diff --git a/.gitignore b/.gitignore index 7fbcbea0..35dc79bb 100644 --- a/.gitignore +++ b/.gitignore @@ -130,4 +130,6 @@ dmypy.json *.sqlite *.sqlite* -/argo-pipeline/src/cartiflette-s3-cache +**/cartiflette-s3-cache/* +*.json +/cartiflette/pipeline/cartiflette-s3-cache diff --git a/Dockerfile b/Dockerfile index 9863757b..73b6d1af 100644 --- a/Dockerfile +++ b/Dockerfile @@ -31,4 +31,6 @@ COPY docker/test.py . RUN curl https://install.python-poetry.org/ | python - RUN poetry install --only main --no-interaction +# TODO : is this necessary? This should throw an exception if datasets have not +# already been (manually) uploaded ? CMD ["python", "test.py"] \ No newline at end of file diff --git a/argo-pipeline/api.py b/argo-pipeline/api.py index 4156cac8..859feaf2 100644 --- a/argo-pipeline/api.py +++ b/argo-pipeline/api.py @@ -1,10 +1,12 @@ """A simple API to expose cartiflette files""" + import typing from fastapi import FastAPI, Response from fastapi.responses import FileResponse from cartiflette.api import download_from_cartiflette_inner -from cartiflette.config import PATH_WITHIN_BUCKET +from cartiflette.config import PATH_WITHIN_BUCKET, DATASETS_HIGH_RESOLUTION +from cartiflette.pipeline_constants import COG_TERRITOIRE # , IRIS app = FastAPI( title="API de récupération des fonds de carte avec cartiflette", @@ -42,9 +44,10 @@ def download_from_cartiflette_api( year=2022, crs=4326, simplification=simplification, - provider="IGN", - dataset_family="ADMINEXPRESS", - source="EXPRESS-COG-CARTO-TERRITOIRE", + provider="Cartiflette", + dataset_family="production", + # TODO : source can also be IRIS[DATASETS_HIGH_RESOLUTION] + source=COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION], return_as_json=False, path_within_bucket=PATH_WITHIN_BUCKET, ) diff --git a/argo-pipeline/pipeline.yaml b/argo-pipeline/pipeline.yaml index 0a1b9a0e..22b7b6f8 100644 --- a/argo-pipeline/pipeline.yaml +++ b/argo-pipeline/pipeline.yaml @@ -24,257 +24,102 @@ spec: # --------------------------- dag: + failFast: false tasks: - # STEP 0: RETRIEVE IGN FILE AND DUPLICATE IN MINIO - - name: duplicate-ign - template: duplicate-ign - - name: test-volume - template: test-volume - dependencies: [ duplicate-ign ] - # STEP 1.1. SPLIT BY DEPARTEMENT - - name: prepare-split-departement - template: prepare-split - dependencies: [ duplicate-ign ] - arguments: - parameters: - - name: restrict_field - value: "DEPARTEMENT" - - name: split-departement - template: split-dataset - dependencies: [ prepare-split-departement ] - arguments: - parameters: - - name: split_type - value: "DEPARTEMENT" - - name: format_output - value: "{{item.format-output}}" - - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-departement.outputs.result}}" - # STEP 1.2. SPLIT BY COMMUNE - - name: prepare-split-commune - template: prepare-split - dependencies: [ duplicate-ign ] - arguments: - parameters: - - name: restrict_field - value: "COMMUNE" - - name: split-commune - template: split-dataset - dependencies: [ prepare-split-commune ] - arguments: - parameters: - - name: split_type - value: "COMMUNE" - - name: format_output - value: "{{item.format-output}}" - - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-commune.outputs.result}}" - # STEP 1.3. SPLIT BY REGION - - name: prepare-split-region - template: prepare-split - dependencies: [ duplicate-ign ] - arguments: - parameters: - - name: restrict_field - value: "REGION" - - name: split-region - template: split-dataset - dependencies: [ prepare-split-region ] - arguments: - parameters: - - name: split_type - value: "REGION" - - name: format_output - value: "{{item.format-output}}" - - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-region.outputs.result}}" - # STEP 1.4. SPLIT BY BASSIN VIE - - name: prepare-split-bassin-vie - template: prepare-split - dependencies: [ duplicate-ign ] - arguments: - parameters: - - name: restrict_field - value: "BASSIN_VIE" - - name: split-bassin-vie - template: split-dataset - dependencies: [ prepare-split-bassin-vie ] - arguments: - parameters: - - name: split_type - value: "BASSIN_VIE" - - name: format_output - value: "{{item.format-output}}" - - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-bassin-vie.outputs.result}}" - # STEP 1.5. SPLIT BY ZONE_EMPLOI - - name: prepare-split-zone-emploi - template: prepare-split - dependencies: [ duplicate-ign ] + + # TASK 0 : MOUNT VOLUMES AND CHECK PERMISSIONS + - name: init-and-test-volume + template: init-and-test-volume + + # TASK 1 : RETRIEVE ALL (NEW) FILES FROM SOURCES AND UPLOAD TO MINIO + - name: download-all-sources + template: download-all-sources + dependencies: [ init-and-test-volume ] + + # TASK 2 : CHECK WICH VINTAGE SHOULD BE RE-PROCESSED FROM (NEW) RAW SOURCES + - name: select-downstream-vintage-to-process + template: select-downstream-vintage-to-process + dependencies: [ download-all-sources ] arguments: parameters: - - name: restrict_field - value: "ZONE_EMPLOI" - - name: split-zone-emploi - template: split-dataset - dependencies: [ prepare-split-zone-emploi ] + - name: download_results + value: "{{tasks.download-all-sources.outputs.parameters.download_all_results}}" + + # TASK 3.1 : CREATE BASE GEODATASETS ON MINIO FROM RAW TERRITORIAL FILES + - name: make-geodatasets + template: make-geodatasets + dependencies: [ select-downstream-vintage-to-process ] arguments: parameters: - - name: split_type - value: "ZONE_EMPLOI" - - name: format_output - value: "{{item.format-output}}" - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-zone-emploi.outputs.result}}" - # STEP 1.6. SPLIT BY UNITE_URBAINE - - name: prepare-split-unite-urbaine - template: prepare-split - dependencies: [ duplicate-ign ] - arguments: - parameters: - - name: restrict_field - value: "UNITE_URBAINE" - - name: split-unite-urbaine - template: split-dataset - dependencies: [ prepare-split-unite-urbaine ] + value: "{{item}}" + withParam: "{{tasks.select-downstream-vintage-to-process.outputs.parameters.geodatasets_vintage_to_update}}" + + # TASK 3.2 : CREATE METADATA FILES ON MINIO FROM RAW INSEE FILES + - name: make-metadata + template: make-metadata + dependencies: [ select-downstream-vintage-to-process ] arguments: parameters: - - name: split_type - value: "UNITE_URBAINE" - - name: format_output - value: "{{item.format-output}}" - - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-unite-urbaine.outputs.result}}" - # STEP 1.6. SPLIT BY AIRE_ATTRACTION_VILLES - - name: prepare-split-aire-attraction - template: prepare-split - dependencies: [ duplicate-ign ] + - name: years + value: "{{tasks.select-downstream-vintage-to-process.outputs.parameters.metadata_vintage_to_update}}" + + # TASK 4: SELECT DOWNSTREAM YEARS TO GENERATE + # (FAN-OUT STEP TO ENSURE SCALIBILITY AND RESULTS' MAX LENGTH IN NEXT STEP) + - name: operationnal-selection-of-vintages-to-generate + template: operationnal-selection-of-vintages-to-generate + dependencies: [ make-geodatasets, make-metadata ] arguments: parameters: - - name: restrict_field - value: "AIRE_ATTRACTION_VILLES" - - name: split-aire-attraction - template: split-dataset - dependencies: [ prepare-split-aire-attraction ] + - name: years_geodatasets + value: "{{tasks.make-geodatasets.outputs.parameters.updated_geodata}}" + - name: years_metadata + value: "{{tasks.make-metadata.outputs.parameters.updated_metadata}}" + + # TASK 5 : TASK WITH 2 NESTED STEPS FOR + # step 5.1 selecting geodatasets to generate + # step 5.2 creating selected geodatasets + - name: generate-downstream-datasets + template: generate-downstream-datasets + dependencies: [ operationnal-selection-of-vintages-to-generate ] arguments: parameters: - - name: split_type - value: "AIRE_ATTRACTION_VILLES" - - name: format_output - value: "{{item.format-output}}" - name: year - value: "{{item.year}}" - - name: crs - value: "{{item.crs}}" - - name: source - value: "{{item.source}}" - - name: simplification - value: "{{item.simplification}}" - - name: level_polygons - value: "{{item.level-polygons}}" - - name: filter_by - value: "{{item.filter-by}}" - withParam: "{{tasks.prepare-split-aire-attraction.outputs.result}}" - + value: "{{item}}" + withParam: "{{tasks.operationnal-selection-of-vintages-to-generate.outputs.parameters.years}}" + continueOn: + failed: true + + # TASK 6 : GENERATE CATALOG + - name: make-catalog + template: make-catalog + dependencies: [generate-downstream-datasets] # -------------------------- # TEMPLATES DEFINITION # --------------------------- - # First step: retrieving and duplicating IGN tiles ------------------ - - name: duplicate-ign + - name: init-and-test-volume inputs: artifacts: - name: code path: /mnt/bin git: repo: https://github.com/inseefrlab/cartiflette - revision: "main" + revision: "feat/refacto_pipeline_first_steps" container: - image: inseefrlab/cartiflette + image: inseefrlab/cartiflette:latest command: [sh, -c] - args: [" - mkdir -p $LOCAL_DATA_PATH ; - mkdir -p /mnt/bin/src ; + args: ["mkdir -p /mnt/bin/src ; mv /mnt/bin/argo-pipeline/src/* /mnt/bin/src ; - python /mnt/bin/src/duplicate_in_bucket.py --path $PATH_WRITING_S3 --localpath $LOCAL_DATA_PATH ; + echo $ENVIRONMENT; "] volumeMounts: - name: volume-workflow-tmp mountPath: /mnt env: &env_parameters - - name: PATH_WRITING_S3 - value: "production" - name: PYTHONPATH value: "${PYTHONPATH}:/mnt/bin" - - name: LOCAL_DATA_PATH - value: "/mnt/data" - name: AWS_ACCESS_KEY_ID valueFrom: secretKeyRef: @@ -291,62 +136,182 @@ spec: value: minio.lab.sspcloud.fr - name: MC_HOST_s3 value: https://$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY@$AWS_S3_ENDPOINT + - name: ENVIRONMENT + # set value to "test" to simplify pipeline execution (2 years, only topojson, etc.), use "preprod" or "prod" else + # -> this will also configure the path_within_bucket constant + value: test + + - name: download-all-sources + outputs: + parameters: + - name: download_all_results + valueFrom: + path: download_all_results.json + container: + image: inseefrlab/cartiflette:latest + command: [sh, -c] + args: [" + python /mnt/bin/src/download_all_sources.py; + "] + volumeMounts: + - name: volume-workflow-tmp + mountPath: /mnt + env: *env_parameters + + - name: select-downstream-vintage-to-process + inputs: + parameters: + - name: download_results + outputs: + parameters: + - name: geodatasets_vintage_to_update + valueFrom: + path: geodatasets_years.json + - name: metadata_vintage_to_update + valueFrom: + path: metadata_years.json + container: + image: inseefrlab/cartiflette:latest + command: [sh, -c] + volumeMounts: + - name: volume-workflow-tmp + mountPath: /mnt + args: [" + python /mnt/bin/src/select_downstream_vintage_to_process.py --download_results '{{inputs.parameters.download_results}}' + "] + env: *env_parameters - - name: test-volume + - name: make-geodatasets + inputs: + parameters: + - name: year + outputs: + parameters: + - name: updated_geodata + valueFrom: + path: "geodataset_years/{{inputs.parameters.year}}.json" container: - image: inseefrlab/cartiflette + image: inseefrlab/cartiflette:latest command: [sh, -c] - args: ["echo $PATH_WRITING_S3 ; - head -n 1 ${LOCAL_DATA_PATH}/tagc.csv"] + volumeMounts: + - name: volume-workflow-tmp + mountPath: /mnt + args: [" + python /mnt/bin/src/make_geodata_datasets.py --year '{{inputs.parameters.year}}'; + "] env: *env_parameters + + - name: make-metadata + inputs: + parameters: + - name: years + outputs: + parameters: + - name: updated_metadata + valueFrom: + path: metadata_years.json + container: + image: inseefrlab/cartiflette:latest + command: [sh, -c] volumeMounts: - name: volume-workflow-tmp mountPath: /mnt + args: [" + python /mnt/bin/src/make_metadata_datasets.py --years '{{inputs.parameters.years}}'; + "] + env: *env_parameters - # Step 2: creating template task for splitting ------------------ - - name: prepare-split + - name: operationnal-selection-of-vintages-to-generate inputs: parameters: - - name: restrict_field + - name: years_geodatasets + - name: years_metadata + outputs: + parameters: + - name: years + valueFrom: + path: vintages_operationnal_generation.json container: - image: inseefrlab/cartiflette + image: inseefrlab/cartiflette:latest command: [sh, -c] volumeMounts: - name: volume-workflow-tmp mountPath: /mnt + env: *env_parameters args: [" - python /mnt/bin/src/crossproduct.py --restrictfield '{{inputs.parameters.restrict_field}}' + python /mnt/bin/src/filter_vintages_operationnal.py --years-geodatasets '{{inputs.parameters.years_geodatasets}}' --years-metadata '{{inputs.parameters.years_metadata}}'; "] - - name: split-dataset + - name : generate-downstream-datasets + inputs: + parameters: + - name: year + steps: + - - name: select-configs-for-generation + template: select-configs-for-generation + arguments: + parameters: + - name: year + value: "{{inputs.parameters.year}}" + - - name: generate-datasets + template: generate-datasets + arguments: + parameters: + - name: year + value: "{{inputs.parameters.year}}" + - name: config_generation + value: "{{steps.select-configs-for-generation.outputs.parameters.configs}}" + + - name: select-configs-for-generation + inputs: + parameters: + - name: year + outputs: + parameters: + - name: configs + valueFrom: + path: "configs_datasets_to_generate/{{ inputs.parameters.year }}.json" + container: + image: inseefrlab/cartiflette:latest + command: [sh, -c] + volumeMounts: + - name: volume-workflow-tmp + mountPath: /mnt + env: *env_parameters + args: [" + python /mnt/bin/src/crossproduct.py --year '{{inputs.parameters.year}}'; + "] + + - name: generate-datasets inputs: parameters: - - name: split_type - - name: format_output - name: year - - name: crs - - name: source - - name: simplification - - name: level_polygons - - name: filter_by + - name: config_generation + outputs: + parameters: + - name: result + valueFrom: + path: "generation/{{ inputs.parameters.year }}/result.json" container: - image: inseefrlab/cartiflette + image: inseefrlab/cartiflette:latest command: ["sh", "-c"] args: [" - mkdir -p temp/ && cp /mnt/data/tagc.csv temp/tagc.csv ; - python /mnt/bin/src/split_merge_tiles.py \ - --path $PATH_WRITING_S3 \ - --format_output {{inputs.parameters.format_output}} \ - --year {{inputs.parameters.year}} \ - --crs {{inputs.parameters.crs}} \ - --source {{inputs.parameters.source}} \ - --simplification {{inputs.parameters.simplification}} \ - --level_polygons {{inputs.parameters.level_polygons}} \ - --filter_by {{inputs.parameters.filter_by}}" + python /mnt/bin/src/split_merge_tiles_multithreading.py \ + --year '{{inputs.parameters.year}}' \ + --configs '{{inputs.parameters.config_generation}}'" ] volumeMounts: - name: volume-workflow-tmp mountPath: /mnt env: *env_parameters - + + - name: make-catalog + container: + image: inseefrlab/cartiflette:latest + command: ["sh", "-c"] + args: ["python /mnt/bin/src/catalog.py"] + volumeMounts: + - name: volume-workflow-tmp + mountPath: /mnt + env: *env_parameters diff --git a/argo-pipeline/src/catalog.py b/argo-pipeline/src/catalog.py new file mode 100644 index 00000000..1b91bfec --- /dev/null +++ b/argo-pipeline/src/catalog.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Create cartiflette's catalog +""" + +import logging + +from s3fs import S3FileSystem + +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, +) +from cartiflette.s3 import make_s3_inventory + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +logger.info("=" * 50) +logger.info("\n%s", __doc__) +logger.info("=" * 50) + +# Nota : no parsed needed for this command + + +def main( + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, + fs: S3FileSystem = FS, +): + + make_s3_inventory( + fs=fs, bucket=bucket, path_within_bucket=path_within_bucket + ) + + logger.info("Success!") + + +if __name__ == "__main__": + main( + bucket=BUCKET, + path_within_bucket=PATH_WITHIN_BUCKET, + fs=FS, + ) diff --git a/argo-pipeline/src/crossproduct.py b/argo-pipeline/src/crossproduct.py index 26da9839..cb7b49da 100644 --- a/argo-pipeline/src/crossproduct.py +++ b/argo-pipeline/src/crossproduct.py @@ -1,67 +1,126 @@ -import json +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +4.2th step of pipeline + +Prepare arguments for next step +""" + import argparse +import json +import logging +import os +from typing import List + +from s3fs import S3FileSystem + from cartiflette.pipeline import crossproduct_parameters_production +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, +) +from cartiflette.pipeline_constants import ( + PIPELINE_SIMPLIFICATION_LEVELS, + # PIPELINE_FORMATS, + # PIPELINE_CRS, +) + +logger = logging.getLogger(__name__) + +logging.basicConfig(level=logging.INFO) + +logger.info("=" * 50) +logger.info("\n" + __doc__) +logger.info("=" * 50) + parser = argparse.ArgumentParser(description="Crossproduct Script") + parser.add_argument( - "--restrictfield", type=str, default=None, help="Field to restrict level-polygons" + "-y", + "--year", + default="2023", + help="Filter downstream vintage to process", ) +# parser.add_argument( +# "-f", +# "--formats", +# default=",".join(PIPELINE_FORMATS), +# help="Desired output formats, as a comma separated values list", +# ) + +# parser.add_argument( +# "-c", +# "--crs", +# default=",".join([str(x) for x in PIPELINE_CRS]), +# help="Desired projections as EPSG codes, as a comma separated values list", +# ) + +parser.add_argument( + "-s", + "--simplifications", + default=",".join([str(x) for x in PIPELINE_SIMPLIFICATION_LEVELS]), + help="Desired simplifications levels, as a comma separated values list", +) -# parameters -formats = ["topojson", "geojson"] -years = [2022] -crs_list = [4326] -sources = ["EXPRESS-COG-CARTO-TERRITOIRE"] - -croisement_decoupage_level = { - # structure -> niveau geo: [niveau decoupage macro], - "COMMUNE": [ - "BASSIN_VIE", - "ZONE_EMPLOI", - "UNITE_URBAINE", - "AIRE_ATTRACTION_VILLES", # zonages d'études - "DEPARTEMENT", - "REGION", # zonages administratifs - "TERRITOIRE", - "FRANCE_ENTIERE", - "FRANCE_ENTIERE_DROM_RAPPROCHES", - ], - "DEPARTEMENT": [ - "REGION", - "TERRITOIRE", - "FRANCE_ENTIERE", - "FRANCE_ENTIERE_DROM_RAPPROCHES", - ], - "REGION": ["TERRITOIRE", "FRANCE_ENTIERE", "FRANCE_ENTIERE_DROM_RAPPROCHES"], - "BASSIN_VIE": ["TERRITOIRE", "FRANCE_ENTIERE", "FRANCE_ENTIERE_DROM_RAPPROCHES"], - "ZONE_EMPLOI": ["TERRITOIRE", "FRANCE_ENTIERE", "FRANCE_ENTIERE_DROM_RAPPROCHES"], - "UNITE_URBAINE": ["TERRITOIRE", "FRANCE_ENTIERE", "FRANCE_ENTIERE_DROM_RAPPROCHES"], - "AIRE_ATTRACTION_VILLES": ["TERRITOIRE", "FRANCE_ENTIERE", "FRANCE_ENTIERE_DROM_RAPPROCHES"], -} args = parser.parse_args() +year = args.year +# formats = args.formats.split(",") +# crs = args.crs.split(",") +simplifications = args.simplifications.split(",") + + +# TODO : convert bucket & path_within_bucket to parsable arguments -def main(): - tempdf = crossproduct_parameters_production( - croisement_filter_by_borders=croisement_decoupage_level, - list_format=formats, - years=years, - crs_list=crs_list, - sources=sources, - simplifications=[0, 50], + +def main( + year: int = None, + simplifications: List[str] = None, + formats: List[str] = None, + crs: List[int] = None, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, + fs: S3FileSystem = FS, +): + + simplifications = ( + simplifications if simplifications else PIPELINE_SIMPLIFICATION_LEVELS + ) + + logger.info("Crossproduct with year=%s", year) + logger.info("Crossproduct with simplifications=%s", simplifications) + logger.info("Crossproduct with formats=%s", formats) + logger.info("Crossproduct with crs=%s", crs) + + configs = crossproduct_parameters_production( + # list_format=formats, + year=year, + # crs_list=crs, + simplifications=simplifications, + fs=fs, + bucket=bucket, + path_within_bucket=path_within_bucket, ) - tempdf.columns = tempdf.columns.str.replace("_", "-") - # Apply filtering if restrictfield is provided - if args.restrictfield: - tempdf = tempdf.loc[tempdf["level-polygons"] == args.restrictfield] + try: + os.makedirs("configs_datasets_to_generate") + except FileExistsError: + pass - output = tempdf.to_json(orient="records") - parsed = json.loads(output) - print(json.dumps(parsed)) + with open(f"configs_datasets_to_generate/{year}.json", "w") as out: + json.dump(configs, out) + return configs if __name__ == "__main__": - main() + configs = main( + year=year, + simplifications=simplifications, + bucket=BUCKET, + path_within_bucket=PATH_WITHIN_BUCKET, + fs=FS, + ) diff --git a/argo-pipeline/src/download_all_sources.py b/argo-pipeline/src/download_all_sources.py new file mode 100644 index 00000000..8bbc61ce --- /dev/null +++ b/argo-pipeline/src/download_all_sources.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +First step of pipeline + +Performs a full pipeline to download data and store them on MinIO. The +target files are described in cartiflette/constants.py under the +constant PIPELINE_DOWNLOAD_ARGS. Those files' characteristics must also be +described in the cartiflette/utils/sources.yaml file. + +Note: to perform an easy debugging task, please overwrite +cartiflette.config.THREADS_DOWNLOAD to 1 (to avoid multithreading which +could be gruesome to debug). + +During the operation: + * GIS files should be reprojected to 4326 if current projection has no EPSG + code + * each file should be re-encoded in UTF-8 + * unvalid geometries will try to be fixed using a 0 buffer + +""" + +import argparse +from datetime import date +import logging +import os +import json + +from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS +from cartiflette.pipeline import download_all + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +logger.info("=" * 50) +logger.info("\n" + __doc__) +logger.info("=" * 50) + + +# Initialize ArgumentParser +parser = argparse.ArgumentParser( + description="Run Cartiflette pipeline download script." +) + +default_years = ",".join(str(x) for x in range(2020, date.today().year + 1)) +parser.add_argument( + "--years", + type=str, + help="List of years to perform download on (as comma separated values)", + default=default_years, +) + +parser.add_argument( + "--skip", + action="store_true", + help="Skip download for speeding debugging purposes", +) + +# Parse arguments +args = parser.parse_args() + +bucket = BUCKET +years = args.years +skip = args.skip + +if os.environ.get("ENVIRONMENT", None) == "test": + logging.warning( + "test environment -> restrict download to 2023 & 2024 only" + ) + years = "2023,2024" + +if years: + years = [int(x) for x in years.split(",")] + +fs = FS + + +try: + if not skip: + results = download_all( + bucket, PATH_WITHIN_BUCKET, fs=fs, upload=True, years=years + ) + else: + results = dict() + logger.warning( + "\n\n!!!! Download skipped !!!\n\n" + "To reset download, remove --skip flag from pipeline yaml (from " + "download-all-sources template)!" + ) + + with open("download_all_results.json", "w") as out: + json.dump(results, out) +except Exception: + try: + os.unlink("download_all_results.json") + except FileNotFoundError: + pass + raise + +logger.info(results) diff --git a/argo-pipeline/src/duplicate_in_bucket.py b/argo-pipeline/src/duplicate_in_bucket.py deleted file mode 100644 index 1853ff99..00000000 --- a/argo-pipeline/src/duplicate_in_bucket.py +++ /dev/null @@ -1,74 +0,0 @@ -import argparse -import os - -from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS -from cartiflette.utils import create_path_bucket -from cartiflette.pipeline.combine_adminexpress_france import ( - combine_adminexpress_territory, -) -from cartiflette.pipeline.prepare_cog_metadata import prepare_cog_metadata - -# Initialize ArgumentParser -parser = argparse.ArgumentParser(description="Run Cartiflette pipeline script.") -parser.add_argument( - "-p", "--path", help="Path within bucket", default=PATH_WITHIN_BUCKET -) -parser.add_argument( - "-lp", "--localpath", help="Path within bucket", default="temp" -) - -# Parse arguments -args = parser.parse_args() - -bucket = BUCKET -path_within_bucket = args.path -local_path = args.localpath - -year = 2022 -fs = FS - -os.makedirs(local_path, exist_ok=True) - -# PART 1/ COMBINE RAW FILES TOGETHER AND WRITE TO S3 - - -def main(path_within_bucket, localpath, bucket=BUCKET, year=year): - - path_combined_files = combine_adminexpress_territory( - path_within_bucket=path_within_bucket, - intermediate_dir=localpath - ) - - path_raw_s3 = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": "france", - "crs": 4326, - "filter_by": "preprocessed", - "value": "before_cog", - "vectorfile_format": "geojson", - "provider": "IGN", - "dataset_family": "ADMINEXPRESS", - "source": "EXPRESS-COG-CARTO-TERRITOIRE", - "territory": "france", - "filename": "raw.geojson", - "simplification": 0, - } - ) - - fs.put_file(path_combined_files, path_raw_s3) - - # Retrieve COG metadata - tagc_metadata = prepare_cog_metadata( - path_within_bucket, local_dir=localpath) - tagc_metadata.drop(columns=["LIBGEO"]).to_csv(f"{localpath}/tagc.csv") - - data = {"preprocessed": path_combined_files, "metadata": f"{localpath}/tagc.csv"} - - return data - - -if __name__ == "__main__": - main(path_within_bucket, localpath=local_path) diff --git a/argo-pipeline/src/filter_vintages_operationnal.py b/argo-pipeline/src/filter_vintages_operationnal.py new file mode 100644 index 00000000..f7daf740 --- /dev/null +++ b/argo-pipeline/src/filter_vintages_operationnal.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +4.1th step of pipeline + +Filter years for which geodata OR metadata have been successfuly generated. +This is a dummy task in the dag, only used to force a fan-out step, which +should ensure next step does not reach ARGO's maximum 262,144 characters for +output/input. +""" + +import argparse +import json +import os +import logging + +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, +) + +logger = logging.getLogger(__name__) + +logger.info("=" * 50) +logger.info("\n" + __doc__) +logger.info("=" * 50) + + +parser = argparse.ArgumentParser(description="Crossproduct Script") + +parser.add_argument( + "-yg", + "--years-geodatasets", + default=r'["{\"2023\": true}"]', + help="Updated geodataset's vintages", +) + +parser.add_argument( + "-ym", + "--years-metadata", + default="[2023]", + help="Updated metadata's vintages", +) + +args = parser.parse_args() + +years_geodatasets = [json.loads(x) for x in json.loads(args.years_geodatasets)] +years_geodatasets = { + int(year) + for d in years_geodatasets + for (year, result) in d.items() + if result +} + +years_metadata = {int(x) for x in json.loads(args.years_metadata)} + +years = sorted(list(years_geodatasets | years_metadata)) + +if os.environ.get("ENVIRONMENT", None) == "test": + logging.warning("test environment -> restrict generation to 2023, 2024 ") + years = [2023, 2024] + +logger.info( + "selected downstream years for operationnal generation of datasets : %s", + years, +) + +with open("vintages_operationnal_generation.json", "w") as out: + json.dump(years, out) diff --git a/argo-pipeline/src/make_geodata_datasets.py b/argo-pipeline/src/make_geodata_datasets.py new file mode 100644 index 00000000..77bc38ce --- /dev/null +++ b/argo-pipeline/src/make_geodata_datasets.py @@ -0,0 +1,88 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +3rd step of pipeline - part 1 + +Retrieve all territorial cities' files and merge those into single files +for each vintage. +""" + +import argparse +import json +import logging +import os +from typing import List + +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, + INTERMEDIATE_FORMAT, +) +from cartiflette.pipeline_constants import PIPELINE_SIMPLIFICATION_LEVELS +from cartiflette.pipeline.prepare_geodatasets import ( + create_one_year_geodataset_batch, +) + +logging.basicConfig(level=logging.INFO) + +logger = logging.getLogger(__name__) + + +logger.info("=" * 50) +logger.info("\n" + __doc__) +logger.info("=" * 50) + +# Initialize ArgumentParser +parser = argparse.ArgumentParser( + description="Preprocess geodatasets from raw sources" +) +parser.add_argument( + "-y", "--year", help="Vintage to perform computation on", default="2023" +) + +parser.add_argument( + "-s", + "--simplify", + help="Simplifications levels to perform", + default=PIPELINE_SIMPLIFICATION_LEVELS, +) + +# Parse arguments +args = parser.parse_args() +year = args.year +simplifications = args.simplify + +bucket = BUCKET +fs = FS + + +def main( + simplifications: List[int], + bucket=BUCKET, + year: int = None, + path_within_bucket: str = PATH_WITHIN_BUCKET, +): + + created = create_one_year_geodataset_batch( + year, + format_output=INTERMEDIATE_FORMAT, + simplifications_values=simplifications, + bucket=bucket, + path_within_bucket=path_within_bucket, + fs=fs, + ) + + try: + os.makedirs("geodataset_years") + except FileExistsError: + pass + + with open(f"geodataset_years/{year}.json", "w") as out: + json.dump(created, out) + + return created + + +if __name__ == "__main__": + data = main(simplifications=simplifications, year=year) diff --git a/argo-pipeline/src/make_metadata_datasets.py b/argo-pipeline/src/make_metadata_datasets.py new file mode 100644 index 00000000..bd0079c1 --- /dev/null +++ b/argo-pipeline/src/make_metadata_datasets.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +3rd step of pipeline - part 2 + +Update/create vintaged metadata files and send those to S3 +""" + +import argparse +import json +import logging +import os +import tempfile + +from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS +from cartiflette.utils import create_path_bucket +from cartiflette.pipeline.prepare_cog_metadata import prepare_cog_metadata + + +logging.basicConfig(level=logging.INFO) + +logger = logging.getLogger(__name__) + +logger.info("=" * 50) +logger.info("\n%s", __doc__) +logger.info("=" * 50) + +# Initialize ArgumentParser +parser = argparse.ArgumentParser( + description="Preprocess metadata from raw sources" +) + +parser.add_argument( + "-y", "--years", help="Vintage to perform computation on", default="[]" +) + +# Parse arguments +args = parser.parse_args() + +bucket = BUCKET +years = args.years + +years = json.loads(years) + +fs = FS + + +def main( + path_within_bucket: str = PATH_WITHIN_BUCKET, + bucket: str = BUCKET, + years: int = None, +): + + created = [] + + with tempfile.TemporaryDirectory() as tempdir: + for year in years: + logger.info("-" * 50) + logger.info("Computing metadata for year=%s", year) + logger.info("-" * 50) + + config = { + "bucket": bucket, + "path_within_bucket": path_within_bucket, + "year": year, + "crs": None, + "filter_by": "preprocessed", + "value": "tagc", + "vectorfile_format": "csv", + "provider": "Cartiflette", + "dataset_family": "metadata", + "source": "INSEE", + "territory": "france", + "filename": "metadata.csv", + "simplification": 0, + } + + # Retrieve COG metadata + # TODO : update prepare_cog_metadata to send directly to S3 + metadata = prepare_cog_metadata( + bucket=bucket, + path_within_bucket=path_within_bucket, + year=year, + ) + if metadata is None: + continue + + for key in [ + "COMMUNE", + "ARRONDISSEMENT_MUNICIPAL", + "CANTON", + "IRIS", + ]: + try: + metadata_border = metadata[key] + except KeyError: + continue + if metadata_border.empty: + continue + config["borders"] = key + path_raw_s3 = create_path_bucket(config) + localfile = f"{tempdir}/metadata.csv" + metadata_border.to_csv(localfile, index=False) + try: + logger.info("sending %s -> %s", localfile, path_raw_s3) + fs.put_file(localfile, path_raw_s3) + except Exception: + raise + finally: + os.unlink(localfile) + + # if at least one metadata constructed + created.append(year) + + created = sorted(list(set(created))) + + with open("metadata_years.json", "w", encoding="utf8") as out: + json.dump(created, out) + + return created + + +if __name__ == "__main__": + data = main(years=years) diff --git a/argo-pipeline/src/select_downstream_vintage_to_process.py b/argo-pipeline/src/select_downstream_vintage_to_process.py new file mode 100644 index 00000000..b29ed0fd --- /dev/null +++ b/argo-pipeline/src/select_downstream_vintage_to_process.py @@ -0,0 +1,112 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +2nd step of pipeline + +Select which geodatasets should be updated (those where raw datasets components +have been re-downloaded) to select downstream steps +""" + +import argparse +import logging +import os +import json + +from cartiflette.config import ( + DATASETS_HIGH_RESOLUTION, +) +from cartiflette.pipeline_constants import COG_TERRITOIRE, IRIS + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +logger.info("=" * 50) +logger.info("\n%s", __doc__) +logger.info("=" * 50) + +parser = argparse.ArgumentParser(description="Select vintage to update") +parser.add_argument( + "--download_results", + type=str, + default="{}", + help="Results of download pipeline", +) + +args = parser.parse_args() +download_results = args.download_results + +download_results = json.loads(download_results) + +# Example of download_results +# {"IGN": {"ADMINEXPRESS": {"EXPRESS-COG-TERRITOIRE": {"guadeloupe": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=5490/origin=raw/vectorfile_format=shp/territory=guadeloupe/simplification=0/COMMUNE.shp"]}}}, "martinique": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=5490/origin=raw/vectorfile_format=shp/territory=martinique/simplification=0/COMMUNE.shp"]}}}, "guyane": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=2972/origin=raw/vectorfile_format=shp/territory=guyane/simplification=0/COMMUNE.shp"]}}}, "reunion": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=2975/origin=raw/vectorfile_format=shp/territory=reunion/simplification=0/COMMUNE.shp"]}}}, "mayotte": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=4326/origin=raw/vectorfile_format=shp/territory=mayotte/simplification=0/COMMUNE.shp"]}}}, "metropole": {"2024": {"downloaded": true, "paths": {"COMMUNE": ["projet-cartiflette/test/provider=IGN/dataset_family=ADMINEXPRESS/source=EXPRESS-COG-TERRITOIRE/year=2024/administrative_level=None/crs=2154/origin=raw/vectorfile_format=shp/territory=metropole/simplification=0/COMMUNE.shp"]}}}}}}, "Insee": {"COG": {"DEPARTEMENT": {"france_entiere": {"2024": {"downloaded": false, "paths": null}}}, "REGION": {"france_entiere": {"2024": {"downloaded": false, "paths": null}}}}, "TAGC": {"APPARTENANCE": {"france_entiere": {"2024": {"downloaded": true, "paths": {"table-appartenance-geo-communes-2024": ["projet-cartiflette/test/provider=Insee/dataset_family=TAGC/source=APPARTENANCE/year=2024/administrative_level=None/crs=None/origin=raw/vectorfile_format=xlsx/territory=france_entiere/simplification=0/table-appartenance-geo-communes-2024.xlsx"]}}}}}}} + + +if os.environ.get("ENVIRONMENT", None) == "test": + logging.warning("test environment -> force generation of only 2023 & 2024") + + +def store_to_json(name, years): + "util function to store vintage selections to json for argo results" + with open(name, "w", encoding="utf8") as out: + json.dump(years, out) + return years + + +def filter_geodata(results): + "filter the downloaded vintages of geodatasets" + if os.environ.get("ENVIRONMENT", None) == "test": + return store_to_json("geodatasets_years.json", [2023, 2024]) + + years = set() + keys_geo = ( + ("ADMINEXPRESS", COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION]), + ("IRIS", IRIS[DATASETS_HIGH_RESOLUTION]), + ) + try: + raw = [results["IGN"][family][geo] for family, geo in keys_geo] + except KeyError: + years = [] + else: + for dset in raw: + for dict_results in dset.values(): + for year, dict_results_this_year in dict_results.items(): + if dict_results_this_year["downloaded"]: + years.add(year) + + years = sorted(list(years)) + logger.info("selected downstream geodatasets : %s", years) + return store_to_json("geodatasets_years.json", years) + + +def filter_metadata(results): + "filter the downloaded vintages of metadatasets" + if os.environ.get("ENVIRONMENT", None) == "test": + return store_to_json("metadata_years.json", [2023, 2024]) + + years = set() + try: + raw = [dset for provider, dset in results.items() if provider != "IGN"] + except KeyError: + years = [] + else: + + for dset_provider in raw: + for dset_family in dset_provider.values(): + for dset in dset_family.values(): + for dict_results in dset.values(): + for ( + year, + dict_results_this_year, + ) in dict_results.items(): + if dict_results_this_year["downloaded"]: + years.add(year) + + years = sorted(list(years)) + logger.info("selected downstream metadatasets : %s", years) + return store_to_json("metadata_years.json", years) + + +if __name__ == "__main__": + filter_geodata(download_results) + filter_metadata(download_results) diff --git a/argo-pipeline/src/split_merge_tiles.py b/argo-pipeline/src/split_merge_tiles.py deleted file mode 100644 index 1e392614..00000000 --- a/argo-pipeline/src/split_merge_tiles.py +++ /dev/null @@ -1,67 +0,0 @@ -import argparse -from cartiflette.config import PATH_WITHIN_BUCKET -from cartiflette.pipeline import ( - mapshaperize_split_from_s3, - mapshaperize_merge_split_from_s3, -) -import logging - - -parser = argparse.ArgumentParser(description="Process command line arguments.") -logger = logging.getLogger(__name__) - -# Define the arguments with their default values -parser.add_argument( - "--path", type=str, default=PATH_WITHIN_BUCKET, help="Path in bucket" -) -parser.add_argument( - "--format_output", type=str, default="geojson", help="Output format" -) -parser.add_argument("--year", type=int, default=2022, help="Year for the data") -parser.add_argument("--crs", type=int, default=4326, help="Coordinate Reference System") -parser.add_argument( - "--source", type=str, default="EXPRESS-COG-CARTO-TERRITOIRE", help="Data source" -) -parser.add_argument( - "--simplification", type=float, default=0, help="Simplification level" -) -parser.add_argument( - "--level_polygons", type=str, default="COMMUNE", help="Level of polygons" -) -parser.add_argument( - "--filter_by", type=str, default="DEPARTEMENT", help="Splitting criteria" -) - -# Parse the arguments -args = parser.parse_args() - -# Create a dictionary from the parsed arguments -args_dict = { - "path_within_bucket": args.path, - "format_output": args.format_output, - "year": args.year, - "crs": args.crs, - "source": args.source, - "simplification": args.simplification, - "level_polygons": args.level_polygons, - "filter_by": args.filter_by, -} - - -def main(args_dict): - logger.info("Processing with provided arguments") - logger.info("Arguments for mapshaperize_split_from_s3 ---> {0}".format(args_dict)) - mapshaperize_split_from_s3(args_dict) - - if args_dict["level_polygons"] != "COMMUNE": - return None - - logger.info("Also processing for COMMUNE_ARRONDISSEMENT borders") - args_dict["level_polygons"] = "COMMUNE_ARRONDISSEMENT" - mapshaperize_merge_split_from_s3(args_dict) - - return args_dict - - -if __name__ == "__main__": - main(args_dict) diff --git a/argo-pipeline/src/split_merge_tiles_multithreading.py b/argo-pipeline/src/split_merge_tiles_multithreading.py new file mode 100644 index 00000000..61af7d68 --- /dev/null +++ b/argo-pipeline/src/split_merge_tiles_multithreading.