postprocessor.core.multisignal.crosscorr.crosscorr¶
- class crosscorr(parameters)[source]¶
Bases:
PostProcessABC- Attributes
- parameters
Methods
run(trace_dfA[, trace_dfB])Calculates normalised cross-correlations as a function of lag.
as_function
default_parameters
- Attributes
- parameters
Methods
run(trace_dfA[, trace_dfB])Calculates normalised cross-correlations as a function of lag.
as_function
default_parameters
Methods
__init__(parameters)as_function(data, *extra_data, **kwargs)default_parameters(*args, **kwargs)run(trace_dfA[, trace_dfB])Calculates normalised cross-correlations as a function of lag.
Attributes
parameters- run(trace_dfA, trace_dfB=None)[source]¶
Calculates normalised cross-correlations as a function of lag.
Calculates normalised auto- or cross-correlations as a function of lag. Lag is given in multiples of the unknown time interval between data points.
Normalisation is by the product of the standard deviation over time for each replicate for each variable.
For the cross-correlation between sA and sB, the closest peak to zero lag should in the positive lags if sA is delayed compared to signal B and in the negative lags if sA is advanced compared to signal B.
- Parameters
- trace_dfA: dataframe
An array of signal values, with each row a replicate measurement and each column a time point.
- trace_dfB: dataframe (required for cross-correlation only)
An array of signal values, with each row a replicate measurement and each column a time point.
- normalised: boolean (optional)
If True, normalise the result for each replicate by the standard deviation over time for that replicate.
- only_pos: boolean (optional)
If True, return results only for positive lags.
- Returns
- corr: dataframe
An array of the correlations with each row the result for the corresponding replicate and each column a time point
- lags: array
A 1D array of the lags in multiples of the unknown time interval