File size: 32,711 Bytes
dc3477c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 | import xarray as xr
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
import sys
import os
import torch
import random
from torch.utils import data
import torch.nn.functional as F
import matplotlib.pyplot as plt
sfc_vars_1 = ['SSTK', 'TCW', 'TCWV', 'CP', 'MSL', 'TCC', 'U10M', 'V10M', 'T2M', 'TP', 'SKT']
sfc_vars_2 = ['sst', 'tcw', 'tcwv', 'cp', 'msl', 'tcc', 'u10', 'v10', 't2m', 'tp', 'skt']
pl_vars_1 = ["Z", "T", "Q", "W", "D", "U", "V"]
pl_vars_2 = ["z", "t", "q", "w", "d", "u", "v"]
var_map = {}
for var1, var2 in zip(sfc_vars_1+pl_vars_1, sfc_vars_2+pl_vars_2):
var_map[var1] = var2
class Aurora_CDF_Dataset_china(data.Dataset):
"""Dataset class for the era5 upper and surface variables."""
def __init__(self,
nc_path='',
seed=1234,
startDate='2010',
endDate='2020',
freq='12h',
horizon = 12,
surface = ["2m_temperature","10m_u_component_of_wind","10m_v_component_of_wind", "mean_sea_level_pressure", "total_precipitation_6hr"],
upper = ["temperature", "u_component_of_wind", "v_component_of_wind", "relative_humidity", "geopotential"],
level = [50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000],
h = 240,
w = 240,
degree = 0.25,
num_points = 5,
evaluate = False,
):
"""Initialize."""
self.nc_path = nc_path
"""
To do
if start and end is valid date, if the date can be found in the downloaded files, length >= 0
"""
# Prepare the datetime objects for training, validation, and test
self.freq = int(freq[:-1])
self.surface_variables = surface
self.upper_variables = upper
self.levels = level
self.horizon = horizon
self.h = h
self.w = w
self.degree = degree
self.num_points = num_points
self.keys = list(pd.date_range(start=startDate, end=endDate, freq=freq))[:-1]
random.seed(seed)
def __getitem__(self, index):
"""Return input frames, target frames, and its corresponding time steps."""
key = self.keys[index]
input_surfaces = []
input_uppers = []
time_points = []
for p in range(self.num_points):
time_str = datetime.strftime(key, '%Y%m%d%H')
time_points.append(key.timestamp())
data = np.load(os.path.join(self.nc_path,f"{time_str}.npy")).astype(np.float32)
input_surface_variables = data[:,0]
input_upper_variables = data[:,1:]
input_surfaces.append(input_surface_variables[np.newaxis, ...])
input_uppers.append(input_upper_variables[np.newaxis, ...])
key = key + timedelta(hours=self.horizon)
return np.concatenate(input_surfaces,axis=0), np.concatenate(input_uppers, axis=0), time_points
def __len__(self):
return len(self.keys)
def __repr__(self):
return self.__class__.__name__
class Dataset_Postprocessing(data.Dataset):
def __init__(self,
data_path='',
seed=1234,
start_date='1998-01-01',
end_date='2015-12-31',
val = False,
surface = ['SSTK', 'TCW', 'TCWV', 'CP', 'MSL', 'TCC', 'U10M', 'V10M', 'T2M', 'TP', 'SKT'],
upper = ["Z", "T", "Q", "W", "D", "U", "V"],
levels = [500, 850],
target_surface= ["T2M",'U10M', 'V10M'],
target_upper = ["Z", "T"],
target_level = [500, 850],
H = 361,
W = 720,
full = False,
):
"""Initialize."""
