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How to use _draw_core_dimensions method in hypothesis

Best Python code snippet using hypothesis

numpy.py

Source:numpy.py

...710        # We don't usually have a gufunc signature; do the common case first & fast.711        if self.signature is None:712            return self._draw_loop_dimensions(data)713        # When we *do*, draw the core dims, then draw loop dims, and finally combine.714        core_in, core_res = self._draw_core_dimensions(data)715        # If some core shape has omitted optional dimensions, it's an error to add716        # loop dimensions to it.  We never omit core dims if min_dims >= 1.717        # This ensures that we respect Numpy's gufunc broadcasting semantics and user718        # constraints without needing to check whether the loop dims will be719        # interpreted as an invalid substitute for the omitted core dims.720        # We may implement this check later!721        use = [None not in shp for shp in core_in]722        loop_in, loop_res = self._draw_loop_dimensions(data, use=use)723        def add_shape(loop, core):724            return tuple(x for x in (loop + core)[-32:] if x is not None)725        return BroadcastableShapes(726            input_shapes=tuple(add_shape(l, c) for l, c in zip(loop_in, core_in)),727            result_shape=add_shape(loop_res, core_res),728        )729    def _draw_core_dimensions(self, data):730        # Draw gufunc core dimensions, with None standing for optional dimensions731        # that will not be present in the final shape.  We track omitted dims so732        # that we can do an accurate per-shape length cap.733        dims = {}734        shapes = []735        for shape in self.signature.input_shapes + (self.signature.result_shape,):736            shapes.append([])737            for name in shape:738                if name.isdigit():739                    shapes[-1].append(int(name))740                    continue741                if name not in dims:742                    dim = name.strip("?")743                    dims[dim] = data.draw(self.side_strat)...

_array_helpers.py

Source:_array_helpers.py

...431        # We don't usually have a gufunc signature; do the common case first & fast.432        if self.signature is None:433            return self._draw_loop_dimensions(data)434        # When we *do*, draw the core dims, then draw loop dims, and finally combine.435        core_in, core_res = self._draw_core_dimensions(data)436        # If some core shape has omitted optional dimensions, it's an error to add437        # loop dimensions to it.  We never omit core dims if min_dims >= 1.438        # This ensures that we respect Numpy's gufunc broadcasting semantics and user439        # constraints without needing to check whether the loop dims will be440        # interpreted as an invalid substitute for the omitted core dims.441        # We may implement this check later!442        use = [None not in shp for shp in core_in]443        loop_in, loop_res = self._draw_loop_dimensions(data, use=use)444        def add_shape(loop, core):445            return tuple(x for x in (loop + core)[-NDIM_MAX:] if x is not None)446        return BroadcastableShapes(447            input_shapes=tuple(add_shape(l_in, c) for l_in, c in zip(loop_in, core_in)),448            result_shape=add_shape(loop_res, core_res),449        )450    def _draw_core_dimensions(self, data):451        # Draw gufunc core dimensions, with None standing for optional dimensions452        # that will not be present in the final shape.  We track omitted dims so453        # that we can do an accurate per-shape length cap.454        dims = {}455        shapes = []456        for shape in self.signature.input_shapes + (self.signature.result_shape,):457            shapes.append([])458            for name in shape:459                if name.isdigit():460                    shapes[-1].append(int(name))461                    continue462                if name not in dims:463                    dim = name.strip("?")464                    dims[dim] = data.draw(self.side_strat)...

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