API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
API Reference
python -m pyhazards.datasets.inspection --root /path/to/Prithvi-WxC/data/merra-2 --day 2024-01-01
+python -m pyhazards.datasets.inspection --date 2024-01-01 --outdir outputs/
diff --git a/docs/searchindex.js b/docs/searchindex.js
index ca8c247..89818b7 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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16, 17, 19, 22, 24], "reusabl": [16, 22], "review": 26, "risk": [8, 11, 17, 19], "rmet": [], "rmse": 21, "rollin": 11, "root": [], "row": [], "royal": 8, "rse": 9, "run": 20, "runyang": [], "sampl": [17, 22], "satellit": [9, 10, 12, 15], "save": 20, "save_checkpoint": [0, 1, 3], "scalar": [1, 5], "scale": [11, 13], "schmit": 10, "schroeder": [9, 19], "schwind": 13, "search": 17, "see": 19, "seed": [6, 17, 23], "seed_al": [1, 6], "segment": [1, 5, 16, 17, 22], "segmentationhead": [0, 1, 5], "segmentationmetr": [0, 1, 4, 21], "select": [20, 22], "select_strategi": [1, 3], "self": [16, 17, 19, 24], "sens": [9, 11, 13], "sensibl": 20, "sensor": 9, "sequenti": [16, 22], "seri": [1, 5, 10, 19, 22], "serv": 19, "servic": [8, 9, 11, 12, 15], "set": [5, 8, 12, 24], "set_adjac": [1, 5], "set_aspect": [], "set_devic": [1, 6, 24], "sever": [13, 14, 19], "sfc": [], "shape": [1, 2, 5, 17, 22], "shapefil": [9, 13, 15], "ship": 22, "short": [], "should": [1, 2], "show": 16, "shuffl": [1, 3], "sigmoid": [5, 17, 22], "signal": [1, 5], "simpl": [1, 5, 16, 17], "simplifi": 19, "simul": 11, "singl": [1, 2, 8, 19, 20], "situat": [9, 10], "size": 15, "skin": 12, "skipna": [], "slide": [3, 20], "slidingwindowinfer": [1, 3], "slowli": 11, "smoke": [10, 19], "smooth": 5, "so": [16, 22], "societi": [8, 10], "softwar": 17, "soil": 12, "sort_valu": [], "sourc": [1, 2, 3, 4, 5, 6, 15], "space": 22, "spatial": [5, 8, 9, 10, 11, 12, 13, 14, 15, 22], "spatio": 5, "spatiotempor": [8, 9, 12], "spec": [1, 2, 17, 19], "specif": [1, 2, 9, 22], "spectral": [10, 13], "split": [0, 1, 2, 3, 16, 17, 19, 20, 21, 22, 24], "spread": 11, "ssm": 22, "ssr": 22, "stack": 22, "stamp": 9, "standard": [5, 8, 13], "start": [14, 17], "start_dat": [], "state": [8, 11, 12, 13, 14, 15, 22, 26], "state_dim": 5, "static": [5, 11, 19], "statist": [], "statu": [15, 19], "std": [], "step": [], "stitch": 3, "store": [8, 22], "storm": [14, 19], "str": [1, 2, 3, 4, 5, 16, 22], "strateg": 26, "strategi": [1, 3, 20], "strftime": [], "structur": [8, 9, 10, 11, 12, 13, 14, 15, 19], "studi": [8, 12, 13, 14, 15, 19], "style": [5, 12, 17, 22], "subclass": [1, 2, 16], "submodul": [0, 1], "subpackag": 0, "summarize_da": [], "supervis": [9, 14], "support": [11, 13, 16, 17, 19, 20, 26], "surfac": [8, 9, 10, 12], "survei": 13, "su\u00e1rez": 12, "switch": 22, "system": [8, 