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a/space2stats_api/src/pyproject.toml b/space2stats_api/src/pyproject.toml index 27887df6..ea93c5b7 100644 --- a/space2stats_api/src/pyproject.toml +++ b/space2stats_api/src/pyproject.toml @@ -21,7 +21,8 @@ starlette-cramjam = ">=0.3,<0.4" mangum = "*" asgi-s3-response-middleware = "^0.0.4" boto3 = "^1.35.11" -numpy = "^2.1.1" +numpy = "^1.24.0" +h3ronpy = "^0.21.0" [tool.poetry.group.lambda.dependencies] mangum = "*" diff --git a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index de597c48..e8f946cf 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -1,145 +1,77 @@ -from typing import Any, Dict, Iterable, List, Literal, Optional, Set +from typing import Any, Dict, List, Literal -import h3 -from shapely.geometry import MultiPolygon, Point, Polygon, mapping, shape +import pyarrow as pa +from h3ronpy import ContainmentMode +from h3ronpy.arrow.vector import ( + cells_to_wkb_points, + cells_to_wkb_polygons, + geometry_to_cells, +) +from shapely import wkb +from shapely.geometry import mapping, shape -# https://h3geo.org/docs/core-library/restable -MAX_RESOLUTION = 15 - - -def _recursive_polyfill( - aoi_geojson: Dict[str, Any], resolution: int, original_resolution: int -) -> Set[str]: - # Attempt to get H3 IDs at the current resolution - h3_ids = h3.polyfill(aoi_geojson, resolution, geo_json_conformant=True) - - # If valid H3 IDs are found, return them - if h3_ids: - return {h3.h3_to_parent(h3_id, original_resolution) for h3_id in h3_ids} - - # If we haven't reached the maximum resolution, try the next higher resolution - if resolution < MAX_RESOLUTION: - return _recursive_polyfill(aoi_geojson, resolution + 1, original_resolution) - - return set() - - -def _find_within(aoi_geojson: Dict[str, Any], h3_ids: Iterable[str]) -> Set[str]: - # Convert AOI GeoJSON to Shapely geometry - aoi_shape = shape(aoi_geojson) - - # Find H3 IDs that are within the polygon - contained_h3_ids = [ - h3_id - for h3_id in h3_ids - if aoi_shape.contains(Polygon(h3.h3_to_geo_boundary(h3_id, geo_json=True))) - ] - - return set(contained_h3_ids) - - -def _find_touches(aoi_geojson: Dict[str, Any], h3_ids: Iterable[str]) -> Set[str]: - aoi_shape = shape(aoi_geojson) - - neighbors = set() - for h3_id in h3_ids: - parent_id = h3.h3_to_parent(h3_id, h3.h3_get_resolution(h3_id) - 1) - - for n_h3_id in h3.h3_to_children( - parent_id, h3.h3_get_resolution(parent_id) + 1 - ): - boundary = Polygon(h3.h3_to_geo_boundary(n_h3_id, geo_json=True)) - if h3.h3_indexes_are_neighbors(h3_id, n_h3_id) and aoi_shape.intersects( - boundary - ): - neighbors.add(n_h3_id) - - return neighbors.union(h3_ids) - - -def _generate_h3_ids( - aoi_geojson: Dict[str, Any], - resolution: int, - spatial_join_method: Literal["touches", "within", "centroid"], -) -> Set[str]: - if spatial_join_method not in ["touches", "within", "centroid"]: - raise ValueError(f"Invalid spatial join method: {spatial_join_method}") - - # Generate H3 hexagons covering the AOI - # Polyfill defines containment based on centroid: - # https://h3geo.org/docs/3.x/api/regions/#polyfill - h3_ids = _recursive_polyfill(aoi_geojson, resolution, resolution) - - if spatial_join_method == "within": - h3_ids = _find_within(aoi_geojson, h3_ids) - - if spatial_join_method == "touches": - h3_ids = _find_touches(aoi_geojson, h3_ids) - - return h3_ids +CONTAINMENT_MODE_MAP = { + "centroid": ContainmentMode.ContainsCentroid, + "touches": ContainmentMode.IntersectsBoundary, + "within": ContainmentMode.ContainsBoundary, +} def generate_h3_ids( aoi_geojson: Dict[str, Any], resolution: int, - spatial_join_method: Literal["touches", "within", "centroid"], -) -> Set[str]: + spatial_join_method: Literal["touches", "within", "centroid"] = "centroid", +) -> List[int]: """ - Generate H3 hexagon IDs for a given AOI GeoJSON object, supporting both Polygon and MultiPolygon. - - Parameters: - aoi_geojson (Dict[str, Any]): The AOI GeoJSON object. - resolution (int): The H3 resolution. - spatial_join_method (str): The spatial join method to use. - - Returns: - Set[str]: A set of H3 hexagon IDs. + Generate H3 IDs using h3ronpy's geometry_to_cells with the correct containment mode. + Returns the H3 IDs in uint64 format for geometry creation. """ - if spatial_join_method not in ["touches", "within", "centroid"]: - raise ValueError("Invalid spatial join method") + geom = shape(aoi_geojson) + containment_mode = CONTAINMENT_MODE_MAP.get(spatial_join_method) - aoi_shape = shape(aoi_geojson) + if containment_mode is None: + raise ValueError(f"Invalid spatial join method: {spatial_join_method}") - if isinstance(aoi_shape, MultiPolygon): - h3_ids = set() - for geom in aoi_shape.geoms: - # Convert each geometry to GeoJSON - geom_geojson = mapping(geom) - h3_ids.update( - _generate_h3_ids(geom_geojson, resolution, spatial_join_method) - ) - else: - # Single polygon case - h3_ids = _generate_h3_ids(aoi_geojson, resolution, spatial_join_method) + # Generate H3 IDs as uint64 + h3_ids_uint64 = geometry_to_cells( + geom, resolution, containment_mode=containment_mode + ) - return h3_ids + return h3_ids_uint64 def generate_h3_geometries( - h3_ids: List[str], geometry_type: Optional[str] -) -> List[Dict[str, Any]]: + h3_ids_uint64: List[int], geometry_type: Literal["polygon", "point"] = "polygon" +) -> List[Dict]: """ - Generate geometries (polygon or point) for a list of H3 hexagon IDs. + Generate geometries (GeoJSON polygons or points) for a list of H3 hexagon IDs (as uint64). Parameters: - h3_ids (List[str]): A list of H3 hexagon IDs. + h3_ids_uint64 (List[int]): A list of H3 hexagon IDs in uint64 format. geometry_type (Optional[str]): The type of geometry to generate ('polygon' or 'point'). Returns: - List[Dict[str, Any]]: A list of geometries in GeoJSON format. + List[Dict]: A list of GeoJSON geometries (Polygon or Point). """ - if geometry_type not in ["polygon", "point"]: - raise ValueError("Invalid geometry type") - - geometries = [] - for h3_id in h3_ids: - if geometry_type == "polygon": - hex_boundary = h3.h3_to_geo_boundary(h3_id, geo_json=True) - geometries.append(mapping(Polygon(hex_boundary))) - elif geometry_type == "point": - # h3_to_geo does not have geo_json parameter to invert order of coords - x, y = h3.h3_to_geo(h3_id) - geometries.append(mapping(Point(y, x))) - - return geometries + if geometry_type == "polygon": + # Convert H3 cells to WKB polygons + wkb_geometries = cells_to_wkb_polygons(h3_ids_uint64) + elif geometry_type == "point": + # Convert H3 cells to WKB points (centroid of each hexagon) + wkb_geometries = cells_to_wkb_points(h3_ids_uint64) + else: + raise ValueError("Invalid geometry type. Use 'polygon' or 'point'.") + + # Convert pyarrow Array to GeoJSON geometries + geojson_geometries = [] + for geom in wkb_geometries: + if isinstance(geom, pa.Scalar): + # Convert to buffer and then to bytes + shapely_geom = wkb.loads(geom.as_buffer().to_pybytes()) + # Convert Shapely geometry to GeoJSON-like dict + geojson_geometries.append(mapping(shapely_geom)) + else: + raise TypeError(f"Expected pyarrow Scalar, but got {type(geom).__name__}") + + return geojson_geometries diff --git a/space2stats_api/src/tests/test_h3_utils.py b/space2stats_api/src/tests/test_h3_utils.py index 86031780..6de9de7a 100644 --- a/space2stats_api/src/tests/test_h3_utils.py +++ b/space2stats_api/src/tests/test_h3_utils.py @@ -1,4 +1,5 @@ import pytest +from h3ronpy.arrow import cells_parse from shapely.geometry import MultiPolygon, Polygon, mapping from space2stats.h3_utils import generate_h3_geometries, generate_h3_ids @@ -83,7 +84,7 @@ def test_generate_h3_sliver_polygon_touches(): "touches", ) - for h in ["867a74817ffffff", "867a74807ffffff"]: + for h in cells_parse(["867a74817ffffff", "867a74807ffffff"]): assert h in data, f"Missing {h} in generated hexagons" assert len(data) == 2 @@ -123,9 +124,7 @@ def test_generate_h3_sliver_polygon_centroid(): 6, "centroid", ) - h = "867a74817ffffff" - assert "867a74817ffffff" in data, f"{h} not in generated hexagons" - assert len(data) == 1 + assert len(data) == 0 if __name__ == "__main__": From 4283cc8793df70bbf366cfcc13d5523b3c8a95d6 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 3 Oct 2024 10:53:34 -0700 Subject: [PATCH 02/18] Update lib.py for use of h3ronpy --- space2stats_api/src/space2stats/lib.py | 15 ++++++++++++--- space2stats_api/src/tests/test_module.py | 3 +-- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/space2stats_api/src/space2stats/lib.py b/space2stats_api/src/space2stats/lib.py index 6b8e4eb7..b3bf2f59 100644 --- a/space2stats_api/src/space2stats/lib.py +++ b/space2stats_api/src/space2stats/lib.py @@ -3,6 +3,7 @@ import psycopg as pg from geojson_pydantic import Feature +from h3ronpy.arrow import cells_to_string from psycopg import Connection from .h3_utils import generate_h3_geometries, generate_h3_ids @@ -36,7 +37,7 @@ def __exit__(self, exc_type, exc_value, traceback) -> None: if self.conn: self.conn.close() - def _get_summaries(self, fields: List[str], h3_ids: List[str]): + def _get_summaries(self, fields: List[str], h3_ids: List[int]): colnames = ["hex_id"] + fields cols = [pg.sql.Identifier(c) for c in colnames] sql_query = pg.sql.SQL( @@ -53,7 +54,7 @@ def _get_summaries(self, fields: List[str], h3_ids: List[str]): cur.execute( sql_query, [ - h3_ids, + cells_to_string(h3_ids).to_pylist(), ], ) rows = cur.fetchall() @@ -72,6 +73,10 @@ def summaries( if not isinstance(aoi, Feature): aoi = AoiModel.model_validate(aoi) + invalid_fields = [field for field in fields if field not in self.fields()] + if invalid_fields: + raise ValueError(f"Invalid fields: {invalid_fields}") + # Get H3 ids from geometry resolution = 6 h3_ids = generate_h3_ids( @@ -137,6 +142,10 @@ def aggregate( if not isinstance(aoi, Feature): aoi = AoiModel.model_validate(aoi) + invalid_fields = [field for field in fields if field not in self.fields()] + if invalid_fields: + raise ValueError(f"Invalid fields: {invalid_fields}") + # Get H3 ids from geometry resolution = 6 h3_ids = list( @@ -168,7 +177,7 @@ def aggregate( with self.conn.cursor() as cur: cur.execute( sql_query, - [h3_ids], + [cells_to_string(h3_ids).to_pylist()], ) row = cur.fetchone() # Get a single row of results colnames = [desc[0] for desc in cur.description] diff --git a/space2stats_api/src/tests/test_module.py b/space2stats_api/src/tests/test_module.py index 4e2be5ee..1e61ac1f 100644 --- a/space2stats_api/src/tests/test_module.py +++ b/space2stats_api/src/tests/test_module.py @@ -1,4 +1,3 @@ -import psycopg as pg import pytest from geojson_pydantic import Feature from space2stats.lib import Settings, StatsTable @@ -151,7 +150,7 @@ def test_aggregate_invalid_field(mock_env, database, aoi_example): ) with StatsTable.connect(settings) as stats_table: - with pytest.raises(pg.errors.UndefinedColumn): + with pytest.raises(ValueError): stats_table.aggregate( aoi=aoi_example, spatial_join_method="centroid", From 66e3aeaec59cb03dd4b90c75b46e6f5b213bc851 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 3 Oct 2024 10:58:16 -0700 Subject: [PATCH 03/18] Update ValueError for invalid fields --- space2stats_api/src/space2stats/api/app.py | 4 ++-- space2stats_api/src/tests/test_api.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/space2stats_api/src/space2stats/api/app.py b/space2stats_api/src/space2stats/api/app.py index 182d7a1a..1556543a 100644 --- a/space2stats_api/src/space2stats/api/app.py +++ b/space2stats_api/src/space2stats/api/app.py @@ -62,8 +62,8 @@ def get_summary(body: SummaryRequest, table: StatsTable = Depends(stats_table)): body.fields, body.geometry, ) - except pg.errors.UndefinedColumn as e: - raise HTTPException(status_code=400, detail=e.diag.message_primary) from e + except ValueError as e: + raise HTTPException(status_code=400, detail=str(e)) from e @app.post("/aggregate", response_model=Dict[str, float]) def get_aggregate(body: AggregateRequest, table: StatsTable = Depends(stats_table)): diff --git a/space2stats_api/src/tests/test_api.py b/space2stats_api/src/tests/test_api.py index b1e6d68a..bb9b76a9 100644 --- a/space2stats_api/src/tests/test_api.py +++ b/space2stats_api/src/tests/test_api.py @@ -53,7 +53,7 @@ def test_bad_fields_validated(client): response = client.post("/summary", json=request_payload) assert response.status_code == 400 - assert response.json() == {"error": 'column "a_non_existent_field" does not exist'} + assert response.json() == {"error": "Invalid fields: ['a_non_existent_field']"} @pytest.mark.parametrize("aggregation_type", ["sum", "avg", "count", "max", "min"]) From 1507512867d034c9f9ea6c27808e1a36cd75b9a0 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 3 Oct 2024 11:25:45 -0700 Subject: [PATCH 04/18] Add documentation on bug --- notebooks/user-docs/space2stats_api_bug.ipynb | 344 ++++++++++++++++++ space2stats_api/src/space2stats/h3_utils.py | 10 +- 2 files changed, 350 insertions(+), 4 deletions(-) create mode 100644 notebooks/user-docs/space2stats_api_bug.ipynb diff --git a/notebooks/user-docs/space2stats_api_bug.ipynb b/notebooks/user-docs/space2stats_api_bug.ipynb new file mode 100644 index 00000000..9af5affc --- /dev/null +++ b/notebooks/user-docs/space2stats_api_bug.ipynb @@ -0,0 +1,344 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict\n", + "\n", + "import numpy as np\n", + "import requests\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from shapely.geometry import shape\n", + "from lonboard import Map, ScatterplotLayer\n", + "from geojson_pydantic import Feature, Polygon" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_URL = \"http://localhost:8000\"\n", + "FIELDS_ENDPOINT = f\"{BASE_URL}/fields\"\n", + "SUMMARY_ENDPOINT = f\"{BASE_URL}/summary\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available Fields: ['sum_pop_2020', 'ogc_fid', 'sum_pop_f_0_2020', 'sum_pop_f_10_2020', 'sum_pop_f_15_2020', 'sum_pop_f_1_2020', 'sum_pop_f_20_2020', 'sum_pop_f_25_2020', 'sum_pop_f_30_2020', 'sum_pop_f_35_2020', 'sum_pop_f_40_2020', 'sum_pop_f_45_2020', 'sum_pop_f_50_2020', 'sum_pop_f_55_2020', 'sum_pop_f_5_2020', 'sum_pop_f_60_2020', 'sum_pop_f_65_2020', 'sum_pop_f_70_2020', 'sum_pop_f_75_2020', 'sum_pop_f_80_2020', 'sum_pop_m_0_2020', 'sum_pop_m_10_2020', 'sum_pop_m_15_2020', 'sum_pop_m_1_2020', 'sum_pop_m_20_2020', 'sum_pop_m_25_2020', 'sum_pop_m_30_2020', 'sum_pop_m_35_2020', 'sum_pop_m_40_2020', 'sum_pop_m_45_2020', 'sum_pop_m_50_2020', 'sum_pop_m_55_2020', 'sum_pop_m_5_2020', 'sum_pop_m_60_2020', 'sum_pop_m_65_2020', 'sum_pop_m_70_2020', 'sum_pop_m_75_2020', 'sum_pop_m_80_2020', 'sum_pop_m_2020', 'sum_pop_f_2020']\n" + ] + } + ], + "source": [ + "response = requests.get(FIELDS_ENDPOINT)\n", + "if response.status_code != 200:\n", + " raise Exception(f\"Failed to get fields: {response.text}\")\n", + "\n", + "available_fields = response.json()\n", + "print(\"Available Fields:\", available_fields)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "AOIModel = Feature[Polygon, Dict]\n", + "\n", + "# ~kenya\n", + "aoi = {\n", + " \"type\": \"Feature\",\n", + " \"geometry\": {\n", + " \"type\": \"Polygon\",\n", + " \"coordinates\": [\n", + " [\n", + " [33.78593974945852, 5.115816884114494],\n", + " [33.78593974945852, -4.725410543134203],\n", + " [41.94362577283266, -4.725410543134203],\n", + " [41.94362577283266, 5.115816884114494],\n", + " [33.78593974945852, 5.115816884114494]\n", + " ]\n", + " ]\n", + " },\n", + " \"properties\": {\n", + " \"name\": \"Updated AOI\"\n", + " }\n", + "}\n", + " \n", + " \n", + "\n", + "feat = AOIModel(**aoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Request Payload\n", + "request_payload = {\n", + " \"aoi\": aoi,\n", + " \"spatial_join_method\": \"centroid\",\n", + " \"fields\": [\"sum_pop_2020\"], \n", + " \"geometry\": \"point\"\n", + "}\n", + "\n", + "# Get Summary Data\n", + "response = requests.post(SUMMARY_ENDPOINT, json=request_payload)\n", + "if response.status_code != 200:\n", + " raise Exception(f\"Failed to get summary: {response.text}\")\n", + "\n", + "summary_data = response.json()\n", + "df = pd.DataFrame(summary_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + 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1):\n", + " if breaks[i] <= value < breaks[i + 1]:\n", + " return colors[i]\n", + " return colors[-1] # In case value exceeds all breaks\n", + "\n", + "# Map sum_pop_2020 values to colors using the custom function\n", + "gdf['color'] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", + "colors = np.uint8(gdf['color'].tolist())\n", + "\n", + "# Create the scatterplot layer with the assigned colors\n", + "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=colors)\n", + "\n", + "m = Map(layer)\n", + "m" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index e8f946cf..01222704 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -66,12 +66,14 @@ def generate_h3_geometries( # Convert pyarrow Array to GeoJSON geometries geojson_geometries = [] for geom in wkb_geometries: - if isinstance(geom, pa.Scalar): - # Convert to buffer and then to bytes - shapely_geom = wkb.loads(geom.as_buffer().to_pybytes()) + if isinstance(geom, pa.LargeBinaryScalar): + # Convert the LargeBinaryScalar to bytes + shapely_geom = wkb.loads(geom.as_py()) # Convert Shapely geometry to GeoJSON-like dict geojson_geometries.append(mapping(shapely_geom)) else: - raise TypeError(f"Expected pyarrow Scalar, but got {type(geom).