(self, key, value)
| 1904 | return self._scalarfunc(operator.index) |
| 1905 | |
| 1906 | def __setitem__(self, key, value): |
| 1907 | if value is np.ma.masked: |
| 1908 | value = np.ma.masked_all((), dtype=self.dtype) |
| 1909 | |
| 1910 | if not is_dask_collection(value) and self.dtype.kind in "iu": |
| 1911 | if np.isnan(value).any(): |
| 1912 | raise ValueError("cannot convert float NaN to integer") |
| 1913 | if np.isinf(value).any(): |
| 1914 | raise ValueError("cannot convert float infinity to integer") |
| 1915 | |
| 1916 | # Suppress dtype broadcasting; __setitem__ can't change the dtype. |
| 1917 | # Use asanyarray to retain e.g. np.ma objects. |
| 1918 | value = asanyarray(value, dtype=self.dtype, like=self) |
| 1919 | |
| 1920 | if isinstance(key, Array) and ( |
| 1921 | key.dtype.kind in "iu" |
| 1922 | or (key.dtype == bool and key.ndim == 1 and self.ndim > 1) |
| 1923 | ): |
| 1924 | key = (key,) |
| 1925 | |
| 1926 | ## Use the "where" method for cases when key is an Array of bools |
| 1927 | if isinstance(key, Array): |
| 1928 | from dask.array.routines import where |
| 1929 | |
| 1930 | left_shape = np.array(key.shape) |
| 1931 | right_shape = np.array(self.shape) |
| 1932 | |
| 1933 | # We want to treat unknown shape on *either* sides as a match |
| 1934 | match = left_shape == right_shape |
| 1935 | match |= np.isnan(left_shape) | np.isnan(right_shape) |
| 1936 | |
| 1937 | if not match.all(): |
| 1938 | raise IndexError( |
| 1939 | f"boolean index shape {key.shape} must match indexed array's " |
| 1940 | f"{self.shape}." |
| 1941 | ) |
| 1942 | |
| 1943 | # If value has ndim > 0, they must be broadcastable to self.shape[idx]. |
| 1944 | # This raises when the bool mask causes the size to become unknown, |
| 1945 | # e.g. this is valid in numpy but raises here: |
| 1946 | # x = da.array([1,2,3]) |
| 1947 | # x[da.array([True, True, False])] = [4, 5] |
| 1948 | if value.ndim: |
| 1949 | value = broadcast_to(value, self[key].shape) |
| 1950 | |
| 1951 | y = where(key, value, self) |
| 1952 | # FIXME does any backend allow mixed ops vs. numpy? |
| 1953 | # If yes, is it wise to let them change the meta? |
| 1954 | self._meta = y._meta |
| 1955 | self.dask = y.dask |
| 1956 | self._name = y.name |
| 1957 | self._chunks = y.chunks |
| 1958 | return |
| 1959 | |
| 1960 | if np.isnan(self.shape).any(): |
| 1961 | raise ValueError(f"Arrays chunk sizes are unknown. {unknown_chunk_message}") |
| 1962 | |
| 1963 | # Still here? Then apply the assignment to other type of |
nothing calls this directly
no test coverage detected