cf.RaggedContiguousArray¶

class
cf.
RaggedContiguousArray
(compressed_array=None, shape=None, size=None, ndim=None, count_variable=None, source=None, copy=True)[source]¶ Bases:
cf.data.array.mixin.compressedarraymixin.CompressedArrayMixin
,cf.data.array.mixin.arraymixin.ArrayMixin
,cf.mixin_container.Container
,cfdm.data.raggedcontiguousarray.RaggedContiguousArray
An underlying contiguous ragged array.
A collection of features stored using a contiguous ragged array combines all features along a single dimension (the “sample dimension”) such that each feature in the collection occupies a contiguous block.
The information needed to uncompress the data is stored in a “count variable” that gives the size of each block.
It is assumed that the compressed dimension is the leftmost dimension in the compressed array.
See CF section 9 “Discrete Sampling Geometries”.
New in version 3.0.0.
Initialisation
 Parameters
 compressed_array: array_like
The compressed data.
 shape:
tuple
The shape of the uncompressed array.
 count_variable:
Count
The count variable required to uncompress the data, corresponding to a CFnetCDF count variable.
 source: optional
Convert source, which can be any type of object, to a
RaggedContiguousArray
instance.All other parameters, apart from copy, are ignored and their values are instead inferred from source by assuming that it has the
RaggedContiguousArray
API. Any parameters that can not be retrieved from source in this way are assumed to have their default value.Note that if
x
is also aRaggedContiguousArray
instance thencf.RaggedContiguousArray(source=x)
is equivalent tox.copy()
.New in version (cfdm): 1.10.0.0
 copy:
bool
, optional If True (the default) then deep copy the input parameters prior to initialisation. By default the parameters are not deep copied.
New in version (cfdm): 1.10.0.0
 size:
int
Deprecated at version 1.10.0.0. Ignored if set.
Number of elements in the uncompressed array.
 ndim:
int
Deprecated at version 1.10.0.0. Ignored if set.
The number of uncompressed array dimensions.
Methods
Mapping of compressed to uncompressed dimensions. 

The data as required by the decompression algorithm. 

Return a deep copy of the array. 

The calendar of the array. 

Return axes that are compressed in the underlying array. 

Returns the compressed dimension’s position in the array. 

Returns the array’s compression type. 

Return the count variable for the compressed array. 



Return the names of any files containing the compressed data. 

Return the index variable for the compressed array. 

Return the Subarray class. 

Return a subspace, defined by indices, of a numpy array. 

The units of the array. 

Return the underlying array object. 

Create the subarray shapes along each uncompressed dimension. 

Return descriptors for every subarray. 

Nondata parameters required by the 

Convert the data to a 

Bring data on disk into memory. 
Attributes
Returns a numpy array containing the uncompressed data. 

Returns an independent numpy array with the compressed data. 

Datatype of the uncompressed data. 

Number of array dimensions. 

Shape of the uncompressed data. 

Number of elements in the array. 

The 