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1820 1821 1822 1823 1824 1825 1826 | import time as timelib
from time import time
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from loader_utils_new import *
from data_shapes import *
class WeatherDataset(Dataset):
"""
Base weather dataset class
"""
def __init__(
self,
device,
hadisd_mode,
start_date,
end_date,
lead_time,
era5_mode="train",
res=1,
filter_dates=None,
diff=None,
):
super().__init__()
# Setup
self.device = device
self.mode = hadisd_mode
self.data_path = "path_to_data/"
self.aux_data_path = "path_to_auxiliary_data/"
self.start_date = start_date
self.end_date = end_date
self.lead_time = lead_time
self.era5_mode = era5_mode
self.res = res
self.filter_dates = filter_dates
self.diff = diff
# Date indexing
self.dates = pd.date_range(start_date, end_date, freq="6H")
if self.filter_dates == "start":
self.index = np.array([i for i, d in enumerate(self.dates) if d.month < 7])
elif self.filter_dates == "end":
self.index = np.array([i for i, d in enumerate(self.dates) if d.month >= 7])
else:
self.index = np.array(range(len(self.dates)))
# Load the input modalities
print("Loading IGRA")
self.load_igra()
print("Loading AMSU-A")
self.load_amsua()
print("Loading AMSU-B")
self.load_amsub()
print("Loading ICOADS")
self.load_icoads()
print("Loading IASI")
self.load_iasi()
print("Loading GEO")
self.load_sat_data()
print("Loading HADISD")
self.load_hadisd(self.mode)
print("Loading ASCAT")
self.load_ascat_data()
self.load_hirs_data()
# Load the ground truth data for training
print("Loading ERA5")
self.era5_sfc = [
self.load_era5(year)
for year in range(int(start_date[:4]), int(end_date[:4]) + 1)
]
# Internal grid to longitude latitude correspondence
self.era5_x = [
self.to_tensor(
np.load(self.data_path + "era5/era5_x_{}.npy".format(self.res))
)
/ LATLON_SCALE_FACTOR,
self.to_tensor(
np.load(self.data_path + "era5/era5_y_{}.npy".format(self.res))
)
/ LATLON_SCALE_FACTOR,
]
# Orography
self.era5_elev = self.to_tensor(
np.load(self.data_path + "era5/elev_vars_{}.npy".format(self.res))
)
self.era5_elev = torch.flip(self.era5_elev.permute(0, 2, 1), [-1])
xx, yy = torch.meshgrid(self.era5_x[0], self.era5_x[1])
self.era5_lonlat = torch.stack([xx, yy])
# Climatology
self.climatology = np.memmap(
self.data_path + "climatology_data.mmap",
dtype="float32",
mode="r",
shape=CLIMATOLOGY_SHAPE,
)
# Setup normalisation factors
if self.diff:
self.era5_mean_spatial = np.load(
self.aux_data_path + "era5_spatial_means.npy"
)[0, ...]
self.means = np.load(self.aux_data_path + "era5_avdiff_means.npy")[
:, np.newaxis, np.newaxis, ...
]
self.stds = np.load(self.aux_data_path + "era5_avdiff_stds.npy")[
:, np.newaxis, np.newaxis, ...
]
else:
self.means = np.load(
self.aux_data_path
+ "norm_factors/mean_{}_{}.npy".format(self.era5_mode, self.res)
)[:, np.newaxis, np.newaxis, ...]
self.stds = np.load(
self.aux_data_path
+ "norm_factors/std_{}_{}.npy".format(self.era5_mode, self.res)
)[:, np.newaxis, np.newaxis, ...]
def load_icoads(self):
"""
Load the ICOADS data
"""
self.icoads_y = np.memmap(
self.data_path + "icoads/1999_2021_icoads_y.mmap",
dtype="float32",
mode="r",
shape=ICOADS_Y_SHAPE,
)
self.icoads_x = (
np.memmap(
self.data_path + "icoads/1999_2021_icoads_x.mmap",
dtype="float32",
mode="r",
shape=ICOADS_X_SHAPE,
)
/ LATLON_SCALE_FACTOR
)
self.icoads_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_icoads.npy")
)
self.icoads_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_icoads.npy")
)
self.icoads_means = self.to_tensor(
np.nanmean(self.icoads_y[-365 * 4 :, ...], axis=(0, 2))[:, np.newaxis]
)
self.icoads_stds = self.to_tensor(
np.nanstd(self.icoads_y[-365 * 4 :, ...], axis=(0, 2))[:, np.newaxis]
)
self.icoads_index_offset = ICOADS_OFFSETS[self.start_date]
return
def load_igra(self):
"""
Load the IGRA data
"""
self.igra_y = np.memmap(
self.data_path + "igra/1999_2021_igra_y.mmap",
dtype="float32",
mode="r",
shape=IGRA_Y_SHAPE,
)
self.igra_x = np.copy(
np.memmap(
self.data_path + "igra/1999_2021_igra_x.mmap",
dtype="float32",
mode="r",
shape=IGRA_X_SHAPE,
)
)
self.igra_x = self.igra_x / LATLON_SCALE_FACTOR
self.igra_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_igra.npy")
)
self.igra_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_igra.npy")
)
self.igra_index_offset = IGRA_OFFSETS[self.start_date]
return
def load_amsua(self):
"""
Load the AMSU-A data
"""
self.amsua_y = np.memmap(
self.data_path + "amsua/2007_2021_amsua.mmap",
dtype="float32",
mode="r",
shape=AMSUA_Y_SHAPE,
)
self.amsua_index_offset = AMSUA_OFFSETS[self.start_date]
xx = np.linspace(-180, 179, 360, dtype=np.float32)
xx = ((xx + 360) % 360) / LATLON_SCALE_FACTOR
yy = np.linspace(90, -90, 180, dtype=np.float32) / LATLON_SCALE_FACTOR
self.amsua_x = [xx, yy]
self.amsua_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_amsua.npy")
)
self.amsua_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_amsua.npy")
)
return
def load_amsub(self):
"""
Load the AMSU-B data
"""
self.amsub_y = np.memmap(
self.data_path + "amsub_mhs/2007_2021_amsub.mmap",
dtype="float32",
mode="r",
shape=AMSUB_Y_SHAPE,
)
self.amsub_index_offset = AMSUB_OFFSETS[self.start_date]
xx = np.linspace(0, 359, 360, dtype=np.float32)
xx = ((xx + 360) % 360) / LATLON_SCALE_FACTOR
yy = np.linspace(90, -90, 181, dtype=np.float32) / LATLON_SCALE_FACTOR
self.amsub_x = [xx, yy]
self.amsub_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_amsub.npy")
)
self.amsub_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_amsub.npy")