__name__}") + raise TypeError( + f"Expected pa.LargeBinaryScalar, but got {type(geom).__name__}" + ) return geojson_geometries From a32650d350147eb2d67cdf1a13f9913debe4522b Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 3 Oct 2024 14:17:34 -0700 Subject: [PATCH 05/18] Fix issue with Point generation h3ronpy currently has a bug with cells_to_wkb_points which returns matching coordinates. --- notebooks/user-docs/space2stats_api_bug.ipynb | 344 ------------------ .../user-docs/space2stats_api_demo.ipynb | 54 +-- space2stats_api/src/space2stats/h3_utils.py | 47 ++- 3 files changed, 50 insertions(+), 395 deletions(-) delete mode 100644 notebooks/user-docs/space2stats_api_bug.ipynb diff --git a/notebooks/user-docs/space2stats_api_bug.ipynb b/notebooks/user-docs/space2stats_api_bug.ipynb deleted file mode 100644 index 9af5affc..00000000 --- a/notebooks/user-docs/space2stats_api_bug.ipynb +++ /dev/null @@ -1,344 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Dict\n", - "\n", - "import numpy as np\n", - "import requests\n", - "import pandas as pd\n", - "import geopandas as gpd\n", - "from shapely.geometry import shape\n", - "from lonboard import Map, ScatterplotLayer\n", - "from geojson_pydantic import Feature, Polygon" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "BASE_URL = \"http://localhost:8000\"\n", - "FIELDS_ENDPOINT = f\"{BASE_URL}/fields\"\n", - "SUMMARY_ENDPOINT = f\"{BASE_URL}/summary\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available Fields: ['sum_pop_2020', 'ogc_fid', 'sum_pop_f_0_2020', 'sum_pop_f_10_2020', 'sum_pop_f_15_2020', 'sum_pop_f_1_2020', 'sum_pop_f_20_2020', 'sum_pop_f_25_2020', 'sum_pop_f_30_2020', 'sum_pop_f_35_2020', 'sum_pop_f_40_2020', 'sum_pop_f_45_2020', 'sum_pop_f_50_2020', 'sum_pop_f_55_2020', 'sum_pop_f_5_2020', 'sum_pop_f_60_2020', 'sum_pop_f_65_2020', 'sum_pop_f_70_2020', 'sum_pop_f_75_2020', 'sum_pop_f_80_2020', 'sum_pop_m_0_2020', 'sum_pop_m_10_2020', 'sum_pop_m_15_2020', 'sum_pop_m_1_2020', 'sum_pop_m_20_2020', 'sum_pop_m_25_2020', 'sum_pop_m_30_2020', 'sum_pop_m_35_2020', 'sum_pop_m_40_2020', 'sum_pop_m_45_2020', 'sum_pop_m_50_2020', 'sum_pop_m_55_2020', 'sum_pop_m_5_2020', 'sum_pop_m_60_2020', 'sum_pop_m_65_2020', 'sum_pop_m_70_2020', 'sum_pop_m_75_2020', 'sum_pop_m_80_2020', 'sum_pop_m_2020', 'sum_pop_f_2020']\n" - ] - } - ], - "source": [ - "response = requests.get(FIELDS_ENDPOINT)\n", - "if response.status_code != 200:\n", - " raise Exception(f\"Failed to get fields: {response.text}\")\n", - "\n", - "available_fields = response.json()\n", - "print(\"Available Fields:\", available_fields)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "AOIModel = Feature[Polygon, Dict]\n", - "\n", - "# ~kenya\n", - "aoi = {\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"Polygon\",\n", - " \"coordinates\": [\n", - " [\n", - " [33.78593974945852, 5.115816884114494],\n", - " [33.78593974945852, -4.725410543134203],\n", - " [41.94362577283266, -4.725410543134203],\n", - " [41.94362577283266, 5.115816884114494],\n", - " [33.78593974945852, 5.115816884114494]\n", - " ]\n", - " ]\n", - " },\n", - " \"properties\": {\n", - " \"name\": \"Updated AOI\"\n", - " }\n", - "}\n", - " \n", - " \n", - "\n", - "feat = AOIModel(**aoi)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Define the Request Payload\n", - "request_payload = {\n", - " \"aoi\": aoi,\n", - " \"spatial_join_method\": \"centroid\",\n", - " \"fields\": [\"sum_pop_2020\"], \n", - " \"geometry\": \"point\"\n", - "}\n", - "\n", - "# Get Summary Data\n", - "response = requests.post(SUMMARY_ENDPOINT, json=request_payload)\n", - "if response.status_code != 200:\n", - " raise Exception(f\"Failed to get summary: {response.text}\")\n", - "\n", - "summary_data = response.json()\n", - "df = pd.DataFrame(summary_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"gdf" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dcab21a133cc4fe5b7419ea14c15fe52", - "version_major": 2, - "version_minor": 1 - }, - "text/plain": [ - "Map(basemap_style=\n", - "[\n", - " [\n", - " 2…" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Define custom breaks and corresponding RGBA colors\n", - "breaks = [0, 1, 1000, 10000, 50000, 100000, 200000, gdf[\"sum_pop_2020\"].max()]\n", - "colors = np.array([\n", - " [211, 211, 211, 255], # Light gray for 0\n", - " [255, 255, 0, 255], # Yellow for 1-1000\n", - " [255, 165, 0, 255], # Orange for 1000-10000\n", - " [255, 0, 0, 255], # Red for 10000-50000\n", - " [128, 0, 128, 255], # Purple for 50000-100000\n", - " [0, 0, 255, 255], # Blue for 100000-200000\n", - " [0, 0, 139, 255], # Dark blue for 200000+\n", - "])\n", - "\n", - "# Function to assign colors based on custom bins\n", - "def assign_color(value, breaks, colors):\n", - " for i in range(len(breaks) - 1):\n", - " if breaks[i] <= value < breaks[i + 1]:\n", - " return colors[i]\n", - " return colors[-1] # In case value exceeds all breaks\n", - "\n", - "# Map sum_pop_2020 values to colors using the custom function\n", - "gdf['color'] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", - "colors = np.uint8(gdf['color'].tolist())\n", - "\n", - "# Create the scatterplot layer with the assigned colors\n", - "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=colors)\n", - "\n", - "m = Map(layer)\n", - "m" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/user-docs/space2stats_api_demo.ipynb b/notebooks/user-docs/space2stats_api_demo.ipynb index 2d7f0619..7bf7da24 100644 --- a/notebooks/user-docs/space2stats_api_demo.ipynb +++ b/notebooks/user-docs/space2stats_api_demo.ipynb @@ -23,7 +23,7 @@ "metadata": {}, "outputs": [], "source": [ - "BASE_URL = \"https://space2stats.ds.io\"\n", + "BASE_URL = \"http://localhost:8000\"\n", "FIELDS_ENDPOINT = f\"{BASE_URL}/fields\"\n", "SUMMARY_ENDPOINT = f\"{BASE_URL}/summary\"" ] @@ -141,31 +141,31 @@ " \n", " 0\n", " 866a4a017ffffff\n", - " POINT (36.40245 -0.10117)\n", + " POINT (35.90001 0.6494)\n", " 676.912804\n", " \n", " \n", " 1\n", " 866a4a01fffffff\n", - " POINT (36.29170 -0.39057)\n", + " POINT (34.79718 2.12642)\n", " 347.182722\n", " \n", " \n", " 2\n", " 866a4a04fffffff\n", - " POINT (39.40696 -2.61300)\n", + " POINT (39.42863 -0.40995)\n", " 285.943490\n", " \n", " \n", " 3\n", " 866a4a057ffffff\n", - 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"execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fc98ebd574fd4a989e4040baf6218300", + "model_id": "a916785affa547218fd0bb1c80afe06b", "version_major": 2, - "version_minor": 0 + "version_minor": 1 }, "text/plain": [ - "Map(layers=[ScatterplotLayer(get_fill_color=\n", + "Map(layers=[ScatterplotLayer(get_fill_color=\n", "[\n", " [\n", " 2…" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index 01222704..b3fe0fa7 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -2,13 +2,13 @@ import pyarrow as pa from h3ronpy import ContainmentMode -from h3ronpy.arrow.vector import ( - cells_to_wkb_points, +from h3ronpy.arrow.vector import ( # Import cells_to_coordinates for point geometries + cells_to_coordinates, cells_to_wkb_polygons, geometry_to_cells, ) from shapely import wkb -from shapely.geometry import mapping, shape +from shapely.geometry import Point, mapping, shape CONTAINMENT_MODE_MAP = { "centroid": ContainmentMode.ContainsCentroid, @@ -43,37 +43,36 @@ def generate_h3_ids( def generate_h3_geometries( h3_ids_uint64: List[int], geometry_type: Literal["polygon", "point"] = "polygon" ) -> List[Dict]: - """ - Generate geometries (GeoJSON polygons or points) for a list of H3 hexagon IDs (as uint64). - - Parameters: - h3_ids_uint64 (List[int]): A list of H3 hexagon IDs in uint64 format. - geometry_type (Optional[str]): - The type of geometry to generate ('polygon' or 'point'). - - Returns: - List[Dict]: A list of GeoJSON geometries (Polygon or Point). - """ if geometry_type == "polygon": - # Convert H3 cells to WKB polygons - wkb_geometries = cells_to_wkb_polygons(h3_ids_uint64) + return _get_polygon(h3_ids_uint64) elif geometry_type == "point": - # Convert H3 cells to WKB points (centroid of each hexagon) - wkb_geometries = cells_to_wkb_points(h3_ids_uint64) + return _get_point(h3_ids_uint64) else: raise ValueError("Invalid geometry type. Use 'polygon' or 'point'.") - # Convert pyarrow Array to GeoJSON geometries + +def _get_polygon(h3_ids_uint64: List[int]) -> List[Dict]: + wkb_geometries = cells_to_wkb_polygons(h3_ids_uint64) geojson_geometries = [] for geom in wkb_geometries: - if isinstance(geom, pa.LargeBinaryScalar): - # Convert the LargeBinaryScalar to bytes + if isinstance(geom, pa.LargeBinaryScalar) or isinstance(geom, pa.BinaryScalar): shapely_geom = wkb.loads(geom.as_py()) - # Convert Shapely geometry to GeoJSON-like dict - geojson_geometries.append(mapping(shapely_geom)) + geojson_geom = mapping(shapely_geom) + geojson_geometries.append(geojson_geom) else: raise TypeError( - f"Expected pa.LargeBinaryScalar, but got {type(geom).__name__}" + f"Expected pa.LargeBinaryScalar or pa.BinaryScalar, but got {type(geom).__name__}" ) + return geojson_geometries + +def _get_point(h3_ids_uint64: List[int]) -> List[Dict]: + coords_table = cells_to_coordinates(h3_ids_uint64) + latitudes = coords_table["lat"].to_numpy() + longitudes = coords_table["lng"].to_numpy() + + geojson_geometries = [] + for lat, lon in zip(latitudes, longitudes): + point_geom = Point(lon, lat) + geojson_geometries.append(mapping(point_geom)) return geojson_geometries From 536d36ff1f324027158d2cf3b74b3035006c69b3 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Fri, 4 Oct 2024 14:11:48 -0700 Subject: [PATCH 06/18] Integrate release from h3ronpy that solves cells_to_wkb_points --- space2stats_api/src/poetry.lock | 44 ++++++++++----------- space2stats_api/src/space2stats/h3_utils.py | 42 +++++--------------- space2stats_api/src/tests/test_h3_utils.py | 17 ++++++-- 3 files changed, 45 insertions(+), 58 deletions(-) diff --git a/space2stats_api/src/poetry.lock b/space2stats_api/src/poetry.lock index a5dae79b..5e6c5168 100644 --- a/space2stats_api/src/poetry.lock +++ b/space2stats_api/src/poetry.lock @@ -123,17 +123,17 @@ testing = ["boto3-stubs[s3] (>=1.35.11)", "coverage", "moto (>=5.0.13)", "pytest [[package]] name = "boto3" -version = "1.35.31" +version = "1.35.34" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.35.31-py3-none-any.whl", hash = "sha256:2e9af74d10d8af7610a8d8468d2914961f116912a024fce17351825260385a52"}, - {file = "boto3-1.35.31.tar.gz", hash = "sha256:8c593af260c4ea3eb6f079c09908f94494ca2222aa4e40a7ff490fab1cee8b39"}, + {file = "boto3-1.35.34-py3-none-any.whl", hash = "sha256:291e7b97a34967ed93297e6171f1bebb8529e64633dd48426760e3fdef1cdea8"}, + {file = "boto3-1.35.34.tar.gz", hash = "sha256:57e6ee8504e7929bc094bb2afc879943906064179a1e88c23b4812e2c6f61532"}, ] [package.dependencies] -botocore = ">=1.35.31,<1.36.0" +botocore = ">=1.35.34,<1.36.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -142,13 +142,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.35.31" +version = "1.35.34" description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" files = [ - {file = "botocore-1.35.31-py3-none-any.whl", hash = "sha256:4cee814875bc78656aef4011d3d6b2231e96f53ea3661ee428201afb579d5c31"}, - {file = "botocore-1.35.31.tar.gz", hash = "sha256:f7bfa910cf2cbcc8c2307c1cf7b93495d614c2d699883417893e0a337fe4eb63"}, + {file = "botocore-1.35.34-py3-none-any.whl", hash = "sha256:ccb0fe397b11b81c9abc0c87029d17298e17bf658d8db5c0c5a551a12a207e7a"}, + {file = "botocore-1.35.34.tar.gz", hash = "sha256:789b6501a3bb4a9591c1fe10da200cc315c1fa5df5ada19c720d8ef06439b3e3"}, ] [package.dependencies] @@ -157,7 +157,7 @@ python-dateutil = ">=2.1,<3.0.0" urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""} [package.extras] -crt = ["awscrt (==0.21.5)"] +crt = ["awscrt (==0.22.0)"] [[package]] name = "certifi" @@ -769,16 +769,16 @@ test = ["flake8", "pylint", "pytest", "pytest-cov"] [[package]] name = "h3ronpy" -version = "0.21.0" +version = "0.21.1" description = "Data science toolkit for the H3 geospatial grid" optional = false python-versions = "*" files = [ - {file = "h3ronpy-0.21.0-cp38-abi3-macosx_10_14_x86_64.whl", hash = "sha256:49ac8d38a0cb8e2c9ea598473268b22fa99787669a141c8a20fc006ae1879602"}, - 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{file = "rich-13.9.1-py3-none-any.whl", hash = "sha256:b340e739f30aa58921dc477b8adaa9ecdb7cecc217be01d93730ee1bc8aa83be"}, - {file = "rich-13.9.1.tar.gz", hash = "sha256:097cffdf85db1babe30cc7deba5ab3a29e1b9885047dab24c57e9a7f8a9c1466"}, + {file = "rich-13.9.2-py3-none-any.whl", hash = "sha256:8c82a3d3f8dcfe9e734771313e606b39d8247bb6b826e196f4914b333b743cf1"}, + {file = "rich-13.9.2.tar.gz", hash = "sha256:51a2c62057461aaf7152b4d611168f93a9fc73068f8ded2790f29fe2b5366d0c"}, ] [package.dependencies] @@ -2169,13 +2169,13 @@ test = ["brotlipy", "httpx", "pytest", "pytest-cov"] [[package]] name = "tomli" -version = "2.0.1" +version = "2.0.2" description = "A lil' TOML parser" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, - {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, + {file = "tomli-2.0.2-py3-none-any.whl", hash = "sha256:2ebe24485c53d303f690b0ec092806a085f07af5a5aa1464f3931eec36caaa38"}, + {file = "tomli-2.0.2.tar.gz", hash = "sha256:d46d457a85337051c36524bc5349dd91b1877838e2979ac5ced3e710ed8a60ed"}, ] [[package]] diff --git a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index b3fe0fa7..9c3a60fc 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -1,14 +1,13 @@ from typing import Any, Dict, List, Literal -import pyarrow as pa from h3ronpy import ContainmentMode from h3ronpy.arrow.vector import ( # Import cells_to_coordinates for point geometries - cells_to_coordinates, + cells_to_wkb_points, cells_to_wkb_polygons, geometry_to_cells, ) -from shapely import wkb -from shapely.geometry import Point, mapping, shape +from shapely import from_wkb, to_geojson +from shapely.geometry import shape CONTAINMENT_MODE_MAP = { "centroid": ContainmentMode.ContainsCentroid, @@ -44,35 +43,12 @@ def generate_h3_geometries( h3_ids_uint64: List[int], geometry_type: Literal["polygon", "point"] = "polygon" ) -> List[Dict]: if geometry_type == "polygon": - return _get_polygon(h3_ids_uint64) + wkb_geometries = cells_to_wkb_polygons(h3_ids_uint64) elif geometry_type == "point": - return _get_point(h3_ids_uint64) + wkb_geometries = cells_to_wkb_points(h3_ids_uint64) else: - raise ValueError("Invalid geometry type. Use 'polygon' or 'point'.") + raise ValueError( + f"Invalid geometry type. Use 'polygon' or 'point', not {geometry_type}" + ) - -def _get_polygon(h3_ids_uint64: List[int]) -> List[Dict]: - wkb_geometries = cells_to_wkb_polygons(h3_ids_uint64) - geojson_geometries = [] - for geom in wkb_geometries: - if isinstance(geom, pa.LargeBinaryScalar) or isinstance(geom, pa.BinaryScalar): - shapely_geom = wkb.loads(geom.as_py()) - geojson_geom = mapping(shapely_geom) - geojson_geometries.append(geojson_geom) - else: - raise TypeError( - f"Expected pa.LargeBinaryScalar or pa.BinaryScalar, but got {type(geom).