self.data_path = data_path
self.surface_variables = surface
self.upper_variables = upper
self.levels = levels
self.target_sfc_variables = target_surface
self.target_pl_variables = target_upper
self.target_pl_level = target_level
self.H = 361
self.W = 720
self.full = full
time_range = slice(start_date, end_date)
if self.full:
self.ensemble_path = os.path.join(data_path, "ensemble")
self.T = 10
self.sfc_value_range = {"sst": (260., 305.), "tcw": (0., 60.), "tcwv": (0., 60.), "cp": (0., 0.04), "msl": (97000., 1.1e5), "tcc": (0., 1.0), "u10": (-13., 11.), "v10": (-10., 15.), "t2m":(218, 304), "tp": (0., 0.07), "skt": (210., 310.)}
self.pl_value_range = [{"z": (48200, 58000), "t": (230, 269), "q": (0., 4e-3), "w": (-0.7, 1.4), "d": (-5e-5, 8e-5), "u": (-7., 27.), "v": (-7., 7.)},
{"z": (10000, 15500), "t":(240, 299), "q": (0., 1.5e-2), "w": (-1.2, 1.8), "d": (-1.9e-4, 1.6e-4), "u": (-16., 17.5), "v": (-10., 16.)}]
else:
self.T = 2
self.sfc_ens_mean_normalized = xr.open_dataset(os.path.join(data_path, "ENS10_sfc_mean_normalized.nc"), engine="h5netcdf").sel(time=time_range)
self.sfc_ens_std_normalized = xr.open_dataset(os.path.join(data_path, "ENS10_sfc_std_normalized.nc"), engine="h5netcdf").sel(time=time_range)
self.sfc_ens_mean = xr.open_dataset(os.path.join(data_path, "ENS10_sfc_mean.nc"), engine="h5netcdf").sel(time=time_range)
self.sfc_ens_std = xr.open_dataset(os.path.join(data_path, "ENS10_sfc_std.nc"), engine="h5netcdf").sel(time=time_range)
self.pl_ens_mean_normalized = [xr.open_dataset(os.path.join(data_path, f"ENS10_pl_mean_{str(l)}_normalized.nc"), engine="h5netcdf").sel(time=time_range) for l in levels]
self.pl_ens_std_normalized = [xr.open_dataset(os.path.join(data_path, f"ENS10_pl_std_{str(l)}_normalized.nc"), engine="h5netcdf").sel(time=time_range) for l in levels]
self.pl_ens_mean = [xr.open_dataset(os.path.join(data_path, f"ENS10_pl_mean_{str(l)}.nc"), engine="h5netcdf").sel(time=time_range) for l in levels]
self.pl_ens_std = [xr.open_dataset(os.path.join(data_path, f"ENS10_pl_std_{str(l)}.nc"), engine="h5netcdf").sel(time=time_range) for l in levels]
self.era5 = xr.open_dataset(os.path.join(data_path, "ERA5.nc"), engine="h5netcdf").sel(time=time_range)
if val:
self.keys = self.sfc_ens_mean.drop_sel(time="2017-01-02").time.values
# self.keys = [self.sfc_ens_mean.sel(time="2017-12-01").time.values]
else:
self.keys = self.sfc_ens_mean.time.values
self.era5_sfc_scale = {}
random.seed(seed)
def __getitem__(self, index):
"""Return input frames, target frames, and its corresponding time steps."""