)
return
def load_ascat_data(self):
"""
Load the ASCAT data
"""
self.ascat_y = np.memmap(
self.data_path + "ascat/2007_2021_ascat.mmap",
dtype="float32",
mode="r",
shape=ASCAT_Y_SHAPE,
)
self.ascat_index_offset = ASCAT_OFFSETS[self.start_date]
xx = np.linspace(0, 359, 360, dtype=np.float32)
xx = ((xx + 360) % 360) / LATLON_SCALE_FACTOR
yy = np.linspace(-90, 90, 181, dtype=np.float32) / LATLON_SCALE_FACTOR
self.ascat_x = [xx, np.copy(yy[::-1])]
self.ascat_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_ascat.npy")
)
self.ascat_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_ascat.npy")
)
return
def load_hirs_data(self):
"""
Load the HIRS data
"""
self.hirs_y = np.memmap(
self.data_path + "hirs/2007_2021_hirs.mmap",
dtype="float32",
mode="r",
shape=HIRS_Y_SHAPE,
)
self.hirs_index_offset = ASCAT_OFFSETS[self.start_date]
xx = np.linspace(0, 359, 360, dtype=np.float32)
xx = ((xx + 360) % 360) / LATLON_SCALE_FACTOR
yy = np.linspace(-90, 90, 181, dtype=np.float32) / LATLON_SCALE_FACTOR
self.hirs_x = [xx, np.copy(yy[::-1])]
self.hirs_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/hirs_means.npy")
)
self.hirs_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/hirs_stds.npy")
)
return
def load_sat_data(self):
"""
Load the GRIDSAT data
"""
self.sat_y = np.memmap(
self.data_path + "gridsat/gridsat_data.mmap",
dtype="float32",
mode="r",
shape=GRIDSAT_Y_SHAPE,
)
xx = np.load(self.data_path + "gridsat/sat_x.npy") / LATLON_SCALE_FACTOR
yy = np.load(self.data_path + "gridsat/sat_y.npy") / LATLON_SCALE_FACTOR
self.sat_x = [xx, yy]
self.sat_index_offset = SAT_OFFSETS[self.start_date]
self.sat_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_sat.npy")
)
self.sat_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_sat.npy")
)
return
def load_iasi(self):
"""
Load the IASI data
"""
self.iasi = np.memmap(
self.data_path + "2007_2021_iasi_subset.mmap",
dtype="float32",
mode="r",
shape=IASI_Y_SHAPE,
)
self.iasi_index_offset = ASCAT_OFFSETS[self.start_date]
xx = np.linspace(0, 359, 360, dtype=np.float32)
xx = ((xx + 360) % 360) / LATLON_SCALE_FACTOR
yy = np.linspace(-90, 90, 181, dtype=np.float32) / LATLON_SCALE_FACTOR
self.iasi_x = [xx, np.copy(yy[::-1])]
self.iasi_means = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/mean_iasi.npy")
)
self.iasi_stds = self.to_tensor(
np.load(self.aux_data_path + "norm_factors/std_iasi.npy")
)
return
def load_hadisd(self, mode):
"""
Load the HADISD data
"""
self.hadisd_x = []
self.hadisd_alt = []
self.hadisd_y = []
hadisd_vars = ["tas", "tds", "psl", "u", "v"]
for var in hadisd_vars:
lon = lon_to_0_360(
np.load(
self.data_path + "hadisd_processed/{}_lon_{}.npy".format(var, mode)
)
)
lat = np.load(
self.data_path + "hadisd_processed/{}_lat_{}.npy".format(var, mode)
)
alt = np.load(
self.data_path + "hadisd_processed/{}_alt_{}.npy".format(var, mode)
)
vals = np.memmap(
self.data_path
+ "hadisd_processed/{}_vals_{}.memmap".format(var, self.mode),
dtype="float32",
mode="r",
shape=get_hadisd_shape(mode),
)
self.hadisd_x.append(np.stack([lon, lat], axis=-1) / LATLON_SCALE_FACTOR)
self.hadisd_alt.append(alt)
self.hadisd_y.append(vals)
self.hadisd_index_offset = HADISD_OFFSETS[self.start_date]
self.hadisd_means = [
self.to_tensor(
np.load(
self.aux_data_path + "norm_factors/mean_hadisd_{}.npy".format(var)
)
)
for var in hadisd_vars
]
self.hadisd_stds = [
self.to_tensor(
np.load(
self.aux_data_path + "norm_factors/std_hadisd_{}.npy".format(var)
)
)
for var in hadisd_vars
]
return
def load_era5(self, year):
"""
Load the ERA5 training data
"""
if year % 4 == 0:
d = 366 * 4
else:
d = 365 * 4
if self.era5_mode == "sfc":
levels = 4
elif self.era5_mode == "13u":
levels = 69
else:
levels = 24
if self.res == 1:
x = 240
y = 121
elif self.res == 5:
x = 64
y = 32
mmap = np.memmap(
self.data_path
+ "/era5/era5_{}_{}_6_{}.memmap".format(self.era5_mode, self.res, year),
dtype="float32",
mode="r",
shape=(d, levels, x, y),
)
return mmap
def norm_era5(self, x):
x = (x - self.means) / self.stds
return x
def unnorm_era5(self, x):
x = x * self.stds + self.means
return x
def norm_data(self, x, means, stds):
return (x - means) / stds
def norm_hadisd(self, x):
for i in range(5):
x[i] = (x[i] - self.hadisd_means[i]) / self.hadisd_stds[i]
return x
def __len__(self):
return self.index.shape[0] - 1 - 1
def to_tensor(self, arr):
return torch.from_numpy(arr).float().to(self.device)
def get_time_aux(self, current_date):
"""
Return the auxiliary temporal channels given a date
"""
doy = current_date.dayofyear
year = (current_date.year - 2007) / 15
time_of_day = current_date.hour
return np.array(
[
np.cos(np.pi * 2 * doy / DAYS_IN_YEAR),
np.sin(np.pi * 2 * doy / DAYS_IN_YEAR),
np.cos(np.pi * 2 * time_of_day / 24),
np.sin(np.pi * 2 * time_of_day / 24),
year,
]
)
class WeatherDatasetAssimilation(WeatherDataset):
"""
Encoder training loader
"""
def __init__(
self,
device,
hadisd_mode,
start_date,
end_date,
lead_time,
era5_mode="sfc",
res=1,
filter_dates=None,
var_start=0,
var_end=24,
diff=False,
two_frames=False,
):
super().__init__(
device,
hadisd_mode,
start_date,
end_date,
lead_time,
era5_mode,
res=res,
filter_dates=filter_dates,
diff=diff,
)
# Setup
self.var_start = var_start
self.var_end = var_end
self.diff = diff
self.two_frames = two_frames
def load_era5_time(self, index):
"""
ERA5 ground truth data loading
"""
date = self.dates[index]
year = date.year
hour = date.hour
doy = (date.dayofyear - 1) * 4 + (hour // 6)
era5 = self.era5_sfc[year - int(self.start_date[:4])][doy, ...]
