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import os
import pandas as pd
import regionmask
import xarray as xr
from huggingface_hub import hf_hub_download
LAND_FEATURES = ['c3ann', 'c3nfx', 'c3per','c4ann', 'c4per',
'pastr', 'primf', 'primn', 'range', 'secdf', 'secdn', 'urban', 'cell_area']
LAND_DIFF_FEATURES = ['c3ann_diff', 'c3nfx_diff', 'c3per_diff','c4ann_diff', 'c4per_diff',
'pastr_diff', 'primf_diff', 'primn_diff', 'range_diff', 'secdf_diff', 'secdn_diff', 'urban_diff']
FEATURES = LAND_FEATURES + LAND_DIFF_FEATURES
LABEL = "ELUC"
PATH_TO_DATASET = "merged_aggregated_dataset_1850_2022.zarr.zip"
def import_data(path, update_path):
raw = xr.open_zarr(path, consolidated=True)
# Get updated ELUC
if update_path:
eluc = xr.open_dataset(update_path)
raw = raw.drop_vars(["ELUC", "cell_area"])
raw = raw.merge(eluc)
# Shift actions back a year
raw[LAND_DIFF_FEATURES] = raw[LAND_DIFF_FEATURES].shift(time=-1)
# Old time shifting
# raw['ELUC'] = raw['ELUC'].shift(time=1)
# raw['ELUC_diff'] = raw['ELUC_diff'].shift(time=1)
# raw['time'] = raw.time - 1
# assert(list(np.unique(raw.time)) == list(range(1849, 2022)))
mask = raw["ELUC_diff"].isnull().compute()
raw = raw.where(~mask, drop=True)
country_mask = regionmask.defined_regions.natural_earth_v5_0_0.countries_110.mask(raw)
raw["country"] = country_mask
return raw
def da_to_df(da, countries_df):
df = da.to_dataframe()
df = df.dropna()
df['country_name'] = countries_df.loc[df['country'], 'names'].values
return df
def main():
raw = import_data(PATH_TO_DATASET, None)
countries_df = regionmask.defined_regions.natural_earth_v5_0_0.countries_110.to_dataframe()
df = da_to_df(raw, countries_df)
df = df.loc[1982:][FEATURES + [LABEL]]
df.to_csv("processed/eluc_1982.csv", index=True)
if __name__ == "__main__":
main() |