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +""" +Last step of pipeline (with multithreading) + +Create all geodatasets served by cartiflette +""" + +import argparse +import json +import logging +import os + +from s3fs import S3FileSystem + +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, +) +from cartiflette.pipeline import mapshaperize_split_from_s3_multithreading + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO) + +logger.info("=" * 50) +logger.info("\n%s", __doc__) +logger.info("=" * 50) + +parser = argparse.ArgumentParser(description="Process command line arguments.") + +# Define the arguments with their default values +parser.add_argument( + "-y", + "--year", + default="2023", + help="Filter downstream vintage to process", +) + +parser.add_argument( + "-c", + "--configs", + default='[{"mesh_init":"ARRONDISSEMENT_MUNICIPAL","source_geodata":"EXPRESS-COG-CARTO-TERRITOIRE","simplification":40,"dissolve_by":"ARRONDISSEMENT_MUNICIPAL","territories":["FRANCE_ENTIERE_DROM_RAPPROCHES","FRANCE_ENTIERE"]}]', + help="Configurations for child datasets", +) + +# Parse the arguments +args = parser.parse_args() + + +def main( + year, + config_generation: list, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, + fs: S3FileSystem = FS, +): + + result = mapshaperize_split_from_s3_multithreading( + year=year, + configs=config_generation, + bucket=bucket, + path_within_bucket=path_within_bucket, + fs=fs, + ) + + out_path = f"generation/{year}/result.json" + try: + os.makedirs(os.path.dirname(out_path)) + except FileExistsError: + pass + with open(out_path, "w", encoding="utf8") as out: + json.dump(result, out) + + return result + + +if __name__ == "__main__": + main( + year=args.year, + config_generation=json.loads(args.configs), + # config_generation=[ + # { + # "mesh_init": "IRIS", + # "source_geodata": "CONTOUR-IRIS", + # "simplification": "40", + # "dissolve_by": "IRIS", + # "territories": [ + # "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + # ], + # } + # ], + bucket=BUCKET, + path_within_bucket=PATH_WITHIN_BUCKET, + fs=FS, + ) diff --git a/cartiflette/api/output.py b/cartiflette/api/output.py index c17854e6..89cfbef1 100644 --- a/cartiflette/api/output.py +++ b/cartiflette/api/output.py @@ -8,7 +8,7 @@ import s3fs import geopandas as gpd -from cartiflette.download.scraper import MasterScraper +from cartiflette.download import Scraper from cartiflette.utils import create_path_bucket, standardize_inputs from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS @@ -305,7 +305,7 @@ def download_vectorfile_single( fs.download(remote_file, local_path) else: - with MasterScraper(*args, **kwargs) as s: + with Scraper(*args, **kwargs) as s: # Note that python should cleanup all tmpfile by itself if format_read == "shp": @@ -314,9 +314,7 @@ def download_vectorfile_single( successes = [] for remote_file in files: remote = os.path.splitext(url)[0] + f".{ext}" - success, tmp = s.download_to_tempfile_http( - url=remote - ) + success, tmp = s.download_to_tempfile_http(url=remote) successes.append(success) shutil.copy(tmp, f"{tdir.name}/raw.{ext}") local_path = f"{tdir.name}/raw.shp" diff --git a/cartiflette/config.py b/cartiflette/config.py index e0c44844..3a60aa25 100644 --- a/cartiflette/config.py +++ b/cartiflette/config.py @@ -1,12 +1,13 @@ # -*- coding: utf-8 -*- import os +import warnings from dotenv import load_dotenv import s3fs -load_dotenv() +load_dotenv(override=True) BUCKET = "projet-cartiflette" -PATH_WITHIN_BUCKET = "production" +PATH_WITHIN_BUCKET = os.environ.get("ENVIRONMENT", "test") ENDPOINT_URL = "https://minio.lab.sspcloud.fr" kwargs = {} @@ -16,9 +17,27 @@ except KeyError: continue FS = s3fs.S3FileSystem(client_kwargs={"endpoint_url": ENDPOINT_URL}, **kwargs) +# Double the standard timeouts +FS.read_timeout = 30 +FS.connect_timeout = 10 THREADS_DOWNLOAD = 5 # Nota : each thread may also span the same number of children threads; # set to 1 for debugging purposes (will deactivate multithreading) -LEAVE_TQDM = False +RETRYING = True # WHETHER TO USE RETRYING MODULE ON DOWNLOAD/UPLOAD + +# ============================================================================= +# PIPELINE CONFIG +# ============================================================================= + +# set to low resolution datasets for test environment, high for anything else +INTERMEDIATE_FORMAT = "geojson" +DATASETS_HIGH_RESOLUTION = os.environ.get("ENVIRONMENT", "test") != "test" +MAPSHAPER_QUIET = os.environ.get("ENVIRONMENT", "test") != "test" + +if not DATASETS_HIGH_RESOLUTION: + warnings.warn( + "cartiflette is running with test configuration, using only low " + "resolution datasets" + ) diff --git a/cartiflette/constants.py b/cartiflette/constants.py index 28e10a12..3463cc16 100644 --- a/cartiflette/constants.py +++ b/cartiflette/constants.py @@ -28,46 +28,17 @@ "geometry": box(44.7437, -13.2733, 45.507, -12.379), }, { - "location": "saint_pierre_et_miquelon", + "location": "saint-pierre-et-miquelon", "geometry": box(-56.6975, 46.5488, -55.9066, 47.3416), }, + { + "location": "saint-barthelemy", + "geometry": box(-62.951118, 17.870818, -62.789027, 17.974103), + }, + { + "location": "saint-martin", + "geometry": box(-63.153327, 18.046591, -62.970338, 18.125203), + }, ] REFERENCES = gpd.GeoDataFrame(REFERENCES, crs=4326) - -DOWNLOAD_PIPELINE_ARGS = { - "ADMIN-EXPRESS": [ - "IGN", - "ADMINEXPRESS", - "EXPRESS-COG-TERRITOIRE", - [ - "guadeloupe", - "martinique", - "guyane", - "reunion", - "mayotte", - "metropole", - ], - ], - "BDTOPO": ["IGN", "BDTOPO", "ROOT", "france_entiere"], - "IRIS": ["IGN", "CONTOUR-IRIS", "ROOT", None], - "COG": [ - "Insee", - "COG", - [ - "COMMUNE", - "CANTON", - "ARRONDISSEMENT", - "DEPARTEMENT", - "REGION", - "COLLECTIVITE", - "PAYS", - ], - "france_entiere", - ], - "BV 2022": ["Insee", "BV", "FondsDeCarte_BV_2022", "france_entiere"], -} - -# EXPRESS-COG ? -# EXPRESS-COG-CARTO-TERRITOIRE ? -# EXPRESS-COG-CARTO ? diff --git a/cartiflette/download/__init__.py b/cartiflette/download/__init__.py index c8e07b4e..1f675160 100644 --- a/cartiflette/download/__init__.py +++ b/cartiflette/download/__init__.py @@ -1,14 +1,6 @@ -# from cartiflette.download.dev import ( -# get_vectorfile_communes_arrondissement, -# # get_BV, -# ) +# -*- coding: utf-8 -*- +from .scraper import Scraper +from .download import _download_and_store_sources -from cartiflette.download.pipeline import ( - download_all, -) - - -__all__ = [ - "download_all", -] +__all__ = ["Scraper", "_download_and_store_sources"] diff --git a/cartiflette/download/dataset.py b/cartiflette/download/dataset.py index 018a6f56..65556185 100644 --- a/cartiflette/download/dataset.py +++ b/cartiflette/download/dataset.py @@ -6,23 +6,25 @@ import json import logging import os -import pebble -import py7zr import re -import s3fs import tempfile from typing import Tuple +import warnings import zipfile +import pebble +import py7zr +import s3fs + from cartiflette.utils import import_yaml_config, hash_file, deep_dict_update from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS logger = logging.getLogger(__name__) -class Dataset: +class RawDataset: """ - Class representing a dataset stored in the yaml meant to be retrieved + Class representing a raw dataset stored in the yaml meant to be retrieved """ md5 = None @@ -40,7 +42,7 @@ def __init__( fs: s3fs.S3FileSystem = FS, ): """ - Initialize a Dataset object. + Initialize a RawDataset object. Parameters ---------- @@ -85,7 +87,10 @@ def __str__(self): territory = self.territory provider = self.provider - name = f"" + name = ( + f"" + ) return name def __repr__(self): @@ -113,15 +118,15 @@ def _md5(file_path: str) -> str: def _get_last_md5(self) -> None: """ Read the last md5 hash value of the target on the s3 and store it - as an attribute of the Dataset : self.md5 + as an attribute of the RawDataset : self.md5 """ try: with self.fs.open(self.json_md5, "r") as f: all_md5 = json.load(f) - except Exception as e: - logger.error(e) - logger.error("md5 json not found on MinIO") + except FileNotFoundError as e: + # use warnings instead of logging to display this only once + warnings.warn(f"md5 json not found on MinIO - {e}") return try: md5 = all_md5[self.provider][self.dataset_family][self.source][ @@ -129,8 +134,7 @@ def _get_last_md5(self) -> None: ][str(self.year)] self.md5 = md5 except Exception as e: - logger.debug(e) - logger.debug("file not referenced in md5 json") + logger.debug("file not referenced in md5 json %s", e) @pebble.synchronized def update_json_md5(self, md5: str) -> bool: @@ -213,6 +217,9 @@ def get_path_from_provider(self) -> str: d = d[key] try: self.pattern = d["pattern"] + if isinstance(self.pattern, str): + self.pattern = [self.pattern] + break except KeyError: continue @@ -269,7 +276,16 @@ def get_path_from_provider(self) -> str: url = url.format(**kwargs) - logger.debug(f"using {url}") + try: + # check if {territory} is part of self.pattern: + if territory != "": + self.pattern = [ + x.format(**{"territory": territory}) for x in self.pattern + ] + except UnboundLocalError: + pass + + logger.debug("using %s", url) return url @@ -295,7 +311,7 @@ def unpack(self, protocol: str) -> Tuple[str, Tuple[Tuple[str, ...], ...]]: the decompressed files. Note that this folder will be stored in the temporary cache, but requires manual cleanup. If nested archives (ie zip in zip), will unpack all nested data and - look for target pattern **INSIDE** the nested archive only + look for target pattern(s) **INSIDE** the nested archive only Every file Path @@ -326,7 +342,7 @@ def unpack(self, protocol: str) -> Tuple[str, Tuple[Tuple[str, ...], ...]]: # unzip in temp directory location = tempfile.mkdtemp() - logger.debug(f"Extracting to {location}") + logger.debug("Extracting to %s", location) year = self.year source = self.source @@ -351,7 +367,7 @@ def get_utils_from_protocol(protocol): list_files = "namelist" extract = "extractall" targets_kw = "members" - # TODO + # TODO : other archives formats? (rar, tar, gz, ...) # rar files, see https://pypi.org/project/rarfile/ # tar files # gz files @@ -362,14 +378,18 @@ def get_utils_from_protocol(protocol): archives_to_process = [(self.temp_archive_path, protocol)] while archives_to_process: archive, protocol = archives_to_process.pop() - loader, list_files, extract, targets_kw = get_utils_from_protocol(protocol) + loader, list_files, extract, targets_kw = get_utils_from_protocol( + protocol + ) with loader(archive, mode="r") as archive: everything = getattr(archive, list_files)() # Handle nested archives (and presume there is no mixup in # formats...) archives = [ - x for x in everything if x.endswith(".zip") or x.endswith(".7z") + x + for x in everything + if x.endswith(".zip") or x.endswith(".7z") ] archives = [(x, x.split(".")[-1]) for x in archives] for nested_archive, protocol in archives: @@ -378,10 +398,16 @@ def get_utils_from_protocol(protocol): (io.BytesIO(nested.read()), protocol) ) - files = filter_case_insensitive(self.pattern, everything) + files = [ + file + for pattern in self.pattern + for file in filter_case_insensitive(pattern, everything) + ] if year <= 2020 and source.endswith("-TERRITOIRE"): - territory_code = sources["territory"][territory].split("_")[0] + territory_code = sources["territory"][territory].split( + "_" + )[0] files = {x for x in files if territory_code in x} # Find all auxiliary files sharing the same name as those found @@ -414,18 +440,24 @@ def get_utils_from_protocol(protocol): # when using dbf) -> return only target but extract all patterns = {x.rsplit(".", maxsplit=1)[0] for x in targets} real_extracts = { - x for x in everything if x.rsplit(".", maxsplit=1)[0] in patterns + x + for x in everything + if x.rsplit(".", maxsplit=1)[0] in patterns } kwargs = {"path": location, targets_kw: real_extracts} getattr(archive, extract)(**kwargs) - extracted += [os.path.join(location, target) for target in targets] + extracted += [ + os.path.join(location, target) for target in targets + ] # self._list_levels(extracted) if any(x.lower().endswith(".shp") for x in extracted): shapefiles_pattern = { - os.path.splitext(x)[0] for x in files if x.lower().endswith(".shp") + os.path.splitext(x)[0] + for x in files + if x.lower().endswith(".shp") } extracted = [ diff --git a/cartiflette/download/download.py b/cartiflette/download/download.py index f782d955..9e977461 100644 --- a/cartiflette/download/download.py +++ b/cartiflette/download/download.py @@ -5,28 +5,56 @@ # pipeline, please refer yourself to cartiflette\download\pipeline.py # ============================================================================= -from collections import OrderedDict +from collections import OrderedDict, Counter from itertools import product import logging -from pebble import ThreadPool -import s3fs import shutil import traceback from typing import Union -from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS, THREADS_DOWNLOAD +from pebble import ThreadPool +from retrying import retry +import s3fs + +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, + THREADS_DOWNLOAD, + RETRYING, +) from cartiflette.utils import ( deep_dict_update, create_path_bucket, ) -from cartiflette.download.scraper import MasterScraper -from cartiflette.download.dataset import Dataset +from .scraper import Scraper +from .dataset import RawDataset logger = logging.getLogger(__name__) +if not RETRYING: + # patch retrying + def retry(*args, **kwargs): + def decorator(func): + return func + + return decorator + + +def _result_is_ko(result): + """ + return True if result is ko + used to check if _upload_raw_dataset_to_s3 should be retried + """ + return result is not None and len(result) == 0 + + +@retry( + retry_on_result=_result_is_ko, stop_max_attempt_number=3, wait_fixed=2000 +) def _upload_raw_dataset_to_s3( - dataset: Dataset, + dataset: RawDataset, result: dict, bucket: str = BUCKET, path_within_bucket: str = PATH_WITHIN_BUCKET, @@ -40,8 +68,8 @@ def _upload_raw_dataset_to_s3( Parameters ---------- - dataset : Dataset - Dataset object to store into s3 + dataset : RawDataset + RawDataset object to store into s3 result : dict result of the dataset's download bucket : str, optional @@ -80,7 +108,7 @@ def _upload_raw_dataset_to_s3( try: # DUPLICATE SOURCES IN BUCKET errors_encountered = False - dataset_paths = dict() + dataset_paths = {} for key, layer in result["layers"].items(): layer_paths = [] for path, rename_basename in layer.files_to_upload.items(): @@ -105,7 +133,7 @@ def _upload_raw_dataset_to_s3( layer_paths.append(path_within) - logger.debug(f"upload to {path_within}") + logger.info("upload to %s", path_within) try: fs.put(path, path_within, recursive=True) @@ -114,7 +142,9 @@ def _upload_raw_dataset_to_s3( errors_encountered = True if any(x.lower().endswith(".shp") for x in layer_paths): - layer_paths = [x for x in layer_paths if x.lower().endswith(".shp")] + layer_paths = [ + x for x in layer_paths if x.lower().endswith(".shp") + ] dataset_paths[key] = layer_paths @@ -131,22 +161,20 @@ def _upload_raw_dataset_to_s3( # to allow for further tentatives) dataset.update_json_md5(result["hash"]) return dataset_paths - else: - return {} + return {} -def _download_sources( +def _download_and_store_sources( providers: Union[list[str, ...], str], dataset_families: Union[list[str, ...], str], sources: Union[list[str, ...], str], - territories: Union[list[str, ...], str], years: Union[list[str, ...], str], + territories: Union[list[str, ...], str] = None, bucket: str = BUCKET, path_within_bucket: str = PATH_WITHIN_BUCKET, fs: s3fs.S3FileSystem = FS, upload: bool = True, ) -> dict: - # TODO : contrôler return """ Main function to perform downloads of datasets and store them the s3. All available combinations will be tested; hence an unfound file might not @@ -162,10 +190,12 @@ def _download_sources( List of datasets family in the yaml file sources : list[str, ...] List of sources in the yaml file - territories : list[str, ...] - List of territoires in the yaml file years : list[int, ...] List of years in the yaml file + territories : list[str, ...], optional + List of territoires in the yaml file. The default is None (corresponds + to datasets where that field is absent), which will set + "territory=france_entiere" when uploading to the S3 FileSystem. bucket : str, optional Bucket to use. The default is BUCKET. path_within_bucket : str, optional @@ -178,10 +208,6 @@ def _download_sources( Returns ------- - dict - DESCRIPTION. - - files : dict Structure of the nested dict will use the following keys : provider @@ -192,22 +218,23 @@ def _download_sources( {downloaded: bool, paths: list:str} For instance: { - 'IGN': { - 'BDTOPO': { - 'ROOT': { + 'Insee': { + 'COG': { + 'COMMUNE': { 'france_entiere': { - 2017: { + 2023: { 'downloaded': True, 'paths': { - 'CHEF_LIEU': [ - 'projet-cartiflette/diffusion/shapefiles-test4/year=2017/administrative_level=None/crs=4326/None=None/vectorfile_format=shp/provider=IGN/dataset_family=BDTOPO/source=ROOT/territory=martinique/CHEF_LIEU.shp' - ], - 'COMMUNE': [ - 'projet-cartiflette/diffusion/shapefiles-test4/year=2017/administrative_level=None/crs=4326/None=None/vectorfile_format=shp/provider=IGN/dataset_family=BDTOPO/source=ROOT/territory=martinique/COMMUNE.shp' - ], - 'ARRONDISSEMENT': [ - 'projet-cartiflette/diffusion/shapefiles-test4/year=2017/administrative_level=None/crs=4326/None=None/vectorfile_format=shp/provider=IGN/dataset_family=BDTOPO/source=ROOT/territory=metropole/ARRONDISSEMENT.shp' - ] + 'dummy_file_2023': [ + 'projet-cartiflette/.../dummmy.csv' + ] + } + }, + 2024: { + 'downloaded': True, + 'paths': { + 'dummy_file_2024': [ + 'projet-cartiflette/.../dummy.csv'] } } } @@ -215,7 +242,12 @@ def _download_sources( } } } + """ + + if not territories: + territories = "france_entiere" + kwargs = OrderedDict() items = [ ("sources", sources), @@ -225,28 +257,89 @@ def _download_sources( ("dataset_families", dataset_families), ] for key, val in items: - if isinstance(val, str) or isinstance(val, int): + if isinstance(val, (str, int)): kwargs[key] = [val] elif not val: kwargs[key] = [None] - elif isinstance(val, list) or isinstance(val, tuple) or isinstance(val, set): + elif isinstance(val, (list, tuple, set)): kwargs[key] = list(val) combinations = list(product(*kwargs.values())) + order = "source", "territory", "year", "provider", "dataset_family" + combinations = [dict(zip(order, x)) for x in combinations] + + # Check wether (some) urls are reused in this batch + reused_urls = set() + datasets = [ + RawDataset( + bucket=bucket, + path_within_bucket=path_within_bucket, + **x, + ) + for y in years + for x in combinations + if x["year"] == y + ] + + def try_get_path(dset): + try: + return dset.get_path_from_provider() + except ValueError: + # Do not bother to log this, there will be warning later on + # when Cartiflette tries to retrieve the datasets + pass + + reused = { + (url, md5) + for (url, md5), count in Counter( + (try_get_path(dset), dset.md5) + for dset in datasets + if try_get_path(dset) + ).items() + if count > 1 + } + reused_urls.update(reused) + reused_urls = list(reused_urls) + # reused_urls = [(url_1, md5_1), (url_2, md5_2)] + + # -> proceed to immediate download of reused urls (and don't do anything + # about it), and resort to requests-cache to dispatch it to the different + # datasets later + if reused_urls: + with Scraper() as s: + threads = min(THREADS_DOWNLOAD, len(reused_urls)) + if threads > 1: + with ThreadPool( + threads, + ) as pool: + iterator = pool.map( + s.simple_download, *zip(*reused_urls), timeout=60 * 10 + ).result() + index = 0 + while True: + try: + next(iterator) + except StopIteration: + break + except Exception: + logger.error(traceback.format_exc()) + logger.error("url was %s", reused_urls[index]) + finally: + index += 1 + else: + for url, md5 in reused_urls: + try: + s.simple_download(url, md5) + except Exception: + logger.error(traceback.format_exc()) + logger.error("url was %s", (url, md5)) files = {} - with MasterScraper() as s: - - def func(args): - source, territory, year, provider, dataset_family = args - datafile = Dataset( - dataset_family, - source, - year, - provider, - territory, - bucket, - path_within_bucket, + with Scraper() as s: + + def func(kwargs): + datafile = RawDataset( + bucket=bucket, path_within_bucket=path_within_bucket, **kwargs ) try: result = s.download_unpack(datafile) @@ -254,11 +347,11 @@ def func(args): logger.warning(e) this_result = { - provider: { - dataset_family: { - source: { - territory: { - year: { + kwargs["provider"]: { + kwargs["dataset_family"]: { + kwargs["source"]: { + kwargs["territory"]: { + kwargs["year"]: { "downloaded": False, "paths": None, } @@ -283,24 +376,39 @@ def func(args): result["paths"] = paths this_result = { - provider: {dataset_family: {source: {territory: {year: result}}}} + kwargs["provider"]: { + kwargs["dataset_family"]: { + kwargs["source"]: { + kwargs["territory"]: {kwargs["year"]: result} + } + } + } } return this_result if THREADS_DOWNLOAD > 1: with ThreadPool(THREADS_DOWNLOAD) as pool: - iterator = pool.map(func, combinations).result() + iterator = pool.map( + func, combinations, timeout=60 * 10 + ).result() + index = 0 while True: try: files = deep_dict_update(files, next(iterator)) except StopIteration: break - except Exception as e: - logger.error(e) + except Exception: logger.error(traceback.format_exc()) + logger.error("config was %s", combinations[index]) + finally: + index += 1 else: for args in combinations: - files = deep_dict_update(files, func(args)) + try: + files = deep_dict_update(files, func(args)) + except Exception: + logger.error(traceback.format_exc()) + logger.error("config was %s", args) return files diff --git a/cartiflette/download/layer.py b/cartiflette/download/layer.py index ef7160dc..4fda3f58 100644 --- a/cartiflette/download/layer.py +++ b/cartiflette/download/layer.py @@ -1,24 +1,32 @@ # -*- coding: utf-8 -*- + +import logging +import os + from charset_normalizer import from_bytes, is_binary import fiona import geopandas as gpd -import logging -import os +import pyogrio from shapely.geometry import box -from cartiflette.download.dataset import Dataset +from .dataset import RawDataset from cartiflette.constants import REFERENCES logger = logging.getLogger(__name__) class Layer: - def __init__(self, dataset: Dataset, cluster_name: str, files: dict): + """ + Layer present in a dataset. + """ + + def __init__(self, dataset: RawDataset, cluster_name: str, files: dict): """ - Layer present in a dataset. A layer is defined by a distinctive - combination of path and basename (without extension). To that effect, - each auxialary file associated to a shapefile shall be present in the - same layer. + Layer present in a dataset. + + A layer is defined by a distinctive combination of path and basename + (without extension). To that effect, each auxiliary file associated to + a shapefile shall be present in the same layer. Nota : distinction between selected and unselected files in `files` argument helps to evaluate territory using a shapefile even if the @@ -26,8 +34,8 @@ def __init__(self, dataset: Dataset, cluster_name: str, files: dict): Parameters ---------- - dataset : Dataset - Dataset containing layers + dataset : RawDataset + RawDataset containing layers cluster_name : str Unique name for a layer (computed by the scraper after data unpacking) and corresponding to the minimum recursive distinct path @@ -63,14 +71,18 @@ def __repr__(self): return self.__str__() def _get_format(self): - if any(x.lower().split(".")[-1] == "shp" for x in self.files_to_upload): + if any( + x.lower().split(".")[-1] == "shp" for x in self.files_to_upload + ): self.format = "shp" else: # assume there is only one file self.format = list(self.files_to_upload)[0].split(".")[-1] def _get_encoding(self): - ref_cpg_file = [x for x in self.files if x.lower().split(".")[-1] == "cpg"] + ref_cpg_file = [ + x for x in self.files if x.lower().split(".")[-1] == "cpg" + ] try: ref_cpg_file = ref_cpg_file[0] except IndexError: @@ -81,7 +93,9 @@ def _get_encoding(self): return encoding.lower() def _get_gis_file(self): - ref_gis_file = [x for x in self.files if x.lower().split(".")[-1] == "shp"] + ref_gis_file = [ + x for x in self.files if x.lower().split(".")[-1] == "shp" + ] try: ref_gis_file = ref_gis_file[0] except IndexError: @@ -96,13 +110,22 @@ def _gis_and_encoding_evaluation(self): ref_gis_file = self._get_gis_file() try: # Note : read all rows to evaluate bbox / territory + + # Disable fiona logger + fiona_logger = logging.getLogger("fiona") + init = fiona_logger.level + fiona_logger.setLevel(logging.CRITICAL) gdf = gpd.read_file(ref_gis_file, **kwargs) + + fiona_logger.setLevel(init) + self.crs = gdf.crs.to_epsg() if not self.crs: - logger.warning( - f"{self} - projection without known EPSG, " - "layer will be reprojected to 4326" + logger.info( + "%s - projection without known EPSG, " + "layer will be reprojected to 4326", + self, ) # Let's reproject... @@ -110,20 +133,35 @@ def _gis_and_encoding_evaluation(self): self.crs = 4326 # let's overwrite initial files - gdf.to_file(ref_gis_file, encoding="utf-8") + gdf.to_file(ref_gis_file, encoding="utf-8", engine="fiona") elif encoding and encoding != "utf-8": - logger.warning( - f"{self} - encoding={encoding}, " "layer will be re-encoded to UTF8" + logger.info( + "%s - encoding=%s, layer will be re-encoded to UTF8", + self, + encoding, ) # let's overwrite initial files with utf8... - gdf.to_file(ref_gis_file, encoding="utf-8") - - except (AttributeError, fiona.errors.DriverError): + gdf.to_file(ref_gis_file, encoding="utf-8", engine="fiona") + + except ( + AttributeError, + fiona.errors.DriverError, + pyogrio.errors.DataSourceError, + pyogrio.errors.CRSError, + ): # Non-native-GIS dataset self.crs = None + fiona_logger.setLevel(init) if self.crs: + # check if geometries are valid: + geometries_valid = gdf["geometry"].is_valid.all() + if not geometries_valid: + # try to fix geometries and overwrite file + gdf["geometry"] = gdf["geometry"].buffer(0) + gdf.to_file(ref_gis_file, encoding="utf-8", engine="fiona") + bbox = box(*gdf.total_bounds) bbox = gpd.GeoSeries([bbox], crs=gdf.crs) @@ -139,17 +177,19 @@ def _gis_and_encoding_evaluation(self): elif len(intersects) > 1 and "metropole" in intersects: self.territory = "france_entiere" else: - logger.warning( - f"{self} : spatial join used for territory recognition " - "failed, dataset's raw description will be used instead" + logger.info( + "%s : spatial join used for territory recognition " + "failed, dataset's raw description will be used instead", + self, ) self.territory = self.dataset.territory elif not self.crs: # TODO : chercher un champ de clefs INSEE ? logger.info( - f"{self} : coverage analysis of non-gis files is not yet " - "implemented, dataset's raw description will be used instead" + "%s : coverage analysis of non-gis files is not yet " + "implemented, dataset's raw description will be used instead", + self, ) self.territory = self.dataset.territory @@ -175,9 +215,11 @@ def _gis_and_encoding_evaluation(self): pass else: if encoding != "utf_8": - logger.warning( - f"{self} - encoding={encoding}, " - "layer will be re-encoded to UTF8" + logger.info( + "%s - encoding=%s, " + "layer will be re-encoded to UTF8", + self, + encoding, ) with open(file, "w", encoding="utf8"): data.decode(encoding) diff --git a/cartiflette/download/scraper.py b/cartiflette/download/scraper.py index b5f4e95e..066d1c67 100644 --- a/cartiflette/download/scraper.py +++ b/cartiflette/download/scraper.py @@ -1,27 +1,36 @@ # -*- coding: utf-8 -*- -import magic from glob import glob import logging -import numpy as np import os import re -import requests -import requests_cache +from retrying import retry import tempfile -from tqdm import tqdm from typing import TypedDict + +import magic +import requests +import requests_cache from unidecode import unidecode from cartiflette.utils import hash_file -from cartiflette.download.dataset import Dataset -from cartiflette.download.layer import Layer -from cartiflette.config import LEAVE_TQDM +from .dataset import RawDataset +from .layer import Layer +from cartiflette.config import RETRYING + +if not RETRYING: + # patch retrying + def retry(*args, **kwargs): + def decorator(func): + return func + + return decorator + logger = logging.getLogger(__name__) -class MasterScraper(requests_cache.CachedSession): +class Scraper(requests_cache.CachedSession): """ Scraper class which could be used to perform either http/https get downloads. @@ -76,7 +85,35 @@ def __init__( except KeyError: continue - def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: + def simple_download(self, url: str, hash_: str = None, **kwargs): + """ + Trigger a simple download to temporary file (immediatly cleaned) + + Use this only to cache http response (for instance, when you know that + a request will be queried multiple times) + + Parameters + ---------- + url : str + url to download + hash_ : str, optional + previous hash signature of the file at latest download. The default + is None. + **kwargs : + Optional arguments to pass to requests.Session object. + + Returns + ------- + None. + + """ + done, _, temp = download_to_tempfile_http(url, hash_, self, **kwargs) + if done: + os.unlink(temp) + + def download_unpack( + self, datafile: RawDataset, **kwargs + ) -> DownloadReturn: """ Performs a download (through http, https) to a tempfile which will be cleaned automatically ; unzip targeted files to a 2nd @@ -96,8 +133,8 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: Parameters ---------- - datafile : Dataset - Dataset object to download. + datafile : RawDataset + RawDataset object to download. **kwargs : Optional arguments to pass to requests.Session object. @@ -117,17 +154,17 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: 'hash': '5435fca3e488ca0372505b9bcacfde30', 'layers': { 'CONTOURS-IRIS_2-1_SHP_RGR92UTM40S_REU-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_RGR92UTM40S_REU - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> , + < Layer CONTOURS - IRIS_2 - 1_SHP_RGR92UTM40S_REU - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> , 'CONTOURS-IRIS_2-1_SHP_RGAF09UTM20_GLP-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_RGAF09UTM20_GLP - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> , + < Layer CONTOURS - IRIS_2 - 1_SHP_RGAF09UTM20_GLP - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> , 'CONTOURS-IRIS_2-1_SHP_RGAF09UTM20_MTQ-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_RGAF09UTM20_MTQ - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> , + < Layer CONTOURS - IRIS_2 - 1_SHP_RGAF09UTM20_MTQ - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> , 'CONTOURS-IRIS_2-1_SHP_LAMB93_FXX-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_LAMB93_FXX - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> , + < Layer CONTOURS - IRIS_2 - 1_SHP_LAMB93_FXX - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> , 'CONTOURS-IRIS_2-1_SHP_UTM22RGFG95_GUF-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_UTM22RGFG95_GUF - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> , + < Layer CONTOURS - IRIS_2 - 1_SHP_UTM22RGFG95_GUF - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> , 'CONTOURS-IRIS_2-1_SHP_RGM04UTM38S_MYT-2022_CONTOURS-IRIS': - < Layer CONTOURS - IRIS_2 - 1_SHP_RGM04UTM38S_MYT - 2022_CONTOURS - IRIS from < Dataset IGN CONTOUR - IRIS ROOT None 2022 >> + < Layer CONTOURS - IRIS_2 - 1_SHP_RGM04UTM38S_MYT - 2022_CONTOURS - IRIS from < RawDataset IGN CONTOUR - IRIS ROOT None 2022 >> }, 'root_cleanup': 'C:\\Users\\tintin.milou\\AppData\\Local\\Temp\\tmpnbvoes9g' } @@ -135,7 +172,7 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: """ - hash_ = None + hash_ = datafile.md5 url = datafile.get_path_from_provider() # Download to temporary file @@ -164,13 +201,13 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: datafile.set_temp_file_path(temp_archive_file_raw) + simple_copy = ["Microsoft Excel 2007+", "Unicode text", "CSV text"] + if "7-zip" in filetype: root_folder, files_locations = datafile.unpack(protocol="7z") elif "Zip archive" in filetype: - root_folder, files_locations = datafile.unpack( - protocol="zip" - ) - elif "Unicode text" in filetype or "CSV text" in filetype: + root_folder, files_locations = datafile.unpack(protocol="zip") + elif any(x for x in simple_copy if x in filetype): # copy in temp directory without processing root_folder = tempfile.mkdtemp() with open(temp_archive_file_raw, "rb") as f: @@ -178,13 +215,17 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: filename = "_".join( x for x in re.split(r"\W+", filename) if x ) - path = os.path.join(root_folder, filename + ".csv") + if filetype == "Microsoft Excel 2007+": + ext = ".xlsx" + else: + ext = ".csv" + + path = os.path.join(root_folder, filename + ext) with open(path, "wb") as out: out.write(f.read()) - logger.debug(f"Storing CSV to {root_folder}") + logger.debug("Storing file to %s", root_folder) files_locations = ((path,),) - else: raise NotImplementedError(f"{filetype} encountered") except Exception as e: @@ -211,7 +252,7 @@ def download_unpack(self, datafile: Dataset, **kwargs) -> DownloadReturn: for basename, cluster in basenames.items() } - layers = dict() + layers = {} for cluster_name, cluster_filtered in paths.items(): cluster_pattern = { os.path.splitext(x)[0] for x in cluster_filtered @@ -253,6 +294,7 @@ def validate_file(file_path, hash_): return hash_file(file_path) == hash_ +@retry(stop_max_attempt_number=3, wait_fixed=2000) def download_to_tempfile_http( url: str, hash_: str = None, @@ -312,55 +354,64 @@ def download_to_tempfile_http( head = r.headers if not r.ok: - raise IOError(f"download failed with {r.status_code} code") + raise IOError(f"download failed with {r.status_code} code at {url=}") try: expected_md5 = head["content-md5"] - logger.debug(f"File MD5 is {expected_md5}") + logger.debug("File MD5 is %s", expected_md5) except KeyError: expected_md5 = None - logger.debug(f"md5 not found in header at url {url}") + logger.debug("md5 not found in header at url %s", url) else: if hash_ and expected_md5 == hash_: # unchanged file -> exit - logger.info(f"md5 matched at {url} - download prevented") + logger.info("md5 matched at %s - download prevented", url) return False, None, None - finally: - try: - # No MD5 in header -> check requested file's size - expected_file_size = int(head["Content-length"]) - logger.debug(f"File size is {expected_file_size}") - except KeyError: - expected_file_size = None - msg = f"Content-Length not found in header at url {url}" - logger.debug(msg) with tempfile.NamedTemporaryFile("wb", delete=False) as temp_file: file_path = temp_file.name - logger.debug(f"Downloading to {file_path}") - - logger.debug(f"starting download at {url}") + logger.debug("Downloading to %s", file_path) + logger.info("starting download at %s", url) r = session.get(url, stream=True, **kwargs) if not r.ok: raise IOError(f"download failed with {r.status_code} code") - if expected_file_size: - total = int(np.ceil(expected_file_size / block_size)) - else: - total = None - with tqdm( - desc="Downloading: ", - total=total, - unit="iB", - unit_scale=True, - unit_divisor=1024, - leave=LEAVE_TQDM, - ) as pbar: - for chunk in r.iter_content(chunk_size=block_size): - if chunk: # filter out keep-alive new chunks - size = temp_file.write(chunk) - pbar.update(size) + try: + # Nota : check Content-length after full request (not only head + # request), some sites are adapting the header to the kind of + # http request performed + head = r.headers + expected_file_size = int(head["Content-length"]) + logger.debug("File size is %s", expected_file_size) + except KeyError: + expected_file_size = None + msg = f"Content-Length not found in header at url {url}" + logger.debug(msg) + + # ===================================================================== + # This is not working (yet) with requests-cache: + # ===================================================================== + # if expected_file_size: + # total = int(np.ceil(expected_file_size / block_size)) + # else: + # total = None + # with tqdm( + # desc="Downloading: ", + # total=total, + # unit="iB", + # unit_scale=True, + # unit_divisor=1024, + # leave=LEAVE_TQDM, + # ) as pbar: + # for chunk in r.iter_content(chunk_size=block_size): + # if chunk: # filter out keep-alive new chunks + # size = temp_file.write(chunk) + # pbar.update(size) + + for chunk in r.iter_content(chunk_size=block_size): + if chunk: + temp_file.write(chunk) # Check that the downloaded file has the expected characteristics if expected_md5: @@ -370,16 +421,25 @@ def download_to_tempfile_http( elif expected_file_size: # check that the downloaded file is the expected size if not expected_file_size == os.path.getsize(file_path): + print(expected_file_size, os.path.getsize(file_path)) os.unlink(file_path) raise IOError("download failed (corrupted file)") # if there's a hash value, check if there are any changes if hash_ and validate_file(file_path, hash_): # unchanged file -> exit (after deleting the downloaded file) - logger.debug(f"md5 matched at {url} after download") + logger.info("md5 matched at %s after download", url) os.unlink(file_path) return False, None, None + logger.info( + "NO md5 match at %s after download : hash_=%s and new hash=%s " + "-> keeping new file", + url, + hash_, + hash_file(file_path), + ) + filetype = magic.from_file(file_path) return True, filetype, file_path diff --git a/cartiflette/mapshaper/__init__.py b/cartiflette/mapshaper/__init__.py index b5b57a06..d53c7cd7 100644 --- a/cartiflette/mapshaper/__init__.py +++ b/cartiflette/mapshaper/__init__.py @@ -1,13 +1,40 @@ """ Handling spatial data with mapshaper behind the stage """ -from .mapshaperize import mapshaperize_split, mapshaperize_split_merge -from .mapshaper_convert_mercator import mapshaper_convert_mercator + +from .mapshaper_split import mapshaper_split +from .mapshaper_convert_reproject import mapshaper_convert_reproject from .mapshaper_closer import mapshaper_bring_closer +from .mapshaper_enrich import mapshaper_enrich +from .mapshaper_dissolve import mapshaper_dissolve +from .mapshaper_concat import mapshaper_concat +from .mapshaper_remove_cities_with_districts import ( + mapshaper_remove_cities_with_districts, +) +from .mapshaper_process_communal_districts import ( + mapshaper_process_communal_districts, +) +from .mapshaper_combine_districts_and_cities import ( + mapshaper_combine_districts_and_cities, +) +from .mapshaper_simplify import mapshaper_simplify +from .mapshaper_add_field import mapshaper_add_field +from .mapshaper_capture_cities_from_ultramarine_territories import ( + mapshaper_capture_cities_from_ultramarine_territories, +) + __all__ = [ - "mapshaperize_split", - "mapshaperize_split_merge", - "mapshaper_convert_mercator", + "mapshaper_convert_reproject", "mapshaper_bring_closer", + "mapshaper_enrich", + "mapshaper_split", + "mapshaper_dissolve", + "mapshaper_concat", + "mapshaper_remove_cities_with_districts", + "mapshaper_process_communal_districts", + "mapshaper_combine_districts_and_cities", + "mapshaper_simplify", + "mapshaper_add_field", + "mapshaper_capture_cities_from_ultramarine_territories", ] diff --git a/cartiflette/mapshaper/mapshaper_add_field.py b/cartiflette/mapshaper/mapshaper_add_field.py new file mode 100644 index 00000000..729119f1 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_add_field.py @@ -0,0 +1,68 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os + +from .utils import run + + +def mapshaper_add_field( + input_file: str, + label: str, + value: str, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Add a field (= a column/attribute) to the dataset. + To add a static value, please set it between double+singles quotes, + ("'blah'" for instance). To add a dynamic field (from existing fields), use + a single double quote string (for isntance "INSEE_DEP+INSEE_CAN"). + + Parameters + ---------- + input_file : str + Path to the input file. + label : str + The added field's name. + value : str + The value of the added field. + output_dir : str + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + output = f"{output_dir}/{output_name}.{output_format}" + quiet = "-quiet " if quiet else " " + + cmd = ( + f"mapshaper {input_file} " + f'-each "{label}={value}" ' + f"{quiet}" + "-proj EPSG:4326 " + f" -o {output} " + f'format={output_format} extension=".{output_format}" force' + ) + + # Run Mapshaper command + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_capture_cities_from_ultramarine_territories.py b/cartiflette/mapshaper/mapshaper_capture_cities_from_ultramarine_territories.py new file mode 100644 index 00000000..0afe8d4f --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_capture_cities_from_ultramarine_territories.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_capture_cities_from_ultramarine_territories( + input_city_file: str, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Remove cities from departements, and keep only cities from ultramarine + territories (Saint-Martin, etc.). + + Parameters + ---------- + input_city_file : str + Path to the input file. + output_dir : str + Directory to store the output file. The default is "temp". + output_name : str, optional + Name of the written file, without extension. The default is "output". + output_format : str, optional + Format for output file. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + output = f"{output_dir}/{output_name}.{output_format}" + quiet = "-quiet " if quiet else " " + + cmd = ( + f"mapshaper {input_city_file} name='COMMUNE' -proj EPSG:4326 " + "-filter \"'saint-barthelemy,saint-pierre-et-miquelon,saint-martin'" + '.indexOf(AREA) > -1" ' + "-drop fields=TYP_IRIS " + f"{quiet}" + "-o force " + f'{output} format={output_format} extension=".{output_format}" singles' + ) + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_closer.py b/cartiflette/mapshaper/mapshaper_closer.py index d17fb7e0..b825b5a4 100644 --- a/cartiflette/mapshaper/mapshaper_closer.py +++ b/cartiflette/mapshaper/mapshaper_closer.py @@ -1,134 +1,186 @@ -import subprocess +import logging import os +from cartiflette.mapshaper.utils import run + +logger = logging.getLogger(__name__) + +# TODO : TOM (St-Martin, St-Barthelemy, St-Pierre-et-Miquelon) + logical_conditions = { "EMPRISES": { + # left, bottom, right, top (epsg=3857) + "ile de france": "IDF==1", "metropole": "bbox=-572324.2901945524,5061666.243842439,1064224.7522608414,6638201.7541528195", "guadeloupe": "bbox=-6880639.760944527,1785277.734007631,-6790707.017202182,1864381.5053494961", - "martinique": 'bbox=-6815985.711078632,1618842.9696702233,-6769303.6899859235,1675227.3853840816', + "martinique": "bbox=-6815985.711078632,1618842.9696702233,-6769303.6899859235,1675227.3853840816", "guyane": "bbox=-6078313.094526156,235057.05702474713,-5746208.123095576,641016.7211362486", - "reunion": 'bbox=6146675.557436854,-2438398.996947137,6215705.133130206,-2376601.891080389', - "mayotte": 'bbox=5011418.778972076,-1460351.1566339568,5042772.003914668,-1418243.6428180535' - }, - "DEPARTEMENT": { - "ile de france": "['75', '92', '93', '94'].includes(INSEE_DEP)", - "zoom idf": 4, - }, - "REGION": { - "ile de france": "INSEE_REG == 11", - "zoom idf": 1.5 - }, - "BASSIN_VIE": { - "ile de france": "BV2012 == 75056", - "zoom idf": 1.5 - }, - "UNITE_URBAINE": { - "ile de france": "UU2020 == '00851'", - "zoom idf": 1.5 + "reunion": "bbox=6146675.557436854,-2438398.996947137,6215705.133130206,-2376601.891080389", + "mayotte": "bbox=5011418.778972076,-1460351.1566339568,5042772.003914668,-1418243.6428180535", + "saint-martin": "bbox=-7034906.766337046, 2038329.0872462029, -7009537.630813715, 2056865.7060235194", + "saint-pierre-et-miquelon": "bbox=-6298822.299318486, 5894013.594517256, -6239181.296921183, 5973004.907786214", + "saint-barthelemy": "bbox=-7003557.376380256, 2018598.440800959, -6985037.106437805, 2033965.5078367123", }, - "ZONE_EMPLOI": { - "ile de france": 'ZE2020 == 1109', - "zoom idf": 1.5 - }, - "AIRE_ATTRACTION_VILLES": { - "ile de france": "AAV2020 == '001'", - "zoom idf": 1.5 - } - + "IRIS": 8, } shift = { - 'guadeloupe': '6355000,3330000', - 'martinique': '6480000,3505000', - 'guyane': '5760000,4720000', - 'reunion': '-6170000,7560000', - 'mayotte': '-4885000,6590000', + # X, Y shift + "guadeloupe": "6355000,3330000", + "martinique": "6480000,3505000", + "guyane": "5760000,4720000", + "reunion": "-6170000,7560000", + "mayotte": "-4885000,6590000", + "saint-martin": "5690000,-900000", + "saint-pierre-et-miquelon": "2880000,-2910000", + "saint-barthelemy": "5670000,-730000", } scale = { - 'guadeloupe': '1.5', - 'martinique': '1.5', - 'guyane': '0.35', - 'reunion': '1.5', - 'mayotte': '1.5' + "guadeloupe": "1.5", + "martinique": "1.5", + "guyane": "0.35", + "reunion": "1.5", + "mayotte": "1.5", + "saint-martin": "2.5", + "saint-pierre-et-miquelon": "2", + "saint-barthelemy": "2.5", } def mapshaper_bring_closer( - france_vector_path="temp.geojson", - level_agreg="DEPARTEMENT" - ): - - output_path = "temp/preprocessed_transformed/idf_combined.geojson" - output_dir = os.path.dirname(output_path) + input_file: str, + bring_out_idf: str = True, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + level_agreg: str = "DEPARTEMENT", + quiet: bool = True, +): + """ + Bring DROM closer and zoom over IDF. + + Parameters + ---------- + input_file : str + Path to the input file. + bring_out_idf : bool, optional + If True, will extract IdF and zoom on it. The default is True. + output_dir : str + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + level_agreg : str, optional + Desired aggregation configuration. The default is "DEPARTEMENT". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + str + Local path to the output file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + logical_idf = logical_conditions["EMPRISES"]["ile de france"] + zoom_idf = logical_conditions.get(level_agreg, 5) + if zoom_idf < 5: + shift_idf = "-650000,275000" + elif zoom_idf < 6: + shift_idf = "-650000,320000" + else: + shift_idf = "-650000,450000" - logical_idf = logical_conditions[level_agreg]["ile de france"] - zoom_idf = logical_conditions[level_agreg]["zoom idf"] logical_metropole = logical_conditions["EMPRISES"]["metropole"] - idf_zoom = ( - f"mapshaper -i {france_vector_path} " - f"-proj EPSG:3857 " - f'-filter "{logical_idf}" ' - f"-affine shift=-650000,275000 scale={zoom_idf} " - f"-o {output_dir}/idf_zoom.geojson" - ) - - france_metropolitaine = ( - f"mapshaper -i {france_vector_path} " - f"-proj EPSG:3857 " - f'-filter "{logical_metropole}" ' - f"-o {output_dir}/metropole.geojson" - ) - - subprocess.run( - idf_zoom, - shell=True, - check=True, - ) - - subprocess.run( - france_metropolitaine, - shell=True, - check=True, - ) - - for region, shift_value in shift.items(): - print(f"Processing {region}") - cmd = ( - f"mapshaper -i {france_vector_path} " + quiet = "-quiet " if quiet else " " + + try: + france_metropolitaine = ( + f"mapshaper -i {input_file} " f"-proj EPSG:3857 " - f'-filter "{logical_conditions["EMPRISES"][region]}" ' - f'-affine shift={shift_value} scale={scale[region]} ' - f"-o {output_dir}/{region}.geojson" + f'-filter "{logical_metropole}" ' + f"{quiet}" + f"-o {output_dir}/metropole.{output_format}" + ) + + if bring_out_idf: + idf_zoom = ( + f"mapshaper -i {input_file} " + f"-proj EPSG:3857 " + f'-filter "{logical_idf}" ' + f"-affine shift={shift_idf} scale={zoom_idf} " + f"{quiet}" + f"-o {output_dir}/idf_zoom.{output_format}" + ) + + run(idf_zoom) + + run(france_metropolitaine) + + for region, shift_value in shift.items(): + logger.info("Processing %s", region) + cmd = ( + f"mapshaper -i {input_file} " + f"-proj EPSG:3857 " + f'-filter "{logical_conditions["EMPRISES"][region]}" ' + f"-affine shift={shift_value} scale={scale[region]} " + f"{quiet}" + f"-o {output_dir}/{region}.{output_format}" + ) + run(cmd) + + # fix_geo = "fix-geometry" if output_format == "topojson" else "" + + output = f"{output_dir}/{output_name}.{output_format}" + bring_out_idf = ( + f"{output_dir}/idf_zoom.{output_format} " if bring_out_idf else "" ) - subprocess.run( - cmd, - shell=True, - check=True, + cmd_combined = ( + f"mapshaper " + f"{output_dir}/metropole.{output_format} " + + bring_out_idf + + f"{output_dir}/guadeloupe.{output_format} " + f"{output_dir}/martinique.{output_format} " + f"{output_dir}/guyane.{output_format} " + f"{output_dir}/reunion.{output_format} " + f"{output_dir}/mayotte.{output_format} " + f"snap combine-files " + f'-proj wgs84 init="EPSG:3857" target=* ' + f"-rename-layers FRANCE,IDF,GDP,MTQ,GUY,REU,MAY " + f"-merge-layers target=FRANCE,IDF,GDP,MTQ,GUY,REU,MAY force " + f"-rename-layers FRANCE_TRANSFORMED " + "-explode " + f"{quiet}" + f"-o {output} " + # f"{fix_geo}" ) - cmd_combined = ( - f"mapshaper " - f"{output_dir}/metropole.geojson " - f"{output_dir}/idf_zoom.geojson " - f"{output_dir}/guadeloupe.geojson " - f"{output_dir}/martinique.geojson " - f"{output_dir}/guyane.geojson " - f"{output_dir}/reunion.geojson " - f"{output_dir}/mayotte.geojson " - f"snap combine-files " - f'-proj wgs84 init="EPSG:3857" target=* ' - f"-rename-layers FRANCE,IDF,GDP,MTQ,GUY,REU,MAY " - f"-merge-layers target=FRANCE,IDF,GDP,MTQ,GUY,REU,MAY force " - f"-rename-layers FRANCE_TRANSFORMED " - f"-o {output_dir}/idf_combined.geojson " - ) - - subprocess.run( - cmd_combined, - shell=True, - check=True, - ) - - return f"{output_dir}/idf_combined.geojson" \ No newline at end of file + run(cmd_combined) + except Exception: + raise + + finally: + for tempfile in [ + "metropole", + "idf_zoom", + "guadeloupe", + "martinique", + "guyane", + "reunion", + "mayotte", + ]: + try: + os.unlink(f"{output_dir}/{tempfile}.{output_format}") + except FileNotFoundError: + pass + + return output diff --git a/cartiflette/mapshaper/mapshaper_combine_districts_and_cities.py b/cartiflette/mapshaper/mapshaper_combine_districts_and_cities.py new file mode 100644 index 00000000..42d68c1f --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_combine_districts_and_cities.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_combine_districts_and_cities( + input_city_file: str, + input_communal_districts_file: str, + output_dir: str, + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Combine cities' dataset with communal districts', ensure layer renamming + before merging. + + Parameters + ---------- + input_communal_districts_file : str + Path to the input file. + output_dir : str + Directory to store the output file. + output_name : str, optional + Name of the written file, without extension. The default is "output". + output_format : str, optional + Format for output file. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + # fix_geo = "fix-geometry" if output_format == "topojson" else "" + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + rename = "INSEE_ARM=INSEE_COG,NOM_ARM=NOM" + drop = "STATUT,INSEE_ARR,INSEE_CAN,INSEE_DEP,INSEE_REG,SIREN_EPCI" + + cmd = ( + f"mapshaper {input_city_file} {input_communal_districts_file} " + "snap combine-files " + "-proj EPSG:4326 " + "-rename-layers COMMUNE,ARRONDISSEMENT_MUNICIPAL " + "-merge-layers target=COMMUNE,ARRONDISSEMENT_MUNICIPAL force " + "-rename-layers ARRONDISSEMENT_MUNICIPAL " + f"-rename-fields {rename} " + f"-drop fields={drop} " + f"-o {output} " + # f"{fix_geo} " + f"{quiet}" + f"format={output_format} " + f'extension=".{output_format}"' + ) + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_concat.py b/cartiflette/mapshaper/mapshaper_concat.py new file mode 100644 index 00000000..5c1959b7 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_concat.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_concat( + input_dir: str, + input_format: str = "*", + output_dir: str = "temp", + output_name: str = "concatenated", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Concat multiple files (all files should have the same projection). + + Parameters + ---------- + input_dir : str + Directory containing the files to concat + input_format : str, optional + Input file's format. If "*", will match every kind of files. + The default is "*" + output_dir : str + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + cmd = ( + f"mapshaper -i {input_dir}/*.{input_format}" + f" combine-files name='{output_name}' " + f"-proj EPSG:4326 " + f"-merge-layers " + f"{quiet}" + f'-o {output} format={output_format} extension=".{output_format}" ' + "singles" + ) + + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_convert_mercator.py b/cartiflette/mapshaper/mapshaper_convert_mercator.py deleted file mode 100644 index ce66b9c9..00000000 --- a/cartiflette/mapshaper/mapshaper_convert_mercator.py +++ /dev/null @@ -1,33 +0,0 @@ -import subprocess - - -def mapshaper_convert_mercator( - local_dir="temp", - territory="reunion", - file="COMMUNE", - extension_initial="shp", - format_intermediate="geojson", - identifier="", - output_path=None, -): - if output_path is None: - output_path = f"{local_dir}/preprocessed" - - output_name = f"{output_path}/{territory}.{format_intermediate}" - - if identifier != "": - identifier = f"-each \"AREA='{identifier}'\" " - - subprocess.run( - ( - f"mapshaper {local_dir}/{territory}/COMMUNE.{extension_initial} name='COMMUNE' " - f"-proj EPSG:4326 " - f"{identifier}" - f"-o {output_name} " - f'format={format_intermediate} extension=".{format_intermediate}" singles' - ), - shell=True, - check=True, - ) - - return output_name diff --git a/cartiflette/mapshaper/mapshaper_convert_reproject.py b/cartiflette/mapshaper/mapshaper_convert_reproject.py new file mode 100644 index 00000000..a51b868f --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_convert_reproject.py @@ -0,0 +1,68 @@ +import os + +from .utils import run + + +def mapshaper_convert_reproject( + input_file: str, + epsg: int = 4326, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + filter_by: str = "", + quiet: bool = True, +) -> str: + """ + Project a file to a given EPSG (into a given format). + If identifier is given, will filter the file based on the following + criteria: AREA='{identifier}' + + Parameters + ---------- + input_file : str + Path to the input file. + epsg : int, optional + EPSG code to project into. The default is 4326. + output_dir : str, optional + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + filter_by: str, optional + The criteria to filter the input file, based on AREA field. The default + is "", which will not perform any filter. + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + if filter_by != "": + filter_by = f"-each \"AREA='{filter_by}'\" " + + cmd = ( + f"mapshaper {input_file} name='{output_name}' " + f"-proj EPSG:{epsg} " + f"{filter_by} " + f"{quiet}" + f"-o {output} force " + f'format={output_format} extension=".{output_format}" singles' + ) + + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_dissolve.py b/cartiflette/mapshaper/mapshaper_dissolve.py new file mode 100644 index 00000000..ad1b2cd6 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_dissolve.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +from typing import List + +from .utils import run + + +def mapshaper_dissolve( + input_file: str, + by: str, + copy_fields: List[str] = None, + calc: List[str] = None, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Dissolve geometries + + Dissolve geometries on field `by`, keeping fields `copy_fields`. Other + fields should be computaded using javascript functions with `calc` + argument. + + + Parameters + ---------- + input_file : str + Path to the input file. + by : str + Field used to dissolve + copy_fields : List[str], optional + Copies values from the first feature in each group of dissolved + features. The default is None. + calc : Listr[str], optional + Fields on which computed should be operated, describing valid js + functions. For instance ["POPULATION=sum(POPULATION)"]. The default + is None. + output_dir : str + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + name = "_".join(by) + by = ",".join(by) + cmd = ( + f"mapshaper {input_file} " + f"name='{name}' " + "-proj EPSG:4326 " + f"-dissolve {by} " + ) + if calc: + calc = ",".join(calc) + cmd += f"calc='{calc}' " + if copy_fields: + cmd += "copy-fields=" + ",".join(copy_fields) + + # fix_geo = "fix-geometry" if output_format == "topojson" else "" + + cmd += ( + f" {quiet}" + f" -o {output} force " + # f"{fix_geo}" + ) + + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_enrich.py b/cartiflette/mapshaper/mapshaper_enrich.py new file mode 100644 index 00000000..ca6c68ea --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_enrich.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os +from typing import List + +from .utils import run + + +def mapshaper_enrich( + input_geodata_file: str, + input_metadata_file: str, + keys: List[str], + dtype: str = None, + drop: list = None, + rename: dict = None, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Enriches an initial geodata file with additional data using Mapshaper. + + Parameters + ---------- + input_geodata_file : str + Path to the input geodata file. + input_metadata_file : str + Path to the input metadata file to join to the geodata file. + keys : List[str] + List of fields used for joining the dataframes. Should be a tuple + corresponding to left-field and right-field, for instance + ['INSEE_COM', 'CODGEO'] + dtype : dict, optional + Dtypes (among "str", "string", "num", "number"), for + instance {"INSEE_REG": "str"} . Default is None. + drop : list, optional + List of columns to drop (if not None). Default is None. + rename : dict, optional + List of columns to rename (if not None) in a pandas' syntax-like. + To rename A -> B, pass {"A": "B"}. The default is None. + output_dir : str, optional + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + dtype = ",".join( + [f"{key}:{val}" for key, val in dtype.items()] if dtype else [] + ) + keys = ",".join(keys) + drop = ",".join(drop if drop else []) + + # Warning : for mapshaper, to rename A -> B, use B=A syntax! + rename = ",".join( + [f"{val}={key}" for key, val in rename.items()] if rename else [] + ) + + # Mapshaper command for the enrichment process + cmd = ( + f"mapshaper {input_geodata_file} " + "name='' -proj EPSG:4326 " + f"-join {input_metadata_file} keys={keys} field-types={dtype} " + f"-filter-fields {drop} invert " + f"-rename-fields {rename} " + "-each \"PAYS='France'\" " + f"{quiet}" + f"-o {output} force" + ) + + # Run Mapshaper command + run(cmd) + + return output + + +# %% diff --git a/cartiflette/mapshaper/mapshaper_process_communal_districts.py b/cartiflette/mapshaper/mapshaper_process_communal_districts.py new file mode 100644 index 00000000..4fa8cdf0 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_process_communal_districts.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_process_communal_districts( + input_communal_districts_file: str, + output_dir: str, + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Preprocess communal districts files to ensure + + Parameters + ---------- + input_communal_districts_file : str + Path to the input file. + output_dir : str + Directory to store the output file. The default is "temp". + output_name : str, optional + Name of the written file, without extension. The default is "output". + output_format : str, optional + Format for output file. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + cmd = ( + f"mapshaper {input_communal_districts_file} " + "name='ARRONDISSEMENT_MUNICIPAL' " + "-proj EPSG:4326 " + "-rename-fields INSEE_COG=INSEE_ARM " + "-each 'STATUT=\"Arrondissement municipal\"' " + f"{quiet}" + "-o force " + f'{output} format={output_format} extension=".{output_format}"' + ) + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_remove_cities_with_districts.py b/cartiflette/mapshaper/mapshaper_remove_cities_with_districts.py new file mode 100644 index 00000000..840d693e --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_remove_cities_with_districts.py @@ -0,0 +1,57 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_remove_cities_with_districts( + input_city_file: str, + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + Remove cities with communal districts (Paris, Lyon, Marseille) from the + base cities geodataset. + + Parameters + ---------- + input_city_file : str + Path to the input file. + output_dir : str + Directory to store the output file. The default is "temp". + output_name : str, optional + Name of the written file, without extension. The default is "output". + output_format : str, optional + Format for output file. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + cmd = ( + f"mapshaper {input_city_file} name='COMMUNE' -proj EPSG:4326 " + "-filter \"'69123,13055,75056'.indexOf(INSEE_COM) > -1\" invert " + '-each "INSEE_COG=INSEE_COM" ' + f"{quiet}" + "-o force " + f'{output} format={output_format} extension=".{output_format}" singles' + ) + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_simplify.py b/cartiflette/mapshaper/mapshaper_simplify.py new file mode 100644 index 00000000..9d9287c6 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_simplify.py @@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import os + +from .utils import run + + +def mapshaper_simplify( + input_file: str, + option_simplify: str = "", + output_dir: str = "temp", + output_name: str = "output", + output_format: str = "geojson", + quiet: bool = True, +) -> str: + """ + SImplify geometries + + + Parameters + ---------- + input_file : str + Path to the input file. + option_simplify : str, optional + Additional options for simplifying geometries, for instance + "-simplify 50%". The default is "". + output_dir : str + Directory to store the output file. The default is "temp" + output_name : str, optional + The path to write the file to (without extension). + The default is "concatenated" + output_format : str, optional + The format to write the outputfile. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + output : str + Path of the created file + + """ + + try: + os.makedirs(output_dir) + except FileExistsError: + pass + + # fix_geo = "fix-geometry" if output_format == "topojson" else "" + + quiet = "-quiet " if quiet else " " + output = f"{output_dir}/{output_name}.{output_format}" + + cmd = ( + f"mapshaper {input_file} " + "-proj EPSG:4326 " + f"{option_simplify} " + f"{quiet}" + f" -o {output} force " + # f"{fix_geo}" + ) + + run(cmd) + + return output diff --git a/cartiflette/mapshaper/mapshaper_split.py b/cartiflette/mapshaper/mapshaper_split.py new file mode 100644 index 00000000..b1e54675 --- /dev/null +++ b/cartiflette/mapshaper/mapshaper_split.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +from glob import glob +import os +from typing import List + +from .utils import run + + +def mapshaper_split( + input_file: str, + layer_name: str = "", + split_variable: str = "DEPARTEMENT", + output_dir: str = "temp", + output_format: str = "geojson", + crs: int = 4326, + option_simplify: str = "", + quiet: bool = True, +) -> List[str]: + """ + Splits a GeoJSON file based on a specified variable using Mapshaper. + + Parameters + ---------- + input_file : str + The input file to be split (default is "temp.geojson"). + layer_name : str, optional + The name of the layer within the file. The default is "". + split_variable : str, optional + The variable used for splitting the file. The default is "DEPARTEMENT". + output_dir : str, optional + Directory to store output files. The default is "temp". + output_format : str, optional + Format for output files. The default is "geojson". + crs : int, optional + The coordinate reference system EPSG code. The default is 4326. + option_simplify : str, optional + Additional options for simplifying geometries, for instance + "-simplify 50%". The default is "". + quiet : bool, optional + If True, inhibits console messages. The default is True. + + Returns + ------- + final_files : List[str] + List of paths of created files + + """ + + # make a temporary inner directory to retrieve the full list of produced + # files at the end + temp_output_dir = os.path.join(output_dir, "this_is_a_dumb_temp_directory") + try: + os.makedirs(temp_output_dir) + except FileExistsError: + pass + + quiet = "-quiet " if quiet else " " + + # Mapshaper command for the splitting process + cmd = ( + f"mapshaper {input_file} name='{layer_name}' -proj EPSG:{crs} " + f"{option_simplify} " + f"-split {split_variable} " + "-drop fields=IDF " # remove IDF used for tagging IdF entities on every level + f"{quiet}" + f"-o {temp_output_dir}/ " + f'format={output_format} extension=".{output_format}" singles' + ) + + # Run Mapshaper command + run(cmd) + + produced_files = glob(os.path.join(temp_output_dir, f"*.{output_format}")) + final_files = [ + file.replace(temp_output_dir, output_dir) for file in produced_files + ] + [os.replace(src, dst) for src, dst in zip(produced_files, final_files)] + os.rmdir(temp_output_dir) + + return final_files diff --git a/cartiflette/mapshaper/mapshaper_wrangling.py b/cartiflette/mapshaper/mapshaper_wrangling.py deleted file mode 100644 index 827db5aa..00000000 --- a/cartiflette/mapshaper/mapshaper_wrangling.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -Data wrangling (geo)operations wrappers from mapshaper. -""" - -import subprocess -from cartiflette.utils import DICT_CORRESP_ADMINEXPRESS - - -def mapshaper_enrich( - local_dir: str = "temp", - filename_initial: str = "COMMUNE", - extension_initial: str = "shp", - output_path: str = "temp.geojson", - metadata_file: str = "temp/tagc.csv", - dict_corresp: dict = DICT_CORRESP_ADMINEXPRESS, -) -> None: - """ - Enriches an initial shapefile with additional data using Mapshaper and a specified - correspondence dictionary. - - Parameters: - - local_dir (str): The local directory where the initial shapefile is stored and - Mapshaper will be executed (default is "temp"). - - filename_initial (str): The name of the initial shapefile without extension - (default is "COMMUNE"). - - extension_initial (str): The extension of the initial shapefile (default is "shp"). - - output_path (str): The path for the output GeoJSON file after enrichment - (default is "temp.geojson"). - - dict_corresp (dict): A dictionary containing correspondences for field renaming - and value assignment (default is DICT_CORRESP_ADMINEXPRESS). - - Returns: - - None: The function runs Mapshaper with the specified commands and enriches - the initial shapefile. - """ - - # Mapshaper command for the enrichment process - cmd_step1 = ( - f"mapshaper {local_dir}/{filename_initial}.{extension_initial} " - f"name='' -proj EPSG:4326 " - f"-join {metadata_file} " - f"keys=INSEE_COM,CODGEO field-types=INSEE_COM:str,CODGEO:str " - f"-filter-fields INSEE_CAN,INSEE_ARR,SIREN_EPCI,INSEE_DEP,INSEE_REG,NOM_M invert " - f"-rename-fields INSEE_DEP=DEP,INSEE_REG=REG " - f"-each \"{dict_corresp['FRANCE_ENTIERE']}='France'\" " - f"-o {output_path}" - ) - - # Run Mapshaper command - subprocess.run(cmd_step1, shell=True, check=True) - - -def mapshaper_split( - input_file: str = "temp.geojson", - layer_name: str = "", - split_variable: str = "DEPARTEMENT", - output_path: str = "temp2.geojson", - format_output: str = "geojson", - crs: int = 4326, - option_simplify: str = "", - source_identifier: str = "", -) -> None: - """ - Splits a GeoJSON file based on a specified variable using Mapshaper. - - Parameters: - - input_file (str): The input GeoJSON file to be split (default is "temp.geojson"). - - layer_name (str): The name of the layer within the GeoJSON file (default is ""). - - split_variable (str): The variable used for splitting the GeoJSON file - (default is "DEPARTEMENT"). - - output_path (str): The path for the output GeoJSON file after splitting - (default is "temp2.geojson"). - - format_output (str): The format for the output GeoJSON file (default is "geojson"). - - crs (int): The coordinate reference system EPSG code (default is 4326). - - option_simplify (str): Additional options for simplifying geometries (default is ""). - - source_identifier (str): Identifier for the data source (default is ""). - - Returns: - - None: The function runs Mapshaper with the specified commands and splits the GeoJSON file. - """ - - # Mapshaper command for the splitting process - cmd_step2 = ( - f"mapshaper {input_file} name='{layer_name}' -proj EPSG:{crs} " - f"{option_simplify}" - f"-each \"SOURCE='{source_identifier}'\" " - f"-split {split_variable} " - f'-o {output_path} format={format_output} extension=".