era5 = np.copy(era5)
if self.diff:
era5 = era5 - self.era5_mean_spatial
era5 = self.norm_era5(era5[np.newaxis, ...])[0, ...]
return era5
def load_year_end(self, year, doy):
data_1 = self.era5_sfc[year - int(self.start_date[:4])][doy : doy + 1, ...]
missing = self.lead_time - data_1.shape[0] + 1
data_2 = self.era5_sfc[year - int(self.start_date[:4]) + 1][:missing, ...]
data = np.concatenate([data_1, data_2])
return data
def load_era5_slice(self, index):
"""
ERA5 ground truth data loading
"""
date = self.dates[index]
year = date.year
doy = (date.dayofyear - 1) * 4
next_date = self.dates[index + 1]
next_year = next_date.year
if next_year != year:
era5 = self.load_year_end(year, doy)
else:
era5 = self.era5_sfc[year - int(self.start_date[:4])][doy : doy + 1, ...]
era5 = self.norm_era5(np.copy(era5))
return era5
def __getitem__(self, index):
if self.two_frames:
# Case 1: loading t=0 and t=-1
index = index + 1
current = self.get_index(index, "current")
prev = self.get_index(index - 1, "prev")
current["y_target"] = current["y_target_current"]
return {**current, **prev}
else:
# Case 2: loading t=0
current = self.get_index(index, "current")
current["y_target"] = current["y_target_current"]
return {**current}
def unnorm_pred(self, x):
dev = x.device
x = x.detach().cpu().numpy()
x = (
x
* self.stds[np.newaxis, ...].transpose(0, 2, 3, 1)[
..., self.var_start : self.var_end
]
+ self.means[np.newaxis, ...].transpose(0, 2, 3, 1)[
..., self.var_start : self.var_end
]
)
if bool(self.diff):
x = (
x
+ self.era5_mean_spatial[np.newaxis, ...].transpose(0, 3, 2, 1)[
..., self.var_start : self.var_end
]
)
return torch.from_numpy(x).float().to(dev)
def get_index(self, index, prefix):
"""
Load data for the relevant index respecting different offsets depending on the modality
"""
index = self.index[index]
date = self.dates[index]
# ICOADS
icoads_x = self.icoads_x[index + self.icoads_index_offset, ...]
icoads_y = self.icoads_y[index + self.icoads_index_offset, ...]
icoads_x = [icoads_x[0, :], icoads_x[1, :]]
icoads_x = [self.to_tensor(i) for i in icoads_x]
icoads_y = self.to_tensor(icoads_y)
icoads_y = self.norm_data(icoads_y, self.icoads_means, self.icoads_stds)
# GRIDSAT
sat_y = self.sat_y[index + self.sat_index_offset, ...]
sat_x = [self.to_tensor(i) for i in self.sat_x]
sat_y = self.to_tensor(sat_y)
sat_y = self.norm_data(sat_y, self.sat_means, self.sat_stds)
# AMSU-A
amsua_y = self.to_tensor(self.amsua_y[index + self.amsua_index_offset, ...])
amsua_y[amsua_y < -998] = torch.nan
amsua_x = [self.to_tensor(i) for i in self.amsua_x]
amsua_y[amsua_y < -998] = np.nan
amsua_y = self.norm_data(amsua_y, self.amsua_means, self.amsua_stds)
# AMSU-B
amsub_y = self.to_tensor(self.amsub_y[index + self.amsub_index_offset, ...])
amsub_y[amsub_y < -998] = torch.nan
amsub_x = [self.to_tensor(i) for i in self.amsub_x]
amsub_y[amsub_y < -998] = np.nan
amsub_y = self.norm_data(amsub_y, self.amsub_means, self.amsub_stds)
# IASI
iasi_y = self.to_tensor(self.iasi[index + self.iasi_index_offset, ...])
iasi_x = [self.to_tensor(i) for i in self.iasi_x]
iasi_y = self.norm_data(iasi_y, self.iasi_means, self.iasi_stds)
# IGRA
igra_y = self.to_tensor(self.igra_y[index + self.igra_index_offset, ...])
igra_x = [self.igra_x[:, 0], self.igra_x[:, 1]]
igra_x = [self.to_tensor(i) for i in igra_x]
igra_y = self.norm_data(igra_y, self.igra_means, self.igra_stds)
# ASCAT
ascat_y = self.to_tensor(self.ascat_y[index + self.ascat_index_offset, ...])
ascat_x = [self.to_tensor(i) for i in self.ascat_x]
ascat_y[..., 4][ascat_y[..., 4] < -990] = np.nan
ascat_y = self.norm_data(ascat_y, self.ascat_means, self.ascat_stds)
# HIRS
hirs_y = self.to_tensor(self.hirs_y[index + self.hirs_index_offset, ...])
