diff --git a/docs/api/pgstac.md b/docs/api/pgstac.md index 640d9f6..da56f5d 100644 --- a/docs/api/pgstac.md +++ b/docs/api/pgstac.md @@ -5,7 +5,3 @@ - Rehydrating the dehydrated items - Partitioning by time - Injecting dynamic links and assets from a STAC API - -::: stac_geoparquet.pgstac_reader.CollectionConfig - options: - show_if_no_docstring: true diff --git a/docs/examples/dump_pgstac_to_parquet.ipynb b/docs/examples/dump_pgstac_to_parquet.ipynb new file mode 100644 index 0000000..62e2f7b --- /dev/null +++ b/docs/examples/dump_pgstac_to_parquet.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "13b420d4-8ff4-4332-aa76-4314438caefa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2mUsing Python 3.10.17 environment at: /home/bitner/data/stac-geoparquet/.venv\u001b[0m\n", + "\u001b[2mAudited \u001b[1m5 packages\u001b[0m \u001b[2min 4ms\u001b[0m\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%uv pip install psycopg psycopg[binary] fsspec datetime pathlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d6705a8c-0bd8-40d4-ba4c-82ece55ef36a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"user=username password=password dbname=postgis host=localhost port=5439 options=' -c search_path=pgstac,public -c statement_timeout=300000'\"" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from typing import Any\n", + "\n", + "from stac_geoparquet.pgstac_reader import (\n", + " pgstac_dsn,\n", + " pgstac_to_arrow,\n", + " pgstac_to_iter,\n", + " sync_pgstac_to_parquet,\n", + ")\n", + "\n", + "# pgstac test items derived from naip\n", + "db = pgstac_dsn(\n", + " \"postgres://username:password@localhost:5439/postgis\", statement_timeout=300000\n", + ")\n", + "db" + ] + }, + { + "cell_type": "markdown", + "id": "443f7c5d-5691-4d11-9c4d-18f77433c770", + "metadata": {}, + "source": [ + "Create Functions to Modify Each Item" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "45aa91ee-f34d-46d1-bd98-9d6faf249775", + "metadata": {}, + "outputs": [], + "source": [ + "def inject_links(item: dict[str, Any]) -> dict[str, Any]:\n", + " item[\"links\"] = [\n", + " {\n", + " \"rel\": \"collection\",\n", + " \"type\": \"application/json\",\n", + " \"href\": f\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{item['collection']}\", # noqa: E501\n", + " },\n", + " {\n", + " \"rel\": \"parent\",\n", + " \"type\": \"application/json\",\n", + " \"href\": f\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{item['collection']}\", # noqa: E501\n", + " },\n", + " {\n", + " \"rel\": \"root\",\n", + " \"type\": \"application/json\",\n", + " \"href\": \"https://planetarycomputer.microsoft.com/api/stac/v1/\",\n", + " },\n", + " {\n", + " \"rel\": \"self\",\n", + " \"type\": \"application/geo+json\",\n", + " \"href\": f\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{item['collection']}/items/{item['id']}\", # noqa: E501\n", + " },\n", + " {\n", + " \"rel\": \"preview\",\n", + " \"href\": f\"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection={item['collection']}&item={item['id']}\", # noqa: E501\n", + " \"title\": \"Map of item\",\n", + " \"type\": \"text/html\",\n", + " },\n", + " ]\n", + " return item\n", + "\n", + "\n", + "def inject_assets(item: dict[str, Any], render_config) -> dict[str, Any]:\n", + " item[\"assets\"][\"tilejson\"] = {\n", + " \"href\": (\n", + " \"https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?\"\n", + " f\"collection={item['collection']}\"\n", + " f\"&item={item['id']}&{render_config}\"\n", + " ),\n", + " \"roles\": [\"tiles\"],\n", + " \"title\": \"TileJSON with default rendering\",\n", + " \"type\": \"application/json\",\n", + " }\n", + " item[\"assets\"][\"rendered_preview\"] = {\n", + " \"href\": (\n", + " \"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?\"\n", + " f\"collection={item['collection']}\"\n", + " f\"&item={item['id']}&{render_config}\"\n", + " ),\n", + " \"rel\": \"preview\",\n", + " \"roles\": [\"overview\"],\n", + " \"title\": \"Rendered preview\",\n", + " \"type\": \"image/png\",\n", + " }\n", + " return item\n", + "\n", + "\n", + "def naip_year_to_int(item: dict[str, Any]) -> dict[str, Any]:\n", + " \"\"\"Convert the year to an integer.\"\"\"\n", + " if \"naip:year\" in item[\"properties\"] and isinstance(\n", + " item[\"properties\"][\"naip:year\"], str\n", + " ):\n", + " item[\"properties\"][\"naip:year\"] = int(item[\"properties\"][\"naip:year\"])\n", + " return item\n", + "\n", + "\n", + "render_config = \"render=myrenderconfig\"\n", + "\n", + "\n", + "def clean_item(item: dict[str, Any]) -> dict[str, Any]:\n", + " \"\"\"Clean items by making sure that naip:year is an int and injecting links and assets.\"\"\"\n", + " return naip_year_to_int(inject_links(inject_assets(item, render_config)))" + ] + }, + { + "cell_type": "markdown", + "id": "cb7bf760-3c9a-465c-b0db-7007bb02b5c2", + "metadata": {}, + "source": [ + "Demonstrate Injecting Additional Links and Assets." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9b49de7b-1330-46c4-96ba-88402dece003", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'id': 'pgstac-test-item-0003', 'geometry': {'type': 'Polygon', 'coordinates': (((-85.309412, 30.933949), (-85.308201, 31.002658), (-85.378084, 31.003555), (-85.379245, 30.934843), (-85.309412, 30.933949)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-85.379245, 30.933949, -85.308201, 31.003555], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30085/m_3008506_nw_16_1_20110825.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30085/m_3008506_nw_16_1_20110825.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30085/m_3008506_nw_16_1_20110825.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0003&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0003&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-08-25T00:00:00Z', 'naip:year': 2011, 'proj:bbox': [654842, 3423507, 661516, 3431125], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'al', 'proj:shape': [7618, 6674], 'eo:cloud_cover': 28, 'proj:transform': [1, 0, 654842, 0, -1, 3431125, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0003'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0003', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0004', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.121772, 30.934795), (-87.121858, 31.002794), (-87.190775, 31.002711), (-87.190639, 30.934712), (-87.121772, 30.934795)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.190775, 30.934712, -87.121772, 31.002794], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008707_ne_16_1_20110824.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30087/m_3008707_ne_16_1_20110824.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008707_ne_16_1_20110824.