{format_output}" singles' - ) - - # Run Mapshaper command - subprocess.run(cmd_step2, shell=True, check=True) diff --git a/cartiflette/mapshaper/mapshaperize.py b/cartiflette/mapshaper/mapshaperize.py deleted file mode 100644 index 54d97506..00000000 --- a/cartiflette/mapshaper/mapshaperize.py +++ /dev/null @@ -1,262 +0,0 @@ -import os -import subprocess - -from cartiflette.utils import DICT_CORRESP_ADMINEXPRESS -from .mapshaper_wrangling import mapshaper_enrich, mapshaper_split -from .mapshaper_closer import mapshaper_bring_closer - - -def mapshaperize_split( - local_dir="temp", - config_file_city={}, - format_output="topojson", - niveau_polygons="COMMUNE", - niveau_agreg="DEPARTEMENT", - provider="IGN", - source="EXPRESS-COG-CARTO-TERRITOIRE", - territory="metropole", - crs=4326, - simplification=0, - dict_corresp=DICT_CORRESP_ADMINEXPRESS, -): - """ - Processes shapefiles and splits them based on specified parameters using Mapshaper. - - Parameters - ---------- - local_dir : str, optional - The local directory for file storage, by default "temp". - filename_initial : str, optional - The initial filename, by default "COMMUNE". - extension_initial : str, optional - The initial file extension, by default "shp". - format_output : str, optional - The output format, by default "topojson". - niveau_agreg : str, optional - The level of aggregation for the split, by default "DEPARTEMENT". - provider : str, optional - The data provider, by default "IGN". - source : str, optional - The data source, by default "EXPRESS-COG-CARTO-TERRITOIRE". - year : int, optional - The year of the data, by default 2022. - dataset_family : str, optional - The dataset family, by default "ADMINEXPRESS". - territory : str, optional - The territory of the data, by default "metropole". - crs : int, optional - The coordinate reference system (CRS) code, by default 4326. - simplification : int, optional - The degree of simplification, by default 0. - dict_corresp: dict - A dictionary giving correspondance between niveau_agreg argument - and variable names. - - Returns - ------- - str - The output path of the processed and split shapefiles. - - """ - - simplification_percent = simplification if simplification is not None else 0 - - # City level borders, file location - directory_city = config_file_city.get("location", local_dir) - initial_filename_city = config_file_city.get("filename", "COMMUNE") - extension_initial_city = config_file_city.get("extension", "shp") - - output_path = ( - f"{local_dir}/{territory}/{niveau_agreg}/{format_output}/{simplification=}" - ) - - os.makedirs(output_path, exist_ok=True) - - if simplification_percent != 0: - option_simplify = f"-simplify {simplification_percent}% " - else: - option_simplify = "" - - temp_filename = "temp.geojson" - - # STEP 1: ENRICHISSEMENT AVEC COG - mapshaper_enrich( - local_dir=directory_city, - filename_initial=initial_filename_city, - extension_initial=extension_initial_city, - dict_corresp=dict_corresp, - output_path=temp_filename, - ) - - if niveau_polygons != initial_filename_city: - csv_list_vars = ( - f"{dict_corresp[niveau_polygons]}," f"{dict_corresp[niveau_agreg]}" - ) - libelle_niveau_polygons = dict_corresp.get("LIBELLE_" + niveau_polygons, "") - if libelle_niveau_polygons != "": - libelle_niveau_polygons = f",{libelle_niveau_polygons}" - libelle_niveau_agreg = dict_corresp.get("LIBELLE_" + niveau_agreg, "") - if libelle_niveau_polygons != "": - libelle_niveau_agreg = f",{libelle_niveau_agreg}" - csv_list_vars = ( - f"{csv_list_vars}{libelle_niveau_polygons}{libelle_niveau_agreg}" - ) - - # STEP 1B: DISSOLVE IF NEEDED - cmd_dissolve = ( - f"mapshaper {temp_filename} " - f"name='' -proj EPSG:4326 " - f"-dissolve {dict_corresp[niveau_polygons]} " - f"calc='POPULATION=sum(POPULATION)' " - f"copy-fields={csv_list_vars} " - "-o temp.geojson force" - ) - subprocess.run(cmd_dissolve, shell=True, check=True) - - # IF WE DESIRE TO BRING "DROM" CLOSER TO FRANCE - if niveau_agreg.upper() == "FRANCE_ENTIERE_DROM_RAPPROCHES": - niveau_filter_drom = "DEPARTEMENT" - if niveau_polygons != "COMMUNE": - niveau_filter_drom = niveau_polygons - input_path = mapshaper_bring_closer( - temp_filename, level_agreg=niveau_filter_drom - ) - else: - input_path = "temp.geojson" - - print(input_path) - - # STEP 2: SPLIT ET SIMPLIFIE - mapshaper_split( - input_file=input_path, - layer_name="", - split_variable=dict_corresp[niveau_agreg], - output_path=output_path, - format_output=format_output, - crs=crs, - option_simplify=option_simplify, - source_identifier=f"{provider}:{source}", - ) - - return output_path - - -def mapshaperize_split_merge( - format_output="topojson", - niveau_agreg="DEPARTEMENT", - provider="IGN", - source="EXPRESS-COG-CARTO-TERRITOIRE", - territory="metropole", - config_file_city={}, - config_file_arrondissement={}, - local_dir="temp", - crs=4326, - simplification=0, - dict_corresp=DICT_CORRESP_ADMINEXPRESS, -): - simplification_percent = simplification if simplification is not None else 0 - - # City level borders, file location - directory_city = config_file_city.get("location", local_dir) - initial_filename_city = config_file_city.get("filename", "COMMUNE") - extension_initial_city = config_file_city.get("extension", "shp") - - # Arrondissement level borders, file location - directory_arrondissement = config_file_arrondissement.get("location", local_dir) - initial_filename_arrondissement = config_file_arrondissement.get( - "filename", "ARRONDISSEMENT_MUNICIPAL" - ) - extension_initial_arrondissement = config_file_arrondissement.get( - "extension", "shp" - ) - - # Intermediate output location - output_path = ( - f"{local_dir}/{territory}/{niveau_agreg}/{format_output}/{simplification=}" - ) - - if simplification_percent != 0: - option_simplify = f"-simplify {simplification_percent}% " - else: - option_simplify = "" - - format_intermediate = "geojson" - - # PREPROCESS CITIES - file_city = f"{directory_city}/{initial_filename_city}.{extension_initial_city}" - subprocess.run( - ( - f"mapshaper {file_city} name='COMMUNE' " - f"-proj EPSG:4326 " - f"-filter '\"69123,13055,75056\".indexOf(INSEE_COM) > -1' invert " - f'-each "INSEE_COG=INSEE_COM" ' - f"-o {output_path}/communes_simples.{format_intermediate} " - f'format={format_intermediate} extension=".{format_intermediate}" singles' - ), - shell=True, - check=True, - ) - - # PREPROCESS ARRONDISSEMENT - file_arrondissement = ( - f"{directory_arrondissement}/" - f"{initial_filename_arrondissement}.{extension_initial_arrondissement}" - ) - subprocess.run( - ( - f"mapshaper {file_arrondissement} " - f"name='ARRONDISSEMENT_MUNICIPAL' " - f"-proj EPSG:4326 " - f"-rename-fields INSEE_COG=INSEE_ARM " - f"-each 'STATUT=\"Arrondissement municipal\" ' " - f"-o {output_path}/arrondissements.{format_intermediate} " - f'format={format_intermediate} extension=".{format_intermediate}"' - ), - shell=True, - check=True, - ) - - # MERGE CITIES AND ARRONDISSEMENT - subprocess.run( - ( - f"mapshaper " - f"{output_path}/communes_simples.{format_intermediate} " - f"{output_path}/arrondissements.{format_intermediate} snap combine-files " - f"-proj EPSG:4326 " - f"-rename-layers COMMUNE,ARRONDISSEMENT_MUNICIPAL " - f"-merge-layers target=COMMUNE,ARRONDISSEMENT_MUNICIPAL force " - f"-rename-layers COMMUNE_ARRONDISSEMENT " - f"-o {output_path}/raw.{format_intermediate} " - f'format={format_intermediate} extension=".{format_intermediate}"' - ), - shell=True, - check=True, - ) - - # STEP 1: ENRICHISSEMENT AVEC COG - mapshaper_enrich( - local_dir=output_path, - filename_initial="raw", - extension_initial=format_intermediate, - output_path=f"{output_path}/raw2.{format_intermediate}", - dict_corresp=DICT_CORRESP_ADMINEXPRESS, - ) - - input_path = f"{output_path}/raw2.{format_intermediate}" - - if niveau_agreg.upper() == "FRANCE_ENTIERE_DROM_RAPPROCHES": - input_path = mapshaper_bring_closer(input_path) - - # TRANSFORM AS NEEDED - mapshaper_split( - input_file=input_path, - layer_name="", - split_variable=dict_corresp[niveau_agreg], - output_path=output_path, - format_output=format_output, - crs=crs, - option_simplify=option_simplify, - source_identifier=f"{provider}:{source}", - ) - - return output_path diff --git a/cartiflette/mapshaper/utils.py b/cartiflette/mapshaper/utils.py new file mode 100644 index 00000000..a83bf783 --- /dev/null +++ b/cartiflette/mapshaper/utils.py @@ -0,0 +1,32 @@ +# -*- coding: utf-8 -*- +""" +Utils to ensure subprocess is ran with same level of debugging between windows +& linux +""" +import logging +import os +import subprocess + + +def run(cmd): + if os.name == "nt": + kwargs = {"shell": True, "text": True, "capture_output": True} + result = subprocess.run(cmd, **kwargs) + logging.info(result.stdout) + if not result.returncode == 0: + logging.warning(result.stderr) + raise subprocess.CalledProcessError(result.returncode, cmd) + else: + # on windows, mapshaper's output seem to always be in stderr, + # whether there was an error or not + logging.info(result.stderr) + + else: + kwargs = { + "shell": True, + "check": True, + "text": True, + } + subprocess.run(cmd, **kwargs) + + return diff --git a/cartiflette/pipeline/__init__.py b/cartiflette/pipeline/__init__.py index 355511e8..6ed4c100 100644 --- a/cartiflette/pipeline/__init__.py +++ b/cartiflette/pipeline/__init__.py @@ -1,18 +1,15 @@ from .cross_product_parameters import ( - restructure_nested_dict_borders, crossproduct_parameters_production, ) - -from .prepare_mapshaper import prepare_local_directory_mapshaper from .mapshaper_split_from_s3 import ( mapshaperize_split_from_s3, - mapshaperize_merge_split_from_s3, + mapshaperize_split_from_s3_multithreading, ) +from .download import download_all __all__ = [ - "restructure_nested_dict_borders", "crossproduct_parameters_production", - "prepare_local_directory_mapshaper", "mapshaperize_split_from_s3", - "mapshaperize_merge_split_from_s3", + "mapshaperize_split_from_s3_multithreading", + "download_all", ] diff --git a/cartiflette/pipeline/combine_adminexpress_france.py b/cartiflette/pipeline/combine_adminexpress_france.py deleted file mode 100644 index 43d6d078..00000000 --- a/cartiflette/pipeline/combine_adminexpress_france.py +++ /dev/null @@ -1,60 +0,0 @@ -import subprocess - -from cartiflette.config import FS, PATH_WITHIN_BUCKET -from cartiflette.utils import import_yaml_config -from cartiflette.mapshaper import mapshaper_convert_mercator -from cartiflette.s3 import upload_s3_raw -from .prepare_mapshaper import prepare_local_directory_mapshaper - - -def combine_adminexpress_territory( - intermediate_dir="temp", path_within_bucket=PATH_WITHIN_BUCKET, fs=FS -): - local_dir = intermediate_dir - format_intermediate = "geojson" - - yaml = import_yaml_config() - - list_territories = yaml["IGN"]["ADMINEXPRESS"]["EXPRESS-COG-TERRITOIRE"][ - "territory" - ].keys() - - list_location_raw = { - territ: upload_s3_raw( - path_within_bucket=path_within_bucket, year=2022, territory=territ - ) - for territ in list_territories - } - - for territory, path_bucket in list_location_raw.items(): - prepare_local_directory_mapshaper( - path_bucket, - borders="COMMUNE", - territory=territory, - niveau_agreg="COMMUNE", - format_output="geojson", - simplification=0, - local_dir=local_dir, - fs=fs, - ) - - for territ in list_territories: - mapshaper_convert_mercator( - local_dir=local_dir, territory=territ, identifier=territ - ) - - output_path = f"{local_dir}/preprocessed_combined/raw.{format_intermediate}" - - subprocess.run( - ( - f"mapshaper -i {local_dir}/preprocessed/*.geojson combine-files name='COMMUNE' " - f"-proj EPSG:4326 " - f"-merge-layers " - f"-o {output_path} " - f'format={format_intermediate} extension=".{format_intermediate}" singles' - ), - shell=True, - check=True, - ) - - return output_path diff --git a/cartiflette/pipeline/cross_product_parameters.py b/cartiflette/pipeline/cross_product_parameters.py index 9282d7e2..414f9fef 100644 --- a/cartiflette/pipeline/cross_product_parameters.py +++ b/cartiflette/pipeline/cross_product_parameters.py @@ -1,109 +1,331 @@ -import itertools +import logging + import pandas as pd +from pebble import ThreadPool +from s3fs import S3FileSystem +from cartiflette.config import ( + FS, + BUCKET, + PATH_WITHIN_BUCKET, + THREADS_DOWNLOAD, + INTERMEDIATE_FORMAT, +) +from cartiflette.pipeline_constants import ( + AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH, + AVAILABLE_TERRITORIAL_SPLITS_FOR_BORDERS, + PIPELINE_DOWNLOAD_ARGS, + PIPELINE_SIMPLIFICATION_LEVELS, +) +from cartiflette.s3 import S3GeoDataset -def restructure_nested_dict_borders(dict_with_list: dict): - """ - Restructures a nested dictionary by flattening its values and their corresponding keys. +logger = logging.getLogger(__name__) - Parameters: - ----------- - dict_with_list : dict - A dictionary with list values to be restructured. - Returns: - -------- - list - A list of lists containing key-value pairs obtained by flattening the input dictionary. +def flatten_dict_to_list(dict_with_list: dict) -> list: + """ + Restructures a nested dictionary by flattening its values and their + corresponding keys. - Example: - -------- - Example usage: - sample_dict = {'a': [1, 2, 3], 'b': [4, 5]} - result = restructure_nested_dict_borders(sample_dict) - print(result) + Parameters: + ----------- + dict_with_list : dict + A dictionary with list values to be restructured. + + Returns: + -------- + flattened_list : list + A list of lists containing key-value pairs obtained by flattening + the input dictionary. + ValueError + Example: + -------- + Example usage: + sample_dict = {'a': [1, 2, 3], 'b': [4, 5]} + result = flatten_dict(sample_dict) + print(result) - This will output: - [['a', 1], ['a', 2], ['a', 3], ['b', 4], ['b', 5]] + This will output: + [['a', 1], ['a', 2], ['a', 3], ['b', ValueError4], ['b', 5]] """ - croisement_filter_by_borders_flat = [ + flattened_list = [ [key, inner_value] for key, values in dict_with_list.items() for inner_value in values ] - return croisement_filter_by_borders_flat + return flattened_list def crossproduct_parameters_production( - croisement_filter_by_borders: dict, - list_format: list, - years: list, - crs_list: list, - sources: list, - simplifications: list, -) -> pd.DataFrame: + year: int, + simplifications: list = None, + fs: S3FileSystem = FS, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, +) -> list: """ - Generates a DataFrame by performing a cross-product of the given parameters. + Generates a dict of arguments commanding the generation of output + geodatasets. Only the best available process to generate a given dataset + are kep (for instance among available IRIS and COMMUNE candidates). + + Note that the length of the return represents the number of downstream + pods. Parameters: ----------- - croisement_filter_by_borders : dict - A dictionary with nested lists for cross-product generation. - list_format : list - A list of formats for cross-product generation. - years : list - A list of years for cross-product generation. - crs_list : list - A list of CRS (Coordinate Reference Systems) for cross-product generation. - sources : list - A list of sources for cross-product generation. - simplifications : list - A list of simplifications for cross-product generation. + year : int + Desired vintage. For ex. 2023 + simplifications : list, optional + A list of simplification for cross-product generation. The default is + None and will result to PIPELINE_SIMPLIFICATION_LEVELS. + fs : S3FileSystem, optional + S3FileSystem used for storage. The default is FS. + bucket : str, optional + The bucket used for storage on fs. The default is BUCKET. + path_within_bucket : str, optional + The path within the bucket used for storage on fs. The default is + PATH_WITHIN_BUCKET. Returns: -------- - pd.DataFrame - A pandas DataFrame containing the cross-product of the input parameters. + combinations : list + A list of dicts used for commanding the generation of a downstream + dataset. + + Each dict has 5 keys: + * mesh_init: str (for instance 'DEPARTEMENT') + * source_geodata: str (for instance 'EXPRESS-COG-CARTO-TERRITOIRE') + * simplification: str (for instance '2021') + * dissolve_by: str (for instance 'ARRONDISSEMENT') + * config: List[str] of territorial splits Example: -------- Example usage: - sample_dict = {'a': [1, 2, 3], 'b': [4, 5]} - formats = ['geojson', 'gpkg'] - years = [2022, 2022] - crs_list = [4326, 2154] - sources = ['source1', 'source2'] - simplifications = [0, 40] - result = crossproduct_parameters_production( - sample_dict, formats, years, crs_list, sources, simplifications - ) - print(result) + >>> year = 2023 + >>> simplifications = [0, 40] + >>> result = crossproduct_parameters_production(year, simplifications) + >>> print(result) + >>> [{ + 'mesh_init': 'ARRONDISSEMENT_MUNICIPAL', + 'source_geodata': 'EXPRESS-COG-CARTO-TERRITOIRE', + 'simplification': 40, + 'dissolve_by': 'ARRONDISSEMENT_MUNICIPAL', + 'territories': ['ZONE_EMPLOI', ..., 'AIRE_ATTRACTION_VILLES'] + }, ..., { + 'mesh_init': 'IRIS', + 'source_geodata': 'CONTOUR-IRIS', + 'simplification': 40, + 'dissolve_by': 'IRIS', + 'territories': ['ZONE_EMPLOI', ..., 'AIRE_ATTRACTION_VILLES'] + } + ] - This will output: - A pandas DataFrame with the cross-product of the provided parameters. """ - croisement_filter_by_borders_flat = restructure_nested_dict_borders( - croisement_filter_by_borders + + if not simplifications: + simplifications = PIPELINE_SIMPLIFICATION_LEVELS + + # prepare a list of (potential) sources from cartiflette's config + # (the result will depend of the resolution in the config) + sources = { + "ARRONDISSEMENT_MUNICIPAL": PIPELINE_DOWNLOAD_ARGS["ADMIN-EXPRESS"], + "COMMUNE": PIPELINE_DOWNLOAD_ARGS["ADMIN-EXPRESS"], + "IRIS": PIPELINE_DOWNLOAD_ARGS["IRIS"], + "CANTON": PIPELINE_DOWNLOAD_ARGS["ADMIN-EXPRESS"], + } + sources = pd.DataFrame(sources).T + sources.columns = [ + "geodata_provider", + "geodata_dataset_family", + "geodata_source", + "geodata_territorial_components", + ] + sources.index.name = "mesh_init" + sources = sources.reset_index(drop=False) + sources["geodata_territorial_components"] = ( + sources.geodata_territorial_components.apply(", ".join) + ) + + sources = sources.drop( + ["geodata_provider", "geodata_dataset_family"], axis=1 + ) + + # prepare a list of tuples ( + # administrative_level = polygon level = borders, + # territory used for splitting the file's boundaries = territory + # ), + croisement_filter_by_borders_flat = pd.DataFrame( + flatten_dict_to_list(AVAILABLE_TERRITORIAL_SPLITS_FOR_BORDERS), + columns=["borders", "territory"], ) - combinations = list( - itertools.product( - list_format, + # prepare a list of tuples ( + # raw source's polygon level, + # mesh created after dissolve + # ), + geometries_dissolutions = pd.DataFrame( + flatten_dict_to_list(AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH), + columns=["mesh_init", "dissolve_by"], + ) + + combinations = sources.merge( + geometries_dissolutions.merge( croisement_filter_by_borders_flat, - years, - crs_list, - sources, - simplifications, + left_on="dissolve_by", + right_on="borders", ) ) + combinations = ( + combinations + # .join( + # pd.Series(list_format, name="format_output"), how="cross" + # ) + # .join(pd.Series(crs_list, name="epsg"), how="cross") + .join(pd.Series(simplifications, name="simplification"), how="cross") + ) - tempdf = pd.DataFrame( - combinations, - columns=["format_output", "nested", "year", "crs", "source", "simplification"], + combinations = combinations.drop( + ["geodata_territorial_components", "borders"], axis=1 ) - tempdf["level_polygons"] = tempdf["nested"].apply(lambda tup: tup[0]) - tempdf["filter_by"] = tempdf["nested"].apply(lambda tup: tup[1]) - tempdf.drop("nested", axis="columns", inplace=True) - return tempdf + def geodataset_exists(borders, geodata_source, simplification): + "check if preprocessed geodata file is found on S3" + config = { + "bucket": bucket, + "path_within_bucket": path_within_bucket, + "provider": "Cartiflette", + "dataset_family": "geodata", + "source": geodata_source, + "year": year, + "borders": borders, + "crs": 4326, + "filter_by": "preprocessed", + "value": "before_cog", + "vectorfile_format": INTERMEDIATE_FORMAT, + "territory": "france", + "simplification": simplification, + "fs": fs, + } + try: + S3GeoDataset(**config, build_from_local=False) + return True + except ValueError: + # raw file does not exist + return False + + def metadataset_exists(borders): + "check if preprocessed metadata file is found on S3" + config = { + "bucket": bucket, + "path_within_bucket": path_within_bucket, + "provider": "Cartiflette", + "dataset_family": "metadata", + "source": "*", + "year": year, + "borders": borders, + "crs": None, + "filter_by": "preprocessed", + "value": "tagc", + "vectorfile_format": "csv", + "territory": "france", + "simplification": 0, + "fs": fs, + } + try: + S3GeoDataset(**config, build_from_local=False) + return True + except ValueError: + # raw file does not exist + return False + + # remove combinations having no available upstream source + geodata_unique = combinations[ + ["mesh_init", "geodata_source", "simplification"] + ].drop_duplicates() + metadata_unique = combinations[["mesh_init"]].drop_duplicates() + + if THREADS_DOWNLOAD == 1: + + geodata_unique["upstream_geodata_exists"] = geodata_unique.apply( + lambda tup: geodataset_exists(*tup), axis=1 + ) + + metadata_unique["upstream_metadata_exists"] = metadata_unique.apply( + lambda tup: metadataset_exists(*tup), axis=1 + ) + + else: + with ThreadPool(min(THREADS_DOWNLOAD, len(combinations))) as pool: + geodata_unique["upstream_geodata_exists"] = list( + pool.map( + geodataset_exists, *zip(*geodata_unique.values.tolist()) + ).result() + ) + + metadata_unique["upstream_metadata_exists"] = list( + pool.map( + metadataset_exists, *zip(*metadata_unique.values.tolist()) + ).result() + ) + + combinations = combinations.merge(geodata_unique).merge(metadata_unique) + combinations["upstream_exists"] = ( + combinations["upstream_geodata_exists"] + & combinations["upstream_metadata_exists"] + ) + + ix = combinations[~combinations.upstream_exists].index + combinations = combinations.drop(ix).drop( + [ + "upstream_exists", + "upstream_geodata_exists", + "upstream_metadata_exists", + ], + axis=1, + ) + + logger.debug( + "found %s combinations of downstream geodatasets", len(combinations) + ) + + if len(combinations) == 0: + raise ValueError(f"no combination available for {year=}") + + # get best combination available among COMMUNE/IRIS/CANTON + # -> for each geodataset to generate, keep COMMUNE if available, IRIS + # otherwise (and CANTON for border=CANTON generation) + dups = [ + "dissolve_by", + "territory", + # "format_output", + # "epsg", + "simplification", + "mesh_init", + ] + combinations = combinations.sort_values(dups, ascending=False) + combinations = combinations.drop_duplicates(dups[:-1], keep="last") + + keys = ["mesh_init", "geodata_source", "simplification", "dissolve_by"] + combinations = ( + combinations.set_index(keys) + .groupby(keys)["territory"] + .agg(list) + .to_dict() + ) + logger.info("%s batch datasets will be created", len(combinations)) + logger.info("combinations are %s", combinations) + + combinations = [ + { + "mesh_init": key[0], + "source_geodata": key[1], + "simplification": key[2], + "dissolve_by": key[3], + "territories": val, + } + for key, val in combinations.items() + ] + + return combinations diff --git a/cartiflette/download/pipeline.py b/cartiflette/pipeline/download.py similarity index 69% rename from cartiflette/download/pipeline.py rename to cartiflette/pipeline/download.py index 4b8761d8..c6ecaeef 100644 --- a/cartiflette/download/pipeline.py +++ b/cartiflette/pipeline/download.py @@ -3,12 +3,20 @@ from datetime import date import json import logging +import traceback +from typing import List + from pebble import ThreadPool import s3fs -from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS, THREADS_DOWNLOAD -from cartiflette.constants import DOWNLOAD_PIPELINE_ARGS -from cartiflette.download.download import _download_sources +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, + THREADS_DOWNLOAD, +) +from cartiflette.pipeline_constants import PIPELINE_DOWNLOAD_ARGS +from cartiflette.download import _download_and_store_sources from cartiflette.utils import deep_dict_update logger = logging.getLogger(__name__) @@ -19,11 +27,12 @@ def download_all( path_within_bucket: str = PATH_WITHIN_BUCKET, fs: s3fs.S3FileSystem = FS, upload: bool = True, + years: List[int] = None, ) -> dict: """ Performs a full pipeline to download data and store them on MinIO. The target files are described in cartiflette/constants.py under the - constant DOWNLOAD_PIPELINE_ARGS. Those files' characteristics must also be + constant PIPELINE_DOWNLOAD_ARGS. Those files' characteristics must also be described in the cartiflette/utils/sources.yaml file. Note: to perform an easy debugging task, please overwrite @@ -42,6 +51,9 @@ def download_all( Whether to store data on MinIO or not. This argument should only be used for debugging purposes. The default is True, to upload data on MinIO. + years : List[int], optional + Years to perform download on. If not set, will result to + range(2015, date.today().year + 1). The default is None. Returns ------- @@ -100,8 +112,15 @@ def download_all( """ + if not years: + years = list(range(2015, date.today().year + 1))[-1::-1] + + logger.info(f"performing download on {years=}") + if not upload: - logger.warning("no upload to s3 will be done, set upload=True to upload") + logger.warning( + "no upload to s3 will be done, set upload=True to upload" + ) # Initialize MD5 json if absent json_md5 = f"{bucket}/{path_within_bucket}/md5.json" @@ -117,7 +136,6 @@ def download_all( "fs": fs, "upload": upload, } - years = list(range(2015, date.today().year + 1))[-1::-1] results = {} @@ -125,11 +143,32 @@ def download_all( def func(args): key, args = args - results = _download_sources(*args, years=years, **kwargs) + try: + providers, dataset_families, sources, territories = args + except ValueError: + # No territories set in constant (will ultimately be stored at + # "france_entiere" on the S3 FileSystem) + providers, dataset_families, sources = args + territories = None + logger.info( + "looking for %s %s %s %s", + providers, + dataset_families, + sources, + territories, + ) + results = _download_and_store_sources( + providers=providers, + dataset_families=dataset_families, + sources=sources, + years=years, + territories=territories, + **kwargs, + ) logger.info(f"{key} done") return results - datasets_args = DOWNLOAD_PIPELINE_ARGS + datasets_args = PIPELINE_DOWNLOAD_ARGS if THREADS_DOWNLOAD > 1: with ThreadPool(THREADS_DOWNLOAD) as pool: @@ -139,97 +178,17 @@ def func(args): results = deep_dict_update(results, next(iterator)) except StopIteration: break - except Exception as e: - logger.error(e) + except Exception: + logger.error(traceback.format_exc()) else: for args in datasets_args.items(): results = deep_dict_update(results, func(args)) return results -# def download_all_option2(): -# # Dérouler le yaml comme dans le test - -# yaml = import_yaml_config() - -# with MasterScraper() as scraper: -# for provider, provider_yaml in yaml.items(): -# if not isinstance(provider_yaml, dict): -# continue - -# for dataset_family, dataset_family_yaml in provider_yaml.items(): -# if not isinstance(dataset_family_yaml, dict): -# continue - -# for source, source_yaml in dataset_family_yaml.items(): -# str_yaml = f"{dataset_family}/{source}" - -# if not isinstance(source_yaml, dict): -# logger.error( -# f"yaml {str_yaml} contains '{source_yaml}'" -# ) -# continue -# elif "FTP" in set(source_yaml.keys()): -# logger.info("yaml {str_yaml} not checked (FTP)") -# continue - -# years = set(source_yaml.keys()) - {"field", "FTP"} -# try: -# territories = set(source_yaml["field"].keys()) -# except KeyError: -# territories = {""} - -# for year in years: -# for territory in territories: -# str_yaml = ( -# f"{dataset_family}/{source}/{year}/" -# f"{provider}/{territory}" -# ) - -# if territory == "": -# territory = None -# try: -# ds = Dataset( -# dataset_family, -# source, -# int(year), -# provider, -# territory, -# ) -# except Exception: -# logger.error( -# f"error on yaml {str_yaml} : " -# "dataset not constructed" -# ) -# continue -# try: -# url = ds.get_path_from_provider() -# except Exception: -# logger.error( -# f"error on yaml {str_yaml} : " -# "url no reconstructed" -# ) -# continue - -# try: -# r = scraper.get(url, stream=True) -# except Exception: -# logger.error( -# f"error on yaml {str_yaml} : " -# f"https get request failed on {url}" -# ) -# continue -# if not r.ok: -# logger.error( -# f"error on yaml {str_yaml} : " -# "https get request " -# f"got code {r.status_code} on {url}" -# ) - if __name__ == "__main__": logging.basicConfig( - level=logging.INFO, + level=logging.WARNING, format="%(levelname)s :%(filename)s:%(lineno)d (%(funcName)s) - %(message)s", ) - results = download_all(upload=True) diff --git a/cartiflette/pipeline/mapshaper_split_from_s3.py b/cartiflette/pipeline/mapshaper_split_from_s3.py index 57e68e55..3a80ecd2 100644 --- a/cartiflette/pipeline/mapshaper_split_from_s3.py +++ b/cartiflette/pipeline/mapshaper_split_from_s3.py @@ -1,200 +1,292 @@ -import os -import shutil - -from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS -from cartiflette.utils import create_path_bucket -from cartiflette.mapshaper import mapshaperize_split, mapshaperize_split_merge -from .prepare_mapshaper import prepare_local_directory_mapshaper - - -def mapshaperize_split_from_s3(config, fs=FS): - format_output = config.get("format_output", "topojson") - filter_by = config.get("filter_by", "DEPARTEMENT") - territory = config.get("territory", "metropole") - level_polygons = config.get("level_polygons", "COMMUNE") - territory = config.get("territory", "metropole") - - provider = config.get("provider", "IGN") - source = config.get("source", "EXPRESS-COG-CARTO-TERRITOIRE") - year = config.get("year", 2022) - dataset_family = config.get("dataset_family", "ADMINEXPRESS") - territory = config.get("territory", "metropole") - crs = config.get("crs", 4326) - simplification = config.get("simplification", 0) - - bucket = config.get("bucket", BUCKET) - path_within_bucket = config.get("path_within_bucket", PATH_WITHIN_BUCKET) - local_dir = config.get("local_dir", "temp") - - path_raw_s3_combined = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": "france", - "crs": 4326, - "filter_by": "preprocessed", - "value": "before_cog", - "vectorfile_format": "geojson", - "provider": "IGN", - "dataset_family": "ADMINEXPRESS", - "source": "EXPRESS-COG-CARTO-TERRITOIRE", - "territory": "france", - "filename": "raw.geojson", - "simplification": 0, - } - ) +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- - fs.download(path_raw_s3_combined, "temp/preprocessed_combined/COMMUNE.geojson") - - output_path = mapshaperize_split( - local_dir=local_dir, - config_file_city={ - "location": "temp/preprocessed_combined", - "filename": "COMMUNE", - "extension": "geojson", - }, - format_output=format_output, - niveau_agreg=filter_by, - niveau_polygons=level_polygons, - provider=provider, - source=source, - crs=crs, - simplification=simplification, - ) +import logging +import traceback +from typing import List - for values in os.listdir(output_path): - path_s3 = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": level_polygons, - "crs": crs, - "filter_by": filter_by, - "value": values.replace(f".{format_output}", ""), - "vectorfile_format": format_output, - "provider": provider, - "dataset_family": dataset_family, - "source": source, - "territory": territory, - "simplification": simplification, - } - ) - fs.put(f"{output_path}/{values}", path_s3) - - shutil.rmtree(output_path) - - -def mapshaperize_merge_split_from_s3(config, fs=FS): - format_output = config.get("format_output", "topojson") - filter_by = config.get("filter_by", "DEPARTEMENT") - territory = config.get("territory", "metropole") - - provider = config.get("provider", "IGN") - source = config.get("source", "EXPRESS-COG-CARTO-TERRITOIRE") - year = config.get("year", 2022) - dataset_family = config.get("dataset_family", "ADMINEXPRESS") - territory = config.get("territory", "metropole") - crs = config.get("crs", 4326) - simplification = config.get("simplification", 0) - - bucket = config.get("bucket", BUCKET) - path_within_bucket = config.get("path_within_bucket", PATH_WITHIN_BUCKET) - local_dir = config.get("local_dir", "temp") - - path_raw_s3_combined = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": "france", - "crs": 4326, - "filter_by": "preprocessed", - "value": "before_cog", - "vectorfile_format": "geojson", - "provider": "IGN", - "dataset_family": "ADMINEXPRESS", - "source": "EXPRESS-COG-CARTO-TERRITOIRE", - "territory": "france", - "filename": "raw.geojson", - "simplification": 0, - } - ) +from pebble import ThreadPool +from s3fs import S3FileSystem - fs.download(path_raw_s3_combined, "temp/preprocessed_combined/COMMUNE.geojson") - - path_raw_s3_arrondissement = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": None, - "crs": 2154, - "filter_by": "origin", - "value": "raw", - "vectorfile_format": "shp", - "provider": "IGN", - "dataset_family": "ADMINEXPRESS", - "source": "EXPRESS-COG-CARTO-TERRITOIRE", - "territory": "metropole", - "filename": "ARRONDISSEMENT_MUNICIPAL.shp", - "simplification": 0, - } - ) - path_raw_s3_arrondissement = path_raw_s3_arrondissement.rsplit("/", maxsplit=1)[0] +from cartiflette.config import ( + BUCKET, + PATH_WITHIN_BUCKET, + FS, + INTERMEDIATE_FORMAT, + THREADS_DOWNLOAD, +) +from cartiflette.s3 import S3GeoDataset, S3Dataset - # retrieve arrondissement - prepare_local_directory_mapshaper( - path_raw_s3_arrondissement, - borders="ARRONDISSEMENT_MUNICIPAL", - territory="metropole", - niveau_agreg=filter_by, - format_output="topojson", - simplification=simplification, - local_dir="temp", - fs=FS, + +logger = logging.getLogger(__name__) + + +def mapshaperize_split_from_s3( + year: int, + init_geometry_level: str, + source: str, + simplification: int, + dissolve_by: str, + territorial_splits: list, + fs: S3FileSystem = FS, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, +): + logger.info( + "processing %s from '%s' geometries and dissolve on '%s'", + year, + init_geometry_level, + dissolve_by, ) - output_path = mapshaperize_split_merge( - local_dir=local_dir, - config_file_city={ - "location": "temp/preprocessed_combined", - "filename": "COMMUNE", - "extension": "geojson", - }, - config_file_arrondissement={ - "location": "temp/metropole", - "filename": "ARRONDISSEMENT_MUNICIPAL", - "extension": "shp", - }, - format_output=format_output, - niveau_agreg=filter_by, - provider=provider, + kwargs = { + "fs": fs, + "bucket": bucket, + "path_within_bucket": path_within_bucket, + "year": year, + "borders": init_geometry_level, + "filter_by": "preprocessed", + "provider": "Cartiflette", + "territory": "france", + } + with S3Dataset( + dataset_family="metadata", + source="*", + crs=None, + value="tagc", + vectorfile_format="csv", + **kwargs, + ) as metadata, S3GeoDataset( + dataset_family="geodata", source=source, - crs=crs, + crs=4326, + value="before_cog", + vectorfile_format=INTERMEDIATE_FORMAT, simplification=simplification, - ) + **kwargs, + ) as gis_file: + + failed = [] + success = [] + skipped = [] + for niveau_agreg in territorial_splits: + + # Check that both niveau_agreg and dissolve_by correspond to + # definitive fields from either metadata/geodata + available = set(gis_file._get_columns()) | set( + metadata._get_columns() + ) + + warnings = [] + for field in niveau_agreg, dissolve_by: + if field in [ + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ]: + continue + try: + metadata.find_column_name(field, available) + except (ValueError, IndexError) as exc: + warnings.append(str(exc)) + if warnings: + skipped.append( + { + "warning": " - ".join(warnings), + "aggreg": niveau_agreg, + } + ) + continue + + with gis_file.copy() as gis_copy: + try: + gis_copy.create_downstream_geodatasets( + metadata, + niveau_agreg=niveau_agreg, + init_geometry_level=init_geometry_level, + dissolve_by=dissolve_by, + simplification=simplification, + ) + except Exception as exc: + failed.append( + { + "error": exc, + "aggreg": niveau_agreg, + "traceback": traceback.format_exc(), + } + ) + else: + success.append( + { + "aggreg": niveau_agreg, + } + ) + + warning_traceback = [] + error_traceback = [] + if skipped: + for one_skipped in skipped: + msg = "\n".join( + [ + "-" * 50, + one_skipped["warning"], + f"aggregation: {one_skipped['aggreg']}", + ] + ) + logger.warning(msg) + warning_traceback.append(msg) + if failed: + for one_failed in failed: + msg = "\n".join( + [ + "=" * 50, + f"error: {one_failed['error']}", + f"aggregation: {one_failed['aggreg']}", + "-" * 50, + f"traceback:\n{one_failed['traceback']}", + ] + ) + logger.error(msg) + error_traceback.append(msg) + + return { + "success": success, + "skipped": skipped, + "failed": failed, + "warning_traceback": warning_traceback, + "error_traceback": error_traceback, + } + + +def mapshaperize_split_from_s3_multithreading( + year: int, + configs: List[dict], + fs: S3FileSystem = FS, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, +): + + results = { + "success": 0, + "skipped": 0, + "failed": 0, + "warning_traceback": [], + "error_traceback": [], + } + if THREADS_DOWNLOAD > 1: + with ThreadPool(min(len(configs), THREADS_DOWNLOAD)) as pool: + args = [ + ( + year, + d["mesh_init"], + d["source_geodata"], + d["simplification"], + d["dissolve_by"], + d["territories"], + fs, + bucket, + path_within_bucket, + ) + for d in configs + ] + iterator = pool.map( + mapshaperize_split_from_s3, *zip(*args), timeout=60 * 10 + ).result() + + failed = False + index = 0 + while True: + try: + this_result = next(iterator) + except StopIteration: + break + except Exception: + logger.error(traceback.format_exc()) + logger.error("args were %s", args[index]) + else: + for key in "success", "skipped", "failed": + results[key] += len(this_result[key]) + for key in "warning_traceback", "error_traceback": + results[key] += this_result[key] + finally: + index += 1 + else: + for d in configs: + + d["init_geometry_level"] = d.pop("mesh_init") + d["source"] = d.pop("source_geodata") + d["territorial_splits"] = d.pop("territories") + try: + this_result = mapshaperize_split_from_s3( + year=year, + fs=fs, + bucket=bucket, + path_within_bucket=path_within_bucket, + **d, + ) + except Exception: + logger.error(traceback.format_exc()) + logger.error("args were %s", d) + else: + for key in "success", "skipped", "failed": + results[key] += len(this_result[key]) + for key in "warning_traceback", "error_traceback": + results[key] += this_result[key] + + skipped = results["skipped"] + success = results["success"] + failed = results["failed"] + warnings = results["warning_traceback"] + errors = results["error_traceback"] + + if warnings or errors: + level = "warning" + if errors: + level = "error" + log_func = getattr(logger, level) + log_func("=" * 50) + log_func("Traceback recaps") + for msg in warnings: + logger.warning(msg) + logger.info("%s", "-" * 50) + for msg in errors: + logger.error(msg) + logger.info("%s", "-" * 50) + + logger.info("%s file(s) generation(s) were skipped", skipped) + logger.info("%s file(s) generation(s) succeeded", success) + logger.error("%s file(s) generation(s) failed", failed) + + if failed: + raise ValueError("some datasets' generation failed") + + return { + "success": success, + "skipped": skipped, + "failed": failed, + } + + +# if __name__ == "__main__": +# import logging +# from cartiflette.pipeline_constants import COG_TERRITOIRE +# from cartiflette.config import DATASETS_HIGH_RESOLUTION + +# logging.basicConfig(level=logging.INFO) - for values in os.listdir(output_path): - path_s3 = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": "COMMUNE_ARRONDISSEMENT", - "crs": crs, - "filter_by": filter_by, - "value": values.replace(f".