hirs_y[hirs_y < -998] = np.nan
hirs_x = [self.to_tensor(i) for i in self.hirs_x]
hirs_y = self.norm_data(hirs_y, self.hirs_means, self.hirs_stds)
# HadISD
x_context_hadisd = self.hadisd_x
y_context_hadisd = [
i[index + self.hadisd_index_offset, :] for i in self.hadisd_y
]
x_context_hadisd = [self.to_tensor(i).permute(1, 0) for i in x_context_hadisd]
y_context_hadisd = [self.to_tensor(i) for i in y_context_hadisd]
y_context_hadisd = self.norm_hadisd(y_context_hadisd)
# ERA5
era5 = self.to_tensor(self.load_era5_time(index))
era5_target = era5.permute(2, 1, 0)
era5_x = self.era5_x
# AUxiliary variables
aux_time = self.to_tensor(self.get_time_aux(date))
climatology = self.climatology[date.hour // 6, date.dayofyear - 1, ...]
task = {
"x_context_hadisd_{}".format(prefix): x_context_hadisd,
"y_context_hadisd_{}".format(prefix): y_context_hadisd,
"climatology_{}".format(prefix): self.to_tensor(climatology),
"sat_x_{}".format(prefix): sat_x,
"sat_{}".format(prefix): sat_y,
"icoads_x_{}".format(prefix): icoads_x,
"icoads_{}".format(prefix): icoads_y,
"igra_x_{}".format(prefix): igra_x,
"igra_{}".format(prefix): igra_y,
"amsua_{}".format(prefix): amsua_y,
"amsua_x_{}".format(prefix): amsua_x,
"amsub_{}".format(prefix): amsub_y,
"amsub_x_{}".format(prefix): amsub_x,
"iasi_{}".format(prefix): iasi_y,
"iasi_x_{}".format(prefix): iasi_x,
"ascat_{}".format(prefix): ascat_y,
"ascat_x_{}".format(prefix): ascat_x,
"hirs_{}".format(prefix): hirs_y,
"hirs_x_{}".format(prefix): hirs_x,
"y_target_{}".format(prefix): era5_target[
..., self.var_start : self.var_end
],
"era5_x_{}".format(prefix): era5_x,
"era5_elev_{}".format(prefix): self.era5_elev,
"era5_lonlat_{}".format(prefix): self.era5_lonlat,
"aux_time_{}".format(prefix): aux_time,
"lt": torch.Tensor([self.var_start]),
}
return task
class HadISDDataset(Dataset):
"""
HadISD dataset for decoder training
"""
def __init__(self, var, mode, device, start_date, end_date):
super().__init__()
# Setup
if not mode in ["train", "val", "test"]:
raise Exception(f"mode is {mode}. Must be train, val, or test.")
self.var = var
self.mode = mode
self.start_date = start_date
self.device = device
dates = pd.date_range(start_date, end_date, freq="6H")
self.index = np.array(range(len(dates)))
# Load the hadISD data
self.load_hadisd()
def load_hadisd(self):
"""
Load the raw HadISD data
"""
data_path = "path_to_data/"
aux_data_path = "path_to_auxiliary_data/"
var = self.var
mode = self.mode
vals = np.memmap(
data_path + f"hadisd_processed/{var}_vals_{mode}.memmap",
dtype="float32",
mode="r",
shape=get_hadisd_shape(mode),
)
lon = lon_to_0_360(
np.load(data_path + f"hadisd_processed/{var}_lon_{mode}.npy")
)
lat = np.load(data_path + f"hadisd_processed/{var}_lat_{mode}.npy")
self.hadisd_x = np.stack([lon, lat], axis=-1) / LATLON_SCALE_FACTOR
self.hadisd_alt = np.load(
data_path + f"hadisd_processed/{var}_alt_{mode}_final.npy"
)
self.hadisd_y = vals
self.hadisd_index_offset = HADISD_OFFSETS[self.start_date]
self.hadisd_means = self.to_tensor(
np.load(aux_data_path + f"norm_factors/mean_hadisd_{var}.npy")
)
self.hadisd_stds = self.to_tensor(
np.load(aux_data_path + f"norm_factors/std_hadisd_{var}.npy")
)
return
def norm_hadisd(self, x):
return (x - self.hadisd_means) / self.hadisd_stds
def unnorm_pred(self, x):
return self.hadisd_means + self.hadisd_stds * x
def __len__(self):
return self.index.shape[0] - 2
def to_tensor(self, arr):
return torch.from_numpy(np.array(arr)).float().to(self.device)
def __getitem__(self, index):
index = self.index[index]
# Get longitude-latitude locations
x_target = self.to_tensor(self.hadisd_x).permute(1, 0)
# Get altitude and normalise
m_alt = np.expand_dims(np.load("path_to_mean_alt.npy"), 1)
s_alt = np.expand_dims(np.load("path_to_std_alt.npy"), 1)
alt_target = self.to_tensor((self.hadisd_alt - m_alt) / s_alt)[:, :]
# Get observations
y_target = self.norm_hadisd(
self.to_tensor(self.hadisd_y[index + self.hadisd_index_offset, :])
)
assert x_target.shape[0] == 2
n_stations = x_target.shape[1]
assert alt_target.shape[1] == n_stations
assert y_target.shape[0] == n_stations
return {"x": x_target, "altitude": alt_target, "y": y_target}
class AardvarkICDataset(Dataset):
"""
Helper dataset to handle initial condition loading for decoder training
"""
def __init__(self, device, start_date, end_date, lead_time=0):
super().__init__()
# Setup
if lead_time == 0:
# If leadtime is 0 load the output of the encoder
if start_date == "2007-01-02" and end_date == "2017-12-31":
ic_fname = "ic_train.mmap"
elif start_date == "2019-01-01" and end_date == "2019-12-01":
ic_fname = "ic_val.mmap"
elif start_date == "2018-01-01" and end_date == "2018-12-31":
ic_fname = "ic_test.mmap"
else:
print((start_date, end_date))
raise Exception("Invalid start and end date")
dates = pd.date_range(start_date, end_date, freq="6H")
self.data = np.memmap(
"path_to_encoder_predictions/" + ic_fname,
dtype="float32",
mode="r",
shape=(len(dates), 121, 240, 24), # shape of the output
)
else:
# if leadtime >0 load the forecast generated from the encoder prediction
if start_date == "2007-01-02" and end_date == "2017-12-31":
ic_fname = f"ic_train_{lead_time}.mmap"
elif start_date == "2019-01-01" and end_date == "2019-12-01":
ic_fname = f"ic_val_{lead_time}.mmap"
elif start_date == "2018-01-01" and end_date == "2018-12-31":
ic_fname = f"ic_test_{lead_time}.mmap"
else:
print((start_date, end_date))
raise Exception("Invalid start and end date.")
