iode.Scalars.from_frame

Scalars.from_frame(df: DataFrame)[source]

Copy the pandas DataFrame df into the IODE Scalars database. The scalar names to copy are deduced from the index of the DataFrame.

Parameters:
df: DataFrame

pandas DataFrame containing the scalars to copy into the Scalars database. The passed DataFrame should have the following structure:

  • index: names of the scalars

  • a column labeled ‘value’: values of the scalars.

  • a column labeled ‘relax’ (optional): relaxation parameter values of the scalars.

  • a column labeled ‘std’ (optional): standard deviation values resulting from estimation.

If the DataFrame does not contain a ‘relax’ column, relax values will be all set to 1. If the DataFrame does not contain a ‘std’ column, std values will be all set to \(NA\).

See also

Scalars.from_series
Scalars.to_frame

Notes

The index of the passed DataFrame is sorted in alphabetical order before copying to IODE Scalars database.

Examples

>>> from iode import scalars
>>> import pandas as pd
>>> import numpy as np
>>> scalars.clear()
>>> len(scalars)
0

>>> # create the pandas DataFrame
>>> data = {"alpha_0": [0.9, 1., np.nan],
...         "alpha_1": [0.1, 0.8, np.nan],
...         "alpha_": [5.5, 0., np.nan],
...         "beta_0": [0.9, 1., 0.05246],
...         "beta_1": [0.01, 0.8, 0.25687],
...         "beta_": [3.6, 0., 0.]}
>>> df = pd.DataFrame.from_dict(data, orient='index', dtype="float64", 
...                             columns=["value", "relax", "std"])
>>> # display the pandas series
>>> df
        value  relax      std
alpha_0   0.90    1.0      NaN
alpha_1   0.10    0.8      NaN
alpha_    5.50    0.0      NaN
beta_0    0.90    1.0  0.05246
beta_1    0.01    0.8  0.25687
beta_     3.60    0.0  0.00000

>>> # load into the IODE Scalars database
>>> scalars.from_frame(df)
>>> len(scalars)
6

>>> scalars.names
['alpha_', 'alpha_0', 'alpha_1', 'beta_', 'beta_0', 'beta_1']
>>> df.loc["alpha_1"]
value    0.1
relax    0.8
std      NaN
Name: alpha_1, dtype: float64
>>> scalars["alpha_1"]
Scalar(0.1, 0.8, na)
>>> df.loc["beta_1"]
value    0.01000
relax    0.80000
std      0.25687
Name: beta_1, dtype: float64
>>> scalars["beta_1"]
Scalar(0.01, 0.8, 0.25687)