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"description": "We determine efficient coastal adaptation pathways for each coastal floodplain management unit at the European coastline based on a dynamic (inter-temporal) cost-benefit analysis. Protection, retreat and accommodation are considered as potential adaptation options; the height of a protection measure and the retreat height under which all people and assets retreat are determined by the optimisation. Accommodation is binary; buildings can be flood-proofed by up to an extreme event of one meter. The output of this cost-benefit analysis are the most cost-efficient adaptation trajectories, including adaptation options and their height per time step for each floodplain. We further provide the expected residual flood damages and adaptation costs in US$2024. This could be useful to coastal decision-makers to initiate coastal adaptation processes and for global institutions to estimate costs on coastal adaptation on global scale. It is used for a publication that will be send to Nature Climate Change. The files 'GCF.open.CBA_country.SSP245' included aggregated information based on the detailed trajectories on country level. The ending 'SSP126' defines the shared socioeconomic pathway that was used. The variables are the following: country (country name), nuts (EU NUTS two letter code), coast_length (coast length of that country in our model in km), protection_2050 (proportion of the entire coastline of that country where protection is efficient in the year 2050), retreat_2050 (proportion of the entire coastline of that country where retreat is efficient in the year 2050), acc_2050 (proportion of the entire coastline of that country where accommodation is efficient in the year 2050), protection_retreat_2050 (proportion of the entire coastline of that country where protection&retreat simultaneously is efficient in the year 2050), no_adaptation_2050 (proportion of the entire coastline of that country where no adaptation is efficient in the year 2050), costs_adaptation (the costs for implementing and maintaining adaptation options over the entire time horizon from 2020-2150), costs_damage (the costs for (residual) flood damage over the entire time horizon from 2020-2150), costs= costs_adaptation + costs_damage The files 'GCF.open.CBA.SSP126' contain the detailed adaptation trajectories for each floodplain and all time steps. The ending SSP126 defines the shared socioeconomic pathway that was used. The variables are the following: fpid (ID of each floodplain), year (year), protection_height (efficient protection height for specific floodplain and year), protection_level (return period of this protection height), retreat_height (efficient retreat height for specific floodplain and year), accommodation (efficient binary indicator for accommodation for specific floodplain and year), total_costs (= total_adaptation_costs + total_flood_damages), total_adaptation_costs (adaptation costs, including maintenance, for this floodplain under the efficient adaptation trajectory for year x until 2150), total_flood_damages (flood damages for this floodplain under the efficient adaptation trajectory for year x until 2150), current_adaptation_cost (adaptation costs for this floodplain under the efficient adaptation trajectory at specific year x), annual_flood_damage (flood damage for this floodplain under the efficient adaptation trajectory at specific year x)", + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "CoCliCo STAC Catalog" + }, + { + "rel": "item", + "href": "./items/GCF.open.CBA_country.all.