use_local_data = False + +if use_local_data: + ds_dir = tmp_dir +else: + ds_dir = coclico_data_dir + +if not ds_dir.exists(): + raise FileNotFoundError(f"Data dir does not exist, {str(ds_dir)}") + +# # directory to export result +# cog_dirs = ds_dir.joinpath("cogs") +ds_path = ds_dir.joinpath("XX_NUTS") +ds_fp = ds_path.joinpath("NUTS_RG_01M_2021_3035_CM.parquet") # file directory + +# # load metadata template +metadata_fp = ds_path.joinpath("metadata", ds_fp.name).with_suffix(".json") +with open(metadata_fp, "r") as f: + metadata = json.load(f) + +# # extend keywords +metadata["KEYWORDS"].extend(["Full-Track", "Background Layers"]) + +# # data output configurations +HREF_PREFIX = urljoin( + GCS_PROTOCOL, BUCKET_NAME, BUCKET_PROJ, PROJ_NAME +) # cloud export directory +TMP_DIR = pathlib.Path.home() / "tmp" + +PARQUET_MEDIA_TYPE = "application/vnd.apache.parquet" + +# CONTAINER_NAME = "transects" +# PREFIX = f"gcts-{TRANSECT_LENGTH}m.parquet" +# BASE_URL = f"gs://{CONTAINER_NAME}/{PREFIX}" +GEOPARQUET_STAC_ITEMS_HREF = ( + f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{COLLECTION_ID}/{ds_fp.name}" +) + + +# %% +# %% +def read_parquet_schema_df(uri: str) -> List: # pd.DataFrame: + """Return a Pandas dataframe corresponding to the schema of a local URI of a parquet file. + + The returned dataframe has the columns: column, pa_dtype + """ + # Ref: https://stackoverflow.com/a/64288036/ + # Ref: https://stackoverflow.com/questions/41567081/get-schema-of-parquet-file-in-python + schema = pyarrow.parquet.read_schema(uri, memory_map=True) + # schema = pd.DataFrame(({"name": name, "type": str(pa_dtype)} for name, pa_dtype in zip(schema.names, schema.types))) + schema = [ + { + "name": name, + "type": str(pa_dtype), + "description": "", + } # TODO: add column descriptions once received from the VU + for name, pa_dtype in zip(schema.names, schema.types) + ] + # schema = schema.reindex(columns=["name", "type"], fill_value=pd.NA) # Ensures columns in case the parquet file has an empty dataframe. + return schema + + +def partition_dataframe(df: pd.DataFrame, batch_size: int) -> list[pd.DataFrame]: + """ + Splits a DataFrame into partitions approximately equal to or smaller than the specified batch size. + + Args: + df (pd.DataFrame): The DataFrame to be partitioned. + batch_size (int): The maximum number of rows each partition should have. + + Returns: + List[pd.DataFrame]: A list of DataFrames, each with a size up to the specified batch size. + """ + n_rows = len(df) + if n_rows <= batch_size: + return [df] + + num_partitions = (n_rows + batch_size - 1) // batch_size + partition_size = (n_rows + num_partitions - 1) // num_partitions + + partitions = [ + df.iloc[i : i + partition_size] for i in range(0, n_rows, partition_size) + ] + return partitions + + +@dataclasses.dataclass +class PathParts: + """ + Parses a path into its component parts, supporting variations with and without hive partitioning, + and with and without geographical bounds. + """ + + path: str + container: str | None = None + prefix: str | None = None + name: str | None = None + stac_item_id: str | None = None + + def __post_init__(self) -> None: + # Strip any protocol pattern like "xyz://" + stripped_path = re.sub(r"^\w+://", "", self.path) + split = stripped_path.rstrip("/").split("/") + + # Extract container + self.container = split[0] + + # Determine if there is hive partitioning and extract it + hive_partition_info = [ + "_".join(part.split("=")) for part in split[1:-1] if "=" in part + ] + + # Extract name, which is the filename with the .parquet extension + self.name = split[-1] + + # Construct the stac_item_id + # Include hive partitioning info if present, then add the file name, replacing ".parquet" and ensuring underscores + parts_to_join = hive_partition_info + [self.name.replace(".parquet", "")] + self.stac_item_id = "_".join(parts_to_join) + + +def create_collection( + description: str | None = None, extra_fields: dict[str, Any] | None = None +) -> pystac.Collection: + + providers = [ + pystac.Provider( + name=metadata["PROVIDERS"]["name"], + roles=[ + pystac.provider.ProviderRole.PRODUCER, + pystac.provider.ProviderRole.LICENSOR, + ], + url=metadata["PROVIDERS"]["url"], + ), + pystac.Provider( + name="Deltares", + roles=[ + pystac.provider.ProviderRole.PROCESSOR, + pystac.provider.ProviderRole.HOST, + ], + url="https://deltares.nl", + ), + ] + + start_datetime = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + + extent = pystac.Extent( + pystac.SpatialExtent([metadata["SPATIAL_EXTENT"]]), + pystac.TemporalExtent([[start_datetime, None]]), + ) + + # double check, this is hard-coded! + # links = [ + # pystac.Link( + # pystac.RelType.LICENSE, + # target="https://creativecommons.org/publicdomain/zero/1.0/", + # media_type="text/html", + # title="CC License", + # ) + # ] + + if "Creative Commons" in metadata["LICENSE"] and "4.0" in metadata["LICENSE"]: + metadata["LICENSE"] = "CC-BY-4.0" + + collection = pystac.Collection( + id=COLLECTION_ID, + title=metadata["TITLE"], + description=metadata["DESCRIPTION"], + license=metadata["LICENSE"], + providers=providers, + extent=extent, + catalog_type=pystac.CatalogType.RELATIVE_PUBLISHED, + ) + + collection.add_asset( + "thumbnail", + pystac.Asset( + f"https://storage.googleapis.com/coclico-data-public/coclico/assets/thumbnails/{COLLECTION_ID}.jpeg", + title="Thumbnail", + media_type=pystac.MediaType.JPEG, + ), + ) + # collection.links = links + collection.keywords = metadata["KEYWORDS"] + + pystac.extensions.item_assets.ItemAssetsExtension.add_to(collection) + + collection.extra_fields["item_assets"] = { + "data": { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + "roles": ["data"], + "type": stac_table.PARQUET_MEDIA_TYPE, + **ASSET_EXTRA_FIELDS, + } + } + + if extra_fields: + collection.extra_fields.update(extra_fields) + + pystac.extensions.scientific.ScientificExtension.add_to(collection) + collection.extra_fields["sci:citation"] = metadata["CITATION"] + + collection.stac_extensions.append(stac_table.SCHEMA_URI) + + pystac.extensions.version.VersionExtension.add_to(collection) + collection.extra_fields["version"] = "1" + + return collection + + +def create_item( + asset_href: str, + storage_options: dict[str, Any] | None = None, + asset_extra_fields: dict[str, Any] | None = None, +) -> pystac.Item: + """Create a STAC Item + + For + + Args: + asset_href (str): The HREF pointing to an asset associated with the item + + Returns: + Item: STAC Item object + """ + + parts = PathParts(asset_href) + + properties = { + "title": metadata["TITLE_ABBREVIATION"], + "description": metadata["SHORT_DESCRIPTION"], + } + + dt = datetime.datetime.strptime( + metadata["TEMPORAL_EXTENT"][0].split("T")[0], "%Y-%m-%d" + ) + # shape = shapely.box(*bbox) + # geometry = shapely.geometry.mapping(shape) + template = pystac.Item( + id=parts.stac_item_id, + properties=properties, + geometry=None, + bbox=None, + datetime=dt, + stac_extensions=[], + ) + + item = stac_table.generate( + uri=asset_href, + template=template, + infer_bbox=True, + infer_geometry=None, + datetime_column=None, + infer_datetime=stac_table.InferDatetimeOptions.no, + count_rows=True, + asset_key="data", + asset_extra_fields=asset_extra_fields, + proj=True, + storage_options=storage_options, + validate=False, + ) + assert isinstance(item, pystac.Item) + + item.common_metadata.created = datetime.datetime.utcnow() + + # add descriptions to item properties + if "table:columns" in ASSET_EXTRA_FIELDS and "table:columns" in item.properties: + source_lookup = { + col["name"]: col for col in ASSET_EXTRA_FIELDS["table:columns"] + } + + for target_col in item.properties["table:columns"]: + source_col = source_lookup.get(target_col["name"]) + if source_col: + target_col.setdefault("description", source_col.get("description")) + + # TODO: make configurable upstream + item.assets["data"].title = metadata["TITLE_ABBREVIATION"] + item.assets["data"].description = metadata["SHORT_DESCRIPTION"] + + return item + + +# %% +# ## Do the work +if __name__ == "__main__": + log = logging.getLogger() + log.setLevel(logging.ERROR) + + # loading credentials + load_google_credentials( + google_token_fp=cred_data_dir.joinpath("google_credentials_new.json") + ) + + # %% test if file is multi-indexed, if we need to write to the cloud and whether we need to split files + dum = gpd.read_parquet(ds_fp) # read parquet file + split = "N" # value to determine if we need to split the files + for file in os.listdir(ds_path): + if os.path.getsize(ds_path.joinpath(file)) / 10**6 < MAX_FILE_SIZE: + split = "Y" # change slit to Yes + break + + # bucket content + uri = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{PROJ_NAME}" + # storage_options = {"account_name": "coclico", "credential": sas_token} + # fs, token, [root] = fsspec.get_fs_token_paths(uri, storage_options=storage_options) + fs = gcsfs.GCSFileSystem( + gcs_project=GCS_PROJECT, token=os.environ["GOOGLE_APPLICATION_CREDENTIALS"] + ) + paths = fs.glob(uri + "/*.parquet") + uris = ["gs://" + p for p in paths] + + # TODO: build something in for assessing size of parquet data, do this in both the if and elif statements + # if ( + # dum.index.nlevels > 1 or split == "Y" + # ) and paths == []: # if multi-indexed or split and there is nothing in the cloud + # files = os.listdir(ds_path) # list all files in the directory + # files_clean = [k for k in files if ".parquet" in k] # only select parquet files + + # for file in files_clean: + # print(file) + # file_size = os.path.getsize(ds_path.joinpath(file)) / 10**6 + + # if file_size < MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + + # # write to the cloud, single file + # dspd.to_parquet( + # f"{uri}/{file}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif file_size > MAX_FILE_SIZE: # test if file size is smaller than 500MB + # dspd = gpd.read_parquet(ds_path.joinpath(file)) # read parquet file + + # batch_size = int( + # np.ceil(len(dspd) / np.ceil(file_size / MAX_FILE_SIZE)) + # ) # calc batch size (max number of rows per partition) + # if dum.index.nlevels > 1: + # dspd = dspd.reset_index() # reset multi-index + # splitted_dspd = partition_dataframe(dspd, batch_size) # calc partitions + + # # write to the cloud, all split files + # for idx, split_dspd in enumerate(splitted_dspd): + # file_name = ( + # file.split(".")[0] + # + "_{:02d}.".format(idx + 1) + # + file.split(".")[1] + # ) # add zero-padded index (+1 to start at 1) to file name + # split_dspd.to_parquet( + # f"{uri}/{file_name}", engine="pyarrow" + # ) # or supply with local path if needed + + # elif ( + # dum.index.nlevels == 1 and split == "N" and paths == [] + # ): # if not multi-indexed and no need to split and cloud file does not exist + + # upload directory to the cloud (files already parquet) + file_to_google_cloud( + file_path=str(ds_fp), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + file_name=ds_fp.name, + ) + + # %% get descriptions + COLUMN_DESCRIPTIONS = read_parquet_schema_df( + uris[0] + ) # select first file of the cloud directory + + ASSET_EXTRA_FIELDS = { + "table:storage_options": {"account_name": "coclico"}, + "table:columns": COLUMN_DESCRIPTIONS, + } + + # %% add to STAC + catalog = pystac.Catalog.from_file(str(STAC_DIR / "catalog.json")) + + stac_io = DefaultStacIO() + layout = ParquetLayout() + + collection = create_collection(extra_fields={"base_url": uri}) + + for uri in uris: + print(uri) + item = create_item(uri) + collection.add_item(item) + + collection.update_extent_from_items() + + items = list(collection.get_all_items()) + items_as_json = [i.to_dict() for i in items] + item_extents = stac_geoparquet.to_geodataframe(items_as_json) + + with fsspec.open(GEOPARQUET_STAC_ITEMS_HREF, mode="wb") as f: + item_extents.to_parquet(f) + + collection.add_asset( + "geoparquet-stac-items", + pystac.Asset( + GEOPARQUET_STAC_ITEMS_HREF, + title="GeoParquet STAC items", + description="Snapshot of the collection's STAC items exported to GeoParquet format.", + media_type=PARQUET_MEDIA_TYPE, + roles=["data"], + ), + ) + + # Set thumbnail directory + THUMB_DIR = pathlib.Path(__file__).parent.parent.joinpath("thumbnails") + THUMB_FILE = THUMB_DIR.joinpath(COLLECTION_ID + ".png") + + # Make sure image is reshaped to desired aspect ratio (default = 16/9) + cropped_im = reshape_aspectratio_image(str(THUMB_FILE)) + + # Overwrite image with cropped version + cv2.imwrite(str(THUMB_FILE), cropped_im) + + # Upload thumbnail to cloud + THUMB_URL = file_to_google_cloud( + str(THUMB_FILE), + GCS_PROJECT, + BUCKET_NAME, + BUCKET_PROJ, + "assets/thumbnails", + THUMB_FILE.name, + return_URL=True, + ) + + # Add thumbnail + collection.add_asset( + "thumbnail", + pystac.Asset( + THUMB_URL, # noqa: E501 + title="Thumbnail", + media_type=pystac.MediaType.PNG, + ), + ) + + if catalog.get_child(collection.id): + catalog.remove_child(collection.id) + print(f"Removed child: {collection.id}.") + + catalog.add_child(collection) + + collection.normalize_hrefs(str(STAC_DIR / collection.id), layout) + + collection.validate_all() + + catalog.save( + catalog_type=pystac.CatalogType.SELF_CONTAINED, + dest_href=str(STAC_DIR), + stac_io=stac_io, + ) + +# %% From ea0c70cf8547d69f4d8f5471466caec51ecf1ba9 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:59:15 +0100 Subject: [PATCH 8/9] Adding NUTS CM Update STAC catalog --- current/NUTS_CM/collection.json | 163 ++++++++++++++++++ .../items/NUTS_RG_01M_2021_3035_CM.json | 140 +++++++++++++++ current/catalog.json | 6 + .../thumbnails/{NUTS _CM.png => NUTS_CM.png} | Bin 4 files changed, 309 insertions(+) create mode 100644 current/NUTS_CM/collection.json create mode 100644 current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json rename scripts/thumbnails/{NUTS _CM.png => NUTS_CM.png} (100%) diff --git a/current/NUTS_CM/collection.json b/current/NUTS_CM/collection.json new file mode 100644 index 0000000000..a8b0f8da75 --- /dev/null +++ b/current/NUTS_CM/collection.json @@ -0,0 +1,163 @@ +{ + "type": "Collection", + "id": "NUTS_CM", + "stac_version": "1.0.0", + "description": "The GISCO statistical unit dataset represents the NUTS (nomenclature of territorial units for statistics) and statistical regions by means of multipart polygon, polyline and point topology. The NUTS geographical information is completed by attribute tables and a set of cartographic help lines to better visualise multipart polygonal regions.", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "item", + "href": "./items/NUTS_RG_01M_2021_3035_CM.json", + "type": "application/json" + }, + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + } + ], + "stac_extensions": [ + "https://stac-extensions.github.io/item-assets/v1.0.0/schema.json", + "https://stac-extensions.github.io/scientific/v1.0.0/schema.json", + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/version/v1.2.0/schema.json" + ], + "item_assets": { + "data": { + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "roles": [ + "data" + ], + "type": "application/vnd.apache.parquet", + "table:storage_options": { + "account_name": "coclico" + }, + "table:columns": [ + { + "name": "NUTS_ID", + "type": "string", + "description": "" + }, + { + "name": "LEVL_CODE", + "type": "int64", + "description": "" + }, + { + "name": "CNTR_CODE", + "type": "string", + "description": "" + }, + { + "name": "NAME_LATN", + "type": "binary", + "description": "" + }, + { + "name": "NUTS_NAME", + "type": "binary", + "description": "" + }, + { + "name": "MOUNT_TYPE", + "type": "double", + "description": "" + }, + { + "name": "URBN_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "COAST_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "__index_level_0__", + "type": "int64", + "description": "" + } + ] + } + }, + "base_url": "gs://coclico-data-public/coclico/NUTS_CM", + "sci:citation": "None.", + "version": "1", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask", + "extent": { + "spatial": { + "bbox": [ + [ + -90.23528381409483, + -29.857082115784582, + 103.456913582018, + 39.79889221318682 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2024-01-22T00:00:00Z", + "2024-01-22T00:00:00Z" + ] + ] + } + }, + "license": "CC-BY-4.0", + "keywords": [ + "Local", + "Adminstrative", + "Units", + "European Union", + "Full-Track", + "Background