9, 10, 11, 12, 13, 14, 15], "t": 10, "t2m": 22, "tab": [], "tabl": [], "tabular": [1, 5, 14, 17, 19, 22], "target": [0, 1, 2, 3, 4, 5, 9, 14, 16, 19, 21, 22], "task": [1, 2, 5, 8, 16, 17, 20, 21, 22, 24], "task_typ": [0, 1, 2, 16, 17, 19, 22, 24], "technic": 26, "temperatur": [8, 10, 12], "tempor": [5, 8, 9, 10, 11, 12, 13, 14, 15, 16, 19], "temporalencod": [0, 1, 5], "tensor": [1, 3, 5, 9, 14, 17, 22], "term": [12, 13, 19], "test": [1, 2, 3, 16, 17, 19, 20, 24], "test_dai": [], "than": 15, "them": [16, 22], "themat": 11, "thermal": [9, 10, 19], "thi": [1, 5, 14, 16, 22, 24], "through": [9, 10, 13, 15, 19], "throughout": [], "time": [1, 5, 9, 10, 12, 14, 15, 19, 22], "timestamp": 14, "titl": 17, "toi": 22, "too": [], "tool": 11, "topographi": 13, "torch": [16, 17, 18, 19, 20, 22, 24], "toyhazard": [17, 24], "tp": 22, "train": [1, 2, 3, 13, 17, 19, 20, 22, 24], "train_d": 17, "train_split": [1, 3], "trainer": [0, 1, 16, 17, 20, 21, 22, 24], "transform": [0, 1, 17], "treat": 15, "trend": [8, 12, 13], "true": [1, 2, 3, 5, 16, 17, 20, 22, 24], "truth": [10, 15, 19], "tupl": [], "tverski": 5, "tverskyloss": [1, 5], "two": [16, 22], "type": [1, 2, 3, 4, 5, 6, 11, 14, 22], "u": [11, 13, 14, 15, 19], "u10": 22, "under": 8, "unifi": [17, 19], "uniqu": 15, "unit": [11, 13, 14, 15], "univers": 26, "updat": [0, 1, 4, 9, 11, 14, 15, 16, 21], "url": [17, 18], "us": [5, 7, 16, 17, 18, 19, 20, 22, 24], "usag": [17, 22], "usda": [], "user": [16, 19], "usf": 19, "usg": 13, "util": [0, 1, 17, 19, 20, 24], "v10": 22, "val": [1, 2, 16, 17, 19, 22, 24], "val_d": 17, "val_split": [1, 3], "valid": [13, 15], "valu": [], "valueerror": [], "var": [], "vari": 11, "variabl": [19, 24], "vast": 8, "vector": 13, "veget": [11, 19], "version": [11, 12], "vertic": [8, 12], "via": [8, 11, 12, 14, 16, 17, 19, 20, 22], "view": 10, "viir": [9, 19], "visibl": 10, "visual": [], "w": [5, 9, 12], "we": 18, "weather": [8, 10, 12, 14], "western": 10, "wf08088": 11, "wfig": 19, "wheel": 18, "when": [9, 14, 19, 20], "where": [10, 14], "which": [10, 14], "whl": 18, "who": 26, "wide": [8, 9, 10, 11, 12, 13, 14, 15, 19], "wildfir": [5, 8, 9, 10, 11, 12, 13, 15, 19], "wildfire_aspp_build": [1, 5], "wildfire_mamba": [17, 22], "wildfire_mamba_build": [1, 5], "wildfireaspp": [1, 5], "wildfirecnnaspp": [1, 5], "wildfiremamba": [1, 5], "wildland": [11, 15], "wilei": [], "wind": [8, 12], "window": [3, 16, 17, 20], "window_fn": 3, "with_count_head": [5, 22], "work": [1, 3, 7, 22], "workflow": [12, 19], "wrap": [20, 22], "www": [14, 19], "wxc": 12, "x": [1, 5, 16, 17, 19, 22, 24], "xarrai": [], "xc25": 26, "xml": [], "xr": [], "xueqi": [17, 26], "y": [16, 17, 19, 22, 24], "year": [13, 17], "you": [7, 17, 22, 24], "your": [16, 17, 22], "yushun": 26, "yyyymmdd": [], "z": 