Return all the non-masked data as a 1-D array. This function is equivalent to calling the "compressed" method of a `ma.MaskedArray`, see `ma.MaskedArray.compressed` for details. See Also -------- ma.MaskedArray.compressed : Equivalent method. Examples --------
(x)
| 7273 | |
| 7274 | |
| 7275 | def compressed(x): |
| 7276 | """ |
| 7277 | Return all the non-masked data as a 1-D array. |
| 7278 | |
| 7279 | This function is equivalent to calling the "compressed" method of a |
| 7280 | `ma.MaskedArray`, see `ma.MaskedArray.compressed` for details. |
| 7281 | |
| 7282 | See Also |
| 7283 | -------- |
| 7284 | ma.MaskedArray.compressed : Equivalent method. |
| 7285 | |
| 7286 | Examples |
| 7287 | -------- |
| 7288 | >>> import numpy as np |
| 7289 | |
| 7290 | Create an array with negative values masked: |
| 7291 | |
| 7292 | >>> import numpy as np |
| 7293 | >>> x = np.array([[1, -1, 0], [2, -1, 3], [7, 4, -1]]) |
| 7294 | >>> masked_x = np.ma.masked_array(x, mask=x < 0) |
| 7295 | >>> masked_x |
| 7296 | masked_array( |
| 7297 | data=[[1, --, 0], |
| 7298 | [2, --, 3], |
| 7299 | [7, 4, --]], |
| 7300 | mask=[[False, True, False], |
| 7301 | [False, True, False], |
| 7302 | [False, False, True]], |
| 7303 | fill_value=999999) |
| 7304 | |
| 7305 | Compress the masked array into a 1-D array of non-masked values: |
| 7306 | |
| 7307 | >>> np.ma.compressed(masked_x) |
| 7308 | array([1, 0, 2, 3, 7, 4]) |
| 7309 | |
| 7310 | """ |
| 7311 | return asanyarray(x).compressed() |
| 7312 | |
| 7313 | |
| 7314 | def concatenate(arrays, axis=0): |
nothing calls this directly
no test coverage detected
searching dependent graphs…