time_points = []
key = self.keys[index]
time_points.append(key.item())
sfc_inputs = np.zeros((self.T, len(self.surface_variables), self.H, self.W)).astype(np.float32)
pl_inputs = np.zeros((self.T, len(self.upper_variables), len(self.levels), self.H, self.W)).astype(np.float32)
ds_targets = self.era5.sel(time=key)
time_str = (pd.to_datetime(key)-timedelta(days=2)).strftime("%Y%m%d")
sfc_targets = np.zeros((len(self.target_sfc_variables), self.H, self.W)).astype(np.float32)
sfc_scale = np.zeros((2, len(self.target_sfc_variables), self.H, self.W)).astype(np.float32)
if self.target_sfc_variables:
for i in range(len(self.target_sfc_variables)):
sfc_targets[i] = ds_targets[self.target_sfc_variables[i]].values.astype(np.float32)
sfc_scale[0,i] = self.sfc_ens_mean[self.target_sfc_variables[i]].sel(time=key).values.astype(np.float32)
sfc_scale[1,i] = self.sfc_ens_std[self.target_sfc_variables[i]].sel(time=key).values.astype(np.float32)
pl_targets = np.zeros((len(self.target_pl_variables), self.H, self.W)).astype(np.float32)
pl_scale = np.zeros((2, len(self.target_pl_variables), self.H, self.W)).astype(np.float32)
if self.target_pl_variables:
for i in range(len(self.target_pl_variables)):
pl_targets[i] = ds_targets[self.target_pl_variables[i]].values.astype(np.float32)[0]
pl_scale[0,i] = self.pl_ens_mean[self.levels.index(self.target_pl_level[i])][self.target_pl_variables[i]].sel(time=key,plev=self.target_pl_level[i]*1e2).values.astype(np.float32)
pl_scale[1,i] = self.pl_ens_std[self.levels.index(self.target_pl_level[i])][self.target_pl_variables[i]].sel(time=key,plev=self.target_pl_level[i]*1e2).values.astype(np.float32)
if self.full:
sfc_ds = xr.open_dataset(os.path.join(self.ensemble_path, f"output.sfc.{time_str}.grib"), backend_kwargs={"indexpath":""}).fillna(9999.0)
pl_ds = xr.open_dataset(os.path.join(self.ensemble_path, f"output.pl.{time_str}.grib"), backend_kwargs={"indexpath":""}).sel(isobaricInhPa=self.levels).fillna(9999.0)
for i, var in enumerate(self.surface_variables):
value = sfc_ds[var_map[var]].values.astype(np.float32)
minval, maxval = self.sfc_value_range[var_map[var]]
sfc_inputs[:,i] = (value - minval) / (maxval - minval)
for i, var in enumerate(self.upper_variables):
value = pl_ds[var_map[var]].values.astype(np.float32)
for j in range(len(self.levels)):
minval, maxval = self.pl_value_range[j][var_map[var]]
pl_inputs[:,i,j] = (value[:,j] - minval) / (maxval - minval)
else:
for i, var in enumerate(self.surface_variables):
sfc_inputs[1,i] = self.sfc_ens_mean_normalized[var].sel(time=key).values.astype(np.float32)
sfc_inputs[0,i] = self.sfc_ens_std_normalized[var].sel(time=key).values.astype(np.float32)
for i, var in enumerate(self.upper_variables):
for j, l in enumerate(self.levels):
pl_inputs[1,i,j] = self.pl_ens_mean_normalized[j][var].sel(time=key).values[0].astype(np.float32)
pl_inputs[0,i,j] = self.pl_ens_std_normalized[j][var].sel(time=key).values[0].astype(np.float32)
return sfc_inputs, pl_inputs, sfc_scale[:,:,:-1], sfc_targets[:,:-1], pl_scale[:,:,:-1],pl_targets[:,:-1], time_points
def __len__(self):
return len(self.keys)
def __repr__(self):
return self.__class__.__name__
class Aurora_downscale(data.Dataset):
"""Dataset class for the era5 upper and surface variables."""
def __init__(self,
nc_path='',
seed=1234,
startDate='1999',
endDate='2020',
freq='6h',
surface_prefix = ["2m_temperature","10m_u_component_of_wind","10m_v_component_of_wind"],
upper_prefix = ["temperature", "u_component_of_wind", "v_component_of_wind", "specific_humidity", "geopotential"],
surface = ["t2m","u10","v10"],
upper = ["t", "u", "v", "q", "z"],
levels = [50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000],
num_points = 2,
degree = ("5.625", "1.40625"),
lr_dir = None,
hr_dir = None,
lr_degree = None,
hr_degree = None,
spatial_multiple = 4,
):
"""Initialize."""