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0004&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0004&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-08-24T00:00:00Z', 'naip:year': 2011, 'proj:bbox': [481788, 3422382, 488367, 3429918], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'al', 'proj:shape': [7536, 6579], 'eo:cloud_cover': 23, 'proj:transform': [1, 0, 481788, 0, -1, 3429918, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0004'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0004', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0005', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.059311, 30.934794), (-87.059353, 31.002757), (-87.128238, 31.002707), (-87.128147, 30.934744), (-87.059311, 30.934794)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.128238, 30.934744, -87.059311, 31.002757], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008708_nw_16_1_20110824.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30087/m_3008708_nw_16_1_20110824.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008708_nw_16_1_20110824.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0005&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0005&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-08-24T00:00:00Z', 'naip:year': 2011, 'proj:bbox': [487758, 3422377, 494334, 3429909], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'al', 'proj:shape': [7532, 6576], 'eo:cloud_cover': 3, 'proj:transform': [1, 0, 487758, 0, -1, 3429909, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0005'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0005', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0006', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.184223, 30.934794), (-87.184354, 31.00282), (-87.253322, 31.002703), (-87.253142, 30.934677), (-87.184223, 30.934794)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.253322, 30.934677, -87.184223, 31.00282], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008707_nw_16_1_20110824.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30087/m_3008707_nw_16_1_20110824.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008707_nw_16_1_20110824.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0006&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0006&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-08-24T00:00:00Z', 'naip:year': 2011, 'proj:bbox': [475817, 3422390, 482401, 3429929], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'al', 'proj:shape': [7539, 6584], 'eo:cloud_cover': 100, 'proj:transform': [1, 0, 475817, 0, -1, 3429929, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0006'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0006', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0007', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.371617, 30.622297), (-87.371878, 30.690415), (-87.440935, 30.690199), (-87.440626, 30.622081), (-87.371617, 30.622297)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.440935, 30.622081, -87.371617, 30.690415], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008721_se_16_1_20110817.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 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'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0084', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0085', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.746345, 30.93481), (-87.746877, 31.00311), (-87.816195, 31.002691), (-87.815614, 30.934391), (-87.746345, 30.93481)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.816195, 30.934391, -87.746345, 31.00311], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008702_ne_16_1_20110801.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30087/m_3008702_ne_16_1_20110801.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008702_ne_16_1_20110801.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0085&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0085&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-08-01T00:00:00Z', 'naip:year': 2012, 'proj:bbox': [422082, 3422616, 428700, 3430186], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'xx', 'proj:shape': [7570, 6618], 'eo:cloud_cover': 3, 'proj:transform': [1, 0, 422082, 0, -1, 3430186, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0085'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0085', 'title': 'Map of item', 'type': 'text/html'}]}\n", + "{'id': 'pgstac-test-item-0100', 'geometry': {'type': 'Polygon', 'coordinates': (((-87.74637, 30.497308), (-87.746892, 30.565604), (-87.816179, 30.565188), (-87.815608, 30.496894), (-87.74637, 30.497308)),)}, 'collection': 'pgstac-test-collection', 'bbox': [-87.816179, 30.496894, -87.74637, 30.565604], 'assets': {'image': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008726_se_16_1_20110731.tif', 'type': 'image/tiff; application=geotiff; profile=cloud-optimized', 'roles': ['data'], 'title': 'RGBIR COG tile', 'eo:bands': [{'name': 'Red', 'common_name': 'red'}, {'name': 'Green', 'common_name': 'green'}, {'name': 'Blue', 'common_name': 'blue'}, {'name': 'NIR', 'common_name': 'nir', 'description': 'near-infrared'}]}, 'metadata': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_fgdc_2011/30087/m_3008726_se_16_1_20110731.txt', 'type': 'text/plain', 'roles': ['metadata'], 'title': 'FGDC Metdata'}, 'thumbnail': {'href': 'https://naipeuwest.blob.core.windows.net/naip/v002/al/2011/al_100cm_2011/30087/m_3008726_se_16_1_20110731.200.jpg', 'type': 'image/jpeg', 'roles': ['thumbnail'], 'title': 'Thumbnail'}, 'tilejson': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection=pgstac-test-collection&item=pgstac-test-item-0100&render=myrenderconfig', 'roles': ['tiles'], 'title': 'TileJSON with default rendering', 'type': 'application/json'}, 'rendered_preview': {'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=pgstac-test-collection&item=pgstac-test-item-0100&render=myrenderconfig', 'rel': 'preview', 'roles': ['overview'], 'title': 'Rendered preview', 'type': 'image/png'}}, 'stac_extensions': ['eo', 'projection'], 'properties': {'gsd': 1, 'datetime': '2011-07-31T00:00:00Z', 'naip:year': 2013, 'proj:bbox': [421730, 3374130, 428375, 3381699], 'proj:epsg': 26916, 'providers': [{'url': 'https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/', 'name': 'USDA Farm Service Agency', 'roles': ['producer', 'licensor']}], 'naip:state': 'zz', 'proj:shape': [7569, 6645], 'eo:cloud_cover': 50, 'proj:transform': [1, 0, 421730, 0, -1, 3381699, 0, 0, 1]}, 'type': 'Feature', 'stac_version': '1.0.0-beta.2', 'links': [{'rel': 'collection', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'parent', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection'}, {'rel': 'root', 'type': 'application/json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/'}, {'rel': 'self', 'type': 'application/geo+json', 'href': 'https://planetarycomputer.microsoft.com/api/stac/v1/collections/pgstac-test-collection/items/pgstac-test-item-0100'}, {'rel': 'preview', 'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection=pgstac-test-collection&item=pgstac-test-item-0100', 