iode.Identities.execute

Identities.execute(identities: str | List[str] = None, from_period: str | Period = None, to_period: str | Period = None, var_files: str | List[str] = None, scalar_files: str | List[str] = None, trace: bool = False) → bool

Execute the specified identity(ies).

Parameters:

identitiesstr or List[str], optional

The identities to execute. If not provided, all identities are executed.

from_periodstr or Period, optional

The starting period of the execution range. Defaults to the starting period of the current Variables sample.

to_periodstr or Period, optional

The ending period of the execution range. Defaults to the ending period of the current Variables sample.

var_filesstr or List[str], optional

The variable files from which the required Variables are temporarily loaded. The Variables database is left unchanged after the execution of the identity(ies). Defaults to None (current content of the Variables database is used).

scalar_filesstr or List[str], optional

The scalar files from which the required Scalars are temporarily loaded. The Variables database is left unchanged after the execution of the identity(ies). Defaults to None (current content of the Scalars database is used).

tracebool, optional

If True, prints the source of the variables and scalars.

Returns:

bool

Examples

>>> from iode import identities, variables, SAMPLE_DATA_DIR
>>> identities.load(f"{SAMPLE_DATA_DIR}/fun.idt")
Loading .../fun.idt
48 objects loaded 
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
Loading .../fun.var
394 objects loaded
>>> sample = variables.sample
>>> sample
Sample("1960Y1:2015Y1")
>>> identities["GAP_"]
Identity('100*((QAF_/Q_F)-1)')
>>> identities["GAP2"]
Identity('100*(QAFF_/(Q_F+Q_I))')
>>> # reset variables GAP_ and GAP2
>>> variables["GAP_"] = 0.
>>> variables["GAP_"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  ...  2013Y1  2014Y1  2015Y1
GAP_          0.00    0.00    0.00  ...    0.00    0.00    0.00

>>> variables["GAP2"] = 0.
>>> variables["GAP2"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  ...  2013Y1  2014Y1  2015Y1
GAP2          0.00    0.00    0.00  ...    0.00    0.00    0.00

>>> # compute GAP_ and GAP2 (assuming Scalars and Variables are already loaded)
>>> identities.execute("GAP_;GAP2")
True
>>> variables["GAP_"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  ...  2013Y1  2014Y1  2015Y1
GAP_         -3.20   -3.98   -2.12  ...    4.06    3.78    3.24

>>> variables["GAP2"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  ...  2013Y1  2014Y1  2015Y1
GAP2         96.93   97.40   98.37  ...  102.50  102.15  101.59

>>> # compute GAP_ and GAP2 over a subset of the sample
>>> variables["GAP_"] = 0.
>>> variables["GAP2"] = 0.
>>> identities.execute("GAP_;GAP2", "2000Y1", "2005Y1")
True
>>> variables["GAP_", "2000Y1:2005Y1"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 2000Y1:2005Y1
mode: LEVEL

name        2000Y1  2001Y1  2002Y1  2003Y1  2004Y1  2005Y1
GAP_          4.51    3.31    2.62    3.46    5.48    5.58

>>> variables["GAP2", "2000Y1:2005Y1"]
Workspace: Variables
nb variables: 1
filename: ...fun.var
description: Modèle fun - Simulation 1
sample: 2000Y1:2005Y1
mode: LEVEL

name        2000Y1  2001Y1  2002Y1  2003Y1  2004Y1  2005Y1
GAP2        104.61  103.06  102.17  102.82  104.47  104.36

>>> # compute GAP_ and GAP2 assuming Variables are not already loaded
>>> variables.clear()
>>> variables.sample = "1960Y1:2015Y1"
>>> variables.names
[]
>>> # setting the var_files argument will fetch the required values of 
>>> # 'QAF_', 'QAFF_', 'Q_F' and 'Q_I' from the passed Variables file
>>> success = identities.execute("GAP_;GAP2", var_files=f"{SAMPLE_DATA_DIR}/fun.var")
Loading ...fun.var
394 objects loaded
>>> success
True
>>> variables["GAP_"]
Workspace: Variables
nb variables: 1
filename: ws
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  1963Y1  ...  2012Y1  2013Y1  2014Y1  2015Y1
GAP_         -3.20   -3.98   -2.12   -2.65  ...    3.61    4.06    3.78    3.24

>>> variables["GAP2"]
Workspace: Variables
nb variables: 1
filename: ws
sample: 1960Y1:2015Y1
mode: LEVEL

name        1960Y1  1961Y1  1962Y1  1963Y1  ...  2012Y1  2013Y1  2014Y1  2015Y1
GAP2         96.93   97.40   98.37   97.71  ...  102.19  102.50  102.15  101.59

>>> # note that the variables 'QAF_', 'QAFF_', 'Q_F' and 'Q_I' are not 
>>> # present in the Variables database after running identities.execute
>>> variables.names
['GAP2', 'GAP_']