self.nc_path = nc_path
"""
To do
if start and end is valid date, if the date can be found in the downloaded files, length >= 0
"""
# Prepare the datetime objects for training, validation, and test
self.freq = int(freq[:-1])
self.surface_variables = surface
self.upper_variables = upper
self.levels = levels
self.spatial_multiple = int(spatial_multiple)
self.num_points = num_points
self.keys = list(pd.date_range(start=startDate, end=endDate, freq=freq))[:-2]
self.lr_datasets = {}
self.hr_datasets = {}
years = [datetime.strftime(key, '%Y') for key in list(pd.date_range(start=startDate[:4], end=endDate[:4], freq="YE"))]
if not years:
raise ValueError("No years were resolved from startDate/endDate.")
if lr_degree is None:
lr_degree = degree[0]
if hr_degree is None:
hr_degree = degree[1]
# Resolve actual folder/file naming in datasets (e.g., 1.40625 vs 1.5_nc).
sample_prefix = surface_prefix[0]
sample_year = years[0]
lr_dir, lr_degree = self._resolve_dataset_layout(
nc_root=self.nc_path,
prefix=sample_prefix,
year=sample_year,
requested_dir=lr_dir,
requested_degree=lr_degree,
candidates=(("5.625", "5.625"), ("5.625_nc", "5.625")),
kind="LR",
)
hr_dir, hr_degree = self._resolve_dataset_layout(
nc_root=self.nc_path,
prefix=sample_prefix,
year=sample_year,
requested_dir=hr_dir,
requested_degree=hr_degree,
candidates=(
("1.40625", "1.40625"),
("1.40625_nc", "1.40625"),
("1.5_nc", "1.5"),
("1.5", "1.5"),
),
kind="HR",
)
self.lr_dir = lr_dir
self.hr_dir = hr_dir
self.lr_degree = lr_degree
self.hr_degree = hr_degree
print(f"[Aurora_downscale] LR={self.lr_dir} ({self.lr_degree}deg), HR={self.hr_dir} ({self.hr_degree}deg)")
for i, var in enumerate(self.surface_variables):
for year in years:
self.lr_datasets[f"{var}_{year}"] = xr.open_dataset(
os.path.join(self.nc_path, self.lr_dir, surface_prefix[i], f'{surface_prefix[i]}_{year}_{self.lr_degree}deg.nc')
)
self.hr_datasets[f"{var}_{year}"] = xr.open_dataset(
os.path.join(self.nc_path, self.hr_dir, surface_prefix[i], f'{surface_prefix[i]}_{year}_{self.hr_degree}deg.nc')
)
for i, var in enumerate(self.upper_variables):
for year in years:
self.lr_datasets[f"{var}_{year}"] = xr.open_dataset(
os.path.join(self.nc_path, self.lr_dir, upper_prefix[i], f'{upper_prefix[i]}_{year}_{self.lr_degree}deg.nc')
)
self.hr_datasets[f"{var}_{year}"] = xr.open_dataset(
os.path.join(self.nc_path, self.hr_dir, upper_prefix[i], f'{upper_prefix[i]}_{year}_{self.hr_degree}deg.nc')
)
random.seed(seed)
def _trim_width_to_multiple(self, value):
"""Trim the last longitude columns if width is not divisible by `self.spatial_multiple`."""
if self.spatial_multiple <= 0:
return value
w = value.shape[-1]
w_new = (w // self.spatial_multiple) * self.spatial_multiple
if w_new == 0:
return value
if w_new == w:
return value
return value[..., :w_new]
@staticmethod
def _lat_name(da):
if "latitude" in da.coords:
return "latitude"
if "lat" in da.coords:
return "lat"
raise KeyError("Latitude coordinate not found.")
@staticmethod
def _lon_name(da):
if "longitude" in da.coords:
return "longitude"
if "lon" in da.coords:
return "lon"
raise KeyError("Longitude coordinate not found.")