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": "CBA", + "description": "The cost-benefit analysis determines efficient adaptation trajectories over time for each European floodplain, considering protection, retreat and accommodation.", + "roles": [ + "data" + ], + "type": "application/vnd.apache.parquet", + "table:storage_options": { + "account_name": "coclico" + }, + "table:columns": [ + { + "name": "country", + "type": "string", + "description": "" + }, + { + "name": "coast_length", + "type": "double", + "description": "" + }, + { + "name": "protection_2050", + "type": "double", + "description": "" + }, + { + "name": "retreat_2050", + "type": "double", + "description": "" + }, + { + "name": "acc_2050", + "type": "double", + "description": "" + }, + { + "name": "protect_retreat_2050", + "type": "double", + "description": "" + }, + { + "name": "no_adaptation_2050", + "type": "double", + "description": "" + }, + { + "name": "protection_2100", + "type": "double", + "description": "" + }, + { + "name": "retreat_2100", + "type": "double", + "description": "" + }, + { + "name": "acc_2100", + "type": "double", + "description": "" + }, + { + "name": "protect_retreat_2100", + "type": "double", + "description": "" + }, + { + "name": "no_adaptation_2100", + "type": "double", + "description": "" + }, + { + "name": "protection_2150", + "type": "double", + "description": "" + }, + { + "name": "retreat_2150", + "type": "double", + "description": "" + }, + { + "name": "acc_2150", + "type": "double", + "description": "" + }, + { + "name": "protect_retreat_2150", + "type": "double", + "description": "" + }, + { + "name": "no_adaptation_2150", + "type": "double", + "description": "" + }, + { + "name": "costs_adaptation", + "type": "double", + "description": "" + }, + { + "name": "costs_damage", + "type": "double", + "description": "" + }, + { + "name": "costs", + "type": "double", + "description": "" + }, + { + "name": "NUTS_ID", + "type": "string", + "description": "" + }, + { + "name": "geometry", + "type": "binary", + "description": "" + }, + { + "name": "scenarios", + "type": "string", + "description": "" + } + ] + } + }, + "base_url": "gs://coclico-data-public/coclico/CBA_stats", + "sci:citation": "V\u00c3\u00b6lz, V., Lincke, D., Hinkel, J., Honsel, L. (2024). Efficient adaptation pathways for European floodplains. tbd", + "version": "1", + "title": "Cost-benefit analysis of coastal adaptation per floodplain", + "extent": { + "spatial": { + "bbox": [ + [ + -90.23528381409483, + -29.857082115784582, + 103.456913582018, + 39.79889221318682 + ] + ] + }, + "temporal": { + "interval": [ + [ + "2020-01-01T00:00:00Z", + "2020-01-01T00:00:00Z" + ] + ] + } + }, + "license": "CC-BY-4.0", + "keywords": [ + "cost-benefit analysis", + "floodplains", + "adaptation", + "costs", + "Full-Track", + "Risk & Adaptation" + ], + "providers": [ + { + "name": "Global Climate Forum", + "roles": [ + "producer", + "licensor" + ], + "url": "https://globalclimateforum.org/" + }, + { + "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%2Fcba_stats.png?alt=media", + "type": "image/png", + "title": "Thumbnail" + }, + "geoparquet-stac-items": { + "href": "https://storage.googleapis.com/coclico-data-public/coclico/CBA_stats/GCF.open.CBA_country.all.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/cba/items/GCF.open.CBA_country.all.json b/current/cba_stats/items/GCF.open.CBA_country.all.json similarity index 95% rename from current/cba/items/GCF.open.CBA_country.all.json rename to current/cba_stats/items/GCF.open.CBA_country.all.json index 2006dc860f..f874dcc27b 100644 --- a/current/cba/items/GCF.open.CBA_country.all.json +++ b/current/cba_stats/items/GCF.open.CBA_country.all.json @@ -11,11 +11,6 @@ "type": "string", "description": "" }, - { - "name": "nuts", - "type": "string", - "description": "" - }, { "name": "coast_length", "type": "double", @@ -135,7 +130,7 @@ ], "proj:epsg": 3035, "table:row_count": 105, - "created": "2024-11-21T09:28:12.205348Z", + "created": "2025-01-15T07:06:10.833749Z", "datetime": "2020-01-01T00:00:00Z" }, "geometry": { @@ -187,7 +182,7 @@ ], "assets": { "data": { - "href": "https://storage.googleapis.com/coclico-data-public/coclico/CBA/GCF.open.CBA_country.all.parquet", + "href": "https://storage.googleapis.com/coclico-data-public/coclico/CBA_stats/GCF.open.CBA_country.all.parquet", "type": "application/vnd.apache.parquet", "title": "CBA", "description": "The cost-benefit analysis determines efficient adaptation trajectories over time for each European floodplain, considering protection, retreat and