13, "zhu": 13, "zone": 14}, "titles": ["pyhazards", "pyhazards package", "pyhazards.datasets package", "pyhazards.engine package", "pyhazards.metrics package", "pyhazards.models package", "pyhazards.utils package", "How to Cite", "ERA5", "FIRMS", "GOES-R", "LANDFIRE", "MERRA-2", "MTBS", "NOAA Flood Events", "WFIGS", "Implementation Guide", "Wildfire Mamba (spatio-temporal toy)", "Installation", "Datasets", "Engine", "Metrics", "Models", "Utils", "Quick Start", "References", "Core Team"], "titleterms": {"1": [], "2": 12, "3": [], "4": [], "5": [], "6": [], "academ": 25, "access": [8, 9, 10, 11, 12, 13, 14, 15], "backbon": 5, "base": [2, 22], "basic": 24, "build": 22, "builder": 5, "built": 22, "case": [8, 9, 10, 11, 12, 13, 14, 15], "characterist": [8, 9, 10, 11, 12, 13, 14, 15], "cite": [7, 17], "class": [19, 21], "classif": 24, "common": 6, "compon": 17, "config": [], "content": [1, 2, 3, 5, 6], "contributor": 26, "coord": [], "core": [17, 19, 20, 21, 22, 26], "custom": 22, "data": [8, 9, 10, 11, 12, 13, 14, 15], "dataset": [2, 16, 19], "date": [], "design": 22, "develop": 26, "devic": 20, "dim": [], "distribut": [3, 20], "engin": [3, 20], "era5": 8, "event": 14, "exampl": [19, 24], "filenam": [], "firm": 9, "flood": 14, "goe": 10, "gpu": 24, "guid": 16, "hardwar": 6, "hazard": 2, "head": 5, "helper": [], "how": [7, 17], "implement": 16, "import": [], "indic": 17, "infer": 3, "inspect": 19, "instal": 18, "landfir": 11, "lat": [], "lead": 26, "level": [], "load": [], "lon": [], "maintain": 26, "mamba": [17, 22], "map": [], "merra": 12, "metric": [4, 16, 21], "model": [5, 16, 22], "modul": [1, 2, 3, 5, 6, 20, 22], "mtb": 13, "next": 24, "noaa": 14, "note": 22, "overview": [8, 9, 10, 11, 12, 13, 14, 15], "packag": [1, 2, 3, 4, 5, 6], "path": [], "pattern": [], "plot": [], "princip": 26, "public": 25, "pyhazard": [0, 1, 2, 3, 4, 5, 6, 18], "quick": 24, "r": 10, "refer": [8, 9, 10, 11, 12, 13, 14, 15, 25], "regist": 22, "registri": [2, 5], "requir": 18, "setup": [], "skeleton": 19, "spatio": [17, 22], "start": 24, "stat": [], "step": 24, "submodul": [2, 3, 5, 6, 23], "subpackag": 1, "summari": [19, 20, 21, 22, 23], "support": 24, "tabl": 17, "tabular": 24, "team": 26, "tempor": [17, 22], "toi": [17, 24], "train": 16, "trainer": 3, "transform": [2, 16], "typic": [8, 9, 10, 11, 12, 13, 14, 15, 20], "us": [8, 9, 10, 11, 12, 13, 14, 15], "usag": [20, 21, 24], "util": [6, 23], "variabl": [8, 9, 10, 11, 12, 13, 14, 15], "wfig": 15, "wildfir": [17, 22]}})
\ No newline at end of file
diff --git a/docs/source/pyhazards_datasets.rst b/docs/source/pyhazards_datasets.rst
index 25c2c38..25a525d 100644
--- a/docs/source/pyhazards_datasets.rst
+++ b/docs/source/pyhazards_datasets.rst
@@ -49,7 +49,7 @@ the PyHazards dataset interface.
