iode.Variables

class iode.Variables

IODE Variables database.

Parameters:

filepath: str, optional: file containing the IODE variables to load.

Returns:

Variables

Examples

>>> from iode import variables, SAMPLE_DATA_DIR
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
>>> len(variables)
394
>>> variables           
Workspace: Variables
nb variables: 394
filename: ...\tests\data\fun.var
description: Modèle fun - Simulation 1
sample: 1960Y1:2015Y1
mode: LEVEL

 name       1960Y1  1961Y1  1962Y1  1963Y1  1964Y1  1965Y1  ...     2009Y1  2010Y1  2011Y1  2012Y1  2013Y1  2014Y1  2015Y1
ACAF            na      na      na      na      na      na  ...     -37.46  -37.83  -44.54  -55.56  -68.89  -83.34  -96.41
ACAG            na      na      na      na      na      na  ...      27.23   28.25   29.28   30.32   31.37   32.42   33.47
AOUC            na    0.25    0.25    0.26    0.28    0.29  ...       1.29    1.31    1.33    1.36    1.39    1.42    1.46
AOUC_           na      na      na      na      na      na  ...       1.23    1.25    1.27    1.30    1.34    1.37    1.41
AQC           0.22    0.22    0.22    0.23    0.24    0.25  ...       1.45    1.46    1.48    1.51    1.56    1.61    1.67
...            ...     ...     ...     ...     ...     ...  ...        ...     ...     ...     ...     ...     ...     ...
ZJ              na      na      na      na      na      na  ...       1.49    1.51    1.53    1.56    1.59    1.63    1.67
ZKF           0.80    0.81    0.82    0.81    0.83    0.82  ...       0.87    0.87    0.87    0.87    0.87    0.87    0.87
ZKFO          1.00    1.00    1.00    1.00    1.00    1.00  ...       1.02    1.02    1.02    1.02    1.02    1.02    1.02
ZX            0.00    0.00    0.00    0.00    0.00    0.00  ...       0.00    0.00    0.00    0.00    0.00    0.00    0.00
ZZF_          0.69    0.69    0.69    0.69    0.69    0.69  ...       0.69    0.69    0.69    0.69    0.69    0.69    0.69

Attributes:

filename: str
description: str
mode: int
sample: Sample
nb_periods: int
periods: list(str)
periods_as_float: list(float)

Methods

`clear`(self)	Delete all objects from the current database.
`copy`(self)	Create a copy of an IODE database.
`copy_from`(self, input_files[, objects_names])	Copy (a subset of) objects from the input file(s) 'input_files' into the current database.
`extrapolate`(self, method[, from_period, ...])	Extrapolate variables using one the method described below, based on previous periods.
`from_array`(self, array[, time_axis_name, sep])	Copies the Array array into the IODE Variables database.
`from_frame`(self, df)	Copy the pandas DataFrame df into the IODE Variables database.
`get_names`(self[, pattern])	Returns the list of objects names given a pattern.
`high_to_low`(self, type_of_series, filepath, ...)	Build series of lower periodicity by (summing the / taking the average of the / taking the last observation of) sub-periods.
`is_copy_subset`(self)	Whether or not the present object represents of a subset of a global IODE database and if the IODE objects of the subset represent deep copies of the IODE objects from the global IODE database.
`is_subset`(self)	Whether or not the present object represents a subset of a global IODE database.
`load`(self, filepath)	Load objects stored in file 'filepath' into the current database.
`low_to_high`(self, type_of_series, method, ...)	Build series with higher periodicity for stock data (Unemployment, Debt, ...) or flow data (GNP, Deficit, ...).
`merge`(self, other[, overwrite])
`merge_from`(self, input_file)	Merge all objects stored in the input file 'input_file' into the current database.
`periods_subset`(self[, from_period, ...])	Return a subset of the periods from the current Variables sample.
`remove`(self, names)	Delete the object(s) named 'names' from the current database.
`rename`(self, old_name, new_name)	Rename an object of the database.
`save`(self, filepath)	Save objects of the current database into the file 'filepath'.
`search`(self, pattern[, word, ...])	Return a list of all objects from the current database having a specific pattern in their names or LEC expression, comment...
`seasonal_adjustment`(self, input_file[, ...])	Eliminate seasonal variations in monthly series (= variables).
`to_array`(self[, vars_axis_name, ...])	Creates an Array from the current IODE Variables database.
`to_frame`(self[, vars_axis_name, ...])	Create a pandas DataFrame from the current Variables database.
`trend_correction`(self, input_file, lambda_)	Implementation of the Hodrick-Prescott method for trend series (= variables) construction.

__init__(*args, **kwargs)

Methods

`__init__`(args, *kwargs)
`clear`(self)	Delete all objects from the current database.
`copy`(self)	Create a copy of an IODE database.
`copy_from`(self, input_files[, objects_names])	Copy (a subset of) objects from the input file(s) 'input_files' into the current database.
`extrapolate`(self, method[, from_period, ...])	Extrapolate variables using one the method described below, based on previous periods.
`from_array`(self, array[, time_axis_name, sep])	Copies the Array array into the IODE Variables database.
`from_frame`(self, df)	Copy the pandas DataFrame df into the IODE Variables database.
`get_names`(self[, pattern])	Returns the list of objects names given a pattern.
`high_to_low`(self, type_of_series, filepath, ...)	Build series of lower periodicity by (summing the / taking the average of the / taking the last observation of) sub-periods.
`is_copy_subset`(self)	Whether or not the present object represents of a subset of a global IODE database and if the IODE objects of the subset represent deep copies of the IODE objects from the global IODE database.
`is_subset`(self)	Whether or not the present object represents a subset of a global IODE database.
`load`(self, filepath)	Load objects stored in file 'filepath' into the current database.
`low_to_high`(self, type_of_series, method, ...)	Build series with higher periodicity for stock data (Unemployment, Debt, ...) or flow data (GNP, Deficit, ...).
`merge`(self, other[, overwrite])
`merge_from`(self, input_file)	Merge all objects stored in the input file 'input_file' into the current database.
`periods_subset`(self[, from_period, ...])	Return a subset of the periods from the current Variables sample.
`remove`(self, names)	Delete the object(s) named 'names' from the current database.
`rename`(self, old_name, new_name)	Rename an object of the database.
`save`(self, filepath)	Save objects of the current database into the file 'filepath'.
`search`(self, pattern[, word, ...])	Return a list of all objects from the current database having a specific pattern in their names or LEC expression, comment...
`seasonal_adjustment`(self, input_file[, ...])	Eliminate seasonal variations in monthly series (= variables).
`to_array`(self[, vars_axis_name, ...])	Creates an Array from the current IODE Variables database.
`to_frame`(self[, vars_axis_name, ...])	Create a pandas DataFrame from the current Variables database.
`trend_correction`(self, input_file, lambda_)	Implementation of the Hodrick-Prescott method for trend series (= variables) construction.

Attributes

`description`	description: str
`df`	df: DataFrame
`filename`	filename: str
`mode`	mode: str
`names`	names: List[str]
`nb_periods`	nb_periods: int
`periods`	periods: List[str]
`periods_as_float`	periods_as_float: List[float]
`sample`	sample: Sample