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006ea64 | 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 | from __future__ import annotations
import argparse
from datetime import datetime
from pathlib import Path
from typing import Any
import h5py
import matplotlib
import numpy as np
import yaml
matplotlib.use("Agg")
from matplotlib import pyplot as plt
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Visualize StormCast HDF5 forecasts")
parser.add_argument("--config", type=Path, default=Path("conf/config.yaml"))
parser.add_argument("--input", type=Path, default="./outputs/inference/forecast.h5")
parser.add_argument("--output-dir", type=Path)
parser.add_argument("--state-variable")
parser.add_argument("--background-variable")
parser.add_argument("--step", type=int, action="append")
parser.add_argument(
"--normalized",
action="store_true",
help="Plot model-space values instead of applying dataset statistics",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
config_path = args.config.resolve()
with config_path.open("r", encoding="utf-8") as handle:
config = yaml.safe_load(handle)
project_root = config_path.parent.parent
data_root = Path(config["data"]["root_dir"])
if not data_root.is_absolute():
data_root = (project_root / data_root).resolve()
output_dir = args.output_dir or Path(config["inference"]["output_dir"]) / "plots"
if not output_dir.is_absolute():
output_dir = (project_root / output_dir).resolve()
visualize(
input_path=args.input,
output_dir=output_dir,
data_root=data_root,
state_variable=args.state_variable
or config["inference"]["plot_state_variable"],
background_variable=args.background_variable
or config["inference"]["plot_background_variable"],
steps=args.step,
denormalize=not args.normalized,
)
def visualize(
input_path: Path,
output_dir: Path,
data_root: Path,
state_variable: str,
background_variable: str,
steps: list[int] | None = None,
denormalize: bool = True,
) -> list[Path]:
output_dir.mkdir(parents=True, exist_ok=True)
outputs: list[Path] = []
with h5py.File(input_path, "r") as handle:
state_variables = _decode_strings(handle.attrs["state_variables"])
background_variables = _decode_strings(handle.attrs["background_variables"])
state_index = _variable_index(state_variables, state_variable, "state")
background_index = _variable_index(
background_variables, background_variable, "background"
)
selected_steps = steps or list(range(handle["prediction"].shape[0]))
for step in selected_steps:
if not 0 <= step < handle["prediction"].shape[0]:
raise IndexError(f"Step {step} is outside the forecast range")
source_normalized = bool(handle.attrs.get("normalized", False))
stats_by_year: dict[
int,
tuple[
tuple[np.ndarray, np.ndarray],
tuple[np.ndarray, np.ndarray],
],
] = {}
for step in selected_steps:
prediction = handle["prediction"][step, state_index]
target = handle["target"][step, state_index]
background = handle["background"][step, background_index]
time_index = int(handle["time_index"][step])
if denormalize and source_normalized:
year = int(str(time_index)[:4])
if year not in stats_by_year:
stats_by_year[year] = (
_read_stats(
data_root / "hrrr" / "data" / f"{year}.h5",
state_variables,
),
_read_stats(
data_root / "era5" / "data" / f"{year}.h5",
background_variables,
),
)
state_stats, background_stats = stats_by_year[year]
prediction = _denormalize(prediction, state_stats, state_index)
target = _denormalize(target, state_stats, state_index)
background = _denormalize(
background, background_stats, background_index
)
output = output_dir / f"forecast_{step:03d}_{state_variable}.png"
_save_four_panel(
prediction,
target,
background,
state_variable,
background_variable,
time_index,
output,
normalized=source_normalized and not denormalize,
)
outputs.append(output)
print(f"plot={output}")
return outputs
def _save_four_panel(
prediction: np.ndarray,
target: np.ndarray,
background: np.ndarray,
state_variable: str,
background_variable: str,
time_index: int,
output: Path,
normalized: bool,
) -> None:
error = prediction - target
state_min = float(min(np.nanmin(prediction), np.nanmin(target)))
state_max = float(max(np.nanmax(prediction), np.nanmax(target)))
error_limit = max(float(np.nanmax(np.abs(error))), np.finfo(np.float32).eps)
time_label = datetime.strptime(str(time_index), "%Y%m%d%H").strftime(
"%Y-%m-%d %H:00"
)
units = " (normalized)" if normalized else ""
figure, axes = plt.subplots(1, 4, figsize=(19, 4.8), constrained_layout=True)
panels = (
(
prediction,
f"StormCast {state_variable}{units}",
"viridis",
state_min,
state_max,
),
(target, f"Target {state_variable}{units}", "viridis", state_min, state_max),
(background, f"ERA5 {background_variable}{units}", "magma", None, None),
(
error,
f"Error {state_variable}{units}",
"RdBu_r",
-error_limit,
error_limit,
),
)
for axis, (data, title, cmap, vmin, vmax) in zip(axes, panels):
image = axis.imshow(
data, origin="lower", cmap=cmap, vmin=vmin, vmax=vmax, aspect="auto"
)
axis.set_title(title, fontsize=10)
axis.set_xticks([])
axis.set_yticks([])
figure.colorbar(image, ax=axis, fraction=0.046, pad=0.03)
figure.suptitle(f"StormCast valid time: {time_label}", fontsize=13)
figure.savefig(output, dpi=160)
plt.close(figure)
def _read_stats(
path: Path, expected_variables: list[str]
) -> tuple[np.ndarray, np.ndarray]:
if not path.is_file():
raise FileNotFoundError(f"Missing statistics file: {path}")
with h5py.File(path, "r") as handle:
variables = _decode_strings(handle["fields"].attrs["variables"])
if variables != expected_variables:
raise ValueError(f"Variable order in {path} differs from inference output")
means = np.asarray(handle["global_means"][:], dtype=np.float32).reshape(-1)
stds = np.asarray(handle["global_stds"][:], dtype=np.float32).reshape(-1)
return means, stds
def _denormalize(
data: np.ndarray,
stats: tuple[np.ndarray, np.ndarray],
index: int,
) -> np.ndarray:
means, stds = stats
return data * stds[index] + means[index]
def _decode_strings(values: Any) -> list[str]:
return [
value.decode() if isinstance(value, bytes) else str(value) for value in values
]
def _variable_index(variables: list[str], name: str, kind: str) -> int:
try:
return variables.index(name)
except ValueError as error:
raise ValueError(f"Unknown {kind} variable {name!r}") from error
if __name__ == "__main__":
main()
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