@staticmethod
def _level_name(da):
for c in ("level", "pressure_level", "plev"):
if c in da.dims or c in da.coords:
return c
raise KeyError("Level coordinate not found for upper variable.")
def _read_surface(self, ds, var, key):
da = ds[var].sel(time=key)
lat_name = self._lat_name(da)
lon_name = self._lon_name(da)
lat = da[lat_name].values
if lat[0] < lat[-1]:
da = da.isel({lat_name: slice(None, None, -1)})
da = da.transpose(lat_name, lon_name)
value = da.values.astype(np.float32)
value = self._trim_width_to_multiple(value)
return value
def _read_upper(self, ds, var, key):
da = ds[var].sel(time=key)
lev_name = self._level_name(da)
lat_name = self._lat_name(da)
lon_name = self._lon_name(da)
lat = da[lat_name].values
if lat[0] < lat[-1]:
da = da.isel({lat_name: slice(None, None, -1)})
da = da.transpose(lev_name, lat_name, lon_name)
value = da.values.astype(np.float32)
value = self._trim_width_to_multiple(value)
return value
@staticmethod
def _resolve_dataset_layout(
nc_root,
prefix,
year,
requested_dir,
requested_degree,
candidates,
kind,
):
if requested_dir is not None and requested_degree is None:
for cand_dir, cand_degree in candidates:
if cand_dir != requested_dir:
continue
expected = os.path.join(nc_root, requested_dir, prefix, f"{prefix}_{year}_{cand_degree}deg.nc")
if os.path.exists(expected):
return requested_dir, cand_degree
if requested_degree is not None and requested_dir is None:
for cand_dir, cand_degree in candidates:
if cand_degree != requested_degree:
continue
expected = os.path.join(nc_root, cand_dir, prefix, f"{prefix}_{year}_{requested_degree}deg.nc")
if os.path.exists(expected):
return cand_dir, requested_degree
if requested_dir is not None and requested_degree is not None:
expected = os.path.join(nc_root, requested_dir, prefix, f"{prefix}_{year}_{requested_degree}deg.nc")
if os.path.exists(expected):
return requested_dir, requested_degree
raise FileNotFoundError(
f"[{kind}] Requested path not found: {expected}. "
f"Check --{kind.lower()}_data_dir/--{kind.lower()}_degree_tag."
)
for cand_dir, cand_degree in candidates:
expected = os.path.join(nc_root, cand_dir, prefix, f"{prefix}_{year}_{cand_degree}deg.nc")
if os.path.exists(expected):
return cand_dir, cand_degree
raise FileNotFoundError(
f"[{kind}] Could not auto-resolve dataset layout for prefix={prefix}, year={year}. "
f"Tried: {[(d, deg) for d, deg in candidates]}"
)
def __getitem__(self, index):
"""Return input frames, target frames, and its corresponding time steps."""
key = self.keys[index]
input_surfaces = []
input_uppers = []
target_surfaces = []
target_uppers = []
time_points = []
for _ in range(self.num_points):
time_points.append(key.timestamp())
year = datetime.strftime(key, '%Y')
input_surface_variables = []
input_upper_variables = []
for var in self.surface_variables:
value = self._read_surface(self.lr_datasets[f"{var}_{year}"], var, key)
input_surface_variables.append(value[np.newaxis, ...])
for var in self.upper_variables:
value = self._read_upper(self.lr_datasets[f"{var}_{year}"], var, key)
input_upper_variables.append(value[np.newaxis, ...])
input_surfaces.append(np.concatenate(input_surface_variables, axis=0)[np.newaxis, ...])
input_uppers.append(np.concatenate(input_upper_variables, axis=0)[np.newaxis, ...])
key = key + timedelta(hours=6)
key = key - timedelta(hours=6)
year = datetime.strftime(key, '%Y')
for var in self.surface_variables:
value = self._read_surface(self.hr_datasets[f"{var}_{year}"], var, key)
target_surfaces.append(value[np.newaxis, ...])
for var in self.upper_variables:
value = self._read_upper(self.hr_datasets[f"{var}_{year}"], var, key)
target_uppers.append(value[np.newaxis, ...])
