erlab.analysis.correlation

Macros for correlation analysis.

Functions

acf2(arr[, mode, method])	Calculate the autocorrelation function (ACF) of a 2D array including nans.
acf2stack(arr[, stack_dims, mode, method])
autocorrelate(arr, *args, **kwargs)	Calculate the autocorrelation of a N-dimensional array, normalized to 1.
autocorrelation_lags(in_len, *args, **kwargs)
nanacf(arr, *args, **kwargs)
xcorr1d(in1, in2[, method])	Perform 1-dimensional correlation analysis on xarray.DataArray s.

erlab.analysis.correlation.acf2(arr, mode='full', method='fft')

Calculate the autocorrelation function (ACF) of a 2D array including nans.

Parameters:

• arr – The input array for which the ACF needs to be calculated.

• mode (str) – The mode of the ACF calculation, by default "full". For more information, see scipy.signal.correlate.

• method (str) – The method used for ACF calculation, by default "fft". For more information, see scipy.signal.correlate.

Returns:

The ACF of the input array.

Return type:

xarray.DataArray

Examples

>>> import numpy as np
>>> import xarray as xr
>>> np.random.seed(0)  # Set the random seed for reproducibility
>>> arr = xr.DataArray(np.random.rand(10, 10), dims=("kx", "ky"))
>>> acf = acf2(arr)
>>> acf
<xarray.DataArray (qx: 19, qy: 19)> Size: 3kB
8.403e-05 0.01495 0.01979 0.02734 0.03215 ... 0.02734 0.01979 0.01495 8.403e-05
Coordinates:
* qx       (qx) int64 152B -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9
* qy       (qy) int64 152B -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

erlab.analysis.correlation.acf2stack(arr, stack_dims=('eV',), mode='full', method='fft')

erlab.analysis.correlation.xcorr1d(in1, in2, method='direct')

Perform 1-dimensional correlation analysis on xarray.DataArray s.