cfdm.Data

class cfdm.Data(array=None, units=None, calendar=None, fill_value=None, source=None, copy=True, dtype=None, mask=None, _use_array=True, **kwargs)

Bases: cfdm.mixin.container.Container, cfdm.mixin.netcdf.NetCDFHDF5, cfdm.core.data.data.Data

An orthogonal multidimensional array with masking and units.

New in version (cfdm): 1.7.0

Initialisation

Parameters

array: data_like, optional

The array of values.

A data_like object is any object that can be converted to a Data object, i.e. numpy array_like objects, Data objects, and cfdm instances that contain Data objects.

Ignored if the source parameter is set.

Parameter example:

array=[34.6]

Parameter example:

array=[[1, 2], [3, 4]]

Parameter example:

array=numpy.ma.arange(10).reshape(2, 1, 5)

units: str, optional

The physical units of the data. Ignored if the source parameter is set.

The units may also be set after initialisation with the set_units method.

Parameter example:

units='km hr-1'

Parameter example:

units='days since 2018-12-01'

calendar: str, optional

The calendar for reference time units. Ignored if the source parameter is set.

The calendar may also be set after initialisation with the set_calendar method.

Parameter example:

calendar='360_day'

fill_value: optional

The fill value of the data. By default, or if set to None, the numpy fill value appropriate to the array’s data type will be used (see numpy.ma.default_fill_value). Ignored if the source parameter is set.

The fill value may also be set after initialisation with the set_fill_value method.

Parameter example:

fill_value=-999.

dtype: data-type, optional

The desired data-type for the data. By default the data-type will be inferred form the array parameter.

The data-type may also be set after initialisation with the dtype attribute.

Parameter example:

dtype=float

Parameter example:

dtype='float32'

Parameter example:

dtype=numpy.dtype('i2')

mask: data_like, optional

Apply this mask to the data given by the array parameter. By default, or if mask is None, no mask is applied. May be any data_like object that broadcasts to array. Masking will be carried out where mask elements evaluate to True.

A data_like object is any object that can be converted to a Data object, i.e. numpy array_like objects, Data objects, and cfdm instances that contain Data objects.

This mask will applied in addition to any mask already defined by the array parameter.

source: optional

Initialise the array, units, calendar and fill value from those of source.

Note that if source is a Data instance then cfdm.Data(source=source) is equivalent to source.copy().

copy: bool, optional

If False then do not deep copy input parameters prior to initialisation. By default arguments are deep copied.

kwargs: ignored

Not used. Present to facilitate subclassing.

Inspection

Attributes

array	Return an independent numpy array containing the data.
dtype	Data-type of the data elements.
ndim	Number of data dimensions.
shape	Tuple of data dimension sizes.
size	Number of elements in the data.

Units

del_units	Delete the units.
get_units	Return the units.
has_units	Whether units have been set.
set_units	Set the units.
del_calendar	Delete the calendar.
get_calendar	Return the calendar.
has_calendar	Whether a calendar has been set.
set_calendar	Set the calendar.

Data creation routines

Ones and zeros

empty

Create a new data array without initialising the elements.

From existing data

copy	Return a deep copy.

Data manipulation routines

Changing data shape

flatten

Flatten axes of the data.

Transpose-like operations

transpose

Permute the axes of the data array.

Changing number of dimensions

insert_dimension	Expand the shape of the data array.
squeeze	Remove size 1 axes from the data.

Adding and removing elements

unique

The unique elements of the data.

Date-time support

del_calendar	Delete the calendar.
get_calendar	Return the calendar.
has_calendar	Whether a calendar has been set.
set_calendar	Set the calendar.

Attributes

datetime_array	Returns an independent numpy array of datetimes.
datetime_as_string	Returns an independent numpy array with datetimes as strings.

Indexing routines

Single value selection

first_element	Return the first element of the data as a scalar.
second_element	Return the second element of the data as a scalar.
last_element	Return the last element of the data as a scalar.

Logic functions

Truth value testing

any	Test whether any data array elements evaluate to True.

Comparison

equals

Whether two data arrays are the same.

Mask support

apply_masking	Apply masking.
filled	Replace masked elements with the fill value.
del_fill_value	Delete the fill value.
get_fill_value	Return the missing data value.
has_fill_value	Whether a fill value has been set.
set_fill_value	Set the missing data value.

Attributes

mask	The Boolean missing data mask of the data array.

Mathematical functions

Sums, products, differences

sum	Return the sum of an array or the sum along axes.

Set routines

Making proper sets

unique

The unique elements of the data.

Sorting, searching, and counting

Statistics

Order statistics

maximum	Return the maximum of an array or the maximum along axes.
minimum	Return the minimum of an array or minimum along axes.
max	Alias for maximum.
min	Alias for minimum.

Sums

sum	Return the sum of an array or the sum along axes.

Compression by convention

get_compressed_axes	Returns the dimensions that are compressed in the array.
get_compressed_dimension	Returns the compressed dimension’s array position.
get_compression_type	Returns the type of compression applied to the array.
get_count	Return the count variable for a compressed array.
get_index	Return the index variable for a compressed array.
get_list	Return the list variable for a compressed array.
uncompress	Uncompress the underlying array.

Attributes

compressed_array

Returns an independent numpy array of the compressed data.

Miscellaneous

creation_commands	Return the commands that would create the data object.
get_filenames	Return the name of the file containing the data array.
source	Return the underlying array object.

Performance

nc_clear_hdf5_chunksizes	Clear the HDF5 chunksizes for the data.
nc_hdf5_chunksizes	Return the HDF5 chunksizes for the data.
nc_set_hdf5_chunksizes	Set the HDF5 chunksizes for the data.
to_memory	Bring data on disk into memory and retain it there.

Special

__array__	The numpy array interface.
__deepcopy__	Called by the copy.deepcopy function.
__getitem__	Return a subspace of the data defined by indices.
__int__	Called by the int built-in function.
__iter__	Called when an iterator is required.
__repr__	Called by the repr built-in function.
__setitem__	Assign to data elements defined by indices.
__str__	Called by the str built-in function.

Docstring substitutions

Methods

_docstring_special_substitutions	Return the special docstring substitutions.
_docstring_substitutions	Returns the substitutions that apply to methods of the class.
_docstring_package_depth	Returns the class {{package}} substitutions package depth.
_docstring_method_exclusions	Returns method names excluded in the class substitutions.