__name__}" - ) - return geojson_geometries - - -def _get_point(h3_ids_uint64: List[int]) -> List[Dict]: - coords_table = cells_to_coordinates(h3_ids_uint64) - latitudes = coords_table["lat"].to_numpy() - longitudes = coords_table["lng"].to_numpy() - - geojson_geometries = [] - for lat, lon in zip(latitudes, longitudes): - point_geom = Point(lon, lat) - geojson_geometries.append(mapping(point_geom)) - return geojson_geometries + return to_geojson(from_wkb(wkb_geometries)) diff --git a/space2stats_api/src/tests/test_h3_utils.py b/space2stats_api/src/tests/test_h3_utils.py index 6de9de7a..163d5d0b 100644 --- a/space2stats_api/src/tests/test_h3_utils.py +++ b/space2stats_api/src/tests/test_h3_utils.py @@ -1,5 +1,6 @@ import pytest from h3ronpy.arrow import cells_parse +from shapely import from_geojson from shapely.geometry import MultiPolygon, Polygon, mapping from space2stats.h3_utils import generate_h3_geometries, generate_h3_ids @@ -50,11 +51,15 @@ def test_generate_h3_ids_centroid_multipolygon(): def test_generate_h3_geometries_polygon_multipolygon(): h3_ids = generate_h3_ids(aoi_geojson_multi, resolution, "touches") geometries = generate_h3_geometries(h3_ids, "polygon") + print(geometries) assert len(geometries) == len( h3_ids ), "Expected the same number of geometries as H3 IDs" for geom in geometries: - assert geom["type"] == "Polygon", "Expected Polygon geometry for MultiPolygon" + geojson = from_geojson(geom) + assert ( + geojson.geom_type == "Polygon" + ), "Expected Polygon geometry for MultiPolygon" def test_generate_h3_geometries_point_multipolygon(): @@ -63,8 +68,14 @@ def test_generate_h3_geometries_point_multipolygon(): assert len(geometries) == len( h3_ids ), "Expected the same number of geometries as H3 IDs" - for geom in geometries: - assert geom["type"] == "Point", "Expected Point geometry for MultiPolygon" + print(geometries) + for g in geometries: + geojson = from_geojson(g) + print(dir(geojson)) + assert geojson.geom_type == "Point" + assert len(geojson.coords) == 1 + assert len(geojson.coords[0]) == 2 + assert geojson.coords[0][0] != geojson.coords[0][1] def test_generate_h3_sliver_polygon_touches(): From 5299454c7954a0ee167616abc2a2157cd495b0c1 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Fri, 4 Oct 2024 14:27:17 -0700 Subject: [PATCH 07/18] Remove looping on geometries --- space2stats_api/src/space2stats/lib.py | 2 +- space2stats_api/src/tests/test_api.py | 11 ++++++++--- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/space2stats_api/src/space2stats/lib.py b/space2stats_api/src/space2stats/lib.py index b3bf2f59..bd515bf0 100644 --- a/space2stats_api/src/space2stats/lib.py +++ b/space2stats_api/src/space2stats/lib.py @@ -101,7 +101,7 @@ def summaries( for idx, row in enumerate(rows): summary = {"hex_id": row[0]} - if geometry and geometries: + if geometry and geometries is not None: summary["geometry"] = geometries[idx] summary.update( diff --git a/space2stats_api/src/tests/test_api.py b/space2stats_api/src/tests/test_api.py index bb9b76a9..f57abf31 100644 --- a/space2stats_api/src/tests/test_api.py +++ b/space2stats_api/src/tests/test_api.py @@ -1,4 +1,5 @@ import pytest +from shapely import from_geojson aoi = { "type": "Feature", @@ -114,7 +115,8 @@ def test_get_summary_with_geometry_multipolygon(client): for summary in response_json: assert "hex_id" in summary assert "geometry" in summary - assert summary["geometry"]["type"] == "Polygon" + geometry = from_geojson(summary["geometry"]) + assert geometry.geom_type == "Polygon" assert len(summary) == len(request_payload["fields"]) + 2 @@ -135,7 +137,8 @@ def test_get_summary_with_geometry_polygon(client): for summary in response_json: assert "hex_id" in summary assert "geometry" in summary - assert summary["geometry"]["type"] == "Polygon" + geometry = from_geojson(summary["geometry"]) + assert geometry.geom_type == "Polygon" assert len(summary) == len(request_payload["fields"]) + 2 @@ -148,6 +151,7 @@ def test_get_summary_with_geometry_point(client): } response = client.post("/summary", json=request_payload) + print(response.json()) assert response.status_code == 200 response_json = response.json() assert len(response_json) > 0, "Test query failed to return any summaries" @@ -156,7 +160,8 @@ def test_get_summary_with_geometry_point(client): for summary in response_json: assert "hex_id" in summary assert "geometry" in summary - assert summary["geometry"]["type"] == "Point" + geometry = from_geojson(summary["geometry"]) + assert geometry.geom_type == "Point" assert len(summary) == len(request_payload["fields"]) + 2 From ac4fdd3bcc763cc2cda6c5b597761c80c9e1037d Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Fri, 4 Oct 2024 14:54:27 -0700 Subject: [PATCH 08/18] Update user documentation --- .../user-docs/space2stats_api_demo.ipynb | 198 ++++- .../space2stats_py_package_demo.ipynb | 738 ++++++++++++------ 2 files changed, 671 insertions(+), 265 deletions(-) diff --git a/notebooks/user-docs/space2stats_api_demo.ipynb b/notebooks/user-docs/space2stats_api_demo.ipynb index 7bf7da24..e79fc152 100644 --- a/notebooks/user-docs/space2stats_api_demo.ipynb +++ b/notebooks/user-docs/space2stats_api_demo.ipynb @@ -85,9 +85,142 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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hex_idgeometrysum_pop_2020
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" 866a4a04fffffff\n", - " POINT (39.42863 -0.40995)\n", + " POINT (36.33929 5.11542)\n", " 285.943490\n", " \n", " \n", " 3\n", " 866a4a057ffffff\n", - " POINT (36.91381 3.93637)\n", + " POINT (36.23708 5.10296)\n", " 279.463912\n", " \n", " \n", " 4\n", " 866a4a05fffffff\n", - " POINT (35.69251 -1.6086)\n", + " POINT (36.29318 5.0809)\n", " 235.258474\n", " \n", " \n", @@ -177,31 +311,31 @@ " \n", " 25801\n", " 86969ed97ffffff\n", - " POINT (39.98378 -3.73064)\n", + " POINT (34.80875 -3.47225)\n", " 196.448391\n", " \n", " \n", " 25802\n", " 86969ed9fffffff\n", - " POINT (34.59534 2.69625)\n", + " POINT (34.75041 -3.44944)\n", " 296.740893\n", " \n", " \n", " 25803\n", " 86969eda7ffffff\n", - " POINT (41.47496 -0.99731)\n", + " POINT (34.77046 -3.5715)\n", " 154.818453\n", " \n", " \n", " 25804\n", " 86969edafffffff\n", - " POINT (37.33826 3.60893)\n", + " POINT (34.7121 -3.54868)\n", " 157.875350\n", " \n", " \n", " 25805\n", " 86969edb7ffffff\n", - " POINT (40.73842 -4.35757)\n", + " POINT (34.81878 -3.53328)\n", " 236.127331\n", " \n", " \n", @@ -211,52 +345,54 @@ ], "text/plain": [ " hex_id geometry sum_pop_2020\n", - "0 866a4a017ffffff POINT (35.90001 0.6494) 676.912804\n", - "1 866a4a01fffffff POINT (34.79718 2.12642) 347.182722\n", - "2 866a4a04fffffff POINT (39.42863 -0.40995) 285.943490\n", - "3 866a4a057ffffff POINT (36.91381 3.93637) 279.463912\n", - "4 866a4a05fffffff POINT (35.69251 -1.6086) 235.258474\n", + "0 866a4a017ffffff POINT (36.07869 5.11252) 676.912804\n", + "1 866a4a01fffffff POINT (36.13482 5.09047) 347.182722\n", + "2 866a4a04fffffff POINT (36.33929 5.11542) 285.943490\n", + "3 866a4a057ffffff POINT (36.23708 5.10296) 279.463912\n", + "4 866a4a05fffffff POINT (36.29318 5.0809) 235.258474\n", "... ... ... ...\n", - "25801 86969ed97ffffff POINT (39.98378 -3.73064) 196.448391\n", - "25802 86969ed9fffffff POINT (34.59534 2.69625) 296.740893\n", - "25803 86969eda7ffffff POINT (41.47496 -0.99731) 154.818453\n", - "25804 86969edafffffff POINT (37.33826 3.60893) 157.875350\n", - "25805 86969edb7ffffff POINT (40.73842 -4.35757) 236.127331\n", + "25801 86969ed97ffffff POINT (34.80875 -3.47225) 196.448391\n", + "25802 86969ed9fffffff POINT (34.75041 -3.44944) 296.740893\n", + "25803 86969eda7ffffff POINT (34.77046 -3.5715) 154.818453\n", + "25804 86969edafffffff POINT (34.7121 -3.54868) 157.875350\n", + "25805 86969edb7ffffff POINT (34.81878 -3.53328) 236.127331\n", "\n", "[25806 rows x 3 columns]" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['geometry'] = df['geometry'].apply(lambda geom: shape(geom))\n", + "from shapely import from_geojson\n", + "\n", + "df['geometry'] = df['geometry'].apply(lambda geom: from_geojson(geom))\n", "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n", "gdf" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a916785affa547218fd0bb1c80afe06b", + "model_id": "267540ef06104b718e9f18ffd4225440", "version_major": 2, "version_minor": 1 }, "text/plain": [ - "Map(layers=[ScatterplotLayer(get_fill_color=\n", + "Map(layers=[ScatterplotLayer(get_fill_color=\n", "[\n", " [\n", " 2…" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +445,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/notebooks/user-docs/space2stats_py_package_demo.ipynb b/notebooks/user-docs/space2stats_py_package_demo.ipynb index a0bdff5a..4e9dbb66 100644 --- a/notebooks/user-docs/space2stats_py_package_demo.ipynb +++ b/notebooks/user-docs/space2stats_py_package_demo.ipynb @@ -2,17 +2,92 @@ "cells": [ { "cell_type": "code", - "execution_count": 39, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mFound existing installation: space2stats 0.1.0\n", + "Uninstalling space2stats-0.1.0:\n", + " Successfully uninstalled space2stats-0.1.0\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mCollecting git+https://github.com/worldbank/DECAT_Space2Stats.git@feature/h3ronpy#subdirectory=space2stats_api/src\n", + " Cloning https://github.com/worldbank/DECAT_Space2Stats.git (to revision feature/h3ronpy) to /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-req-build-o_n4xv_w\n", + " Running command git clone --filter=blob:none --quiet https://github.com/worldbank/DECAT_Space2Stats.git /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-req-build-o_n4xv_w\n", + " Running command git checkout -b feature/h3ronpy --track origin/feature/h3ronpy\n", + " Switched to a new branch 'feature/h3ronpy'\n", + " branch 'feature/h3ronpy' set up to track 'origin/feature/h3ronpy'.\n", + " Resolved https://github.com/worldbank/DECAT_Space2Stats.git to commit 5299454c7954a0ee167616abc2a2157cd495b0c1\n", + " Installing build dependencies ... \u001b[?25ldone\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: asgi-s3-response-middleware<0.0.5,>=0.0.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.0.4)\n", + "Requirement already satisfied: boto3<2.0.0,>=1.35.11 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.35.34)\n", + "Requirement already satisfied: fastapi>=0.112.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.115.0)\n", + "Requirement already satisfied: geojson-pydantic in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.1.1)\n", + "Requirement already satisfied: h3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.7.7)\n", + "Requirement already satisfied: h3ronpy<0.22.0,>=0.21.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.21.1)\n", + "Requirement already satisfied: httpx in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.27.2)\n", + "Requirement already satisfied: mangum in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.19.0)\n", + "Requirement already satisfied: numpy<2.0.0,>=1.24.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.26.4)\n", + "Requirement already satisfied: orjson in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.10.7)\n", + "Requirement already satisfied: pandas in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.2.3)\n", + "Requirement already satisfied: psycopg[binary,pool] in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.2.3)\n", + "Requirement already satisfied: pydantic-settings>=2.0.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.5.2)\n", + "Requirement already satisfied: shapely in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.0.6)\n", + "Requirement already satisfied: starlette-cramjam<0.4,>=0.3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.3.3)\n", + "Requirement already satisfied: typing_extensions in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (4.12.2)\n", + "Requirement already satisfied: starlette>=0.38.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from asgi-s3-response-middleware<0.0.5,>=0.0.4->space2stats==0.1.0) (0.38.6)\n", + "Requirement already satisfied: botocore<1.36.0,>=1.35.34 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (1.35.34)\n", + "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (1.0.1)\n", + "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (0.10.2)\n", + "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from fastapi>=0.112.0->space2stats==0.1.0) (2.9.2)\n", + "Requirement already satisfied: pyarrow>=10.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from h3ronpy<0.22.0,>=0.21.0->space2stats==0.1.0) (17.0.0)\n", + "Requirement already satisfied: anyio in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (4.6.0)\n", + "Requirement already satisfied: certifi in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (2024.8.30)\n", + "Requirement already satisfied: httpcore==1.* in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (1.0.6)\n", + "Requirement already satisfied: idna in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (3.10)\n", + "Requirement already satisfied: sniffio in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (1.3.1)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpcore==1.*->httpx->space2stats==0.1.0) (0.14.0)\n", + "Requirement already satisfied: python-dotenv>=0.21.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic-settings>=2.0.0->space2stats==0.1.0) (1.0.1)\n", + "Requirement already satisfied: cramjam<2.9,>=2.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from starlette-cramjam<0.4,>=0.3->space2stats==0.1.0) (2.8.4)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2024.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2024.2)\n", + "Requirement already satisfied: psycopg-binary==3.2.3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from psycopg[binary,pool]->space2stats==0.1.0) (3.2.3)\n", + "Requirement already satisfied: psycopg-pool in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from psycopg[binary,pool]->space2stats==0.1.0) (3.2.3)\n", + "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from botocore<1.36.0,>=1.35.34->boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (2.2.3)\n", + "Requirement already satisfied: annotated-types>=0.6.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi>=0.112.0->space2stats==0.1.0) (0.7.0)\n", + "Requirement already satisfied: pydantic-core==2.23.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi>=0.112.0->space2stats==0.1.0) (2.23.4)\n", + "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->space2stats==0.1.0) (1.16.0)\n", + "Building wheels for collected packages: space2stats\n", + " Building wheel for space2stats (pyproject.toml) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for space2stats: filename=space2stats-0.1.0-py3-none-any.whl size=18428 sha256=ea6987e1b55fe30bed14d734f35195abc39985520ecc888928d1c0df698bc0dc\n", + " Stored in directory: /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-ephem-wheel-cache-x7ltnsoy/wheels/99/9f/50/575302cb376b7ead3e96482cd92b0bf444bc829e19cc250c03\n", + "Successfully built space2stats\n", + "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mInstalling collected packages: space2stats\n", + "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mSuccessfully installed space2stats-0.1.0\n", + "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "%%capture\n", - "%pip install \"git+https://github.com/worldbank/DECAT_Space2Stats.git#subdirectory=space2stats_api/src\"" + "%pip install \"git+https://github.com/worldbank/DECAT_Space2Stats.git@feature/h3ronpy#subdirectory=space2stats_api/src\"" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +101,7 @@ "from lonboard import Map, ScatterplotLayer\n", "from geojson_pydantic import Feature, Polygon\n", "\n", - "from space2stats import StatsTable" + "from space2stats import StatsTable\n" ] }, { @@ -48,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -57,7 +132,7 @@ "True" ] }, - "execution_count": 45, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -68,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -116,7 +191,7 @@ " 'sum_pop_f_2020']" ] }, - "execution_count": 42, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -130,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -163,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -213,8 +288,32 @@ " \n", " \n", " 0\n", + " 866a4a00fffffff\n", + " {\"type\":\"Point\",\"coordinates\":[36.180976490229...\n", + " 476.538185\n", + " 6157253\n", + " 6.070529\n", + " 32.703007\n", + " 27.829216\n", + " 22.437599\n", + " 23.757664\n", + " 20.835018\n", + " ...\n", + " 4.712565\n", + " 5.091442\n", + " 31.273582\n", + " 2.852396\n", + " 2.619311\n", + " 1.270194\n", + " 0.888411\n", + " 0.582864\n", + " 234.480238\n", + " 242.057947\n", + " \n", + " \n", + " 1\n", " 866a4a017ffffff\n", - " {'type': 'Point', 'coordinates': (38.494790325...\n", + " {\"type\":\"Point\",\"coordinates\":[36.078694056627...\n", " 676.912804\n", " 6157254\n", " 8.924362\n", @@ -236,9 +335,9 @@ " 340.830624\n", " \n", " \n", - " 1\n", + " 2\n", " 866a4a01fffffff\n", - " {'type': 'Point', 'coordinates': (40.094390450...\n", + " {\"type\":\"Point\",\"coordinates\":[36.134817927246...\n", " 347.182722\n", " 6157255\n", " 4.515680\n", @@ -260,9 +359,33 @@ " 175.423497\n", " \n", " \n", - " 2\n", + " 3\n", + " 866a4a047ffffff\n", + " {\"type\":\"Point\",\"coordinates\":[36.283201202805...\n", + " 380.988678\n", + " 6157259\n", + " 4.853342\n", + " 26.145809\n", + " 22.249249\n", + " 17.938692\n", + " 18.994072\n", + " 16.657440\n", + " ...\n", + " 3.767660\n", + " 4.070570\n", + " 25.002991\n", + " 2.280469\n", + " 2.094120\n", + " 1.015510\n", + " 0.710278\n", + " 0.465995\n", + " 187.465175\n", + " 193.523504\n", + " \n", + " \n", + " 4\n", " 866a4a04fffffff\n", - " {'type': 'Point', 'coordinates': (34.462341228...\n", + " {\"type\":\"Point\",\"coordinates\":[36.339286712001...\n", " 285.943490\n", " 6157260\n", " 3.642580\n", @@ -284,120 +407,265 @@ " 145.245222\n", " \n", " \n", - " 3\n", - " 866a4a057ffffff\n", - " {'type': 'Point', 'coordinates': (41.436122880...\n", - " 279.463912\n", - " 6157261\n", - " 3.560037\n", - " 19.178549\n", - " 16.320332\n", - " 13.158442\n", - " 13.932587\n", - " 12.218613\n", - " ...\n", - " 2.763665\n", - " 2.985856\n", - " 18.340267\n", - " 1.672776\n", - " 1.536084\n", - " 0.744900\n", - " 0.521005\n", - " 0.341818\n", - " 137.509998\n", - " 141.953914\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 4\n", - " 866a4a05fffffff\n", - " {'type': 'Point', 'coordinates': (41.251660148...\n", - " 235.258474\n", - " 6157262\n", - " 2.996913\n", - " 16.144897\n", - " 13.738791\n", - " 11.077046\n", - " 11.728739\n", - " 10.285879\n", - " ...\n", - " 2.326510\n", - " 2.513555\n", - " 15.439214\n", - " 1.408177\n", - " 1.293108\n", - " 0.627072\n", - " 0.438593\n", - " 0.287750\n", - " 115.758746\n", - " 119.499727\n", + " 26184\n", + " 86969ed97ffffff\n", + " {\"type\":\"Point\",\"coordinates\":[34.808746693532...\n", + " 196.448391\n", + " 8710025\n", + " 4.108127\n", + " 14.176966\n", + " 11.835453\n", + " 15.015658\n", + " 8.398741\n", + " 6.313566\n", + " ...\n", + " 1.962373\n", + " 1.294143\n", + " 16.514441\n", + " 1.084920\n", + " 0.696495\n", + " 0.607390\n", + " 0.353109\n", + " 0.295762\n", + " 96.252689\n", + " 100.195702\n", + " \n", + " \n", + " 26185\n", + " 86969ed9fffffff\n", + " {\"type\":\"Point\",\"coordinates\":[34.750412026186...\n", + " 296.740893\n", + " 8710026\n", + " 6.205442\n", + " 21.414711\n", + " 17.877789\n", + " 22.681580\n", + " 12.686538\n", + " 9.536821\n", + " ...\n", + " 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5 rows × 42 columns

