xray.Variable¶
- class xray.Variable(dims, data, attributes=None, encoding=None)¶
A netcdf-like variable consisting of dimensions, data and attributes which describe a single Array. A single Variable object is not fully described outside the context of its parent Dataset (if you want such a fully described object, use a DataArray instead).
Attributes
T attributes attrs Dictionary of local attributes on this variable. dimensions Tuple of dimension names with which this variable is associated. dtype 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. identical(other) Like equals, but also checks attributes. indexed(**indexers) Return a new array indexed along the specified dimension(s). 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) 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 a 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__(dims, data, attributes=None, encoding=None)¶
Parameters: dims : str or sequence of str
Name(s) of the the data dimension(s). Must be either a string (only for 1D data) or a sequence of strings with length equal to the number of dimensions.
data : array_like
Data array which supports numpy-like data access.
attributes : dict_like or None, optional
Attributes to assign to the new variable. If None (default), an empty attribute dictionary is initialized.
encoding : dict_like or None, optional
Dictionary specifying how to encode this array’s data into a serialized format like netCDF4. Currently used keys (for netCDF) include ‘_FillValue’, ‘scale_factor’, ‘add_offset’ and ‘dtype’. Well behaviored code to serialize a Variable should ignore unrecognized encoding items.
Methods
__init__(dims, data[, attributes, encoding]) Parameters: 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. identical(other) Like equals, but also checks attributes. indexed(**indexers) Return a new array indexed along the specified dimension(s). 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) 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 a 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 attributes attrs Dictionary of local attributes on this variable. dimensions Tuple of dimension names with which this variable is associated. dtype ndim shape size values The variable’s data as a numpy.ndarray