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f637d77 | 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 | # Normalization files
This folder provides the normalization (standardization) parameters used for FuXi-CFD inference.
## Overview
- Inputs are standardized before being fed to the ONNX model.
- Model outputs are de-standardized back to physical scale after inference.
## Files
- `scaler_input.npy` : dict of input normalization stats (mean/std) for static/dynamic input variables
- `scaler_output.npy` : dict of output normalization stats (mean/std) for model outputs
Both are stored as Python dicts inside `.npy` (load with `allow_pickle=True`).
## Input normalization (`scaler_input.npy`)
Keys:
- `low_mean`, `low_std` for low-res dynamic inputs: `["u_100m", "v_100m"]`
- `high_mean`, `high_std` for high-res static inputs: `["dem", "roughness"]`
Final ONNX input channel order **must** be:
`[u_100m, v_100m, dem, roughness]` (C=4)
## Output de-normalization (`scaler_output.npy`)
Keys:
- `mean`, `std`: arrays of shape `(27, 4)` (levels=27, vars=4)
Output variable order:
`[u, v, w, k]`
The ONNX model output tensor is expected as:
`(1, 27, 4, 300, 300)` and de-normalization applies:
`pred = pred * std + mean` (broadcast on `(300,300)`)
## Example loading
```python
import numpy as np
in_stats = np.load("normalization/scaler_input.npy", allow_pickle=True).item()
out_stats = np.load("normalization/scaler_output.npy", allow_pickle=True).item()
```
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