dates = pd.date_range(start_date, end_date, freq="6H")[(lead_time) * 4 :]
ic_shape = (len(dates), 121, 240, 24)
self.data = np.memmap(
self.data_path + "forecast_finetune/" + ic_fname,
dtype="float32",
mode="r",
shape=ic_shape,
)
self.device = device
# Normalisation
aux_data_path = "path_to_auxiliary_data/"
mean_factors_path = aux_data_path + f"norm_factors/mean_4u_1.npy"
std_factors_path = aux_data_path + f"norm_factors/std_4u_1.npy"
self.means = np.load(mean_factors_path)[:, np.newaxis, np.newaxis, ...]
self.stds = np.load(std_factors_path)[:, np.newaxis, np.newaxis, ...]
def __getitem__(self, index):
# Load Aardvark prediction and normalise
data_raw = np.transpose(np.copy(self.data[index, :, :, :]), (2, 1, 0))
data = (data_raw - self.means) / self.stds
return torch.from_numpy(data).to(self.device)
class WeatherDatasetDownscaling(Dataset):
"""
Main decoder training dataset. Uses AardvarkICDataset and HadISDDataset to
handle processor output and station data
"""
def __init__(
self,
device,
hadisd_mode,
start_date,
end_date,
context_mode,
era5_mode="sfc",
res=1,
hadisd_var="tas",
lead_time=1,
):
# The context mode determines whether we make use of ERA5 or our own ICs.
if not context_mode in ["era5", "aardvark"]:
raise Exception(
f"context_mode must be era5 or aardvark, got {context_mode}"
)
super().__init__()
# Setup
self.lead_time = lead_time
self.device = device
self.data_path = "path_to_data/"
self.aux_data_path = "path_to_auxiliary_data/"
self.start_date = start_date
self.end_date = end_date
self.era5_mode = era5_mode
self.res = res
self.context_mode = context_mode
self.dates = pd.date_range(start_date, end_date, freq="6H")
self.index = np.array(range(len(self.dates)))
# Load ERA5 data for pre-training
self.era5_sfc = [
self.load_era5(year)
for year in range(int(start_date[:4]), int(end_date[:4]) + 1)
]
raw_era5_lon = np.load(self.data_path + f"era5/era5_x_{res}.npy")
raw_era5_lat = np.load(self.data_path + f"era5/era5_y_{res}.npy")
self.era5_x = [
self.to_tensor(raw_era5_lon) / LATLON_SCALE_FACTOR,
self.to_tensor(raw_era5_lat) / LATLON_SCALE_FACTOR,
]
# Load orography
elev_path = self.data_path + f"era5/elev_vars_{res}.npy"
self.era5_elev = self.to_tensor(np.load(elev_path)).permute(0, 2, 1)
# Normalisation
mean_factors_path = (
self.aux_data_path + f"norm_factors/mean_{era5_mode}_{res}.npy"
)
std_factors_path = (
self.aux_data_path + f"norm_factors/std_{era5_mode}_{res}.npy"
)
self.means = np.load(mean_factors_path)[:, np.newaxis, np.newaxis, ...]
self.stds = np.load(std_factors_path)[:, np.newaxis, np.newaxis, ...]
# HadISD data
self.hadisd_data = HadISDDataset(
var=hadisd_var,
mode=hadisd_mode,
device=device,
start_date=start_date,
end_date=end_date,
)
if context_mode == "aardvark":
# Load the Aardvark encoder predictions
self.aardvark_data = AardvarkICDataset(
device, start_date, end_date, lead_time
)
def load_era5(self, year):
"""
Load the raw ERA5 data
"""
if year % 4 == 0:
d = 366 * 4
else:
d = 365 * 4
if self.era5_mode == "sfc":
levels = 4
elif self.era5_mode == "13u":
levels = 69
else:
levels = 24
if self.res == 1:
x = 240
y = 121
elif self.res == 5:
x = 64
y = 32
mmap = np.memmap(
self.data_path
+ "era5/era5_{}_{}_6_{}.memmap".format(self.era5_mode, self.res, year),
dtype="float32",
mode="r",
shape=(d, levels, x, y),
)
return mmap
def norm_era5(self, x):
x = (x - self.means) / self.stds
return x
def unnorm_era5(self, x):
x = x * self.stds + self.means
return x
def unnorm_pred(self, x):
return self.hadisd_data.unnorm_pred(x)
def norm_data(self, x, means, stds):
return (x - means) / stds
def __len__(self):
return self.index.shape[0] - (self.lead_time) * 4
def to_tensor(self, arr):
return torch.from_numpy(np.array(arr)).float().to(self.device)
def get_time_aux(self, current_date):
"""
Get auxiliary time variables for a given date
"""
doy = current_date.dayofyear
year = (current_date.year - 2007) / 15
time_of_day = current_date.hour
return np.array(
[
np.cos(np.pi * 2 * doy / DAYS_IN_YEAR),
np.sin(np.pi * 2 * doy / DAYS_IN_YEAR),
np.cos(np.pi * 2 * time_of_day / 24),
np.sin(np.pi * 2 * time_of_day / 24),
year,
]
)
def load_era5_time(self, index):
"""
Load ERA5 training data
"""
date = self.dates[index]
year = date.year
hour = date.hour
doy = (date.dayofyear - 1) * 4 + (hour // 6)
era5 = self.era5_sfc[year - int(self.start_date[:4])][doy, ...]
era5 = np.copy(era5)
era5 = self.norm_era5(era5[np.newaxis, ...])[0, ...]
return era5
def load_year_end(self, year, doy):
data_1 = self.era5_sfc[year - int(self.start_date[:4])][doy : doy + 1, ...]
missing = self.lead_time - data_1.shape[0] + 1
data_2 = self.era5_sfc[year - int(self.start_date[:4]) + 1][:missing, ...]