Layers" + ], + "providers": [ + { + "name": "Eurostat", + "roles": [ + "producer", + "licensor" + ], + "url": "https://ec.europa.eu/eurostat/web/gisco/geodata/statistical-units/territorial-units-statistics" + }, + { + "name": "Deltares", + "roles": [ + "processor", + "host" + ], + "url": "https://deltares.nl" + } + ], + "assets": { + "thumbnail": { + "href": "https://storage.googleapis.com/download/storage/v1/b/coclico-data-public/o/coclico%2Fassets%2Fthumbnails%2FNUTS_CM.png?alt=media", + "type": "image/png", + "title": "Thumbnail" + }, + "geoparquet-stac-items": { + "href": "gs://coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "GeoParquet STAC items", + "description": "Snapshot of the collection's STAC items exported to GeoParquet format.", + "roles": [ + "data" + ] + } + } +} \ No newline at end of file diff --git a/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json new file mode 100644 index 0000000000..f99981c6bc --- /dev/null +++ b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json @@ -0,0 +1,140 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "NUTS_RG_01M_2021_3035_CM", + "properties": { + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "table:columns": [ + { + "name": "NUTS_ID", + "type": "string", + "description": "" + }, + { + "name": "LEVL_CODE", + "type": "int64", + "description": "" + }, + { + "name": "CNTR_CODE", + "type": "string", + "description": "" + }, + { + "name": "NAME_LATN", + "type": "binary", + "description": "" + }, + { + "name": "NUTS_NAME", + "type": "binary", + "description": "" + }, + { + "name": "MOUNT_TYPE", + "type": "double", + "description": "" + }, + { + "name": "URBN_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "COAST_TYPE", + "type": "int64", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "__index_level_0__", + "type": "int64", + "description": "" + } + ], + "proj:bbox": [ + -2824230.8134000003, + -3076162.6068, + 10026010.8334, + 6405005.3475 + ], + "proj:epsg": 3035, + "table:row_count": 921, + "created": "2024-12-12T12:55:33.336278Z", + "datetime": "2024-01-22T00:00:00Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + 103.456913582018, + -29.857082115784582 + ], + [ + 103.456913582018, + 39.79889221318682 + ], + [ + -90.23528381409483, + 39.79889221318682 + ], + [ + -90.23528381409483, + -29.857082115784582 + ], + [ + 103.456913582018, + -29.857082115784582 + ] + ] + ] + }, + "links": [ + { + "rel": "root", + "href": "../../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "collection", + "href": "../collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + }, + { + "rel": "parent", + "href": "../collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + } + ], + "assets": { + "data": { + "href": "gs://coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", + "type": "application/vnd.apache.parquet", + "title": "NUTS", + "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", + "roles": [ + "data" + ] + } + }, + "bbox": [ + -90.23528381409483, + -29.857082115784582, + 103.456913582018, + 39.79889221318682 + ], + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json", + "https://stac-extensions.github.io/projection/v1.1.0/schema.json" + ], + "collection": "NUTS_CM" +} \ No newline at end of file diff --git a/current/catalog.json b/current/catalog.json index 3d064b10c1..c29eea871d 100644 --- a/current/catalog.json +++ b/current/catalog.json @@ -183,6 +183,12 @@ "href": "./LAU_CM/collection.json", "type": "application/json", "title": "Local administrative units, clipped by Coastal Mask" + }, + { + "rel": "child", + "href": "./NUTS_CM/collection.json", + "type": "application/json", + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" } ], "assets": { diff --git a/scripts/thumbnails/NUTS _CM.png b/scripts/thumbnails/NUTS_CM.png similarity index 100% rename from scripts/thumbnails/NUTS _CM.png rename to scripts/thumbnails/NUTS_CM.png From 1b9d554f1a3427ba88c6a06777ed445bc2ee2195 Mon Sep 17 00:00:00 2001 From: EtienneKras <62947009+EtienneKras@users.noreply.github.com> Date: Thu, 12 Dec 2024 