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26189 rows × 42 columns

\n", "" ], "text/plain": [ - " hex_id geometry \\\n", - "0 866a4a017ffffff {'type': 'Point', 'coordinates': (38.494790325... \n", - "1 866a4a01fffffff {'type': 'Point', 'coordinates': (40.094390450... \n", - "2 866a4a04fffffff {'type': 'Point', 'coordinates': (34.462341228... \n", - "3 866a4a057ffffff {'type': 'Point', 'coordinates': (41.436122880... \n", - "4 866a4a05fffffff {'type': 'Point', 'coordinates': (41.251660148... \n", + " hex_id geometry \\\n", + "0 866a4a00fffffff {\"type\":\"Point\",\"coordinates\":[36.180976490229... \n", + "1 866a4a017ffffff {\"type\":\"Point\",\"coordinates\":[36.078694056627... \n", + "2 866a4a01fffffff {\"type\":\"Point\",\"coordinates\":[36.134817927246... \n", + "3 866a4a047ffffff {\"type\":\"Point\",\"coordinates\":[36.283201202805... \n", + "4 866a4a04fffffff {\"type\":\"Point\",\"coordinates\":[36.339286712001... \n", + "... ... ... \n", + "26184 86969ed97ffffff {\"type\":\"Point\",\"coordinates\":[34.808746693532... \n", + "26185 86969ed9fffffff {\"type\":\"Point\",\"coordinates\":[34.750412026186... \n", + "26186 86969eda7ffffff {\"type\":\"Point\",\"coordinates\":[34.770464787099... \n", + "26187 86969edafffffff {\"type\":\"Point\",\"coordinates\":[34.712102323801... \n", + "26188 86969edb7ffffff {\"type\":\"Point\",\"coordinates\":[34.818784182799... \n", + "\n", + " sum_pop_2020 ogc_fid sum_pop_f_0_2020 sum_pop_f_10_2020 \\\n", + "0 476.538185 6157253 6.070529 32.703007 \n", + "1 676.912804 6157254 8.924362 49.524105 \n", + "2 347.182722 6157255 4.515680 24.773327 \n", + "3 380.988678 6157259 4.853342 26.145809 \n", + "4 285.943490 6157260 3.642580 19.623219 \n", + "... ... ... ... ... \n", + "26184 196.448391 8710025 4.108127 14.176966 \n", + "26185 296.740893 8710026 6.205442 21.414711 \n", + "26186 154.818453 8710027 3.237562 11.172684 \n", + "26187 157.875350 8710028 3.301488 11.393290 \n", + "26188 236.127331 8710029 4.937892 17.040451 \n", "\n", - " sum_pop_2020 ogc_fid sum_pop_f_0_2020 sum_pop_f_10_2020 \\\n", - "0 676.912804 6157254 8.924362 49.524105 \n", - "1 347.182722 6157255 4.515680 24.773327 \n", - "2 285.943490 6157260 3.642580 19.623219 \n", - "3 279.463912 6157261 3.560037 19.178549 \n", - "4 235.258474 6157262 2.996913 16.144897 \n", + " sum_pop_f_15_2020 sum_pop_f_1_2020 sum_pop_f_20_2020 \\\n", + "0 27.829216 22.437599 23.757664 \n", + "1 36.898296 32.985901 28.963400 \n", + "2 19.462585 16.690670 15.832314 \n", + "3 22.249249 17.938692 18.994072 \n", + "4 16.698730 13.463530 14.255625 \n", + "... ... ... ... \n", + "26184 11.835453 15.015658 8.398741 \n", + "26185 17.877789 22.681580 12.686538 \n", + "26186 9.327369 11.833648 6.618940 \n", + "26187 9.511538 12.067303 6.749631 \n", + "26188 14.225995 18.048544 10.095131 \n", "\n", - " sum_pop_f_15_2020 sum_pop_f_1_2020 sum_pop_f_20_2020 sum_pop_f_25_2020 \\\n", - "0 36.898296 32.985901 28.963400 33.645473 \n", - "1 19.462585 16.690670 15.832314 16.429201 \n", - "2 16.698730 13.463530 14.255625 12.501910 \n", - "3 16.320332 13.158442 13.932587 12.218613 \n", - "4 13.738791 11.077046 11.728739 10.285879 \n", + " sum_pop_f_25_2020 ... sum_pop_m_50_2020 sum_pop_m_55_2020 \\\n", + "0 20.835018 ... 4.712565 5.091442 \n", + "1 33.645473 ... 6.732327 4.131749 \n", + "2 16.429201 ... 3.445144 2.752507 \n", + "3 16.657440 ... 3.767660 4.070570 \n", + "4 12.501910 ... 2.827743 3.055085 \n", + "... ... ... ... ... \n", + "26184 6.313566 ... 1.962373 1.294143 \n", + "26185 9.536821 ... 2.964221 1.954840 \n", + "26186 4.975640 ... 1.546521 1.019898 \n", + "26187 5.073884 ... 1.577057 1.040035 \n", + "26188 7.588790 ... 2.358737 1.555536 \n", "\n", - " ... sum_pop_m_50_2020 sum_pop_m_55_2020 sum_pop_m_5_2020 \\\n", - "0 ... 6.732327 4.131749 53.380043 \n", - "1 ... 3.445144 2.752507 25.548544 \n", - "2 ... 2.827743 3.055085 18.765499 \n", - "3 ... 2.763665 2.985856 18.340267 \n", - "4 ... 2.326510 2.513555 15.439214 \n", + " sum_pop_m_5_2020 sum_pop_m_60_2020 sum_pop_m_65_2020 \\\n", + "0 31.273582 2.852396 2.619311 \n", + "1 53.380043 3.240541 2.475817 \n", + "2 25.548544 1.827761 1.524122 \n", + "3 25.002991 2.280469 2.094120 \n", + "4 18.765499 1.711561 1.571700 \n", + "... ... ... ... \n", + "26184 16.514441 1.084920 0.696495 \n", + "26185 24.945534 1.638802 1.052075 \n", + "26186 13.014819 0.855011 0.548899 \n", + "26187 13.271797 0.871894 0.559737 \n", + "26188 19.850052 1.304053 0.837174 \n", "\n", - " sum_pop_m_60_2020 sum_pop_m_65_2020 sum_pop_m_70_2020 sum_pop_m_75_2020 \\\n", - "0 3.240541 2.475817 0.702667 0.399492 \n", - "1 1.827761 1.524122 0.585427 0.381080 \n", - "2 1.711561 1.571700 0.762171 0.533085 \n", - "3 1.672776 1.536084 0.744900 0.521005 \n", - "4 1.408177 1.293108 0.627072 0.438593 \n", + " sum_pop_m_70_2020 sum_pop_m_75_2020 sum_pop_m_80_2020 \\\n", + "0 1.270194 0.888411 0.582864 \n", + "1 0.702667 0.399492 0.129285 \n", + "2 0.585427 0.381080 0.209030 \n", + "3 1.015510 0.710278 0.465995 \n", + "4 0.762171 0.533085 0.349744 \n", + "... ... ... ... \n", + "26184 0.607390 0.353109 0.295762 \n", + "26185 0.917481 0.533381 0.446756 \n", + "26186 0.478677 0.278281 0.233086 \n", + "26187 0.488128 0.283775 0.237688 \n", + "26188 0.730072 0.424430 0.355500 \n", "\n", - " sum_pop_m_80_2020 sum_pop_m_2020 sum_pop_f_2020 \n", - "0 0.129285 336.082180 340.830624 \n", - "1 0.209030 171.759225 175.423497 \n", - "2 0.349744 140.698267 145.245222 \n", - "3 0.341818 137.509998 141.953914 \n", - "4 0.287750 115.758746 119.499727 \n", + " sum_pop_m_2020 sum_pop_f_2020 \n", + "0 234.480238 242.057947 \n", + "1 336.082180 340.830624 \n", + "2 171.759225 175.423497 \n", + "3 187.465175 193.523504 \n", + "4 140.698267 145.245222 \n", + "... ... ... \n", + "26184 96.252689 100.195702 \n", + "26185 145.392433 151.348460 \n", + "26186 75.855508 78.962945 \n", + "26187 77.353279 80.522071 \n", + "26188 115.693951 120.433380 \n", "\n", - "[5 rows x 42 columns]" + "[26189 rows x 42 columns]" ] }, - "execution_count": 44, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with StatsTable.connect() as stats_table:\n", - " data = stats_table.summaries(aoi=feat, spatial_join_method=\"centroid\", fields=fields, geometry=\"point\")\n", + " data = stats_table.summaries(aoi=feat, spatial_join_method=\"touches\", fields=fields, geometry=\"point\")\n", " df = pd.DataFrame(data)\n", "\n", - "df.head()" + "df" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -447,8 +715,32 @@ " \n", " \n", " 0\n", + " 866a4a00fffffff\n", + " POINT (36.18098 5.12501)\n", + " 476.538185\n", + " 6157253\n", + " 6.070529\n", + " 32.703007\n", + " 27.829216\n", + " 22.437599\n", + " 23.757664\n", + " 20.835018\n", + " ...\n", + " 4.712565\n", + " 5.091442\n", + " 31.273582\n", + " 2.852396\n", + " 2.619311\n", + " 1.270194\n", + " 0.888411\n", + " 0.582864\n", + " 234.480238\n", + " 242.057947\n", + " \n", + " \n", + " 1\n", " 866a4a017ffffff\n", - " POINT (38.49479 -0.20685)\n", + " POINT (36.07869 5.11252)\n", " 676.912804\n", " 6157254\n", " 8.924362\n", @@ -470,9 +762,9 @@ " 340.830624\n", " \n", " \n", - " 1\n", + " 2\n", " 866a4a01fffffff\n", - " POINT (40.09439 0.52663)\n", + " POINT (36.13482 5.09047)\n", " 347.182722\n", " 6157255\n", " 4.515680\n", @@ -494,9 +786,33 @@ " 175.423497\n", " \n", " \n", - " 2\n", + " 3\n", + " 866a4a047ffffff\n", + " POINT (36.2832 5.13748)\n", + " 380.988678\n", + " 6157259\n", + " 4.853342\n", + " 26.145809\n", + " 22.249249\n", + " 17.938692\n", + " 18.994072\n", + " 16.657440\n", + " ...\n", + " 3.767660\n", + " 4.070570\n", + " 25.002991\n", + " 2.280469\n", + " 2.094120\n", + " 1.015510\n", + " 0.710278\n", + " 0.465995\n", + " 187.465175\n", + " 193.523504\n", + " \n", + " \n", + " 4\n", " 866a4a04fffffff\n", - " POINT (34.46234 5.05804)\n", + " POINT (36.33929 5.11542)\n", " 285.943490\n", " 6157260\n", " 3.642580\n", @@ -518,54 +834,6 @@ " 145.245222\n", " \n", " \n", - " 3\n", - " 866a4a057ffffff\n", - " POINT (41.43612 -4.68139)\n", - " 279.463912\n", - " 6157261\n", - " 3.560037\n", - " 19.178549\n", - " 16.320332\n", - " 13.158442\n", - " 13.932587\n", - " 12.218613\n", - " ...\n", - " 2.763665\n", - " 2.985856\n", - " 18.340267\n", - " 1.672776\n", - " 1.536084\n", - " 0.744900\n", - " 0.521005\n", - " 0.341818\n", - " 137.509998\n", - " 141.953914\n", - " \n", - " \n", - " 4\n", - " 866a4a05fffffff\n", - " POINT (41.25166 -0.09407)\n", - " 235.258474\n", - " 6157262\n", - " 2.996913\n", - " 16.144897\n", - " 13.738791\n", - " 11.077046\n", - " 11.728739\n", - " 10.285879\n", - " ...\n", - " 2.326510\n", - " 2.513555\n", - " 15.439214\n", - " 1.408177\n", - " 1.293108\n", - " 0.627072\n", - " 0.438593\n", - " 0.287750\n", - " 115.758746\n", - " 119.499727\n", - " \n", - " \n", " ...\n", " ...\n", " ...\n", @@ -590,9 +858,9 @@ " ...\n", " \n", " \n", - " 25801\n", + " 26184\n", " 86969ed97ffffff\n", - " POINT (36.22291 0.63262)\n", + " POINT (34.80875 -3.47225)\n", " 196.448391\n", " 8710025\n", " 4.108127\n", @@ -614,9 +882,9 @@ " 100.195702\n", " \n", " \n", - " 25802\n", + " 26185\n", " 86969ed9fffffff\n", - " POINT (36.93237 -1.31051)\n", + " POINT (34.75041 -3.44944)\n", " 296.740893\n", " 8710026\n", " 6.205442\n", @@ -638,9 +906,9 @@ " 151.348460\n", " \n", " \n", - " 25803\n", + " 26186\n", " 86969eda7ffffff\n", - " POINT (37.92797 0.89329)\n", + " POINT (34.77046 -3.5715)\n", " 154.818453\n", " 8710027\n", " 3.237562\n", @@ -662,9 +930,9 @@ " 78.962945\n", " \n", " \n", - " 25804\n", + " 26187\n", " 86969edafffffff\n", - " POINT (39.02185 -1.07532)\n", + " POINT (34.7121 -3.54868)\n", " 157.875350\n", " 8710028\n", " 3.301488\n", @@ -686,9 +954,9 @@ " 80.522071\n", " \n", " \n", - " 25805\n", + " 26188\n", " 86969edb7ffffff\n", - " POINT (36.5281 3.44777)\n", + " POINT (34.81878 -3.53328)\n", " 236.127331\n", " 8710029\n", " 4.937892\n", @@ -711,122 +979,124 @@ " \n", " \n", "\n", - "

25806 rows × 42 columns

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26189 rows × 42 columns

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"0 0.399492 0.129285 336.082180 340.830624 \n", - "1 0.381080 0.209030 171.759225 175.423497 \n", - "2 0.533085 0.349744 140.698267 145.245222 \n", - "3 0.521005 0.341818 137.509998 141.953914 \n", - "4 0.438593 0.287750 115.758746 119.499727 \n", + "0 0.888411 0.582864 234.480238 242.057947 \n", + "1 0.399492 0.129285 336.082180 340.830624 \n", + "2 0.381080 0.209030 171.759225 175.423497 \n", + "3 0.710278 0.465995 187.465175 193.523504 \n", + "4 0.533085 0.349744 140.698267 145.245222 \n", "... ... ... ... ... \n", - "25801 0.353109 0.295762 96.252689 100.195702 \n", - "25802 0.533381 0.446756 145.392433 151.348460 \n", - "25803 0.278281 0.233086 75.855508 78.962945 \n", - "25804 0.283775 0.237688 77.353279 80.522071 \n", - "25805 0.424430 0.355500 115.693951 120.433380 \n", + "26184 0.353109 0.295762 96.252689 100.195702 \n", + "26185 0.533381 0.446756 145.392433 151.348460 \n", + "26186 0.278281 0.233086 75.855508 78.962945 \n", + "26187 0.283775 0.237688 77.353279 80.522071 \n", + "26188 0.424430 0.355500 115.693951 120.433380 \n", "\n", - "[25806 rows x 42 columns]" + "[26189 rows x 42 columns]" ] }, - "execution_count": 26, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df['geometry'] = df['geometry'].apply(lambda geom: shape(geom))\n", + "from shapely import from_geojson\n", + "\n", + "df['geometry'] = df['geometry'].apply(lambda geom: from_geojson(geom))\n", "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n", "gdf" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e472e297d8dc4c5582193eab628f4d44", + "model_id": "b73e90e5014643d2a3b7f9ef33032f8f", "version_major": 2, "version_minor": 1 }, "text/plain": [ - "Map(layers=[ScatterplotLayer(get_fill_color=\n", + "Map(layers=[ScatterplotLayer(get_fill_color=\n", "[\n", " [\n", " 2…" ] }, - "execution_count": 38, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } From 9e8c7d7cae4ed5389246004f40073a4b54f66de0 Mon Sep 17 00:00:00 2001 From: Zac Deziel Date: Fri, 11 Oct 2024 07:56:31 -0700 Subject: [PATCH 09/18] Update user doc notebooks (#76) * create stac catalog from sample kenya data * testing metadata * update metadata files * typo * Update catalog with link to self and title based on stac-check best practice * Move METADATA to space2stats_ingest * fix sources link * rewrite catalog with self link and item titles * Rename duplicated variable gdf to adm_gdf * Corrected the color breaks * Adapt based on h3ronpy and adapt colormap * Change formatting, ensure clear runs, and remove unused imports * Add nbqa pre-commit for notebooks --------- Co-authored-by: Andres Chamorro --- .pre-commit-config.yaml | 22 +- .../space2stats_api_adm_example.ipynb | 570 +++ .../user-docs/space2stats_api_demo.ipynb | 269 +- .../space2stats_py_package_demo.ipynb | 276 +- .../Space2Stats Metadata Content.xlsx | Bin 0 -> 31482 bytes .../METADATA/metadata.ipynb | 3586 +++++++++++++++++ .../METADATA/stac/catalog.json | 36 + .../METADATA/stac/space2stats/sources.json | 126 + .../stac/space2stats/space2stats.json | 308 ++ 9 files changed, 4939 insertions(+), 254 deletions(-) create mode 100644 notebooks/user-docs/space2stats_api_adm_example.ipynb create mode 100644 space2stats_api/src/space2stats_ingest/METADATA/Space2Stats Metadata Content.xlsx create mode 100644 space2stats_api/src/space2stats_ingest/METADATA/metadata.ipynb create mode 100644 space2stats_api/src/space2stats_ingest/METADATA/stac/catalog.json create mode 100644 space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/sources.json create mode 100644 space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/space2stats.json diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5e4b1cca..fcf1fb0d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,4 +22,24 @@ repos: files: ^space2stats_api/ exclude: ^space2stats/env/ additional_dependencies: - - types-requests \ No newline at end of file + - types-requests + + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.8.7 + hooks: + - id: nbqa-ruff + args: ["--fix"] + # Match all `.ipynb` files under `notebooks/user-docs/` + files: ^notebooks/user-docs/.*\.ipynb$ + - id: nbqa-isort + args: ["--profile=black"] + additional_dependencies: [isort==5.6.4] + # Match all `.ipynb` files under `notebooks/user-docs/` + files: ^notebooks/user-docs/.*\.ipynb$ + - id: nbqa-black + # Match all `.ipynb` files under `notebooks/user-docs/` + files: ^notebooks/user-docs/.*\.ipynb$ + - id: nbqa-pyupgrade + args: ["--py37-plus"] + # Match all `.ipynb` files under `notebooks/user-docs/` + files: ^notebooks/user-docs/.*\.ipynb$ \ No newline at end of file diff --git a/notebooks/user-docs/space2stats_api_adm_example.ipynb b/notebooks/user-docs/space2stats_api_adm_example.ipynb new file mode 100644 index 00000000..64e3d8ab --- /dev/null +++ b/notebooks/user-docs/space2stats_api_adm_example.ipynb @@ -0,0 +1,570 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from typing import Dict\n", + "\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "import requests\n", + "from lonboard import Map, PolygonLayer, ScatterplotLayer\n", + "from shapely import from_geojson" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_URL = \"http://127.0.0.1:8000\"\n", + "FIELDS_ENDPOINT = f\"{BASE_URL}/fields\"\n", + "SUMMARY_ENDPOINT = f\"{BASE_URL}/summary\"\n", + "AGGREGATION_ENDPOINT = f\"{BASE_URL}/aggregate\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_admin_boundaries(iso3: str, adm: str) -> gpd.GeoDataFrame:\n", + " \"\"\"Fetch administrative boundaries from GeoBoundaries API.\"\"\"\n", + " url = f\"https://www.geoboundaries.org/api/current/gbOpen/{iso3}/{adm}/\"\n", + " res = requests.get(url).json()\n", + " return gpd.read_file(res[\"gjDownloadURL\"])\n", + "\n", + "\n", + "def fetch_summary_data(feature: Dict) -> pd.DataFrame:\n", + " \"\"\"Fetch summary data for each administrative feature.