accommodation.", @@ -206,5 +201,5 @@ "https://stac-extensions.github.io/table/v1.2.0/schema.json", "https://stac-extensions.github.io/projection/v1.1.0/schema.json" ], - "collection": "cba" + "collection": "cba_stats" } \ No newline at end of file diff --git a/notebooks/33_cba.ipynb b/notebooks/33_cba.ipynb index 8ddd1b513f..fa8cfd424b 100644 --- a/notebooks/33_cba.ipynb +++ b/notebooks/33_cba.ipynb @@ -9,9 +9,24 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\kras\\AppData\\Local\\Temp\\ipykernel_11912\\339879207.py:14: 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", @@ -45,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -58,14 +73,14 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\soest\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pystac_client\\client.py:187: NoConformsTo: Server does not advertise any conformance classes.\n", + "c:\\Users\\kras\\AppData\\Local\\mambaforge\\envs\\coclico\\Lib\\site-packages\\pystac_client\\client.py:187: NoConformsTo: Server does not advertise any conformance classes.\n", " warnings.warn(NoConformsTo())\n" ] }, @@ -282,7 +297,7 @@ "[2010 rows x 9 columns]" ] }, - "execution_count": 76, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -314,610 +329,16 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
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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 b'?esko' \n", - "2 DE 0 DE b'Deutschland' \n", - "3 DK 0 DK b'Danmark' \n", - "4 CY 0 CY b'4bfd70726f73' \n", - "5 BE 0 BE b'42656c67697175652f42656c6769eb' \n", - "6 BG 0 BG b'Bulgaria' \n", - "7 CH 0 CH b'Schweiz/Suisse/Svizzera' \n", - "8 AT 0 AT b'd6737465727265696368' \n", - "9 EE 0 EE b'Eesti' \n", - "10 EL 0 EL b'456c6ce16461' \n", - "11 ES 0 ES b'45737061f161' \n", - "12 FI 0 FI b'Suomi/Finland' \n", - "13 FR 0 FR b'France' \n", - "14 HR 0 HR b'Hrvatska' \n", - "15 HU 0 HU b'4d61677961726f72737ae167' \n", - "16 IE 0 IE b'c96972652f4972656c616e64' \n", - "17 IS 0 IS b'cd736c616e64' \n", - "18 IT 0 IT b'Italia' \n", - "21 LV 0 LV b'Latvija' \n", - "24 ME 0 ME b'Crna Gora' \n", - "25 MT 0 MT b'Malta' \n", - "26 NO 0 NO b'Norge' \n", - "29 MK 0 MK b'Severna Makedonija' \n", - "31 LT 0 LT b'Lietuva' \n", - "32 LU 0 LU b'Luxembourg' \n", - "34 NL 0 NL b'Nederland' \n", - "36 SI 0 SI b'Slovenija' \n", - "37 LI 0 LI b'Liechtenstein' \n", - "38 SE 0 SE b'Sverige' \n", - "39 PL 0 PL b'Polska' \n", - "56 SK 0 SK b'Slovensko' \n", - "58 RS 0 RS b'Serbia' \n", - "59 TR 0 TR b'54fc726b697965' \n", - "60 UK 0 UK b'United Kingdom' \n", - "74 RO 0 RO b'526f6de26e6961' \n", - "95 PT 0 PT b'Portugal' \n", - "\n", - " NUTS_NAME MOUNT_TYPE URBN_TYPE COAST_TYPE \\\n", - "0 b'5368716970eb726961' 0.0 0 0 \n", - "1 b'?esko' 0.0 0 0 \n", - "2 b'Deutschland' 0.0 0 0 \n", - "3 b'Danmark' 0.0 0 0 \n", - "4 b'??????' 0.0 0 0 \n", - "5 b'42656c67697175652f42656c6769eb' 0.0 0 0 \n", - "6 b'????????' 0.0 0 0 \n", - "7 b'Schweiz/Suisse/Svizzera' 0.0 0 0 \n", - "8 b'd6737465727265696368' 0.0 0 0 \n", - "9 b'Eesti' 0.0 0 0 \n", - "10 b'??????' 0.0 0 0 \n", - "11 b'45737061f161' 0.0 0 0 \n", - "12 b'Suomi/Finland' 0.0 0 0 \n", - "13 b'France' 0.0 0 0 \n", - "14 b'Hrvatska' 0.0 0 0 \n", - "15 b'4d61677961726f72737ae167' 0.0 0 0 \n", - "16 b'c96972652f4972656c616e64' 0.0 0 0 \n", - "17 b'cd736c616e64' 0.0 0 0 \n", - "18 b'Italia' 0.0 0 0 \n", - "21 b'Latvija' 0.0 0 0 \n", - "24 b'???? ????' 0.0 0 0 \n", - "25 b'Malta' 0.0 0 0 \n", - "26 b'Norge' 0.0 0 0 \n", - "29 b'??????? ??????????' 0.0 0 0 \n", - "31 b'Lietuva' 0.0 0 0 \n", - "32 b'Luxembourg' 0.0 0 0 \n", - "34 b'Nederland' 0.0 0 0 \n", - "36 b'Slovenija' 0.0 0 0 \n", - "37 b'Liechtenstein' 0.0 0 0 \n", - "38 b'Sverige' 0.0 0 0 \n", - "39 b'Polska' 0.0 0 0 \n", - "56 b'Slovensko' 0.0 0 0 \n", - "58 b'Srbija/?p????' 0.0 0 0 \n", - "59 b'54fc726b697965' 0.0 0 0 \n", - "60 b'United Kingdom' 0.0 0 0 \n", - "74 b'526f6de26e6961' 0.0 0 0 \n", - "95 b'Portugal' 0.0 0 0 \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", - "5 MULTIPOLYGON (((3962902.889 3162436.894, 39626... \n", - "6 POLYGON ((5330611.947 2430822.479, 5332044.063... \n", - "7 