return np.concatenate(input_surfaces,axis=0), np.concatenate(input_uppers, axis=0), np.concatenate(target_surfaces,axis=0), np.concatenate(target_uppers, axis=0), time_points
def __len__(self):
return len(self.keys)
def __repr__(self):
return self.__class__.__name__
class AuroraFactorizedST(data.Dataset):
"""Dataset for factorized spatial/temporal SR with commutativity training."""
def __init__(
self,
nc_path="",
seed=1234,
startDate="1999",
endDate="2020",
freq="6h",
surface_prefix=("2m_temperature", "10m_u_component_of_wind", "10m_v_component_of_wind"),
upper_prefix=("temperature", "u_component_of_wind", "v_component_of_wind", "specific_humidity", "geopotential"),
surface=("t2m", "u10", "v10"),
upper=("t", "u", "v", "q", "z"),
levels=(50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000),
lr6h_dir="5.625",
hr6h_dir="1.5_nc",
hr1h_dir="1.5_1h_nc",
lr1h_dir="",
lr6h_degree="5.625",
hr_degree="1.5",
include_endpoints=True,
derive_hr6h_from_hr1h=True,
derive_lr1h_from_hr1h=True,
):
self.nc_path = nc_path
self.surface_prefix = list(surface_prefix)
self.upper_prefix = list(upper_prefix)
self.surface_variables = list(surface)
self.upper_variables = list(upper)
self.levels = tuple(levels)
self.include_endpoints = include_endpoints
self.derive_hr6h_from_hr1h = bool(derive_hr6h_from_hr1h)
self.derive_lr1h_from_hr1h = bool(derive_lr1h_from_hr1h)
self.lr1h_available = bool(lr1h_dir) and (not self.derive_lr1h_from_hr1h)
if self.derive_hr6h_from_hr1h and (not self.include_endpoints):
raise ValueError(
"derive_hr6h_from_hr1h=True requires include_endpoints=True to access 6h endpoints."
)
self.keys = list(pd.date_range(start=startDate, end=endDate, freq=freq))[:-2]
self.hr_offsets = list(range(0, 7)) if include_endpoints else list(range(1, 6))
self.ds_lr6h = {}
self.ds_hr6h = {}
self.ds_hr1h = {}
self.ds_lr1h = {}
self._time_pos_cache = {}
start_year = pd.Timestamp(startDate).year
end_year = pd.Timestamp(endDate).year
years = [str(y) for y in range(start_year, end_year + 1)]
for year in years:
for i, var in enumerate(self.surface_variables):
pref = self.surface_prefix[i]
self.ds_lr6h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, lr6h_dir, pref, f"{pref}_{year}_{lr6h_degree}deg.nc")
)
if not self.derive_hr6h_from_hr1h:
self.ds_hr6h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, hr6h_dir, pref, f"{pref}_{year}_{hr_degree}deg.nc")
)
self.ds_hr1h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, hr1h_dir, pref, f"{pref}_{year}_{hr_degree}deg.nc")
)
if self.lr1h_available:
self.ds_lr1h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, lr1h_dir, pref, f"{pref}_{year}_{lr6h_degree}deg.nc")
)
for i, var in enumerate(self.upper_variables):
pref = self.upper_prefix[i]
self.ds_lr6h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, lr6h_dir, pref, f"{pref}_{year}_{lr6h_degree}deg.nc")
)
if not self.derive_hr6h_from_hr1h:
self.ds_hr6h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, hr6h_dir, pref, f"{pref}_{year}_{hr_degree}deg.nc")
)
self.ds_hr1h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, hr1h_dir, pref, f"{pref}_{year}_{hr_degree}deg.nc")
)
if self.lr1h_available:
self.ds_lr1h[f"{var}_{year}"] = xr.open_dataset(
os.path.join(nc_path, lr1h_dir, pref, f"{pref}_{year}_{lr6h_degree}deg.nc")
)
random.seed(seed)
@staticmethod
def _lat_name(da):
if "latitude" in da.coords:
return "latitude"
if "lat" in da.coords:
return "lat"
raise KeyError("Latitude coordinate not found.")
