(self, x)
| 250 | return None |
| 251 | |
| 252 | def normalize(self, x): |
| 253 | arr = _to_array(x) |
| 254 | if arr is None: |
| 255 | return None |
| 256 | normed = _normalize_array( |
| 257 | arr, |
| 258 | mean=self.mean, |
| 259 | std=self.std, |
| 260 | min_val=self.min_val, |
| 261 | max_val=self.max_val, |
| 262 | q_low=self.q_low, |
| 263 | q_high=self.q_high, |
| 264 | mode=self.mode, |
| 265 | ) |
| 266 | if _uses_bounded_normalized_range(self.mode): |
| 267 | normed = np.clip(normed, -1.0, 1.0) |
| 268 | if self.mask is not None: |
| 269 | normed = np.where(self.mask, normed, arr) |
| 270 | if self.zero_mask is not None: |
| 271 | normed = np.where(self.zero_mask, 0.0, normed) |
| 272 | if torch.is_tensor(x): |
| 273 | return torch.as_tensor(normed, device=x.device, dtype=x.dtype) |
| 274 | return normed |
| 275 | |
| 276 | def unnormalize(self, x): |
| 277 | arr = _to_array(x) |
no test coverage detected