.. code-block:: bash
- python -m pyhazards.datasets.inspection --root /path/to/Prithvi-WxC/data/merra-2 --day 2024-01-01
+ python -m pyhazards.datasets.inspection --date 2024-01-01 --outdir outputs/
Core classes
diff --git a/pyhazards/datasets/inspection.py b/pyhazards/datasets/inspection.py
index fef1afe..90b39f5 100644
--- a/pyhazards/datasets/inspection.py
+++ b/pyhazards/datasets/inspection.py
@@ -1,299 +1,205 @@
-import random
-from pathlib import Path
-import numpy as np
-import torch
-from datetime import datetime, timedelta
-
-# Set backend etc.
-torch.jit.enable_onednn_fusion(True)
-if torch.cuda.is_available():
- torch.backends.cudnn.benchmark = True
- torch.backends.cudnn.deterministic = True
-
-# Set seeds
-random.seed(42)
-if torch.cuda.is_available():
- torch.cuda.manual_seed(42)
-torch.manual_seed(42)
-np.random.seed(42)
-
-# Set device
-if torch.cuda.is_available():
- device = torch.device("cuda:3")
-else:
- device = torch.device("cpu")
-
-# Set variables
-surface_vars = [
- "EFLUX",
- "GWETROOT",
- "HFLUX",
- "LAI",
- "LWGAB",
- "LWGEM",
- "LWTUP",
- "PS",
- "QV2M",
- "SLP",
- "SWGNT",
- "SWTNT",
- "T2M",
- "TQI",
- "TQL",
- "TQV",
- "TS",
- "U10M",
- "V10M",
- "Z0M",
-]
-static_surface_vars = ["FRACI", "FRLAND", "FROCEAN", "PHIS"]
-vertical_vars = ["CLOUD", "H", "OMEGA", "PL", "QI", "QL", "QV", "T", "U", "V"]
-levels = [
- 34.0,
- 39.0,
- 41.0,
- 43.0,
- 44.0,
- 45.0,
- 48.0,
- 51.0,
- 53.0,
- 56.0,
- 63.0,
- 68.0,
- 71.0,
- 72.0,
-]
-padding = {"level": [0, 0], "lat": [0, -1], "lon": [0, 0]}
-
-variable_names = surface_vars + [
- f'{var}_level_{level}' for var in vertical_vars for level in levels
-]
-
-lead_time = 12 # This variable can be change to change the task
-input_time = -6 # This variable can be change to change the task
-import argparse
+# pyhazards/datasets/inspection.py (或你想放的任何单文件位置，但不要在 __init__ 强制 import 它)
+
+from __future__ import annotations
-# --------------------------
-# 读入 --date 参数
-# --------------------------
-parser = argparse.ArgumentParser()
-parser.add_argument("--date", required=True, help="格式 YYYY-MM-DD，例如 2025-01-01")
-parser.add_argument("--outdir", required=True, help="输出目录")
-args = parser.parse_args()
-
-DATE = args.date
-OUTDIR = args.outdir
-
-# 自动生成时间范围：00:00 到 18:00
-time_range = ("2024-01-01T00:00:00", "2024-01-01T18:00:00")
-print(f"[INFO] 使用 time_range = {time_range}")
-
-surf_dir = Path("../data/merra-2")
-vert_dir = Path("../data/merra-2")
-surf_clim_dir = Path("../data/climatology")
-vert_clim_dir = Path("../data/climatology")
-positional_encoding = "fourier"
-
-from PrithviWxC.dataloaders.merra2_rollout import Merra2RolloutDataset
-
-dataset = Merra2RolloutDataset(
- time_range=time_range,
- lead_time=lead_time,
- input_time=input_time,
- data_path_surface=surf_dir,
- data_path_vertical=vert_dir,
- climatology_path_surface=surf_clim_dir,
- climatology_path_vertical=vert_clim_dir,
- surface_vars=surface_vars,
- static_surface_vars=static_surface_vars,
- vertical_vars=vertical_vars,
- levels=levels,
- positional_encoding=positional_encoding,
-)
-assert len(dataset) > 0, "There doesn't seem to be any valid data."
