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| # + | |
| from exactextract import exact_extract | |
| from osgeo import ogr, gdal | |
| from pathlib import Path | |
| from joblib import Parallel, delayed | |
| def extract(raster, vector, layer, output = None): | |
| if output is None: | |
| output = Path(raster).with_suffix('.csv').name | |
| gdal.UseExceptions() | |
| ogr.UseExceptions() | |
| rast = gdal.Open(raster) | |
| vrt = f""" | |
| <OGRVRTDataSource> | |
| <OGRVRTWarpedLayer> | |
| <OGRVRTLayer name="combined"> | |
| <SrcDataSource>{vector}</SrcDataSource> | |
| <SrcLayer>{layer}</SrcLayer> | |
| </OGRVRTLayer> | |
| <TargetSRS>{rast.GetSpatialRef().ExportToWkt()}</TargetSRS> | |
| </OGRVRTWarpedLayer> | |
| </OGRVRTDataSource> | |
| """ | |
| polys = ogr.Open(vrt) | |
| exact_extract(rast, polys, 'mean', output='gdal', output_options = {'filename' : output }) | |
| return output | |
| # - | |
| vector = "/home/rstudio/source.coop/cboettig/pad-us-3/PADUS3_0Geopackage.gpkg" | |
| layer = "PADUS3_0Combined_DOD_TRIB_Fee_Designation_Easement" | |
| rasters = [ | |
| "/home/rstudio/boettiger-lab/us-pa-policy/hfp_2021_100m_v1-2_cog.tif", | |
| '/home/rstudio/source.coop/vizzuality/lg-land-carbon-data/deforest_carbon_100m_cog.tif', | |
| '/home/rstudio/source.coop/vizzuality/lg-land-carbon-data/natcrop_bii_100m_cog.tif', | |
| '/home/rstudio/source.coop/vizzuality/lg-land-carbon-data/natcrop_fii_100m_cog.tif', | |
| '/home/rstudio/source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif', | |
| '/home/rstudio/source.coop/vizzuality/lg-land-carbon-data/natcrop_reduction_100m_cog.tif', | |
| '/home/rstudio/source.coop/cboettig/carbon/cogs/irrecoverable_c_total_2018.tif', | |
| '/home/rstudio/source.coop/cboettig/carbon/cogs/manageable_c_total_2018.tif', | |
| '/home/rstudio/minio/shared-biodiversity/redlist/cog/combined_rwr_2022.tif', | |
| '/home/rstudio/minio/shared-biodiversity/redlist/cog/combined_sr_2022.tif', | |
| #'/home/rstudio/source.coop/cboettig/mobi/species-richness-all/SpeciesRichness_All.tif', | |
| #'/home/rstudio/source.coop/cboettig/mobi/range-size-rarity-all/RSR_All.tif', | |
| ] | |
| # extract(rasters[0], vector, layer) # just one | |
| # + | |
| # %%time | |
| # extract all the raster layers in parallel | |
| parallel_extract = delayed(extract) | |
| runner = Parallel(n_jobs=-1) | |
| runner(parallel_extract(i, vector, layer) for i in rasters) | |
| # + | |
| # bind columns into a single csv | |
| csv_files = list(Path.cwd().glob("*.csv")) | |
| import pandas as pd | |
| data = {} | |
| for file in csv_files: | |
| df = pd.read_csv(file.name) | |
| data[file.stem] = df['mean'] | |
| pd.DataFrame(data).to_csv("pad_raster_means.csv") | |
| # - | |
| [file.unlink() for file in csv_files] | |