gammapy /
gammapy
| @@ 366-381 (lines=16) @@ | ||
| 363 | idx[idx_axis] = slice(None) |
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| 364 | yield tuple(idx), self.quantity[tuple(idx)] |
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| 365 | ||
| 366 | def fill_by_idx(self, idx, weights=None): |
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| 367 | # TODO: too complex, simplify! |
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| 368 | idx = pix_tuple_to_idx(idx) |
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| 369 | ||
| 370 | msk = np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0) |
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| 371 | idx = [t[msk] for t in idx] |
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| 372 | ||
| 373 | if weights is not None: |
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| 374 | if isinstance(weights, u.Quantity): |
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| 375 | weights = weights.to_value(self.unit) |
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| 376 | weights = weights[msk] |
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| 377 | ||
| 378 | idx = np.ravel_multi_index(idx, self.data.T.shape) |
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| 379 | idx, idx_inv = np.unique(idx, return_inverse=True) |
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| 380 | weights = np.bincount(idx_inv, weights=weights).astype(self.data.dtype) |
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| 381 | self.data.T.flat[idx] += weights |
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| 382 | ||
| 383 | def get_by_idx(self, idxs): |
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| 384 | return self.data[idxs[::-1]] |
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| @@ 176-189 (lines=14) @@ | ||
| 173 | ||
| 174 | return vals |
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| 175 | ||
| 176 | def fill_by_idx(self, idx, weights=None): |
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| 177 | idx = pix_tuple_to_idx(idx) |
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| 178 | msk = np.all(np.stack([t != INVALID_INDEX.int for t in idx]), axis=0) |
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| 179 | idx = [t[msk] for t in idx] |
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| 180 | ||
| 181 | if weights is not None: |
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| 182 | if isinstance(weights, u.Quantity): |
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| 183 | weights = weights.to_value(self.unit) |
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| 184 | weights = weights[msk] |
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| 185 | ||
| 186 | idx = np.ravel_multi_index(idx, self.data.T.shape) |
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| 187 | idx, idx_inv = np.unique(idx, return_inverse=True) |
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| 188 | weights = np.bincount(idx_inv, weights=weights).astype(self.data.dtype) |
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| 189 | self.data.T.flat[idx] += weights |
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| 190 | ||
| 191 | def set_by_idx(self, idx, vals): |
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| 192 | idx = pix_tuple_to_idx(idx) |
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