data = np.concatenate([data_1, data_2])
return data
def __getitem__(self, index):
index = self.index[index]
date = self.dates[index + 4 * self.lead_time]
# Get HadISD data
hadisd_slice = self.hadisd_data[index + 4 * self.lead_time]
# Get lon-lat
x_context = self.era5_x
n_lon = x_context[0].shape[0]
n_lat = x_context[1].shape[0]
# Get auxiliary time
aux_time = torch.reshape(self.to_tensor(self.get_time_aux(date)), (-1, 1, 1))
# Load the context (either aardvark or ERA5 for use in pre-training)
if self.context_mode == "era5":
y_context_obs = self.to_tensor(
self.load_era5_time(index + 4 * self.lead_time)
)
elif self.context_mode == "aardvark":
y_context_obs = self.aardvark_data[index]
else:
raise Exception
y_context = torch.cat(
[
y_context_obs,
self.era5_elev.permute(0, 2, 1),
aux_time.repeat(1, n_lon, n_lat),
]
)
assert y_context.shape[1] == n_lon
assert y_context.shape[2] == n_lat
x = hadisd_slice["x"]
alt = hadisd_slice["altitude"]
y = hadisd_slice["y"]
return {
"x_target": x,
"alt_target": alt,
"y_target": y,
"y_context": y_context,
"x_context": x_context,
"aux_time": aux_time,
"lt": torch.Tensor([0]),
}
class ForecasterDatasetDownscaling(Dataset):
"""
Dataset to generate decoder predictions from pre-saved Aardvark forecasts
"""
def __init__(
self,
start_date,
end_date,
lead_time,
hadisd_var,
mode,
device,
forecast_path,
region="global",
):
super().__init__()
# Setup
if not mode in ["train", "val", "test"]:
raise Exception(f"Mode is {mode}. Must be either train, val, or test")
self.device = device
self.start_date = start_date
self.end_date = end_date
self.lead_time = lead_time
self.mode = mode
self.offset = np.timedelta64(lead_time, "D").astype("timedelta64[ns]")
self.dates = pd.date_range(start_date, end_date, freq="6H")[:-30]
# Normalisation
aux_data_path = "auxiliary_data_path/"
self.means = np.load(aux_data_path + "norm_factors/mean_4u_1.npy")
self.stds = np.load(aux_data_path + "norm_factors/std_4u_1.npy")
# Load auxiliary data
self.load_npy_file()
data_path = "data_path/"
res = "1"
raw_era5_lon = np.load(data_path + f"era5/era5_x_{res}.npy")
raw_era5_lat = np.load(data_path + f"era5/era5_y_{res}.npy")
self.era5_x = [
self.to_tensor(raw_era5_lon) / LATLON_SCALE_FACTOR,
self.to_tensor(raw_era5_lat) / LATLON_SCALE_FACTOR,
]
elev_path = data_path + f"era5/elev_vars_{res}.npy"
self.era5_elev = self.to_tensor(np.load(elev_path)).permute(0, 2, 1)
# Load hadISD
self.hadisd_data = HadISDDataset(
var=hadisd_var,
mode="train",
device=device,
start_date=start_date,
end_date=end_date,
)
# Subset to region
self.region = region
if self.region != "global":
self.mask = np.load(
self.data_path + f"hadisd_processed/tas_mask_train_{region}.npy"
)
def date_range(self):
return np.arange(
start=np.datetime64(self.start_date).astype("datetime64[ns]"),
stop=np.datetime64(self.end_date).astype("datetime64[ns]"),
step=np.timedelta64(1, "D").astype("timedelta64[ns]"),
)
def load_npy_file(self):
"""
Load the pre-saved Aardvark forecasts
"""
dates = pd.date_range(self.start_date, self.end_date, freq="6H")
if self.mode == "train":
dates = dates[:-40] # Need 10 day offset at end of year
self.Y_context = np.memmap(
"path_to_forecasts/forecast_{}.mmap".format(self.mode),
dtype="float32",
mode="r",
shape=(len(dates), 121, 240, 24, 11),
)
return
def norm_era5(self, x):
return (x - self.means) / self.stds
def norm_hadisd(self, x):
return self.hadisd_data.norm_hadisd(x)
def unnorm_pred(self, x):
return self.hadisd_data.unnorm_pred(x)
def __len__(self):
return len(self.dates) - 40 # Need 10 day offset at end of year
def to_tensor(self, arr):
return torch.from_numpy(np.array(arr)).float().to(self.device)
def get_time_aux(self, index):
"""
Get the auxiliary time variables
"""
current_date = (self.dates + self.offset)[index]
doy = current_date.dayofyear
year = (current_date.year - 2007) / 15
time_of_day = current_date.hour
return np.array(
[
np.cos(np.pi * 2 * doy / DAYS_IN_YEAR),
np.sin(np.pi * 2 * doy / DAYS_IN_YEAR),
np.cos(np.pi * 2 * time_of_day / 24),
np.sin(np.pi * 2 * time_of_day / 24),
year,
]
)
def __getitem__(self, index):
# Load target data
hadisd_slice = self.hadisd_data[index + 4 * self.lead_time]
x_context = self.era5_x
n_lon = x_context[0].shape[0]
n_lat = x_context[1].shape[0]
# Load auxiliary time
aux_time = torch.reshape(self.to_tensor(self.get_time_aux(index)), (-1, 1, 1))
# Load input
y_context = self.norm_era5(self.Y_context[index, ..., self.lead_time])
y_context = torch.cat(
[
self.to_tensor(y_context).permute(2, 1, 0),
self.era5_elev.permute(0, 2, 1),
aux_time.repeat(1, n_lon, n_lat),
]
)
assert y_context.shape[1] == n_lon
assert y_context.shape[2] == n_lat
# Handle region masking
if self.region != "global":
hadisd_slice["y"][self.mask] = np.nan
return {
"x_target": hadisd_slice["x"],
"alt_target": hadisd_slice["altitude"],
"y_target": hadisd_slice["y"],
"y_context": y_context,
"x_context": x_context,
"aux_time": aux_time,
"lt": torch.Tensor([0]),
}
class ForecastLoader(Dataset):
"""
Loader for finetuning the processor module
"""
def __init__(
self,
device,
mode,
lead_time,
era5_mode="sfc",
res=5,
frequency=24,
norm=True,
diff=False,
rollout=False,
random_lt=False,
u_only=False,
ic_path=None,
finetune_step=None,
finetune_eval_every=100,
eval_steps=False,
):
super().__init__()
# Setup
self.device = device
self.mode = mode
self.data_path = "data_path/"
self.lead_time = lead_time
