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import concurrent.futures
import multiprocessing
import os
import netCDF4
import numpy as np
from ..features.io import load_batch, decode_saved_var_to_rainrate
def fractions_ensemble(observation, forecasts, threshold, max_scale=256):
obs = (observation >= threshold).astype(np.float32)
fc = (forecasts >= threshold).astype(np.float32).mean(axis=-1)
obs_frac = {}
fc_frac = {}
scale = 1
while True:
obs_frac[scale] = obs.copy()
fc_frac[scale] = fc.copy()
scale *= 2
if scale > max_scale:
break
obs = 0.25 * (
obs[...,::2,::2] +
obs[...,1::2,::2] +
obs[...,::2,1::2] +
obs[...,1::2,1::2]
)
fc = 0.25 * (
fc[...,::2,::2] +
fc[...,1::2,::2] +
fc[...,::2,1::2] +
fc[...,1::2,1::2]
)
return (obs_frac, fc_frac)
def frac_from_file(fn, threshold, preproc_fc):
print(fn)
(_, y, y_pred) = load_batch(fn, preproc_fc=preproc_fc)
return fractions_ensemble(y, y_pred, threshold)
def save_fractions_for_dataset(data_dir, result_fn, threshold, preproc_fc=None):
files = sorted(os.listdir(data_dir))
files = [os.path.join(data_dir,fn) for fn in files]
N_threads = multiprocessing.cpu_count()
with concurrent.futures.ProcessPoolExecutor(N_threads) as executor:
futures = []
for fn in files:
args = (frac_from_file, fn, threshold, preproc_fc)
futures.append(executor.submit(*args))
(obs_frac, fc_frac) = zip(*(f.result() for f in futures))
scales = list(obs_frac[0].keys())
obs_frac_dict = {}
fc_frac_dict = {}
for s in scales:
obs_frac_dict[s] = np.concatenate([f[s] for f in obs_frac], axis=0)
fc_frac_dict[s] = np.concatenate([f[s] for f in fc_frac], axis=0)
obs_frac = obs_frac_dict
fc_frac = fc_frac_dict
frac_vars = {}
k = 0
with netCDF4.Dataset(result_fn, 'w') as ds:
ds.createDimension("dim_sample", obs_frac[1].shape[0])
ds.createDimension("dim_channel", obs_frac[1].shape[1])
ds.createDimension("dim_time_future", obs_frac[1].shape[2])
var_params = {"zlib": True, "complevel": 1}
for s in scales:
ds.createDimension(f"dim_h_pool{s}x{s}", obs_frac[s].shape[3])
ds.createDimension(f"dim_w_pool{s}x{s}", obs_frac[s].shape[4])
obs_var = ds.createVariable(
f"obs_frac_scale{s}x{s}", np.float32,
(
"dim_sample", "dim_channel", "dim_time_future",
f"dim_h_pool{s}x{s}", f"dim_w_pool{s}x{s}",
),
chunksizes=(1,)+obs_frac[s].shape[1:],
**var_params
)
obs_var[:] = obs_frac[s]
fc_var = ds.createVariable(
f"fc_frac_scale{s}x{s}", np.float32,
(
"dim_sample", "dim_channel", "dim_time_future",
f"dim_h_pool{s}x{s}", f"dim_w_pool{s}x{s}",
),
chunksizes=(1,)+fc_frac[s].shape[1:],
**var_params
)
fc_var[:] = fc_frac[s]
def load_fractions(fn):
obs_frac = {}
fc_frac = {}
with netCDF4.Dataset(fn, 'r') as ds:
var_list = ds.variables.keys()
scales = {int(v.split("x")[-1]) for v in var_list}
for s in scales:
obs_frac[s] = np.array(ds[f"obs_frac_scale{s}x{s}"][:], copy=False)
fc_frac[s] = np.array(ds[f"fc_frac_scale{s}x{s}"][:], copy=False)
return (obs_frac, fc_frac)
def fractions_skill_score(
obs_frac, fc_frac,
frac_axes=None, fss_axes=None, use_timesteps=None
):
if isinstance(obs_frac, dict):
return {
s: fractions_skill_score(
obs_frac[s], fc_frac[s],
frac_axes=frac_axes, fss_axes=fss_axes,
use_timesteps=use_timesteps
)
for s in sorted(obs_frac)
}
if use_timesteps != None:
obs_frac = obs_frac[:,:,:use_timesteps,...]
fc_frac = fc_frac[:,:,:use_timesteps,...]
fbs = ((obs_frac - fc_frac)**2).mean(axis=frac_axes)
fbs_ref = (obs_frac**2).mean(axis=frac_axes) + \
(fc_frac**2).mean(axis=frac_axes)
fss = 1 - fbs/fbs_ref
if isinstance(fss, np.ndarray):
fss[~np.isfinite(fss)] = 1
fss = fss.mean(axis=fss_axes)
return fss
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