POLYGON ((4214016.748 2744590.195, 4214310.948... \n", - "8 MULTIPOLYGON (((4354123.395 2712436.711, 43552... \n", - "9 MULTIPOLYGON (((5200046.875 4159874.733, 52002... \n", - "10 MULTIPOLYGON (((6083864.252 1676459.388, 60836... \n", - "11 MULTIPOLYGON (((3815030.229 1904966.807, 38157... \n", - "12 MULTIPOLYGON (((5003266.058 5307016.629, 50049... \n", - "13 MULTIPOLYGON (((9981288.972 -3029239.216, 9982... \n", - "14 MULTIPOLYGON (((4809428.353 2624702.723, 48094... \n", - "15 MULTIPOLYGON (((5164844.563 2893731.778, 51653... \n", - "16 MULTIPOLYGON (((3235383.615 3716183.571, 32356... \n", - "17 MULTIPOLYGON (((3187191.804 5040669.782, 31871... \n", - "18 MULTIPOLYGON (((4490080.814 2665777.451, 44913... \n", - "21 MULTIPOLYGON (((5211682.040 3982144.747, 52122... \n", - "24 MULTIPOLYGON (((5014573.760 2200024.633, 50152... \n", - "25 MULTIPOLYGON (((4718428.714 1448639.239, 47190... \n", - "26 MULTIPOLYGON (((4961367.759 5413266.131, 49622... \n", - "29 POLYGON ((5332379.725 2222835.909, 5332041.814... \n", - "31 MULTIPOLYGON (((5232018.536 3800170.443, 52324... \n", - "32 POLYGON ((4038552.051 3012685.848, 4039867.283... \n", - "34 MULTIPOLYGON (((4105407.002 3377924.206, 41082... \n", - "36 MULTIPOLYGON (((4796604.936 2660336.468, 47980... \n", - "37 POLYGON ((4288094.362 2673014.898, 4288583.148... \n", - "38 MULTIPOLYGON (((4972382.336 4789635.007, 49721... \n", - "39 MULTIPOLYGON (((4854947.286 3556285.285, 48556... \n", - "56 MULTIPOLYGON (((5003133.934 2988592.054, 50037... \n", - "58 MULTIPOLYGON (((5065273.952 2612464.116, 50671... \n", - "59 MULTIPOLYGON (((6349101.036 2450900.951, 63518... \n", - "60 MULTIPOLYGON (((3546135.140 4022028.934, 35466... \n", - "74 MULTIPOLYGON (((5550222.999 2933295.763, 55512... \n", - "95 MULTIPOLYGON (((2828396.621 2295946.291, 28279... " + "(37, 9)" ] }, - "execution_count": 77, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -925,12 +346,12 @@ "source": [ "# Only get NUTS0 level\n", "NUTS_data = NUTS_data[NUTS_data['LEVL_CODE'] == 0]\n", - "NUTS_data" + "NUTS_data.shape" ] }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -977,6 +398,7 @@ " CBA.append(cur_CBA)\n", "\n", "CBA = pd.concat(CBA, ignore_index=True)\n", + "CBA.drop(\"nuts\", axis=1, inplace=True)\n", "\n", "# Write as parquet\n", "CBA.to_parquet(str(CBA_file.parent.joinpath('GCF.open.CBA_country.all.parquet')))" @@ -984,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -993,7 +415,7 @@ "'P:\\\\11207608-coclico\\\\FULLTRACK_DATA\\\\WP6\\\\data\\\\CBA\\\\GCF.open.CBA_country.all.parquet'" ] }, - "execution_count": 87, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1005,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1030,7 +452,6 @@ " \n", " \n", " country\n", - " nuts\n", " coast_length\n", " protection_2050\n", " retreat_2050\n", @@ -1039,6 +460,7 @@ " no_adaptation_2050\n", " protection_2100\n", " retreat_2100\n", + " acc_2100\n", " ...\n", " retreat_2150\n", " acc_2150\n", @@ -1056,7 +478,6 @@ " \n", " 0\n", " Aaland\n", - " None\n", " 3886.0\n", " 0.0\n", " 3.8\n", @@ -1065,6 +486,7 @@ " 94.7\n", " 0.0\n", " 3.8\n", + " 1.5\n", " ...\n", " 3.8\n", " 1.5\n", @@ -1080,7 +502,6 @@ " \n", " 1\n", " Albania\n", - " AL\n", " 753.0\n", " 10.7\n", " 3.8\n", @@ -1089,6 +510,7 @@ " 45.5\n", " 27.6\n", " 3.8\n", + " 23.1\n", " ...\n", " 9.3\n", " 1.0\n", @@ -1104,7 +526,6 @@ " \n", " 2\n", " Belgium\n", - " BE\n", " 319.0\n", " 97.3\n", " 2.0\n", @@ -1113,6 +534,7 @@ " 0.7\n", " 97.3\n", " 2.0\n", + " 0.0\n", " ...\n", " 2.0\n", " 0.0\n", @@ -1128,7 +550,6 @@ " \n", " 3\n", " Bulgaria\n", - " BG\n", " 466.0\n", " 0.4\n", " 0.0\n", @@ -1137,6 +558,7 @@ " 76.5\n", " 0.4\n", " 0.0\n", + " 24.4\n", " ...\n", " 0.0\n", " 18.5\n", @@ -1152,7 +574,6 @@ " \n", " 4\n", " Croatia\n", - " HR\n", " 5309.0\n", " 3.5\n", " 7.7\n", @@ -1161,6 +582,7 @@ " 87.6\n", " 4.6\n", " 7.7\n", + " 0.0\n", " ...\n", " 7.7\n", " 0.0\n", @@ -1200,7 +622,6 @@ " \n", " 100\n", " Sweden\n", - " SE\n", " 24115.0\n", " 2.9\n", " 20.4\n", @@ -1209,6 +630,7 @@ " 75.2\n", " 2.9\n", " 19.6\n", + " 1.2\n", " ...