# Filtering Data#

There are many filters in `pept.tracking`

, you can check out the Manual at the top of the page for a complete list. Here are examples with the most important ones.

## Remove#

Simply remove a column:

```
from pept.tracking import *
trajectories = Remove("label").fit(trajectories)
```

Or multiple columns:

```
trajectories = Remove("label", "error").fit(trajectories)
```

## Condition#

One of the most important filters, selecting only data that satisfies a condition:

```
from pept.tracking import *
trajectories = Condition("error < 15").fit(trajectories)
```

Or multiple ones:

```
trajectories = Condition("error < 15, label >= 0").fit(trajectories)
```

In the simplest case, you just use the column name **as the first argument** followed by a comparison. If the column name is not the first argument, you must use single quotes:

```
trajectories = Condition("0 <= 'label'").fit(trajectories)
```

You can also use filtering functions from NumPy in the condition string (i.e. anything returning a boolean mask):

```
# Remove all NaNs and Infs from the 'x' column
trajectories = Condition("np.isfinite('x')")
```

Finally, you can supply your own function receiving a NumPy array of the data and returning a boolean mask:

```
def last_column_filter(data):
return data[:, -1] > 10
trajectories = Condition(last_column_filter).fit(trajectories)
```

Or using inline functions (i.e. `lambda`

):

```
# Select points within a vertical cylinder with radius 10
trajectories = Condition(lambda x: x[:, 1]**2 + x[:, 3]**2 < 10**2).fit(trajectories)
```

## SamplesCondition#

While `Condition`

is applied on individual points, we could filter entire samples - for example, select only trajectories with more than 30 points:

```
import pept.tracking as pt
long_trajectories_filter = pept.Pipeline([
# Segregate points - appends "label" column
pt.Segregate(window = 20, cut_distance = 10),
# Group points into samples; e.g. sample 1 contains all points with label 1
pt.GroupBy("label"),
# Now each sample is an entire trajectory which we can filter
pt.SamplesCondition("sample_size > 30"),
# And stack all remaining samples back into a single PointData
pt.Stack(),
])
long_trajectories = long_trajectories_filter.fit(trajectories)
```

The condition can be based on the sample itself, e.g. keep only samples that lie completely beyond x=0:

```
# Keep only samples for which all points' X coordinates are bigger than 0
Condition("np.all(sample['x'] > 0)")
```

## GroupBy#

Stack all samples (i.e. `LineData`

or `PointData`

) and split them into a list according to a named / numeric column index:

```
from pept.tracking import *
group_list = GroupBy("label").fit(trajectories)
```

## RemoveStatic#

Remove tracer locations when it spends more than time_window without moving more than max_distance:

```
from pept.tracking import *
# Remove positions that spent more than 2 seconds without moving more than 20 mm
nonstatic = RemoveStatic(time_window = 2000, max_distance = 20).fit(trajectories)
```