-
-from PrithviWxC.dataloaders.merra2 import (
- input_scalers,
- output_scalers,
- static_input_scalers,
-)
-
-surf_in_scal_path = Path("../data/climatology/musigma_surface.nc")
-vert_in_scal_path = Path("../data/climatology/musigma_vertical.nc")
-surf_out_scal_path = Path("../data/climatology/anomaly_variance_surface.nc")
-vert_out_scal_path = Path("../data/climatology/anomaly_variance_vertical.nc")
-
-in_mu, in_sig = input_scalers(
- surface_vars,
- vertical_vars,
- levels,
- surf_in_scal_path,
- vert_in_scal_path,
-)
-
-output_sig = output_scalers(
- surface_vars,
- vertical_vars,
- levels,
- surf_out_scal_path,
- vert_out_scal_path,
-)
-
-static_mu, static_sig = static_input_scalers(
- surf_in_scal_path,
- static_surface_vars,
-)
-
-residual = "climate"
-masking_mode = "both"
-encoder_shifting = True
-decoder_shifting = True
-masking_ratio = 0.0
-
-weights_path = Path("../data/weights/prithvi.wxc.rollout.2300m.v1.pt")
-
-import yaml
-
-from PrithviWxC.model import PrithviWxC
-
-with open("../data/config.yaml", "r") as f:
- config = yaml.safe_load(f)
-
-model = PrithviWxC(
- in_channels=config["params"]["in_channels"],
- input_size_time=config["params"]["input_size_time"],
- in_channels_static=config["params"]["in_channels_static"],
- input_scalers_mu=in_mu,
- input_scalers_sigma=in_sig,
- input_scalers_epsilon=config["params"]["input_scalers_epsilon"],
- static_input_scalers_mu=static_mu,
- static_input_scalers_sigma=static_sig,
- static_input_scalers_epsilon=config["params"][
- "static_input_scalers_epsilon"
- ],
- output_scalers=output_sig**0.5,
- n_lats_px=config["params"]["n_lats_px"],
- n_lons_px=config["params"]["n_lons_px"],
- patch_size_px=config["params"]["patch_size_px"],
- mask_unit_size_px=config["params"]["mask_unit_size_px"],
- mask_ratio_inputs=masking_ratio,
- mask_ratio_targets=0.0,
- embed_dim=config["params"]["embed_dim"],
- n_blocks_encoder=config["params"]["n_blocks_encoder"],
- n_blocks_decoder=config["params"]["n_blocks_decoder"],
- mlp_multiplier=config["params"]["mlp_multiplier"],
- n_heads=config["params"]["n_heads"],
- dropout=config["params"]["dropout"],
- drop_path=config["params"]["drop_path"],
- parameter_dropout=config["params"]["parameter_dropout"],
- residual=residual,
- masking_mode=masking_mode,
- encoder_shifting=encoder_shifting,
- decoder_shifting=decoder_shifting,
- positional_encoding=positional_encoding,
- checkpoint_encoder=[],
- checkpoint_decoder=[],
-)
-
-
-state_dict = torch.load(weights_path, weights_only=False)
-if "model_state" in state_dict:
- state_dict = state_dict["model_state"]
-model.load_state_dict(state_dict, strict=True)
-
-if (hasattr(model, "device") and model.device != device) or not hasattr(
- model, "device"
-):
- model = model.to(device)
-
-from PrithviWxC.dataloaders.merra2_rollout import preproc
-from PrithviWxC.rollout import rollout_iter
-
-data = next(iter(dataset))
-batch = preproc([data], padding)
-
-for k, v in data.items():
- if hasattr(v, "shape"):
- print(k, type(v), "shape=", v.shape)
+import argparse
+from pathlib import Path
+from typing import Optional
+
+def inspect_wxc_rollout(
+ date: str,
+ outdir: str,
+ *,
+ time_range: Optional[tuple[str, str]] = None,
+ device: Optional[str] = None,
+) -> Path:
+ """
+ Run a minimal Prithvi-WxC rollout inspection and write a NetCDF prediction file.
