| 883 | |
| 884 | |
| 885 | class Random(IO): |
| 886 | _parameters = [ |
| 887 | "rng", |
| 888 | "distribution", |
| 889 | "size", |
| 890 | "chunks", |
| 891 | "extra_chunks", |
| 892 | "args", |
| 893 | "kwargs", |
| 894 | ] |
| 895 | _defaults = {"extra_chunks": ()} |
| 896 | |
| 897 | @cached_property |
| 898 | def kwargs(self): |
| 899 | return self.operand("kwargs") |
| 900 | |
| 901 | @property |
| 902 | def chunks(self): |
| 903 | size = self.operand("size") |
| 904 | chunks = self.operand("chunks") |
| 905 | |
| 906 | # shapes = list( |
| 907 | # { |
| 908 | # ar.shape |
| 909 | # for ar in chain(args, kwargs.values()) |
| 910 | # if isinstance(ar, (Array, np.ndarray)) |
| 911 | # } |
| 912 | # ) |
| 913 | # if size is not None: |
| 914 | # shapes.append(size) |
| 915 | shapes = [size] |
| 916 | # broadcast to the final size(shape) |
| 917 | size = broadcast_shapes(*shapes) |
| 918 | return normalize_chunks( |
| 919 | chunks, |
| 920 | size, # ideally would use dtype here |
| 921 | dtype=self.kwargs.get("dtype", np.float64), |
| 922 | ) |
| 923 | |
| 924 | @cached_property |
| 925 | def _info(self): |
| 926 | sizes = list(product(*self.chunks)) |
| 927 | if isinstance(self.rng, Generator): |
| 928 | bitgens = _spawn_bitgens(self.rng._bit_generator, len(sizes)) |
| 929 | bitgen_token = tokenize(bitgens) |
| 930 | bitgens = [_bitgen._seed_seq for _bitgen in bitgens] |
| 931 | func_applier = _apply_random_func |
| 932 | gen = type(self.rng._bit_generator) |
| 933 | elif isinstance(self.rng, RandomState): |
| 934 | bitgens = random_state_data(len(sizes), self.rng._numpy_state) |
| 935 | bitgen_token = tokenize(bitgens) |
| 936 | func_applier = _apply_random |
| 937 | gen = self.rng._RandomState |
| 938 | else: |
| 939 | raise TypeError( |
| 940 | "Unknown object type: Not a Generator and Not a RandomState" |
| 941 | ) |
| 942 | token = tokenize(bitgen_token, self.size, self.chunks, self.args, self.kwargs) |
no outgoing calls
no test coverage detected