xray.Coordinate¶
- class xray.Coordinate(*args, **kwargs)¶
Subclass of Variable which caches its data as a pandas.Index instead of a numpy.ndarray.
Coordinates must always be 1-dimensional. In addition to Variable methods, they support some pandas.Index methods directly (e.g., get_indexer).
Attributes
T as_index The variable’s data as a pandas.Index attributes attrs Dictionary of local attributes on this variable. dimensions Tuple of dimension names with which this variable is associated. dtype is_monotonic ndim shape size values The variable’s data as a numpy.ndarray Methods
all([dimension, axis]) Reduce this Variable’s data’ by applying numpy.all along some dimension(s). any([dimension, axis]) Reduce this Variable’s data’ by applying numpy.any along some dimension(s). argmax([dimension, axis]) Reduce this Variable’s data’ by applying numpy.argmax along some dimension(s). argmin([dimension, axis]) Reduce this Variable’s data’ by applying numpy.argmin along some dimension(s). argsort(*args, **kwargs) astype(*args, **kwargs) clip(*args, **kwargs) concat(variables[, dimension, indexers, ...]) Concatenate variables along a new or existing dimension. conj(*args, **kwargs) conjugate(*args, **kwargs) copy([deep]) Returns a copy of this object. equals(other) True if two Variables have the same dimensions and values; otherwise False. get_axis_num(dimension) Return axis number(s) corresponding to dimension(s) in this array. get_indexer(label) get_loc(label) identical(other) Like equals, but also checks attributes. indexed(**indexers) Return a new array indexed along the specified dimension(s). is_numeric() item() Calls numpy.ndarray.item on this array’s values max([dimension, axis]) Reduce this Variable’s data’ by applying numpy.max along some dimension(s). mean([dimension, axis]) Reduce this Variable’s data’ by applying numpy.mean along some dimension(s). min([dimension, axis]) Reduce this Variable’s data’ by applying numpy.min along some dimension(s). prod([dimension, axis]) Reduce this Variable’s data’ by applying numpy.prod along some dimension(s). ptp([dimension, axis]) Reduce this Variable’s data’ by applying numpy.ptp along some dimension(s). reduce(func[, dimension, axis]) Reduce this array by applying func along some dimension(s). round(*args, **kwargs) slice_indexer([start, stop, step]) slice_locs([start, stop]) squeeze([dimension]) Return a new Variable object with squeezed data. std([dimension, axis]) Reduce this Variable’s data’ by applying numpy.std along some dimension(s). sum([dimension, axis]) Reduce this Variable’s data’ by applying numpy.sum along some dimension(s). to_coord() Return this variable as an Coordinate transpose(*dimensions) Return a new Variable object with transposed dimensions. var([dimension, axis]) Reduce this Variable’s data’ by applying numpy.var along some dimension(s). - __init__(*args, **kwargs)¶
Methods
__init__(*args, **kwargs) all([dimension, axis]) Reduce this Variable’s data’ by applying numpy.all along some dimension(s). any([dimension, axis]) Reduce this Variable’s data’ by applying numpy.any along some dimension(s). argmax([dimension, axis]) Reduce this Variable’s data’ by applying numpy.argmax along some dimension(s). argmin([dimension, axis]) Reduce this Variable’s data’ by applying numpy.argmin along some dimension(s). argsort(*args, **kwargs) astype(*args, **kwargs) clip(*args, **kwargs) concat(variables[, dimension, indexers, ...]) Concatenate variables along a new or existing dimension. conj(*args, **kwargs) conjugate(*args, **kwargs) copy([deep]) Returns a copy of this object. equals(other) True if two Variables have the same dimensions and values; otherwise False. get_axis_num(dimension) Return axis number(s) corresponding to dimension(s) in this array. get_indexer(label) get_loc(label) identical(other) Like equals, but also checks attributes. indexed(**indexers) Return a new array indexed along the specified dimension(s). is_numeric() item() Calls numpy.ndarray.item on this array’s values max([dimension, axis]) Reduce this Variable’s data’ by applying numpy.max along some dimension(s). mean([dimension, axis]) Reduce this Variable’s data’ by applying numpy.mean along some dimension(s). min([dimension, axis]) Reduce this Variable’s data’ by applying numpy.min along some dimension(s). prod([dimension, axis]) Reduce this Variable’s data’ by applying numpy.prod along some dimension(s). ptp([dimension, axis]) Reduce this Variable’s data’ by applying numpy.ptp along some dimension(s). reduce(func[, dimension, axis]) Reduce this array by applying func along some dimension(s). round(*args, **kwargs) slice_indexer([start, stop, step]) slice_locs([start, stop]) squeeze([dimension]) Return a new Variable object with squeezed data. std([dimension, axis]) Reduce this Variable’s data’ by applying numpy.std along some dimension(s). sum([dimension, axis]) Reduce this Variable’s data’ by applying numpy.sum along some dimension(s). to_coord() Return this variable as an Coordinate transpose(*dimensions) Return a new Variable object with transposed dimensions. var([dimension, axis]) Reduce this Variable’s data’ by applying numpy.var along some dimension(s). Attributes
T as_index The variable’s data as a pandas.Index attributes attrs Dictionary of local attributes on this variable. dimensions Tuple of dimension names with which this variable is associated. dtype is_monotonic ndim shape size values The variable’s data as a numpy.ndarray