self.era5_mode = era5_mode
self.res = res
self.frequency = frequency
self.norm = norm
self.diff = diff
self.rollout = rollout
self.random_lt = random_lt
self.u_only = u_only
self.ic_path = ic_path
self.finetune_step = finetune_step
self.finetune_eval_every = finetune_eval_every
self.eval_steps = eval_steps
if self.frequency == 6:
self.lead_time = self.lead_time * 4
freq = "6H"
else:
freq = "1D"
if self.mode == "train":
self.dates = pd.date_range("1979-01-01", "2017-12-31", freq=freq)
elif self.mode == "tune":
self.dates = pd.date_range("2018-01-01", "2018-12-31", freq=freq)
elif self.mode == "test":
self.dates = pd.date_range("2018-01-01", "2018-12-31", freq=freq)
elif self.mode == "val":
self.dates = pd.date_range("2019-01-01", "2019-12-31", freq=freq)
# Load the predictions from the previous leadtime to be the new context set
if self.finetune_step is not None:
if self.mode == "train":
self.dates = pd.date_range("2007-01-02", "2017-12-31", freq=freq)
ic_shape = (
len(self.dates) - max(0, (self.finetune_step - 1) * 4),
121,
240,
24,
)
elif self.mode == "val":
self.dates = pd.date_range("2019-01-01", "2019-12-31", freq=freq)
ic_shape = (
len(self.dates) - max(0, (self.finetune_step - 1) * 4),
121,
240,
24,
)
elif self.mode == "test":
self.dates = pd.date_range("2018-01-01", "2018-12-31", freq=freq)
ic_shape = (
len(self.dates) - max(0, (self.finetune_step - 1) * 4),
121,
240,
24,
)
if self.finetune_step > 1:
print(ic_shape)
self.ic = np.memmap(
self.ic_path
+ "ic_{}_{}.mmap".format(self.mode, self.finetune_step - 1),
dtype="float32",
mode="r",
shape=ic_shape,
)
elif self.ic_path is not None:
self.ic = np.memmap(
self.ic_path + "ic_{}.mmap".format(self.mode),
dtype="float32",
mode="r",
shape=ic_shape,
)
elif self.ic_path is not None:
if self.mode == "train":
self.dates = pd.date_range("2007-01-02", "2017-12-31", freq=freq)
ic_shape = (len(self.dates), 121, 240, 24)
self.ic = np.memmap(
self.ic_path + "/ic_{}.mmap".format(self.mode),
dtype="float32",
mode="r",
shape=ic_shape,
)
# Orography
self.era5_elev = np.float32(
np.load(self.data_path + "era5/elev_vars_{}.npy".format(res))
)
elev_mean = self.era5_elev.mean(axis=(1, 2))[:, np.newaxis, np.newaxis]
elev_std = self.era5_elev.std(axis=(1, 2))[:, np.newaxis, np.newaxis]
self.era5_elev = (self.era5_elev - elev_mean) / elev_std
# ERA5 ground truth data for training
self.era5_sfc = [
self.load_era5(year)
for year in range(int(self.dates[0].year), int(self.dates[-1].year) + 1)
]
# Noramalisation factors
self.means = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/mean_{}_{}.npy".format(self.era5_mode, self.res)
)
)
.unsqueeze(1)
.unsqueeze(1)
)
self.stds = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/std_{}_{}.npy".format(self.era5_mode, self.res)
)
)
.unsqueeze(1)
.unsqueeze(1)
)
self.diff_means = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/mean_diff_{}_{}.npy".format(
self.era5_mode, self.res
)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.diff_stds = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/std_diff_{}_{}.npy".format(self.era5_mode, self.res)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.diff_means_1 = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/mean_diff_{}_{}_6h.npy".format(
self.era5_mode, self.res
)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.diff_stds_1 = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/std_diff_{}_{}_6h.npy".format(
self.era5_mode, self.res
)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.diff_means_2 = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/mean_diff_{}_{}_12h.npy".format(
self.era5_mode, self.res
)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.diff_stds_2 = (
self.to_tensor(
np.load(
self.data_path
+ "norm_factors/std_diff_{}_{}_12h.npy".format(
self.era5_mode, self.res
)
)
)
.unsqueeze(0)
.unsqueeze(0)
)
self.means_dict = {
0: self.diff_means,
2: self.diff_means_2,
3: self.diff_means_1,
}
self.stds_dict = {0: self.diff_stds, 2: self.diff_stds_2, 3: self.diff_stds_1}
def __len__(self):
if np.logical_and(self.eval_steps, self.mode == "train"):
return self.finetune_eval_every * 12 * 4
return self.dates.shape[0] - self.lead_time
def to_tensor(self, arr):
return torch.from_numpy(arr).float().to(self.device)
def norm_era5(self, x):
x = (x - self.means) / self.stds
return x
def norm_era5_tendency(self, x, lt_offset):
x = (x - self.means_dict[lt_offset]) / self.stds_dict[lt_offset]
return x
def unnorm_pred(self, x):
x = x * self.diff_stds.unsqueeze(0) + self.diff_means.unsqueeze(0)
return x
def unnorm_base_context(self, x):
x = x * self.stds.unsqueeze(0) + self.means.unsqueeze(0)
return x
def load_era5(self, year):
"""
Load ERA5 data for training
"""
if year % 4 == 0:
d = 366
else:
d = 365
if self.frequency == 6:
d = d * 4
if self.era5_mode == "sfc":
levels = 4
elif self.era5_mode == "13u":
levels = 69
else:
levels = 24
if self.res == 1:
x = 240
y = 121
elif self.res == 5:
x = 64
y = 32
mmap = np.memmap(
self.data_path
+ "era5/era5_{}_{}_{}_{}.memmap".format(
self.era5_mode, self.res, self.frequency, year
),
dtype="float32",
mode="r",
shape=(d, levels, x, y),
)
return mmap
def load_era5_time(self, index):
"""
Load ERA5 data for training
"""
date = self.dates[index]
year = date.year
doy = date.dayofyear - 1
hour = date.hour
if self.frequency == 6:
era5 = self.era5_sfc[year - int(self.dates[0].year)][
doy * 4 + hour // 6, ...
