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How to use _draw_loop_dimensions method in hypothesis

Best Python code snippet using hypothesis

numpy.py

Source:numpy.py

...708        self.size_one_allowed = self.min_side <= 1 <= self.max_side709    def do_draw(self, data):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)744                    if self.min_dims == 0 and not data.draw_bits(3):745                        dims[dim + "?"] = None746                    else:747                        dims[dim + "?"] = dims[dim]748                shapes[-1].append(dims[name])749        return tuple(tuple(s) for s in shapes[:-1]), tuple(shapes[-1])750    def _draw_loop_dimensions(self, data, use=None):751        # All shapes are handled in column-major order; i.e. they are reversed752        base_shape = self.base_shape[::-1]753        result_shape = list(base_shape)754        shapes = [[] for _ in range(self.num_shapes)]755        if use is None:756            use = [True for _ in range(self.num_shapes)]757        else:758            assert len(use) == self.num_shapes759            assert all(isinstance(x, bool) for x in use)760        for dim_count in range(1, self.max_dims + 1):761            dim = dim_count - 1762            # We begin by drawing a valid dimension-size for the given763            # dimension. This restricts the variability across the shapes764            # at this dimension such that they can only choose between...

_array_helpers.py

Source:_array_helpers.py

...429        self.size_one_allowed = self.min_side <= 1 <= self.max_side430    def do_draw(self, data):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)465                    if self.min_dims == 0 and not data.draw_bits(3):466                        dims[dim + "?"] = None467                    else:468                        dims[dim + "?"] = dims[dim]469                shapes[-1].append(dims[name])470        return tuple(tuple(s) for s in shapes[:-1]), tuple(shapes[-1])471    def _draw_loop_dimensions(self, data, use=None):472        # All shapes are handled in column-major order; i.e. they are reversed473        base_shape = self.base_shape[::-1]474        result_shape = list(base_shape)475        shapes = [[] for _ in range(self.num_shapes)]476        if use is None:477            use = [True for _ in range(self.num_shapes)]478        else:479            assert len(use) == self.num_shapes480            assert all(isinstance(x, bool) for x in use)481        for dim_count in range(1, self.max_dims + 1):482            dim = dim_count - 1483            # We begin by drawing a valid dimension-size for the given484            # dimension. This restricts the variability across the shapes485            # at this dimension such that they can only choose between...

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