cf.CellMeasure


class cf.CellMeasure(measure=None, properties=None, data=None, source=None, copy=True, _use_data=True)[source]

Bases: cf.mixin.propertiesdata.PropertiesData, cfdm.cellmeasure.CellMeasure

A cell measure construct of the CF data model.

A cell measure construct provides information that is needed about the size or shape of the cells and that depends on a subset of the domain axis constructs. Cell measure constructs have to be used when the size or shape of the cells cannot be deduced from the dimension or auxiliary coordinate constructs without special knowledge that a generic application cannot be expected to have.

The cell measure construct consists of a numeric array of the metric data which spans a subset of the domain axis constructs, and properties to describe the data. The cell measure construct specifies a “measure” to indicate which metric of the space it supplies, e.g. cell horizontal areas, and must have a units property consistent with the measure, e.g. square metres. It is assumed that the metric does not depend on axes of the domain which are not spanned by the array, along which the values are implicitly propagated. CF-netCDF cell measure variables correspond to cell measure constructs.

NetCDF interface

The netCDF variable name of the construct may be accessed with the nc_set_variable, nc_get_variable, nc_del_variable and nc_has_variable methods.

Initialisation

Parameters
measure: str, optional

Set the measure that indicates which metric given by the data array. Ignored if the source parameter is set.

The measure may also be set after initialisation with the set_measure method.

Parameter example:

measure='area'

properties: dict, optional

Set descriptive properties. The dictionary keys are property names, with corresponding values. Ignored if the source parameter is set.

Properties may also be set after initialisation with the set_properties and set_property methods.

Parameter example:

properties={'standard_name': 'cell_area'}

data: data_like, optional

Set the data. Ignored if the source parameter is set.

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 contain Data objects.

The data also may be set after initialisation with the set_data method.

source: optional

Initialize the measure, properties and data from those of source.

Note that if source is a CellMeasure instance then cf.CellMeasure(source=source) is equivalent to source.copy().

copy: bool, optional

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

Inspection

Methods

dump

A full description of the cell measure construct.

identity

Return the canonical identity.

identities

Return all possible identities.

inspect

Inspect the object for debugging.

Attributes

construct_type

Return a description of the construct type.

id

An identity for the CellMeasure object.

Measure

Methods

del_measure

Remove the measure.

get_measure

Return the measure.

has_measure

Whether the measure has been set.

set_measure

Set the measure.

Attributes

measure

TODO

Selection

Methods

match_by_identity

Whether or not the construct identity satisfies conditions.

match_by_naxes

Whether or not the data has a given dimensionality.

match_by_ncvar

Whether or not the netCDF variable name satisfies conditions.

match_by_property

Whether or not properties satisfy conditions.

match_by_units

Whether or not the construct has given units.

Properties

Methods

del_property

Remove a property.

get_property

Get a CF property.

has_property

Whether a property has been set.

set_property

Set a property.

properties

Return all properties.

clear_properties

Remove all properties.

set_properties

Set properties.

Attributes

add_offset

The add_offset CF property.

calendar

The calendar CF property.

comment

The comment CF property.

_FillValue

The _FillValue CF property.

history

The history CF property.

leap_month

The leap_month CF property.

leap_year

The leap_year CF property.

long_name

The long_name CF property.

missing_value

The missing_value CF property.

month_lengths

The month_lengths CF property.

scale_factor

The scale_factor CF property.

standard_name

The standard_name CF property.

units

The units CF property.

valid_max

The valid_max CF property.

valid_min

The valid_min CF property.

valid_range

The valid_range CF property.

Units

Methods

override_units

Override the units.

override_calendar

Override the calendar of date-time units.

Attributes

Units

The cf.Units object containing the units of the data array.

Data

Attributes

array

A numpy array deep copy of the data array.

Data

The Data object containing the data array.

data

The Data object containing the data array.

datetime_array

An independent numpy array of date-time objects.

datum

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

dtype

The numpy data type of the data array.

isscalar

True if the data array is scalar.

ndim

The number of dimensions in the data array.

shape

A tuple of the data array’s dimension sizes.

size

The number of elements in the data array.

varray

A numpy array view of the data array.

Methods

__getitem__

Return a subspace defined by indices

del_data

Remove the data.

get_data

Return the data.

has_data

Whether a data has been set.

set_data

Set the data.

Rearranging elements

flatten

Flatten axes of the data

flip

Flip (reverse the direction of) data dimensions.

insert_dimension

Expand the shape of the data array.

roll

Roll the data along an axis.

squeeze

Remove size one axes from the data array.

swapaxes

Interchange two axes of an array.

transpose

Permute the axes of the data array.

