# Full Example¶

Here is a worked example to get to a dataset that is ready for analysis.

All of the following steps assume that you’ve executed import span in a Python interpreter.

## 1. Read a TDT file¶

import span
tankname = 'some/path/to/a/tdt/tank/file'
tank = span.tdt.PandasTank(tankname)
sp = tank.spik # spikes is a computed property based on the names of events


## 2. Threshold the data¶

# create an array of bools indicating which spikes have voltage values
# greater than 4 standard deviations from the mean
thr = sp.threshold(4 * sp.std())


## 3. Clear the refractory period¶

# clear the refractory period of any spikes; in place to save memory
thr.clear_refrac(inplace=True)


## 4. Bin the data¶

# bin the data in 1 second bins
binned = clr.resample('S', how='sum')


## 5. Compute the cross correlation¶

# compute the cross-correlation of all channels
# note that there are a lot more options to this method
# you should explore the docs
xcorr = sp.xcorr(binned)


## Full Code Block¶

import span
tankname = 'some/path/to/a/tdt/tank/file'
tank = span.tdt.TdtTank(tankname)
sp = tank.spik

# create an array of bools indicating which spikes have voltage values
# greater than 4 standard deviations
thr = sp.threshold(4 * sp.std())

# clear the refractory period of any spikes
thr.clear_refrac(inplace=True)

# binned the data in 1 second bins
binned = clr.resample('S', how='sum')

# compute the cross-correlation of all channels
xcorr = sp.xcorr(binned)


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