diff --git a/README.md b/README.md index 838e78e..7e2376e 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # SWRData/Boundaries -Ready-to-use, timestamped, tiled boundary data for Germany. +Ready-to-use, timestamped boundary data for Germany. [![deploy demo](https://github.com/SWRdata/boundaries/actions/workflows/deploy-demo.yaml/badge.svg)](https://github.com/SWRdata/boundaries/actions/workflows/deploy-demo.yaml) [![deploy pipeline](https://github.com/SWRdata/boundaries/actions/workflows/deploy-pipeline.yaml/badge.svg)](https://github.com/SWRdata/boundaries/actions/workflows/deploy-pipeline.yaml) [![ty](https://github.com/SWRdata/boundaries/actions/workflows/ty.yaml/badge.svg)](https://github.com/SWRdata/boundaries/actions/workflows/ty.yaml) @@ -14,15 +14,17 @@ See [demo](https://static.datenhub.net/apps/boundaries/main/index.html) for code ### Tilesets -| Name | Description | Source | License | -| ------------------ | --------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------- | -| `admin_boundaries` | Staat, Länder, Kreise, Gemeinden 1:250,000 | [BKG VG250](https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/verwaltungsgebiete/verwaltungsgebiete-1-250-000-stand-01-01-vg250-01-01.html) | [DNN 2.0](https://sgx.geodatenzentrum.de/web_public/gdz/lizenz/deu/nutzungsbedingungen_vg250.pdf) | -| `admin_labels` | Label points for all features in `admin_boundaries` | [BKG VG250](https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/verwaltungsgebiete/verwaltungsgebiete-1-250-000-stand-01-01-vg250-01-01.html) | [DNN 2.0](https://sgx.geodatenzentrum.de/web_public/gdz/lizenz/deu/nutzungsbedingungen_vg250.pdf) | +| Name | Description | Sources | License | +| ------------------ | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| `admin_boundaries` | Staat, Länder, Kreise, Gemeinden 1:250,000 | [BKG VG250](https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/verwaltungsgebiete/verwaltungsgebiete-1-250-000-stand-01-01-vg250-01-01.html), [Natural Earth 10m Cultural Vectors](https://www.naturalearthdata.com/downloads/10m-cultural-vectors/) | [DNN 2.0](https://sgx.geodatenzentrum.de/web_public/gdz/lizenz/deu/nutzungsbedingungen_vg250.pdf), [Public Domain](https://www.naturalearthdata.com/about/terms-of-use/) | +| `admin_labels` | Label points for all features in `admin_boundaries` | [BKG VG250](https://gdz.bkg.bund.de/index.php/default/digitale-geodaten/verwaltungsgebiete/verwaltungsgebiete-1-250-000-stand-01-01-vg250-01-01.html) | [DNN 2.0](https://sgx.geodatenzentrum.de/web_public/gdz/lizenz/deu/nutzungsbedingungen_vg250.pdf) | ### Timestamps + `2024-01-01`, `2025-01-01` + ### Fields diff --git a/demo/package.json b/demo/package.json index bd4669d..b711358 100644 --- a/demo/package.json +++ b/demo/package.json @@ -9,9 +9,9 @@ "sync": "svelte-kit sync" }, "devDependencies": { - "@sveltejs/kit": "^2.61.1", "@sveltejs/adapter-auto": "^7.0.1", "@sveltejs/adapter-static": "^3.0.10", + "@sveltejs/kit": "^2.61.1", "@sveltejs/vite-plugin-svelte": "^7.1.2", "dotenv": "^17.4.2", "prettier": "^3.8.3", diff --git a/demo/src/lib/App.svelte b/demo/src/lib/App.svelte index 3d3c122..4d6dd87 100644 --- a/demo/src/lib/App.svelte +++ b/demo/src/lib/App.svelte @@ -1,7 +1,16 @@
