solarwindpy.tools

Utility functions for manipulating solar wind data.

This module contains helper functions that are not yet organized into their own submodules. The functions are primarily used for handling proton data and for converting log-normal parameters to their normal form.

Functions

swap_protons

Swap beam and core proton labels when the beam density exceeds the core density.

normal_parameters

Convert log-normal distribution parameters to normal parameters.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> columns = pd.MultiIndex.from_tuples([
...     ('n', '', 'p1'), ('n', '', 'p2')
... ], names=['M', 'C', 'S'])
>>> df = pd.DataFrame([[1, 0.1], [2, 0.2]], columns=columns)
>>> new_df, mask = swap_protons(df)
>>> 'swapped_protons' in new_df.columns.get_level_values('M')
True

Functions

normal_parameters(m, s)

Convert log-normal parameters to normal distribution parameters.

swap_protons(data[, logger])

Swap beam and core proton labels when the beam density dominates.
swap_protons(data, logger=None)[source]

Swap beam and core proton labels when the beam density dominates.

Parameters:

  • data (pandas.DataFrame) – Data containing proton information. Proton species are stored in the S level of the column index.

  • logger (logging.Logger, optional) – Logger used to report indices of swapped protons. If None a simple logger is created.

Returns:

  • new_data (pandas.DataFrame) – Copy of data with p1 and p2 columns swapped where the beam density exceeds the core density.

  • swap (pandas.Series) – Boolean mask indicating where swaps occurred.

Examples

>>> import pandas as pd
>>> import numpy as np
>>> columns = pd.MultiIndex.from_tuples([
...     ('n', '', 'p1'), ('n', '', 'p2')
... ], names=['M', 'C', 'S'])
>>> df = pd.DataFrame([[2, 1], [1, 2]], columns=columns)  # p1 < p2 in first row
>>> new_df, mask = swap_protons(df)
>>> mask.iloc[0]  # First row should be swapped
True
normal_parameters(m, s)[source]

Convert log-normal parameters to normal distribution parameters.

Parameters:
Returns:

Data frame with columns mu and sigma.

Return type:

pandas.DataFrame

Notes

The conversion uses

\[\mu = \exp[m + s^2/2]\]
\[\sigma = \sqrt{\exp[s^2 + 2m]\,(\exp[s^2] - 1)}\]

These expressions apply to both natural logarithms and base-10 logarithms.

Examples

>>> import numpy as np
>>> m, s = 1.0, 0.5  # log-normal parameters
>>> mu, sigma = normal_parameters(m, s)
>>> mu > 1.0  # Normal mean should be > 1
True