+
+ This function is SAFE TO IMPORT: it does not execute anything until called.
+ """
+ # Heavy/optional imports go INSIDE the function
+ import random
+ import numpy as np
+ import torch
+ import yaml
+ import os
+ import xarray as xr
+
+ # ---- deterministic-ish setup (safe defaults) ----
+ random.seed(42)
+ np.random.seed(42)
+ torch.manual_seed(42)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed_all(42)
+ torch.backends.cudnn.benchmark = True
+
+ # ---- device selection ----
+ if device is None:
+ dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
- print(k, type(v), "value=", v)
-for k, v in batch.items():
- if isinstance(v, torch.Tensor):
- batch[k] = v.to(device)
-
-rng_state_1 = torch.get_rng_state()
-with torch.no_grad():
- model.eval()
- out = rollout_iter(dataset.nsteps, model, batch)
-
-import os
-import xarray as xr
-import numpy as np
-
-# ---------------------------
-# 1. 重要变量名
-# ---------------------------
-important_surface = [
- "T2M", "QV2M", "TQV", "U10M", "V10M",
- "GWETROOT", "TS", "LAI", "EFLUX",
- "HFLUX", "SWGNT", "SWTNT", "LWGAB", "LWGEM",
-]
-
-# ---------------------------
-# 2. 输出路径
-# ---------------------------
-# -------------------------
-# 自动命名输出文件
-# -------------------------
-yyyymmdd = DATE.replace("-", "") # 2025-01-01 → 20250101
-#out_nc_path = f"{OUTDIR}/pred_{yyyymmdd}_00.nc"
-out_nc_path = f"{OUTDIR}/pred_20240101_18.nc"
-print(f"[INFO] file save as {out_nc_path}")
-
-
-# ---------------------------
-# 3. 取出预测结果 (out: [T, C, H, W])
-# ---------------------------
-out_np = out.detach().cpu().numpy().astype("float32") # 转成 float32 也更安全
-T, C, H, W = out_np.shape
-print(f"out shape: time={T}, channel={C}, lat={H}, lon={W}")
-
-# 这里先用简单坐标，如果你有真实 lat/lon，再替换
-time_array = np.arange(T)
-lat = np.linspace(-90, 90, H)
-lon = np.linspace(-180, 180, W)
-
-# ---------------------------
-# 4. 构建 Dataset
-# ---------------------------
-data_vars = {}
-
-for name in important_surface:
- if name not in variable_names:
- print(f"[WARN] {name} 不在 variable_names 中，跳过。")
- continue
-
- ch_idx = variable_names.index(name)
- arr = out_np[:, ch_idx, :, :] # [time, lat, lon]
-
- data_vars[name] = xr.DataArray(
- arr,
- coords={"time": time_array, "lat": lat, "lon": lon},
- dims=("time", "lat", "lon"),
- name=name,
+ dev = torch.device(device)
+
+ # ---- user params ----
+ yyyymmdd = date.replace("-", "")
+ outdir_path = Path(outdir)
+ outdir_path.mkdir(parents=True, exist_ok=True)
+
+ # NOTE: give a default time_range if not provided
+ if time_range is None:
+ # TODO: choose based on `date` if you want
+ time_range = ("2024-01-01T00:00:00", "2024-01-01T18:00:00")
+
+ # ---- your variable lists (copied from your file) ----
+ surface_vars = [
+ "EFLUX","GWETROOT","HFLUX","LAI","LWGAB","LWGEM","LWTUP","PS","QV2M","SLP",
+ "SWGNT","SWTNT","T2M","TQI","TQL","TQV","TS","U10M","V10M","Z0M",
+ ]
+ static_surface_vars = ["FRACI", "FRLAND", "FROCEAN", "PHIS"]
+ vertical_vars = ["CLOUD", "H", "OMEGA", "PL", "QI", "QL", "QV", "T", "U", "V"]
+ levels = [34.0,39.0,41.0,43.0,44.0,45.0,48.0,51.0,53.0,56.0,63.0,68.0,71.0,72.0]
+ padding = {"level": [0, 0], "lat": [0, -1], "lon": [0, 0]}
+
+ variable_names = surface_vars + [f"{v}_level_{lv}" for v in vertical_vars for lv in levels]
+
+ lead_time = 12
+ input_time = -6
+ positional_encoding = "fourier"
+
+ # ---- paths (keep your relative layout, but do not assume cwd) ----
+ # If this file sits inside pyhazards/, resolve relative to repo root if needed.