{format_output}", ""), - "vectorfile_format": format_output, - "provider": provider, - "dataset_family": dataset_family, - "source": source, - "territory": territory, - "simplification": simplification, - } - ) - fs.put(f"{output_path}/{values}", path_s3) - - shutil.rmtree(output_path) +# mapshaperize_split_from_s3( +# year=2023, +# init_geometry_level="ARRONDISSEMENT_MUNICIPAL", +# source=COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION], +# simplification=40, +# dissolve_by="DEPARTEMENT", +# config_generation={ +# "FRANCE_ENTIERE_DROM_RAPPROCHES": [ +# {"format_output": "gpkg", "epsg": "4326"}, +# {"format_output": "geojson", "epsg": "4326"}, +# {"format_output": "gpkg", "epsg": "2154"}, +# {"format_output": "geojson", "epsg": "2154"}, +# ] +# }, +# ) diff --git a/cartiflette/pipeline/prepare_cog_metadata.py b/cartiflette/pipeline/prepare_cog_metadata.py index 5706975d..614c26a8 100644 --- a/cartiflette/pipeline/prepare_cog_metadata.py +++ b/cartiflette/pipeline/prepare_cog_metadata.py @@ -1,21 +1,101 @@ -import os +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import io +import logging +import re +import warnings + +from diskcache import Cache import pandas as pd +import numpy as np +import polars as pl +from pebble import ThreadPool import s3fs -from cartiflette.config import FS -from cartiflette.s3 import upload_s3_raw +from cartiflette.config import FS, BUCKET, PATH_WITHIN_BUCKET, THREADS_DOWNLOAD + + +cache = Cache("cartiflette-s3-cache", timeout=3600) +logger = logging.getLogger(__name__) + + +def s3_to_df( + fs: s3fs.S3FileSystem, path_in_bucket: str, **kwargs +) -> pd.DataFrame: + """ + Retrieve DataFrame from S3 with cache handling. + + Parameters + ---------- + fs : s3fs.S3FileSystem + An S3FileSystem object for interacting with the S3 bucket + path_in_bucket : str + Target file's path on S3 bucket + **kwargs : + Optionnal kwargs to pass to either pandas.read_excel or pandas.read_csv + + Returns + ------- + df : pd.DataFrame + Download dataset as dataframe + + """ + + try: + return cache[("metadata", path_in_bucket)] + except KeyError: + pass + + try: + + with fs.open(path_in_bucket, mode="rb") as remote_file: + remote = io.BytesIO(remote_file.read()) + if path_in_bucket.endswith("csv") or path_in_bucket.endswith("txt"): + df = pl.read_csv( + remote, infer_schema_length=0, **kwargs + ).to_pandas() + + elif path_in_bucket.endswith("xls") or path_in_bucket.endswith("xlsx"): + # carefull, with polars skip_rows and header_row are summed ! + kwargs = {"header_row": kwargs["skip_rows"]} + df = pl.read_excel( + remote, + has_header=True, + infer_schema_length=0, + read_options=kwargs, + ).to_pandas() + + except Exception as e: + warnings.warn(f"could not read {path_in_bucket=}: {e}") + raise + df.columns = [x.upper() for x in df.columns] + + # Remove 'ZZZZZ'-like values from INSEE datasets + for col in df.columns: + ix = df[df[col].fillna("").str.fullmatch("Z+", case=False)].index + df.loc[ix, col] = pd.NA + + cache[("metadata", path_in_bucket)] = df + + return df def prepare_cog_metadata( - path_within_bucket: str, local_dir: str = "temp", fs: s3fs.core.S3FileSystem = FS + year: int, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, + fs: s3fs.core.S3FileSystem = FS, ) -> pd.DataFrame: """ - Prepares and retrieves COG (French Census Geographic Code) metadata by fetching and merging - relevant datasets from remote sources, such as DEPARTEMENT, REGION, and TAGC (Appartenance). + Prepares and retrieves COG (French Census Geographic Code) metadata by + merging relevant datasets from raw sources stored on S3, such as + DEPARTEMENT, REGION, and TAGC (Appartenance). Parameters: - - path_within_bucket (str): The path within the S3 bucket where the datasets will be stored. - - local_dir (str): Local directory where the datasets will be downloaded. + - year (int): The COG metadata's vintage + - bucket (str): The bucket where the dataset are stored + - path_within_bucket (str): The path within the S3 bucket where the datasets are stored. - fs (s3fs.core.S3FileSystem): An S3FileSystem object for interacting with the S3 bucket. Returns: @@ -23,81 +103,413 @@ def prepare_cog_metadata( and TAGC information. """ - # Create the local directory if it does not exist - os.makedirs(local_dir, exist_ok=True) - - # Fetch and upload DEPARTEMENT dataset to S3 - path_bucket_cog_departement = upload_s3_raw( - provider="Insee", - dataset_family="COG", - source="DEPARTEMENT", - territory="france_entiere", - borders="DATASET_INSEE_COG_DEPARTEMENT_FRANCE_ENTIERE_2022", - year=2022, - vectorfile_format="csv", - path_within_bucket=path_within_bucket, - ) - - # Fetch and upload REGION dataset to S3 - path_bucket_cog_region = upload_s3_raw( - provider="Insee", - dataset_family="COG", - source="REGION", - territory="france_entiere", - borders="DATASET_INSEE_COG_REGION_FRANCE_ENTIERE_2022", - year=2022, - vectorfile_format="csv", - path_within_bucket=path_within_bucket, - ) + # TODO : calcul des tables BANATIC, etc. - # Fetch and upload TAGC APPARTENANCE dataset to S3 - path_bucket_tagc_appartenance = upload_s3_raw( - provider="Insee", - dataset_family="TAGC", - source="APPARTENANCE", - territory="france_entiere", - borders="table-appartenance-geo-communes-22", - year=2022, - vectorfile_format="xlsx", - path_within_bucket=path_within_bucket, - ) + # ========================================================================= + # Part 1. : retrieve all paths on S3 + # ========================================================================= + paths_bucket = {} - # Retrieve paths for the uploaded datasets - path_tagc = fs.ls(path_bucket_tagc_appartenance)[0] - path_bucket_cog_departement = fs.ls(path_bucket_cog_departement)[0] - path_bucket_cog_region = fs.ls(path_bucket_cog_region)[0] - - # Read datasets from S3 into Pandas DataFrames - with fs.open(path_tagc, mode="rb") as remote_file: - tagc = pd.read_excel( - remote_file, - skiprows=5, - dtype_backend="pyarrow", - dtype={"REG": "string[pyarrow]"}, + def retrieve_path(provider, family: str, source: str, ext: str): + path = ( + f"{bucket}/{path_within_bucket}/" + f"provider={provider}/dataset_family={family}/source={source}" + f"/year={year}/**/*.{ext}" ) + logger.debug(path) + try: + path = paths_bucket[(family, source)] = fs.glob(path)[0] + except IndexError: + warnings.warn(f"missing {family} {source} file for {year=}") - with fs.open(path_bucket_cog_departement, mode="rb") as remote_file: - cog_dep = pd.read_csv( - remote_file, dtype_backend="pyarrow", dtype={"REG": "string[pyarrow]"} - ) + args = [ + ("Insee", "COG", "COMMUNE-OUTRE-MER", "csv"), + ("Insee", "COG", "CANTON", "csv"), + ("Insee", "COG", "COMMUNE", "csv"), + ("Insee", "COG", "ARRONDISSEMENT", "csv"), + ("Insee", "COG", "DEPARTEMENT", "csv"), + ("Insee", "COG", "REGION", "csv"), + ("Insee", "TAGC", "APPARTENANCE", "xlsx"), + ("Insee", "TAGIRIS", "APPARTENANCE", "xlsx"), + ("DGCL", "BANATIC", "CORRESPONDANCE-SIREN-INSEE-COMMUNES", "xlsx"), + ("Insee", "ZONAGES", "EPCI-FP", "xlsx"), + ("Insee", "ZONAGES", "EPT", "xlsx"), + ("Insee", "ZONAGES", "UNITES-URBAINES", "xlsx"), + ("Insee", "ZONAGES", "BASSINS-VIE", "xlsx"), + ("Insee", "ZONAGES", "AIRES-ATTRACTION-VILLES", "xlsx"), + ("Insee", "ZONAGES", "ZONES-EMPLOI", "xlsx"), + ("Insee", "POPULATION", "POPULATION-IRIS-COM", "xlsx"), + ("Insee", "POPULATION", "POPULATION-IRIS-FRANCE-HORS-MAYOTTE", "xlsx"), + ] + if THREADS_DOWNLOAD > 1: + with ThreadPool(THREADS_DOWNLOAD) as pool: + list(pool.map(retrieve_path, *zip(*args)).result()) + else: + for provider, family, source, ext in args: + retrieve_path( + provider=provider, family=family, source=source, ext=ext + ) - with fs.open(path_bucket_cog_region, mode="rb") as remote_file: - cog_region = pd.read_csv( - remote_file, dtype_backend="pyarrow", dtype={"REG": "string[pyarrow]"} - ) + try: + [ + paths_bucket[("COG", x)] + for x in ("REGION", "DEPARTEMENT", "ARRONDISSEMENT") + ] + except KeyError: + warnings.warn(f"{year=} metadata not constructed!") + return + + # ========================================================================= + # Part 2. : download and read all datasets from S3 + # ========================================================================= + + def download(key, skip_rows): + try: + path = paths_bucket[key] + return key, s3_to_df(fs, path, skip_rows=skip_rows) + except KeyError: + # not there + return key, pd.DataFrame() + + args = [ + (("COG", "COMMUNE"), 0), + (("COG", "ARRONDISSEMENT"), 0), + (("COG", "DEPARTEMENT"), 0), + (("COG", "REGION"), 0), + (("COG", "COMMUNE-OUTRE-MER"), 0), + (("BANATIC", "CORRESPONDANCE-SIREN-INSEE-COMMUNES"), 0), + (("POPULATION", "POPULATION-IRIS-FRANCE-HORS-MAYOTTE"), 5), + (("POPULATION", "POPULATION-IRIS-COM"), 5), + (("ZONAGES", "EPCI-FP"), 5), + (("ZONAGES", "EPT"), 5), + (("ZONAGES", "UNITES-URBAINES"), 5), + (("ZONAGES", "BASSINS-VIE"), 5), + (("ZONAGES", "AIRES-ATTRACTION-VILLES"), 5), + (("ZONAGES", "ZONES-EMPLOI"), 5), + (("TAGIRIS", "APPARTENANCE"), 5), + (("TAGC", "APPARTENANCE"), 5), + (("COG", "CANTON"), 0), + ] + if THREADS_DOWNLOAD > 1: + with ThreadPool(THREADS_DOWNLOAD) as pool: + ddf = dict(pool.map(download, *zip(*args)).result()) + else: + ddf = {key: download(key, skip)[-1] for key, skip in args} - # Merge DEPARTEMENT and REGION COG metadata + # Merge ARR, DEPARTEMENT and REGION COG metadata cog_metadata = ( - cog_dep.loc[:, ["DEP", "REG", "LIBELLE"]] + # Note : Mayotte (976) not in ARR + # -> take DEP & REG from cog dep & cog reg + ddf[("COG", "ARRONDISSEMENT")] + .loc[:, ["ARR", "DEP", "LIBELLE"]] + .rename({"LIBELLE": "LIBELLE_ARRONDISSEMENT"}, axis=1) .merge( - cog_region.loc[:, ["REG", "LIBELLE"]], - on="REG", - suffixes=["_DEPARTEMENT", "_REGION"], + ddf[("COG", "DEPARTEMENT")] + .loc[:, ["DEP", "REG", "LIBELLE"]] + .merge( + ddf[("COG", "REGION")].loc[:, ["REG", "LIBELLE"]], + on="REG", + suffixes=["_DEPARTEMENT", "_REGION"], + ), + on="DEP", + how="outer", # Nota : Mayotte not in ARR file ) - .drop(columns=["REG"]) ) + # Ex. cog_metadata : + # ARR DEP LIBELLE_ARRONDISSEMENT REG LIBELLE_DEPARTEMENT \ + # 0 011 01 Belley 84 Ain + # 1 012 01 Bourg-en-Bresse 84 Ain + + # LIBELLE_REGION + # 0 Auvergne-Rhône-Alpes + # 1 Auvergne-Rhône-Alpes + + # Compute metadata at COMMUNE level + tagc = ddf[("TAGC", "APPARTENANCE")] + if tagc.empty: + warnings.warn(f"{year=} metadata for cities not constructed!") + cities = pd.DataFrame() + arm = pd.DataFrame() + else: + drop = {"CANOV", "CV"} & set(tagc.columns) + tagc = tagc.drop(list(drop), axis=1) + + # Add various labels for zoning plans + zoning = { + "EPCI-FP": ("EPCI", "LIBEPCI"), + "EPT": ("EPT", "LIBEPT"), + "UNITES-URBAINES": ("UU", "LIBUU"), + "BASSINS-VIE": ("BV", "LIBBV"), + "AIRES-ATTRACTION-VILLES": ("AAV", "LIBAAV"), + "ZONES-EMPLOI": ("ZE", "LIBZE"), + } + for file_key, (key, label) in zoning.items(): + labels = ddf[("ZONAGES", file_key)] + if not labels.empty: + labels = labels.dropna() + + def find_from_pattern(target): + found = [ + x + for x in labels.columns + if re.match(target + "([0-9]{4})?", x) + ] + if len(found) > 1 or not found: + warnings.warn( + f"could not find {target} in zonage {file_key}" + ) + else: + return found[0] + + pk_insee = find_from_pattern(key) + label_insee = find_from_pattern(label) + if not (pk_insee and label_insee): + continue + + labels = labels.loc[:, [pk_insee, label_insee]] + labels = labels.rename( + { + label_insee: f"LIBELLE_{file_key.replace('-', '_')}", + }, + axis=1, + ) + try: + tagc = tagc.merge(labels, on=pk_insee, how="left") + except KeyError as e: + pass + + cities = tagc.merge( + cog_metadata, on=["ARR", "DEP", "REG"], how="inner" + ) + cities = cities.rename({"LIBGEO": "LIBELLE_COMMUNE"}, axis=1) + + cog_tom = ddf[("COG", "COMMUNE-OUTRE-MER")] + if not cog_tom.empty: + keep = ["COM_COMER", "LIBELLE", "COMER", "LIBELLE_COMER"] + cog_tom = cog_tom.query("NATURE_ZONAGE=='COM'").loc[:, keep] + cog_tom = cog_tom.rename( + { + "COMER": "DEP", + "LIBELLE_COMER": "LIBELLE_DEPARTEMENT", + "COM_COMER": "CODGEO", + "LIBELLE": "LIBELLE_COMMUNE", + }, + axis=1, + ) + cities = pd.concat([cities, cog_tom], ignore_index=True) + + cog_arm = ddf[("COG", "COMMUNE")].query("TYPECOM=='ARM'") + cog_arm = cog_arm.loc[:, ["TYPECOM", "COM", "LIBELLE", "COMPARENT"]] + + arm = cities.merge( + cog_arm.drop("TYPECOM", axis=1).rename( + { + "COM": "CODE_ARM", + "LIBELLE": "LIBELLE_ARRONDISSEMENT_MUNICIPAL", + }, + axis=1, + ), + how="left", + left_on="CODGEO", + right_on="COMPARENT", + ).drop("COMPARENT", axis=1) + ix = arm[arm.CODE_ARM.isnull()].index + arm.loc[ix, "CODE_ARM"] = arm.loc[ix, "CODGEO"] + arm.loc[ix, "LIBELLE_ARRONDISSEMENT_MUNICIPAL"] = arm.loc[ + ix, "LIBELLE_COMMUNE" + ] + # Set unique ARR code (as "NumDEP" + "NumARR") to ensure dissolution + # is ok + for df in arm, cities: + ix = df[(df.ARR.notnull())].index + df.loc[ix, "INSEE_ARR"] = df.loc[ix, "DEP"] + df.loc[ix, "ARR"] + + siren = ddf[("BANATIC", "CORRESPONDANCE-SIREN-INSEE-COMMUNES")] + if not siren.empty: + pop_communes = { + "PTOT_([0-9]{4})": "POPULATION_TOTALE", + "PMUN_[0-9]{4}": "POPULATION_MUNICIPALE", + "PCAP_[0-9]{4}": "POPULATION_COMPTEE_A_PART", + } + rename = { + col: f"{new}_" + re.findall("[0-9]{4}", col)[0] + for pattern, new in pop_communes.items() + for col in siren.columns + if re.match(pattern, col) + } + rename.update({"SIREN": "SIREN_COMMUNE"}) + siren = siren.drop( + ["REG_COM", "DEP_COM", "NOM_COM"], axis=1 + ).rename(rename, axis=1) + + cities = cities.merge( + siren, how="left", left_on="CODGEO", right_on="INSEE" + ).drop("INSEE", axis=1) + + # Do not keep populations for ARM (info is not available on ARM + # level for LYON or MARSEILLE) + drop = { + col: f"{new}_" + re.findall("[0-9]{4}", col)[0] + for pattern, new in pop_communes.items() + for col in siren.columns + if re.match(pattern, col) + } + arm = arm.merge( + siren[["SIREN_COMMUNE", "INSEE"]], + how="left", + left_on="CODGEO", + right_on="INSEE", + ).drop( + ["INSEE"], + axis=1, + ) + for df in arm, cities: + df["SOURCE_METADATA"] = "Cartiflette, d'après INSEE & DGCL" + + # Compute metadata at IRIS level + iris = ddf[("TAGIRIS", "APPARTENANCE")] + if iris.empty: + warnings.warn(f"{year=} metadata for iris not constructed!") + iris = pd.DataFrame() + else: + iris = iris.drop(columns=["LIBCOM", "UU2020", "REG", "DEP"]) + rename = {"DEPCOM": "CODE_ARM", "LIB_IRIS": "LIBELLE_IRIS"} + iris = iris.rename(rename, axis=1) + + # retrieve populations + pop_iris = pd.concat( + [ + ddf[("POPULATION", "POPULATION-IRIS-FRANCE-HORS-MAYOTTE")], + ddf[("POPULATION", "POPULATION-IRIS-COM")], + ], + ignore_index=True, + ) + if pop_iris.empty: + # all iris population dataframe are empty, triggering an exception + pop_iris = pd.DataFrame() + else: + pop_iris_field = re.compile("P[0-9]{2}_POP$") + pop_iris_field = [ + x for x in pop_iris.columns if pop_iris_field.match(x) + ][0] + pop_iris = pop_iris.loc[:, ["IRIS", pop_iris_field]].rename( + { + pop_iris_field: "POPULATION_" + + re.findall("([0-9]{2})", pop_iris_field)[0] + } + ) + + iris = arm.merge(iris, on="CODE_ARM", how="left") + + # Compute metadata at CANTON level + cantons = ddf[("COG", "CANTON")] + if cantons.empty: + warnings.warn(f"{year=} metadata for cantons not constructed!") + else: + + # Set pure "CANTON" code (without dep part) to prepare for + # join with IGN's CANTON geodataset + cantons["INSEE_CAN"] = cantons["CAN"].str[-2:] + + # Add Lyon if missing (<2024): single CANTON since creation of the + # metropole, not covering the whole dept, so this should be added + # before the merge operation like Paris, Martinique, etc. + ix = cantons[ + (cantons.DEP == "69") & (cantons.NCC.str.contains("LYON")) + ].index + if ix.empty: + cantons = pd.concat( + [ + cantons, + pd.DataFrame( + [ + { + "CAN": "69NR", + "DEP": "69", + "REG": "84", + "INSEE_CAN": "NR", + "LIBELLE": "Lyon", + "INSEE_CAN": "NR", + } + ] + ), + ], + ignore_index=True, + ) + + # Merge CANTON metadata with COG metadata + cantons = cantons.merge( + # Nota : we do not have the CANTON -> ARR imbrication as of yet + # (except of course as a geospatial join...) + cog_metadata.drop( + ["ARR", "LIBELLE_ARRONDISSEMENT"], axis=1 + ).drop_duplicates(), + on=["REG", "DEP"], + # Note : Martinique (972) and Guyane (973) missing from CANTON + # as well as Paris (75) for older vintages + # -> go for outer join + how="outer", + ) + keep = [ + "INSEE_CAN", + "CAN", + "DEP", + "REG", + "BURCENTRAL", + "TYPECT", + "LIBELLE", + "LIBELLE_DEPARTEMENT", + "LIBELLE_REGION", + ] + cantons = cantons.loc[:, keep].rename( + {"LIBELLE": "LIBELLE_CANTON"}, axis=1 + ) + + # Hack to set PARIS, GUYANE and MARTINIQUE with the same key as IGN's + # dataset (if trully missing) + for dep, label in { + # Paris missing for year <2024 / for year 2024, key is 99 in IGN + # datasets, NR in INSEE's + "75": "Paris", + "973": "Guyane", + "972": "Martinique", + }.items(): + ix = cantons[cantons.DEP == dep].index + if dep == "75" or cantons.loc[ix, "CAN"].isnull().all(): + cantons.loc[ix, "INSEE_CAN"] = "NR" + cantons.loc[ix, "CAN"] = ( + cantons.loc[ix, "DEP"] + cantons.loc[ix, "INSEE_CAN"] + ) + if cantons.loc[ix, "CAN"].isnull().all(): + cantons.loc[ix, "LIBELLE_CANTON"] = label + + cantons["SOURCE_METADATA"] = "Cartiflette d'après INSEE" + + rename = { + "DEP": "INSEE_DEP", + "REG": "INSEE_REG", + # "ARR": "INSEE_ARR", <- carefull, there is a INSEE_ARR already there! + "CODGEO": "INSEE_COM", + # "CAN": "INSEE_CAN", <- carefull, there is a INSEE_CAN already there! + "CODE_ARM": "INSEE_ARM", + } + + # Prepare field used for bringing IDF closer in further pipeline steps + return_dict = {} + ile_de_france = pd.DataFrame({"DEP": ["75", "92", "93", "94"]}) + ile_de_france["IDF"] = 1 - # Merge TAGC metadata with COG metadata - tagc_metadata = tagc.merge(cog_metadata) + for label, df in [ + ("IRIS", iris), + ("COMMUNE", cities), + ("CANTON", cantons), + ("ARRONDISSEMENT_MUNICIPAL", arm), + ]: + if not df.empty: + df = df.replace(np.nan, pd.NA) + df = df.merge(ile_de_france, on="DEP", how="left") + df["IDF"] = df["IDF"].fillna(0).astype(int) + df = df.rename(rename, axis=1) + return_dict[label] = df - return tagc_metadata + return return_dict diff --git a/cartiflette/pipeline/prepare_geodatasets.py b/cartiflette/pipeline/prepare_geodatasets.py new file mode 100644 index 00000000..2a1488ba --- /dev/null +++ b/cartiflette/pipeline/prepare_geodatasets.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from contextlib import ExitStack, nullcontext +from copy import deepcopy +from functools import partial +from itertools import product +import logging +import os +import re +from tempfile import TemporaryDirectory +import traceback +from typing import Union, List +import warnings + +import geopandas as gpd +from pebble import ThreadPool +import s3fs + +from cartiflette.config import ( + FS, + BUCKET, + PATH_WITHIN_BUCKET, + THREADS_DOWNLOAD, + INTERMEDIATE_FORMAT, + DATASETS_HIGH_RESOLUTION, +) +from cartiflette.pipeline_constants import ( + PIPELINE_DOWNLOAD_ARGS, + PIPELINE_SIMPLIFICATION_LEVELS, + COG_TERRITOIRE, +) +from cartiflette.s3.geodataset import ( + S3GeoDataset, + concat_s3geodataset, + from_frame, +) + +logger = logging.getLogger(__name__) + + +COMPILED_TERRITORY = re.compile(r"territory=([a-z\-]*)/", flags=re.IGNORECASE) + + +def make_one_geodataset( + dset: S3GeoDataset, + with_municipal_district: bool, + simplification: int, + communal_districts: S3GeoDataset = None, +) -> str: + """ + Generate one geodataset and upload it to S3FileSystem + + Parameters + ---------- + dset : S3GeoDataset + Basic geodataset with full France coverage, already downloaded. This + dataset should have a basic geometric mesh coherent with the `mesh` + argument. At the time of the docstring redaction, the dataset should + either be composed of cities or cantons. + with_municipal_district : bool + Whether to substitutes main cities (Paris, Lyon, Marseille) with + their municipal districts. Obviously, this can only be used with a + cities dataset. + simplification : int + Level of desired simplification. + communal_districts : S3GeoDataset, optional + Geodataset for communal districts (only : no cities are in), already + downloaded. Only needed if `mesh == 'COMMUNE'`. The default is None. + + Returns + ------- + uploaded : str + Uploaded file's path on S3FileSystem + + """ + + mesh = dset.config["borders"] + + if mesh != "COMMUNE" and with_municipal_district: + raise ValueError( + "with_municipal_district is not authorized with this S3GeoDataset " + f"(found {mesh=} instead of 'COMMUNE')" + ) + + log = "Create %s geodatasets with simplification=%s" + if with_municipal_district: + log += " with municipal districts substitution" + logger.info(log, mesh, simplification) + + kwargs = {"format_output": INTERMEDIATE_FORMAT} + + # Note : must escape the ' for the js command in mapshaper, + # hence the raw string + source = r"Cartiflette d\'après IGN simplifié à " + f"{simplification} %" + new_dset = dset.copy() + if with_municipal_district: + # substitute communal districts + districts = new_dset.substitute_municipal_districts( + communal_districts=communal_districts.copy(), **kwargs + ) + else: + districts = nullcontext() + + with new_dset, districts: + processed_dset = districts if with_municipal_district else new_dset + processed_dset.simplify(simplification=simplification, **kwargs) + processed_dset.add_field("GEODATA_SOURCE", f"'{source}'") + processed_dset.to_s3() + uploaded = processed_dset.s3_dirpath + + return uploaded + + +def create_one_year_geodataset_batch( + year: Union[str, int], + format_output: str = "geojson", + simplifications_values: List[int] = None, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, + fs: s3fs.S3FileSystem = FS, +) -> dict: + """ + Merge cities datasets into a single file (full France territory). + + All files are retrieved from S3, projected to 4326 coordinates, then + merged using mapshaper. Every computation is done on the disk, inside + a temporary dir. + + Note that multithreading will be used . + To debug code, please consider deactivating the threading, using + `cartiflette.config.THREADS = 1` beforehand. + + Parameters + ---------- + year : Union[str, int] + Desired vintage + format_output : str, optional + Final (and intermediate) formats to use. The default is "geojson" + simplifications_values : List[int], optional + List of simplifications' levels to compute (as percentage values + casted to integers). The default is None, which will result to + PIPELINE_SIMPLIFICATION_LEVELS. + bucket : str, optional + Storage bucket on S3 FileSystem. The default is BUCKET. + path_within_bucket : str, optional + Path within S3 bucket used for storage. The default is + PATH_WITHIN_BUCKET. + fs : s3fs.FyleSystem, optional + S3 file system used for storage of raw data. The default is FS. + + Returns + ------- + success : dict + {"year": True/False} + + """ + + logger.info("-" * 50) + logger.info(f"Merging territorial files of cities for {year=}") + logger.info("-" * 50) + + if not simplifications_values: + simplifications_values = PIPELINE_SIMPLIFICATION_LEVELS + + paths = ( + f"{bucket}/{path_within_bucket}/" + "provider=IGN/dataset_family=*/" + "source=*/" + f"year={year}/" + "administrative_level=None/" + "crs=*/" + "origin=raw/" + "vectorfile_format=*/" + "territory=*/**/*.shp" + ) + + paths = fs.glob(paths) + dirs = {os.path.dirname(x) for x in paths} + territories = {t for x in dirs for t in COMPILED_TERRITORY.findall(x)} + territories = territories - {"france_entiere"} + + if not territories: + warnings.warn(f"{year} not constructed (no territories available)") + return + + logger.info("Territoires identifiés:\n%s", "\n".join(territories)) + + config = { + "bucket": bucket, + "path_within_bucket": path_within_bucket, + "provider": "IGN", + "dataset_family": "ADMINEXPRESS", + "source": COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION], + "borders": None, + "crs": "*", + "filter_by": "origin", + "value": "raw", + "vectorfile_format": "shp", + "simplification": 0, + "year": year, + "fs": fs, + } + + uploaded = [] + + # Construct S3GeoDataset for municipal districts + raw_config = deepcopy(config) + kwargs = {"territory": "metropole", "filename": "ARRONDISSEMENT_MUNICIPAL"} + try: + districts = S3GeoDataset(**kwargs, **raw_config) + except ValueError: + # ARM is missing + districts = None + + input_geodatasets = {} + # Retrieve raw files of cities, cantons and iris + dset_source_configs = { + "COMMUNE": PIPELINE_DOWNLOAD_ARGS["ADMIN-EXPRESS"][:3], + "CANTON": PIPELINE_DOWNLOAD_ARGS["ADMIN-EXPRESS"][:3], + "IRIS": PIPELINE_DOWNLOAD_ARGS["IRIS"][:3], + } + for mesh in "CANTON", "COMMUNE", "IRIS": + + provider, family, source = dset_source_configs[mesh] + # Construct S3GeoDatasets for each territory (Guyane, metropole, ...) + # at mesh level (COMMUNE or CANTON) + mesh_config = deepcopy(config) + mesh_config["provider"] = provider + # Nota : filename for IRIS might be CONTOURS-IRIS.shp or IRIS_GE.shp + # while COMMUNE and CANTON are COMMUNE.shp and CANTON.shp + mesh_config["filename"] = f"*{mesh}*" + mesh_config["dataset_family"] = family + mesh_config["source"] = source + geodatasets = [] + for territory in territories: + try: + geodatasets.append( + S3GeoDataset(territory=territory, **mesh_config) + ) + except ValueError: + # not present for this territory and this mesh + logger.warning( + "file not found for %s on mesh=%s", territory, mesh + ) + input_geodatasets[mesh] = None + continue + + with TemporaryDirectory() as tempdir: + with ExitStack() as stack: + # download all datasets in context: download at enter + if THREADS_DOWNLOAD > 1: + threads = min(THREADS_DOWNLOAD, len(geodatasets)) + with ThreadPool(threads) as pool: + geodatasets = list( + pool.map( + stack.enter_context, + geodatasets, + timeout=60 * 2, + ).result() + ) + else: + geodatasets = [ + stack.enter_context(dset) for dset in geodatasets + ] + + if not geodatasets: + logger.warning( + "base geodataset from mesh=%s was not generated", mesh + ) + continue + + # concat S3GeoDataset + mesh_config.update( + { + "vectorfile_format": format_output, + "crs": 4326, + "borders": mesh, + "filter_by": "preprocessed", + "value": "before_cog", + "territory": "france", + "provider": "Cartiflette", + "dataset_family": "geodata", + } + ) + dset = concat_s3geodataset( + geodatasets, + output_dir=tempdir, + output_name=mesh, + **mesh_config, + ) + + input_geodatasets[mesh] = dset.copy() + + # clean intermediate datasets from local disk at exit (keep + # only concatenated S3GeoDataset, which exists only on local + # disk) + + try: + # Capture ultramarine territories geometries from IRIS to complete the + # COMMUNE geodataset + with TemporaryDirectory() as tempdir: + with input_geodatasets["IRIS"].copy() as temp: + tom_from_iris = temp.only_ultramarine_territories().to_frame() + tom_from_iris = tom_from_iris.rename( + {"NOM_COM": "NOM"}, axis=1 + ) + cities = input_geodatasets["COMMUNE"].to_frame() + concat = gpd.pd.concat( + [tom_from_iris, cities], ignore_index=True + ) + full_cities = from_frame( + concat, fs=fs, **input_geodatasets["COMMUNE"].config + ) + input_geodatasets["COMMUNE"] = full_cities + except (AttributeError, KeyError, IndexError): + # AttributeError : input_geodatasets["IRIS"] is None + # IndexError : INSEE_COM field was not found in the dataset + pass + + with ( + input_geodatasets["COMMUNE"] + if input_geodatasets["COMMUNE"] + else nullcontext() + ) as commune, ( + input_geodatasets["CANTON"] + if input_geodatasets["CANTON"] + else nullcontext() + ) as canton, ( + input_geodatasets["IRIS"] + if input_geodatasets["IRIS"] + else nullcontext() + ) as iris, ( + districts if districts else nullcontext() + ) as districts: + # download communal_districts and enter context for commune/canton/iris + + if districts: + with_districts = [False, True] + else: + with_districts = [False] + warnings.warn("ARM could not be fetched") + args = ( + list(product([commune], with_districts, simplifications_values)) + + list((product([canton], [False], simplifications_values))) + + list((product([iris], [False], simplifications_values))) + ) + args = [x for x in args if x[0]] # remove dsets with nullcontext + + func = partial( + make_one_geodataset, + communal_districts=districts, + ) + + if THREADS_DOWNLOAD > 1: + # create geodatasets with multithreading + threads = min(THREADS_DOWNLOAD, len(args)) + logger.info( + "Parallelizing simplifications with %s threads", threads + ) + with ThreadPool(threads) as pool: + iterator = pool.map(func, *list(zip(*args))).result() + + while True: + try: + uploaded.append(next(iterator)) + except StopIteration: + break + except Exception: + logger.error(traceback.format_exc()) + else: + # create geodatasets using a simple loop + for dset, with_municipal_district, simplification in args: + try: + uploaded.append( + func( + dset=dset, + with_municipal_district=with_municipal_district, + simplification=simplification, + ) + ) + except Exception: + logger.error(traceback.format_exc()) + + logger.info(f"Created files are : {uploaded}") + + success = True if uploaded else False + + return {year: success} + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + created = create_one_year_geodataset_batch(2023) diff --git a/cartiflette/pipeline/prepare_mapshaper.py b/cartiflette/pipeline/prepare_mapshaper.py deleted file mode 100644 index 3aae9457..00000000 --- a/cartiflette/pipeline/prepare_mapshaper.py +++ /dev/null @@ -1,56 +0,0 @@ -import os - -from cartiflette.config import FS -from cartiflette.s3 import list_raw_files_level, download_files_from_list - - -def prepare_local_directory_mapshaper( - path_bucket, - borders="COMMUNE", - territory="metropole", - niveau_agreg="DEPARTEMENT", - format_output="topojson", - simplification=0, - local_dir="temp", - fs=FS, -): - """ - Prepares the local directory for processing with Mapshaper. - - This function creates a local directory structure and downloads - raw shapefiles from the specified path in the file system. - - Parameters - ---------- - path_bucket : str - The path to the bucket in the file system. - borders : str, optional - The type of borders, by default "COMMUNE". - niveau_agreg : str, optional - The level of aggregation, by default "DEPARTEMENT". - format_output : str, optional - The output format, by default "topojson". - simplification : int, optional - The degree of simplification, by default 0. - local_dir : str, optional - The local directory for file storage, by default "temp". - fs : FileSystem, optional - The file system object, by default fs. - - Returns - ------- - dict - A dictionary containing paths for the original and destination directories. - - """ - local_dir = f"{local_dir}/{territory}" - os.makedirs(local_dir, exist_ok=True) - # Get all raw shapefiles from Minio - list_raw_files = list_raw_files_level(fs, path_bucket, borders=borders) - download_files_from_list(fs, list_raw_files, local_dir=local_dir) - local_path_destination = ( - f"{local_dir}/{niveau_agreg}/{format_output}/{simplification=}" - ) - os.makedirs(local_path_destination, exist_ok=True) - paths = {"path_origin": local_dir, "path_destination": local_path_destination} - return paths diff --git a/cartiflette/pipeline_constants.py b/cartiflette/pipeline_constants.py new file mode 100644 index 00000000..e3347d87 --- /dev/null +++ b/cartiflette/pipeline_constants.py @@ -0,0 +1,268 @@ +# -*- coding: utf-8 -*- + +import os +from cartiflette.config import DATASETS_HIGH_RESOLUTION + +# Keys for COG_TERRITOIRE and IRIS are True for high resolution and False for +# low resolution of datasets +COG_TERRITOIRE = { + False: "EXPRESS-COG-CARTO-TERRITOIRE", + True: "EXPRESS-COG-TERRITOIRE", +} +IRIS = { + # Keys are DATASETS_HIGH_RESOLUTION's potential value + False: "CONTOUR-IRIS", + True: "IRIS-GE", +} + +PIPELINE_DOWNLOAD_ARGS = { + "ADMIN-EXPRESS": [ + "IGN", + "ADMINEXPRESS", + COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION], + [ + "guadeloupe", + "martinique", + "guyane", + "reunion", + "mayotte", + "metropole", + ], + ], + "IRIS": [ + "IGN", + "IRIS", + IRIS[DATASETS_HIGH_RESOLUTION], + [ + "guadeloupe", + "martinique", + "guyane", + "reunion", + "mayotte", + "metropole", + "saint-pierre-et-miquelon", + "saint-barthelemy", + "saint-martin", + ], + ], + "COG": [ + "Insee", + "COG", + [ + "COMMUNE", + "CANTON", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "COMMUNE-OUTRE-MER", + ], + "france_entiere", + ], + "TAGC": ["Insee", "TAGC", "APPARTENANCE"], + "TAGIRIS": ["Insee", "TAGIRIS", "APPARTENANCE"], + "CORRESPONDANCE-SIREN-INSEE-COMMUNES": [ + "DGCL", + "BANATIC", + "CORRESPONDANCE-SIREN-INSEE-COMMUNES", + ], + "EPCI-FP": ["Insee", "ZONAGES", "EPCI-FP"], + "EPT": ["Insee", "ZONAGES", "EPT"], + "UNITES-URBAINES": ["Insee", "ZONAGES", "UNITES-URBAINES"], + "BASSINS-VIE": ["Insee", "ZONAGES", "BASSINS-VIE"], + "AIRES-ATTRACTION-VILLES": ["Insee", "ZONAGES", "AIRES-ATTRACTION-VILLES"], + "ZONES-EMPLOI": ["Insee", "ZONAGES", "ZONES-EMPLOI"], + "POPULATION": [ + "Insee", + "POPULATION", + "POPULATION-IRIS-FRANCE-HORS-MAYOTTE", + ], + "POPULATION-COM": ["Insee", "POPULATION", "POPULATION-IRIS-COM"], +} + +if os.environ.get("ENVIRONMENT", "test") != "test": + PIPELINE_CRS = [2154, 4326, 3857] + PIPELINE_SIMPLIFICATION_LEVELS = [100, 40] + PIPELINE_FORMATS = ["geojson", "topojson", "gpkg"] +else: + PIPELINE_CRS = [4326] + PIPELINE_SIMPLIFICATION_LEVELS = [40] + PIPELINE_FORMATS = ["topojson"] + + +# which dissolutions can be operated from a given raw geodataset, depending +# of it's source (either from IRIS or from COMMUNES) +AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH = { + "IRIS": [ + "IRIS", + "COMMUNE", + "ARRONDISSEMENT_MUNICIPAL", + "EPCI", + "EPT", + "UNITE_URBAINE", + "ZONE_EMPLOI", + "BASSIN_VIE", + "AIRE_ATTRACTION_VILLES", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + ], + "ARRONDISSEMENT_MUNICIPAL": [ + "ARRONDISSEMENT_MUNICIPAL", + ], + "COMMUNE": [ + "COMMUNE", + "EPCI", + "EPT", + "UNITE_URBAINE", + "ZONE_EMPLOI", + "BASSIN_VIE", + "AIRE_ATTRACTION_VILLES", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + ], + "CANTON": [ + "CANTON", + ], +} + +# which territorial splits can be derived from a given geodataset (which +# borders' levels has been deduced from raw sources by dissolution) +AVAILABLE_TERRITORIAL_SPLITS_FOR_BORDERS = { + # borders -> [filter_by1, filter_by2, ... ] + "IRIS": [ + # "COMMUNE" -> too much files generated, trigger this only if usecase + # CANTON -> if INSEE can prepare a junction between IRIS and CANTON + "BASSIN_VIE", + "ZONE_EMPLOI", + "UNITE_URBAINE", + "AIRE_ATTRACTION_VILLES", + "EPT", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "ARRONDISSEMENT_MUNICIPAL": [ + "BASSIN_VIE", + "ZONE_EMPLOI", + "UNITE_URBAINE", + "AIRE_ATTRACTION_VILLES", + "EPT", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "COMMUNE": [ + "BASSIN_VIE", + "ZONE_EMPLOI", + "UNITE_URBAINE", + "AIRE_ATTRACTION_VILLES", + "EPT", + "ARRONDISSEMENT", + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "EPCI": [ + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "EPT": [ + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "CANTON": [ + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "ARRONDISSEMENT": [ + "DEPARTEMENT", + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "DEPARTEMENT": [ + "REGION", + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ], + "REGION": [ + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "BASSIN_VIE": [ + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "ZONE_EMPLOI": [ + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "UNITE_URBAINE": [ + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], + "AIRE_ATTRACTION_VILLES": [ + "TERRITOIRE", + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + ], +} + +# Check integrity +all_dissolutions = { + dissolution + for key, dissolutions in AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH.items() + for dissolution in dissolutions +} + +all_borders = { + split + for key, splits in AVAILABLE_TERRITORIAL_SPLITS_FOR_BORDERS.items() + for split in splits +} | { + # unwanted splits (too much files without due use case) + "IRIS", # -> will never need to make a map for a given IRIS + "COMMUNE", # -> should never need to make a map for a given COMMUNE + "ARRONDISSEMENT_MUNICIPAL", # -> should never need to make a map for a given ARM + "CANTON", # -> might need it ? + "EPCI", # -> might need it ? +} + +differences = (all_borders ^ all_dissolutions) - { + "FRANCE_ENTIERE", + "FRANCE_ENTIERE_DROM_RAPPROCHES", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", +} +if differences: + raise ValueError( + "keys of AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH must be the same as " + "every available dissolution from " + "AVAILABLE_DISSOLUTIONS_FROM_RAW_MESH. Found the following " + f"differences : {differences}" + ) diff --git a/cartiflette/s3/__init__.py b/cartiflette/s3/__init__.py index 1f124522..6a12633b 100644 --- a/cartiflette/s3/__init__.py +++ b/cartiflette/s3/__init__.py @@ -1,8 +1,12 @@ -from .upload_raw_s3 import upload_s3_raw -from .list_files_s3 import download_files_from_list, list_raw_files_level from .download_vectorfile import download_vectorfile_url_all +from .geodataset import S3GeoDataset, concat_s3geodataset +from .dataset import S3Dataset +from .inventory import make_s3_inventory __all__ = [ - "upload_s3_raw", "download_files_from_list", "list_raw_files_level", - "download_vectorfile_url_all" + "download_vectorfile_url_all", + "S3GeoDataset", + "S3Dataset", + "concat_s3geodataset", + "make_s3_inventory", ] diff --git a/cartiflette/s3/dataset.py b/cartiflette/s3/dataset.py new file mode 100644 index 00000000..5f9b7820 --- /dev/null +++ b/cartiflette/s3/dataset.py @@ -0,0 +1,299 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Classe générique pour travailler autour d'un dataset présent sur le S3 +""" + +import logging +import os +import shutil +import tempfile +from typing import List +import warnings + +from diskcache import Cache +import pandas as pd +from s3fs import S3FileSystem +from retrying import retry + + +from cartiflette.config import FS, RETRYING, MAPSHAPER_QUIET +from cartiflette.utils import ( + create_path_bucket, + ConfigDict, + DICT_CORRESP_ADMINEXPRESS, +) + +logger = logging.getLogger(__name__) +cache = Cache("cartiflette-s3-cache", timeout=3600) + +if not RETRYING: + # patch retrying + def retry(*args, **kwargs): + def decorator(func): + return func + + return decorator + + +class S3Dataset: + """ + Base class representing a dataset stored on the S3 + + This class is used on it's own only for tabular datasets (to be joined to + S3GeoDataset for enrichment) + """ + + files = None + main_filename = None + s3_dirpath = None + local_dir = None + + def __init__( + self, + fs: S3FileSystem = FS, + filename: str = "*", + build_from_local: str = None, + **config: ConfigDict, + ): + """ + Create a S3Dataset. + + Parameters + ---------- + fs : S3FileSystem, optional + S3FileSystem used for storage. The default is FS. + filename : str, optional + In case there are multiple files into the same folder define it + to avoid catching the wrong file from S3FileSystem + (this should only occur with the download of raw datasets with + COMMUNE.shp and ARRONDISSEMENT_MUNICIPAL.shp being stored in the + same directory). + The default is "*". + For instance, "COMMUNE.shp" + build_from_local : str, optional + If the object is generated from local files, should be the path + to the main file of the dataset. + If None, the path will be deduced from the S3 and the main filename + also. + **config : ConfigDict + Other arguments to define the path on the S3 to the dataset. + """ + self.fs = fs + self.config = config + self.build_from_local = build_from_local + self.local_files = [] + + self.filename = filename.rsplit(".", maxsplit=1)[0] + + self.source = ( + f"{config.get('provider', '')}:{config.get('source', '')}" + ) + + if build_from_local and not os.path.exists(build_from_local): + raise ValueError(f"File not found at {build_from_local}") + + self.get_path_of_dataset() + + def __str__(self): + return f"" + + def __repr__(self): + return self.