14:12:35 +0100 Subject: [PATCH 9/9] update LAU & NUTS CM with https link iso gs url --- current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json | 4 ++-- current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json | 4 ++-- current/catalog.json | 8 ++++---- scripts/create_stacs/99_LAU_CM_stacs.py | 4 +++- scripts/create_stacs/99_NUTS_CM_stacs.py | 4 +++- 5 files changed, 14 insertions(+), 10 deletions(-) diff --git a/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json b/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json index a3e66dfc6d..d683206aac 100644 --- a/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json +++ b/current/LAU_CM/items/LAU_2020_NUTS_2021_01M_3035_CM.json @@ -80,7 +80,7 @@ ], "proj:epsg": 3035, "table:row_count": 11965, - "created": "2024-12-12T12:41:14.628282Z", + "created": "2024-12-12T13:10:41.632774Z", "datetime": "2024-01-22T00:00:00Z" }, "geometry": { @@ -132,7 +132,7 @@ ], "assets": { "data": { - "href": "gs://coclico-data-public/coclico/LAU_CM/LAU_2020_NUTS_2021_01M_3035_CM.parquet", + "href": "https://storage.googleapis.com/coclico-data-public/coclico/LAU_CM/LAU_2020_NUTS_2021_01M_3035_CM.parquet", "type": "application/vnd.apache.parquet", "title": "LAU", "description": "A system of local administrative units (LAUs) compatible with NUTS, clipped only to the coastal area", diff --git a/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json index f99981c6bc..37c24f1947 100644 --- a/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json +++ b/current/NUTS_CM/items/NUTS_RG_01M_2021_3035_CM.json @@ -65,7 +65,7 @@ ], "proj:epsg": 3035, "table:row_count": 921, - "created": "2024-12-12T12:55:33.336278Z", + "created": "2024-12-12T13:09:43.503823Z", "datetime": "2024-01-22T00:00:00Z" }, "geometry": { @@ -117,7 +117,7 @@ ], "assets": { "data": { - "href": "gs://coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", + "href": "https://storage.googleapis.com/coclico-data-public/coclico/NUTS_CM/NUTS_RG_01M_2021_3035_CM.parquet", "type": "application/vnd.apache.parquet", "title": "NUTS", "description": "The NUTS are a hierarchical system divided into 3 levels, clipped only to the coastal area", diff --git a/current/catalog.json b/current/catalog.json index c29eea871d..faced0a89f 100644 --- a/current/catalog.json +++ b/current/catalog.json @@ -180,15 +180,15 @@ }, { "rel": "child", - "href": "./LAU_CM/collection.json", + "href": "./NUTS_CM/collection.json", "type": "application/json", - "title": "Local administrative units, clipped by Coastal Mask" + "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" }, { "rel": "child", - "href": "./NUTS_CM/collection.json", + "href": "./LAU_CM/collection.json", "type": "application/json", - "title": "Nomenclature of territorial units for statistics, clipped by Coastal Mask" + "title": "Local administrative units, clipped by Coastal Mask" } ], "assets": { diff --git a/scripts/create_stacs/99_LAU_CM_stacs.py b/scripts/create_stacs/99_LAU_CM_stacs.py index 4b963dcb17..28c47ded72 100644 --- a/scripts/create_stacs/99_LAU_CM_stacs.py +++ b/scripts/create_stacs/99_LAU_CM_stacs.py @@ -460,8 +460,10 @@ def create_item( collection = create_collection(extra_fields={"base_url": uri}) for uri in uris: - print(uri) + GCS_url = urljoin(HREF_PREFIX, uri.split("/")[-1]) + print(GCS_url) item = create_item(uri) + item.assets["data"].href = GCS_url # replace with https link iso gs uri collection.add_item(item) collection.update_extent_from_items() diff --git a/scripts/create_stacs/99_NUTS_CM_stacs.py b/scripts/create_stacs/99_NUTS_CM_stacs.py index f4c54ea509..d27d458dfa 100644 --- a/scripts/create_stacs/99_NUTS_CM_stacs.py +++ b/scripts/create_stacs/99_NUTS_CM_stacs.py @@ -460,8 +460,10 @@ def create_item( collection = create_collection(extra_fields={"base_url": uri}) for uri in uris: - print(uri) + GCS_url = urljoin(HREF_PREFIX, uri.split("/")[-1]) + print(GCS_url) item = create_item(uri) + item.assets["data"].href = GCS_url # replace with https link iso gs uri collection.add_item(item) collection.update_extent_from_items()