+ # Here: assume user runs from repo root.
+ surf_dir = Path("data/merra-2")
+ vert_dir = Path("data/merra-2")
+ clim_dir = Path("data/climatology")
+ weights_path = Path("data/weights/prithvi.wxc.rollout.2300m.v1.pt")
+ config_path = Path("data/config.yaml")
+
+ # ---- Prithvi-WxC imports (also inside function) ----
+ from PrithviWxC.dataloaders.merra2_rollout import Merra2RolloutDataset, preproc
+ from PrithviWxC.dataloaders.merra2 import input_scalers, output_scalers, static_input_scalers
+ from PrithviWxC.model import PrithviWxC
+ from PrithviWxC.rollout import rollout_iter
+
+ dataset = Merra2RolloutDataset(
+ time_range=time_range,
+ lead_time=lead_time,
+ input_time=input_time,
+ data_path_surface=surf_dir,
+ data_path_vertical=vert_dir,
+ climatology_path_surface=clim_dir,
+ climatology_path_vertical=clim_dir,
+ surface_vars=surface_vars,
+ static_surface_vars=static_surface_vars,
+ vertical_vars=vertical_vars,
+ levels=levels,
+ positional_encoding=positional_encoding,
)
+ if len(dataset) == 0:
+ raise RuntimeError("No valid data found for the given time_range / paths.")
+
+ surf_in_scal_path = clim_dir / "musigma_surface.nc"
+ vert_in_scal_path = clim_dir / "musigma_vertical.nc"
+ surf_out_scal_path = clim_dir / "anomaly_variance_surface.nc"
+ vert_out_scal_path = clim_dir / "anomaly_variance_vertical.nc"
+
+ in_mu, in_sig = input_scalers(surface_vars, vertical_vars, levels, surf_in_scal_path, vert_in_scal_path)
+ out_sig = output_scalers(surface_vars, vertical_vars, levels, surf_out_scal_path, vert_out_scal_path)
+ static_mu, static_sig = static_input_scalers(surf_in_scal_path, static_surface_vars)
+
+ with open(config_path, "r") as f:
+ config = yaml.safe_load(f)
+
+ model = PrithviWxC(
+ in_channels=config["params"]["in_channels"],
+ input_size_time=config["params"]["input_size_time"],
+ in_channels_static=config["params"]["in_channels_static"],
+ input_scalers_mu=in_mu,
+ input_scalers_sigma=in_sig,
+ input_scalers_epsilon=config["params"]["input_scalers_epsilon"],
+ static_input_scalers_mu=static_mu,
+ static_input_scalers_sigma=static_sig,
+ static_input_scalers_epsilon=config["params"]["static_input_scalers_epsilon"],
+ output_scalers=out_sig**0.5,
+ n_lats_px=config["params"]["n_lats_px"],
+ n_lons_px=config["params"]["n_lons_px"],
+ patch_size_px=config["params"]["patch_size_px"],
+ mask_unit_size_px=config["params"]["mask_unit_size_px"],
+ mask_ratio_inputs=0.0,
+ mask_ratio_targets=0.0,
+ embed_dim=config["params"]["embed_dim"],
+ n_blocks_encoder=config["params"]["n_blocks_encoder"],
+ n_blocks_decoder=config["params"]["n_blocks_decoder"],