'title': 'Map of item', 'type': 'text/html'}]}\n" + ] + } + ], + "source": [ + "render_config = \"render=myrenderconfig\"\n", + "items = pgstac_to_iter(\n", + " db,\n", + " row_func=clean_item,\n", + ")\n", + "\n", + "for item in items:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "7d21b29d-0a3c-4344-9adb-b97f854784f3", + "metadata": {}, + "source": [ + "Inject Additional Links and Assets, Convert to RecordBatchReader, and Dump to Parquet (We need to refetch the data as the iterator was spent)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "59c124ab-02b5-4e01-a1a4-4b8feb0a47bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "assets: struct>, href: string, roles: list, title: string, type: string>, metadata: struct, title: string, type: string>, rendered_preview: struct, title: string, type: string>, thumbnail: struct, title: string, type: string>, tilejson: struct, title: string, type: string>>\n", + " child 0, image: struct>, href: string, roles: list, title: string, type: string>\n", + " child 0, eo:bands: list>\n", + " child 0, item: struct\n", + " child 0, common_name: string\n", + " child 1, description: string\n", + " child 2, name: string\n", + " child 1, href: string\n", + " child 2, roles: list\n", + " child 0, item: string\n", + " child 3, title: string\n", + " child 4, type: string\n", + " child 1, metadata: struct, title: string, type: string>\n", + " child 0, href: string\n", + " child 1, roles: list\n", + " child 0, item: string\n", + " child 2, title: string\n", + " child 3, type: string\n", + " child 2, rendered_preview: struct, title: string, type: string>\n", + " child 0, href: string\n", + " child 1, rel: string\n", + " child 2, roles: list\n", + " child 0, item: string\n", + " child 3, title: string\n", + " child 4, type: string\n", + " child 3, thumbnail: struct, title: string, type: string>\n", + " child 0, href: string\n", + " child 1, roles: list\n", + " child 0, item: string\n", + " child 2, title: string\n", + " child 3, type: string\n", + " child 4, tilejson: struct, title: string, type: string>\n", + " child 0, href: string\n", + " child 1, roles: list\n", + " child 0, item: string\n", + " child 2, title: string\n", + " child 3, type: string\n", + "bbox: struct\n", + " child 0, xmin: double\n", + " child 1, ymin: double\n", + " child 2, xmax: double\n", + " child 3, ymax: double\n", + "collection: string\n", + "geometry: binary\n", + " -- field metadata --\n", + " ARROW:extension:name: 'geoarrow.wkb'\n", + " ARROW:extension:metadata: '{\"crs\":{\"$schema\":\"https://proj.org/schemas/' + 1380\n", + "id: string\n", + "links: list>\n", + " child 0, item: struct\n", + " child 0, href: string\n", + " child 1, rel: string\n", + " child 2, title: string\n", + " child 3, type: string\n", + "stac_extensions: list\n", + " child 0, item: string\n", + "stac_version: string\n", + "type: string\n", + "datetime: timestamp[us, tz=UTC]\n", + "eo:cloud_cover: int64\n", + "gsd: int64\n", + "naip:state: string\n", + "naip:year: int64\n", + "proj:bbox: list\n", + " child 0, item: int64\n", + "proj:epsg: int64\n", + "proj:shape: list\n", + " child 0, item: int64\n", + "proj:transform: list\n", + " child 0, item: int64\n", + "providers: list, url: string>>\n", + " child 0, item: struct, url: string>\n", + " child 0, name: string\n", + " child 1, roles: list\n", + " child 0, item: string\n", + " child 2, url: string" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%config Application.log_level=\"INFO\"\n", + "\n", + "arrow = pgstac_to_arrow(\n", + " db,\n", + " row_func=clean_item,\n", + ")\n", + "arrow.schema" + ] + }, + { + "cell_type": "markdown", + "id": "5af96ffb-5f81-4afb-9dae-6b236d9e4845", + "metadata": {}, + "source": [ + "Dump any partition in pgstac that has been updated after a given time in order to incrementally dump only new records to parquet." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "29aab686-7e72-4580-b578-4bd9e99daf21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/tmp/pgstactoparquet2/pgstac-test-collection/items.parquet'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%config Application.log_level=\"INFO\"\n", + "sync_pgstac_to_parquet(\n", + " db,\n", + " output_path=\"/tmp/pgstactoparquet2\",\n", + " row_func=clean_item,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6092c5fc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index 7e2dc79..784762f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,19 +37,9 @@ deltalake = [ ] pgstac = [ - "fsspec", - "psycopg[binary,pool]", + "psycopg[binary]", + "psycopg-pool", "pypgstac", - "python-dateutil", - "tqdm", -] - -pc = [ - "adlfs", - "azure-data-tables", - "psycopg[binary,pool]", - "pypgstac", - "tqdm" ] [dependency-groups] @@ -69,6 +59,7 @@ dev = [ "types-python-dateutil", "types-requests", "vcrpy>=7.0.0", + "docker", ] docs = [ "griffe-inherited-docstrings", @@ -86,8 +77,6 @@ default-groups = ["dev", "docs"] [project.urls] Home = "https://github.com/stac-utils/stac-geoparquet" -[project.scripts] -pc-geoparquet = "stac_geoparquet.cli:main" [tool.pytest.ini_options] minversion = "6.0" diff --git a/stac_geoparquet/cli.py b/stac_geoparquet/cli.py deleted file mode 100644 index 2200f43..0000000 --- a/stac_geoparquet/cli.py +++ /dev/null @@ -1,146 +0,0 @@ -from __future__ import annotations - -import argparse -import logging -import os -import sys - -from stac_geoparquet import pc_runner - -logger = logging.getLogger("stac_geoparquet.pgstac_reader") - - -def parse_args(args: list[str] | None = None) -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument( - "--output-protocol", - help="fsspec protocol for writing (e.g. 'abfs').", - default=None, - ) - parser.add_argument( - "-c", - "--connection-info", - default=os.environ.get("STAC_GEOPARQUET_CONNECTION_INFO"), - ) - - parser.add_argument( - "--table-credential", - default=os.environ.get("STAC_GEOPARQUET_TABLE_CREDENTIAL"), - help="Azure data tables client SAS credential for reading.", - ) - parser.add_argument( - "--table-name", - help="Azure data tables name with the collection config.", - default=os.environ.get("STAC_GEOPARQUET_TABLE_NAME"), - ) - parser.add_argument( - "--table-account-url", - help="Azure data tables account URL name with the collection config.", - default=os.environ.get("STAC_GEOPARQUET_TABLE_ACCOUNT_URL"), - ) - parser.add_argument( - "--storage-options-account-name", - default=os.environ.get("STAC_GEOPARQUET_STORAGE_OPTIONS_ACCOUNT_NAME"), - ) - parser.add_argument( - "--storage-options-credential", - default=os.environ.get("STAC_GEOPARQUET_STORAGE_OPTIONS_CREDENTIAL"), - ) - parser.add_argument("--extra-skip", help="Extra collections to skip") - return parser.parse_args(args) - - -def setup_logging() -> None: - import logging - import warnings - - import rich.logging - - warnings.filterwarnings("ignore", message=".*initial implementation of Parquet.