Data array mask

apply_masking

Apply masking as defined by the CF conventions.

count

Count the non-masked elements of the data.

count_masked

Count the masked elements of the data.

fill_value

Return the data array missing data value.

binary_mask

A binary (0 and 1) missing data mask of the data array.

hardmask

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

mask

The mask of the data array.

mask_invalid

Mask the array where invalid values occur.

Changing data values

__setitem__

Called to implement assignment to x[indices]

halo

Expand the data by adding a halo.

mask_invalid

Mask the array where invalid values occur.

subspace

Return a new variable whose data is subspaced.

where

Set data array elements depending on a condition.

Miscellaneous

chunk

Partition the data array.

close

Close all files referenced by the construct.

convert_reference_time

Convert reference time data values to have new units.

cyclic

Set the cyclicity of an axis.

period

Return or set the period of the data.

iscyclic

Whether or a not a given axis is cyclic.

isperiodic

TODO

get_filenames

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

has_bounds

Whether or not there are cell bounds.

Miscellaneous

Methods

concatenate

Join a sequence of variables together.

copy

Return a deep copy.

creation_commands

Return the commands that would create the cell measure construct.

equals

Whether two instances are the same.

uncompress

Uncompress the construct.

Attributes

T

True if and only if the data are coordinates for a CF ‘T’ axis.

X

Always False.

Y

Always False.

Z

Always False.

id

An identity for the CellMeasure object.

Mathematical operations

Methods

Trigonometrical and hyperbolic functions

arccos

Take the trigonometric inverse cosine of the data element-wise.

arccosh

Take the inverse hyperbolic cosine of the data element-wise.

arcsin

Take the trigonometric inverse sine of the data element-wise.

arcsinh

Take the inverse hyperbolic sine of the data element-wise.

arctan

Take the trigonometric inverse tangent of the data element-wise.

arctanh

Take the inverse hyperbolic tangent of the data element-wise.

cos

Take the trigonometric cosine of the data element-wise.

cosh

Take the hyperbolic cosine of the data element-wise.

sin

Take the trigonometric sine of the data element-wise.

sinh

Take the hyperbolic sine of the data element-wise.

tan

Take the trigonometric tangent of the data element-wise.

tanh

Take the hyperbolic tangent of the data array.

Rounding and truncation

ceil

The ceiling of the data, element-wise.

clip

Limit the values in the data.

floor

Floor the data array, element-wise.

rint

Round the data to the nearest integer, element-wise.

round

Round the data to the given number of decimals.

trunc

Truncate the data, element-wise.

Statistical collapses

max

Alias for maximum.

mean

The unweighted mean the data array.

mid_range

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

min

Alias for minimum.

range

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

sample_size

The number of non-missing data elements in the data array.

sum

The sum of the data array.

sd

Alias for standard_deviation

var

Alias for variance

standard_deviation

The unweighted sample standard deviation of the data array.

variance

The unweighted sample variance of the data array.

maximum

The maximum of the data array.

minimum

The minimum of the data array.

Exponential and logarithmic functions

exp

The exponential of the data, element-wise.

log

The logarithm of the data array.

Date-time operations

Attributes

day

The day of each date-time data array element.

datetime_array

An independent numpy array of date-time objects.

hour

The hour of each date-time data array element.

minute

The minute of each date-time data array element.

month

The month of each date-time data array element.

reference_datetime

The reference date-time of units of elapsed time.

second

The second of each date-time data array element.

year

The year of each date-time data array element.

Logic functions

Truth value testing

all

Test whether all data elements evaluate to True.

any

Test whether any data elements evaluate to True.

Comparison

allclose

Test whether all data are element-wise equal to other, broadcastable data.

equals

Whether two instances are the same.

equivalent

True if two constructs are equal, False otherwise.

Set operations

unique

The unique elements of the data.

NetCDF

Methods

nc_del_variable

Remove the netCDF variable name.

nc_get_variable

Return the netCDF variable name.

nc_has_variable

Whether the netCDF variable name has been set.

nc_set_variable

Set the netCDF variable name.

nc_get_external

Whether the construct corresponds to an external netCDF variable.

nc_set_external

Set external status of a netCDF variable.

Aliases

Methods

match

Alias for match_by_identity.

Attributes

dtarray

Alias for datetime_array.

Arithmetic and comparison operations

Arithmetic, bitwise and comparison operations are defined as element-wise operations on the data, which yield a new construct or, for augmented assignments, modify the construct’s data in-place.

Comparison operators

__lt__

The rich comparison operator <

__le__

The rich comparison operator <=

__eq__

The rich comparison operator ==

__ne__

The rich comparison operator !=

__gt__

The rich comparison operator >

__ge__

The rich comparison operator >=

Binary arithmetic operators

__add__

The binary arithmetic operation +

__sub__

The binary arithmetic operation -

__mul__

The binary arithmetic operation *

__div__

The binary arithmetic operation /

__truediv__

The binary arithmetic operation / (true division)

__floordiv__

The binary arithmetic operation //

__pow__

The binary arithmetic operations ** and pow

__mod__

The binary arithmetic operation %

Binary arithmetic operators with reflected (swapped) operands

__radd__

The binary arithmetic operation + with reflected operands

__rsub__

The binary arithmetic operation - with reflected operands

__rmul__

The binary arithmetic operation * with reflected operands

__rdiv__

The binary arithmetic operation / with reflected operands

__rtruediv__

The binary arithmetic operation / (true division) with reflected operands

__rfloordiv__

The binary arithmetic operation // with reflected operands

__rpow__

The binary arithmetic operations ** and pow with reflected operands

__rmod__

The binary arithmetic operation % with reflected operands

Augmented arithmetic assignments

__iadd__

The augmented arithmetic assignment +=

__isub__

The augmented arithmetic assignment -=

__imul__

The augmented arithmetic assignment *=

__idiv__

The augmented arithmetic assignment /=

__itruediv__

The augmented arithmetic assignment /= (true division)

__ifloordiv__

The augmented arithmetic assignment //=

__ipow__

The augmented arithmetic assignment **=

__imod__

The binary arithmetic operation %=

Unary arithmetic operators

__neg__

The unary arithmetic operation -

__pos__

The unary arithmetic operation +

__abs__

The unary arithmetic operation abs

Binary bitwise operators

__and__

The binary bitwise operation &

__or__

The binary bitwise operation |

__xor__

The binary bitwise operation ^

__lshift__

The binary bitwise operation <<

__rshift__

The binary bitwise operation >>

Binary bitwise operators with reflected (swapped) operands

__rand__

The binary bitwise operation & with reflected operands

__ror__

The binary bitwise operation | with reflected operands

__rxor__

The binary bitwise operation ^ with reflected operands

__rlshift__

The binary bitwise operation << with reflected operands

__rrshift__

The binary bitwise operation >> with reflected operands

Augmented bitwise assignments

__iand__

The augmented bitwise assignment &=

__ior__

The augmented bitwise assignment |=

__ixor__

The augmented bitwise assignment ^=

__ilshift__

The augmented bitwise assignment <<=

__irshift__

The augmented bitwise assignment >>=

Unary bitwise operators

__invert__

The unary bitwise operation ~

Groups

Methods

nc_variable_groups

Return the netCDF variable group hierarchy.

nc_clear_variable_groups

Remove the netCDF variable group hierarchy.

nc_set_variable_groups

Set the netCDF variable group hierarchy.

Special

Methods

__contains__

Called to implement membership test operators.

__deepcopy__

Called by the copy.deepcopy function.

__getitem__

Return a subspace defined by indices

__repr__

Called by the repr built-in function.

__setitem__

Called to implement assignment to x[indices]

__str__

Called by the str built-in function.

__array__

Returns a numpy array representation of the data.

__data__

Returns a new reference to the data.

__query_set__

TODO

__query_wi__

TODO

__query_wo__

TODO

Deprecated

Methods

asdatetime

Deprecated at version 3.0.0.

asreftime

Deprecated at version 3.0.0.

attributes

Deprecated at version 3.0.0.

delprop

Deprecated at version 3.0.0, use method del_property instead.

dtvarray

Deprecated at version 3.0.0.

expand_dims

Deprecated at version 3.0.0, use insert_dimension method instead.

getprop

Deprecated at version 3.0.0, use method get_property instead.

hasbounds

Deprecated at version 3.0.0, use has_bounds method instead.

hasdata

Deprecated at version 3.0.0, use has_data method instead.

hasprop

Deprecated at version 3.0.0, use method has_property instead.

insert_data

Deprecated at version 3.0.0, use set_data method instead.

isauxiliary

Deprecated at version 3.7.0, use construct_type attribute instead.

isdimension

Deprecated at version 3.7.0, use construct_type attribute instead.

isdomainancillary

Deprecated at version 3.7.0, use construct_type attribute instead.

isfieldancillary

Deprecated at version 3.7.0, use construct_type attribute instead.

ismeasure

Deprecated at version 3.7.0, use construct_type attribute instead.

name

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

remove_data

Deprecated at version 3.0.0, use method del_data instead.

select

Deprecated at version 3.0.0.

setprop

Deprecated at version 3.0.0, use method set_property instead.

unsafe_array

Deprecated at version 3.0.0, use array attribute instead.