__str__() + + def __enter__(self): + "download file into local folder at enter" + if not self.build_from_local: + self.local_dir = tempfile.mkdtemp() + self.to_local_folder_for_mapshaper() + return self + + def _get_columns(self, **kwargs): + "return the current dataset's columns" + df = self.to_frame(**kwargs, nrows=5) + return df.columns.tolist() + + @staticmethod + def find_column_name(column: str, columns: List[str]) -> str: + """ + Retrieve a column's full name among available columns, using a + compiled regex expression from DICT_CORRESP_ADMINEXPRESS. + + Parameters + ---------- + column : str + The searched column + columns : List[str] + The list of columns to search into. + + Raises + ------ + ValueError + If the searched column corresponds to more than one result. + IndexError + If the searched column is not found. + + + Returns + ------- + str + The column's full name. + + """ + compiled = DICT_CORRESP_ADMINEXPRESS[column] + founds = [col for col in columns if compiled.match(col)] + if len(founds) > 1: + raise ValueError(f"{column=} matched multiple columns : {founds=}") + try: + return founds[0] + except IndexError as exc: + raise IndexError( + f"{column=}/{compiled=} not found among {columns=}" + ) from exc + + def to_frame(self, **kwargs) -> pd.DataFrame: + return pd.read_csv( + os.path.join(self.local_dir, self.main_filename), **kwargs + ) + + def clean(self): + "remove files from local dir" + try: + try: + shutil.rmtree(self.local_dir) + except FileNotFoundError: + pass + except Exception as exc: + warnings.warn(exc) + + def __exit__(self, *args, **kwargs): + "remove tempfiles at exit" + self.clean() + + def get_path_of_dataset(self): + "retrieve dataset's full paths on S3" + path = os.path.dirname(create_path_bucket(self.config)) + search = f"{path}/**/{self.filename}" + if self.filename != "*": + search += ".*" + + init_level = logging.getLogger("botocore.credentials").level + if MAPSHAPER_QUIET: + logging.getLogger("botocore.credentials").setLevel( + logging.CRITICAL + ) + self.s3_files = self.fs.glob(search) + logging.getLogger("botocore.credentials").setLevel(init_level) + + if self.build_from_local: + # This S3Dataset has been created from a local file + self.s3_dirpath = path + self.local_dir = os.path.dirname(self.build_from_local) + self.main_filename = os.path.basename(self.build_from_local) + + return + + if not self.s3_files: + raise ValueError( + f"this dataset is not available on S3 on {search}" + ) + + if len(self.s3_files) > 1: + main_filename = ( + self.s3_files[0].rsplit(".", maxsplit=1)[0] + ".shp" + ) + else: + main_filename = self.s3_files[0] + + self.main_filename = os.path.basename(main_filename) + self.s3_dirpath = os.path.dirname(main_filename) + + @retry(stop_max_attempt_number=3, wait_fixed=2000) + def to_s3(self): + "upload file to S3" + target = self.s3_dirpath + if not target.endswith("/"): + target += "/" + logger.debug("sending %s -> %s", self.local_dir, target) + + init_level = logging.getLogger("botocore.credentials").level + if MAPSHAPER_QUIET: + logging.getLogger("botocore.credentials").setLevel( + logging.CRITICAL + ) + self.fs.put(self.local_dir + "/*", target, recursive=True) + logging.getLogger("botocore.credentials").setLevel(init_level) + + def _read(self, src: str) -> bytes: + """ + Read bytes from a file on S3FileSystem with disk cache support + + Parameters + ---------- + src : str + Source of file + + Returns + ------- + bytes + File content + + """ + try: + return cache[src] + except KeyError: + init_level = logging.getLogger("botocore.credentials").level + if MAPSHAPER_QUIET: + logging.getLogger("botocore.credentials").setLevel( + logging.CRITICAL + ) + with self.fs.open(src, "rb") as f: + content = f.read() + logging.getLogger("botocore.credentials").setLevel(init_level) + cache[src] = content + return content + + def download(self, src: str, dest: str): + """ + Download a file from S3FileSystem to localdir with cache support + + Parameters + ---------- + src : str + Path of source file on S3FileSystem + dest : str + Path to write the file's content on local directory. + + Returns + ------- + None. + + """ + "download to dest with disk cache" + content = self._read(src) + with open(dest, "wb") as f: + f.write(content) + + def to_local_folder_for_mapshaper(self): + "download to local dir and prepare for use with mapshaper" + + if not self.s3_files: + raise ValueError( + f"this dataset is not available on S3 : {self.s3_dirpath}" + ) + + files = [] + + # Get all files (plural in case of shapefile) from Minio + logger.debug("downloading %s to %s", self.s3_files, self.local_dir) + for file in self.s3_files: + path = f"{self.local_dir}/{file.rsplit('/', maxsplit=1)[-1]}" + self.download(file, path) + logger.info("file written to %s", path) + files.append(path) + + self.local_files = files + + def update_s3_path_evaluation(self): + path = os.path.dirname(create_path_bucket(self.config)) + self.s3_dirpath = path diff --git a/cartiflette/s3/geodataset.py b/cartiflette/s3/geodataset.py new file mode 100644 index 00000000..3379ab2a --- /dev/null +++ b/cartiflette/s3/geodataset.py @@ -0,0 +1,1077 @@ +# -*- coding: utf-8 -*- + +from contextlib import ExitStack +from copy import deepcopy +from glob import glob +from itertools import product +import logging +import os +import re +import shutil +import tempfile +from typing import List + +try: + from typing import Self +except ImportError: + # python < 3.11 + Self = "S3GeoDataset" + +import fiona +import geopandas as gpd +from pebble import ThreadPool +from s3fs import S3FileSystem + +from .dataset import S3Dataset +from cartiflette.mapshaper import ( + mapshaper_convert_reproject, + mapshaper_enrich, + mapshaper_bring_closer, + mapshaper_split, + mapshaper_dissolve, + mapshaper_concat, + mapshaper_remove_cities_with_districts, + mapshaper_process_communal_districts, + mapshaper_combine_districts_and_cities, + mapshaper_simplify, + mapshaper_add_field, + mapshaper_capture_cities_from_ultramarine_territories, +) +from cartiflette.utils import ConfigDict +from cartiflette.config import ( + FS, + THREADS_DOWNLOAD, + INTERMEDIATE_FORMAT, + MAPSHAPER_QUIET, +) +from cartiflette.pipeline_constants import PIPELINE_CRS, PIPELINE_FORMATS +from cartiflette.utils.dict_correspondance import ( + create_format_driver, + create_format_standardized, +) + +logger = logging.getLogger(__name__) + + +class S3GeoDataset(S3Dataset): + """ + Base class representing a geodataset stored on the S3 + + An instance can either be an existing file loaded from the S3 or a new + geodataset in the process of creation. In that case, a warning will be + displayed at creation to alert that the file is not present on the S3 + (yet). + """ + + def __str__(self): + return f"" + + def __copy__(self): + """ + Copy a S3GeoDataset. If the original S3GeoDataset has already a + local_dir attribute, this will create a new tempdir inside it. + Note that this new tempdir will be removed at the primary S3GeoDataset + object's __exit__ method execution. + + Returns + ------- + new : S3GeoDataset + Copied S3GeoDataset. + + """ + + if os.path.exists(os.path.join(self.local_dir, self.main_filename)): + # file is already on local disk -> create a new tempdir that should + # be cleaned on __exit__method anyway + new_tempdir = tempfile.mkdtemp() + target_name = self.main_filename.rsplit(".", maxsplit=1)[0] + for file in glob(os.path.join(self.local_dir, f"{target_name}.*")): + shutil.copy(file, new_tempdir) + + new = S3GeoDataset( + self.fs, + self.filename, + build_from_local=os.path.join( + self.local_dir, self.main_filename + ), + **deepcopy(self.config), + ) + new.local_dir = new_tempdir + + else: + new = S3GeoDataset( + self.fs, + self.filename, + self.build_from_local, + **deepcopy(self.config), + ) + + new.main_filename = self.main_filename + + return new + + def to_format(self, format_output: str, epsg: int): + if format_output == INTERMEDIATE_FORMAT and epsg == 4326: + return self + + if format_output in { + "shapefile", + "geojson", + "topojson", + "json", + "dbf", + "csv", + "tsv", + "svg", + }: + self.reproject(epsg=epsg, format_output=format_output) + else: + getattr(self, f"to_{format_output}")(epsg) + return self + + def to_gpkg(self, epsg: int): + """ + Replace the current main_file by a geopackage format (not handled by + mapshaper, needs geopandas) + """ + + # init_level = logging.getLogger("pyogrio").level + # if MAPSHAPER_QUIET: + # logging.getLogger("pyogrio._io").setLevel(logging.CRITICAL) + + # try: + path = os.path.join(self.local_dir, self.main_filename) + path = path.rsplit(".", maxsplit=1)[0] + ".gpkg" + gdf = self.to_frame() + if epsg != 4326: + gdf = gdf.to_crs(epsg) + gdf.to_file(path, driver="GPKG", engine="fiona") + self._substitute_main_file(path) + self.config["vectorfile_format"] = "gpkg" + self.config["crs"] = epsg + self.update_s3_path_evaluation() + # except Exception: + # raise + # finally: + # logging.getLogger("pyogrio").setLevel(init_level) + + # def to_shapefile(self): + # """ + # TODO Quick and dirty hack, to be removed to handle native mapshaper + # output + # Replace the current main_file by a shapefile format (using geopandas) + # """ + # path = os.path.join(self.local_dir, self.main_filename) + # path = path.rsplit(".", maxsplit=1)[0] + ".shp" + # self.to_frame().to_file(path) + # self._substitute_main_file(path) + # self.config["vectorfile_format"] = "shp" + # self.update_s3_path_evaluation() + + def to_frame(self, **kwargs) -> gpd.GeoDataFrame: + "Read the geodataset from local file" + with fiona.Env(OGR_GEOJSON_MAX_OBJ_SIZE="0"): + return gpd.read_file( + os.path.join(self.local_dir, self.main_filename), + engine="fiona", + **kwargs, + ) + + def _get_columns(self, **kwargs): + "Get the columns of the dataset" + df = self.to_frame(**kwargs, rows=5) + return df.columns.tolist() + + def copy(self): + """ + Create a deepcopy of the S3GeoDataset (with a copy of initial file on + a new local dir if the initial object has a local file) + """ + return self.__copy__() + + def _substitute_main_file(self, new_file: str): + "Set a new file as reference for the S3GeoDataset from local disk" + if not os.path.dirname(new_file) == self.local_dir: + raise ValueError( + f"cannot substitute main_file with {new_file=} and " + f"{self.local_dir=} : directories are not identical" + ) + + if os.path.basename(new_file) == self.main_filename: + return + + os.unlink(f"{self.local_dir}/{self.main_filename}") + self.main_filename = os.path.basename(new_file) + + def reproject( + self, + epsg: int = 4326, + format_output: str = "geojson", + quiet: bool = MAPSHAPER_QUIET, + ): + "project to a given EPSG using mapshaper" + input_file = f"{self.local_dir}/{self.main_filename}" + + new_file = mapshaper_convert_reproject( + input_file=input_file, + epsg=epsg, + output_dir=self.local_dir, + output_name=self.main_filename.rsplit(".", maxsplit=1)[0], + output_format=format_output, + filter_by=self.config["territory"], + quiet=quiet, + ) + self._substitute_main_file(new_file) + self.config["crs"] = epsg + self.config["vectorfile_format"] = format_output + self.update_s3_path_evaluation() + return new_file + + def add_field( + self, + label: str, + value: str, + format_output: str = "geojson", + quiet: bool = MAPSHAPER_QUIET, + ): + "add a static/dynamic field using mapshaper" + input_geodata = f"{self.local_dir}/{self.main_filename}" + output = mapshaper_add_field( + input_file=input_geodata, + label=label, + value=value, + output_dir=self.local_dir, + output_name=self.main_filename.rsplit(".", maxsplit=1)[0], + output_format=format_output, + quiet=quiet, + ) + self._substitute_main_file(output) + + def enrich( + self, + metadata_file: S3Dataset, + keys: list, + dtype: dict, + drop: list, + rename: dict, + format_output: str = "geojson", + quiet: bool = MAPSHAPER_QUIET, + ): + "enrich with metadata using mapshaper" + input_metadata = ( + f"{metadata_file.local_dir}/{metadata_file.main_filename}" + ) + input_geodata = f"{self.local_dir}/{self.main_filename}" + output = mapshaper_enrich( + input_geodata_file=input_geodata, + input_metadata_file=input_metadata, + keys=keys, + dtype=dtype, + drop=drop, + rename=rename, + output_dir=self.local_dir, + output_name=self.main_filename.rsplit(".", maxsplit=1)[0], + output_format=format_output, + quiet=quiet, + ) + self._substitute_main_file(output) + + def simplify( + self, + format_output: str, + simplification: int = 0, + quiet: bool = MAPSHAPER_QUIET, + ): + "simplify the geometries" + simplification = simplification if simplification else 0 + if simplification != 0: + option_simplify = ( + f"-simplify {simplification}% interval=.5 -clean " + ) + else: + option_simplify = "" + + input_geodata = f"{self.local_dir}/{self.main_filename}" + output = mapshaper_simplify( + input_geodata, + option_simplify=option_simplify, + output_dir=self.local_dir, + output_name=self.main_filename.rsplit(".", maxsplit=1)[0], + output_format=format_output, + quiet=quiet, + ) + + # update path on S3 + self.config["simplification"] = simplification + self._substitute_main_file(output) + self.update_s3_path_evaluation() + + if format_output.lower() == "topojson": + # cannot fix geometries with geopandas anyway + return + + format_standardized = create_format_standardized() + gpd_driver = create_format_driver() + format_write = format_standardized[format_output.lower()] + driver = gpd_driver[format_write] + + # Ensure geometries' validity + gdf = gpd.read_file(output, engine="fiona") + if not gdf["geometry"].is_valid.all(): + gdf["geometry"] = gdf["geometry"].buffer(0) + gdf.to_file(output, driver=driver, engine="fiona") + + def dissolve( + self, + by: List[str], + copy_fields: List[str] = None, + calc: List[str] = None, + format_output: str = "geojson", + quiet: bool = MAPSHAPER_QUIET, + ): + """ + Dissolve geometries and rename local file using mapshaper. + + Dissolve geometries on field `bv`, keeping fields `copy_fields`. Other + fields should be computaded using javascript functions with `calc` + argument. The original file will be overwritten, then renamed to + {by}.{formate_intermediate}. self.main_filename will be updated. + + + Parameters + ---------- + by : List[str] + Fields used to dissolve + calc : Listr[str], optional + Fields on which computed should be operated, describing valid js + functions. For instance ["POPULATION=sum(POPULATION)"]. The default + is None. + copy_fields : List[str], optional + Copies values from the first feature in each group of dissolved + features. The default is None. + format_output : str, optional + Output format. The default is geojson + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + + Returns + ------- + None. + + """ + init = f"{self.local_dir}/{self.main_filename}" + out = mapshaper_dissolve( + input_file=init, + by=by, + copy_fields=copy_fields, + calc=calc, + output_dir=self.local_dir, + output_name="_".join(by), + output_format=format_output, + quiet=quiet, + ) + self._substitute_main_file(out) + + def bring_drom_closer( + self, + level_agreg: str = "DEPARTEMENT", + format_output: str = "geojson", + bring_out_idf: bool = True, + quiet: bool = MAPSHAPER_QUIET, + ): + """ + Bring ultramarine territories closer to France. This method is executed + **IN PLACE** and the attribute self.main_file will reference the new + geodataset. + + Parameters + ---------- + level_agreg : str, optional + The desired agregation. The default is "DEPARTEMENT". + Should be among ['AIRE_ATTRACTION_VILLES', 'BASSIN_VIE', + 'DEPARTEMENT', 'EMPRISES', 'REGION', 'UNITE_URBAINE', + 'ZONE_EMPLOI'] + format_output : str, optional + The desired output format (which will also be used for intermediate + files creation). The default is "geojson". + bring_out_idf : bool, optional + If True, will extract IdF and zoom on it. The default is True. + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + + Returns + ------- + None. + + """ + + out = mapshaper_bring_closer( + input_file=f"{self.local_dir}/{self.main_filename}", + bring_out_idf=bring_out_idf, + output_dir=self.local_dir, + output_name="idf_combined", + output_format=format_output, + level_agreg=level_agreg, + quiet=quiet, + ) + self._substitute_main_file(out) + + def split_file( + self, + split_variable: str, + crs: int = 4326, + format_output: str = "geojson", + simplification: int = 0, + quiet: bool = MAPSHAPER_QUIET, + **kwargs, + ) -> list[Self]: + """ + Split a file into singletons, based on one field (including + reprojection, simplification and format conversion if need be) + + Parameters + ---------- + split_variable : str + Variable to split files onto + crs : int, optional + EPSG to project the splitted file onto. The default is 4326. + format_output : str, optional + Choosen format to write the output on. The default is "geojson". + simplification : int, optional + Degree of simplification. The default is 0. + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + kwargs : + Optional values for ConfigDict to ensure the correct generation of + the afferant geodatasets. For instance, `borders='DEPARTEMENT` + + Returns + ------- + list[S3GeoDataset] + return a list of S3GeoDataset objects + + """ + + if simplification != 0: + option_simplify = ( + f"-simplify {simplification}% interval=.5 -clean " + ) + else: + option_simplify = "" + + files = mapshaper_split( + input_file=f"{self.local_dir}/{self.main_filename}", + layer_name="", + split_variable=split_variable, + output_dir=f"{self.local_dir}/splitted", + output_format=format_output, + crs=crs, + option_simplify=option_simplify, + quiet=quiet, + ) + + geodatasets = [] + + for file in files: + new_config = deepcopy(self.config) + new_config.update(kwargs) + new_config.update( + { + "crs": crs, + "value": os.path.basename(file).replace( + f".{format_output}", "" + ), + "vectorfile_format": format_output, + "simplification": simplification, + } + ) + + geodatasets.append( + from_file( + file_path=file, + fs=self.fs, + **new_config, + ).copy() + ) + + return geodatasets + + def create_downstream_geodatasets( + self, + metadata: S3Dataset, + init_geometry_level="IRIS", + dissolve_by="COMMUNE", + niveau_agreg="DEPARTEMENT", + simplification=0, + ) -> List[Self]: + """ + TODO : update docstring (arguments also) + Create "children" geodatasets based on arguments and send them to S3. + + Do the following processes: + - join the current geodataset with the metadata to enrich it; + - dissolve geometries if init_geometry_level != dissolve_by + - bring ultramarine territories closer + if niveau_agreg == "FRANCE_ENTIERE_DROM_RAPPROCHES" + - extract IDF if niveau_agreg=="FRANCE_ENTIERE_IDF_DROM_RAPPROCHES" + - split the geodataset based on niveau_agreg + - project the geodataset into the given CRS + - convert the file into the chosen output + - upload those datasets to S3 storage system + + The "children" may result to a single file depending of niveau_agreg. + + Note that some of those steps are done **IN PLACE** on the parent + geodataset (enrichment, dissolution, agregation). Therefore, the + geodataset should not be re-used after a call to this method. + + Parameters + ---------- + metadata : S3Dataset + The metadata file to use to enrich the geodataset + format_output : str, optional + The output format, by default "geojson". + init_geometry_level : str, optional + The level of basic mesh for the geometries. The default is IRIS. + Should be among ['IRIS', 'CANTON', 'ARRONDISSEMENT_MUNICIPAL'] + dissolve_by : str, optional + The level of basic mesh for the geometries. The default is COMMUNE. + Should be among [ + 'REGION', 'DEPARTEMENT', 'BASSIN_VIE', + 'AIRE_ATTRACTION_VILLES', 'UNITE_URBAINE', 'ZONE_EMPLOI', + 'TERRITOIRE', 'ARRONDISSEMENT_MUNICIPAL', 'EPCI', 'EPT', + ] + niveau_agreg : str, optional + The level of aggregation for splitting the dataset into singletons, + by default "DEPARTEMENT". + Should be among ['REGION', 'DEPARTEMENT', 'FRANCE_ENTIERE', + 'FRANCE_ENTIERE_DROM_RAPPROCHES', 'LIBELLE_REGION', + 'LIBELLE_DEPARTEMENT', 'BASSIN_VIE', 'AIRE_ATTRACTION_VILLES', + 'UNITE_URBAINE', 'ZONE_EMPLOI', 'TERRITOIRE'] + crs : int, optional + The coordinate reference system (CRS) code to project the children + datasets into. By default 4326. + simplification : int, optional + The degree of wanted simplification, by default 0. + + Returns + ------- + List[S3GeoDataset] + The output path of the processed and split shapefiles. + + """ + + output_crs_conf = [ + {"epsg": x[0], "format_output": x[1]} + for x in product(PIPELINE_CRS, PIPELINE_FORMATS) + ] + + niveau_agreg = niveau_agreg.upper() + init_geometry_level = init_geometry_level.upper() + + simplification = simplification if simplification else 0 + + # Enrich files with metadata (COG, etc.) + + available_columns = set(self._get_columns()) | set( + metadata._get_columns() + ) + + if init_geometry_level == "IRIS": + keys = ["CODE_IRIS", "CODE_IRIS"] + drop = ["ID", "NOM_COM"] + elif init_geometry_level == "ARRONDISSEMENT_MUNICIPAL": + keys = ["INSEE_ARM", "INSEE_ARM"] + drop = [ + "POPULATION", + "ID", + "NOM_M", + ] + elif init_geometry_level == "COMMUNE": + keys = ["INSEE_COM", "INSEE_COM"] + drop = [ + "POPULATION", + "ID", + "NOM_M", + ] + elif init_geometry_level == "CANTON": + keys = ["CAN", "CAN"] + drop = ["ID"] + self.add_field("CAN", "INSEE_DEP+INSEE_CAN") + else: + # TODO if new base mesh + pass + + if len(set(keys) & available_columns) < len(set(keys)): + raise ValueError( + f"keys must be among {available_columns}, " + f"found {set(keys)} instead" + ) + + if len(set(drop) & available_columns) < len(drop): + missing = set(drop) - available_columns + raise ValueError( + f"drop must be among {available_columns}, following columns " + f"are missing : {missing}" + ) + + dtype = set(keys) | { + "SIREN_EPCI", + "SIREN_COMMUNE", + "INSEE_DEP", + "INSEE_REG", + "CAN", + "BURCENTRAL", + "REG", + "ZE[0-9]{4}", + "TUU[0-9]{4}", + "TDUU[0-9]{4}", + "TAAV[0-9]{4}", + "TDAAV[0-9]{4}", + "CATEAAV[0-9]{4}", + } + dtype = { + col: "str" + for x in dtype + for col in available_columns + if re.match(x, col) + } + + self.enrich( + metadata_file=metadata, + keys=keys, + dtype=dtype, + drop=drop, + rename={}, + format_output=INTERMEDIATE_FORMAT, + ) + + logger.info("new columns are %s", self._get_columns()) + + if init_geometry_level != dissolve_by: + # Dissolve geometries if desired (will replace the local file + # geodata file based on a communal mesh with one using the desired + # mesh + + # Dissolve by both dissolve_by AND niveau_agreg to ensure both + # dissolution and splitability + gdf = self.to_frame() + available_columns = gdf.columns.tolist() + by = self.find_column_name(dissolve_by, available_columns) + if niveau_agreg not in ( + "FRANCE_ENTIERE_DROM_RAPPROCHES", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ): + aggreg_col = self.find_column_name( + niveau_agreg, available_columns + ) + else: + aggreg_col = "AREA" + keys = [by, aggreg_col] + + # And keep all columns which are identical in each subgroup after + # dissolution + summable columns + keep = ( + gdf.drop("geometry", axis=1) + .groupby(keys, dropna=False) + .nunique() + == 1 + ).all() + keep = keep[keep].index.tolist() + + calc = [] + pops = [ + x for x in available_columns if re.match("POPULATION.*", x) + ] + if pops: + calc += [f"{x}=sum({x})" for x in pops] + if "IDF" in available_columns: + calc += ["IDF=max(IDF)"] + + by_keys = [by, aggreg_col] + + self.dissolve( + by=by_keys, + copy_fields=keep, + calc=calc, + format_output=INTERMEDIATE_FORMAT, + ) + + # Bring ultramarine territories closer to France if needed + if niveau_agreg in ( + "FRANCE_ENTIERE_DROM_RAPPROCHES", + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES", + ): + self.bring_drom_closer( + level_agreg=dissolve_by, + format_output=INTERMEDIATE_FORMAT, + bring_out_idf=( + niveau_agreg == "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES" + ), + ) + + # Split datasets, based on the desired "niveau_agreg" and proceed to + # desired level of simplification + columns = self._get_columns() + split_by = self.find_column_name(niveau_agreg, columns) + + new_datasets = self.split_file( + crs=4326, + format_output=INTERMEDIATE_FORMAT, + simplification=simplification, + split_variable=split_by, + filter_by=niveau_agreg, + borders=dissolve_by, + ) + + # fix config for storage on S3 + dataset_family = {"dataset_family": "production"} + [dset.config.update(dataset_family) for dset in new_datasets] + + new_datasets = [ + dset.copy().to_format(**config) + for dset in new_datasets + for config in output_crs_conf + ] + [dset.update_s3_path_evaluation() for dset in new_datasets] + + # Upload new datasets to S3 + with ExitStack() as stack: + # enter context for each new dataset instead of looping to allow + # for multithreading (cleaned locally at exitstack anyway) + [stack.enter_context(dset) for dset in new_datasets] + + if THREADS_DOWNLOAD > 1: + threads = min(THREADS_DOWNLOAD, len(new_datasets)) + with ThreadPool(threads) as pool: + + def upload(dset): + return dset.to_s3() + + list(pool.map(upload, new_datasets).result()) + else: + [dset.to_s3() for dset in new_datasets] + + return new_datasets + + def only_ultramarine_territories( + self, quiet: bool = MAPSHAPER_QUIET + ) -> Self: + """ + Extracts only ultramarine territories from the given IRIS file and + dissolve it to cities. + + Parameters + ---------- + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + + Returns + ------- + S3GeoDataset : new object with only the subset for COM + + """ + iris_file = f"{self.local_dir}/{self.main_filename}" + tom = mapshaper_capture_cities_from_ultramarine_territories( + input_city_file=iris_file, + output_dir=f"{self.local_dir}/tom", + output_name="TOM", + output_format=INTERMEDIATE_FORMAT, + quiet=quiet, + ) + new_config = deepcopy(self.config) + new_config.update( + {"filter_by": "COLLECTIVITE_OUTRE_MER", "value": "France"} + ) + tom = from_file(file_path=tom, **new_config) + + gdf = tom.to_frame() + available_columns = gdf.columns.tolist() + by = self.find_column_name("COMMUNE", available_columns) + # keep all columns which are identical in each subgroup after + # dissolution + summable columns (like pop) + keep = ( + gdf.drop("geometry", axis=1).groupby([by]).nunique() == 1 + ).all() + keep = keep[keep].index.tolist() + + calc = [] + pops = [x for x in available_columns if re.match("POPULATION.*", x)] + if pops: + calc += [f"{x}=sum({x})" for x in pops] + if "IDF" in available_columns: + calc += ["IDF=max(IDF)"] + + tom.dissolve( + by=[by], + copy_fields=keep, + calc=calc, + format_output=INTERMEDIATE_FORMAT, + ) + return tom + + def substitute_municipal_districts( + self, + communal_districts: Self, + format_output: str = "geojson", + quiet: bool = MAPSHAPER_QUIET, + ) -> Self: + """ + Create a new composite S3GeoDataset from communal districts (Paris, + Lyon and Marseille) and other "classical" cities (having no communal + districts) + + Parameters + ---------- + communal_districts : S3GeoDataset + S3GeoDataset representing the communal districts (should be + already downloaded, so this should be generated through a with + statement). + format_output : str, optional + Desired output format. The default is "geojson". + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + + Returns + ------- + S3GeoDataset + New S3GeoDataset object reprensenting the dataset. This dataset is + **NOT** sent to S3. + + """ + + # preprocess cities : remove cities having communal districts + city_file = f"{self.local_dir}/{self.main_filename}" + city_file = mapshaper_remove_cities_with_districts( + input_city_file=city_file, + output_dir=f"{self.local_dir}/singles", + output_name="COMMUNE", + output_format=INTERMEDIATE_FORMAT, + quiet=quiet, + ) + + # note : communal_districts has it's self local_dir which should be + # in f"{self.local_dir}/{communal_districts.config['territory']}" ! + communal_districts.reproject(format_output=format_output, epsg=4326) + communal_districts_file = ( + f"{communal_districts.local_dir}/" + f"{communal_districts.main_filename}" + ) + + communal_districts_file = mapshaper_process_communal_districts( + input_communal_districts_file=communal_districts_file, + output_dir=f"{self.local_dir}/districts", + output_name="ARRONDISSEMENT_MUNICIPAL", + output_format=INTERMEDIATE_FORMAT, + quiet=quiet, + ) + + # MERGE CITIES AND ARRONDISSEMENT + composite = mapshaper_combine_districts_and_cities( + input_city_file=city_file, + input_communal_districts_file=communal_districts_file, + output_dir=self.local_dir, + output_name="ARRONDISSEMENT_MUNICIPAL", + output_format=format_output, + quiet=quiet, + ) + + # move file to new tempdir to isolate this file for new S3GeoDataset + new_tempdir = tempfile.mkdtemp() + shutil.move(composite, composite.replace(self.local_dir, new_tempdir)) + composite = composite.replace(self.local_dir, new_tempdir) + + os.unlink(city_file) + os.unlink(os.path.join(self.local_dir, self.main_filename)) + + new_config = deepcopy(self.config) + new_config.update({"borders": "ARRONDISSEMENT_MUNICIPAL"}) + new_dataset = from_file(file_path=composite, **new_config) + + return new_dataset + + +def from_frame( + gdf: gpd.GeoDataFrame, + fs: S3FileSystem = FS, + **config: ConfigDict, +) -> S3GeoDataset: + """ + Create a new S3GeoDataset from a GeoDataFrame, config and fs. + + The new object will write the the geodataframe into a new tempdir; this + tempdir will be cleaned at __exit__ method's execution. Therefore, the new + object should be created with a with statement, for instance: + >>> new_dset = geodataset.from_frame(gdf, fs, **config) as new_file: + >>> with new_dset: + >>> print(new_file) + + Parameters + ---------- + gdf : gpd.GeoDataFrame + GeoDataFrame to construct the S3GeoDataset from. + fs : S3FileSystem, optional + The S3FileSytem to use for storage. The default is FS. + **config : ConfigDict + Other arguments to define the path on the S3 to the dataset. + + Returns + ------- + dset : S3GeoDataset + New S3GeoDataset object. + + """ + + extension = config.get("vectorfile_format", INTERMEDIATE_FORMAT) + filename = config.get("filename", None) + if not filename: + filename = config.get("borders", "file") + if "." not in filename: + filename = f"{filename}.