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.


Simply remove a column:

from pept.tracking import *

trajectories = Remove("label").fit(trajectories)

Or multiple columns:

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


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)


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

    # 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

long_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)")


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)


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)