@staticmethod
def _lon_name(da):
if "longitude" in da.coords:
return "longitude"
if "lon" in da.coords:
return "lon"
raise KeyError("Longitude coordinate not found.")
@staticmethod
def _level_name(da):
for c in ("level", "pressure_level", "plev"):
if c in da.dims or c in da.coords:
return c
raise KeyError("Level coordinate not found for upper variable.")
@staticmethod
def _year_str(key):
return datetime.strftime(pd.Timestamp(key).to_pydatetime(), "%Y")
def _dataset_for_time(self, ds_dict, var, key):
year = self._year_str(key)
ds_key = f"{var}_{year}"
if ds_key not in ds_dict:
raise KeyError(f"Dataset key {ds_key} is not available.")
return ds_dict[ds_key]
def _time_pos(self, ds, key):
ds_id = id(ds)
if ds_id not in self._time_pos_cache:
if "time" not in ds.indexes:
raise KeyError("Dataset does not have `time` index.")
time_index = pd.DatetimeIndex(ds.indexes["time"])
self._time_pos_cache[ds_id] = {int(ts.value): i for i, ts in enumerate(time_index)}
key_ns = int(pd.Timestamp(key).value)
pos = self._time_pos_cache[ds_id].get(key_ns, None)
if pos is None:
raise KeyError(f"time={pd.Timestamp(key)} not found in dataset time index.")
return pos
def _standardize_lat(self, da):
lat_name = self._lat_name(da)
lat = da[lat_name].values
if lat[0] < lat[-1]:
da = da.isel({lat_name: slice(None, None, -1)})
return da
def _read_surface(self, ds, var, key):
t_pos = self._time_pos(ds, key)
da = ds[var].isel(time=t_pos)
da = self._standardize_lat(da)
lat_name = self._lat_name(da)
lon_name = self._lon_name(da)
da = da.transpose(lat_name, lon_name)
return da.values.astype(np.float32)
def _read_upper(self, ds, var, key):
t_pos = self._time_pos(ds, key)
da = ds[var].isel(time=t_pos)
da = self._standardize_lat(da)
lev_name = self._level_name(da)
lat_name = self._lat_name(da)
lon_name = self._lon_name(da)
if lev_name in da.coords:
da = da.sel({lev_name: list(self.levels)})
da = da.transpose(lev_name, lat_name, lon_name)
return da.values.astype(np.float32)
def _interp_surface_to_lr(self, da_hr, lat_lr, lon_lr):
da_hr = self._standardize_lat(da_hr)
lat_name = self._lat_name(da_hr)
lon_name = self._lon_name(da_hr)
da_lr = da_hr.interp(
{lat_name: xr.DataArray(lat_lr, dims=(lat_name,)),
lon_name: xr.DataArray(lon_lr, dims=(lon_name,))},
method="linear",
)
da_lr = da_lr.transpose(lat_name, lon_name)
return da_lr.values.astype(np.float32)
def _interp_upper_to_lr(self, da_hr, lat_lr, lon_lr):
da_hr = self._standardize_lat(da_hr)
lev_name = self._level_name(da_hr)
lat_name = self._lat_name(da_hr)
lon_name = self._lon_name(da_hr)
da_lr = da_hr.interp(
{lat_name: xr.DataArray(lat_lr, dims=(lat_name,)),
lon_name: xr.DataArray(lon_lr, dims=(lon_name,))},
method="linear",
)
da_lr = da_lr.transpose(lev_name, lat_name, lon_name)
return da_lr.values.astype(np.float32)
def __getitem__(self, index):
key = self.keys[index]
t0 = key
t6 = key + timedelta(hours=6)
times_6h = [t0, t6]
times_1h = [t0 + timedelta(hours=h) for h in self.hr_offsets]
x_lr6h_surface = []