\"\"\"\n", + " request_payload = {\n", + " \"aoi\": feature,\n", + " \"spatial_join_method\": \"touches\",\n", + " \"fields\": [\"sum_pop_2020\"],\n", + " \"geometry\": \"point\",\n", + " }\n", + " response = requests.post(SUMMARY_ENDPOINT, json=request_payload)\n", + " if response.status_code != 200:\n", + " raise Exception(f\"Failed to get summary: {response.text}\")\n", + "\n", + " summary_data = response.json()\n", + " if not summary_data:\n", + " print(f\"Failed to get summary for {feature['id']}\")\n", + " return pd.DataFrame() # Return an empty DataFrame if no data\n", + "\n", + " df = pd.DataFrame(summary_data)\n", + " df[\"adm_id\"] = int(feature[\"id\"])\n", + " df[\"adm_name\"] = feature[\"properties\"][\"shapeName\"]\n", + " df[\"geometry\"] = df[\"geometry\"].apply(lambda geom: from_geojson(geom))\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Turkana 0\n", + "Marsabit 1\n", + "Mandera 2\n", + "Wajir 3\n", + "West Pokot 4\n", + "Samburu 5\n", + "Isiolo 6\n", + "Baringo 7\n", + "Elgeyo-Marakwet 8\n", + "Trans Nzoia 9\n", + "Bungoma 10\n", + "Garissa 11\n", + "Uasin Gishu 12\n", + "Kakamega 13\n", + "Laikipia 14\n", + "Busia 15\n", + "Meru 16\n", + "Nandi 17\n", + "Siaya 18\n", + "Nakuru 19\n", + "Vihiga 20\n", + "Nyandarua 21\n", + "Tharaka 22\n", + "Kericho 23\n", + "Kisumu 24\n", + "Nyeri 25\n", + "Tana River 26\n", + "Kitui 27\n", + "Kirinyaga 28\n", + "Embu 29\n", + "Homa Bay 30\n", + "Bomet 31\n", + "Nyamira 32\n", + "Narok 33\n", + "Kisii 34\n", + "Murang'a 35\n", + "Migori 36\n", + "Kiambu 37\n", + "Machakos 38\n", + "Kajiado 39\n", + "Nairobi 40\n", + "Makueni 41\n", + "Lamu 42\n", + "Kilifi 43\n", + "Taita Taveta 44\n", + "Kwale 45\n", + "Mombasa 46\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " hex_id geometry sum_pop_2020 adm_id adm_name\n", + "0 866a4a407ffffff POINT (35.80117 4.9048) 554.655021 0 Turkana\n", + "1 866a4a417ffffff POINT (35.75483 4.8701) 207.977046 0 Turkana\n", + "2 866a4a41fffffff POINT (35.81106 4.84803) 637.218817 0 Turkana\n", + "3 866a4a427ffffff POINT (35.79128 4.96154) 948.876730 0 Turkana\n", + "4 866a4a437ffffff POINT (35.74495 4.92687) 112.805780 0 Turkana" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ISO3 = \"KEN\"\n", + "ADM = \"ADM1\"\n", + "adm_boundaries = fetch_admin_boundaries(ISO3, ADM)\n", + "geojson_str = adm_boundaries.to_json()\n", + "adm_geojson = json.loads(geojson_str)\n", + "adm_features = adm_geojson[\"features\"]\n", + "\n", + "gdfs = []\n", + "for i, feature in enumerate(adm_features):\n", + " df = fetch_summary_data(feature)\n", + " if not df.empty:\n", + " gdfs.append(gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:4326\"))\n", + " print(feature[\"properties\"][\"shapeName\"], i)\n", + "\n", + "# Concatenate all GeoDataFrames into a single GeoDataFrame\n", + "gdf = pd.concat(gdfs, ignore_index=True)\n", + "\n", + "# Display the GeoDataFrame structure\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Define custom breaks and corresponding RGBA colors for visualization\n", + "breaks = [\n", + " gdf[\"sum_pop_2020\"].min(),\n", + " 1,\n", + " 1000,\n", + " 10000,\n", + " 50000,\n", + " 100000,\n", + " 200000,\n", + " gdf[\"sum_pop_2020\"].max(),\n", + "]\n", + "colors = np.array(\n", + " [\n", + " [211, 211, 211, 255], # Light gray for 0\n", + " [255, 255, 0, 255], # Yellow for 1-1000\n", + " [255, 165, 0, 255], # Orange for 1000-10000\n", + " [255, 0, 0, 255], # Red for 10000-50000\n", + " [128, 0, 128, 255], # Purple for 50000-100000\n", + " [0, 0, 255, 255], # Blue for 100000-200000\n", + " [0, 0, 139, 255], # Dark blue for 200000+\n", + " ]\n", + ")\n", + "\n", + "\n", + "def assign_color(value: float) -> list:\n", + " \"\"\"Assign colors based on population value.\"\"\"\n", + " for i in range(len(breaks) - 1):\n", + " if breaks[i] <= value < breaks[i + 1]:\n", + " return colors[i].tolist() # Convert to list\n", + " return colors[-1].tolist() # In case value exceeds all breaks" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fcab8e1932cd43d49f23111a0f39c97b", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "Map(layers=[ScatterplotLayer(get_fill_color=\n", + "[\n", + " [\n", + " 2…" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Map sum_pop_2020 values to colors using the custom function\n", + "gdf[\"color\"] = gdf[\"sum_pop_2020\"].apply(assign_color)\n", + "\n", + "# Flatten the color list into a 2D array for ScatterplotLayer\n", + "color_list = np.array(gdf[\"color\"].tolist(), dtype=np.uint8)\n", + "\n", + "# Create the scatterplot layer with the assigned colors\n", + "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=color_list)\n", + "\n", + "# Create the map with the scatterplot layer\n", + "m = Map(layer)\n", + "m" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def fetch_aggregated_population(row):\n", + " request_payload = {\n", + " \"aoi\": {\n", + " \"type\": \"Feature\",\n", + " \"geometry\": row.geometry.__geo_interface__,\n", + " \"properties\": {},\n", + " },\n", + " \"spatial_join_method\": \"touches\",\n", + " \"fields\": [\"sum_pop_2020\"],\n", + " \"aggregation_type\": \"sum\",\n", + " }\n", + "\n", + " response = requests.post(AGGREGATION_ENDPOINT, json=request_payload)\n", + "\n", + " if response.status_code == 200:\n", + " result = response.json()\n", + " return result.get(\"sum_pop_2020\", 0)\n", + " else:\n", + " print(response.content)\n", + " return 0" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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geometryshapeNameshapeISOshapeIDshapeGroupshapeTypesum_pop_2020
0POLYGON ((36.05061 4.45622, 35.94395 4.54803, ...TurkanaKE-4332016919B72266624462344KENADM11.374176e+06
1POLYGON ((36.60089 2.40574, 36.60138 2.4053, 3...MarsabitKE-2532016919B63496705134089KENADM14.460342e+05
2POLYGON ((40.99195 2.17919, 40.99245 2.25188, ...ManderaKE-2432016919B2031803566233KENADM12.815273e+06
3POLYGON ((38.96255 2.09739, 38.96272 2.09718, ...WajirKE-4632016919B89873713911655KENADM11.930365e+06
4POLYGON ((34.94278 2.45547, 34.93892 2.45551, ...West PokotKE-4732016919B96045830258165KENADM19.429576e+05
\n", + "
" + ], + "text/plain": [ + " geometry shapeName shapeISO \\\n", + "0 POLYGON ((36.05061 4.45622, 35.94395 4.54803, ... Turkana KE-43 \n", + "1 POLYGON ((36.60089 2.40574, 36.60138 2.4053, 3... Marsabit KE-25 \n", + "2 POLYGON ((40.99195 2.17919, 40.99245 2.25188, ... Mandera KE-24 \n", + "3 POLYGON ((38.96255 2.09739, 38.96272 2.09718, ... Wajir KE-46 \n", + "4 POLYGON ((34.94278 2.45547, 34.93892 2.45551, ... West Pokot KE-47 \n", + "\n", + " shapeID shapeGroup shapeType sum_pop_2020 \n", + "0 32016919B72266624462344 KEN ADM1 1.374176e+06 \n", + "1 32016919B63496705134089 KEN ADM1 4.460342e+05 \n", + "2 32016919B2031803566233 KEN ADM1 2.815273e+06 \n", + "3 32016919B89873713911655 KEN ADM1 1.930365e+06 \n", + "4 32016919B96045830258165 KEN ADM1 9.429576e+05 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adm_gdf = gpd.GeoDataFrame.from_features(adm_features)\n", + "adm_gdf[\"sum_pop_2020\"] = adm_gdf.apply(fetch_aggregated_population, axis=1)\n", + "adm_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "breaks = [\n", + " adm_gdf[\"sum_pop_2020\"].min(),\n", + " 10_000,\n", + " 100_000,\n", + " 1_000_000,\n", + " 5_000_000,\n", + " 10_000_000,\n", + " 20_000_000,\n", + " gdf[\"sum_pop_2020\"].max(),\n", + "]\n", + "colors = np.array(\n", + " [\n", + " [211, 211, 211, 125], # Light gray with transparency for 0 - 10,000\n", + " [255, 255, 0, 125], # Yellow with transparency for 10,000 - 100,000\n", + " [255, 165, 0, 125], # Orange with transparency for 100,000 - 1,000,000\n", + " [255, 0, 0, 125], # Red with transparency for 1,000,000 - 5,000,000\n", + " [128, 0, 128, 125], # Purple with transparency for 5,000,000 - 10,000,000\n", + " [0, 0, 255, 125], # Blue with transparency for 10,000,000 - 20,000,000\n", + " [0, 0, 139, 125], # Dark blue with transparency for 20,000,000 - max\n", + " ]\n", + ")\n", + "\n", + "\n", + "def assign_color(value: float) -> list:\n", + " \"\"\"Assign colors based on population value, including transparency.\"\"\"\n", + " for i in range(len(breaks) - 1):\n", + " if breaks[i] <= value < breaks[i + 1]:\n", + " return colors[i].tolist() # Convert to list\n", + " return colors[-1].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages/lonboard/_geoarrow/ops/reproject.py:23: UserWarning: No CRS exists on data. If no data is shown on the map, double check that your CRS is WGS84.\n", + " warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "94fb588e3bdc4ba99ccd323f7aec0889", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "Map(layers=[PolygonLayer(get_fill_color=\n", + "[\n", + " [\n", + " 255,\n", + "…" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Assign colors to each row in gdf based on sum_pop_2020\n", + "adm_gdf[\"color\"] = adm_gdf[\"sum_pop_2020\"].apply(assign_color)\n", + "\n", + "# Convert the list of colors into a 2D numpy array of uint8\n", + "color_array = np.array(adm_gdf[\"color\"].tolist(), dtype=np.uint8)\n", + "\n", + "# Create the polygon layer using PolygonLayer, referencing the color_array\n", + "layer = PolygonLayer.from_geopandas(\n", + " adm_gdf,\n", + " get_fill_color=color_array, # Pass the 2D numpy array for the colors\n", + " get_line_color=[0, 0, 0, 255], # Optional: Black outline\n", + " line_width_min_pixels=1,\n", + ")\n", + "\n", + "# Create the map with the polygon layer\n", + "m = Map(layer)\n", + "m" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "spacestats", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/user-docs/space2stats_api_demo.ipynb b/notebooks/user-docs/space2stats_api_demo.ipynb index e79fc152..aca1f875 100644 --- a/notebooks/user-docs/space2stats_api_demo.ipynb +++ b/notebooks/user-docs/space2stats_api_demo.ipynb @@ -2,24 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from typing import Dict\n", "\n", + "import geopandas as gpd\n", "import numpy as np\n", - "import requests\n", "import pandas as pd\n", - "import geopandas as gpd\n", - "from shapely.geometry import shape\n", + "import requests\n", + "from geojson_pydantic import Feature, Polygon\n", "from lonboard import Map, ScatterplotLayer\n", - "from geojson_pydantic import Feature, Polygon" + "from shapely import from_geojson" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -69,23 +69,43 @@ " [33.78593974945852, -4.725410543134203],\n", " [41.94362577283266, -4.725410543134203],\n", " [41.94362577283266, 5.115816884114494],\n", - " [33.78593974945852, 5.115816884114494]\n", + " [33.78593974945852, 5.115816884114494],\n", " ]\n", - " ]\n", + " ],\n", " },\n", - " \"properties\": {\n", - " \"name\": \"Updated AOI\"\n", - " }\n", + " \"properties\": {\"name\": \"Updated AOI\"},\n", "}\n", - " \n", - " \n", + "\n", "\n", "feat = AOIModel(**aoi)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the Request Payload\n", + "request_payload = {\n", + " \"aoi\": aoi,\n", + " \"spatial_join_method\": \"touches\",\n", + " \"fields\": [\"sum_pop_2020\"],\n", + " \"geometry\": \"point\",\n", + "}\n", + "\n", + "# Get Summary Data\n", + "response = requests.post(SUMMARY_ENDPOINT, json=request_payload)\n", + "if response.status_code != 200:\n", + " raise Exception(f\"Failed to get summary: {response.text}\")\n", + "\n", + "summary_data = response.json()\n", + "df = pd.DataFrame(summary_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -117,33 +137,33 @@ " \n", " \n", " 0\n", + " 866a4a00fffffff\n", + " {\"type\":\"Point\",\"coordinates\":[36.180976490229...\n", + " 476.538185\n", + " \n", + " \n", + " 1\n", " 866a4a017ffffff\n", " {\"type\":\"Point\",\"coordinates\":[36.078694056627...\n", " 676.912804\n", " \n", " \n", - " 1\n", + " 2\n", " 866a4a01fffffff\n", " {\"type\":\"Point\",\"coordinates\":[36.134817927246...\n", " 347.182722\n", " \n", " \n", - " 2\n", - " 866a4a04fffffff\n", - " {\"type\":\"Point\",\"coordinates\":[36.339286712001...\n", - " 285.943490\n", - " \n", - " \n", " 3\n", - " 866a4a057ffffff\n", - " {\"type\":\"Point\",\"coordinates\":[36.237081352434...\n", - " 279.463912\n", + " 866a4a047ffffff\n", + " {\"type\":\"Point\",\"coordinates\":[36.283201202805...\n", + " 380.988678\n", " \n", " \n", " 4\n", - " 866a4a05fffffff\n", - " {\"type\":\"Point\",\"coordinates\":[36.293182542907...\n", - " 235.258474\n", + " 866a4a04fffffff\n", + " {\"type\":\"Point\",\"coordinates\":[36.339286712001...\n", + " 285.943490\n", " \n", " \n", " ...\n", @@ -152,97 +172,82 @@ " ...\n", " \n", " \n", - " 25801\n", + " 26184\n", " 86969ed97ffffff\n", " {\"type\":\"Point\",\"coordinates\":[34.808746693532...\n", " 196.448391\n", " \n", " \n", - " 25802\n", + " 26185\n", " 86969ed9fffffff\n", " {\"type\":\"Point\",\"coordinates\":[34.750412026186...\n", " 296.740893\n", " \n", " \n", - " 25803\n", + " 26186\n", " 86969eda7ffffff\n", " {\"type\":\"Point\",\"coordinates\":[34.770464787099...\n", " 154.818453\n", " \n", " \n", - " 25804\n", + " 26187\n", " 86969edafffffff\n", " {\"type\":\"Point\",\"coordinates\":[34.712102323801...\n", " 157.875350\n", " \n", " \n", - " 25805\n", + " 26188\n", " 86969edb7ffffff\n", " {\"type\":\"Point\",\"coordinates\":[34.818784182799...\n", " 236.127331\n", " \n", " \n", "\n", - "

25806 rows × 3 columns

\n", + "

26189 rows × 3 columns

\n", "" ], "text/plain": [ " hex_id geometry \\\n", - "0 866a4a017ffffff {\"type\":\"Point\",\"coordinates\":[36.078694056627... \n", - "1 866a4a01fffffff {\"type\":\"Point\",\"coordinates\":[36.134817927246... \n", - "2 866a4a04fffffff {\"type\":\"Point\",\"coordinates\":[36.339286712001... \n", - "3 866a4a057ffffff {\"type\":\"Point\",\"coordinates\":[36.237081352434... \n", - "4 866a4a05fffffff {\"type\":\"Point\",\"coordinates\":[36.293182542907... \n", + "0 866a4a00fffffff {\"type\":\"Point\",\"coordinates\":[36.180976490229... \n", + "1 866a4a017ffffff {\"type\":\"Point\",\"coordinates\":[36.078694056627... \n", + "2 866a4a01fffffff {\"type\":\"Point\",\"coordinates\":[36.134817927246... \n", + "3 866a4a047ffffff {\"type\":\"Point\",\"coordinates\":[36.283201202805... \n", + "4 866a4a04fffffff {\"type\":\"Point\",\"coordinates\":[36.339286712001... \n", "... ... ... \n", - "25801 86969ed97ffffff {\"type\":\"Point\",\"coordinates\":[34.808746693532... \n", - "25802 86969ed9fffffff {\"type\":\"Point\",\"coordinates\":[34.750412026186... \n", - "25803 86969eda7ffffff {\"type\":\"Point\",\"coordinates\":[34.770464787099... \n", - "25804 86969edafffffff {\"type\":\"Point\",\"coordinates\":[34.712102323801... \n", - "25805 86969edb7ffffff {\"type\":\"Point\",\"coordinates\":[34.818784182799... \n", + "26184 86969ed97ffffff {\"type\":\"Point\",\"coordinates\":[34.808746693532... \n", + "26185 86969ed9fffffff {\"type\":\"Point\",\"coordinates\":[34.750412026186... \n", + "26186 86969eda7ffffff {\"type\":\"Point\",\"coordinates\":[34.770464787099... \n", + "26187 86969edafffffff {\"type\":\"Point\",\"coordinates\":[34.712102323801... \n", + "26188 86969edb7ffffff {\"type\":\"Point\",\"coordinates\":[34.818784182799... \n", "\n", " sum_pop_2020 \n", - "0 676.912804 \n", - "1 347.182722 \n", - "2 285.943490 \n", - "3 279.463912 \n", - "4 235.258474 \n", + "0 476.538185 \n", + "1 676.912804 \n", + "2 347.182722 \n", + "3 380.988678 \n", + "4 285.943490 \n", "... ... \n", - "25801 196.448391 \n", - "25802 296.740893 \n", - "25803 154.818453 \n", - "25804 157.875350 \n", - "25805 236.127331 \n", + "26184 196.448391 \n", + "26185 296.740893 \n", + "26186 154.818453 \n", + "26187 157.875350 \n", + "26188 236.127331 \n", "\n", - "[25806 rows x 3 columns]" + "[26189 rows x 3 columns]" ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Define the Request Payload\n", - "request_payload = {\n", - " \"aoi\": aoi,\n", - " \"spatial_join_method\": \"centroid\",\n", - " \"fields\": [\"sum_pop_2020\"], \n", - " \"geometry\": \"point\"\n", - "}\n", - "\n", - "# Get Summary Data\n", - "response = requests.post(SUMMARY_ENDPOINT, json=request_payload)\n", - "if response.status_code != 200:\n", - " raise Exception(f\"Failed to get summary: {response.text}\")\n", - "\n", - "summary_data = response.json()\n", - "df = pd.DataFrame(summary_data)\n", "df" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -274,33 +279,33 @@ " \n", " \n", " 0\n", + " 866a4a00fffffff\n", + " POINT (36.18098 5.12501)\n", + " 476.538185\n", + " \n", + " \n", + " 1\n", " 866a4a017ffffff\n", " POINT (36.07869 5.11252)\n", " 676.912804\n", " \n", " \n", - " 1\n", + " 2\n", " 866a4a01fffffff\n", " POINT (36.13482 5.09047)\n", " 347.182722\n", " \n", " \n", - " 2\n", - " 866a4a04fffffff\n", - " POINT (36.33929 5.11542)\n", - " 285.943490\n", - " \n", - " \n", " 3\n", - " 866a4a057ffffff\n", - " POINT (36.23708 5.10296)\n", - " 279.463912\n", + " 866a4a047ffffff\n", + " POINT (36.2832 5.13748)\n", + " 380.988678\n", " \n", " \n", " 4\n", - " 866a4a05fffffff\n", - " POINT (36.29318 5.0809)\n", - " 235.258474\n", + " 866a4a04fffffff\n", + " POINT (36.33929 5.11542)\n", + " 285.943490\n", " \n", " \n", " ...\n", @@ -309,117 +314,129 @@ " ...\n", " \n", " \n", - " 25801\n", + " 26184\n", " 86969ed97ffffff\n", " POINT (34.80875 -3.47225)\n", " 196.448391\n", " \n", " \n", - " 25802\n", + " 26185\n", " 86969ed9fffffff\n", " POINT (34.75041 -3.44944)\n", " 296.740893\n", " \n", " \n", - " 25803\n", + " 26186\n", " 86969eda7ffffff\n", " POINT (34.77046 -3.5715)\n", " 154.818453\n", " \n", " \n", - " 25804\n", + " 26187\n", " 86969edafffffff\n", " POINT (34.7121 -3.54868)\n", " 157.875350\n", " \n", " \n", - " 25805\n", + " 26188\n", " 86969edb7ffffff\n", " POINT (34.81878 -3.53328)\n", " 236.127331\n", " \n", " \n", "\n", - "

25806 rows × 3 columns

\n", + "

26189 rows × 3 columns

