In this tutorial, we will demonstrate the basic use of nitime in initializing, manipulating and analyzing a simple time-series object. For more advanced usage see the examples section (Examples)

In order to get started, import nitime.timeseries:

In [1]: import nitime.timeseries as ts

Then, you can initialize a simple time-series object, by providing data and some information about the sampling-rate or sampling-interval:

In [2]: t1 = ts.TimeSeries([[1,2,3],[3,6,8]],sampling_rate=0.5)

If you tab-complete, you will see that the object now has several different attributes:

In [3]: t1.
t1.at                  t1.metadata            t1.time
t1.data                t1.sampling_interval   t1.time_unit
t1.duration            t1.sampling_rate
t1.from_time_and_data  t1.t0

Note that the sampling_interval is the inverse of the sampling_rate:

In [4]: t1.sampling_interval
Out[4]: 2.0 s

In addition, the sampling rate is now represented with the units in Hz:

In [5]: t1.sampling_rate
Out[5]: 0.5 Hz

Also - once this object is available to you, you have access to the underlying representation of time:

In [6]: t1.time
Out[6]: UniformTime([ 0.,  2.,  4.], time_unit='s')

Now import the analysis library:

In [7]: import nitime.analysis as nta

and initialize an analyzer for correlation analysis:

In [8]: c = nta.CorrelationAnalyzer(t1)

The simplest use of this analyzer (and also the default output) is to compute the correlation coefficient matrix of the data in the different rows of the time-series:

In [9]: c.corrcoef
array([[ 1.        ,  0.99339927],
       [ 0.99339927,  1.        ]])

but it can also be used in order to generate the cross-correlation function between the channels, which is also a time-series object:

In [63]: x = c.xcorr

In [64]: x.time
Out[64]: UniformTime([-6., -4., -2.,  0.,  2.], time_unit='s')

In [65]: x.data
array([[[   3.,    8.,   14.,    8.,    3.],
        [   8.,   22.,   39.,   24.,    9.]],

       [[   8.,   22.,   39.,   24.,    9.],
        [  24.,   66.,  109.,   66.,   24.]]])