def popweight_outputs_for_input(custom_input):
sim = ("RES" + str(np.round(custom_input[0][0], decimals=1)) + "_IND" +
str(np.round(custom_input[0][1], decimals=1)) + "_TRA" +
str(np.round(custom_input[0][2], decimals=1)) + "_AGR" +
str(np.round(custom_input[0][3], decimals=1)) + "_ENE" +
str(np.round(custom_input[0][4], decimals=1)))
filename = f"ds_{sim}_{output}_popgrid_0.25deg_adjusted_scaled.nc"
print(f"processing for {filename} ...")
with xr.open_dataset(
f"/nobackup/earlacoa/machinelearning/data_annual/predictions/{output}_adjusted_scaled/{filename}"
) as ds:
ds_custom_output = ds[output].load()
clip = rasterize(shapes,
ds_custom_output.coords,
longitude="lon",
latitude="lat")
ds_custom_output_clipped = ds_custom_output.where(clip == 0, other=np.nan)
return filename, np.nansum(
(ds_custom_output_clipped * pop_2015) / pop_2015_clipped.sum())
def crop_ds_to_shapefile(ds, shapefile):
# load shapefile (single multipolygon) and extract shapes
shapefile = gpd.read_file(shapefile)
shapes = [(shape, index) for index, shape in enumerate(shapefile.geometry)]
# apply shapefile to geometry, default: inside shapefile == 0, outside shapefile == np.nan
ds['shapefile'] = rasterize(shapes,
ds.coords,
longitude='lon',
latitude='lat')
# change to more intuitive labelling of 1 for inside shapefile and np.nan for outside shapefile
# if condition preserve (outside shapefile, as inside defaults to 0), otherwise (1, to mark in shapefile)
ds['shapefile'] = ds.shapefile.where(cond=ds.shapefile != 0, other=1)
# example: crop data to shapefile
# if condition (not in shapefile) preserve, otherwise (in shapefile, and scale)
ds = ds.where(cond=ds.shapefile == 1, other=np.nan)
return ds
def popweight_outputs_for_input(custom_input):
sim = 'RES' + str(np.round(custom_input[0][0], decimals=1)) \
+ '_IND' + str(np.round(custom_input[0][1], decimals=1)) \
+ '_TRA' + str(np.round(custom_input[0][2], decimals=1)) \
+ '_AGR' + str(np.round(custom_input[0][3], decimals=1)) \
+ '_ENE' + str(np.round(custom_input[0][4], decimals=1))
filename = f'ds_{sim}_{output}_popgrid.nc'
print(f'processing for {filename} ...')
try:
with xr.open_dataset(path + output + '/' + filename) as ds:
ds_custom_output = ds[output]
clip = rasterize(shapes,
ds_custom_output.coords,
longitude='lon',
latitude='lat')
ds_custom_output_clipped = ds_custom_output.where(clip == 0,
other=np.nan)
return filename, np.nansum(
(ds_custom_output_clipped * pop_2015) / pop_2015_clipped.sum())
except:
RuntimeError
#!/usr/bin/env python3
import numpy as np
import xarray as xr
import salem
from cutshapefile import transform_from_latlon, rasterize
import geopandas as gpd
# load shapefile (single multipolygon) and extract shapes
shapefile = gpd.read_file(
'/nobackup/earlacoa/emissions/shapefiles/CHN_adm0.shp')
shapes = [(shape, index) for index, shape in enumerate(shapefile.geometry)]
# load wrf dataset
ds = salem.xr.open_dataset('/path/wrf.nc')
# apply shapefile to geometry, default: inside shapefile == 0, outside shapefile == np.nan
ds['shapefile'] = rasterize(shapes, ds.coords, longitude='lon', latitude='lat')
# change to more intuitive labelling of 1 for inside shapefile and np.nan for outside shapefile
# if condition preserve (outside shapefile, as inside defaults to 0), otherwise (1, to mark in shapefile)
ds['shapefile'] = ds.shapefile.where(cond=ds.shapefile != 0, other=1)
# example: scale data inside shapefile by 0.5
# if condition (not in shapefile) preserve, otherwise (in shapefile, and scale)
ds = ds.where(cond=ds.shapefile != 1, other=ds * 0.5)
#region = "china_north"
#shapefile = "/nobackup/earlacoa/health/data/CHN_north.shp"
# region = 'china_north_east'
# shapefile = '/nobackup/earlacoa/health/data/CHN_north_east.shp'
# region = 'china_east'
# shapefile = '/nobackup/earlacoa/health/data/CHN_east.shp'
# region = 'china_south_central'
# shapefile = '/nobackup/earlacoa/health/data/CHN_south_central.shp'
# region = 'china_south_west'
# shapefile = '/nobackup/earlacoa/health/data/CHN_south_west.shp'
# region = 'china_north_west'
# shapefile = '/nobackup/earlacoa/health/data/CHN_north_west.shp'
shp = gpd.read_file(shapefile)
shapes = [(shape, n) for n, shape in enumerate(shp.geometry)]
clip = rasterize(shapes, pop_2015.coords, longitude="lon", latitude="lat")
pop_2015_clipped = pop_2015.where(clip == 0, other=np.nan)
def popweight_outputs_for_input(custom_input):
sim = ("RES" + str(np.round(custom_input[0][0], decimals=1)) + "_IND" +
str(np.round(custom_input[0][1], decimals=1)) + "_TRA" +
str(np.round(custom_input[0][2], decimals=1)) + "_AGR" +
str(np.round(custom_input[0][3], decimals=1)) + "_ENE" +
str(np.round(custom_input[0][4], decimals=1)))
filename = f"ds_{sim}_{output}_popgrid_0.25deg_adjusted_scaled.nc"
print(f"processing for {filename} ...")
with xr.open_dataset(
f"/nobackup/earlacoa/machinelearning/data_annual/predictions/{output}_adjusted_scaled/{filename}"
) as ds:
ds_custom_output = ds[output].load()
#region = 'china_north'
#shapefile = '/nobackup/earlacoa/health/data/CHN_north.shp'
#region = 'china_north_east'
#shapefile = '/nobackup/earlacoa/health/data/CHN_north_east.shp'
#region = 'china_east'
#shapefile = '/nobackup/earlacoa/health/data/CHN_east.shp'
#region = 'china_south_central'
#shapefile = '/nobackup/earlacoa/health/data/CHN_south_central.shp'
#region = 'china_south_west'
#shapefile = '/nobackup/earlacoa/health/data/CHN_south_west.shp'
#region = 'china_north_west'
#shapefile = '/nobackup/earlacoa/health/data/CHN_north_west.shp'
shp = gpd.read_file(shapefile)
shapes = [(shape, n) for n, shape in enumerate(shp.geometry)]
clip = rasterize(shapes, pop_2015.coords, longitude='lon', latitude='lat')
pop_2015_clipped = pop_2015.where(clip == 0, other=np.nan)
def popweight_outputs_for_input(custom_input):
sim = 'RES' + str(np.round(custom_input[0][0], decimals=1)) \
+ '_IND' + str(np.round(custom_input[0][1], decimals=1)) \
+ '_TRA' + str(np.round(custom_input[0][2], decimals=1)) \
+ '_AGR' + str(np.round(custom_input[0][3], decimals=1)) \
+ '_ENE' + str(np.round(custom_input[0][4], decimals=1))
filename = f'ds_{sim}_{output}_popgrid.nc'
print(f'processing for {filename} ...')
try:
with xr.open_dataset(path + output + '/' + filename) as ds:
ds_custom_output = ds[output]