\n", "" ], "text/plain": [ " hex_id geometry sum_pop_2020\n", - "0 866a4a017ffffff POINT (36.07869 5.11252) 676.912804\n", - "1 866a4a01fffffff POINT (36.13482 5.09047) 347.182722\n", - "2 866a4a04fffffff POINT (36.33929 5.11542) 285.943490\n", - "3 866a4a057ffffff POINT (36.23708 5.10296) 279.463912\n", - "4 866a4a05fffffff POINT (36.29318 5.0809) 235.258474\n", + "0 866a4a00fffffff POINT (36.18098 5.12501) 476.538185\n", + "1 866a4a017ffffff POINT (36.07869 5.11252) 676.912804\n", + "2 866a4a01fffffff POINT (36.13482 5.09047) 347.182722\n", + "3 866a4a047ffffff POINT (36.2832 5.13748) 380.988678\n", + "4 866a4a04fffffff POINT (36.33929 5.11542) 285.943490\n", "... ... ... ...\n", - "25801 86969ed97ffffff POINT (34.80875 -3.47225) 196.448391\n", - "25802 86969ed9fffffff POINT (34.75041 -3.44944) 296.740893\n", - "25803 86969eda7ffffff POINT (34.77046 -3.5715) 154.818453\n", - "25804 86969edafffffff POINT (34.7121 -3.54868) 157.875350\n", - "25805 86969edb7ffffff POINT (34.81878 -3.53328) 236.127331\n", + "26184 86969ed97ffffff POINT (34.80875 -3.47225) 196.448391\n", + "26185 86969ed9fffffff POINT (34.75041 -3.44944) 296.740893\n", + "26186 86969eda7ffffff POINT (34.77046 -3.5715) 154.818453\n", + "26187 86969edafffffff POINT (34.7121 -3.54868) 157.875350\n", + "26188 86969edb7ffffff POINT (34.81878 -3.53328) 236.127331\n", "\n", - "[25806 rows x 3 columns]" + "[26189 rows x 3 columns]" ] }, - "execution_count": 9, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from shapely import from_geojson\n", - "\n", - "df['geometry'] = df['geometry'].apply(lambda geom: from_geojson(geom))\n", - "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n", + "df[\"geometry\"] = df[\"geometry\"].apply(lambda geom: from_geojson(geom))\n", + "gdf = gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:4326\")\n", "gdf" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "267540ef06104b718e9f18ffd4225440", + "model_id": "897d8bcee7ee4431893b3ba08ee67363", "version_major": 2, "version_minor": 1 }, "text/plain": [ - "Map(layers=[ScatterplotLayer(get_fill_color=\n", + "Map(layers=[ScatterplotLayer(get_fill_color=\n", "[\n", " [\n", " 2…" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Define custom breaks and corresponding RGBA colors\n", - "breaks = [0, 1, 1000, 10000, 50000, 100000, 200000, gdf[\"sum_pop_2020\"].max()]\n", - "colors = np.array([\n", - " [211, 211, 211, 255], # Light gray for 0\n", - " [255, 255, 0, 255], # Yellow for 1-1000\n", - " [255, 165, 0, 255], # Orange for 1000-10000\n", - " [255, 0, 0, 255], # Red for 10000-50000\n", - " [128, 0, 128, 255], # Purple for 50000-100000\n", - " [0, 0, 255, 255], # Blue for 100000-200000\n", - " [0, 0, 139, 255], # Dark blue for 200000+\n", - "])\n", + "breaks = [\n", + " gdf[\"sum_pop_2020\"].min(),\n", + " 1,\n", + " 1000,\n", + " 10000,\n", + " 50000,\n", + " 100000,\n", + " 200000,\n", + " gdf[\"sum_pop_2020\"].max(),\n", + "]\n", + "colors = np.array(\n", + " [\n", + " [211, 211, 211, 255], # Light gray for 0\n", + " [255, 255, 0, 255], # Yellow for 1-1000\n", + " [255, 165, 0, 255], # Orange for 1000-10000\n", + " [255, 0, 0, 255], # Red for 10000-50000\n", + " [128, 0, 128, 255], # Purple for 50000-100000\n", + " [0, 0, 255, 255], # Blue for 100000-200000\n", + " [0, 0, 139, 255], # Dark blue for 200000+\n", + " ]\n", + ")\n", "\n", "# Function to assign colors based on custom bins\n", + "\n", + "\n", "def assign_color(value, breaks, colors):\n", " for i in range(len(breaks) - 1):\n", " if breaks[i] <= value < breaks[i + 1]:\n", " return colors[i]\n", " return colors[-1] # In case value exceeds all breaks\n", "\n", + "\n", "# Map sum_pop_2020 values to colors using the custom function\n", - "gdf['color'] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", - "colors = np.uint8(gdf['color'].tolist())\n", + "gdf[\"color\"] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", + "colors = np.uint8(gdf[\"color\"].tolist())\n", "\n", "# Create the scatterplot layer with the assigned colors\n", "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=colors)\n", @@ -431,7 +448,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "spacestats", "language": "python", "name": "python3" }, @@ -445,7 +462,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/notebooks/user-docs/space2stats_py_package_demo.ipynb b/notebooks/user-docs/space2stats_py_package_demo.ipynb index 4e9dbb66..533b44ec 100644 --- a/notebooks/user-docs/space2stats_py_package_demo.ipynb +++ b/notebooks/user-docs/space2stats_py_package_demo.ipynb @@ -1,90 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0mFound existing installation: space2stats 0.1.0\n", - "Uninstalling space2stats-0.1.0:\n", - " Successfully uninstalled space2stats-0.1.0\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0mCollecting git+https://github.com/worldbank/DECAT_Space2Stats.git@feature/h3ronpy#subdirectory=space2stats_api/src\n", - " Cloning https://github.com/worldbank/DECAT_Space2Stats.git (to revision feature/h3ronpy) to /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-req-build-o_n4xv_w\n", - " Running command git clone --filter=blob:none --quiet https://github.com/worldbank/DECAT_Space2Stats.git /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-req-build-o_n4xv_w\n", - " Running command git checkout -b feature/h3ronpy --track origin/feature/h3ronpy\n", - " Switched to a new branch 'feature/h3ronpy'\n", - " branch 'feature/h3ronpy' set up to track 'origin/feature/h3ronpy'.\n", - " Resolved https://github.com/worldbank/DECAT_Space2Stats.git to commit 5299454c7954a0ee167616abc2a2157cd495b0c1\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: asgi-s3-response-middleware<0.0.5,>=0.0.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.0.4)\n", - "Requirement already satisfied: boto3<2.0.0,>=1.35.11 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.35.34)\n", - "Requirement already satisfied: fastapi>=0.112.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.115.0)\n", - "Requirement already satisfied: geojson-pydantic in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.1.1)\n", - "Requirement already satisfied: h3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.7.7)\n", - "Requirement already satisfied: h3ronpy<0.22.0,>=0.21.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.21.1)\n", - "Requirement already satisfied: httpx in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.27.2)\n", - "Requirement already satisfied: mangum in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.19.0)\n", - "Requirement already satisfied: numpy<2.0.0,>=1.24.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (1.26.4)\n", - "Requirement already satisfied: orjson in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.10.7)\n", - "Requirement already satisfied: pandas in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.2.3)\n", - "Requirement already satisfied: psycopg[binary,pool] in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (3.2.3)\n", - "Requirement already satisfied: pydantic-settings>=2.0.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.5.2)\n", - "Requirement already satisfied: shapely in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (2.0.6)\n", - "Requirement already satisfied: starlette-cramjam<0.4,>=0.3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (0.3.3)\n", - "Requirement already satisfied: typing_extensions in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from space2stats==0.1.0) (4.12.2)\n", - "Requirement already satisfied: starlette>=0.38.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from asgi-s3-response-middleware<0.0.5,>=0.0.4->space2stats==0.1.0) (0.38.6)\n", - "Requirement already satisfied: botocore<1.36.0,>=1.35.34 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (1.35.34)\n", - "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (1.0.1)\n", - "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (0.10.2)\n", - "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from fastapi>=0.112.0->space2stats==0.1.0) (2.9.2)\n", - "Requirement already satisfied: pyarrow>=10.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from h3ronpy<0.22.0,>=0.21.0->space2stats==0.1.0) (17.0.0)\n", - "Requirement already satisfied: anyio in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (4.6.0)\n", - "Requirement already satisfied: certifi in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (2024.8.30)\n", - "Requirement already satisfied: httpcore==1.* in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (1.0.6)\n", - "Requirement already satisfied: idna in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (3.10)\n", - "Requirement already satisfied: sniffio in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpx->space2stats==0.1.0) (1.3.1)\n", - "Requirement already satisfied: h11<0.15,>=0.13 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from httpcore==1.*->httpx->space2stats==0.1.0) (0.14.0)\n", - "Requirement already satisfied: python-dotenv>=0.21.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic-settings>=2.0.0->space2stats==0.1.0) (1.0.1)\n", - "Requirement already satisfied: cramjam<2.9,>=2.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from starlette-cramjam<0.4,>=0.3->space2stats==0.1.0) (2.8.4)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2024.2)\n", - "Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pandas->space2stats==0.1.0) (2024.2)\n", - "Requirement already satisfied: psycopg-binary==3.2.3 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from psycopg[binary,pool]->space2stats==0.1.0) (3.2.3)\n", - "Requirement already satisfied: psycopg-pool in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from psycopg[binary,pool]->space2stats==0.1.0) (3.2.3)\n", - "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from botocore<1.36.0,>=1.35.34->boto3<2.0.0,>=1.35.11->space2stats==0.1.0) (2.2.3)\n", - "Requirement already satisfied: annotated-types>=0.6.0 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi>=0.112.0->space2stats==0.1.0) (0.7.0)\n", - "Requirement already satisfied: pydantic-core==2.23.4 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi>=0.112.0->space2stats==0.1.0) (2.23.4)\n", - "Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/spacestats/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->space2stats==0.1.0) (1.16.0)\n", - "Building wheels for collected packages: space2stats\n", - " Building wheel for space2stats (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25h Created wheel for space2stats: filename=space2stats-0.1.0-py3-none-any.whl size=18428 sha256=ea6987e1b55fe30bed14d734f35195abc39985520ecc888928d1c0df698bc0dc\n", - " Stored in directory: /private/var/folders/md/g6df9cs96bv_bv1kslvxkb4c0000gn/T/pip-ephem-wheel-cache-x7ltnsoy/wheels/99/9f/50/575302cb376b7ead3e96482cd92b0bf444bc829e19cc250c03\n", - "Successfully built space2stats\n", - "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0mInstalling collected packages: space2stats\n", - "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0mSuccessfully installed space2stats-0.1.0\n", - "\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution ~umpy (/opt/anaconda3/envs/spacestats/lib/python3.11/site-packages)\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install \"git+https://github.com/worldbank/DECAT_Space2Stats.git@feature/h3ronpy#subdirectory=space2stats_api/src\"" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -93,15 +8,14 @@ "source": [ "from typing import Dict\n", "\n", + "import geopandas as gpd\n", "import numpy as np\n", - "from dotenv import load_dotenv\n", "import pandas as pd\n", - "import geopandas as gpd\n", - "from shapely.geometry import shape\n", - "from lonboard import Map, ScatterplotLayer\n", + "from dotenv import load_dotenv\n", "from geojson_pydantic import Feature, Polygon\n", - "\n", - "from space2stats import StatsTable\n" + "from lonboard import Map, ScatterplotLayer\n", + "from shapely import from_geojson\n", + "from space2stats import StatsTable" ] }, { @@ -222,16 +136,13 @@ " [33.78593974945852, -4.725410543134203],\n", " [41.94362577283266, -4.725410543134203],\n", " [41.94362577283266, 5.115816884114494],\n", - " [33.78593974945852, 5.115816884114494]\n", + " [33.78593974945852, 5.115816884114494],\n", " ]\n", - " ]\n", + " ],\n", " },\n", - " \"properties\": {\n", - " \"name\": \"Updated AOI\"\n", - " }\n", + " \"properties\": {\"name\": \"Updated AOI\"},\n", "}\n", - " \n", - " \n", + "\n", "\n", "feat = AOIModel(**aoi)" ] @@ -240,6 +151,106 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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hex_idgeometrysum_pop_2020
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4866a4a04fffffff{\"type\":\"Point\",\"coordinates\":[36.339286712001...285.943490
\n", + "
" + ], + "text/plain": [ + " hex_id geometry \\\n", + "0 866a4a00fffffff {\"type\":\"Point\",\"coordinates\":[36.180976490229... \n", + "1 866a4a017ffffff {\"type\":\"Point\",\"coordinates\":[36.078694056627... \n", + "2 866a4a01fffffff {\"type\":\"Point\",\"coordinates\":[36.134817927246... \n", + "3 866a4a047ffffff {\"type\":\"Point\",\"coordinates\":[36.283201202805... \n", + "4 866a4a04fffffff {\"type\":\"Point\",\"coordinates\":[36.339286712001... \n", + "\n", + " sum_pop_2020 \n", + "0 476.538185 \n", + "1 676.912804 \n", + "2 347.182722 \n", + "3 380.988678 \n", + "4 285.943490 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with StatsTable.connect() as stats_table:\n", + " data = stats_table.summaries(\n", + " aoi=feat,\n", + " spatial_join_method=\"touches\",\n", + " fields=[\"sum_pop_2020\"],\n", + " geometry=\"point\",\n", + " )\n", + " df = pd.DataFrame(data)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { @@ -650,14 +661,16 @@ "[26189 rows x 42 columns]" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with StatsTable.connect() as stats_table:\n", - " data = stats_table.summaries(aoi=feat, spatial_join_method=\"touches\", fields=fields, geometry=\"point\")\n", + " data = stats_table.summaries(\n", + " aoi=feat, spatial_join_method=\"touches\", fields=fields, geometry=\"point\"\n", + " )\n", " df = pd.DataFrame(data)\n", "\n", "df" @@ -665,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1064,66 +1077,75 @@ "[26189 rows x 42 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from shapely import from_geojson\n", - "\n", - "df['geometry'] = df['geometry'].apply(lambda geom: from_geojson(geom))\n", - "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n", + "df[\"geometry\"] = df[\"geometry\"].apply(lambda geom: from_geojson(geom))\n", + "gdf = gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:4326\")\n", "gdf" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b73e90e5014643d2a3b7f9ef33032f8f", + "model_id": "6868905940b74debbc4b3f7cd62770a5", "version_major": 2, "version_minor": 1 }, "text/plain": [ - "Map(layers=[ScatterplotLayer(get_fill_color=\n", - "[\n", - " [\n", - " 2…" + "Map(custom_attribution='', layers=(ScatterplotLayer(get_fill_color=arro3.core.ChunkedArrayRK!5*P#-02BZm004jh0I^l=u?G+UfCdr(00{sbNK??(#>v>mNmt3; 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b/space2stats_api/src/space2stats_ingest/METADATA/metadata.ipynb @@ -0,0 +1,3586 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook creates a SpatioTemporal Asset Catalog (STAC) catalog and item for the Space2Stats database. It reads the source parquet file and an additional metadata spreadsheet to create STAC compliant metadata." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict\n", + "import requests\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from shapely.geometry import shape, Polygon\n", + "import h3\n", + "\n", + "import shutil\n", + "import tempfile\n", + "from pathlib import Path\n", + "\n", + "from pystac import Catalog, Item, Asset, CatalogType, get_stac_version\n", + "import fio_stac\n", + "from datetime import datetime, UTC\n", + "import ast\n", + "from os.path import join\n", + "\n", + "import git, os\n", + "git_repo = git.Repo(os.getcwd(), search_parent_directories=True)\n", + "git_root = git_repo.git.rev_parse(\"--show-toplevel\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Current Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "parquet_file = join(git_root, 'postgres', 'space2stats.parquet')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_parquet(parquet_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14117882" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gdf.loc[:, 'geometry'] = gdf.apply(lambda x: Polygon(h3.h3_to_geo_boundary(x['hex_id'], geo_json=True)), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "gdf = gpd.GeoDataFrame(gdf, geometry='geometry', crs='EPSG:4326')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-179.99999562, -89.98750455, 179.99999096, 89.98750455])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf.total_bounds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create STAC" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0.0\n" + ] + } + ], + "source": [ + "print(get_stac_version())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Content" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For now, metadata fields are managed through an Excel Spreadsheet." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "overview = pd.read_excel(\"Space2Stats Metadata Content.xlsx\", sheet_name=\"DDH Dataset\", index_col=\"Field\")\n", + "nada = pd.read_excel(\"Space2Stats Metadata Content.xlsx\", sheet_name=\"NADA\", index_col=\"Field\")\n", + "feature_catalog = pd.read_excel(\"Space2Stats Metadata Content.xlsx\", sheet_name=\"Feature Catalog\")\n", + "sources = pd.read_excel(\"Space2Stats Metadata Content.xlsx\", sheet_name=\"Sources\")\n", + "sources.loc[:, \"Variables\"] = sources.apply(lambda x: ast.literal_eval(x['Variables']), axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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TitleSpace2Stats Database
DescriptionA global dataset of geospatial variables at th...