- + {:else} - + {/if} {/each} @@ -125,7 +157,12 @@ type="line" filter={["==", "admin_level", filter]} paint={{ - "line-width": ["case", ["any", ["boolean", ["feature-state", "hovered"], false]], 1.75, 0], + "line-width": [ + "case", + ["any", ["boolean", ["feature-state", "hovered"], false]], + 1.75, + 0, + ], "line-color": "black", "line-opacity": 1, }} @@ -155,7 +192,12 @@ {/if} {#if hovered} - + {#each Object.entries(hovered.properties) as [k, v]} diff --git a/tasks/make_boundaries/Dockerfile b/tasks/make_boundaries/Dockerfile index 6a2379d..178749b 100644 --- a/tasks/make_boundaries/Dockerfile +++ b/tasks/make_boundaries/Dockerfile @@ -41,4 +41,4 @@ RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ uv sync --frozen --no-dev --no-editable -CMD [".venv/bin/python", "src/main.py"] +CMD [".venv/bin/python", "src/main.py", "--mode=production", "--min_year=2024"] diff --git a/tasks/make_boundaries/exploration/country_snapping.ipynb b/tasks/make_boundaries/exploration/country_snapping.ipynb new file mode 100644 index 0000000..201aa6c --- /dev/null +++ b/tasks/make_boundaries/exploration/country_snapping.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d83ab3d6", + "metadata": {}, + "source": [ + "# `boundaries`: Country snapping\n", + "\n", + "This notebook is for exploring how we're going to snap country boundaries from Natural Earth to our BKG data\n", + "\n", + "max.koehler@swr.de" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e20f696c", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gp\n", + "import shapely as shp\n", + "from matplotlib import pyplot as plt\n", + "from tqdm import tqdm\n", + "tqdm.pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "612b0753", + "metadata": {}, + "outputs": [], + "source": [ + "data = {}\n", + "data[\"ne\"] = gp.read_file(\"zip://../tmp/raw/ne_50m_admin_0_countries.zip/ne_50m_admin_0_countries.shp\")\n", + "for k in [\"STA\"]:\n", + " data[k] = gp.read_file(f\"zip://../tmp/raw/2025-01-01_vg250_01-01.utm32s.shape.ebenen.zip!vg250_01-01.utm32s.shape.ebenen/vg250_ebenen_0101/VG250_{k}.shp\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "489cbac7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG:25832\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 241/241 [00:00<00:00, 3224.12it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG:3857\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(data[\"STA\"].crs)\n", + "germany = (\n", + " data[\"STA\"].loc[data[\"STA\"][\"OBJID\"] == \"DEBKGVG200000CKM\"].to_crs(\"EPSG:3857\")\n", + ")\n", + "ne_countries = data[\"ne\"].loc[data[\"ne\"][\"ADMIN\"] != \"Germany\"].to_crs(\"EPSG:3857\")\n", + "ne_countries[\"name\"] = ne_countries[\"NAME_DE\"]\n", + "ne_countries[\"admin_level\"] = 2\n", + "ne_countries[\"kind\"] = \"Staat\"\n", + "\n", + "neighbours = (\n", + " \"Austria\",\n", + " \"Belgium\",\n", + " \"Czech Republic\",\n", + " \"Denmark\",\n", + " \"France\",\n", + " \"Luxembourg\",\n", + " \"Netherlands\",\n", + " \"Poland\",\n", + " \"Switzerland\",\n", + ")\n", + "\n", + "ne_countries[\"geometry\"] = ne_countries.progress_apply(\n", + " lambda x: (\n", + " x[\"geometry\"].buffer(5_000).difference(germany[\"geometry\"][0])\n", + " if x[\"ADMIN\"] in neighbours\n", + " else x[\"geometry\"]\n", + " ),\n", + " axis=1,\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 10))\n", + "bounds = germany.bounds.to_dict()\n", + "ax.set_xlim(bounds[\"minx\"][0] - 1e5, bounds[\"minx\"][0] + 5e5)\n", + "ax.set_ylim(bounds[\"miny\"][0], bounds[\"miny\"][0] + 1e6)\n", + "\n", + "germany.plot(ax=ax)\n", + "ne_countries.plot(ax=ax, cmap=\"Pastel1\")\n", + "print(ne_countries.crs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "fetch-geometry", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tasks/make_boundaries/exploration/labels.ipynb b/tasks/make_boundaries/exploration/labels.ipynb index 8bf9a70..5ea2775 100644 --- a/tasks/make_boundaries/exploration/labels.ipynb +++ b/tasks/make_boundaries/exploration/labels.ipynb @@ -5,16 +5,14 @@ "id": "d83ab3d6", "metadata": {}, "source": [ - "# `boundaries`: Exploration\n", - "\n", - "This notebook is a place to work out processing steps before we move them into the production pipeline.