*") - logger.setLevel(logging.INFO) - handler = rich.logging.RichHandler() - handler.setLevel(logging.INFO) - logger.addHandler(handler) - - -SKIP = { - "daymet-daily-na", - "daymet-daily-pr", - "daymet-daily-hi", - "daymet-monthly-na", - "daymet-monthly-pr", - "daymet-monthly-hi", - "daymet-annual-na", - "daymet-annual-pr", - "daymet-annual-hi", - "terraclimate", - "gridmet", - "landsat-8-c2-l2", - "gpm-imerg-hhr", - "deltares-floods", - "goes-mcmip", - # errors - "cil-gdpcir-cc0", - "3dep-lidar-intensity", - "cil-gdpcir-cc-by", - "ecmwf-forecast", - "3dep-lidar-copc", - "era5-pds", - "3dep-lidar-classification", - "3dep-lidar-dtm-native", - "cil-gdpcir-cc-by-sa", -} - - -def main(inp: list[str] | None = None) -> int: - import azure.data.tables - - args = parse_args(inp) - - skip = set(SKIP) - if args.extra_skip: - skip |= set(args.extra_skip.split()) - - setup_logging() - - table_client = azure.data.tables.TableClient( - args.table_account_url, - args.table_name, - credential=azure.core.credentials.AzureSasCredential(args.table_credential), - ) - configs = pc_runner.get_configs(table_client) - - configs = {k: v for k, v in configs.items() if k not in skip} - storage_options = { - "account_name": args.storage_options_account_name, - "credential": args.storage_options_credential, - } - - def f(config: pc_runner.CollectionConfig) -> None: - config.export_collection( - args.connection_info, - args.output_protocol, - f"items/{config.collection_id}.parquet", - storage_options, - skip_empty_partitions=True, - ) - - N = len(configs) - success = [] - failure = [] - - for i, config in enumerate(configs.values(), 1): - logger.info(f"processing {config.collection_id} [{i}/{N}]") - try: - f(config) - except Exception as e: - failure.append((config.collection_id, e)) - logger.exception(f"Failed processing {config.collection_id}") - else: - success.append(config.collection_id) - - return len(failure) - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/stac_geoparquet/pc_runner.py b/stac_geoparquet/pc_runner.py deleted file mode 100644 index 3b7b591..0000000 --- a/stac_geoparquet/pc_runner.py +++ /dev/null @@ -1,166 +0,0 @@ -from __future__ import annotations - -import json -import urllib.parse -from typing import Any - -import azure.data.tables -import requests - -from stac_geoparquet.pgstac_reader import CollectionConfig - -PARTITION_FREQUENCIES = { - "3dep-lidar-classification": "YS", - "3dep-lidar-copc": "YS", - "3dep-lidar-dsm": "YS", - "3dep-lidar-dtm": "YS", - "3dep-lidar-dtm-native": "YS", - "3dep-lidar-hag": "YS", - "3dep-lidar-intensity": "YS", - "3dep-lidar-pointsourceid": "YS", - "3dep-lidar-returns": "YS", - "3dep-seamless": None, - "alos-dem": None, - "alos-fnf-mosaic": "YS", - "alos-palsar-mosaic": "YS", - "aster-l1t": "YS", - "chloris-biomass": None, - "cil-gdpcir-cc-by": None, - "cil-gdpcir-cc-by-sa": None, - "cil-gdpcir-cc0": None, - "cop-dem-glo-30": None, - "cop-dem-glo-90": None, - "eclipse": None, - "ecmwf-forecast": "MS", - "era5-pds": None, - "esa-worldcover": None, - "fia": None, - "gap": None, - "gbif": None, - "gnatsgo-rasters": None, - "gnatsgo-tables": None, - "goes-cmi": "W-MON", - "hrea": None, - "io-lulc": None, - "io-lulc-9-class": None, - "jrc-gsw": None, - "landsat-c2-l1": "MS", - "landsat-c2-l2": "MS", - "mobi": None, - "modis-09A1-061": "MS", - "modis-09Q1-061": "MS", - "modis-10A1-061": "MS", - "modis-10A2-061": "MS", - "modis-11A1-061": "MS", - "modis-11A2-061": "MS", - "modis-13A1-061": "MS", - "modis-13Q1-061": "MS", - "modis-14A1-061": "MS", - "modis-14A2-061": "MS", - "modis-15A2H-061": "MS", - "modis-15A3H-061": "MS", - "modis-16A3GF-061": "MS", - "modis-17A2H-061": "MS", - "modis-17A2HGF-061": "MS", - "modis-17A3HGF-061": "MS", - "modis-21A2-061": "MS", - "modis-43A4-061": "MS", - "modis-64A1-061": "MS", - "mtbs": None, - "naip": "YS", - "nasa-nex-gddp-cmip6": None, - "nasadem": None, - "noaa-c-cap": None, - "nrcan-landcover": None, - "planet-nicfi-analytic": "YS", - "planet-nicfi-visual": "YS", - "sentinel-1-grd": "MS", - "sentinel-1-rtc": "MS", - "sentinel-2-l2a": "W-MON", - "us-census": None, -} - - -def build_render_config(render_params: dict[str, Any], assets: dict[str, Any]) -> str: - flat = [] - if assets: - for asset in assets: - flat.append(("assets", asset)) - - for k, v in render_params.items(): - if isinstance(v, list): - flat.extend([(k, v2) for v2 in v]) - else: - flat.append((k, v)) - return urllib.parse.urlencode(flat) - - -def generate_configs_from_storage_table( - table_client: azure.data.tables.TableClient, -) -> dict[str, CollectionConfig]: - configs = {} - for entity in table_client.list_entities(): - collection_id = entity["RowKey"] - data = json.loads(entity["Data"]) - - render_params = data["render_config"]["render_params"] - assets = data["render_config"]["assets"] - render_config = build_render_config(render_params, assets) - configs[collection_id] = CollectionConfig( - collection_id, render_config=render_config - ) - - return configs - - -def generate_configs_from_api(url: str) -> dict[str, CollectionConfig]: - configs = {} - r = requests.get(url) - r.raise_for_status() - - for collection in r.json()["collections"]: - partition_frequency = ( - collection["assets"] - .get("geoparquet-items", {}) - .get("msft:partition_info", {}) - .get("partition_frequency", None) - ) - - configs[collection["id"]] = CollectionConfig( - collection["id"], partition_frequency=partition_frequency - ) - - return configs - - -def merge_configs( - table_configs: dict[str, CollectionConfig], api_configs: dict[str, CollectionConfig] -) -> dict[str, CollectionConfig]: - # what a mess. Get partitioning config from the API, render from the table. - configs = {} - for k in table_configs.keys() | api_configs.keys(): - table_config = table_configs.get(k) - api_config = api_configs.get(k) - config = table_config or api_config - assert config - if api_config: - config.partition_frequency = api_config.partition_frequency - configs[k] = config - return configs - - -def get_configs( - table_client: azure.data.tables.TableClient, -) -> dict[str, CollectionConfig]: - table_configs = generate_configs_from_storage_table(table_client) - api_configs = generate_configs_from_api( - "https://planetarycomputer.microsoft.com/api/stac/v1/collections" - ) - - configs = {**api_configs, **table_configs} - - for k, v in configs.items(): - if v.partition_frequency is None: - v.partition_frequency = PARTITION_FREQUENCIES.get(k) - - return configs diff --git a/stac_geoparquet/pgstac_reader.py b/stac_geoparquet/pgstac_reader.py index 665ac16..ea25ac6 100644 --- a/stac_geoparquet/pgstac_reader.py +++ b/stac_geoparquet/pgstac_reader.py @@ -1,295 +1,60 @@ -from __future__ import annotations - -import collections.abc -import dataclasses -import datetime -import hashlib -import itertools +import functools import logging -import textwrap -from typing import Any - -import dateutil.tz -import fsspec -import pandas as pd +import random +import