Mask using floating point equality. Return a MaskedArray, masked where the data in array `x` are approximately equal to `value`, determined using `isclose`. The default tolerances for `masked_values` are the same as those for `isclose`. For integer types, exact equality is use
(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True)
| 2258 | |
| 2259 | |
| 2260 | def masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True): |
| 2261 | """ |
| 2262 | Mask using floating point equality. |
| 2263 | |
| 2264 | Return a MaskedArray, masked where the data in array `x` are approximately |
| 2265 | equal to `value`, determined using `isclose`. The default tolerances for |
| 2266 | `masked_values` are the same as those for `isclose`. |
| 2267 | |
| 2268 | For integer types, exact equality is used, in the same way as |
| 2269 | `masked_equal`. |
| 2270 | |
| 2271 | The fill_value is set to `value` and the mask is set to ``nomask`` if |
| 2272 | possible. |
| 2273 | |
| 2274 | Parameters |
| 2275 | ---------- |
| 2276 | x : array_like |
| 2277 | Array to mask. |
| 2278 | value : float |
| 2279 | Masking value. |
| 2280 | rtol, atol : float, optional |
| 2281 | Tolerance parameters passed on to `isclose` |
| 2282 | copy : bool, optional |
| 2283 | Whether to return a copy of `x`. |
| 2284 | shrink : bool, optional |
| 2285 | Whether to collapse a mask full of False to ``nomask``. |
| 2286 | |
| 2287 | Returns |
| 2288 | ------- |
| 2289 | result : MaskedArray |
| 2290 | The result of masking `x` where approximately equal to `value`. |
| 2291 | |
| 2292 | See Also |
| 2293 | -------- |
| 2294 | masked_where : Mask where a condition is met. |
| 2295 | masked_equal : Mask where equal to a given value (integers). |
| 2296 | |
| 2297 | Examples |
| 2298 | -------- |
| 2299 | >>> import numpy.ma as ma |
| 2300 | >>> x = np.array([1, 1.1, 2, 1.1, 3]) |
| 2301 | >>> ma.masked_values(x, 1.1) |
| 2302 | masked_array(data=[1.0, --, 2.0, --, 3.0], |
| 2303 | mask=[False, True, False, True, False], |
| 2304 | fill_value=1.1) |
| 2305 | |
| 2306 | Note that `mask` is set to ``nomask`` if possible. |
| 2307 | |
| 2308 | >>> ma.masked_values(x, 2.1) |
| 2309 | masked_array(data=[1. , 1.1, 2. , 1.1, 3. ], |
| 2310 | mask=False, |
| 2311 | fill_value=2.1) |
| 2312 | |
| 2313 | Unlike `masked_equal`, `masked_values` can perform approximate equalities. |
| 2314 | |
| 2315 | >>> ma.masked_values(x, 2.1, atol=1e-1) |
| 2316 | masked_array(data=[1.0, 1.1, --, 1.1, 3.0], |
| 2317 | mask=[False, False, True, False, False], |