analysis.correlation¶
Module: analysis.correlation
¶
Inheritance diagram for nitime.analysis.correlation
:

Classes¶
CorrelationAnalyzer
¶
- class nitime.analysis.correlation.CorrelationAnalyzer(input=None)¶
Bases:
BaseAnalyzer
Analyzer object for correlation analysis. Has the same API as the CoherenceAnalyzer
- __init__(input=None)¶
- Parameters
input : TimeSeries object
Containing the data to analyze.
Examples
>>> np.set_printoptions(precision=4) # for doctesting >>> t1 = ts.TimeSeries(data = np.sin(np.arange(0, ... 10*np.pi,10*np.pi/100)).reshape(2,50), ... sampling_rate=np.pi) >>> c1 = CorrelationAnalyzer(t1) >>> c1 = CorrelationAnalyzer(t1) >>> c1.corrcoef array([[ 1., -1.], [-1., 1.]]) >>> c1.xcorr.sampling_rate 3.141592653... Hz >>> c1.xcorr.t0 -15.91549430915... s
- corrcoef()¶
The correlation coefficient between every pairwise combination of time-series contained in the object
- xcorr()¶
The cross-correlation between every pairwise combination time-series in the object. Uses np.correlation(‘full’).
- Returns
TimeSeries : the time-dependent cross-correlation, with zero-lag
at time=0 :
- xcorr_norm()¶
The cross-correlation between every pairwise combination time-series in the object, where the zero lag correlation is normalized to be equal to the correlation coefficient between the time-series
- Returns
TimeSeries : A TimeSeries object
the time-dependent cross-correlation, with zero-lag at time=0
SeedCorrelationAnalyzer
¶
- class nitime.analysis.correlation.SeedCorrelationAnalyzer(seed_time_series=None, target_time_series=None)¶
Bases:
object
This analyzer takes two time-series. The first is designated as a time-series of seeds. The other is designated as a time-series of targets. The analyzer performs a correlation analysis between each of the channels in the seed time-series and all of the channels in the target time-series.
- __init__(seed_time_series=None, target_time_series=None)¶
- Parameters
seed_time_series : a TimeSeries object
target_time_series : a TimeSeries object
- corrcoef()¶