iode.ComputedTable.plot_data

property ComputedTable.plot_data: Dict[str, Tuple[ndarray, ndarray]]

Returns a dictionary containing the data to be plotted. The keys are the series names, and the values are tuples containing the x and y data as numpy arrays. The x data corresponds to the periods, and the y data corresponds to the values for each series.

Returns:

Dict[str, Tuple[np.ndarray, np.ndarray]]

A dictionary where keys are series names and values are tuples of (x_data, y_data).

Examples

>>> import numpy as np
>>> from iode import SAMPLE_DATA_DIR
>>> from iode import Table, tables, variables
>>> tables.load(f"{SAMPLE_DATA_DIR}/fun.tbl")
Loading .../fun.tbl
46 objects loaded
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
Loading .../fun.var
394 objects loaded
>>> # simple time series (current workspace) - 6 observations
>>> computed_table = tables["C8_1"].compute("2000:6")
>>> series = computed_table.plot_data
>>> x_data = series['Output potentiel'][0]
>>> f"x_data={x_data.tolist()}"
'x_data=[2000.0, 2001.0, 2002.0, 2003.0, 2004.0, 2005.0]'
>>> for series_name, (x_data, y_data) in series.items():
...     print(f"{series_name}: y_data={np.round(y_data, 6).tolist()}")
Output potentiel: y_data=[5495.212782, 5627.858893, ..., 5966.199911, 6103.631844]
Stock de capital: y_data=[8083.551748, 8359.890816, ..., 9175.810569, 9468.886506]
Intensité de capital: y_data=[0.503166, 0.489608, ..., 0.448077, 0.434914]
Productivité totale des facteurs: y_data=[0.993773, 1.003711, ..., 1.034124, 1.044466]