\n", " 19.5\n", " 1.2\n", @@ -1224,7 +646,6 @@ " \n", " 101\n", " Turkey\n", - " TR\n", " 827.0\n", " 0.3\n", " 6.7\n", @@ -1233,6 +654,7 @@ " 88.5\n", " 0.3\n", " 6.7\n", + " 4.4\n", " ...\n", " 6.7\n", " 0.2\n", @@ -1248,7 +670,6 @@ " \n", " 102\n", " Ukraine\n", - " RO\n", " 1732.0\n", " 0.0\n", " 22.7\n", @@ -1257,6 +678,7 @@ " 76.4\n", " 0.0\n", " 22.7\n", + " 0.6\n", " ...\n", " 22.7\n", " 0.0\n", @@ -1272,7 +694,6 @@ " \n", " 103\n", " United Kingdom\n", - " UK\n", " 19867.0\n", " 10.4\n", " 24.8\n", @@ -1281,6 +702,7 @@ " 61.7\n", " 10.4\n", " 24.8\n", + " 0.8\n", " ...\n", " 24.8\n", " 0.7\n", @@ -1296,7 +718,6 @@ " \n", " 104\n", " Total\n", - " None\n", " 214891.0\n", " 8.6\n", " 20.9\n", @@ -1305,6 +726,7 @@ " 67.2\n", " 10.2\n", " 20.9\n", + " 0.7\n", " ...\n", " 20.8\n", " 0.7\n", @@ -1319,48 +741,48 @@ " \n", " \n", "\n", - "

105 rows × 24 columns

\n", + "

105 rows × 23 columns

\n", "" ], "text/plain": [ - " country nuts coast_length protection_2050 retreat_2050 \\\n", - "0 Aaland None 3886.0 0.0 3.8 \n", - "1 Albania AL 753.0 10.7 3.8 \n", - "2 Belgium BE 319.0 97.3 2.0 \n", - "3 Bulgaria BG 466.0 0.4 0.0 \n", - "4 Croatia HR 5309.0 3.5 7.7 \n", - ".. ... ... ... ... ... \n", - "100 Sweden SE 24115.0 2.9 20.4 \n", - "101 Turkey TR 827.0 0.3 6.7 \n", - "102 Ukraine RO 1732.0 0.0 22.7 \n", - "103 United Kingdom UK 19867.0 10.4 24.8 \n", - "104 Total None 214891.0 8.6 20.9 \n", + " country coast_length protection_2050 retreat_2050 acc_2050 \\\n", + "0 Aaland 3886.0 0.0 3.8 1.5 \n", + "1 Albania 753.0 10.7 3.8 40.0 \n", + "2 Belgium 319.0 97.3 2.0 0.0 \n", + "3 Bulgaria 466.0 0.4 0.0 23.1 \n", + "4 Croatia 5309.0 3.5 7.7 1.2 \n", + ".. ... ... ... ... ... \n", + "100 Sweden 24115.0 2.9 20.4 1.1 \n", + "101 Turkey 827.0 0.3 6.7 4.4 \n", + "102 Ukraine 1732.0 0.0 22.7 0.9 \n", + "103 United Kingdom 19867.0 10.4 24.8 0.6 \n", + "104 Total 214891.0 8.6 20.9 2.3 \n", "\n", - " acc_2050 protect_retreat_2050 no_adaptation_2050 protection_2100 \\\n", - "0 1.5 0.0 94.7 0.0 \n", - "1 40.0 0.0 45.5 27.6 \n", - "2 0.0 0.0 0.7 97.3 \n", - "3 23.1 0.0 76.5 0.4 \n", - "4 1.2 0.0 87.6 4.6 \n", - ".. ... ... ... ... \n", - "100 1.1 0.4 75.2 2.9 \n", - "101 4.4 0.0 88.5 0.3 \n", - "102 0.9 0.0 76.4 0.0 \n", - "103 0.6 2.5 61.7 10.4 \n", - "104 2.3 1.0 67.2 10.2 \n", + " protect_retreat_2050 no_adaptation_2050 protection_2100 retreat_2100 \\\n", + "0 0.0 94.7 0.0 3.8 \n", + "1 0.0 45.5 27.6 3.8 \n", + "2 0.0 0.7 97.3 2.0 \n", + "3 0.0 76.5 0.4 0.0 \n", + "4 0.0 87.6 4.6 7.7 \n", + ".. ... ... ... ... \n", + "100 0.4 75.2 2.9 19.6 \n", + "101 0.0 88.5 0.3 6.7 \n", + "102 0.0 76.4 0.0 22.7 \n", + "103 2.5 61.7 10.4 24.8 \n", + "104 1.0 67.2 10.2 20.9 \n", "\n", - " retreat_2100 ... retreat_2150 acc_2150 protect_retreat_2150 \\\n", - "0 3.8 ... 3.8 1.5 0.0 \n", - "1 3.8 ... 9.3 1.0 0.0 \n", - "2 2.0 ... 2.0 0.0 0.0 \n", - "3 0.0 ... 0.0 18.5 0.0 \n", - "4 7.7 ... 7.7 0.0 0.1 \n", - ".. ... ... ... ... ... \n", - "100 19.6 ... 19.5 1.2 1.3 \n", - "101 6.7 ... 6.7 0.2 0.3 \n", - "102 22.7 ... 22.7 0.0 0.1 \n", - "103 24.8 ... 24.8 0.7 2.5 \n", - "104 20.9 ... 20.8 0.7 1.2 \n", + " acc_2100 ... retreat_2150 acc_2150 protect_retreat_2150 \\\n", + "0 1.5 ... 3.8 1.5 0.0 \n", + "1 23.1 ... 9.3 1.0 0.0 \n", + "2 0.0 ... 2.0 0.0 0.0 \n", + "3 24.4 ... 0.0 18.5 0.0 \n", + "4 0.0 ... 7.7 0.0 0.1 \n", + ".. ... ... ... ... ... \n", + "100 1.2 ... 19.5 1.2 1.3 \n", + "101 4.4 ... 6.7 0.2 0.3 \n", + "102 0.6 ... 22.7 0.0 0.1 \n", + "103 0.8 ... 24.8 0.7 2.5 \n", + "104 0.7 ... 