y_hr6h_surface = []
y_hr1h_surface = []
y_lr1h_surface = [] if self.lr1h_available else None
x_lr6h_upper = []
y_hr6h_upper = []
y_hr1h_upper = []
y_lr1h_upper = [] if self.lr1h_available else None
for var in self.surface_variables:
lr_stack = [self._read_surface(self._dataset_for_time(self.ds_lr6h, var, t), var, t) for t in times_6h]
hr1_stack = [self._read_surface(self._dataset_for_time(self.ds_hr1h, var, t), var, t) for t in times_1h]
if self.derive_hr6h_from_hr1h:
hr6_stack = [hr1_stack[0], hr1_stack[-1]]
else:
hr6_stack = [self._read_surface(self._dataset_for_time(self.ds_hr6h, var, t), var, t) for t in times_6h]
if self.lr1h_available:
lr1_stack = [self._read_surface(self._dataset_for_time(self.ds_lr1h, var, t), var, t) for t in times_1h]
else:
lr1_stack = None
x_lr6h_surface.append(np.stack(lr_stack, axis=0))
y_hr6h_surface.append(np.stack(hr6_stack, axis=0))
y_hr1h_surface.append(np.stack(hr1_stack, axis=0))
if y_lr1h_surface is not None:
y_lr1h_surface.append(np.stack(lr1_stack, axis=0))
for var in self.upper_variables:
lr_stack = [self._read_upper(self._dataset_for_time(self.ds_lr6h, var, t), var, t) for t in times_6h]
hr1_stack = [self._read_upper(self._dataset_for_time(self.ds_hr1h, var, t), var, t) for t in times_1h]
if self.derive_hr6h_from_hr1h:
hr6_stack = [hr1_stack[0], hr1_stack[-1]]
else:
hr6_stack = [self._read_upper(self._dataset_for_time(self.ds_hr6h, var, t), var, t) for t in times_6h]
if self.lr1h_available:
lr1_stack = [self._read_upper(self._dataset_for_time(self.ds_lr1h, var, t), var, t) for t in times_1h]
else:
lr1_stack = None
x_lr6h_upper.append(np.stack(lr_stack, axis=0))
y_hr6h_upper.append(np.stack(hr6_stack, axis=0))
y_hr1h_upper.append(np.stack(hr1_stack, axis=0))
if y_lr1h_upper is not None:
y_lr1h_upper.append(np.stack(lr1_stack, axis=0))
# [T, V, ...] layout
x_lr6h_surface = np.stack(x_lr6h_surface, axis=1).astype(np.float32)
y_hr6h_surface = np.stack(y_hr6h_surface, axis=1).astype(np.float32)
y_hr1h_surface = np.stack(y_hr1h_surface, axis=1).astype(np.float32)
if y_lr1h_surface is not None:
y_lr1h_surface = np.stack(y_lr1h_surface, axis=1).astype(np.float32)
x_lr6h_upper = np.stack(x_lr6h_upper, axis=1).astype(np.float32)
y_hr6h_upper = np.stack(y_hr6h_upper, axis=1).astype(np.float32)
y_hr1h_upper = np.stack(y_hr1h_upper, axis=1).astype(np.float32)
if y_lr1h_upper is not None:
y_lr1h_upper = np.stack(y_lr1h_upper, axis=1).astype(np.float32)
time_unix = np.asarray([t.timestamp() for t in times_1h], dtype=np.int64)
out = {
"x_lr6h_surface": x_lr6h_surface,
"x_lr6h_upper": x_lr6h_upper,
"y_hr6h_surface": y_hr6h_surface,
"y_hr6h_upper": y_hr6h_upper,
"y_hr1h_surface": y_hr1h_surface,
"y_hr1h_upper": y_hr1h_upper,
"time_unix": time_unix,
}
if y_lr1h_surface is not None:
out["y_lr1h_surface"] = y_lr1h_surface
if y_lr1h_upper is not None:
out["y_lr1h_upper"] = y_lr1h_upper
return out
def __len__(self):
return len(self.keys)
def __repr__(self):
return self.__class__.__name__
|