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Fix excessive memory usage of _fill_nans_infs_nwp_cascade #425

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Aug 20, 2024
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Fix excessive memory usage of _fill_nans_infs_nwp_cascade #425

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37 changes: 7 additions & 30 deletions pysteps/blending/steps.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@

import math
import time
from copy import deepcopy

import numpy as np
from scipy.linalg import inv
Expand All @@ -55,8 +56,6 @@
from pysteps.postprocessing import probmatching
from pysteps.timeseries import autoregression, correlation

from copy import deepcopy

try:
import dask

Expand Down Expand Up @@ -2443,36 +2442,14 @@ def _fill_nans_infs_nwp_cascade(
"""Ensure that the NWP cascade and fields do no contain any nans or infinite number"""
# Fill nans and infinite numbers with the minimum value present in precip
# (corresponding to zero rainfall in the radar observations)
precip_models_cascade = np.nan_to_num(
precip_models_cascade,
copy=True,
nan=np.nanmin(precip_cascade),
posinf=np.nanmin(precip_cascade),
neginf=np.nanmin(precip_cascade),
)
precip_models_pm = np.nan_to_num(
precip_models_pm,
copy=True,
nan=np.nanmin(precip),
posinf=np.nanmin(precip),
neginf=np.nanmin(precip),
)
min_cascade = np.nanmin(precip_cascade)
min_precip = np.nanmin(precip)
precip_models_cascade[~np.isfinite(precip_models_cascade)] = min_cascade
precip_models_pm[~np.isfinite(precip_models_pm)] = min_precip
# Also set any nans or infs in the mean and sigma of the cascade to
# respectively 0.0 and 1.0
mu_models = np.nan_to_num(
mu_models,
copy=True,
nan=0.0,
posinf=0.0,
neginf=0.0,
)
sigma_models = np.nan_to_num(
sigma_models,
copy=True,
nan=0.0,
posinf=0.0,
neginf=0.0,
)
mu_models[~np.isfinite(mu_models)] = 0.0
sigma_models[~np.isfinite(sigma_models)] = 0.0

return precip_models_cascade, precip_models_pm, mu_models, sigma_models

Expand Down