{extension}" + with tempfile.TemporaryDirectory() as tempdir: + gdf.to_file(f"{tempdir}/{filename}", engine="fiona") + dset = from_file(f"{tempdir}/{filename}", fs, **config) + + return dset + + +def from_file( + file_path: str, + fs: S3FileSystem = FS, + **config: ConfigDict, +) -> S3GeoDataset: + """ + Create a new S3GeoDataset from a local file, config and fs. + + The new object will copy the file(s) into a new tempdir; this tempdir will + be cleaned at __exit__ method's execution. Therefore, the new object should + be created with a with statement, for instance: + >>> new_dset = geodataset.from_file("blah.txt", fs, **config) as new_file: + >>> with new_dset: + >>> print(new_file) + + Parameters + ---------- + file_path : str + Path to the geodataset file to instantiate the new S3GeoDataset. + fs : S3FileSystem, optional + The S3FileSytem to use for storage. The default is FS. + **config : ConfigDict + Other arguments to define the path on the S3 to the dataset. + + Returns + ------- + dset : S3GeoDataset + New S3GeoDataset object. + + """ + if not os.path.exists(file_path): + raise ValueError("file not found from local path") + + local_dir = os.path.dirname(file_path) + filename = os.path.basename(file_path) + vectorfile_format = filename.rsplit(".", maxsplit=1)[1] + + for key in "filename", "vectorfile_format": + try: + del config[key] + except KeyError: + pass + + # Create a new S3GeoDataset + dset = S3GeoDataset( + fs=fs, + filename=filename, + vectorfile_format=vectorfile_format, + build_from_local=file_path, + **config, + ) + dset.local_dir = local_dir + dset.main_filename = filename + + # Then create a copy to ensure the creation of a new tempdir + dset = dset.copy() + + return dset + + +def concat_s3geodataset( + datasets: List[S3GeoDataset], + output_name: str = "COMMUNE", + vectorfile_format: str = "geojson", + output_dir: str = "temp", + fs: S3FileSystem = FS, + quiet: bool = MAPSHAPER_QUIET, + **config_new_dset: ConfigDict, +) -> S3GeoDataset: + """ + Concatenate S3GeoDataset in the manner of a geopandas.concat using + mapshaper. The result is a new S3GeoDataset which will **NOT** be uploaded + on S3. + + Parameters + ---------- + datasets : List[S3GeoDataset] + The list of S3GeoDataset instances to concatenate. + output_name: str, optional + The name of the output layer. The default is 'COMMUNE'. + vectorfile_format : str, optional + The file format to use for creating the new S3GeoDataset. The default + is "geojson". + output_dir : str, optional + The temporary file used for processing the concatenation. The default + is "temp". + fs : S3FileSystem, optional + The S3FileSystem used ultimately to upload the new S3GeoDataset. The + default is FS. + quiet : bool, optional + If True, inhibits console messages. The default is MAPSHAPER_QUIET. + **config_new_dset : ConfigDict + Configuration reprensenting the new S3GeoDataset (used for initiation). + This will determine the path on the S3FileSystem during storage. + + Returns + ------- + S3GeoDataset + New concatenated S3GeoDataset + + """ + + for k, dset in enumerate(datasets): + destination = os.path.join(output_dir, f"{k}.{vectorfile_format}") + + if os.path.exists(os.path.join(dset.local_dir, dset.main_filename)): + # already downloaded, but not sure of the current projection + dset.reproject(format_output=vectorfile_format, epsg=4326) + + shutil.copy( + os.path.join(dset.local_dir, dset.main_filename), destination + ) + else: + with dset: + dset.reproject(format_output=vectorfile_format, epsg=4326) + shutil.copy( + os.path.join(dset.local_dir, dset.main_filename), + destination, + ) + + old_files = glob(f"{output_dir}/*.{vectorfile_format}") + + output_path = mapshaper_concat( + input_dir=output_dir, + input_format=vectorfile_format, + output_dir=f"{output_dir}/preprocessed_combined", + output_name=output_name, + output_format=vectorfile_format, + quiet=quiet, + ) + + logger.info("new S3GeoDataset created at %s", output_path) + + for file in old_files: + os.unlink(file) + + file = glob(f"{output_dir}/preprocessed_combined/*.{vectorfile_format}")[0] + new_dset = from_file(file_path=file, fs=fs, **config_new_dset) + + return new_dset diff --git a/cartiflette/s3/inventory.py b/cartiflette/s3/inventory.py new file mode 100644 index 00000000..7558d7f5 --- /dev/null +++ b/cartiflette/s3/inventory.py @@ -0,0 +1,236 @@ +# -*- coding: utf-8 -*- +""" +Created on Sat Dec 14 19:18:05 2024 +""" + +import json +import logging +import re + +import pandas as pd +from s3fs import S3FileSystem + +from cartiflette.config import FS, BUCKET, PATH_WITHIN_BUCKET +from cartiflette.pipeline_constants import COG_TERRITOIRE, IRIS + +logger = logging.getLogger(__name__) + + +def nested_dict_from_multiindex(df: pd.DataFrame) -> dict: + """ + Convenience function to transform a multiindexed DataFrame do a nested + dict, minimizing the dict's size. + + Parameters + ---------- + df : pd.DataFrame + Multiindexed DataFrame. + + Returns + ------- + dict + Nested dictionnary + + """ + + result = {} + for idx, value in df["simplification"].items(): + d_ref = result + for key in idx[:-1]: + if key not in d_ref: + d_ref[key] = {} + d_ref = d_ref[key] + d_ref[idx[-1]] = value + return result + + +def flatten_dict(d: dict, parent_key: tuple = ()) -> dict: + """ + Convenience function, flattens a nested dictionary and convert it back to + dataframe. + + Parameters + ---------- + d : dict + Nested dictionary + parent_key : tuple, optional + Optional key, used for recursive purposes. The default is (). + + Returns + ------- + dict + flattened dictionnary + + """ + items = [] + for k, v in d.items(): + new_key = parent_key + (k,) + if isinstance(v, dict): + items.extend(flatten_dict(v, new_key).items()) + else: + items.append((new_key, v)) + return dict(items) + + +def make_s3_inventory( + fs: S3FileSystem = FS, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, +): + """ + Compute an inventory of all datasets generated by Cartiflette and push it + to the S3 File System as a single json file. + + The json is pushed to f"{bucket}/{path_within_bucket}/inventory.json". It + uses a nested dictionnary format to ensure the json is small enough to + enhance download performances. + + Parameters + ---------- + fs : S3FileSystem, optional + S3 File System. The default is FS. + bucket : str, optional + Used bucket (both for inventory querying and json storage). The default + is BUCKET. + path_within_bucket : str, optional + Path used within bucket. The default is PATH_WITHIN_BUCKET. + + Returns + ------- + None. + + """ + + paths = ( + f"{bucket}/{path_within_bucket}/" + "provider=Cartiflette/dataset_family=production/" + "**/*" + ) + # debug + # paths = ( + # f"{bucket}/{path_within_bucket}/" + # "provider=Cartiflette/dataset_family=production/" + # "source=CONTOUR-IRIS/" + # "year=2023/" + # "administrative_level=IRIS/" + # "crs=4326/" + # "**/*" + # ) + + paths = fs.glob(paths) + + compiled = re.compile( + ".*?/" + "source=(?P.*?)/" + "year=(?P[0-9]{4})/" + "administrative_level=(?P.*?)/" + "crs=(?P[0-9]{4})/" + "(?P.*?)=(?P.*?)/" + "vectorfile_format=(?P.*?)/" + "territory=(?P.*?)/" + "simplification=(?P[0-9]*?)/" + ".*" + ) + + datasets = [ + x.groupdict() for path in paths for x in compiled.finditer(path) + ] + datasets = pd.DataFrame(datasets) + + # Do not track datasets ran on low resolution files for testing purposes + high_resolution = False + ix = datasets[ + datasets.source.isin( + {IRIS[high_resolution], COG_TERRITOIRE[high_resolution]} + ) + ].index + datasets = datasets.drop(ix) + + cols = [ + "source", + "year", + "borders", + "crs", + "filter_by", + "value", + "vectorfile_format", + "territory", + ] + datasets = datasets.set_index(cols) + + with fs.open( + f"{bucket}/{path_within_bucket}/inventory.json", "w", encoding="utf8" + ) as f: + d = nested_dict_from_multiindex(datasets) + json.dump(d, f) + + +def parse_inventory( + fs: S3FileSystem = FS, + bucket: str = BUCKET, + path_within_bucket: str = PATH_WITHIN_BUCKET, +) -> pd.DataFrame: + """ + Retrieve and load cartiflette's current datasets' inventory (as a + dataframe). + + Inventory columns are [ + 'source', + 'year', + 'borders', + 'crs', + 'filter_by', + 'value', + 'vectorfile_format', + 'territory', + 'simplification' + ] + + Each row corresponds to an available DataFrame. + + Parameters + ---------- + fs : S3FileSystem, optional + S3 File System. The default is FS. + bucket : str, optional + Used bucket (both for inventory querying and json storage). The default + is BUCKET. + path_within_bucket : str, optional + Path used within bucket. The default is PATH_WITHIN_BUCKET. + + Returns + ------- + df : pd.DataFrame + Inventory DataFrame + + """ + with fs.open( + f"{bucket}/{path_within_bucket}/inventory.json", "r", encoding="utf8" + ) as f: + d = json.load(f) + + d = flatten_dict(d) + # Convert the flattened dictionary to a DataFrame + index = pd.MultiIndex.from_tuples(d.keys()) + df = pd.DataFrame( + list(d.values()), index=index, columns=["simplification"] + ) + index.names = [ + "source", + "year", + "borders", + "crs", + "filter_by", + "value", + "vectorfile_format", + "territory", + ] + + df = df.reset_index(drop=False) + return df + + +if __name__ == "__main__": + # df = make_s3_inventory() + df = parse_inventory() + print(df) diff --git a/cartiflette/s3/list_files_s3.py b/cartiflette/s3/list_files_s3.py deleted file mode 100644 index b3d11db6..00000000 --- a/cartiflette/s3/list_files_s3.py +++ /dev/null @@ -1,48 +0,0 @@ -def list_raw_files_level(fs, path_bucket, borders): - """ - Lists raw files at a specific level within the file system. - - Parameters - ---------- - fs : FileSystem - The file system object. - path_bucket : str - The path to the bucket in the file system. - borders : str - The specific level for which raw files are to be listed. - - Returns - ------- - list - A list of raw files at the specified level in the file system. - """ - list_raw_files = fs.ls(path_bucket) - list_raw_files = [ - chemin - for chemin in list_raw_files - if chemin.rsplit("/", maxsplit=1)[-1].startswith(f"{borders}.") - ] - return list_raw_files - - -def download_files_from_list(fs, list_raw_files, local_dir="temp"): - """ - Downloads files from a list of raw files to a specified local directory. - - Parameters - ---------- - fs : FileSystem - The file system object. - list_raw_files : list - A list of raw files to be downloaded. - local_dir : str, optional - The local directory where the files will be downloaded, by default "temp". - - Returns - ------- - str - The path of the local directory where the files are downloaded. - """ - for files in list_raw_files: - fs.download(files, f"{local_dir}/{files.rsplit('/', maxsplit=1)[-1]}") - return local_dir diff --git a/cartiflette/s3/preprocess.py b/cartiflette/s3/preprocess.py deleted file mode 100644 index 3d470997..00000000 --- a/cartiflette/s3/preprocess.py +++ /dev/null @@ -1,286 +0,0 @@ -# -*- coding: utf-8 -*- - -from datetime import date -from typing import TypedDict -import io -import logging -import pandas as pd -import s3fs - -from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET, FS -from cartiflette.utils import magic_csv_reader - -logger = logging.getLogger(__name__) - - -class CogDict(TypedDict): - "Used only for typing hints" - COMMUNE: pd.DataFrame - CANTON: pd.DataFrame - ARRONDISSEMENT: pd.DataFrame - DEPARTEMENT: pd.DataFrame - REGION: pd.DataFrame - COLLECTIVITE: pd.DataFrame - PAYS: pd.DataFrame - - -def get_cog_year( - year: int = None, - bucket: str = BUCKET, - path_within_bucket: str = PATH_WITHIN_BUCKET, - fs: s3fs.S3FileSystem = FS, -) -> CogDict: - """ - Retrieve all COG files on S3, concat all territories and store it into a - dict - - Parameters - ---------- - year : int, optional - Desired vintage. If None (default), will use the current date's year. - bucket : str, optional - Bucket to use. The default is BUCKET. - path_within_bucket : str, optional - path within bucket. The default is PATH_WITHIN_BUCKET. - fs : s3fs.S3FileSystem, optional - S3 file system to use. The default is FS. - - Returns - ------- - CogDict - Dictionnary of dataframes. Each key represent a "layer" of the COG's - yearly dataset. It might change from year to year, according to what's - really present in the dataset. - - If no data is present for the desired vintage, empty dataframes will be - returned in the dictionnary. - - Ex. : - { - 'COMMUNE': - TYPECOM COM REG ... LIBELLE CAN COMPARENT - 0 COM 01001 84.0 ... L'Abergement-Clémenciat 0108 NaN - 1 COM 01002 84.0 ... L'Abergement-de-Varey 0101 NaN - 2 COM 01004 84.0 ... Ambérieu-en-Bugey 0101 NaN - 3 COM 01005 84.0 ... Ambérieux-en-Dombes 0122 NaN - 4 COM 01006 84.0 ... Ambléon 0104 NaN - ... - [37601 rows x 12 columns], - - 'CANTON': - id_canton id_departement ... libelle actual - 0 0101 01 ... Ambérieu-en-Bugey C - 1 0102 01 ... Attignat C - 2 0103 01 ... Valserhône C - 3 0104 01 ... Belley C - 4 0105 01 ... Bourg-en-Bresse-1 C - ... - [2290 rows x 10 columns], - - 'ARRONDISSEMENT': - ARR DEP ... NCCENR LIBELLE - 0 011 01 ... Belley Belley - 1 012 01 ... Bourg-en-Bresse Bourg-en-Bresse - 2 013 01 ... Gex Gex - 3 014 01 ... Nantua Nantua - 4 021 02 ... Château-Thierry Château-Thierry - ... - [332 rows x 8 columns], - - 'DEPARTEMENT': - DEP REG ... NCCENR LIBELLE - 0 01 84 ... Ain Ain - 1 02 32 ... Aisne Aisne - 2 03 84 ... Allier Allier - 3 04 93 ... Alpes-de-Haute-Provence Alpes-de-Haute-Provence - 4 05 93 ... Hautes-Alpes Hautes-Alpes - ... - [101 rows x 7 columns], - - 'REGION': - REG CHEFLIEU ... NCCENR LIBELLE - 0 1 97105 ... Guadeloupe Guadeloupe - 1 2 97209 ... Martinique Martinique - 2 3 97302 ... Guyane Guyane - 3 4 97411 ... La Réunion La Réunion - 4 6 97608 ... Mayotte Mayotte - 5 11 75056 ... Île-de-France Île-de-France - ... - [18 rows x 6 columns], - - 'COLLECTIVITE': - CTCD ... LIBELLE - 0 01D ... Conseil départemental de L'Ain - 1 02D ... Conseil départemental de L'Aisne - 2 03D ... Conseil départemental de L'Allier - 3 04D ... Conseil départemental des Alpes-de-Haute-Provence - 4 05D ... Conseil départemental des Hautes-Alpes - ... - [100 rows x 6 columns], - - 'PAYS': - COG ACTUAL CAPAY CRPAY ... ANCNOM CODEISO2 CODEISO3 CODENUM3 - 0 99101 1 NaN NaN ... NaN DK DNK 208.0 - 1 99101 3 99102 NaN ... NaN FO FRO 234.0 - 2 99102 1 NaN NaN ... NaN IS ISL 352.0 - 3 99103 1 NaN NaN ... NaN NO NOR 578.0 - 4 99103 3 NaN NaN ... NaN BV BVT 74.0 - ... - [282 rows x 11 columns] - } - - """ - - if not year: - year = date.today().year - - levels = [ - "COMMUNE", - "CANTON", - "ARRONDISSEMENT", - "DEPARTEMENT", - "REGION", - "COLLECTIVITE", - "PAYS", - ] - - dict_cog = {} - for level in levels: - pattern = ( - f"{bucket}/{path_within_bucket}/{year=}/**/" - f"provider=Insee/dataset_family=COG/source={level}/**/*.*" - ) - files = fs.glob(pattern) # , refresh=True) - # see issue : https://github.com/fsspec/s3fs/issues/504 - data = [] - for file in files: - with fs.open(file, "rb") as f: - dummy = io.BytesIO(f.read()) - df = magic_csv_reader(dummy) - data.append(df) - if data: - dict_cog[level] = pd.concat(data) - else: - dict_cog[level] = pd.DataFrame() - - return dict_cog - - -# def get_BV( -# year: int = None, -# bv_source: str = "FondsDeCarte_BV_2022", -# ign_source: str = "EXPRESS-COG-TERRITOIRE", -# bucket: str = BUCKET, -# path_within_bucket: str = PATH_WITHIN_BUCKET, -# fs: s3fs.S3FileSystem = FS, -# ) -> gpd.GeoDataFrame: -# """ -# Reconstruct living areas ("Bassins de vie") from AdminExpress' cities' -# geometries and Insee's inventory. - -# Parameters -# ---------- -# year : int, optional -# Desired vintage. Will use the current year if set to None (which is -# default). -# bv_source : str, optional -# Dataset's source to use for living area. The default is -# "FondsDeCarte_BV_2022". -# ign_source : str, optional -# Dataset's source to use for geometries (should be a dataset from the -# dataset_family AdminExpress. The default is "EXPRESS-COG-TERRITOIRE". -# bucket : str, optional -# Bucket to use. The default is BUCKET. -# path_within_bucket : str, optional -# path within bucket. The default is PATH_WITHIN_BUCKET. -# fs : s3fs.S3FileSystem, optional -# S3 file system to use. The default is FS. - -# Raises -# ------ -# ValueError -# If no file has been found on S3 for the given parameters. - -# Returns -# ------- -# bv : gpd.GeoDataFrame -# GeoDataFrame of living areas, constructed from cities geometries - -# Ex.: -# bv libbv dep reg \ -# 0 01004 Ambérieu-en-Bugey 01 84 -# 1 01033 Valserhône 01 84 -# 2 01033 Valserhône 74 84 -# 3 01034 Belley 01 84 -# 4 01053 Bourg-en-Bresse 01 84 - -# geometry POPULATION -# 0 POLYGON ((5.31974 45.92194, 5.31959 45.92190, ... 46645 -# 1 POLYGON ((5.72192 46.03413, 5.72165 46.03449, ... 25191 -# 2 POLYGON ((5.85098 45.99099, 5.85094 45.99070, ... 4566 -# 3 POLYGON ((5.61963 45.66754, 5.61957 45.66773, ... 25620 -# 4 POLYGON ((5.18709 46.05114, 5.18692 46.05085, ... 83935 - -# """ - -# if not year: -# year = date.today().year - -# territory = "france_entiere" -# provider = "Insee" -# dataset_family = "BV" -# source = bv_source -# pattern = ( -# f"{bucket}/{path_within_bucket}/{year=}/**/" -# f"{provider=}/{dataset_family=}/{source=}/{territory=}/**/" -# f"*.dbf" -# ).replace("'", "") -# files = fs.glob(pattern) # , refresh=True) -# # see issue : https://github.com/fsspec/s3fs/issues/504 -# if not files: -# raise ValueError( -# "No file retrieved with the set parameters, resulting to the " -# f"following {pattern=}" -# ) -# data = [] -# for file in files: -# with tempfile.TemporaryDirectory() as tempdir: -# tmp_dbf = os.path.join(tempdir, os.path.basename(file)) -# with open(tmp_dbf, "wb") as tf: -# with fs.open(file, "rb") as fsf: -# tf.write(fsf.read()) - -# df = gpd.read_file(tmp_dbf, encoding="utf8") -# df = df.drop("geometry", axis=1) -# data.append(df) - -# bv = pd.concat(data) - -# communes = get_vectorfile_ign( -# borders="COMMUNE", -# year=year, -# territory="*", -# provider="IGN", -# source=ign_source, -# ) - -# bv = communes.merge(bv, left_on="INSEE_COM", right_on="codgeo", how="right") -# if bv_source == "FondsDeCarte_BV_2022": -# rename = ["bv2022", "libbv2022"] -# elif bv_source == "FondsDeCarte_BV_2012": -# rename = ["bv2012", "libbv2012"] -# bv = bv.rename(dict(zip(rename, ["bv", "libbv"])), axis=1) -# by = ["bv", "libbv", "dep", "reg"] - -# bv = bv.dissolve(by=by, aggfunc={"POPULATION": "sum"}, as_index=False, dropna=False) - -# return bv - - -# if __name__ == "__main__": -# logging.basicConfig(level=logging.INFO) - -# ret = get_cog_year(2022) -# ret = get_vectorfile_ign(source="EXPRESS-COG-TERRITOIRE", year=2022) -# ret = get_vectorfile_communes_arrondissement(year=2022) -# ret = get_BV(year=2022) diff --git a/cartiflette/s3/upload_raw_s3.py b/cartiflette/s3/upload_raw_s3.py deleted file mode 100644 index db90f9b5..00000000 --- a/cartiflette/s3/upload_raw_s3.py +++ /dev/null @@ -1,82 +0,0 @@ -from cartiflette.download.download import _download_sources -from cartiflette.utils import create_path_bucket, official_epsg_codes -from cartiflette.config import PATH_WITHIN_BUCKET - - -def upload_s3_raw( - provider="IGN", - source="EXPRESS-COG-CARTO-TERRITOIRE", - year=2022, - dataset_family="ADMINEXPRESS", - territory="metropole", - borders="COMMUNE", - path_within_bucket=PATH_WITHIN_BUCKET, - vectorfile_format="shp", - bucket="projet-cartiflette", -): - """ - Uploads raw data to an S3 bucket and returns the path to the bucket. - - Parameters - ---------- - provider : str, optional - The provider of the data, by default "IGN". - source : str, optional - The data source, by default "EXPRESS-COG-CARTO-TERRITOIRE". - year : int, optional - The year of the data, by default 2022. - dataset_family : str, optional - The dataset family, by default "ADMINEXPRESS". - territory : str, optional - The territory of the data, by default "metropole". - borders : str, optional - The type of borders, by default "COMMUNE". - path_within_bucket : str, optional - The path within the S3 bucket, by default cartiflette.config.PATH_WITHIN_BUCKET. - bucket : str, optional - The S3 bucket name, by default "projet-cartiflette". - - Returns - ------- - str - The path to the S3 bucket where the raw data is uploaded. - - """ - - x = _download_sources( - upload=True, - providers=provider, - dataset_families=dataset_family, - sources=source, - territories=territory, - years=year, - path_within_bucket=path_within_bucket, - ) - - rawpaths = x[provider][dataset_family][source][territory][year]["paths"] - - if rawpaths is None: - path_raw_s3 = create_path_bucket( - { - "bucket": bucket, - "path_within_bucket": path_within_bucket, - "year": year, - "borders": None, - "crs": official_epsg_codes()[territory], - "filter_by": "origin", - "value": "raw", - "vectorfile_format": vectorfile_format, - "provider": provider, - "dataset_family": dataset_family, - "source": source, - "territory": territory, - "filename": "COMMUNE.shp", - "simplification": 0, - } - ) - else: - path_raw_s3 = rawpaths[borders][0] - - path_bucket = path_raw_s3.rsplit("/", maxsplit=1)[0] - - return path_bucket diff --git a/cartiflette/utils/__init__.py b/cartiflette/utils/__init__.py index 7721186b..b8b06ad1 100644 --- a/cartiflette/utils/__init__.py +++ b/cartiflette/utils/__init__.py @@ -17,7 +17,7 @@ from .dict_update import deep_dict_update from .csv_magic import magic_csv_reader -from .create_path_bucket import create_path_bucket +from .create_path_bucket import create_path_bucket, ConfigDict from .standardize_inputs import standardize_inputs diff --git a/cartiflette/utils/create_path_bucket.py b/cartiflette/utils/create_path_bucket.py index 9ae6e323..50e0e414 100644 --- a/cartiflette/utils/create_path_bucket.py +++ b/cartiflette/utils/create_path_bucket.py @@ -1,7 +1,7 @@ """Module for communication with Minio S3 Storage """ -from typing import Optional, TypedDict +from typing import Optional, TypedDict, Union from cartiflette.config import BUCKET, PATH_WITHIN_BUCKET @@ -13,13 +13,16 @@ class ConfigDict(TypedDict): bucket: Optional[str] path_within_bucket: Optional[str] provider: str + dataset_family: str source: str - vectorfile_format: str + year: str borders: str + crs: Optional[Union[int, str]] filter_by: str - year: str - crs: Optional[int] value: str + vectorfile_format: str + territory: str + simplification: Optional[Union[int, str]] filename: Optional[str] @@ -45,16 +48,15 @@ def create_path_bucket(config: ConfigDict) -> str: path_within_bucket = config.get("path_within_bucket", PATH_WITHIN_BUCKET) provider = config.get("provider") + dataset_family = config.get("dataset_family") source = config.get("source") - - vectorfile_format = config.get("vectorfile_format") + year = config.get("year") borders = config.get("borders") - dataset_family = config.get("dataset_family") - territory = config.get("territory") + crs = config.get("crs", 2154) filter_by = config.get("filter_by") - year = config.get("year") value = config.get("value") - crs = config.get("crs", 2154) + vectorfile_format = config.get("vectorfile_format") + territory = config.get("territory") simplification = config.get("simplification", 0) filename = config.get("filename") @@ -81,8 +83,11 @@ def create_path_bucket(config: ConfigDict) -> str: f"/{simplification=}" ).replace("'", "") - if filename: + if filename and "." not in filename: + # small hack to avoid rewriting the S3Dataset and S3GeoDataset classes write_path += f"/{filename}.{vectorfile_format}" + elif filename: + write_path += f"/{filename}" elif vectorfile_format == "shp": write_path += "/" else: diff --git a/cartiflette/utils/dict_correspondance.py b/cartiflette/utils/dict_correspondance.py index eb4f1db1..b19a7db3 100644 --- a/cartiflette/utils/dict_correspondance.py +++ b/cartiflette/utils/dict_correspondance.py @@ -2,18 +2,38 @@ Collection of utils to reformat inputs """ +import re + +# TODO : rename and explicit purpose of constant! +# only keys used for splitting/dissolution DICT_CORRESP_ADMINEXPRESS = { - "REGION": "INSEE_REG", - "DEPARTEMENT": "INSEE_DEP", - "FRANCE_ENTIERE": "PAYS", - "FRANCE_ENTIERE_DROM_RAPPROCHES": "PAYS", - "LIBELLE_REGION": "LIBELLE_REGION", - "LIBELLE_DEPARTEMENT": "LIBELLE_DEPARTEMENT", - "BASSIN_VIE": "BV2012", - "AIRE_ATTRACTION_VILLES": "AAV2020", - "UNITE_URBAINE": "UU2020", - "ZONE_EMPLOI": "ZE2020", - "TERRITOIRE": "AREA", + "IRIS": re.compile("IRIS"), + "REGION": re.compile("INSEE_REG"), + "DEPARTEMENT": re.compile("INSEE_DEP"), + "ARRONDISSEMENT": re.compile("ARR"), + "FRANCE_ENTIERE": re.compile("PAYS"), + "FRANCE_ENTIERE_DROM_RAPPROCHES": re.compile("PAYS"), + "FRANCE_ENTIERE_IDF_DROM_RAPPROCHES": re.compile("PAYS"), + # "LIBELLE_REGION": re.compile("LIBELLE_REGION"), + # "LIBELLE_DEPARTEMENT": re.compile("LIBELLE_DEPARTEMENT"), + "BASSIN_VIE": re.compile("BV[0-9]{4}"), + "AIRE_ATTRACTION_VILLES": re.compile("AAV[0-9]{4}"), + "UNITE_URBAINE": re.compile("UU[0-9]{4}"), + "ZONE_EMPLOI": re.compile("ZE[0-9]{4}"), + "TERRITOIRE": re.compile("AREA"), + "EPCI": re.compile("EPCI"), + # "LIBELLE_EPCI": re.compile("LIBELLE_EPCI"), + "EPT": re.compile("EPT"), + # "LIBELLE_EPT": re.compile("LIBELLE_EPT"), + "COMMUNE": re.compile("INSEE_COM"), + # "LIBELLE_COMMUNE": re.compile("LIBELLE_COMMUNE"), + # "LIBELLE_CANTON": re.compile("LIBELLE_CANTON"), + "CANTON": re.compile("CAN"), + "ARRONDISSEMENT_MUNICIPAL": re.compile("INSEE_ARM"), + # "LIBELLE_ARRONDISSEMENT_MUNICIPAL": re.compile( + # "LIBELLE_ARRONDISSEMENT_MUNICIPAL" + # ), + # TODO: code SIREN ? } diff --git a/cartiflette/utils/sources.yaml b/cartiflette/utils/sources.yaml index 5f300d20..90d9381b 100644 --- a/cartiflette/utils/sources.yaml +++ b/cartiflette/utils/sources.yaml @@ -1,8 +1,27 @@ +DGCL: + BANATIC: + CORRESPONDANCE-SIREN-INSEE-COMMUNES: + file: https://www.data.gouv.fr/fr/datasets/r/42b16d68-958e-4518-8551-93e095fe8fda + 2024: + pattern: "*2024.xlsx" + 2023: + pattern: "*2023.xlsx" + 2022: + pattern: "*2022.xlsx" + 2021: + pattern: "*2021.xlsx" + 2020: + pattern: "*2020.xlsx" + IGN: ADMINEXPRESS: - pattern: "*DONNEES_LIVRAISON*.shp" + pattern: + pattern: + - "*DONNEES_LIVRAISON*{territory}*/COMMUNE.shp" + - "*DONNEES_LIVRAISON*{territory}*/ARRONDISSEMENT_MUNICIPAL.shp" + - "*DONNEES_LIVRAISON*{territory}*/CANTON.shp" EXPRESS-COG-TERRITOIRE: @@ -38,21 +57,19 @@ IGN: url_prefix: https://data.geopf.fr/telechargement/download/ structure: "{url_prefix}{prefix}/{prefix}_{version}__SHP_{territory}_{date}/{prefix}_{version}__SHP_{territory}_{date}.7z" 2020: - # Nota : n'existe plus sous forme territoriale avec les URLs de la - # géoplateforme, mais les anciennes URLs semblent toujours fonctionner + # Nota : fichier 7Z unique pour tous les territoires prefix: ADMIN-EXPRESS-COG - date: "2019-09-24" - version: "2-0" - url_prefix: https://wxs.ign.fr/x02uy2aiwjo9bm8ce5plwqmr/telechargement/prepackage/ADMINEXPRESS-COG_SHP_TERRITOIRES_PACK_09-2019 - structure: "{url_prefix}${prefix}_{version}__SHP__FRA_{date}/file/{prefix}_{version}__SHP__FRA_{date}.7z" + date: "2020-11-20" + version: "2-1" + url_prefix: https://data.geopf.fr/telechargement/download/ + structure: "{url_prefix}{prefix}/{prefix}_{version}__SHP__FRA_{date}/{prefix}_{version}__SHP__FRA_{date}.7z" 2019: - # Nota : n'existe plus sous forme territoriale avec les URLs de la - # géoplateforme, mais les anciennes URLs semblent toujours fonctionner + # Nota : fichier 7Z unique pour tous les territoires prefix: ADMIN-EXPRESS-COG date: "2019-09-24" version: "2-0" - url_prefix: https://wxs.ign.fr/x02uy2aiwjo9bm8ce5plwqmr/telechargement/prepackage/ADMINEXPRESS-COG_SHP_TERRITOIRES_PACK_09-2019 - structure: "{url_prefix}${prefix}_{version}__SHP__FRA_{date}/file/{prefix}_{version}__SHP__FRA_{date}.7z" + url_prefix: https://data.geopf.fr/telechargement/download/ + structure: "{url_prefix}{prefix}/{prefix}_{version}__SHP_FRA_{date}/{prefix}_{version}__SHP_FRA_{date}.7z" EXPRESS-COG: 2024: @@ -174,88 +191,74 @@ IGN: version: "2-0" url_prefix: https://data.geopf.fr/telechargement/download/ - CONTOUR-IRIS: #variables INSEE_COM NOM_COM IRIS CODE_IRIS NOM_IRIS TYP_IRIS ; manque la var Grand-Quartier (GRD_QUART) présente sur les tables insee https://www.insee.fr/fr/information/2017499 - - pattern: "*DONNEES_LIVRAISON*.shp" - structure: "{url_prefix}{prefix}$CONTOURS-IRIS_{version}__{local}_{date}/file/CONTOURS-IRIS_{version}__{local}_{date}.7z" - ROOT: + IRIS: + CONTOUR-IRIS: + #variables INSEE_COM NOM_COM IRIS CODE_IRIS NOM_IRIS TYP_IRIS ; manque la var Grand-Quartier (GRD_QUART) présente sur les tables insee https://www.insee.fr/fr/information/2017499 + structure: "{url_prefix}CONTOURS-IRIS_{version}__SHP__{local}_{date}/CONTOURS-IRIS_{version}__SHP__{local}_{date}.7z" + pattern: "*DONNEES_LIVRAISON*{territory}*.shp" + territory: + metropole: LAMB93_FXX + guadeloupe: RGAF09UTM20_GLP + martinique: RGAF09UTM20_MTQ + guyane: UTM22RGFG95_GUF + reunion: RGR92UTM40S_REU + saint-pierre-et-miquelon: RGSPM06U21_SPM + mayotte: RGM04UTM38S_MYT + saint-barthelemy: RGAF09UTM20_BLM + saint-martin: RGAF09UTM20_MAF + 2023: + url_prefix: https://data.geopf.fr/telechargement/download/CONTOURS-IRIS/ + date: 2023-01-01 + version: "3-0" + local: "FRA" 2022: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-PACK_2022-01 + url_prefix: https://data.geopf.fr/telechargement/download/CONTOURS-IRIS/ date: 2022-01-01 version: "2-1" - local: "SHP__FRA" + local: "FRA" 2021: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-PACK_2021-01 + url_prefix: https://data.geopf.fr/telechargement/download/CONTOURS-IRIS/ date: 2021-01-01 version: "2-1" - local: "SHP__FRA" + local: "FRA" 2020: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2020-01-01 + url_prefix: https://data.geopf.fr/telechargement/download/CONTOURS-IRIS/ date: 2020-01-01 version: "2-1" - local: "SHP__FRA" - 2019: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2019-01-01 - date: 2019-01-01 - version: "2-1" - local: "SHP__FRA" - 2018: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2018-01-02 - date: 2018-01-01 - version: "2-1" - local: "SHP__FRA" - 2017: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2017-01-01 - date: 2018-06-08 - version: "2-1" - local: "SHP__FRA" - 2016: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2017-06-30 - date: 2017-06-30 - version: "2-1" - local: "SHP__FRA" - 2015: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2015-01-01 - date: 2016-11-10 - version: "2-1" - local: "SHP_LAMB93_FXX" - 2014: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2014-01-01 - date: 2014-01-01 - version: "2-0" - local: "SHP_LAMB93_FXX" - 2013: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-PACK_2013-01 - date: 2013-01-01 - version: "1-0" - local: "SHP_FRA" - 2010: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-2017-08-09 - date: 2010-01-01 - version: "1-0" - local: "SHP__FRA" - 2009: - url_prefix: https://wxs.ign.fr/1yhlj2ehpqf3q6dt6a2y7b64/telechargement/inspire/ - prefix: CONTOURS-IRIS-PACK_2009-01-01 - date: 2009-01-01 - version: "1-0" - local: "SHP_LAMB93_FRA" + local: "FRA" + IRIS-GE: + pattern: "*DONNEES_LIVRAISON*{territory}*.shp" + territory: + metropole: LAMB93_FXX + guadeloupe: RGAF09UTM20_GLP + martinique: RGAF09UTM20_MTQ + guyane: UTM22RGFG95_GUF + reunion: RGR92UTM40S_REU + saint-pierre-et-miquelon: RGSPM06U21_SPM + mayotte: RGM04UTM38S_MYT + saint-barthelemy: RGAF09UTM20_BLM + saint-martin: RGAF09UTM20_MAF + 2024: + prefix: IRIS-GE + date: "2024-01-01" + version: "3-0" + url_prefix: https://data.geopf.fr/telechargement/download/ + structure: "{url_prefix}{prefix}/{prefix}_{version}__SHP_{territory}_{date}/{prefix}_{version}__SHP_{territory}_{date}.7z" + 2023: + prefix: IRIS-GE + date: "2023-01-01" + version: "3-0" + url_prefix: https://data.geopf.fr/telechargement/download/ + # Nota : attention, c'est un fichier global (FRA) mais qui comprend des fichiers territoriaux -> territory reste dans pattern + structure: "{url_prefix}{prefix}/{prefix}_{version}__SHP__FRA_{date}/{prefix}_{version}__SHP__FRA_{date}.7z" Insee: COG: structure: https://www.insee.fr/fr/statistiques/fichier/{id}/{filename} - COMMUNE: + COMMUNE: + 2024: + id: 7766585 + filename: v_commune_2024.csv 2023: id: 6800675 filename: v_commune_2023.csv @@ -282,7 +285,20 @@ Insee: pattern: "*.txt" id: 2666684 filename: comsimp2017-txt.zip + COMMUNE-OUTRE-MER: + 2024: + id: 7766585 + filename: v_commune_comer_2024.csv + 2023: + id: 6800675 + filename: v_commune_comer_2023.csv + 2022: + id: 6051727 + filename: com_comer_2022.csv CANTON: + 2024: + id: 7766585 + filename: v_canton_2024.csv 2023: id: 6800675 filename: v_canton_2023.csv @@ -310,6 +326,9 @@ Insee: id: 2666684 filename: canton2017-txt.zip ARRONDISSEMENT: + 2024: + id: 7766585 + filename: v_arrondissement_2024.csv 2023: id: 6800675 filename: v_arrondissement_2023.csv @@ -337,6 +356,9 @@ Insee: id: 2666684 filename: arrond2017-txt.zip DEPARTEMENT: + 2024: + id: 7766585 + filename: v_departement_2024.csv 2023: id: 6800675 filename: v_departement_2023.csv @@ -364,6 +386,9 @@ Insee: id: 2666684 filename: depts2017-txt.zip REGION: + 2024: + id: 7766585 + filename: v_region_2024.csv 2023: id: 6800675 filename: v_region_2023.csv @@ -391,6 +416,9 @@ Insee: id: 2666684 filename: reg2017-txt.zip COLLECTIVITE: + 2024: + id: 7766585 + filename: v_ctcd_2024.csv 2023: id: 6800675 filename: v_CTCD_2023.csv @@ -402,6 +430,9 @@ Insee: id: 5057840 filename: CTCD2021-csv.zip PAYS: + 2024: + id: 7766585 + filename: v_pays_territoire_2024.csv 2023: id: 6800675 filename: v_pays_2023.csv @@ -452,109 +483,167 @@ Insee: pattern: "*.xlsx" filename: "table_passage_annuelle_2024.zip" - BV: - pattern: "com_bv*.dbf" - #-> nota : zip dans des zip - FondsDeCarte_BV_2022: + TAGIRIS: + structure: https://www.insee.fr/fr/statistiques/fichier/{article}/{filename} + APPARTENANCE: + 2024: + article: "7708995" + pattern: "*.xlsx" + filename: "reference_IRIS_geo2024.zip" + 2023: + article: "7708995" + pattern: "*.xlsx" + filename: "reference_IRIS_geo2023.zip" + 2022: + article: "7708995" + pattern: "*.xlsx" + filename: "reference_IRIS_geo2022.zip" + ZONAGES: + structure: https://www.insee.fr/fr/statistiques/fichier/{article}/{filename} + EPCI-FP: + 2024: + article: "2510634" + pattern: "*.xlsx" + filename: "epci_au_01-01-2024.zip" 2023: - file: https://www.insee.fr/fr/statistiques/fichier/6676988/fonds_bv2022_2023.zip + article: "2510634" + pattern: "*.xlsx" + filename: "Intercommunalite_Metropole_au_01-01-2023.zip" 2022: - file: https://www.insee.fr/fr/statistiques/fichier/6676988/fonds_bv2022_2022.zip + article: "2510634" + pattern: "*.xlsx" + filename: "Intercommunalite_Metropole_au_01-01-2022.zip" + 2021: + article: "2510634" + pattern: "*.xlsx" + filename: "Intercommunalite_Metropole_au_01-01-2021.zip" + 2020: + article: "2510634" + pattern: "*.xlsx" + filename: "Intercommunalite_Metropole_au_01-01-2020_v1.zip" + EPT: + 2024: + article: "2510634" + pattern: "*.xlsx" + filename: "ept_au_01-01-2024.zip" + 2023: + article: "2510634" + pattern: "*.xlsx" + filename: "ept_au_01-01-2023.zip" + UNITES-URBAINES: + 2024: + article: "4802589" + filename: "UU2020_au_01-01-2024.zip" + pattern: "*.xlsx" + 2023: + article: "4802589" + filename: "UU2020_au_01-01-2023.zip" + pattern: "*.xlsx" + 2022: + article: "4802589" + filename: "UU2020_au_01-01-2022.zip" + pattern: "*.xlsx" + 2021: + article: "4802589" + filename: "UU2020_au_01-01-2021.zip" + pattern: "*.xlsx" + 2020: + article: "4802589" + filename: "UU2020_au_01-01-2020.zip" + pattern: "*.xlsx" + BASSINS-VIE: + 2024: + article: "6676988" + filename: "BV2022_au_01-01-2024.zip" + pattern: "*.xlsx" + 2023: + article: "6676988" + filename: "BV2022_au_01-01-2023.zip" + pattern: "*.xlsx" + 2022: + # Newest datasets (BV 2022) + article: "6676988" + filename: "BV2022_au_01-01-2022.zip" + pattern: "*.xlsx" + 2021: + # Oldest datasets (BV 2012) + article: "2115016" + filename: "BV2012_au_01-01-2021.zip" + pattern: "*.xlsx" + 2020: + article: "2115016" + filename: "BV2012_au_01-01-2020_v1.zip" + pattern: "*.xlsx" + AIRES-ATTRACTION-VILLES: + 2024: + article: "4803954" + filename: "AAV2020_au_01-01-2024.zip" + pattern: "*.xlsx" + 2023: + article: "4803954" + filename: "AAV2020_au_01-01-2023.zip" + pattern: "*.xlsx" + 2022: + article: "4803954" + filename: "AAV2020_au_01-01-2022.zip" + pattern: "*.xlsx" + 2021: + article: "4803954" + filename: "AAV2020_au_01-01-2021.zip" + pattern: "*.xlsx" + 2020: + article: "4803954" + filename: "AAV2020_au_01-01-2020.zip" + pattern: "*.xlsx" + ZONES-EMPLOI: + 2024: + article: "4652957" + filename: "ZE2020_au_01-01-2024.zip" + pattern: "*.xlsx" + 2023: + article: "4652957" + filename: "ZE2020_au_01-01-2023.zip" + pattern: "*.xlsx" + 2022: + article: "4652957" + filename: "ZE2020_au_01-01-2022.zip" + pattern: "*.xlsx" + 2021: + article: "4652957" + filename: "ZE2020_au_01-01-2021.zip" + pattern: "*.xlsx" + 2020: + article: "4652957" + filename: "ZE2020_au_01-01-2020_v1.zip" + pattern: "*.xlsx" + + POPULATION: + structure: https://www.insee.fr/fr/statistiques/fichier/{article}/{filename} - # Source disparue : - # FondsDeCarte_BV_2012: - # 2022: - # file: https://www.insee.fr/fr/statistiques/fichier/2115016/fonds_bv2012_2022.zip - + POPULATION-IRIS-FRANCE-HORS-MAYOTTE: + 2022: + # Nota : pop 2020 with 2022 geometries + article: "7704076" + pattern: "*.xlsx" + filename: "base-ic-evol-struct-pop-2020_xlsx.zip" + 2021: + # Nota : pop 2019 with 2021 geometries + article: "6543200" + pattern: "*.xlsx" + filename: "base-ic-evol-struct-pop-2019.zip" + + POPULATION-IRIS-COM: + 2022: + # Nota : pop 2020 with 2022 geometries + article: "7704076" + pattern: "*.xlsx" + filename: "base-ic-evol-struct-pop-2020-com_xlsx.zip" + 2021: + # Nota : pop 2019 with 2021 geometries + article: "6543200" + pattern: "*.xlsx" + filename: "base-ic-evol-struct-pop-2019-com.zip" - # UU2020: - # 2022: #Pour appariement sur code commune : au sein du zip, fichier XLSX "UU2020_au_01-01-2022_v2022-09-27.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO UU2020 LIBUU2020 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 4802589-2022 - # file: https://www.insee.fr/fr/statistiques/fichier/4802589/UU2020_au_01-01-2022_V2.zip - # 2021: #Pour appariement sur code commune : au sein du zip, fichier XLSX "UU2020_au_01-01-2021_v2022-09-27.