string +from collections.abc import Iterator +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Callable, Union + +import orjson +import psycopg +import pyarrow as pa import pyarrow.fs -import pypgstac.db import pypgstac.hydration -import pystac import shapely.wkb -import tqdm.auto +from psycopg.types.json import set_json_dumps, set_json_loads -from stac_geoparquet import to_geodataframe +from stac_geoparquet.arrow import ( + DEFAULT_JSON_CHUNK_SIZE, + DEFAULT_PARQUET_SCHEMA_VERSION, + SUPPORTED_PARQUET_SCHEMA_VERSIONS, + parse_stac_items_to_arrow, + to_parquet, +) +from stac_geoparquet.arrow._schema.models import InferredSchema logger = logging.getLogger(__name__) -def _pairwise( - iterable: collections.abc.Iterable, -) -> Any: - # pairwise('ABCDEFG') --> AB BC CD DE EF FG - a, b = itertools.tee(iterable) - next(b, None) - return zip(a, b) - - -@dataclasses.dataclass -class CollectionConfig: +def dumps(data: dict) -> str: """ - Additional collection-based configuration to inject, matching the - dynamic properties from the API. + Custom JSON dumps function for psycopg. """ + return orjson.dumps(data).decode() - collection_id: str - partition_frequency: str | None = None - stac_api: str = "https://planetarycomputer.microsoft.com/api/stac/v1" - should_inject_dynamic_properties: bool = True - render_config: str | None = None - - def __post_init__(self) -> None: - self._collection: pystac.Collection | None = None - - @property - def collection(self) -> pystac.Collection: - if self._collection is None: - self._collection = pystac.read_file( - f"{self.stac_api}/collections/{self.collection_id}" - ) # type: ignore - assert self._collection is not None - return self._collection - - def inject_links(self, item: dict[str, Any]) -> None: - item["links"] = [ - { - "rel": "collection", - "type": "application/json", - "href": f"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{self.collection_id}", # noqa: E501 - }, - { - "rel": "parent", - "type": "application/json", - "href": f"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{self.collection_id}", # noqa: E501 - }, - { - "rel": "root", - "type": "application/json", - "href": "https://planetarycomputer.microsoft.com/api/stac/v1/", - }, - { - "rel": "self", - "type": "application/geo+json", - "href": f"https://planetarycomputer.microsoft.com/api/stac/v1/collections/{self.collection_id}/items/{item['id']}", # noqa: E501 - }, - { - "rel": "preview", - "href": f"https://planetarycomputer.microsoft.com/api/data/v1/item/map?collection={self.collection_id}&item={item['id']}", # noqa: E501 - "title": "Map of item", - "type": "text/html", - }, - ] - - def inject_assets(self, item: dict[str, Any]) -> None: - item["assets"]["tilejson"] = { - "href": f"https://planetarycomputer.microsoft.com/api/data/v1/item/tilejson.json?collection={self.collection_id}&item={item['id']}&{self.render_config}", # noqa: E501 - "roles": ["tiles"], - "title": "TileJSON with default rendering", - "type": "application/json", - } - item["assets"]["rendered_preview"] = { - "href": f"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection={self.collection_id}&item={item['id']}&{self.render_config}", # noqa: E501 - "rel": "preview", - "roles": ["overview"], - "title": "Rendered preview", - "type": "image/png", - } - - def generate_endpoints( - self, since: datetime.datetime | None = None - ) -> list[tuple[datetime.datetime, datetime.datetime]]: - if self.partition_frequency is None: - raise ValueError("Set partition_frequency") - - start_datetime, end_datetime = self.collection.extent.temporal.intervals[0] - - # https://github.com/dateutil/dateutil/issues/349 - if start_datetime and start_datetime.tzinfo == dateutil.tz.tz.tzlocal(): - start_datetime = start_datetime.astimezone(datetime.timezone.utc) - - if end_datetime and end_datetime.tzinfo == dateutil.tz.tz.tzlocal(): - end_datetime = end_datetime.astimezone(datetime.timezone.utc) - - if end_datetime is None: - end_datetime = pd.Timestamp.utcnow() - - # we need to ensure that the `end_datetime` is past the end of the last partition - # to avoid missing out on the last partition of data. - offset = pd.tseries.frequencies.to_offset(self.partition_frequency) - - if not offset.is_on_offset(start_datetime): - start_datetime = start_datetime - offset - - if not offset.is_on_offset(end_datetime): - end_datetime = end_datetime + offset - - idx = pd.date_range(start_datetime, end_datetime, freq=self.partition_frequency) - - if since: - idx = idx[idx >= since] - pairs = _pairwise(idx) - return list(pairs) +set_json_dumps(dumps) +set_json_loads(orjson.loads) - def export_partition( - self, - conninfo: str, - query: str, - output_protocol: str, - output_path: str, - storage_options: dict[str, Any] | None = None, - rewrite: bool = False, - skip_empty_partitions: bool = False, - ) -> str | None: - storage_options = storage_options or {} - az_fs = fsspec.filesystem(output_protocol, **storage_options) - if az_fs.exists(output_path) and not rewrite: - logger.debug("Path %s already exists.", output_path) - return output_path - - db = pypgstac.db.PgstacDB(conninfo) - with db: - assert db.connection is not None - db.connection.execute("set statement_timeout = 300000;") - # logger.debug("Reading base item") - # TODO: proper escaping - base_item = db.query_one( - f"select * from collection_base_item('{self.collection_id}');" - ) - records = list(db.query(query)) - - if skip_empty_partitions and len(records) == 0: - logger.debug("No records found for query %s.", query) - return None - - items = self.make_pgstac_items(records, base_item) # type: ignore[arg-type] - df = to_geodataframe(items) - filesystem = pyarrow.fs.PyFileSystem(pyarrow.fs.FSSpecHandler(az_fs)) - df.to_parquet(output_path, index=False, filesystem=filesystem) - return output_path - - def export_partition_for_endpoints( - self, - endpoints: tuple[datetime.datetime, datetime.datetime], - conninfo: str, - output_protocol: str, - output_path: str, - storage_options: dict[str, Any], - part_number: int | None = None, - total: int | None = None, - rewrite: bool = False, - skip_empty_partitions: bool = False, - ) -> str | None: - """ - Export results for a pair of endpoints. - """ - a, b = endpoints - base_query = textwrap.dedent( - f"""\ - select * - from pgstac.items - where collection = '{self.collection_id}' - """ - ) - - query = ( - base_query - + f"and datetime >= '{a.isoformat()}' and datetime < '{b.isoformat()}'" - ) - - partition_path = _build_output_path(output_path, part_number, total, a, b) - return self.export_partition( - conninfo, - query, - output_protocol=output_protocol, - output_path=partition_path, - storage_options=storage_options, - rewrite=rewrite, - skip_empty_partitions=skip_empty_partitions, - ) +logger = logging.getLogger(__name__) - def export_collection( - self, - conninfo: str, - output_protocol: str, - output_path: str, - storage_options: dict[str, Any], - rewrite: