# Running mean¶

Authors: | Jose Guzman |
---|---|

Updated: | 20 February, 2019 |

The running mean (or running average) is simple way to smooth the data. Given a certain set of points, a running average will create a new set of data points which will be computed by adding a series of averages of different subsets of the full data set.

Given for example a sequence of points, we can create a new set of data points of length by taking the average of a subset of points from the original data set for every point within the set:

## The running mean function¶

The following Python function calculates the running mean of the current channel. Both trace and channel can be selected as zero-based indices. The width of the running average (refereed to here as binwidth) can be selected. The resulting average will appear in a new Stimfit window.

```
# load main Stimfit module
import stf
# load NumPy for numerical analysis
import numpy as np
def rmean(binwidth, trace=-1,channel=-1):
"""
Calculates a running mean of a single trace
Arguments:
binwidth -- size of the bin in sampling points (pt).
Obviously, it should be smaller than the length of the trace.
trace: -- ZERO-BASED index of the trace within the channel.
Note that this is one less than what is shown in the drop-down box.
The default value of -1 returns the currently displayed trace.
channel -- ZERO-BASED index of the channel. This is independent
of whether a channel is active or not. The default value of -1
returns the currently active channel.
Returns:
A smoothed traced in a new stf window.
"""
# loads the current trace of the channel in a 1D Numpy Array
sweep = stf.get_trace(trace,channel)
# creates a destination python list to append the data
dsweep = np.empty((len(sweep)))
# running mean algorithm
for i in range(len(sweep)):
if (len(sweep)-i) > binwidth:
# append to list the running mean of `binwidth` values
# np.mean(sweep) calculates the mean of list
# sweep[p0:p10] takes the values in the vector between p0 and p10 (zero-based)
dsweep[i] = np.mean( sweep[i:(binwidth+i)] )
else:
# use all remaining points for the average:
dsweep[i] = np.mean( sweep[i:] )
stf.new_window(dsweep)
```

## Code commented¶

Stimfit commonly uses the value -1 to set the current trace/Channel. In this function the default argument values are -1 (see the function arguments *trace=-1* and *channel=-1*).

```
>>> sweep = stf.get_trace(trace,channel)
```

`stf.get_trace()`

imports the **trace** of the **channel** into a 1D-Numpy array that we called sweep. The default values provided by the function are -1. This means that by default, the current trace/channel will be imported.

We create a new stf window with the following

```
>>> stf.new_window(dsweep)
```

where dsweep is the 1D-NumPy array obtained after performing the running average.

## Usage¶

To perform the running average of 10 sampling points of the current trace, type:

```
>>> spells.rmean(10)
```

A new window with the running mean will appear.