How to use _draw_core_dimensions method in hypothesis

Best Python code snippet using hypothesis

numpy.py

Source: numpy.py Github

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...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)...

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_array_helpers.py

Source: _array_helpers.py Github

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...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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