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO UU2020 LIBUU2020 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 4802589-2021 - # file: https://www.insee.fr/fr/statistiques/fichier/4802589/UU2020_au_01-01-2021_V2.zip - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "UU2020_au_01-01-2020_v2022-09-27.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO UU2020 LIBUU2020 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 4802589-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/4802589/UU2020_au_01-01-2020_V2.zip - # UU2010: - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "UU2010_au_01-01-2020.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO UU2010 LIBUU2010 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 2115018-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/2115018/UU2010_au_01-01-2020.zip - # 2019: #Pour appariement sur code commune : au sein du zip, fichier XLS "UU2010_au_01-01-2019.xls", onglet "Composition_communale", variables CODGEO LIBGEO UU2010 LIBUU2010 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 2115018-2019 - # file: https://www.insee.fr/fr/statistiques/fichier/2115018/UU2010_au_01-01-2019.zip - # 2018: #Pour appariement sur code commune : au sein du zip, fichier XLS "UU2010_au_01-01-2018.xls", onglet "Composition_communale", variables CODGEO LIBGEO UU2010 LIBUU2010 TYPE_COM STATUT_2017 DEP REG (début du tab, ligne 6) - # id: 2115018-2018 - # file: https://www.insee.fr/fr/statistiques/fichier/2115018/UU2010_au_01-01-2018.zip - # AAV2020: - # 2022: #Pour appariement sur code commune : au sein du zip, fichier XLSX "AAV2020_au_01-01-2022.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2022 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_au_01-01-2022.zip - # 2021: #Pour appariement sur code commune : au sein du zip, fichier XLSX "AAV2020_au_01-01-2021.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2021 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_au_01-01-2021.zip - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "AAV2020_au_01-01-2020_v1.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_au_01-01-2020.zip - # 2019: #Pour appariement sur code commune : fichier multizip 2014-2019, au sein du zip AAV2020_au_01-01-2019.zip, fichier XLSX "AAV2020_au_01-01-2019.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2019 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_2014-2019.zip - # 2018: #Pour appariement sur code commune : fichier multizip 2014-2019, au sein du zip AAV2020_au_01-01-2018.zip, fichier XLSX "AAV2020_au_01-01-2018.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2018 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_2014-2019.zip - # 2017: #Pour appariement sur code commune : fichier multizip 2014-2019, au sein du zip AAV2020_au_01-01-2017.zip, fichier XLSX "AAV2020_au_01-01-2017.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AAV2020 LIBAAV2020 CATEAAV2020 DEP REG (début du tab, ligne 6) - # id: 4803954-2017 - # file: https://www.insee.fr/fr/statistiques/fichier/4803954/AAV2020_2014-2019.zip - # AU2010: - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "AU2010_au_01-01-2020.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO AU2010 LIBAU2010 CATAEU2010 DEP REG (début du tab, ligne 6) - # id: 2115011-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/2115011/AU2010_au_01-01-2020.zip - # 2019: #Pour appariement sur code commune : au sein du zip, fichier XLS "AU2010_au_01-01-2019.xls, onglet "Composition_communale", variables CODGEO LIBGEO AU2010 LIBAU2010 CATAEU2010 DEP REG (début du tab, ligne 6) - # id: 2115011-2019 - # file: https://www.insee.fr/fr/statistiques/fichier/2115011/AU2010_au_01-01-2019.zip - # 2018: #Pour appariement sur code commune : au sein du zip, fichier XLS "AU2010_au_01-01-2018_V2.xls", onglet "Composition_communale", variables CODGEO LIBGEO AU2010 LIBAU2010 CATAEU2010 DEP REG (début du tab, ligne 6) - # id: 2115011-2018 - # file: https://www.insee.fr/fr/statistiques/fichier/2115011/AU2010_au_01-01-2018_V2.zip - # ZE2020: - # 2022: #Pour appariement sur code commune : au sein du zip, fichier XLSX "ZE2020_au_01-01-2022.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO ZE2020 LIBZE2020 ZE2020_PARTIE_REG DEP REG (début du tab, ligne 6) - # id: 4652957-2022 - # file: https://www.insee.fr/fr/statistiques/fichier/4652957/ZE2020_au_01-01-2022.zip - # 2021: #Pour appariement sur code commune : au sein du zip, fichier XLSX "ZE2020_au_01-01-2021.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO ZE2020 LIBZE2020 ZE2020_PARTIE_REG DEP REG (début du tab, ligne 6) - # id: 4652957-2021 - # file: https://www.insee.fr/fr/statistiques/fichier/4652957/ZE2020_au_01-01-2021.zip - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "ZE2020_au_01-01-2020_v1.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO ZE2020 LIBZE2020 ZE2020_PARTIE_REG DEP REG (début du tab, ligne 6) - # id: 4652957-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/4652957/ZE2020_au_01-01-2020_v1.zip - # ZE2010: - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "ZE2010_au_01-01-2020.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO ZE2010 LIBZE2010 DEP REG (début du tab, ligne 6) - # id: 2114596-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/2114596/ZE2010_au_01-01-2020.zip - # 2019: #Pour appariement sur code commune : au sein du zip, fichier XLS "ZE2010_au_01-01-2019.xls", onglet "Composition_communale", variables CODGEO LIBGEO ZE2010 LIBZE2010 DEP REG (début du tab, ligne 6) - # id: 2114596-2019 - # file: https://www.insee.fr/fr/statistiques/fichier/2114596/ZE2010_au_01-01-2019.zip - # 2018: #Pour appariement sur code commune : au sein du zip, fichier XLS "ZE2010_au_01-01-2018.xls", onglet "Composition_communale", variables CODGEO LIBGEO ZE2010 LIBZE2010 DEP REG (début du tab, ligne 6) - # id: 2114596-2018 - # file: https://www.insee.fr/fr/statistiques/fichier/2114596/ZE2010_au_01-01-2018.zip - # EPCI: - # 2022: #Pour appariement sur code commune : au sein du zip, fichier XLSX "Intercommunalite_Metropole_au_01-01-2022.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2022 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2022.zip - # 2021: #Pour appariement sur code commune : au sein du zip, fichier XLSX "Intercommunalite-Metropole_au_01-01-2021.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2021 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2021.zip - # 2020: #Pour appariement sur code commune : au sein du zip, fichier XLSX "Intercommunalite-Metropole_au_01-01-2020_v1.xlsx", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2020 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2020_v1.zip - # 2019: #Pour appariement sur code commune : au sein du zip, fichier XLS "Intercommunalite_Metropole_au_01-01-2019.xls", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2019 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2019.zip - # 2018: #Pour appariement sur code commune : au sein du zip, fichier XLS "Intercommunalite_Metropole_au_01-01-2018.xls", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2018 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2018.zip - # 2017: #Pour appariement sur code commune : au sein du zip, fichier XLS "Intercommunalite_Metropole_au_01-01-2017.xls", onglet "Composition_communale", variables CODGEO LIBGEO EPCI LIBEPCI DEP REG (début du tab, ligne 6) - # id: 2510634-2017 - # file: https://www.insee.fr/fr/statistiques/fichier/2510634/Intercommunalite_Metropole_au_01-01-2017.zip # DENS: #grille communale de densité 7 niveaux # 2022: #Pour appariement, variables ppales "Code commune", "Libellé commune", "Typo degré densité" ,"Libellé degré densité" # id: 6439600-2022 diff --git a/docker/test.py b/docker/test.py index f2382f9e..d91df028 100644 --- a/docker/test.py +++ b/docker/test.py @@ -1,5 +1,7 @@ import subprocess from cartiflette import carti_download +from cartiflette.pipeline_constants import COG_TERRITOIRE +from cartiflette.config import DATASETS_HIGH_RESOLUTION ile_de_france = carti_download( values=["11"], @@ -7,10 +9,14 @@ borders="DEPARTEMENT", vectorfile_format="geojson", filter_by="REGION", - source="EXPRESS-COG-CARTO-TERRITOIRE", + source=COG_TERRITOIRE[DATASETS_HIGH_RESOLUTION], year=2022, + provider="Cartiflette", + dataset_family="production", ) ile_de_france.to_file("idf.json") -subprocess.run("mapshaper idf.json -o idf.topojson", check=False, shell=True) +subprocess.run( + "mapshaper idf.json -o idf.topojson", check=False, shell=True, text=True +) diff --git a/misc/debug-download.ipynb b/misc/debug-download.ipynb index b52c1bcb..5539c67e 100644 --- a/misc/debug-download.ipynb +++ b/misc/debug-download.ipynb @@ -9,7 +9,7 @@ "from cartiflette.utils import import_yaml_config\n", "from cartiflette.config import FS\n", "from cartiflette.s3 import upload_s3_raw\n", - "from cartiflette.download.download import _download_sources\n", + "from cartiflette.download.download import _download_and_store_sources\n", "from cartiflette.utils import create_path_bucket, official_epsg_codes\n", "from cartiflette.config import PATH_WITHIN_BUCKET\n", "from cartiflette.utils import hash_file\n", @@ -109,7 +109,7 @@ "metadata": {}, "outputs": [], "source": [ - "x = _download_sources(\n", + "x = _download_and_store_sources(\n", " upload=True,\n", " providers=provider,\n", " dataset_families=dataset_family,\n", diff --git a/poetry.lock b/poetry.lock index b2c5470e..7afb4ac0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,135 +1,135 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.9" -content-hash = "231ab4e9b65d01a7d27d478df77b0b78a1ddef521073379e767b6d6957107cf6" +content-hash = "0184ebb638b4b2155de3057b34350ca61175830761a6fb19cf0c0d98df1b0988" diff --git a/pyproject.toml b/pyproject.toml index 00ba9a98..b9feac17 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,8 +1,8 @@ [tool.poetry] name = "cartiflette" -version = "0.0.2" +version = "0.0.3" description = "Tools to easily retrieve French borders for geographic data analytics" -authors = ["Lino Galiana ", "Thomas Grandjean "] +authors = ["Lino Galiana ", "Thomas Grandjean ", "Alica Burlot "] license = "OPEN LICENCE 2.0/LICENCE OUVERTE 2.0" readme = "README.md" @@ -40,7 +40,7 @@ fastexcel = "^0.11.6" PyQt5 = {version = "^5.15", markers = "sys_platform == 'win32'"} PyQt5-Qt5 = {version = "5.15.2", markers = "sys_platform == 'win32'"} PyQtWebEngine-Qt5 = {version = "5.15.2", markers = "sys_platform == 'win32'"} -spyder = "^6.0.0" +spyder = "^6.0.2" pytest = "^8.1.1" [build-system] diff --git a/python-package/cartiflette/cartiflette/__init__.py b/python-package/cartiflette/cartiflette/__init__.py index 82cd7367..8701aaa8 100644 --- a/python-package/cartiflette/cartiflette/__init__.py +++ b/python-package/cartiflette/cartiflette/__init__.py @@ -1,8 +1,8 @@ from importlib.metadata import version from .config import _config -from .client import carti_download +from .client import carti_download, get_catalog __version__ = version(__package__) -__all__ = ["carti_download"] +__all__ = ["carti_download", "get_catalog"] diff --git a/python-package/cartiflette/cartiflette/client.py b/python-package/cartiflette/cartiflette/client.py index a20078df..df9b1d55 100644 --- a/python-package/cartiflette/cartiflette/client.py +++ b/python-package/cartiflette/cartiflette/client.py @@ -1,13 +1,32 @@ -from requests_cache import CachedSession +""" +# TODO +""" + +from datetime import date +from functools import reduce, lru_cache +import logging import os import typing +from warnings import warn + +from requests_cache import CachedSession import geopandas as gpd -from datetime import date -import logging +import pandas as pd + +from cartiflette.constants import ( + DIR_CACHE, + CACHE_NAME, + BUCKET, + PATH_WITHIN_BUCKET, + CATALOG, +) -from cartiflette.constants import DIR_CACHE, CACHE_NAME, BUCKET, PATH_WITHIN_BUCKET from cartiflette.config import _config -from cartiflette.utils import create_path_bucket, standardize_inputs +from cartiflette.utils import ( + create_path_bucket, + standardize_inputs, + flatten_dict, +) logger = logging.getLogger(__name__) @@ -15,13 +34,20 @@ class CartifletteSession(CachedSession): + """ + Inner class used to perform low level Cartiflette queries. + + Basically performs http(s) requests on Cartiflette's storage system with a + client cache. + + To use behind corporate proxy, please set up http_proxy and https_proxy as + environment variable beforehand. + """ CACHE_NAME = os.path.join(DIR_CACHE, CACHE_NAME) def __init__( - self, - expire_after: int = _config["DEFAULT_EXPIRE_AFTER"], - **kwargs, + self, expire_after: int = _config["DEFAULT_EXPIRE_AFTER"], **kwargs ): super().__init__( cache_name=self.CACHE_NAME, @@ -38,11 +64,8 @@ def __init__( def download_cartiflette_single( self, - *args, - bucket: str = BUCKET, - path_within_bucket: str = PATH_WITHIN_BUCKET, - provider: str = "IGN", - dataset_family: str = "ADMINEXPRESS", + provider: str = "Cartiflette", + dataset_family: str = "production", source: str = "EXPRESS-COG-TERRITOIRE", vectorfile_format: str = "geojson", borders: str = "COMMUNE", @@ -53,19 +76,87 @@ def download_cartiflette_single( crs: typing.Union[list, str, int, float] = 2154, simplification: typing.Union[str, int, float] = None, filename: str = "raw", - **kwargs, - ): + ) -> gpd.GeoDataFrame: + """ + Download a single geodataset from Cartiflette + + Parameters + ---------- + provider : str, optional + Deprecated. The default is "Cartiflette". + dataset_family : str, optional + Deprecated. The default is "production". + source : str, optional + DESCRIPTION. The default is "EXPRESS-COG-TERRITOIRE". + vectorfile_format : str, optional + DESCRIPTION. The default is "geojson". + borders : str, optional + DESCRIPTION. The default is "COMMUNE". + filter_by : str, optional + DESCRIPTION. The default is "region". + territory : str, optional + DESCRIPTION. The default is "metropole". + year : typing.Union[str, int, float], optional + DESCRIPTION. The default is None. + value : typing.Union[str, int, float], optional + DESCRIPTION. The default is "28". + crs : typing.Union[list, str, int, float], optional + DESCRIPTION. The default is 2154. + simplification : typing.Union[str, int, float], optional + DESCRIPTION. The default is None. + filename : str, optional + DESCRIPTION. The default is "raw". + : TYPE + DESCRIPTION. + + Returns + ------- + TYPE + DESCRIPTION. + + """ + + if provider: + warn( + "provider is deprecated and will be removed in a future " + "version. You can safely drop this argument.", + DeprecationWarning, + stacklevel=2, + ) + else: + provider = "Cartiflette" + + if dataset_family: + warn( + "dataset_family is deprecated and will be removed in a future " + "version. You can safely drop this argument.", + DeprecationWarning, + stacklevel=2, + ) + else: + dataset_family = "production" + + if borders == "COMMUNE_ARRONDISSEMENT": + warn( + "'COMMUNE_ARRONDISSESMENT' is deprecated for borders and will " + "be removed in a future version. Please use " + "'ARRONDISSEMENT_MUNICIPAL' instead.", + DeprecationWarning, + stacklevel=2, + ) + borders = "ARRONDISSEMENT_MUNICIPAL" + if not year: year = str(date.today().year) - corresp_filter_by_columns, format_read, driver = standardize_inputs( + _corresp_filter_by_columns, format_read, _driver = standardize_inputs( vectorfile_format ) url = create_path_bucket( { - "bucket": bucket, - "path_within_bucket": path_within_bucket, + "bucket": BUCKET, + "path_within_bucket": PATH_WITHIN_BUCKET, "vectorfile_format": format_read, "territory": territory, "borders": borders, @@ -73,8 +164,8 @@ def download_cartiflette_single( "year": year, "value": value, "crs": crs, - "provider": provider, - "dataset_family": dataset_family, + "provider": "Cartiflette", + "dataset_family": "production", "source": source, "simplification": simplification, "filename": filename, @@ -88,16 +179,132 @@ def download_cartiflette_single( gdf = gpd.read_file(r.content) except Exception as e: logger.error( - f"There was an error while reading the file from the URL: {url}" + "There was an error while reading the file from the URL: %s", + url, ) - logger.error(f"Error message: {str(e)}") - else: - return gdf + logger.error("Error message: %s", str(e)) + return gpd.GeoDataFrame() + return gdf + + def get_catalog(self, **kwargs) -> pd.DataFrame: + """ + Retrieve and load cartiflette's current datasets' catalog (as a + dataframe), filtered on any of the following columns: + [ + 'source', + 'year', + 'borders', + 'crs', + 'filter_by', + 'value', + 'vectorfile_format', + 'territory', + 'simplification' + ] + + Each row corresponds to an available DataFrame. + + Parameters + ---------- + kwargs: dict + pairs of column/filter values + + Returns + ------- + df : pd.DataFrame + Filtered catalog as DataFrame + + Example + ------- + >>> kwargs = {"territory": "france", "source": "CONTOUR-IRIS"} + >>> with CartifletteSession() as carti_session: + return carti_session.get_catalog(**kwargs) + + source year ... territory simplification + 0 CONTOUR-IRIS 2023 ... france 40 + 1 CONTOUR-IRIS 2023 ... france 40 + 2 CONTOUR-IRIS 2023 ... france 40 + 3 CONTOUR-IRIS 2023 ... france 40 + 4 CONTOUR-IRIS 2023 ... france 40 + ... ... ... ... ... + 5745 CONTOUR-IRIS 2023 ... france 40 + 5746 CONTOUR-IRIS 2023 ... france 40 + 5747 CONTOUR-IRIS 2023 ... france 40 + 5748 CONTOUR-IRIS 2023 ... france 40 + 5749 CONTOUR-IRIS 2023 ... france 40 + + [5750 rows x 9 columns] + + """ + df = self._get_full_catalog() + if kwargs: + mask = reduce( + lambda x, y: x & y, [df[k] == v for k, v in kwargs.items()] + ) + df = df[mask].copy() + return df + + def get_full_catalog(self) -> pd.DataFrame: + """ + Retrieve and load cartiflette's current datasets' catalog (as a + dataframe). + + Inventory columns are [ + 'source', + 'year', + 'borders', + 'crs', + 'filter_by', + 'value', + 'vectorfile_format', + 'territory', + 'simplification' + ] + + Each row corresponds to an available DataFrame. + + Returns + ------- + df : pd.DataFrame + Inventory DataFrame + + """ + + url = CATALOG + try: + r = self.get(url) + d = r.json() + except Exception: + logger.error( + "There was an error while reading the file from the URL: %s", + url, + ) + raise + + d = flatten_dict(d) + + index = pd.MultiIndex.from_tuples(d.keys()) + df = pd.DataFrame( + list(d.values()), index=index, columns=["simplification"] + ) + index.names = [ + "source", + "year", + "borders", + "crs", + "filter_by", + "value", + "vectorfile_format", + "territory", + ] + + df = df.reset_index(drop=False) + + return df def get_dataset( self, values: typing.List[typing.Union[str, int, float]], - *args, borders: str = "COMMUNE", filter_by: str = "region", territory: str = "metropole", @@ -105,15 +312,13 @@ def get_dataset( year: typing.Union[str, int, float] = None, crs: typing.Union[list, str, int, float] = 2154, simplification: typing.Union[str, int, float] = None, - bucket: str = BUCKET, - path_within_bucket: str = PATH_WITHIN_BUCKET, - provider: str = "IGN", - dataset_family: str = "ADMINEXPRESS", + provider: str = "Cartiflette", + dataset_family: str = "production", source: str = "EXPRESS-COG-TERRITOIRE", filename: str = "raw", return_as_json: bool = False, - **kwargs, ) -> typing.Union[gpd.GeoDataFrame, str]: + # TODO : fix docstring """ Downloads and aggregates official geographic datasets using the Cartiflette API for a set of specified values. @@ -139,12 +344,13 @@ def get_dataset( The coordinate reference system (default is 2154). - simplification (Union[str, int, float], optional): The simplification parameter (default is None). - - bucket, path_within_bucket, provider, dataset_family, source: + - provider, dataset_family, source: Other parameters required for accessing the Cartiflette API. - return_as_json (bool, optional): - If True, the function returns a JSON string representation of the aggregated GeoDataFrame. - If False, it returns a GeoDataFrame. Default is False. + If True, the function returns a JSON string representation of the + aggregated GeoDataFrame. If False, it returns a GeoDataFrame. Default + is False. Returns: - Union[gpd.GeoDataFrame, str]: @@ -168,8 +374,6 @@ def get_dataset( for value in values: gdf_single = self.download_cartiflette_single( value=value, - bucket=bucket, - path_within_bucket=path_within_bucket, provider=provider, dataset_family=dataset_family, source=source, @@ -195,7 +399,6 @@ def get_dataset( def carti_download( values: typing.List[typing.Union[str, int, float]], - *args, borders: str = "COMMUNE", filter_by: str = "region", territory: str = "metropole", @@ -203,14 +406,11 @@ def carti_download( year: typing.Union[str, int, float] = None, crs: typing.Union[list, str, int, float] = 2154, simplification: typing.Union[str, int, float] = None, - bucket: str = BUCKET, - path_within_bucket: str = PATH_WITHIN_BUCKET, provider: str = "IGN", dataset_family: str = "ADMINEXPRESS", source: str = "EXPRESS-COG-TERRITOIRE", filename: str = "raw", return_as_json: bool = False, - **kwargs, ) -> typing.Union[gpd.GeoDataFrame, str]: """ Calls CartifletteSession.get_dataset @@ -238,9 +438,8 @@ def carti_download( The coordinate reference system (default is 2154). - simplification (Union[str, int, float], optional): The simplification parameter (default is None). - - bucket, path_within_bucket, provider, dataset_family, source: + - provider, dataset_family, source: Other parameters required for accessing the Cartiflette API. - - return_as_json (bool, optional): If True, the function returns a JSON string representation of the aggregated GeoDataFrame. If False, it returns a GeoDataFrame. Default is False. @@ -256,7 +455,6 @@ def carti_download( with CartifletteSession() as carti_session: return carti_session.get_dataset( values=values, - *args, borders=borders, filter_by=filter_by, territory=territory, @@ -264,12 +462,93 @@ def carti_download( year=year, crs=crs, simplification=simplification, - bucket=bucket, - path_within_bucket=path_within_bucket, provider=provider, dataset_family=dataset_family, source=source, filename=filename, return_as_json=return_as_json, - **kwargs, ) + + +@lru_cache(maxsize=128) +def _get_full_catalog() -> pd.DataFrame: + """ + Retrieve Cartiflette's complete catalog. + + This is an inner function, used for caching purposes only. Please use + get_catalog() instead. + + Returns + ------- + pd.DataFrame + Catalog of available datasets. + + Example + ------- + >>> get_catalog(territory="france", source="CONTOUR-IRIS") + + source year ... territory simplification + 0 CONTOUR-IRIS 2023 ... france 40 + 1 CONTOUR-IRIS 2023 ... france 40 + 2 CONTOUR-IRIS 2023 ... france 40 + 3 CONTOUR-IRIS 2023 ... france 40 + 4 CONTOUR-IRIS 2023 ... france 40 + ... ... ... ... ... + 5745 CONTOUR-IRIS 2023 ... france 40 + 5746 CONTOUR-IRIS 2023 ... france 40 + 5747 CONTOUR-IRIS 2023 ... france 40 + 5748 CONTOUR-IRIS 2023 ... france 40 + 5749 CONTOUR-IRIS 2023 ... france 40 + + [5750 rows x 9 columns] + + """ + with CartifletteSession() as carti_session: + return carti_session.get_full_catalog() + + +def get_catalog(**kwargs) -> pd.DataFrame: + """ + Retrieve Cartiflette's catalog. If kwargs are specified, will filter that + catalog according to the pairs of column/values given. + + This function is cached. + + Parameters + ---------- + kwargs : + Pairs of keys/values from the catalog, optional. + + Returns + ------- + pd.DataFrame + Catalog of available datasets. + + Example + ------- + >>> get_catalog(territory="france", source="CONTOUR-IRIS") + + source year ... territory simplification + 0 CONTOUR-IRIS 2023 ... france 40 + 1 CONTOUR-IRIS 2023 ... france 40 + 2 CONTOUR-IRIS 2023 ... france 40 + 3 CONTOUR-IRIS 2023 ... france 40 + 4 CONTOUR-IRIS 2023 ... france 40 + ... ... ... ... ... + 5745 CONTOUR-IRIS 2023 ... france 40 + 5746 CONTOUR-IRIS 2023 ... france 40 + 5747 CONTOUR-IRIS 2023 ... france 40 + 5748 CONTOUR-IRIS 2023 ... france 40 + 5749 CONTOUR-IRIS 2023 ... france 40 + + [5750 rows x 9 columns] + + """ + + df = _get_full_catalog() + if kwargs: + mask = reduce( + lambda x, y: x & y, [df[k] == v for k, v in kwargs.items()] + ) + df = df[mask].copy() + return df diff --git a/python-package/cartiflette/cartiflette/constants.py b/python-package/cartiflette/cartiflette/constants.py index c662279d..b0d889f6 100644 --- a/python-package/cartiflette/cartiflette/constants.py +++ b/python-package/cartiflette/cartiflette/constants.py @@ -9,4 +9,9 @@ DIR_CACHE = platformdirs.user_cache_dir(APP_NAME, ensure_exists=True) CACHE_NAME = "cartiflette_http_cache.sqlite" BUCKET = "projet-cartiflette" -PATH_WITHIN_BUCKET = "production" +PATH_WITHIN_BUCKET = "test" + +CATALOG = url = ( + "https://minio.lab.sspcloud.fr/" + f"{BUCKET}/{PATH_WITHIN_BUCKET}/inventory.json" +) diff --git a/python-package/cartiflette/cartiflette/utils.py b/python-package/cartiflette/cartiflette/utils.py index 0961fcc5..7a1a42f0 100644 --- a/python-package/cartiflette/cartiflette/utils.py +++ b/python-package/cartiflette/cartiflette/utils.py @@ -152,3 +152,31 @@ def create_path_bucket(config: ConfigDict) -> str: write_path += f"/raw.{vectorfile_format}" return write_path + + +def flatten_dict(d: dict, parent_key: tuple = ()) -> dict: + """ + Convenience function, flattens a nested dictionary and convert it back to + dataframe. + + Parameters + ---------- + d : dict + Nested dictionary + parent_key : tuple, optional + Optional key, used for recursive purposes. The default is (). + + Returns + ------- + dict + flattened dictionnary + + """ + items = [] + for k, v in d.items(): + new_key = parent_key + (k,) + if isinstance(v, dict): + items.extend(flatten_dict(v, new_key).items()) + else: + items.append((new_key, v)) + return dict(items) diff --git a/python-package/cartiflette/poetry.lock b/python-package/cartiflette/poetry.lock index 5caa60e8..2f286ff2 100644 --- a/python-package/cartiflette/poetry.lock +++ b/python-package/cartiflette/poetry.lock @@ -1,24 +1,418 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. + +[[package]] +name = "aiohappyeyeballs" +version = "2.4.4" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"}, + 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a/tests/manual_writing_tests.py +++ b/tests/manual_writing_tests.py @@ -117,7 +117,7 @@ mapshaperize_merge_split_from_s3( { "path_within_bucket": path_within_bucket, - "level_polygons": "COMMUNE_ARRONDISSEMENT", + "level_polygons": "ARRONDISSEMENT_MUNICIPAL", "filter_by": "FRANCE_ENTIERE_DROM_RAPPROCHES", "simplification": 50, } @@ -127,8 +127,8 @@ { "path_within_bucket": path_within_bucket, "level_polygons": "BASSIN_VIE", - "format": "topojson", + "format": "topojson", "filter_by": "FRANCE_ENTIERE_DROM_RAPPROCHES", "simplification": 50, } -) \ No newline at end of file +) diff --git a/tests/mockups.py b/tests/mockups.py index 3f93e53d..9fba07bf 100644 --- a/tests/mockups.py +++ b/tests/mockups.py @@ -2,7 +2,6 @@ import pytest import requests from requests_cache import CachedSession -import s3fs import logging from tests.conftest import ( @@ -12,22 +11,22 @@ ) from cartiflette.config import FS -from cartiflette.download.dataset import Dataset -from cartiflette.download.scraper import MasterScraper +from cartiflette.download.dataset import RawDataset +from cartiflette.download.scraper import Scraper logging.basicConfig(level=logging.INFO) @pytest.fixture -def mock_Dataset_without_s3(monkeypatch): - monkeypatch.setattr(Dataset, "_get_last_md5", lambda x: None) +def mock_RawDataset_without_s3(monkeypatch): + monkeypatch.setattr(RawDataset, "_get_last_md5", lambda x: None) # monkeypatch.setattr("FS") @pytest.fixture def total_mock_s3(monkeypatch): - monkeypatch.setattr(Dataset, "_get_last_md5", lambda x: None) + monkeypatch.setattr(RawDataset, "_get_last_md5", lambda x: None) def mock_unpack(self, x, validate=True): return { @@ -37,7 +36,7 @@ def mock_unpack(self, x, validate=True): "root_cleanup": None, } - monkeypatch.setattr(MasterScraper, "download_unpack", mock_unpack) + monkeypatch.setattr(Scraper, "download_unpack", mock_unpack) # monkeypatch.setattr("cartiflette.THREADS_DOWNLOAD", 1) def mock_ls(folder): diff --git a/tests/test_download.py b/tests/test_download.py index 2bec0db7..71c70a09 100644 --- a/tests/test_download.py +++ b/tests/test_download.py @@ -5,14 +5,14 @@ import requests_cache import logging -from cartiflette.download.dataset import Dataset +from cartiflette.download.dataset import RawDataset from cartiflette.download.scraper import ( - MasterScraper, + Scraper, validate_file, download_to_tempfile_http, ) -from cartiflette.download.download import _download_sources -from cartiflette.download import download_all +from cartiflette.download.download import _download_and_store_sources +from cartiflette.pipeline import download_all from cartiflette.utils import import_yaml_config from tests.conftest import ( DUMMY_FILE_1, @@ -24,14 +24,14 @@ mock_httpscraper_download_success, mock_httpscraper_download_success_corrupt_hash, mock_httpscraper_download_success_corrupt_length, - mock_Dataset_without_s3, + mock_RawDataset_without_s3, total_mock_s3, ) logger = logging.getLogger(__name__) -def test_Dataset(): +def test_RawDataset(): """ __md5__ __get_last_md5__ @@ -70,7 +70,7 @@ def test_http_proxy(): del os.environ["https_proxy"] except KeyError: pass - dummy_scraper = MasterScraper() + dummy_scraper = Scraper() try: assert isinstance(dummy_scraper, requests_cache.CachedSession) assert dummy_scraper.proxies == { @@ -88,7 +88,7 @@ def test_http_download(mock_httpscraper_download_success): """ # Initialisation - dummy_scraper = MasterScraper() + dummy_scraper = Scraper() dummy = "https://dummy" # Fourniture du même hash -> pas de téléchargement @@ -120,7 +120,7 @@ def test_download_ko_length( le Content-length ne correspond pas au contenu (simule un fichier corrompu) -> doit déclencher un IOError """ - dummy_scraper = MasterScraper() + dummy_scraper = Scraper() with pytest.raises(IOError): result = download_to_tempfile_http("dummy", session=dummy_scraper) @@ -133,12 +133,12 @@ def test_download_ko_md5( avec son contenu (simule un fichier corrompu) -> doit déclencher un IOError """ - dummy_scraper = MasterScraper() + dummy_scraper = Scraper() with pytest.raises(IOError): result = download_to_tempfile_http("dummy", session=dummy_scraper) -# def test_MasterScraper_ko(): +# def test_Scraper_ko(): # """ # download_unzip # * Tester que si échec du téléchargement, le fichier temporaire est supprimé @@ -148,7 +148,7 @@ def test_download_ko_md5( # pass -# def test_MasterScraper_ok(): +# def test_Scraper_ok(): # """ # download_unzip # * Si succès, contrôle de la présence du fichier temporaire (puis le @@ -159,7 +159,7 @@ def test_download_ko_md5( # pass -def test_sources_yaml(mock_Dataset_without_s3): +def test_sources_yaml(mock_RawDataset_without_s3): yaml = import_yaml_config() errors_type0 = [] @@ -168,7 +168,7 @@ def test_sources_yaml(mock_Dataset_without_s3): errors_type3 = [] errors_type4 = [] - with MasterScraper() as scraper: + with Scraper() as scraper: for provider, provider_yaml in yaml.items(): if not isinstance(provider_yaml, dict): continue @@ -208,7 +208,7 @@ def test_sources_yaml(mock_Dataset_without_s3): territory = None try: print(str_yaml) - ds = Dataset( + ds = RawDataset( dataset_family, source, int(year), @@ -250,24 +250,20 @@ def test_sources_yaml(mock_Dataset_without_s3): f"got code {r.status_code} on {url}" ) if errors_type0: - logger.warning( - "Champs du YAML non testés\n" + "\n".join(errors_type0) - ) + logger.warning("Champs du YAML non testés\n" + "\n".join(errors_type0)) if errors_type1 + errors_type2 + errors_type3 + errors_type4: if errors_type1: logger.error("=" * 50) logger.error( - "Objet(s) Dataset(s) non instancié(s)\n" + "Objet(s) RawDataset(s) non instancié(s)\n" + "\n".join(errors_type1) ) logger.error("-" * 50) if errors_type2: logger.error("=" * 50) - logger.error( - "URL non reconstituées:\n" + "\n".join(errors_type2) - ) + logger.error("URL non reconstituées:\n" + "\n".join(errors_type2)) logger.error("-" * 50) if errors_type3: @@ -278,14 +274,11 @@ def test_sources_yaml(mock_Dataset_without_s3): if errors_type4: logger.error("=" * 50) logger.error( - "Requête HTTP avec code d'erreur:\n" - + "\n".join(errors_type4) + "Requête HTTP avec code d'erreur:\n" + "\n".join(errors_type4) ) logger.error("-" * 50) - assert ( - len(errors_type1 + errors_type2 + errors_type3 + errors_type4) == 0 - ) + assert len(errors_type1 + errors_type2 + errors_type3 + errors_type4) == 0 def test_download_all(total_mock_s3): diff --git a/tests/test_download_from_cartiflette.py b/tests/test_download_from_cartiflette.py index c08fc0cd..26e68de6 100644 --- a/tests/test_download_from_cartiflette.py +++ b/tests/test_download_from_cartiflette.py @@ -64,7 +64,7 @@ def test_carti_download_commune_arrondissement(self): ile_de_france_commune_arrondissement = carti_download( values=["75"], crs=4326, - borders="COMMUNE_ARRONDISSEMENT", + borders="ARRONDISSEMENT_MUNICIPAL", vectorfile_format="topojson", filter_by="DEPARTEMENT", source="EXPRESS-COG-CARTO-TERRITOIRE", @@ -83,9 +83,7 @@ def test_carti_download_commune_arrondissement(self): ].unique() self.assertCountEqual(unique_insee_com, ["75056"]) - unique_statut = ile_de_france_commune_arrondissement[ - "STATUT" - ].unique() + unique_statut = ile_de_france_commune_arrondissement["STATUT"].unique() self.assertCountEqual(unique_statut, ["Arrondissement municipal"]) unique_insee_cog = ile_de_france_commune_arrondissement[ diff --git a/tests/test_mapshaper_wrangling.py b/tests/test_mapshaper_wrangling.py index 2781d540..8e13123e 100644 --- a/tests/test_mapshaper_wrangling.py +++ b/tests/test_mapshaper_wrangling.py @@ -8,13 +8,16 @@ import geopandas as gpd # Import the functions to be tested -from cartiflette.mapshaper.mapshaper_wrangling import mapshaper_enrich, mapshaper_split +from cartiflette.mapshaper.mapshaper_enrich import mapshaper_enrich +from cartiflette.mapshaper.mapshaper_split import mapshaper_split class TestMapshaperWrangling(unittest.TestCase): def setUp(self): # Create temporary files for testing - self.input_shapefile = tempfile.NamedTemporaryFile(suffix=".geojson", delete=False) + self.input_shapefile = tempfile.NamedTemporaryFile( + suffix=".geojson", delete=False + ) gdf_shapefile = gpd.GeoDataFrame( { "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], @@ -30,7 +33,9 @@ def setUp(self): ) gdf_shapefile.to_file(self.input_shapefile.name, driver="GeoJSON") - self.input_csv = tempfile.NamedTemporaryFile(suffix=".csv", delete=False) + self.input_csv = tempfile.NamedTemporaryFile( + suffix=".csv", delete=False + ) df_csv = pd.DataFrame( { "CODGEO": ["1", "2", "3"], @@ -41,7 +46,9 @@ def setUp(self): ) df_csv.to_csv(self.input_csv.name, index=False) - self.input_geojson = tempfile.NamedTemporaryFile(suffix=".geojson", delete=False) + self.input_geojson = tempfile.NamedTemporaryFile( + suffix=".geojson", delete=False + ) gdf_geojson = gpd.GeoDataFrame( { "geometry": [Point(0, 0), Point(1, 1), Point(2, 2)], @@ -64,14 +71,10 @@ def test_mapshaper_enrich(self): try: # Call the function to be tested mapshaper_enrich( - local_dir=os.path.dirname(self.input_shapefile.name), - filename_initial=os.path.basename(self.input_shapefile.name).replace( - ".geojson", "" - ), - extension_initial="geojson", - metadata_file=self.input_csv.name, - output_path=f"{output_path}/enriched.geojson", - dict_corresp={"FRANCE_ENTIERE": "Country"}, + input_geodata_file=self.input_shapefile.name, + input_metadata_file=self.input_csv.name, + output_dir=output_path, + output_name="enriched", ) # Check if the output GeoJSON file exists @@ -82,7 +85,12 @@ def test_mapshaper_enrich(self): self.assertEqual(len(gdf_output), 3) # Check if the expected fields are present - expected_fields = ["geometry", "INSEE_COM", "INSEE_DEP", "INSEE_REG"] + expected_fields = [ + "geometry", + "INSEE_COM", + "INSEE_DEP", + "INSEE_REG", + ] for field in expected_fields: self.assertIn(field, gdf_output.columns) @@ -104,7 +112,8 @@ def test_mapshaper_split(self): input_file=self.input_geojson.name, layer_name=layer_name, split_variable=split_variable, - output_path=f"{output_path}/new.geojson", + output_dir=output_path, + output_format="geojson", ) # Check if the output GeoJSON file exists @@ -119,7 +128,9 @@ def test_mapshaper_split(self): ) # Check if the number of features is as expected # Count number of features - gdf_output = pd.concat([gpd.read_file(tempgpd) for tempgpd in files]) + gdf_output = pd.concat( + [gpd.read_file(tempgpd) for tempgpd in files] + ) self.assertEqual(len(gdf_output), 3) finally: