cf.Transform

class cf.Transform(*args, **kwargs)

Bases: cf.utils.CfDict

A CF transform object defining a coordinate’s transformation in a dictionary-like object.

The named parameters and their values of the transformation (i.e. the transformation’s mappings) comprise the object’s key-value pairs.

A transformation is equivalent to either a netCDF(CF) ‘formula_terms’ or ‘grid_mapping’ property. The latter is identified by the presence of the ‘grid_mapping_name’ key which contains the mapping’s name.

In the ‘formula_terms’ case, a mapping to a coordinate (as opposed to another space) uses the coordinate’s grid key name as a pointer rather than a copy of the coordinate itself.

Refer to the Grid Structure section of the cf module for details.

Parameters:

• *args –

  Keys and values are initialized exactly as for a built-in dict.

• **kwargs –

  Keys and values are initialized exactly as for a built-in dict.

Overloaded operators:

The in (set membership) operator is overloaded to use numerically tolerant equality.

Special attributes:

Attribute	Description
name	The identifying name of the transformation. Typically a netCDF(CF) ‘formula_terms’ or ‘grid_mapping_name’.

Examples:

>>> t
<CF Transform: atmosphere_sigma_coordinate>
>>> dump(t)
atmosphere_sigma_coordinate transform
-------------------------------------
Transform['ps'] = <CF Space: surface_air_pressure(73, 96)>
Transform['ptop'] = 0.05
Transform['sigma'] = 'dim0'

>>> t
<CF Transform: rotated_latitude_longitude>
>>> dump(t)
rotated_latitude_longitude transform
------------------------------------
Transform['grid_mapping_name'] = 'rotated_latitude_longitude'
Transform['grid_north_pole_latitude'] = 33.67
Transform['grid_north_pole_longitude'] = 190.0

Methods and attributes defined here:

copy()

CFD.copy() -> a deep copy of CFD

dump(id=None, omit=())

Return a string containing a full description of the transformation.

Parameters:

• id (str) – Optional. Set the common prefix of component names. If None then defaults to the class name.
• omit (sequence) – Optional. Omit the given attributes from the description.

Returns:

A string containing the description of the object.

See also

cf.dump

equals(other, rtol=None, atol=None)

Return True if two instances are congruent in that they have

1. Equal sets of keys.
2. For each key, the two values are equal.
3. Both instances have equal attributes.

Equality of numbers is to within a tolerance. Refer to cf for details.

Parameters:

• other (object) – The variable to compare against for equality.
• atol (None or float) – Optional. If None then use the default method for setting the absolute tolerance for equality of real numbers (refer to cf for details). If a float then set the absolute tolerance to this value for all comparisons (refer to the override parameter).
• rtol (None or float) – Optional. If None then use the default method for setting the relative tolerance for equality of real numbers (refer to cf for details). If a float then set the relative tolerance to this value for all comparisons (refer to the override parameter).

Returns:

True if the two objects are congruent, False otherwise.

get_keys(regex=None)

Return a list of the cf dictionary’s key names which match the given regular expression.

Parameters:regex (str) – Optional. The regular expression with which to identify key names. Returns:A list of keys names.

Examples:

>>> d.keys()
['dim2', 'dim0', 'dim1', 'aux0', 'cm0']
>>> d.get_keys()
['dim2', 'dim0', 'dim1', 'aux0', 'cm0']
>>> d.get_keys('dim')
['dim2', 'dim0', 'dim1']
>>> d.get_keys('^aux|^dim')
['dim2', 'dim0', 'dim1', 'aux0']
>>> d.get_keys('dim[123]')
['dim2', 'dim1']

has_key(key)

CFD.has_key(k) -> True if CFD has a key k, else False