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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"
# +
# Can possibly use remote addresses just fine with vsicurl
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',
# CONUS coverage only
'/home/rstudio/source.coop/cboettig/mobi/species-richness-all/mobi-species-richness.tif', # byte-encoded gdal_translate -ot Byte <in> <out>
'/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]
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