cf.List¶

class
cf.
List
(properties=None, data=None, source=None, copy=True, _use_data=True)[source]¶ Bases:
cf.mixin.propertiesdata.PropertiesData
,cfdm.list.List
A list variable required to uncompress a gathered array.
Compression by gathering combines axes of a multidimensional array into a new, discrete axis whilst omitting the missing values and thus reducing the number of values that need to be stored.
The information needed to uncompress the data is stored in a list variable that gives the indices of the required points.
NetCDF interface
The netCDF variable name of the list variable may be accessed with the
nc_set_variable
,nc_get_variable
,nc_del_variable
andnc_has_variable
methods.New in version 3.0.0.
Initialisation
 Parameters
 properties:
dict
, optional Set descriptive properties. The dictionary keys are property names, with corresponding values.
Properties may also be set after initialisation with the
set_properties
andset_property
methods. Parameter example:
properties={'long_name': 'uncompression indices'}
 data: data_like, optional
Set the data.
A data_like object is any object that can be converted to a
Data
object, i.e.numpy
array_like objects,Data
objects, and cf instances that containData
objects.The data also may be set after initialisation with the
set_data
method. source: optional
Convert source, which can be any type of object, to a
List
instance.All other parameters, apart from copy, are ignored and their values are instead inferred from source by assuming that it has the
List
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 aList
instance thencf.List(source=x)
is equivalent tox.copy()
. copy:
bool
, optional If True (the default) then deep copy the input parameters prior to initialisation. By default the parameters are not deep copied.
 properties:
Inspection¶
Methods
A full description of the list variable. 

Return the canonical identity. 

Return all possible identities. 

Inspect the object for debugging. 
Attributes
An identity for the List object. 
Selection¶
Methods
Whether or not the construct identity satisfies conditions. 

Whether or not the data has a given dimensionality. 

Whether or not the netCDF variable name satisfies conditions. 

Whether or not properties satisfy conditions. 

Whether or not the construct has given units. 
Properties¶
Methods
Remove a property. 

Get a CF property. 

Whether a property has been set. 

Set a property. 

Return all properties. 

Remove all properties. 

Set properties. 
Attributes
The add_offset CF property. 

The calendar CF property. 

The comment CF property. 

The _FillValue CF property. 

The history CF property. 

The leap_month CF property. 

The leap_year CF property. 

The long_name CF property. 

The missing_value CF property. 

The month_lengths CF property. 

The scale_factor CF property. 

The standard_name CF property. 

The units CF property. 

The valid_max CF property. 

The valid_min CF property. 

The valid_range CF property. 
Units¶
Methods
Override the units. 

Override the calendar of datetime units. 
Attributes
The 
Data¶
Attributes
A numpy array deep copy of the data. 

The 

An independent numpy array of datetime objects. 

Return an element of the data array as a standard Python scalar. 

The 

True if the data array is scalar. 

The number of dimensions in the data array. 

A tuple of the data array’s dimension sizes. 

The number of elements in the data array. 

A numpy array view of the data. 
Methods
Convert the data to a 

Return a subspace defined by indices. 

Remove the data. 

Return the data. 

Whether or not the construct has data. 

Set the data. 
Rearranging elements
Flatten axes of the data. 

Flip (reverse the direction of) data dimensions. 

Expand the shape of the data array. 

Roll the data along one or more axes. 

Remove size one axes from the data array. 

Interchange two axes of an array. 

Permute the axes of the data array. 
Data array mask
Apply masking as defined by the CF conventions. 

Count the nonmasked elements of the data. 

Count the masked elements of the data. 

Return the data array missing data value. 

Mask the array where invalid values occur (NaN or inf). 
A binary (0 and 1) missing data mask of the data array. 

Whether the mask is hard (True) or soft (False). 

The mask of the data array. 
Changing data values
Called to implement assignment to x[indices] 

Expand the data by adding a halo. 

Mask the array where invalid values occur (NaN or inf). 

Return a new variable whose data is subspaced. 

Set data array elements depending on a condition. 
Miscellaneous
Change the chunk structure of the data. 

Close all files referenced by the construct. 

Convert reference time data values to have new units. 

Set the cyclicity of an axis. 

Return or set the period of the data. 

Whether or not a given axis is cyclic. 

True if a given axis is periodic. 

The names of files containing the original data and metadata. 

Whether or not there are cell bounds. 

Persist the underlying dask array into memory. 
Miscellaneous¶
Methods
Join a sequence of variables together. 

Return a deep copy. 

Return the commands that would create the construct. 

Whether two instances are the same. 

Bring data on disk into memory. 

Uncompress the construct. 
Attributes


Always False. 

Always False. 

Always False. 

An identity for the List object. 
Mathematical operations¶
Methods
Trigonometrical and hyperbolic functions
Take the trigonometric inverse cosine of the data element wise. 

Take the inverse hyperbolic cosine of the data elementwise. 

Take the trigonometric inverse sine of the data elementwise. 

Take the inverse hyperbolic sine of the data elementwise. 

Take the trigonometric inverse tangent of the data element wise. 

Take the inverse hyperbolic tangent of the data elementwise. 

Take the trigonometric cosine of the data elementwise. 

Take the hyperbolic cosine of the data elementwise. 

Take the trigonometric sine of the data elementwise. 

Take the hyperbolic sine of the data elementwise. 

Take the trigonometric tangent of the data elementwise. 

Take the hyperbolic tangent of the data array. 
Rounding and truncation
The ceiling of the data, elementwise. 

Limit the values in the data. 

Floor the data array, elementwise. 

Round the data to the nearest integer, elementwise. 

Round the data to the given number of decimals. 

Truncate the data, elementwise. 
Statistical collapses
Alias for 

The unweighted mean the data array. 

The unweighted average of the maximum and minimum of the data array. 

Alias for 

The absolute difference between the maximum and minimum of the data array. 

The number of nonmissing data elements in the data array. 

The sum of the data array. 

Alias for 

Alias for 

The unweighted sample standard deviation of the data array. 

The unweighted sample variance of the data array. 

The maximum of the data array. 

The minimum of the data array. 
Exponential and logarithmic functions
The exponential of the data, elementwise. 

The logarithm of the data array. 
Datetime operations¶
Attributes
The day of each datetime data array element. 

An independent numpy array of datetime objects. 

The hour of each datetime data array element. 

The minute of each datetime data array element. 

The month of each datetime data array element. 

The reference datetime of units of elapsed time. 

The second of each datetime data array element. 

The year of each datetime data array element. 
Logic functions¶
Truth value testing
Test whether all data elements evaluate to True. 

Test whether any data elements evaluate to True. 
Comparison
Test whether all data are elementwise equal to other, broadcastable data. 

Whether two instances are the same. 

True if two constructs are equal, False otherwise. 
Set operations
The unique elements of the data. 
NetCDF¶
Methods
Remove the netCDF variable name. 

Return the netCDF variable name. 

Whether the netCDF variable name has been set. 

Set the netCDF variable name. 
Aliases¶
Methods
Alias for 
Attributes
Alias for 
Arithmetic and comparison operations¶
Arithmetic, bitwise and comparison operations are defined as elementwise operations on the data, which yield a new construct or, for augmented assignments, modify the construct’s data inplace.
Comparison operators
The rich comparison operator 

The rich comparison operator 

The rich comparison operator 

The rich comparison operator 

The rich comparison operator 

The rich comparison operator 
Binary arithmetic operators
The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operations 

The binary arithmetic operation 
Binary arithmetic operators with reflected (swapped) operands
The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operation 

The binary arithmetic operations 

The binary arithmetic operation 
Augmented arithmetic assignments
The augmented arithmetic assignment 

The augmented arithmetic assignment 

The augmented arithmetic assignment 

The augmented arithmetic assignment 

The augmented arithmetic assignment 

The augmented arithmetic assignment 

The augmented arithmetic assignment 

The binary arithmetic operation 
Unary arithmetic operators
The unary arithmetic operation 

The unary arithmetic operation 

The unary arithmetic operation 
Binary bitwise operators
The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 
Binary bitwise operators with reflected (swapped) operands
The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 

The binary bitwise operation 
Augmented bitwise assignments
The augmented bitwise assignment 

The augmented bitwise assignment 

The augmented bitwise assignment 

The augmented bitwise assignment 

The augmented bitwise assignment 
Unary bitwise operators
The unary bitwise operation 
Groups¶
Methods
Return the netCDF variable group hierarchy. 

Remove the netCDF variable group hierarchy. 

Set the netCDF variable group hierarchy. 
Special¶
Methods
Called to implement membership test operators. 

Called by the 

Return a subspace defined by indices. 

Called by the 

Called to implement assignment to x[indices] 

Called by the 

Returns a numpy array representation of the data. 

Returns a new reference to the data. 
Deprecated¶
Methods
Deprecated at version 3.0.0. 

Deprecated at version 3.0.0. 

Deprecated at version 3.0.0. 

Partition the data array. 

Deprecated at version 3.0.0, use 

Deprecated at version 3.0.0, use method 

Deprecated at version 3.0.0. 

Deprecated at version 3.0.0, use 

Return the name of the file or files containing the data. 

Deprecated at version 3.0.0, use method 

Deprecated at version 3.0.0, use 

Deprecated at version 3.0.0, use 

Deprecated at version 3.0.0, use method 

Deprecated at version 3.0.0, use 

Deprecated at version 3.7.0, use 

Deprecated at version 3.7.0, use 

Deprecated at version 3.7.0, use 

Deprecated at version 3.7.0, use 

Deprecated at version 3.7.0, use 

Mask the array where invalid values occur. 

Deprecated at version 3.0.0, use method ‘identity’ instead. 

Deprecated at version 3.0.0, use method 

Deprecated at version 3.0.0. 

Deprecated at version 3.0.0, use method 

Deprecated at version 3.0.0, use 