\n", + "# `boundaries`: Label points exploration\n", "\n", "max.koehler@swr.de" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "e20f696c", "metadata": {}, "outputs": [], @@ -26,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "612b0753", "metadata": {}, "outputs": [], @@ -35,7 +33,7 @@ "for k in [\"STA\", \"LAN\", \"KRS\"]:\n", " data[k] = gp.read_file(f\"zip://../tmp/raw/2025-01-01_vg250_01-01.utm32s.shape.ebenen.zip!vg250_01-01.utm32s.shape.ebenen/vg250_ebenen_0101/VG250_{k}.shp\")\n", "\n", - "data[\"subs\"] = gp.read_file(\"../label_substitutions.geojson\")\n" + "data[\"subs\"] = gp.read_file(\"../label_substitutions.geojson\")" ] }, { @@ -182,7 +180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.10" + "version": "3.12.2" } }, "nbformat": 4, diff --git a/tasks/make_boundaries/pyproject.toml b/tasks/make_boundaries/pyproject.toml index 302df5e..ec66a9d 100644 --- a/tasks/make_boundaries/pyproject.toml +++ b/tasks/make_boundaries/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "fetch-geometry" +name = "make_boundaries" version = "0.1.0" description = "" readme = "README.md" @@ -9,8 +9,11 @@ dependencies = [ "dotenv>=0.9.9", "geopandas>=1.1.2", "google-cloud-storage>=3.9.0", + "ipykernel>=7.3.0", "matplotlib>=3.10.9", "pandas>=3.0.3", "requests>=2.34.2", "shapely>=2.1.2", + "tqdm>=4.68.2", + "typed-argument-parser>=1.12.0", ] diff --git a/tasks/make_boundaries/src/globals.py b/tasks/make_boundaries/src/globals.py index f7bd880..0dbe3c7 100644 --- a/tasks/make_boundaries/src/globals.py +++ b/tasks/make_boundaries/src/globals.py @@ -5,3 +5,15 @@ "Dillingen a.d.Donau": "Dillingen an der Donau", "Mühldorf a.Inn": "Mühldorf am Inn", } + +NEIGHBOURS = ( + "Austria", + "Belgium", + "Czechia", + "Denmark", + "France", + "Luxembourg", + "Netherlands", + "Poland", + "Switzerland", +) diff --git a/tasks/make_boundaries/src/main.py b/tasks/make_boundaries/src/main.py index 90c4b87..2e2c415 100644 --- a/tasks/make_boundaries/src/main.py +++ b/tasks/make_boundaries/src/main.py @@ -1,9 +1,10 @@ import datetime import os -from typing import Dict +from typing import Dict, Literal from dotenv import load_dotenv from google.cloud import storage +from tap import Tap from entities.Tileset import Tileset from usecases.fetch_bkg_years import fetch_bkg_years @@ -12,35 +13,51 @@ from usecases.make_admin_labels import make_admin_labels from usecases.upload_blob import upload_blob -gcs_project = "swr-data-1" -gcs_bucket = "datenhub-net-static" -gcs_path = "data/boundaries/" -min_year = 2024 -raw_dir = "./tmp/raw/" -processed_dir = "./tmp/processed/" -manifest_path = os.path.join(processed_dir, "manifest.csv") +class ArgParser(Tap): + mode: Literal["production", "dev"] = "dev" + """dev mode disables reading/writing to GCS and some data fetches""" + min_year: int = 2024 + gcs_project: str = "swr-data-1" + gcs_bucket: str = "datenhub-net-static" + gcs_path: str = "data/boundaries/" + raw_dir: str = "./tmp/raw/" + processed_dir: str = "./tmp/processed/" -tilesets: Dict[str, Tileset] = {} +def main(args: ArgParser): + tilesets: Dict[str, Tileset] = {} - -def run(): load_dotenv() + os.makedirs(os.path.dirname(args.raw_dir), exist_ok=True) + os.makedirs(os.path.dirname(args.processed_dir), exist_ok=True) + manifest_path = os.path.join(args.processed_dir, "manifest.csv") + + print(f"running in {args.mode} mode") - os.makedirs(os.path.dirname(raw_dir), exist_ok=True) - os.makedirs(os.path.dirname(processed_dir), exist_ok=True) + if args.mode == "production": + print(f"{args.get_reproducibility_info()}") - storage_client = storage.Client(project=gcs_project) + if args.mode == "production": + storage_client = storage.Client(project=args.gcs_project) - print("Fetching BKG files... ", end="") - available_years = fetch_bkg_years() - # available_years = [2025] + # Fetch available years on the BKG wesite + if args.mode == "production": + print("fetching available bkg files... ", end="") + available_years = fetch_bkg_years() + else: + print("skipping fetching bkg files in dev mode... ", end="") + available_years = [2025] print(f"found {len(available_years)}\n") - print("Fetching existing files... ", end="") - existing_files = fetch_existing(storage_client, gcs_bucket, gcs_path) - # existing_files = [] + # Fetch existing tilesets + if args.mode == "production": + print("fetching existing files... ", end="") + existing_files = fetch_existing(storage_client, args.gcs_bucket, args.gcs_path) + else: + print("skipping fetching existing files in dev mode... ", end="") + existing_files = [] + if len(existing_files) > 0: print(f"found {len(existing_files)}:\n- {'\n- '.join(existing_files)}") else: @@ -49,25 +66,25 @@ def run(): new_files: list[str] = [] failed_files: list[str] = [] - for y in [y for y in available_years if y >= min_year]: + for y in [y for y in available_years if y >= args.min_year]: date = datetime.date(y, 1, 1) tilesets[f"admin_boundaries_{date.strftime('%Y-%m-%d')}"] = Tileset( name=f"Administrative Boundaries {y}", make_fn=make_admin, make_args={ - "cache_dir": raw_dir, - "output_dir": processed_dir, + "cache_dir": args.raw_dir, + "output_dir": args.processed_dir, "date": date, }, ) - + continue tilesets[f"admin_labels_{date.strftime('%Y-%m-%d')}"] = Tileset( name=f"Administrative Labels {date}", make_fn=make_admin_labels, make_args={ - "cache_dir": raw_dir, - "output_dir": processed_dir, + "cache_dir": args.raw_dir, + "output_dir": args.processed_dir, "date": date, }, ) @@ -96,12 +113,20 @@ def run(): f.write(f"name\n{'\n'.join([*existing_files, *new_files])}") print(f"Wrote manifest to {manifest_path}") - for f in [*new_files, manifest_path]: - upload_blob( - storage_client, f, gcs_bucket, os.path.join(gcs_path, os.path.basename(f)) + if args.mode == "production": + for f in [*new_files, manifest_path]: + upload_blob( + storage_client, + f, + args.gcs_bucket, + os.path.join(args.gcs_path, os.path.basename(f)), + ) + print(f"Uploaded {len(new_files)} new files ({len(failed_files)} failed)") + else: + print( + f"skipping updloading {len(new_files)} new files in dev mode ({len(failed_files)} failed)" ) - print(f"Uploaded {len(new_files)} new files, {len(failed_files)} failed") - -run() +args = ArgParser().parse_args() +main(args) diff --git a/tasks/make_boundaries/src/usecases/fetch_bkg_years.py b/tasks/make_boundaries/src/usecases/fetch_bkg_years.py index b6f4821..08e7b8b 100644 --- a/tasks/make_boundaries/src/usecases/fetch_bkg_years.py +++ b/tasks/make_boundaries/src/usecases/fetch_bkg_years.py @@ -5,9 +5,8 @@ from globals import BKG_URL -# Fetches a list of available years from the BKG website - +# Fetches a list of available years from the BKG website def fetch_bkg_years() -> list[int]: # verify=False if the government issued itself a broken SSL cert again diff --git a/tasks/make_boundaries/src/usecases/make_admin.py b/tasks/make_boundaries/src/usecases/make_admin.py index 6dbb777..a72d979 100644 --- a/tasks/make_boundaries/src/usecases/make_admin.py +++ b/tasks/make_boundaries/src/usecases/make_admin.py @@ -4,7 +4,7 @@ import geopandas as gp import pandas as pd -from globals import BKG_URL, NAME_SUBS +from globals import BKG_URL, NAME_SUBS, NEIGHBOURS from usecases.fetch_unless_cached import fetch_unless_cached from usecases.make_versatiles import make_versatiles @@ -14,7 +14,7 @@ # See: https://wiki.openstreetmap.org/wiki/File:Administrative_Gliederung_Deutschlands_admin_level.png def make_admin(cache_dir: str, output_dir: str, date: date) -> list[str]: - # 1. Fetch the BKG data we need + # 1. Fetch the BKG/Natural Earth data we need ds = date.strftime("%Y-%m-%d") json_path = os.path.join(cache_dir, f"admin_boundaries_{ds}.geojson") versatiles_path = os.path.join(output_dir, f"admin_boundaries_{ds}.versatiles") @@ -22,24 +22,54 @@ def make_admin(cache_dir: str, output_dir: str, date: date) -> list[str]: fp = "vg250_01-01.utm32s.shape.ebenen/vg250_ebenen_0101" - zip_name = "vg250_01-01.utm32s.shape.ebenen.zip" - cache_path = f"{cache_dir}/{ds}_{zip_name}" + bkg_zip_name = "vg250_01-01.utm32s.shape.ebenen.zip" + bkg_cache_path = f"{cache_dir}/{ds}_{bkg_zip_name}" + ne_cache_path = f"{cache_dir}/ne_10m_admin_0_countries.zip" - fetch_unless_cached(f"{BKG_URL}/{date.year}/{zip_name}", cache_path) + fetch_unless_cached(f"{BKG_URL}/{date.year}/{bkg_zip_name}", bkg_cache_path) + fetch_unless_cached( + "https://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_countries.zip", + ne_cache_path, + ) # 2. Extract + wrangle shapes # 2.1 Admin 2 print("Staat... ", end="") - country = gp.read_file(f"zip://{cache_path}!{fp}/VG250_STA.shp") - country["admin_level"] = 2 - country["kind"] = "Staat" - country_processed = country.loc[country["OBJID"] == "DEBKGVG200000CKM"] - assert country_processed.shape[0] == 1 - print(f"done ({country_processed.shape[0]} geom)") + + germany = gp.read_file(f"zip://{bkg_cache_path}!{fp}/VG250_STA.shp") + germany["admin_level"] = 2 + germany["kind"] = "Staat" + germany_processed = germany.loc[germany["OBJID"] == "DEBKGVG200000CKM"].to_crs( + "EPSG:3857" + ) + assert germany_processed.shape[0] == 1 + + ne_countries_raw = gp.read_file( + f"zip://{ne_cache_path}!/ne_10m_admin_0_countries.shp" + ) + ne_countries = ne_countries_raw.loc[ne_countries_raw["ADMIN"] != "Germany"].to_crs( + "EPSG:3857" + ) + + ne_countries["GEN"] = ne_countries["ADMIN"] + ne_countries["admin_level"] = 2 + ne_countries["kind"] = "Staat" + ne_countries["geometry"] = ne_countries.apply( + lambda x: ( + x["geometry"].buffer(10_000).difference(germany_processed["geometry"][0]) + if x["ADMIN"] in NEIGHBOURS + else x["geometry"] + ), + axis=1, + ) + countries = pd.concat( + [germany_processed.to_crs("EPSG:25832"), ne_countries.to_crs("EPSG:25832")] + ) + print(f"done ({countries.shape[0]} geoms)") # 2.2 Admin 4 print("Land... ", end="") - laender = gp.read_file(f"zip://{cache_path}!{fp}/VG250_LAN.shp") + laender = gp.read_file(f"zip://{bkg_cache_path}!{fp}/VG250_LAN.shp") laender["admin_level"] = 4 laender["kind"] = "Land" laender["land"] = laender["SN_L"] @@ -49,7 +79,7 @@ def make_admin(cache_dir: str, output_dir: str, date: date) -> list[str]: # 2.3 Admin 6 print("Kreis... ", end="") - kreise = gp.read_file(f"zip://{cache_path}!{fp}/VG250_KRS.shp") + kreise = gp.read_file(f"zip://{bkg_cache_path}!{fp}/VG250_KRS.shp") kreise["admin_level"] = 6 kreise["kind"] = "Kreis" kreise["land"] = kreise["SN_L"] @@ -58,7 +88,7 @@ def make_admin(cache_dir: str, output_dir: str, date: date) -> list[str]: # 2.4 Admin 8 print("Gemeinde... 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