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" + ], + "text/plain": [ + " Value\n", + "Field \n", + "Title Space2Stats Database\n", + "Description A global dataset of geospatial variables at th...\n", + "TTL Ben Stewart\n", + "Business Unit DECSC\n", + "Collaborator Andres Chamorro" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overview.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GroupValue
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TitleIdentificationSpace2Stats Database
IdentifierIdentificationGLO_2024_SPACE2STATS_GEO_v01
Hierarchy levelIdentificationdataset
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Edition DateIdentification2024-09-06 00:00:00
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" + ], + "text/plain": [ + " Group Value\n", + "Field \n", + "Title Identification Space2Stats Database\n", + "Identifier Identification GLO_2024_SPACE2STATS_GEO_v01\n", + "Hierarchy level Identification dataset\n", + "Edition Identification v.1\n", + "Edition Date Identification 2024-09-06 00:00:00" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nada.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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variabledescriptiontypenodata
0hex_idH3 unique identifierstringNaN
1ogc_fidFeature unique identifiernumericNaN
2sum_pop_2020Total population, 2020numericNaN
3sum_pop_f_0_2020Total population female, ages 0 to 1, 2020numericNaN
4sum_pop_f_10_2020Total population female, ages 10 to 15, 2020numericNaN
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" + ], + "text/plain": [ + " variable description type \\\n", + "0 hex_id H3 unique identifier string \n", + "1 ogc_fid Feature unique identifier numeric \n", + "2 sum_pop_2020 Total population, 2020 numeric \n", + "3 sum_pop_f_0_2020 Total population female, ages 0 to 1, 2020 numeric \n", + "4 sum_pop_f_10_2020 Total population female, ages 10 to 15, 2020 numeric \n", + "\n", + " nodata \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_catalog.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ThemeNameDescriptionMethodological NotesVariablesSource DataCitation sourceOrganizationMethodResolution
0DemographicsPopulationGridded population disaggregated by gender.Global raster files are processed for each hex...[sum_pop_2020, sum_pop_f_0_2020, sum_pop_f_10_...WorldPop gridded population, 2020, Unconstrain...Stevens FR, Gaughan AE, Linard C, Tatem AJ (20...World Pop, https://www.worldpop.org/methods/po...sum100 mts
1Socio-economicNighttime LightsSum of luminosity values measured by monthly c...Monthly composites generated by NASA through t...[ntl_sum_yyyymm]World Bank - Light Every Night, https://regist...NaNNASA, World Banksum500 mts
2ExposureFlood AreaArea where flood depth is greater than 50 cm, ...Flood data combines fluvial, pluvial, and coas...[flood_area_100, flood_area_1000]Fathom 3.0 High Resolution Global Flood Maps I...Wing et al. (2024) A 30 m Global Flood Inundat...Fathom, https://www.fathom.global/sum30 mts
3ExposurePopulation Exposed to FloodsPopulation where flood depth is greater than 5...Flood data is intersected with population grid...[flood_pop_100, flood_pop_1000]Fathom 3.0 High Resolution Global Flood Maps I...Wing et al. (2024) A 30 m Global Flood Inundat...Fathom, https://www.fathom.global/sum of intersect30 mts and 100 mts
4ConflictNumber of Conflict EventsSum of conflict events (ACLED).Conflict data is filtered for event types and ...[acled_events_yyyy]Armed Conflict Location and Event Data (ACLED)...https://acleddata.com/article-categories/gener...ACLED, https://acleddata.com/countpoint data
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" + ], + "text/plain": [ + " Theme Name \\\n", + "0 Demographics Population \n", + "1 Socio-economic Nighttime Lights \n", + "2 Exposure Flood Area \n", + "3 Exposure Population Exposed to Floods \n", + "4 Conflict Number of Conflict Events \n", + "\n", + " Description \\\n", + "0 Gridded population disaggregated by gender. \n", + "1 Sum of luminosity values measured by monthly c... \n", + "2 Area where flood depth is greater than 50 cm, ... \n", + "3 Population where flood depth is greater than 5... \n", + "4 Sum of conflict events (ACLED). \n", + "\n", + " Methodological Notes \\\n", + "0 Global raster files are processed for each hex... \n", + "1 Monthly composites generated by NASA through t... \n", + "2 Flood data combines fluvial, pluvial, and coas... \n", + "3 Flood data is intersected with population grid... \n", + "4 Conflict data is filtered for event types and ... \n", + "\n", + " Variables \\\n", + "0 [sum_pop_2020, sum_pop_f_0_2020, sum_pop_f_10_... \n", + "1 [ntl_sum_yyyymm] \n", + "2 [flood_area_100, flood_area_1000] \n", + "3 [flood_pop_100, flood_pop_1000] \n", + "4 [acled_events_yyyy] \n", + "\n", + " Source Data \\\n", + "0 WorldPop gridded population, 2020, Unconstrain... \n", + "1 World Bank - Light Every Night, https://regist... \n", + "2 Fathom 3.0 High Resolution Global Flood Maps I... \n", + "3 Fathom 3.0 High Resolution Global Flood Maps I... \n", + "4 Armed Conflict Location and Event Data (ACLED)... \n", + "\n", + " Citation source \\\n", + "0 Stevens FR, Gaughan AE, Linard C, Tatem AJ (20... \n", + "1 NaN \n", + "2 Wing et al. (2024) A 30 m Global Flood Inundat... \n", + "3 Wing et al. (2024) A 30 m Global Flood Inundat... \n", + "4 https://acleddata.com/article-categories/gener... \n", + "\n", + " Organization Method \\\n", + "0 World Pop, https://www.worldpop.org/methods/po... sum \n", + "1 NASA, World Bank sum \n", + "2 Fathom, https://www.fathom.global/ sum \n", + "3 Fathom, https://www.fathom.global/ sum of intersect \n", + "4 ACLED, https://acleddata.com/ count \n", + "\n", + " Resolution \n", + "0 100 mts \n", + "1 500 mts \n", + "2 30 mts \n", + "3 30 mts and 100 mts \n", + "4 point data " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sources.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Catalog \n", + "\n", + "Basic description of project and dataset. \n", + "Can link to World Bank metadata page with appropriate schema (DDH or NADA). \n", + "See for example, https://nada-demo.ihsn.org/index.php/catalog/55/ or https://datacatalog.worldbank.org/search/dataset/0064614/Harmonized-Sub-National-Food-Security-Data" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "catalog = Catalog(\n", + " id=\"space2stats-catalog\", \n", + " description=overview.loc[\"Description Resource\"].values[0],\n", + " title=overview.loc[\"Title\"].values[0],\n", + " extra_fields={\n", + " \"License\": overview.loc[\"License\"].values[0],\n", + " \"Responsible Party\": nada.loc[\"Responsible party\", \"Value\"],\n", + " \"Purpose\": nada.loc[\"Purpose\", \"Value\"],\n", + " \"Keywords\": [\"space2stats\", \"sub-national\", \"h3\", \"hexagons\", \"global\"]\n", + " },\n", + " href=\"https://worldbank.github.io/DECAT_Space2Stats/stac/catalog.json\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### STAC Item\n", + "\n", + "Represent the global H3 parquet file with column descriptions for each variable." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "data_dict = []\n", + "for column in gdf.columns:\n", + " if column == 'geometry':\n", + " continue\n", + " data_dict.append({\n", + " \"name\": column,\n", + " \"description\": feature_catalog.loc[feature_catalog['variable'] == column, 'description'].values[0],\n", + " \"type\": str(gdf[column].dtype),\n", + " })" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_types = gdf.dtypes.to_dict()\n", + "gdf_types = {k: str(v) for k, v in gdf_types.items()}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the [table](https://github.com/stac-extensions/table) extension here. Fio-stac also builds `vector:layers` property, not sure if it's necessary." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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        • \n", + " sum_pop_f_75_2020\n", + " \"float64\"\n", + "
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        • \n", + " sum_pop_m_70_2020\n", + " \"float64\"\n", + "
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    • \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " links[] 0 items\n", + " \n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " assets\n", + "
      \n", + " \n", + "
    \n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " bbox[] 4 items\n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + "
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    \n", + " \n", + "
    • \n", + " \n", + " \n", + " \n", + "
  • \n", + " stac_extensions[] 1 items\n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + "
    • \n", + " 0\n", + " \"https://stac-extensions.github.io/table/v1.2.0/schema.json\"\n", + "
      • \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + "
    • \n", + " \n", + " \n", + "
\n", + "
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bb = gdf.total_bounds.tolist()\n", + "geom = Polygon.from_bounds(bb[0], bb[1], bb[2], bb[3])\n", + "\n", + "item = Item(\n", + " id=\"space2stats\",\n", + " geometry=geom.__geo_interface__,\n", + " bbox=bb,\n", + " datetime=datetime.now(),\n", + " properties={\n", + " \"name\": \"Space2Stats H3 Data\",\n", + " \"description\": \"GeoParquet dataset with h3 hexagons (level 6) covering the globe. Users can access data through an API, specifying variables and areas of interest.\", \n", + " \"table:primary_geometry\" : \"geometry\",\n", + " \"table:columns\" : data_dict,\n", + " \"vector:layers\" : {\n", + " \"space2stats\": gdf_types,\n", + " }\n", + " },\n", + " stac_extensions = ['https://stac-extensions.github.io/table/v1.2.0/schema.json']\n", + " # assets={\n", + " # \"data\": Asset(href=out_file, media_type=\"application/geo+json\")\n", + " # } \n", + ")\n", + "item" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "
    \n", + " \n", + " \n", + " \n", + "
  • \n", + " rel\n", + " \"item\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " href\n", + " \"https://worldbank.github.io/DECAT_Space2Stats/stac/space2stats/space2stats.json\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " type\n", + " \"application/json\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " title\n", + " \"Space2Stats Item\"\n", + "
    • \n", + " \n", + " \n", + " \n", + "
\n", + "
\n", + "
" + ], + "text/plain": [ + ">" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "catalog.add_item(item, title=\"Space2Stats Item\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n", + "[]\n" + ] + } + ], + "source": [ + "print(list(catalog.get_children()))\n", + "print(list(catalog.get_items()))" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* \n", + " * \n" + ] + } + ], + "source": [ + "catalog.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assets\n", + "\n", + "Can store additional information about authors, the source for input data, how it was processed etc. \n", + "Add another asset for API docs." + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "
    \n", + " \n", + " \n", + " \n", + "
  • \n", + " href\n", + " \"./sources.json\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " type\n", + " \"application/json\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " title\n", + " \"Sources Metadata\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " roles[] 1 items\n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + "
    • \n", + " 0\n", + " \"metadata\"\n", + "
      • \n", + " \n", + " \n", + " \n", + "
    \n", + " \n", + "
    • \n", + " \n", + " \n", + "
\n", + "
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sources_path = join(\".\", \"stac\", \"space2stats\", \"sources.json\") # \"space2stats\"\n", + "asset = Asset(\n", + " href=\"./sources.json\",\n", + " title=\"Sources Metadata\",\n", + " media_type=\"application/json\",\n", + " roles=[\"metadata\"]\n", + " )\n", + "asset" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "item.add_asset(\"sources-metadata\", asset)" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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    \n", + " \n", + " \n", + " \n", + "
  • \n", + " href\n", + " \"https://space2stats.ds.io/docs\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " type\n", + " \"text/html\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " title\n", + " \"API Documentation\"\n", + "
    • \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " roles[] 1 items\n", + " \n", + "
      \n", + " \n", + " \n", + " \n", + "
    • \n", + " 0\n", + " \"metadata\"\n", + "
      • \n", + " \n", + " \n", + " \n", + "
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    • \n", + " \n", + " \n", + "
\n", + "
\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "asset_api = Asset(\n", + " href=\"https://space2stats.ds.io/docs\",\n", + " title=\"API Documentation\",\n", + " media_type=\"text/html\",\n", + " roles=[\"metadata\"]\n", + " )\n", + "asset_api" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "item.add_asset(\"api-docs\", asset_api)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save Demo" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "False\n" + ] + } + ], + "source": [ + "print(catalog.get_self_href() is None)\n", + "print(item.get_self_href() is None)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "# catalog.normalize_hrefs(join(\".\", \"stac\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://worldbank.github.io/DECAT_Space2Stats/stac/catalog.json\n", + "https://worldbank.github.io/DECAT_Space2Stats/stac/space2stats/space2stats.json\n" + ] + } + ], + "source": [ + "print(catalog.get_self_href())\n", + "print(item.get_self_href())" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "catalog.save(catalog_type=CatalogType.RELATIVE_PUBLISHED, dest_href=join(\".\", \"stac\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "sources.to_json(\n", + " sources_path, \n", + " orient = 'records',\n", + " indent = 4\n", + " )" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "titi", + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/space2stats_api/src/space2stats_ingest/METADATA/stac/catalog.json b/space2stats_api/src/space2stats_ingest/METADATA/stac/catalog.json new file mode 100644 index 00000000..0076c8ed --- /dev/null +++ b/space2stats_api/src/space2stats_ingest/METADATA/stac/catalog.json @@ -0,0 +1,36 @@ +{ + "type": "Catalog", + "id": "space2stats-catalog", + "stac_version": "1.0.0", + "description": "This database contains geospatial statistics for the entire globe standardized to a hexagonal grid. The spatial unit of the dataset is the H3 level 6 (approximately 36 sq. km. per cell). The variables cover a wide range of geographic themes relevant to international development, including demographic, socio-economic, environmental, climate, and infrastructure. An API enables users to query, access, and aggregate statistics from the Space2Stats database. The purpose of this API is to facilitate the generation of sub-national geospatial aggregates for any administrative boundary set.", + "links": [ + { + "rel": "root", + "href": "./catalog.json", + "type": "application/json", + "title": "Space2Stats Database" + }, + { + "rel": "self", + "href": "https://worldbank.github.io/DECAT_Space2Stats/stac/catalog.json", + "type": "application/json" + }, + { + "rel": "item", + "href": "./space2stats/space2stats.json", + "type": "application/json", + "title": "Space2Stats Item" + } + ], + "License": "Creative Commons Attribution 4.0", + "Responsible Party": "Ben Stewart (Task Leader), Andres Chamorro (Collaborator), Development Data Group (DECDG), World Bank", + "Purpose": "The purpose of this API is to facilitate the generation of sub-national geospatial aggregates for any administrative boundary set.", + "Keywords": [ + "space2stats", + "sub-national", + "h3", + "hexagons", + "global" + ], + "title": "Space2Stats Database" +} \ No newline at end of file diff --git a/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/sources.json b/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/sources.json new file mode 100644 index 00000000..f346e749 --- /dev/null +++ b/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/sources.json @@ -0,0 +1,126 @@ +[ + { + "Theme":"Demographics", + "Name":"Population", + "Description":"Gridded population disaggregated by gender.", + "Methodological Notes":"Global raster files are processed for each hexagonal grid using zonal statistics.", + "Variables":[ + "sum_pop_2020", + "sum_pop_f_0_2020", + "sum_pop_f_10_2020", + "sum_pop_f_15_2020", + "sum_pop_f_1_2020", + "sum_pop_f_20_2020", + "sum_pop_f_25_2020", + "sum_pop_f_30_2020", + "sum_pop_f_35_2020", + "sum_pop_f_40_2020", + "sum_pop_f_45_2020", + "sum_pop_f_50_2020", + "sum_pop_f_55_2020", + "sum_pop_f_5_2020", + "sum_pop_f_60_2020", + "sum_pop_f_65_2020", + "sum_pop_f_70_2020", + "sum_pop_f_75_2020", + "sum_pop_f_80_2020", + "sum_pop_m_0_2020", + "sum_pop_m_10_2020", + "sum_pop_m_15_2020", + "sum_pop_m_1_2020", + "sum_pop_m_20_2020", + "sum_pop_m_25_2020", + "sum_pop_m_30_2020", + "sum_pop_m_35_2020", + "sum_pop_m_40_2020", + "sum_pop_m_45_2020", + "sum_pop_m_50_2020", + "sum_pop_m_55_2020", + "sum_pop_m_5_2020", + "sum_pop_m_60_2020", + "sum_pop_m_65_2020", + "sum_pop_m_70_2020", + "sum_pop_m_75_2020", + "sum_pop_m_80_2020", + "sum_pop_m_2020", + "sum_pop_f_2020" + ], + "Source Data":"WorldPop gridded population, 2020, Unconstrained, UN-Adjusted, https:\/\/www.worldpop.org\/methods\/top_down_constrained_vs_unconstrained\/", + "Citation source":"Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. ", + "Organization":"World Pop, https:\/\/www.worldpop.org\/methods\/populations", + "Method":"sum", + "Resolution":"100 mts" + }, + { + "Theme":"Socio-economic", + "Name":"Nighttime Lights", + "Description":"Sum of luminosity values measured by monthly composites from VIIRS satellite.", + "Methodological Notes":"Monthly composites generated by NASA through the Lights Every Night partnership.", + "Variables":[ + "ntl_sum_yyyymm" + ], + "Source Data":"World Bank - Light Every Night, https:\/\/registry.opendata.aws\/wb-light-every-night\/", + "Citation source":null, + "Organization":"NASA, World Bank", + "Method":"sum", + "Resolution":"500 mts" + }, + { + "Theme":"Exposure", + "Name":"Flood Area", + "Description":"Area where flood depth is greater than 50 cm, 1-in-100 or 1000 return period.", + "Methodological Notes":"Flood data combines fluvial, pluvial, and coastal flood exposure using the maximum value. Return period indicates likelihood of disaster (1 in 100 years).", + "Variables":[ + "flood_area_100", + "flood_area_1000" + ], + "Source Data":"Fathom 3.0 High Resolution Global Flood Maps Including Climate Scenarios, https:\/\/datacatalog.worldbank.org\/search\/dataset\/0065653\/Fathom-3-0---High-Resolution-Global-Flood-Maps-Including-Climate-Scenarios", + "Citation source":"Wing et al. (2024) A 30 m Global Flood Inundation Model for Any Climate Scenario. https:\/\/doi.org\/10.1029\/2023WR036460", + "Organization":"Fathom, https:\/\/www.fathom.global\/", + "Method":"sum", + "Resolution":"30 mts" + }, + { + "Theme":"Exposure", + "Name":"Population Exposed to Floods", + "Description":"Population where flood depth is greater than 50 cm, 1-in-100 or 1000 return period.", + "Methodological Notes":"Flood data is intersected with population grid to estimate population exposed.", + "Variables":[ + "flood_pop_100", + "flood_pop_1000" + ], + "Source Data":"Fathom 3.0 High Resolution Global Flood Maps Including Climate Scenarios, https:\/\/datacatalog.worldbank.org\/search\/dataset\/0065653\/Fathom-3-0---High-Resolution-Global-Flood-Maps-Including-Climate-Scenarios", + "Citation source":"Wing et al. (2024) A 30 m Global Flood Inundation Model for Any Climate Scenario. https:\/\/doi.org\/10.1029\/2023WR036460", + "Organization":"Fathom, https:\/\/www.fathom.global\/", + "Method":"sum of intersect", + "Resolution":"30 mts and 100 mts" + }, + { + "Theme":"Conflict", + "Name":"Number of Conflict Events", + "Description":"Sum of conflict events (ACLED).", + "Methodological Notes":"Conflict data is filtered for event types and then aggregated by hexagon (count).", + "Variables":[ + "acled_events_yyyy" + ], + "Source Data":"Armed Conflict Location and Event Data (ACLED), https:\/\/acleddata.com\/data\/", + "Citation source":"https:\/\/acleddata.com\/article-categories\/general-methodology\/", + "Organization":"ACLED, https:\/\/acleddata.com\/", + "Method":"count", + "Resolution":"point data" + }, + { + "Theme":"Conflict", + "Name":"Number of Conflict Fatalities", + "Description":"Sum of estimated fatalities from conflcit events (ACLED).", + "Methodological Notes":"Conflict data is filtered for event types and then aggregated by hexagon (sum of fatalities).", + "Variables":[ + "acled_fatalities_yyyy" + ], + "Source Data":"Armed Conflict Location and Event Data (ACLED), https:\/\/acleddata.com\/data\/", + "Citation source":"https:\/\/acleddata.com\/article-categories\/general-methodology\/", + "Organization":"ACLED, https:\/\/acleddata.com\/", + "Method":"sum", + "Resolution":"point data" + } +] \ No newline at end of file diff --git a/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/space2stats.json b/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/space2stats.json new file mode 100644 index 00000000..c1350885 --- /dev/null +++ b/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats/space2stats.json @@ -0,0 +1,308 @@ +{ + "type": "Feature", + "stac_version": "1.0.0", + "id": "space2stats", + "properties": { + "name": "Space2Stats H3 Data", + "description": "GeoParquet dataset with h3 hexagons (level 6) covering the globe. Users can access data through an API, specifying variables and areas of interest.", + "table:primary_geometry": "geometry", + "table:columns": [ + { + "name": "hex_id", + "description": "H3 unique identifier", + "type": "object" + }, + { + "name": "sum_pop_f_0_2020", + "description": "Total population female, ages 0 to 1, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_10_2020", + "description": "Total population female, ages 10 to 15, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_15_2020", + "description": "Total population female, ages 15 to 20, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_1_2020", + "description": "Total population female, ages 1 to 10, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_20_2020", + "description": "Total population female, ages 20 to 25, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_25_2020", + "description": "Total