iode.Variables.to_array

Variables.to_array(self, vars_axis_name: str = 'names', time_axis_name: str = 'time', sample_as_floats: bool = False) → Array

Creates an Array from the current IODE Variables database.

Parameters:

vars_axis_name: str, optional

Name of the axis representing the Variables names. Defaults to ‘names’.

time_axis_name: str, optional

Name of the axis representing the periods. Defaults to ‘time’.

sample_as_floats: bool, optional

Whether or not to convert periods as float values. Defaults to False (periods are exported as string values).

Warning

IODE and LArray don’t use the same constant to represent NaN values. When exporting IODE variables as a LArray Array, the IODE NaN values (\(NA\)) are converted to LArray NaN values (\(nan\)).

Examples

>>> from iode import SAMPLE_DATA_DIR
>>> from iode import variables
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
>>> len(variables)
394
>>> variables.sample
'1960Y1:2015Y1'
>>> variables.nb_periods
56

>>> # Export the IODE Variables database as an (larray) Array object
>>> array = variables.to_array()
>>> array.shape
(394, 56)
>>> array.axes.info
394 x 56
 names [394]: 'ACAF' 'ACAG' 'AOUC' ... 'ZKFO' 'ZX' 'ZZF_'
 time [56]: '1960Y1' '1961Y1' '1962Y1' ... '2013Y1' '2014Y1' '2015Y1'
>>> variables["AOUC"]               
[nan, 0.24783191606766575, ..., 1.4237139558484628, 1.4608626117037322]
>>> array["AOUC"]
time  1960Y1  ...              2014Y1              2015Y1
         nan  ...  1.4237139558484628  1.4608626117037322
>>> variables["ZKFO"]               
[1.0, 1.0, ..., 1.0159901, 1.0159901]
>>> array["ZKFO"]
time  1960Y1  1961Y1  1962Y1  ...     2012Y1     2013Y1     2014Y1     2015Y1
         1.0     1.0     1.0  ...  1.0159901  1.0159901  1.0159901  1.0159901

>>> # Export a subset of the IODE Variables database as an (larray) Array object
>>> array = variables["A*;*_"].to_array()
>>> array.shape
(33, 56)
>>> array.axes.info
33 x 56
 names [33]: 'ACAF' 'ACAG' 'AOUC' ... 'WNF_' 'YDH_' 'ZZF_'
 time [56]: '1960Y1' '1961Y1' '1962Y1' ... '2013Y1' '2014Y1' '2015Y1'
>>> variables["AOUC"]               
[nan, 0.24783191606766575, ..., 1.4237139558484628, 1.4608626117037322]
>>> array["AOUC"]
time  1960Y1  ...              2014Y1              2015Y1
         nan  ...  1.4237139558484628  1.4608626117037322
>>> variables["ZZF_"]               
[0.68840039, 0.68840039, ..., 0.68840039, 0.68840039]
>>> array["ZZF_"]
time      1960Y1      1961Y1  ...      2013Y1      2014Y1      2015Y1
      0.68840039  0.68840039  ...  0.68840039  0.68840039  0.68840039