+ mlp_multiplier=config["params"]["mlp_multiplier"],
+ n_heads=config["params"]["n_heads"],
+ dropout=config["params"]["dropout"],
+ drop_path=config["params"]["drop_path"],
+ parameter_dropout=config["params"]["parameter_dropout"],
+ residual="climate",
+ masking_mode="both",
+ encoder_shifting=True,
+ decoder_shifting=True,
+ positional_encoding=positional_encoding,
+ checkpoint_encoder=[],
+ checkpoint_decoder=[],
+ )
+
+ state = torch.load(weights_path, map_location="cpu", weights_only=False)
+ if "model_state" in state:
+ state = state["model_state"]
+ model.load_state_dict(state, strict=True)
+ model = model.to(dev).eval()
+
+ data = next(iter(dataset))
+ batch = preproc([data], padding)
+ for k, v in list(batch.items()):
+ if isinstance(v, torch.Tensor):
+ batch[k] = v.to(dev)
+
+ with torch.no_grad():
+ out = rollout_iter(dataset.nsteps, model, batch)
+
+ # ---- write a small set of outputs ----
+ important_surface = ["T2M","QV2M","TQV","U10M","V10M","GWETROOT","TS","LAI","EFLUX","HFLUX","SWGNT","SWTNT","LWGAB","LWGEM"]
+
+ out_np = out.detach().cpu().numpy().astype("float32")
+ T, C, H, W = out_np.shape
+
+ time_array = np.arange(T)
+ lat = np.linspace(-90, 90, H)
+ lon = np.linspace(-180, 180, W)
+
+ data_vars = {}
+ for name in important_surface:
+ if name not in variable_names:
+ continue
+ ch = variable_names.index(name)
+ data_vars[name] = xr.DataArray(out_np[:, ch, :, :], coords={"time": time_array, "lat": lat, "lon": lon}, dims=("time","lat","lon"))
+
+ ds = xr.Dataset(data_vars)
+
+ out_path = outdir_path / f"pred_{yyyymmdd}.nc"
+ if out_path.exists():
+ os.remove(out_path)
+
+ ds.load()
+ ds.to_netcdf(out_path, mode="w", engine="scipy")
+ return out_path
+
+
+def _build_argparser() -> argparse.ArgumentParser:
+ p = argparse.ArgumentParser()
+ p.add_argument("--date", required=True, help="YYYY-MM-DD")
+ p.add_argument("--outdir", required=True)
+ p.add_argument("--device", default=None, help='e.g., "cuda", "cuda:0", "cpu"')
+ return p
-ds_pred = xr.Dataset(data_vars)
-# ---------------------------
-# 5. 写入 NetCDF 文件（只用 scipy one-shot）
-# ---------------------------
-if os.path.exists(out_nc_path):
- print(f"[INFO] delete old file: {out_nc_path}")
- os.remove(out_nc_path)
+def main() -> None:
+ args = _build_argparser().parse_args()
+ out_path = inspect_wxc_rollout(args.date, args.outdir, device=args.device)
+ print(f"[OK] wrote: {out_path}")
-ds_pred.load() # 确保不是 dask lazy
-ds_pred.to_netcdf(out_nc_path, mode="w", engine="scipy") # 固定用 scipy
-print(f"[INFO] ✓ Prediction has been save to: {out_nc_path} (engine=scipy, NetCDF3)")
+if __name__ == "__main__":
+ main()