population female, ages 25 to 30, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_30_2020", + "description": "Total population female, ages 30 to 35, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_35_2020", + "description": "Total population female, ages 35 to 40, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_40_2020", + "description": "Total population female, ages 40 to 45, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_45_2020", + "description": "Total population female, ages 45 to 50, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_50_2020", + "description": "Total population female, ages 50 to 55, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_55_2020", + "description": "Total population female, ages 55 to 60, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_5_2020", + "description": "Total population female, ages 5 to 10, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_60_2020", + "description": "Total population female, ages 60 to 65, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_65_2020", + "description": "Total population female, ages 65 to 70, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_70_2020", + "description": "Total population female, ages 70 to 75, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_75_2020", + "description": "Total population female, ages 75 to 80, 2020", + "type": "float64" + }, + { + "name": "sum_pop_f_80_2020", + "description": "Total population female, ages 80 and above, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_0_2020", + "description": "Total population male, ages 0 to 1, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_10_2020", + "description": "Total population male, ages 10 to 15, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_15_2020", + "description": "Total population male, ages 15 to 20, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_1_2020", + "description": "Total population male, ages 1 to 10, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_20_2020", + "description": "Total population male, ages 20 to 25, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_25_2020", + "description": "Total population male, ages 25 to 30, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_30_2020", + "description": "Total population male, ages 30 to 35, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_35_2020", + "description": "Total population male, ages 35 to 40, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_40_2020", + "description": "Total population male, ages 40 to 45, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_45_2020", + "description": "Total population male, ages 45 to 50, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_50_2020", + "description": "Total population male, ages 50 to 55, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_55_2020", + "description": "Total population male, ages 55 to 60, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_5_2020", + "description": "Total population male, ages 5 to 10, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_60_2020", + "description": "Total population male, ages 60 to 65, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_65_2020", + "description": "Total population male, ages 65 to 70, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_70_2020", + "description": "Total population male, ages 70 to 75, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_75_2020", + "description": "Total population male, ages 75 to 80, 2020", + "type": "float64" + }, + { + "name": "sum_pop_m_80_2020", + "description": "Total population male, ages 80 and above, 2020", + "type": "float64" + } + ], + "vector:layers": { + "space2stats": { + "hex_id": "object", + "sum_pop_f_0_2020": "float64", + "sum_pop_f_10_2020": "float64", + "sum_pop_f_15_2020": "float64", + "sum_pop_f_1_2020": "float64", + "sum_pop_f_20_2020": "float64", + "sum_pop_f_25_2020": "float64", + "sum_pop_f_30_2020": "float64", + "sum_pop_f_35_2020": "float64", + "sum_pop_f_40_2020": "float64", + "sum_pop_f_45_2020": "float64", + "sum_pop_f_50_2020": "float64", + "sum_pop_f_55_2020": "float64", + "sum_pop_f_5_2020": "float64", + "sum_pop_f_60_2020": "float64", + "sum_pop_f_65_2020": "float64", + "sum_pop_f_70_2020": "float64", + "sum_pop_f_75_2020": "float64", + "sum_pop_f_80_2020": "float64", + "sum_pop_m_0_2020": "float64", + "sum_pop_m_10_2020": "float64", + "sum_pop_m_15_2020": "float64", + "sum_pop_m_1_2020": "float64", + "sum_pop_m_20_2020": "float64", + "sum_pop_m_25_2020": "float64", + "sum_pop_m_30_2020": "float64", + "sum_pop_m_35_2020": "float64", + "sum_pop_m_40_2020": "float64", + "sum_pop_m_45_2020": "float64", + "sum_pop_m_50_2020": "float64", + "sum_pop_m_55_2020": "float64", + "sum_pop_m_5_2020": "float64", + "sum_pop_m_60_2020": "float64", + "sum_pop_m_65_2020": "float64", + "sum_pop_m_70_2020": "float64", + "sum_pop_m_75_2020": "float64", + "sum_pop_m_80_2020": "float64", + "geometry": "geometry" + } + }, + "datetime": "2024-10-07T11:21:25.944150Z" + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + -179.99999561620714, + -89.98750455101016 + ], + [ + -179.99999561620714, + 89.98750455101016 + ], + [ + 179.99999096313272, + 89.98750455101016 + ], + [ + 179.99999096313272, + -89.98750455101016 + ], + [ + -179.99999561620714, + -89.98750455101016 + ] + ] + ] + }, + "links": [ + { + "rel": "root", + "href": "../catalog.json", + "type": "application/json", + "title": "Space2Stats Database" + }, + { + "rel": "parent", + "href": "../catalog.json", + "type": "application/json", + "title": "Space2Stats Database" + } + ], + "assets": { + "sources-metadata": { + "href": "./sources.json", + "type": "application/json", + "title": "Sources Metadata", + "roles": [ + "metadata" + ] + }, + "api-docs": { + "href": "https://space2stats.ds.io/docs", + "type": "text/html", + "title": "API Documentation", + "roles": [ + "metadata" + ] + } + }, + "bbox": [ + -179.99999561620714, + -89.98750455101016, + 179.99999096313272, + 89.98750455101016 + ], + "stac_extensions": [ + "https://stac-extensions.github.io/table/v1.2.0/schema.json" + ] +} \ No newline at end of file From ad87bf44b35d5397aaecdcd894d0024a21f0d4f1 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Fri, 15 Nov 2024 10:27:54 +0000 Subject: [PATCH 10/18] Add notebook dependency group and update python library example --- notebooks/user-docs/s2s_python_lib.ipynb | 892 +++++++ space2stats_api/src/poetry.lock | 3003 ++++++++++++++++++---- space2stats_api/src/pyproject.toml | 8 + 3 files changed, 3383 insertions(+), 520 deletions(-) create mode 100644 notebooks/user-docs/s2s_python_lib.ipynb diff --git a/notebooks/user-docs/s2s_python_lib.ipynb b/notebooks/user-docs/s2s_python_lib.ipynb new file mode 100644 index 00000000..f39d6d2f --- /dev/null +++ b/notebooks/user-docs/s2s_python_lib.ipynb @@ -0,0 +1,892 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Access S2S via Python Library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture\n", + "%pip install \"git+https://github.com/worldbank/DECAT_Space2Stats.git#subdirectory=space2stats_api/src\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict\n", + "import json\n", + "\n", + "import numpy as np\n", + "from dotenv import load_dotenv\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from shapely.geometry import shape\n", + "from lonboard import Map, ScatterplotLayer\n", + "from geojson_pydantic import Feature, Polygon\n", + "\n", + "from space2stats import StatsTable" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Expected environment variables to be set__ from dotenv file\n", + "\n", + "\n", + "```.env\n", + "PGHOST=\n", + "PGPORT=\n", + "PGDATABASE=\n", + "PGUSER=\n", + "PGPASSWORD=\n", + "PGTABLENAME=space2stats\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_dotenv(\"../../space2stats_api/db.env\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sum_pop_2020',\n", + " 'ogc_fid',\n", + " 'sum_pop_f_0_2020',\n", + " 'sum_pop_f_10_2020',\n", + " 'sum_pop_f_15_2020',\n", + " 'sum_pop_f_1_2020',\n", + " 'sum_pop_f_20_2020',\n", + " 'sum_pop_f_25_2020',\n", + " 'sum_pop_f_30_2020',\n", + " 'sum_pop_f_35_2020',\n", + " 'sum_pop_f_40_2020',\n", + " 'sum_pop_f_45_2020',\n", + " 'sum_pop_f_50_2020',\n", + " 'sum_pop_f_55_2020',\n", + " 'sum_pop_f_5_2020',\n", + " 'sum_pop_f_60_2020',\n", + " 'sum_pop_f_65_2020',\n", + " 'sum_pop_f_70_2020',\n", + " 'sum_pop_f_75_2020',\n", + " 'sum_pop_f_80_2020',\n", + " 'sum_pop_m_0_2020',\n", + " 'sum_pop_m_10_2020',\n", + " 'sum_pop_m_15_2020',\n", + " 'sum_pop_m_1_2020',\n", + " 'sum_pop_m_20_2020',\n", + " 'sum_pop_m_25_2020',\n", + " 'sum_pop_m_30_2020',\n", + " 'sum_pop_m_35_2020',\n", + " 'sum_pop_m_40_2020',\n", + " 'sum_pop_m_45_2020',\n", + " 'sum_pop_m_50_2020',\n", + " 'sum_pop_m_55_2020',\n", + " 'sum_pop_m_5_2020',\n", + " 'sum_pop_m_60_2020',\n", + " 'sum_pop_m_65_2020',\n", + " 'sum_pop_m_70_2020',\n", + " 'sum_pop_m_75_2020',\n", + " 'sum_pop_m_80_2020',\n", + " 'sum_pop_m_2020',\n", + " 'sum_pop_f_2020']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with StatsTable.connect() as stats_table:\n", + " fields = stats_table.fields()\n", + "\n", + "fields" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "AOIModel = Feature[Polygon, Dict]\n", + "\n", + "# ~kenya\n", + "aoi = {\n", + " \"type\": \"Feature\",\n", + " \"geometry\": {\n", + " \"type\": \"Polygon\",\n", + " \"coordinates\": [\n", + " [\n", + " [33.78593974945852, 5.115816884114494],\n", + " [33.78593974945852, -4.725410543134203],\n", + " [41.94362577283266, -4.725410543134203],\n", + " [41.94362577283266, 5.115816884114494],\n", + " [33.78593974945852, 5.115816884114494]\n", + " ]\n", + " ]\n", + " },\n", + " \"properties\": {\n", + " \"name\": \"Updated AOI\"\n", + " }\n", + "}\n", + " \n", + " \n", + "\n", + "feat = AOIModel(**aoi)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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[metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "b02f69014cb169e7b32eadc52b66c2dac50afe243402a9311965395b420aaaec" +content-hash = "744286122d13a456b7cfcabc5b92c8c767ecd212f8431afa29768be14af8c329" diff --git a/space2stats_api/src/pyproject.toml b/space2stats_api/src/pyproject.toml index ea93c5b7..6b79af37 100644 --- a/space2stats_api/src/pyproject.toml +++ b/space2stats_api/src/pyproject.toml @@ -37,6 +37,14 @@ pyarrow = "^17.0.0" adbc-driver-postgresql = "^1.2.0" tqdm = "^4.66.5" + +[tool.poetry.group.notebook.dependencies] +pandas = "*" +jupyter = "*" +geopandas = "*" +shapely = "*" +lonboard = "*" + [tool.poetry.group.test.dependencies] pre-commit = "*" pytest = "*" From c23af154ed54305feceac9801c468b3c28198d0e Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 21 Nov 2024 09:36:29 +0000 Subject: [PATCH 11/18] Update notebook based on nbqa --- notebooks/user-docs/s2s_python_lib.ipynb | 57 +++++++++++++----------- 1 file changed, 30 insertions(+), 27 deletions(-) diff --git a/notebooks/user-docs/s2s_python_lib.ipynb b/notebooks/user-docs/s2s_python_lib.ipynb index f39d6d2f..ced1148d 100644 --- a/notebooks/user-docs/s2s_python_lib.ipynb +++ b/notebooks/user-docs/s2s_python_lib.ipynb @@ -23,17 +23,16 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Dict\n", "import json\n", + "from typing import Dict\n", "\n", + "import geopandas as gpd\n", "import numpy as np\n", - "from dotenv import load_dotenv\n", "import pandas as pd\n", - "import geopandas as gpd\n", - "from shapely.geometry import shape\n", - "from lonboard import Map, ScatterplotLayer\n", + "from dotenv import load_dotenv\n", "from geojson_pydantic import Feature, Polygon\n", - "\n", + "from lonboard import Map, ScatterplotLayer\n", + "from shapely.geometry import shape\n", "from space2stats import StatsTable" ] }, @@ -155,16 +154,13 @@ " [33.78593974945852, -4.725410543134203],\n", " [41.94362577283266, -4.725410543134203],\n", " [41.94362577283266, 5.115816884114494],\n", - " [33.78593974945852, 5.115816884114494]\n", + " [33.78593974945852, 5.115816884114494],\n", " ]\n", - " ]\n", + " ],\n", " },\n", - " \"properties\": {\n", - " \"name\": \"Updated AOI\"\n", - " }\n", + " \"properties\": {\"name\": \"Updated AOI\"},\n", "}\n", - " \n", - " \n", + "\n", "\n", "feat = AOIModel(**aoi)" ] @@ -397,7 +393,9 @@ ], "source": [ "with StatsTable.connect() as stats_table:\n", - " data = stats_table.summaries(aoi=feat, spatial_join_method=\"centroid\", fields=fields, geometry=\"point\")\n", + " data = stats_table.summaries(\n", + " aoi=feat, spatial_join_method=\"centroid\", fields=fields, geometry=\"point\"\n", + " )\n", " df = pd.DataFrame(data)\n", "\n", "df.head()" @@ -810,8 +808,8 @@ } ], "source": [ - "df['geometry'] = df['geometry'].apply(lambda geom: shape(json.loads(geom)))\n", - "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n", + "df[\"geometry\"] = df[\"geometry\"].apply(lambda geom: shape(json.loads(geom)))\n", + "gdf = gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:4326\")\n", "gdf" ] }, @@ -839,26 +837,31 @@ "source": [ "# Define custom breaks and corresponding RGBA colors\n", "breaks = [0, 1, 1000, 10000, 50000, 100000, 200000, gdf[\"sum_pop_2020\"].max()]\n", - "colors = np.array([\n", - " [211, 211, 211, 255], # Light gray for 0\n", - " [255, 255, 0, 255], # Yellow for 1-1000\n", - " [255, 165, 0, 255], # Orange for 1000-10000\n", - " [255, 0, 0, 255], # Red for 10000-50000\n", - " [128, 0, 128, 255], # Purple for 50000-100000\n", - " [0, 0, 255, 255], # Blue for 100000-200000\n", - " [0, 0, 139, 255], # Dark blue for 200000+\n", - "])\n", + "colors = np.array(\n", + " [\n", + " [211, 211, 211, 255], # Light gray for 0\n", + " [255, 255, 0, 255], # Yellow for 1-1000\n", + " [255, 165, 0, 255], # Orange for 1000-10000\n", + " [255, 0, 0, 255], # Red for 10000-50000\n", + " [128, 0, 128, 255], # Purple for 50000-100000\n", + " [0, 0, 255, 255], # Blue for 100000-200000\n", + " [0, 0, 139, 255], # Dark blue for 200000+\n", + " ]\n", + ")\n", "\n", "# Function to assign colors based on custom bins\n", + "\n", + "\n", "def assign_color(value, breaks, colors):\n", " for i in range(len(breaks) - 1):\n", " if breaks[i] <= value < breaks[i + 1]:\n", " return colors[i]\n", " return colors[-1] # In case value exceeds all breaks\n", "\n", + "\n", "# Map sum_pop_2020 values to colors using the custom function\n", - "gdf['color'] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", - "colors = np.uint8(gdf['color'].tolist())\n", + "gdf[\"color\"] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n", + "colors = np.uint8(gdf[\"color\"].tolist())\n", "\n", "# Create the scatterplot layer with the assigned colors\n", "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=colors)\n", From 798b7feecb608f547b36fd9db72eac650f9606c8 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 21 Nov 2024 09:45:05 +0000 Subject: [PATCH 12/18] Remove unused dependencies in core --- space2stats_api/src/poetry.lock | 442 +++++++++++++---------------- space2stats_api/src/pyproject.toml | 5 +- 2 files changed, 202 insertions(+), 245 deletions(-) diff --git a/space2stats_api/src/poetry.lock b/space2stats_api/src/poetry.lock index b3ab4a12..d144f105 100644 --- a/space2stats_api/src/poetry.lock +++ b/space2stats_api/src/poetry.lock @@ -558,17 +558,17 @@ css = ["tinycss2 (>=1.1.0,<1.5)"] [[package]] name = "boto3" -version = "1.35.62" +version = "1.35.66" description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" files = [ - {file = "boto3-1.35.62-py3-none-any.whl", hash = "sha256:e6574047701ab009c2b2bb17b530a3a2fb34de8698b77f8bbb34dd0c9286c117"}, - {file = "boto3-1.35.62.tar.gz", hash = "sha256:f80eefe7506aa01799b1027d03eddfd3c4a60548d6db5c32f139e1dec9f3f4f5"}, + {file = "boto3-1.35.66-py3-none-any.whl", hash = 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a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index 9c3a60fc..fb44f3fc 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -1,11 +1,7 @@ from typing import Any, Dict, List, Literal from h3ronpy import ContainmentMode -from h3ronpy.arrow.vector import ( # Import cells_to_coordinates for point geometries - cells_to_wkb_points, - cells_to_wkb_polygons, - geometry_to_cells, -) +from h3ronpy.vector import cells_to_wkb_points, cells_to_wkb_polygons, geometry_to_cells from shapely import from_wkb, to_geojson from shapely.geometry import shape From b257dd99485eaa31541e8a6d506981bed89b206f Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 21 Nov 2024 12:21:44 +0000 Subject: [PATCH 16/18] Update types of h3_ids from h3ronpy --- space2stats_api/src/space2stats/h3_utils.py | 3 ++- space2stats_api/src/space2stats/lib.py | 20 ++++++++++++-------- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/space2stats_api/src/space2stats/h3_utils.py b/space2stats_api/src/space2stats/h3_utils.py index fb44f3fc..98a0f63e 100644 --- a/space2stats_api/src/space2stats/h3_utils.py +++ b/space2stats_api/src/space2stats/h3_utils.py @@ -1,5 +1,6 @@ from typing import Any, Dict, List, Literal +from arro3.core import Array from h3ronpy import ContainmentMode from h3ronpy.vector import cells_to_wkb_points, cells_to_wkb_polygons, geometry_to_cells from shapely import from_wkb, to_geojson @@ -16,7 +17,7 @@ def generate_h3_ids( aoi_geojson: Dict[str, Any], resolution: int, spatial_join_method: Literal["touches", "within", "centroid"] = "centroid", -) -> List[int]: +) -> Array: """ Generate H3 IDs using h3ronpy's geometry_to_cells with the correct containment mode. Returns the H3 IDs in uint64 format for geometry creation. diff --git a/space2stats_api/src/space2stats/lib.py b/space2stats_api/src/space2stats/lib.py index bd515bf0..68e424d3 100644 --- a/space2stats_api/src/space2stats/lib.py +++ b/space2stats_api/src/space2stats/lib.py @@ -48,8 +48,11 @@ def _get_summaries(self, fields: List[str], h3_ids: List[int]): """ ).format(pg.sql.SQL(", ").join(cols), pg.sql.Identifier(self.table_name)) - # Convert h3_ids to a list to ensure compatibility with psycopg - h3_ids = list(h3_ids) + # Convert h3_ids to integers to ensure compatibility with psycopg + h3_ids = [ + scalar.as_py() if hasattr(scalar, "as_py") else scalar for scalar in h3_ids + ] + with self.conn.cursor() as cur: cur.execute( sql_query, @@ -89,15 +92,13 @@ def summaries( return [] # Get Summaries from H3 ids - rows, colnames = self._get_summaries(fields=fields, h3_ids=list(h3_ids)) + rows, colnames = self._get_summaries(fields=fields, h3_ids=h3_ids) if not rows: return [] # Format Summaries summaries: List[Dict] = [] - geometries = ( - generate_h3_geometries(list(h3_ids), geometry) if geometry else None - ) + geometries = generate_h3_geometries(h3_ids, geometry) if geometry else None for idx, row in enumerate(rows): summary = {"hex_id": row[0]} @@ -156,6 +157,11 @@ def aggregate( ) ) + # Convert H3 scalar objects to integers + h3_ids = [ + scalar.as_py() if hasattr(scalar, "as_py") else scalar for scalar in h3_ids + ] + if not h3_ids: return {} @@ -172,8 +178,6 @@ def aggregate( pg.sql.Identifier(self.table_name), ) - # Convert h3_ids to a list to ensure compatibility with psycopg - h3_ids = list(h3_ids) with self.conn.cursor() as cur: cur.execute( sql_query, From d07bc21307f1787f110a9475ae0835a86f2a0c03 Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 21 Nov 2024 14:56:59 +0000 Subject: [PATCH 17/18] Update pyproject to use wheel from s3 --- space2stats_api/cdk/aws_stack.py | 2 +- space2stats_api/src/poetry.lock | 14 ++++++-------- space2stats_api/src/pyproject.toml | 2 +- 3 files changed, 8 insertions(+), 10 deletions(-) diff --git a/space2stats_api/cdk/aws_stack.py b/space2stats_api/cdk/aws_stack.py index 66aaf2f2..ad0f803a 100644 --- a/space2stats_api/cdk/aws_stack.py +++ b/space2stats_api/cdk/aws_stack.py @@ -31,7 +31,7 @@ def __init__( self, "Space2StatsFunction", entry="../src", - runtime=_lambda.Runtime.PYTHON_3_11, + runtime=_lambda.Runtime.PYTHON_3_9, index="space2stats/api/handler.py", timeout=Duration.seconds(120), handler="handler", diff --git a/space2stats_api/src/poetry.lock b/space2stats_api/src/poetry.lock index 93515d74..5214ac9a 100644 --- a/space2stats_api/src/poetry.lock +++ b/space2stats_api/src/poetry.lock @@ -1295,8 +1295,9 @@ version = "0.21.1" description = "Data science toolkit for the H3 geospatial grid" optional = false python-versions = "*" -files = [] -develop = false +files = [ + {file = "h3ronpy-0.21.1-cp39-abi3-linux_x86_64.whl", hash = "sha256:071536141bc3039fe589a1b2d965ce6292d5ff93873fa35c0b7818bcd602aedc"}, +] [package.dependencies] arro3-core = ">=0.4" @@ -1308,11 +1309,8 @@ polars = ["polars (>=1)"] test = ["h3 (<4)", "pyarrow (>=15)", "pytest (>=6)", "pytest-benchmark", "rasterio (>=1.4)", "shapely (>=1.7)"] [package.source] -type = "git" -url = "https://github.com/nmandery/h3ronpy.git" -reference = "main" -resolved_reference = "ae3d0c9fe4c70c0165983e2bba2b8c0274dd7f43" -subdirectory = "h3ronpy" +type = "url" +url = "https://temp-wheels-cdk.s3.us-east-1.amazonaws.com/h3ronpy-0.21.1-cp39-abi3-linux_x86_64.whl" [[package]] name = "httpcore" @@ -4282,4 +4280,4 @@ files = [ [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.13" -content-hash = "977f5b82b9d7971cf10517dc2fdfaba52fcd2a99d8f2e2ae7d8e39bf6f43885b" +content-hash = "1cb3521e71698dcbc60e8a67325b04248eb07ec887f647d86be0170f11da3347" diff --git a/space2stats_api/src/pyproject.toml b/space2stats_api/src/pyproject.toml index af14e7c2..90a6971a 100644 --- a/space2stats_api/src/pyproject.toml +++ b/space2stats_api/src/pyproject.toml @@ -20,7 +20,7 @@ mangum = "*" asgi-s3-response-middleware = "^0.0.4" boto3 = "^1.35.11" numpy = "^1.24.0" -h3ronpy = { git = "https://github.com/nmandery/h3ronpy.git", branch = "main", subdirectory = "h3ronpy" } +h3ronpy = { url = "https://temp-wheels-cdk.s3.us-east-1.amazonaws.com/h3ronpy-0.21.1-cp39-abi3-linux_x86_64.whl" } [tool.poetry.group.lambda.dependencies] mangum = "*" From d9844d4cf090871c5ec1a4d505f31b836685edab Mon Sep 17 00:00:00 2001 From: Zachary Deziel Date: Thu, 21 Nov 2024 16:01:29 +0000 Subject: [PATCH 18/18] Update python version --- space2stats_api/cdk/aws_stack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/space2stats_api/cdk/aws_stack.py b/space2stats_api/cdk/aws_stack.py index ad0f803a..0a2dd5b1 100644 --- a/space2stats_api/cdk/aws_stack.py +++ b/space2stats_api/cdk/aws_stack.py @@ -31,7 +31,7 @@ def __init__( self, "Space2StatsFunction", entry="../src", - runtime=_lambda.Runtime.PYTHON_3_9, + runtime=_lambda.Runtime.PYTHON_3_10, index="space2stats/api/handler.py", timeout=Duration.seconds(120), handler="handler",