bool = False, - skip_empty_partitions: bool = False, - ) -> list[str | None]: - base_query = textwrap.dedent( - f"""\ - select * - from pgstac.items - where collection = '{self.collection_id}' - """ - ) - if output_protocol: - output_path = f"{output_protocol}://{output_path}" - - if not self.partition_frequency: - logger.info("Exporting single-partition collection %s", self.collection_id) - logger.debug("query=%s", base_query) - results = [ - self.export_partition( - conninfo, - base_query, - output_protocol, - output_path, - storage_options=storage_options, - rewrite=rewrite, - ) - ] - else: - endpoints = self.generate_endpoints() - total = len(endpoints) - logger.info( - "Exporting %d partitions for collection %s", total, self.collection_id - ) - - results = [] - for i, endpoint in tqdm.auto.tqdm(enumerate(endpoints), total=total): - results.append( - self.export_partition_for_endpoints( - endpoints=endpoint, - conninfo=conninfo, - output_protocol=output_protocol, - output_path=output_path, - storage_options=storage_options, - rewrite=rewrite, - skip_empty_partitions=skip_empty_partitions, - part_number=i, - total=total, - ) - ) +class PgstacRowFactory: + """ + Custom row factory for psycopg to return a tuple of the columns. + """ - return results + def __init__(self, cursor: psycopg.Cursor[Any]) -> None: + self.cursor = cursor - def make_pgstac_items( + def __call__( self, - records: list[ - tuple[str, str, str, datetime.datetime, datetime.datetime, dict[str, Any]] - ], - base_item: dict[str, Any], - ) -> list[dict[str, Any]]: + values: tuple[str, str, str, datetime, datetime, dict[str, Any]], + ) -> dict[str, Any]: """ - Make STAC items out of pgstac records. - - Args: - records: list[tuple] - The dehydrated records from pgstac.items table. - base_item: dict[str, Any] - The base item from the ``collection_base_item`` pgstac function for this - collection. Used for rehydration + Convert the values to a dictionary. """ columns = [ "id", @@ -299,61 +64,291 @@ def make_pgstac_items( "end_datetime", "content", ] + item: dict[str, Any] = dict(zip(columns, values)) + # datetime is in the content too + item.pop("datetime") + item.pop("end_datetime") + + geom = shapely.wkb.loads(item["geometry"], hex=True) + + item["geometry"] = geom.__geo_interface__ + content = item.pop("content") + assert isinstance(content, dict) + if "bbox" in content: + item["bbox"] = content["bbox"] + else: + item["bbox"] = list(geom.bounds) - items = [] - - for record in records: - item = dict(zip(columns, record)) - # datetime is in the content too - item.pop("datetime") - item.pop("end_datetime") - - geom = shapely.wkb.loads(item["geometry"], hex=True) - - item["geometry"] = geom.__geo_interface__ - content = item.pop("content") - assert isinstance(content, dict) - if "bbox" in content: - item["bbox"] = content["bbox"] - else: - item["bbox"] = list(geom.bounds) - - item["assets"] = content["assets"] - if "stac_extensions" in content: - item["stac_extensions"] = content["stac_extensions"] - item["properties"] = content["properties"] - - pypgstac.hydration.hydrate(base_item, item) - - if self.should_inject_dynamic_properties: - self.inject_links(item) - self.inject_assets(item) + item["assets"] = content["assets"] + if "stac_extensions" in content: + item["stac_extensions"] = content["stac_extensions"] + item["properties"] = content["properties"] - items.append(item) + base_item = self.get_baseitem(item["collection"]) + pypgstac.hydration.hydrate(base_item, item) + logger.debug(item) + return item - return items + @functools.lru_cache(maxsize=256) + def get_baseitem(self, collection: str) -> dict[str, Any]: + """ + Get the base item for the collection. + """ + logger.info(f"Getting Base Item for {collection}") + conninfo = self.cursor.connection.info + dsn = conninfo.dsn + password = conninfo.password + with psycopg.connect(dsn, password=password) as conn: + with conn.cursor() as cur: + cur.execute( + "select collection_base_item(%s);", + (collection,), + ) + base_item = cur.fetchone() + if base_item is None: + raise ValueError(f"Collection {collection} not found") + return base_item[0] -def _build_output_path( - base_output_path: str, - part_number: int | None, - total: int | None, - start_datetime: datetime.datetime, - end_datetime: datetime.datetime, +def pgstac_dsn(conninfo: Union[str, None], statement_timeout: Union[int, None]) -> str: + """ + Get the DSN from the connection info. + """ + if conninfo is None: + conninfo = "" + connd = psycopg.conninfo.conninfo_to_dict(conninfo) + options: str = str(connd.get("options", "")) + options += " -c search_path=pgstac,public" + if statement_timeout is not None: + options += f" -c statement_timeout={statement_timeout}" + connd["options"] = options + conninfo = psycopg.conninfo.make_conninfo("", **connd) + return conninfo + + +def pgstac_to_iter( + conninfo: Union[str, None], + collection: Union[str, None] = None, + start_datetime: Union[datetime, None] = None, + end_datetime: Union[datetime, None] = None, + search: Union[dict[str, Any], None] = None, + statement_timeout: Union[int, None] = None, + cursor_itersize: int = 10000, + row_func: Union[Callable, None] = None, +) -> Iterator[dict[str, Any]]: + logger.info("Fetching Data from PGStac Into an Iterator of Items") + conninfo = pgstac_dsn(conninfo, statement_timeout) + + if search is not None and ( + collection is not None or start_datetime is not None or end_datetime is not None + ): + raise ValueError("Cannot use search and collection/datetime at the same time") + if start_datetime is not None and end_datetime is None: + end_datetime = datetime.now(timezone.utc) + + query: str + args: Any + + if search is not None: + logger.info(f"Using CQL2 Filter {search}") + query = "SELECT * FROM search(%s);" + args = (search,) + elif ( + collection is not None + and start_datetime is not None + and end_datetime is not None + ): + logger.info( + f"Using Collection {collection}, Start {start_datetime}, End {end_datetime}" + ) + query = "SELECT * FROM items WHERE collection = %s AND datetime >= %s AND datetime < %s;" + args = (collection, start_datetime, end_datetime) + elif collection is not None: + logger.info(f"Using Collection {collection}") + query = "SELECT * FROM items WHERE collection = %s;" + args = (collection,) + else: + logger.info("With no filter, fetching all items") + query = "SELECT * FROM items;" + args = () + curname = "".join(random.choices(string.ascii_lowercase, k=32)) + with psycopg.connect(conninfo) as conn: + with conn.cursor(curname, row_factory=PgstacRowFactory) as cur: + cur.itersize = cursor_itersize + cur.execute(query, args) + for rec in cur: + if row_func is not None: + rec = row_func(rec) + yield rec + + +def pgstac_to_arrow( + conninfo: str, + collection: Union[str, None] = None, + start_datetime: Union[datetime, None] = None, + end_datetime: Union[datetime, None] = None, + search: Union[dict[str, Any], None] = None, + chunk_size: int = DEFAULT_JSON_CHUNK_SIZE, + schema: Union[pa.Schema, InferredSchema, None] = None, + statement_timeout: Union[int, None] = None, + cursor_itersize: int = 10000, + row_func: Union[Callable, None] = None, +) -> pa.RecordBatchReader: + """ + Convert pgstac items to an arrow record batch reader. + """ + items = pgstac_to_iter( + conninfo, + collection, + start_datetime, + end_datetime, + search, + statement_timeout=statement_timeout, + cursor_itersize=cursor_itersize, + row_func=row_func, + ) + return parse_stac_items_to_arrow(items, chunk_size=chunk_size, schema=schema) + + +def pgstac_to_parquet( + conninfo: str, + output_path: Union[str, Path], + collection: Union[str, None] = None, + start_datetime: Union[datetime, None] = None, + end_datetime: Union[datetime, None] = None, + search: Union[dict[str, Any], None] = None, + chunk_size: int = DEFAULT_JSON_CHUNK_SIZE, + schema: Union[pa.Schema, InferredSchema, None] = None, + statement_timeout: Union[int, None] = None, + cursor_itersize: int = 10000, + row_func: Union[Callable, None] = None, + schema_version: SUPPORTED_PARQUET_SCHEMA_VERSIONS = DEFAULT_PARQUET_SCHEMA_VERSION, + filesystem: Union[pyarrow.fs.FileSystem, None] = None, + **kwargs: Any, ) -> str: - a, b = start_datetime, end_datetime - base_output_path = base_output_path.rstrip("/") + """ + Convert pgstac items to a parquet file. + """ + if filesystem is None: + filesystem, filepath = pyarrow.fs.FileSystem.from_uri(output_path) + else: + filepath = output_path + + filedir = Path(filepath).parent + filesystem.create_dir(str(filedir), recursive=True) + + logger.info(f"Exporting PgSTAC to {filesystem} {filepath}") + + record_batch_reader = pgstac_to_arrow( + conninfo, + collection, + start_datetime, + end_datetime, + search, + chunk_size, + schema, + statement_timeout=statement_timeout, + cursor_itersize=cursor_itersize, + row_func=row_func, + ) + + to_parquet( + record_batch_reader, + output_path=filepath, + filesystem=filesystem, + schema_version=schema_version, + **kwargs, + ) + return str(filepath) + + +@dataclass +class Partition: + collection: str + partition: str + start: datetime + end: datetime + last_updated: datetime + + +def get_pgstac_partitions( + conninfo: str, updated_after: Union[datetime, None] = None +) -> Iterator[Partition]: + db = pgstac_dsn(conninfo, None) + with psycopg.connect(db) as conn: + with conn.cursor(row_factory=psycopg.rows.class_row(Partition)) as cur: + q = """ + SELECT + collection, + CASE WHEN lower(partition_dtrange) = '-infinity' OR upper(partition_dtrange) = 'infinity' THEN + 'items.parquet' + ELSE + format( + 'items_%%s_%%s.parquet', + to_char(lower(partition_dtrange),'YYYYMMDD'), + to_char(upper(partition_dtrange),'YYYYMMDD') + ) + END AS partition, + lower(dtrange) as start, + upper(dtrange) as end, + last_updated + FROM partitions_view + """ + args: Any = () + if updated_after is not None: + q += " WHERE last_updated >= %s" + args = (updated_after,) + q += " ORDER BY last_updated asc" + cur.execute(q, args) + for row in cur: + logger.info(f"Found PgSTAC Partition: {row}") + yield row + + +def sync_pgstac_to_parquet( + conninfo: str, + output_path: Union[str, Path], + updated_after: Union[datetime, None] = None, + chunk_size: int = DEFAULT_JSON_CHUNK_SIZE, + schema: Union[pa.Schema, InferredSchema, None] = None, + statement_timeout: Union[int, None] = None, + cursor_itersize: int = 10000, + row_func: Union[Callable, None] = None, + schema_version: SUPPORTED_PARQUET_SCHEMA_VERSIONS = DEFAULT_PARQUET_SCHEMA_VERSION, + filesystem: Union[pyarrow.fs.FileSystem, None] = None, + **kwargs: Any, +) -> str: + """ + Use the last_updated partition metadata in pgstac to sync only changed + items to parquet. + """ - if part_number is not None and total is not None: - output_path = ( - f"{base_output_path}/part-{part_number:0{len(str(total * 10))}}_" - f"{a.isoformat()}_{b.isoformat()}.parquet" - ) + if filesystem is None: + filesystem, filepath = pyarrow.fs.FileSystem.from_uri(output_path) else: - token = hashlib.md5( - "".join([a.isoformat(), b.isoformat()]).encode() - ).hexdigest() - output_path = ( - f"{base_output_path}/part-{token}_{a.isoformat()}_{b.isoformat()}.parquet" + filepath = output_path + + filedir = Path(filepath) + filesystem.create_dir(str(filedir), recursive=True) + + logger.info( + f"Syncing PgSTAC partitions that have been updated since {updated_after} to {output_path} on filesystem {filesystem}." + ) + for p in get_pgstac_partitions(conninfo, updated_after): + of = filedir / p.collection / p.partition + pgstac_to_parquet( + conninfo, + output_path=of, + collection=p.collection, + start_datetime=p.start, + end_datetime=p.end, + row_func=row_func, + chunk_size=chunk_size, + schema=schema, + statement_timeout=statement_timeout, + cursor_itersize=cursor_itersize, + schema_version=schema_version, + filesystem=filesystem, + **kwargs, ) - return output_path + return str(of) diff --git a/tests/test_pgstac_reader.py b/tests/test_pgstac_reader.py index 9fa6fdb..5d4948b 100644 --- a/tests/test_pgstac_reader.py +++ b/tests/test_pgstac_reader.py @@ -1,201 +1,104 @@ -import datetime import json import pathlib -import sys -import dateutil -import pandas as pd -import pystac +import docker +import pypgstac import pytest +from pypgstac.db import PgstacDB +from pypgstac.load import Loader -import stac_geoparquet.pgstac_reader -from stac_geoparquet._compat import PYSTAC_1_7_0 -from stac_geoparquet.utils import assert_equal +from stac_geoparquet.pgstac_reader import ( + pgstac_dsn, + pgstac_to_arrow, + pgstac_to_iter, +) HERE = pathlib.Path(__file__).parent - -@pytest.mark.vcr -@pytest.mark.skipif( - sys.version_info < (3, 10), reason="vcr tests require python3.10 or higher" -) -def test_naip_item(): - base_item = { - "type": "Feature", - "assets": { - "image": { - "type": "image/tiff; application=geotiff; profile=cloud-optimized", - "roles": ["data"], - "title": "RGBIR COG tile", - "eo:bands": [ - {"name": "Red", "common_name": "red"}, - {"name": "Green", "common_name": "green"}, - {"name": "Blue", "common_name": "blue"}, - { - "name": "NIR", - "common_name": "nir", - "description": "near-infrared", - }, - ], - }, - "metadata": { - "type": "text/plain", - "roles": ["metadata"], - "title": "FGDC Metdata", - }, - "thumbnail": { - "type": "image/jpeg", - "roles": ["thumbnail"], - "title": "Thumbnail", - }, +DOCKERIMG = "ghcr.io/stac-utils/pgstac:latest" +NAIPDATA = HERE / "data" / "naip-pc.json" + + +@pytest.fixture(scope="session") +def pgstac_postgres(): + """ + Start a pgstac postgres instance in Docker for testing. + Yields the connection info string. + """ + assert NAIPDATA.exists(), "NAIP data file does not exist." + client = docker.from_env() + client.images.pull(DOCKERIMG) + container = client.containers.run( + DOCKERIMG, + name="test-pgstac", + ports={"5432/tcp": 5433}, + environment={ + "POSTGRES_DB": "pgstac", + "POSTGRES_USER": "postgres", + "POSTGRES_PASSWORD": "pgstac", }, - "collection": "naip", - "stac_version": "1.0.0", - } - - records = [ - ( - "pa_m_4108053_se_17_1_20150725_20151201", - "0103000020E61000000100000005000000D0D03FC1C51754C0D4635B069C8F44407D259012BB1754C0382D78D15798444089601C5C3A1C54C0D94125AE63984440A8E49CD8431C54C0A4FCA4DAA78F4440D0D03FC1C51754C0D4635B069C8F4440", # noqa: E501 - "naip", - datetime.datetime(2015, 7, 25, 0, 0, tzinfo=datetime.timezone.utc), - datetime.datetime(2015, 7, 25, 0, 0, tzinfo=datetime.timezone.utc), - { - "assets": { - "image": { - "href": "https://naipeuwest.blob.core.windows.net/naip/v002/pa/2015/pa_100cm_2015/41080/m_4108053_se_17_1_20150725.tif" # noqa: E501 - }, - "metadata": { - "href": "https://naipeuwest.blob.core.windows.net/naip/v002/pa/2015/pa_fgdc_2015/41080/m_4108053_se_17_1_20150725.txt" # noqa: E501 - }, - "thumbnail": { - "href": "https://naipeuwest.blob.core.windows.net/naip/v002/pa/2015/pa_100cm_2015/41080/m_4108053_se_17_1_20150725.200.jpg" # noqa: E501 - }, - }, - "properties": { - "gsd": 1.0, - "datetime": "2015-07-25T00:00:00Z", - "naip:year": "2015", - "proj:bbox": [546872.0, 4552485.0, 552765.0, 4560060.0], - "proj:epsg": 26917, - "naip:state": "pa", - "proj:shape": [7575, 5893], - "proj:transform": [ - 1.0, - 0.0, - 546872.0, - 0.0, - -1.0, - 4560060.0, - 0.0, - 0.0, - 1.0, - ], - }, - "stac_extensions": [ - "https://stac-extensions.github.io/eo/v1.0.0/schema.json", - "https://stac-extensions.github.io/projection/v1.0.0/schema.json", - ], - }, - ) - ] - - cfg = stac_geoparquet.pgstac_reader.CollectionConfig( - collection_id="naip", - render_config="assets=image&asset_bidx=image%7C1%2C2%2C3&format=png", + detach=True, + auto_remove=False, + remove=False, ) - result = cfg.make_pgstac_items(records, base_item)[0] - # shapely uses tuples instead of lists - result = pystac.read_dict(result) - - expected = pystac.read_file( - "https://planetarycomputer.microsoft.com/api/stac/v1/collections/naip/items/pa_m_4108053_se_17_1_20150725_20151201" # noqa: E501 + constr = pgstac_dsn( + "postgres://postgres:pgstac@localhost:5433/pgstac", statement_timeout=30000 ) - if PYSTAC_1_7_0: - # https://github.com/stac-utils/pystac/issues/1102 - expected.remove_links(rel=pystac.RelType.SELF) - result.remove_links(rel=pystac.RelType.SELF) - - assert_equal(result, expected, ignore_none=True) - - -@pytest.mark.vcr -@pytest.mark.skipif( - sys.version_info < (3, 10), reason="vcr tests require python3.10 or higher" -) -def test_sentinel2_l2a(): - record = json.loads(HERE.joinpath("record_sentinel2_l2a.json").read_text()) - base_item = json.loads(HERE.joinpath("base_sentinel2_l2a.json").read_text()) - record[3] = dateutil.parser.parse(record[3]) - record[4] = dateutil.parser.parse(record[4]) - - config = stac_geoparquet.pgstac_reader.CollectionConfig( - collection_id="sentinel-2-l2a", - partition_frequency=None, - stac_api="https://planetarycomputer.microsoft.com/api/stac/v1", - should_inject_dynamic_properties=True, - render_config="assets=visual&asset_bidx=visual%7C1%2C2%2C3&nodata=0&format=png", - ) - result = pystac.read_dict(config.make_pgstac_items([record], base_item)[0]) - expected = pystac.read_file( - "https://planetarycomputer.microsoft.com/api/stac/v1/collections/sentinel-2-l2a/items/S2A_MSIL2A_20150704T101006_R022_T35XQA_20210411T133707" # noqa: E501 - ) - if PYSTAC_1_7_0: - # https://github.com/stac-utils/pystac/issues/1102 - expected.remove_links(rel=pystac.RelType.SELF) - result.remove_links(rel=pystac.RelType.SELF) - - expected.remove_links(rel=pystac.RelType.LICENSE) - assert_equal(result, expected, ignore_none=True) - - -@pytest.mark.vcr -@pytest.mark.skipif( - sys.version_info < (3, 10), reason="vcr tests require python3.10 or higher" -) -def test_generate_endpoints(): - cfg = stac_geoparquet.pgstac_reader.CollectionConfig( - collection_id="naip", partition_frequency="YS" - ) - endpoints = cfg.generate_endpoints() - assert endpoints[0][0] == pd.Timestamp("2010-01-01 00:00:00+0000", tz="utc") - assert endpoints[-1][1] >= pd.Timestamp("2021-01-01 00:00:00+0000", tz="utc") - - endpoints = cfg.generate_endpoints(since=pd.Timestamp("2018-01-01", tz="utc")) - assert endpoints[0][0] == pd.Timestamp("2018-01-01 00:00:00+0000", tz="utc") - assert endpoints[-1][1] >= pd.Timestamp("2021-01-01 00:00:00+0000", tz="utc") - - -@pytest.mark.parametrize( - "part_number, total, start_datetime, end_datetime, expected", - [ - ( - None, - None, - pd.Timestamp("2017-05-15 00:16:52+0000", tz="UTC"), - pd.Timestamp("2017-05-22 00:16:52+0000", tz="UTC"), - "items/part-6c6824fb552381663cc7c7a113560cc7_2017-05-15T00:16:52+00:00_2017-05-22T00:16:52+00:00.parquet", - ), - ( - 1, - 2, - pd.Timestamp("2017-05-15 00:16:52+0000", tz="UTC"), - pd.Timestamp("2017-05-22 00:16:52+0000", tz="UTC"), - "items/part-01_2017-05-15T00:16:52+00:00_2017-05-22T00:16:52+00:00.parquet", - ), - ( - 1, - 10, - pd.Timestamp("2017-05-15 00:16:52+0000", tz="UTC"), - pd.Timestamp("2017-05-22 00:16:52+0000", tz="UTC"), - "items/part-001_2017-05-15T00:16:52+00:00_2017-05-22T00:16:52+00:00.parquet", - ), - ], -) -def test_build_output_path(part_number, total, start_datetime, end_datetime, expected): - base_output_path = "items/" - result = stac_geoparquet.pgstac_reader._build_output_path( - base_output_path, part_number, total, start_datetime, end_datetime - ) - assert result == expected + try: + with PgstacDB(constr, debug=True) as db: + db.wait() + assert db.version >= "0.3.4" + loader = Loader(db) + naip_collection = {"id": "naip", "title": "NAIP Imagery Test"} + loader.load_collections([naip_collection]) + collection_count = db.query_one( + "SELECT COUNT(*) FROM collections WHERE id = 'naip'" + ) + assert collection_count == 1 + + items = json.loads(NAIPDATA.read_text()) + assert len(items) == 4, "Expected 4 items in NAIP data file." + + loader.load_items( + items, + pypgstac.load.Methods.ignore, + ) + item_count = db.query_one( + "SELECT COUNT(*) FROM items WHERE collection = 'naip'" + ) + assert item_count == 4 + yield constr + finally: + pass + try: + container.kill() + except Exception: + pass + try: + container.remove(force=True) + except Exception: + pass + + +def test_pgstac_reader_iter(pgstac_postgres): + """ + Test reading from a pgstac instance. + """ + items = list(pgstac_to_iter(pgstac_postgres)) + assert len(items) == 4 + for item in items: + assert item["collection"] == "naip" + assert item["geometry"]["type"] == "Polygon" + + +def test_pgstac_reader_arrow(pgstac_postgres): + """ + Test reading from a pgstac instance. + """ + items = pgstac_to_arrow(pgstac_postgres) + assert items.schema.field("collection").name == "collection" + assert items.schema.field("collection").type == "string" + assert items.schema.field("id").name == "id" + assert items.schema.field("id").type == "string" + assert items.schema.field("geometry").name == 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