20.8 0.7 1.2 \n", "\n", " no_adaptation_2150 costs_adaptation costs_damage costs NUTS_ID \\\n", "0 94.7 0.2 0.0 0.2 None \n", @@ -1388,10 +810,10 @@ "103 MULTIPOLYGON (((3546135.140 4022028.934, 35466... SSP585 \n", "104 None SSP585 \n", "\n", - "[105 rows x 24 columns]" + "[105 rows x 23 columns]" ] }, - "execution_count": 90, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1401,6 +823,51 @@ "test_CBA" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### CUT the file above up into pieces for the F/E map" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "adap_strategy = [\"protection\", \"retreat\", \"acc\", \"protect_retreat\", \"no_adaptation\"]\n", + "scenario = [\"SSP126\", \"SSP245\", \"SSP585\"]\n", + "time = [\"2050\", \"2100\", \"2150\"]\n", + "keepcols = [\"country\", \"coast_length\", \"costs_adaptation\", \"costs_damage\", \"costs\", \"NUTS_ID\", \"geometry\", \"scenarios\"]\n", + "\n", + "# create folder structure & split files\n", + "for adap in adap_strategy:\n", + " for scen in scenario:\n", + " for t in time:\n", + "\n", + " # combine the variables to span to column name\n", + " comb_col = adap+\"_\"+t\n", + " keepcols.append(comb_col) # add the variable column \n", + " #print(keepcols)\n", + " #print(adap, scen, t, comb_col)\n", + "\n", + " # filter the original dataframe\n", + " fil_DF = CBA[keepcols] # filter base columns\n", + " fil_DF = fil_DF[fil_DF['scenarios'] == scen] # filter scenario\n", + " fil_DF.drop(\"scenarios\", axis=1, inplace=True) # drop scenario column\n", + " #print(fil_DF)\n", + "\n", + " # remove the added variable column for the next iteration\n", + " keepcols.remove(comb_col) # remove the variable column\n", + " #print(keepcols)\n", + " \n", + " # make the folder structure and write as parquet & gpkg\n", + " CBA_dir.joinpath(\"maps\", adap, scen).mkdir(parents=True, exist_ok=True)\n", + " fil_DF.to_parquet(str(CBA_dir.joinpath(\"maps\", adap, scen, \"GCF_open_CBA_country_%s_%s_%s.parquet\"%(adap, scen, t))))\n", + " fil_DF.to_file(str(CBA_dir.joinpath(\"maps\", adap, scen, \"GCF_open_CBA_country_%s_%s_%s.gpkg\"%(adap, scen, t))), layer='CBA_%s_%s_%s'%(adap, scen, t), driver='GPKG')" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/scripts/create_stacs/33_cba_stacs.py b/scripts/create_stacs/33_cba_stacs.py index 55cd81ea72..e74881549b 100644 --- a/scripts/create_stacs/33_cba_stacs.py +++ b/scripts/create_stacs/33_cba_stacs.py @@ -23,6 +23,7 @@ import pandas as pd import numpy as np from typing import Any +from pathlib import Path import fsspec import pystac @@ -31,6 +32,7 @@ from posixpath import join as urljoin from dotenv import load_dotenv from pystac.stac_io import DefaultStacIO +from pystac import Catalog, CatalogType, Collection, Summaries from coclicodata.etl.cloud_utils import ( load_google_credentials, @@ -39,9 +41,19 @@ ) from coclicodata.drive_config import p_drive from coclicodata.coclico_stac.reshape_im import reshape_aspectratio_image +from coclicodata.coclico_stac.templates import ( + extend_links, + gen_default_collection_props, + gen_default_item, + gen_default_item_props, + gen_default_summaries, + gen_mapbox_asset, + gen_zarr_asset, + get_template_collection, +) from coastmonitor import stac_table -from coastmonitor.stac.layouts import ParquetLayout +from coclicodata.coclico_stac.layouts import CoCliCoCOGLayout, CoCliCoParquetLayout # %% # ## Define variables @@ -53,7 +65,7 @@ PROJ_NAME = "CBA" # hard-coded STAC templates -STAC_DIR = pathlib.Path.cwd().parent.parent / "current" +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent # hard-coded input params which differ per dataset DATASET_DIR = "WP6" @@ -81,13 +93,13 @@ 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("WP6", "data", "CBA") -ds_fp = ds_path.joinpath("GCF.open.CBA_country.all.parquet") # file directory +parq_dirs = ds_path.joinpath("maps") +ds_fp = ds_path.joinpath("GCF.open.CBA_country.all.parquet") # file directory dummy # Front end makes geopackages to go alongside the parquet data # if this exists define here -FE_gpkg_fp = ds_path.joinpath("GCF_open_CBA_country_all_EPSG4326.gpkg") +# FE_gpkg_fp = ds_path.joinpath("GCF_open_CBA_country_all_EPSG4326.gpkg") # # load metadata template metadata_fp = ds_path.joinpath("metadata_GCF_CBA.json") @@ -374,6 +386,22 @@ def create_item( # %% # ## Do the work if __name__ == "__main__": + + ## Setup folder structure + # List different types on map folders + # item_type = "single" # "single" or "mosaic" + item_properties = ["adaptation strategy", "scenarios", "time"] + adap_strategy = [ + "protection", + "retreat", + "acc", + "protect_retreat", + "no_adaptation", + ] # 5 options + scenarios = ["SSP126", "SSP245", "SSP585"] # 3 options + times = ["2050", "2100", "2150"] # 3 options + + # %% log = logging.getLogger() log.setLevel(logging.ERROR) @@ -382,99 +410,116 @@ def create_item( 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 file.endswith(".parquet"): - if os.path.getsize(ds_path.joinpath(file)) / 10**6 > MAX_FILE_SIZE: - split = "Y" # change slit to Yes - break - - # bucket content + # %% store to cloud folder + + # dir_to_google_cloud( + # dir_path=str(parq_dirs), + # gcs_project=GCS_PROJECT, + # bucket_name=BUCKET_NAME, + # bucket_proj=BUCKET_PROJ, + # dir_name=PROJ_NAME, + # ) + + # %% 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] + # paths = fs.glob(uri + "/**/*.parquet") + # uris = ["gs://" + p for p in paths] + + # %% 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 file.endswith(".parquet"): + # if os.path.getsize(ds_path.joinpath(file)) / 10**6 > MAX_FILE_SIZE: + # split = "Y" # change slit to Yes + # break # 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, - ) - - # Also upload the Front-end geopackage if it exists - if FE_gpkg_fp != None: - # upload directory to the cloud (files already parquet) - file_to_google_cloud( - file_path=str(FE_gpkg_fp), - gcs_project=GCS_PROJECT, - bucket_name=BUCKET_NAME, - bucket_proj=BUCKET_PROJ, - dir_name=PROJ_NAME, - file_name=ds_fp.name, - ) - - elif paths: - print("Dataset already exists in the Google Bucket") + # 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, + # ) + + # # Also upload the Front-end geopackage if it exists + # if FE_gpkg_fp != None: + # # upload directory to the cloud (files already parquet) + # file_to_google_cloud( + # file_path=str(FE_gpkg_fp), + # gcs_project=GCS_PROJECT, + # bucket_name=BUCKET_NAME, + # bucket_proj=BUCKET_PROJ, + # dir_name=PROJ_NAME, + # file_name=ds_fp.name, + # ) + + # elif paths: + # print("Dataset already exists in the Google Bucket") # %% get descriptions + uri_dum = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/CBA_stats" + paths_dum = fs.glob(uri_dum + "/*.parquet") + uris_dum = ["gs://" + p for p in paths_dum] + HREF_PREFIX_dum = urljoin( + GCS_PROTOCOL, BUCKET_NAME, BUCKET_PROJ, "CBA_stats" + ) # cloud export directory + GCS_url_dum = urljoin(HREF_PREFIX_dum, uris_dum[0].split("/")[-1]) COLUMN_DESCRIPTIONS = read_parquet_schema_df( - uris[0] + uris_dum[0] ) # select first file of the cloud directory ASSET_EXTRA_FIELDS = { @@ -486,16 +531,83 @@ def create_item( catalog = pystac.Catalog.from_file(str(STAC_DIR / "catalog.json")) stac_io = DefaultStacIO() - layout = ParquetLayout() + layout = CoCliCoParquetLayout() collection = create_collection(extra_fields={"base_url": uri}) - for uri in uris: - 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) + # for uri in uris: + # GCS_url = urljoin(HREF_PREFIX, strat, scen, 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) + + uris = [] + dimcombs = [] + items = [] + for strat in adap_strategy: + for scen in scenarios: + for time in times: + file_name = f"GCF_open_CBA_country_{strat}_{scen}_{time}.parquet" + uri = f"gs://{BUCKET_NAME}/{BUCKET_PROJ}/{PROJ_NAME}/{strat}/{scen}/{file_name}" + print(uri) + uris.append(uri) + + GCS_url = urljoin(HREF_PREFIX, strat, scen, uri.split("/")[-1]) + item = create_item(uri) + item.assets["data"].href = GCS_url # replace with https link iso gs uri + + # set the file path structure + cur_path = os.path.join(strat, scen, time) + item_href = pathlib.Path(STAC_DIR, COLLECTION_ID, "items", cur_path) + item.set_self_href(item_href.with_suffix(".json")) + item.id = cur_path + ".parquet" + + # TODO: generalize this + dimcomb = { + item_properties[0]: strat, + item_properties[1]: scen, + item_properties[2]: time, + } + dimcombs.append(dimcomb) + + # TODO: include this in our datacube? + # add dimension key-value pairs to stac item properties dict + for k, v in dimcomb.items(): + item.properties[k] = v + + title = COLLECTION_ID + ":" + Path(file_name).stem + # TODO: We need to generalize this `href` somewhat. + vasset = pystac.Asset( # data asset + href="https://coclico.avi.deltares.nl/geoserver/%s/wms?bbox={bbox-epsg-3857}&format=image/png&service=WMS&version=1.1.1&request=GetMap&srs=EPSG:3857&transparent=true&width=256&height=256&layers=%s" + % (COLLECTION_ID, title), + media_type="application/png", + title=title, + description="OGS WMS url", + roles=["visual"], + ) + + item.add_asset("visual", vasset) + + items.append(item) + collection.add_item(item) + + collection.summaries = Summaries({}) + # TODO: check if maxcount is required (inpsired on xstac library) + # stac_obj.summaries.maxcount = 50 + dimvals = {} + for d in dimcombs: + for key, value in d.items(): + if key not in dimvals: + dimvals[key] = [] + if value not in dimvals[key]: + dimvals[key].append(value) + + for k, v in dimvals.items(): + collection.summaries.add(k, v) + + # set extra link properties + extend_links(collection, dimvals.keys()) collection.update_extent_from_items() @@ -508,7 +620,7 @@ def create_item( collection.add_asset( "geoparquet-stac-items", pystac.Asset( - GCS_url, + GCS_url_dum, title="GeoParquet STAC items", description="Snapshot of the collection's STAC items exported to GeoParquet format.", media_type=PARQUET_MEDIA_TYPE, @@ -564,6 +676,3 @@ def create_item( ) # %% - - -# %% diff --git a/scripts/create_stacs/33_cba_stats_stacs.py b/scripts/create_stacs/33_cba_stats_stacs.py new file mode 100644 index 0000000000..51a3000f78 --- /dev/null +++ b/scripts/create_stacs/33_cba_stats_stacs.py @@ -0,0 +1,569 @@ +# %% +# ## 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 json5 +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 = "CBA_stats" + +# hard-coded STAC templates +STAC_DIR = pathlib.Path.cwd() / "current" # .parent.parent + +# hard-coded input params which differ per dataset +DATASET_DIR = "WP6" +# CF_FILE = "Global_merit_coastal_mask_landwards.tif" +COLLECTION_ID = "cba_stats" # 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", "FULLTRACK_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("WP6", "data", "CBA") +ds_fp = ds_path.joinpath("GCF.open.CBA_country.all.parquet") # file directory + +# Front end makes geopackages to go alongside the parquet data +# if this exists define here +FE_gpkg_fp = ds_path.joinpath("GCF_open_CBA_country_all_EPSG4326.gpkg") + +# # load metadata template +metadata_fp = ds_path.joinpath("metadata_GCF_CBA.json") +with open(metadata_fp, "r") as f: + metadata = json5.load(f) + +# # extend keywords +metadata["KEYWORDS"].extend(["Full-Track", "Risk & Adaptation"]) + +# # 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}/items/{COLLECTION_ID}.parquet" +) + + +# %% +# %% +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: + + # NOTE: 2 providers, fixed quickly + 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.add_asset( + # "geoserver_link", + # pystac.Asset( + # "https://coclico.avi.deltares.nl/geoserver/gwc/service/wmts?REQUEST=GetTile&SERVICE=WMTS&VERSION=1.0.0&LAYER=cba:GCF_open_CBA_country_all_EPSG4326&STYLE=&TILEMATRIX=EPSG:900913:{z}&TILEMATRIXSET=EPSG:900913&FORMAT=application/vnd.mapbox-vector-tile&TILECOL={x}&TILEROW={y}", + # title="Geoserver Parquet link", + # media_type="application/vnd.apache.parquet", + # ), + # ) + + # 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 file.endswith(".parquet"): + 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, + ) + + # Also upload the Front-end geopackage if it exists + if FE_gpkg_fp != None: + # upload directory to the cloud (files already parquet) + file_to_google_cloud( + file_path=str(FE_gpkg_fp), + gcs_project=GCS_PROJECT, + bucket_name=BUCKET_NAME, + bucket_proj=BUCKET_PROJ, + dir_name=PROJ_NAME, + file_name=FE_gpkg_fp.name, + ) + + elif paths: + print("Dataset already exists in the Google Bucket") + + # %% 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: + 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() + + 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( + GCS_url, + 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, + ) + +# %% + + +# %% diff --git a/scripts/thumbnails/CBA_stats.PNG b/scripts/thumbnails/CBA_stats.PNG new file mode 100644 index 0000000000..73f1b19721 Binary files /dev/null and b/scripts/thumbnails/CBA_stats.PNG differ