]
else:
era5 = self.era5_sfc[year - int(self.dates[0].year)][doy, ...]
return np.copy(era5)
def make_time_channels(self, index, x, y):
"""
Make auxiliary time channels
"""
date = self.dates[index]
hour = date.hour
doy = date.dayofyear - 1
if date.year % 4 == 0:
n_days = 366
else:
n_days = 365
hour_sin = np.sin(hour * np.pi / 12) * np.float32(np.ones((1, x, y)))
hour_cos = np.cos(hour * np.pi / 12) * np.float32(np.ones((1, x, y)))
doy_sin = np.sin(doy * 2 * np.pi / n_days) * np.float32(np.ones((1, x, y)))
doy_cos = np.cos(doy * 2 * np.pi / n_days) * np.float32(np.ones((1, x, y)))
return np.concatenate([hour_sin, hour_cos, doy_sin, doy_cos])
def __getitem__(self, index):
# Option to offset to random leadtime
lt_offset = 0
if self.random_lt:
lt_offset = np.random.choice([0, 2, 3])
# Load ground truth data
y_target = self.to_tensor(
self.load_era5_time(index + self.lead_time - lt_offset)
)
# Load either initial condition or ERA5 depending on task
if self.ic_path is not None:
era5_ts0 = self.ic[index].copy().transpose(2, 1, 0)
else:
era5_ts0 = self.load_era5_time(index)
# Auxiliary time
time = self.make_time_channels(index, era5_ts0.shape[1], era5_ts0.shape[2])
era5_ts0 = self.to_tensor(
np.concatenate([era5_ts0, self.era5_elev, time], axis=0)
)
y_context = era5_ts0.permute(0, 2, 1)[:, ...]
# Normalisation
if self.diff:
y_target = (y_target - era5_ts0[:24, ...]).permute(2, 1, 0)
y_target = self.norm_era5_tendency(y_target, lt_offset)
y_context[:24, ...] = self.norm_era5(y_context[:24, ...])
else:
if self.norm:
y_context[:24, ...] = self.norm_era5(y_context[:24, ...], lt_offset)
y_target = self.norm_era5(y_target, lt_offset)
y_target = y_target.permute(2, 1, 0)
if self.rollout:
# Option to return entire timeseries of target data
targets = []
for t in range(self.lead_time + 1):
t = self.to_tensor(self.load_era5_time(index + t))
targets.append(t.permute(2, 1, 0))
targets = torch.stack(targets, dim=-1)[..., ::4]
return {
"y_context": y_context.permute(0, 2, 1),
"y_target": y_target,
"targets": targets,
"lt": self.to_tensor(np.array([lt_offset])),
}
else:
return {
"y_context": y_context.permute(0, 2, 1),
"y_target": y_target[..., :],
"lt": self.to_tensor(np.array([lt_offset])),
"target_index": self.to_tensor(np.array([index])),
}
class WeatherDatasetE2E(WeatherDataset):
"""
Dataset for running Aardvark end-to-end
"""
def __init__(
self,
device,
hadisd_mode,
start_date,
end_date,
lead_time,
mode,
hadisd_var,
max_steps_per_epoch=None,
era5_mode="sfc",
res=1,
filter_dates=None,
var_start=0,
var_end=24,
diff=False,
two_frames=False,
region="global",
):
super().__init__(
device,
hadisd_mode,
start_date,
end_date,
lead_time,
era5_mode,
res=res,
filter_dates=filter_dates,
diff=diff,
)
# Setup
self.var_start = var_start
self.var_end = var_end
self.diff = diff
self.two_frames = two_frames
self.region = region
self.lead_time = lead_time
self.mode = mode
self.max_steps_per_epoch = max_steps_per_epoch
# Initialise encoder dataset
self.assimilation_dataset = WeatherDatasetAssimilation(
device="cuda",
hadisd_mode="train",
start_date=start_date,
end_date=end_date,
lead_time=0,
era5_mode="4u",
res=1,
var_start=0,
var_end=24,
diff=False,
two_frames=False,
)
# Initialise forecast dataset
self.forecast_dataset = ForecastLoader(
device="cuda",
mode=mode,
lead_time=lead_time,
era5_mode=era5_mode,
res=1,
frequency=6,
diff=True,
u_only=False,
random_lt=False,
)
# Initialise downscaling dataset
self.downscaling_dataset = ForecasterDatasetDownscaling(
start_date=start_date,
end_date=end_date,
lead_time=lead_time,
hadisd_var=hadisd_var,
mode=mode,
device=device,
forecast_path=None,
region=region,
)
def __len__(self):
if self.max_steps_per_epoch:
return self.max_steps_per_epoch
return len(self.downscaling_dataset) - 40 # Need 10 day offset at end of year
def __getitem__(self, index):
if self.max_steps_per_epoch:
index = np.random.choice(
np.arange(len(self.downscaling_dataset) - 40)
) # Need 10 day offset at end of year
# Get data for each of the three datasets
assimilation = self.assimilation_dataset.__getitem__(index)
forecast = self.forecast_dataset.__getitem__(index)
downscaling = self.downscaling_dataset.__getitem__(index)
# Create task
task = {
"assimilation": assimilation,
"forecast": forecast,
"downscaling": downscaling,
"index": torch.tensor(index),
}
# Add y target to allow for end to end finetuning if needed
task["y_target"] = task["downscaling"]["y_target"]
return task
def unnorm_pred(self, x):
dev = x.device
x = x.detach().cpu().numpy()
x = (
x
* self.stds[np.newaxis, ...].transpose(0, 2, 3, 1)[
..., self.var_start : self.var_end
]
+ self.means[np.newaxis, ...].transpose(0, 2, 3, 1)[
..., self.var_start : self.var_end
]
)
if bool(self.diff):
x = (
x
+ self.era5_mean_spatial[np.newaxis, ...].transpose(0, 3, 2, 1)[
..., self.var_start : self.var_end
]
)
return torch.from_numpy(x).float().to(dev)
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