ExcessStatistics

class plasmapy.analysis.time_series.excess_statistics.ExcessStatistics(signal, thresholds, time_step)[source]

Bases: object

Calculate total time, number of upwards crossings, average time and root-mean-square time above given thresholds of a sequence.

Parameters:

signal (1D array_like) – Signal to be analyzed.
thresholds (1D array_like) – Threshold values.
time_step (int) – Time step of signal.

Raises:

ValueError – If time_step ≤ 0.

Examples

>>> from plasmapy.analysis.time_series.excess_statistics import ExcessStatistics
>>> signal = [0, 0, 2, 2, 0, 4]
>>> thresholds = [1, 3, 5]
>>> time_step = 1
>>> excess_statistics = ExcessStatistics(signal, thresholds, time_step)
>>> excess_statistics.total_time_above_threshold
[3, 1, 0]
>>> excess_statistics.number_of_crossings
[2, 1, 0]
>>> excess_statistics.average_times
[np.float64(1.5), np.float64(1.0), 0]
>>> excess_statistics.rms_times
[np.float64(0.5), np.float64(0.0), 0]

Attributes Summary

`average_times`	Average time above threshold(s).
`number_of_crossings`	Total number of upwards crossings for threshold(s).
`rms_times`	Root-mean-square values of time above threshold(s).
`total_time_above_threshold`	Total time above threshold(s).

Methods Summary

hist([bins])

Computes the probability density function of the time above each value in thresholds.

Attributes Documentation

average_times

Average time above threshold(s).

Returns:: average_times – Average time above each value in thresholds.
Return type:: 1D array_like

number_of_crossings

Total number of upwards crossings for threshold(s).

Returns:: number_of_crossings – Total number of upwards crossings for each value in thresholds.
Return type:: 1D array_like

rms_times

Root-mean-square values of time above threshold(s).

Returns:: rms_times – Root-mean-square values of time above each value in thresholds.
Return type:: 1D array_like

total_time_above_threshold

Total time above threshold(s).

Returns:: total_time_above_threshold – Total time above threshold for each value in thresholds.
Return type:: 1D array_like

Methods Documentation

hist(bins: int = 32)[source]

Computes the probability density function of the time above each value in thresholds.

Parameters:

bins (int, default: 32) – The number of bins in the estimation of the PDF above thresholds.

Returns:

hist (2D ndarray, shape (thresholds.size, bins )) – For each value in thresholds, returns the estimated PDF of time above threshold.
bin_centers (2D ndarray, shape (thresholds.size, bins )) – Bin centers for hist.

Raises:

TypeError – If bins is not a positive integer.

Examples

>>> from plasmapy.analysis.time_series.excess_statistics import ExcessStatistics
>>> signal = [0, 0, 2, 0, 4]
>>> thresholds = [1, 3, 5]
>>> time_step = 1
>>> excess_statistics = ExcessStatistics(signal, thresholds, time_step)
>>> excess_statistics.hist(2)
(array([[0., 2.],
   [0., 2.],
   [0., 0.]]), array([[0.75, 1.25],
   [0.75, 1.25],
   [0.  , 0.  ]]))