PEPT-ML¶

PEPT-ML one pass of clustering recipe¶

import pept
from pept.tracking import *

max_tracers = 1

pipeline = pept.Pipeline([
    Cutpoints(max_distance = 0.5),
    HDBSCAN(true_fraction = 0.15, max_tracers = max_tracers),
    SplitLabels() + Centroids(),
    Stack(),
])

locations = pipeline.fit(lors)

PEPT-ML second pass of clustering recipe¶

import pept
from pept.tracking import *

max_tracers = 1

pipeline = pept.Pipeline([
    Stack(sample_size = 30 * max_tracers, overlap = 30 * max_tracers - 1),
    HDBSCAN(true_fraction = 0.6, max_tracers = max_tracers),
    SplitLabels() + Centroids(),
    Stack(),
])

locations2 = pipeline.fit(lors)

PEPT-ML complete recipe¶

Including two passes of clustering and trajectory separation: Including an example ADAC Forte data initisalisation, two passes of clustering, trajectory separation, plotting and saving trajectories as CSV.

# Import what we need from the `pept` library
import pept
from pept.tracking import *
from pept.plots import PlotlyGrapher, PlotlyGrapher2D


# Open interactive plots in the web browser
import plotly
plotly.io.renderers.default = "browser"


# Initialise data from file and set sample size and overlap
filepath = "DS1.da01"
max_tracers = 1

lors = pept.scanners.adac_forte(
    filepath,
    sample_size = 200 * max_tracers,
    overlap = 150 * max_tracers,
)


# Select only the first 1000 samples of LoRs for testing; comment out for all
lors = lors[:1000]


# Create PEPT-ML processing pipeline
pipeline = pept.Pipeline([

    # First pass of clustering
    Cutpoints(max_distance = 0.5),
    HDBSCAN(true_fraction = 0.15, max_tracers = max_tracers),
    SplitLabels() + Centroids(),

    # Second pass of clustering
    Stack(sample_size = 30 * max_tracers, overlap = 30 * max_tracers - 1),
    HDBSCAN(true_fraction = 0.6, max_tracers = max_tracers),
    SplitLabels() + Centroids(),

    # Trajectory separation
    Segregate(window = 20 * max_tracers, cut_distance = 10),
])


# Process all samples in `lors` in parallel, using `max_workers` threads
trajectories = pipeline.fit(lors, max_workers = 16)


# Save trajectories as CSV
trajectories.to_csv(filepath + ".csv")


# Plot trajectories - first a 2D timeseries, then all 3D positions
PlotlyGrapher2D().add_timeseries(trajectories).show()
PlotlyGrapher().add_points(trajectories).show()

Initialising PEPT Scanner Data The Birmingham Method