Streaming: Processing Unlimited Frames On-Disk¶

A key feature of trackpy is the ability to process an unlimited number of frames.

For feature-finding, this is straightforward: a frame is loaded, features are located, the locations are saved the disk, and the memory is cleared for the next frame. For linking, the problem is more challenging, but trackpy handles all this complexity for you, using as little memory as possible throughout.

When data sets become large, beginning-friendly file formats like CSV or Excel become impractical. We recommend using the HDF5 file format, which is trackpy can read and write out of the box. (HDF5 is widely used; you can be sure it will be around for many, many years to come.)

If you have some other format in mind, see the end of this tutorial, where we explain how to extend trackpy's interface to support other formats.

PyTables¶

You need pytables, which is normally included with the Anaconda distribution. If you find that you don't have it, you can easily install it using conda. Type this command into a Terminal or Command Prompt.

conda install pytables

Locate Features, Streaming Results into an HDF5 File¶

In [1]:

import trackpy as tp
import pims

In [2]:

@pims.pipeline
def gray(image):
    return image[:, :, 1]

images = gray(pims.open('../sample_data/bulk_water/*.png'))
images = images[:10]  # We'll take just the first 10 frames for demo purposes.

In [3]:

# For this demo, we'll first remove the file if it already exists.
!rm -f data.h5

We can use locate inside a loop:

In [4]:

with tp.PandasHDFStore('data.h5') as s:  # This opens an HDF5 file. Data will be stored and retrieved by frame number.
    for image in images:
        features = tp.locate(image, 11, invert=True)  # Find the features in a given frame.
        s.put(features)  # Save the features to the file before continuing to the next frame.

or, equivalently, we can use batch, which accepts the storage file as output.

In [5]:

with tp.PandasHDFStore('data.h5') as s:
    tp.batch(images, 11, invert=True, output=s)

Frame 9: 573 features

We can get the data for a given frame:

In [6]:

with tp.PandasHDFStore('data.h5') as s:
    frame_2_results = s.get(2)
    
frame_2_results.head()  # Display the first few rows.

Out[6]:

	y	x	mass	size	ecc	signal	raw_mass	ep	frame
0	5.509524	497.075000	72.537360	2.870416	0.029523	2.158850	10115.0	0.768330	2
1	5.652962	295.981547	266.747504	2.322843	0.247683	16.407260	10670.0	0.078022	2
2	6.350493	68.049288	236.523604	2.351310	0.044731	10.362480	10878.0	0.058368	2
3	6.405941	336.590347	209.322095	1.996594	0.127966	15.111950	10551.0	0.096638	2
4	6.899098	432.617521	363.723046	2.855660	0.466993	14.334764	10838.0	0.061340	2

Or dump all the data, if your machine has enough memory to hold it:

In [7]:

with tp.PandasHDFStore('data.h5') as s:
    all_results = s.dump()
    
all_results.head()  # Display the first few rows.

Out[7]:

	y	x	mass	size	ecc	signal	raw_mass	ep
0	4.750000	103.668564	192.862485	2.106615	0.066390	10.808405	10714.0	0.073666
1	5.249231	585.779487	164.659302	2.962674	0.078936	4.222033	10702.0	0.075116
2	5.785986	294.792544	244.624615	2.244542	0.219217	15.874846	10686.0	0.077141
3	5.869369	338.173423	187.458282	2.046201	0.185333	13.088304	10554.0	0.099201
4	6.746377	310.584169	151.486558	3.103294	0.053342	4.475355	10403.0	0.147430

You can dump the first N frames using s.dump(N).

Link Trajectories, Streaming From and Updating the HDF5 File¶

In [8]:

with tp.PandasHDFStore('data.h5') as s:
    for linked in tp.link_df_iter(s, 3, neighbor_strategy='KDTree'):
        s.put(linked)

Frame 9: 573 trajectories present.

The original data is overwritten.

In [9]:

with tp.PandasHDFStore('data.h5') as s:
    frame_2_results = s.get(2)
    
frame_2_results.head()  # Display the first few rows.

Out[9]:

	y	x	mass	size	ecc	signal	raw_mass	ep	frame	particle
0	5.509524	497.075000	72.537360	2.870416	0.029523	2.158850	10115.0	0.768330	2	535
1	5.652962	295.981547	266.747504	2.322843	0.247683	16.407260	10670.0	0.078022	2	2
2	6.350493	68.049288	236.523604	2.351310	0.044731	10.362480	10878.0	0.058368	2	8
3	6.405941	336.590347	209.322095	1.996594	0.127966	15.111950	10551.0	0.096638	2	3
4	6.899098	432.617521	363.723046	2.855660	0.466993	14.334764	10838.0	0.061340	2	6

Framewise Data Interfaces¶

Built-in interfaces¶

There are three different interfaces. You can use them interchangeably. They offer different performance advantages.

PandasHDFStore -- fastest for a small (~100) number of frames
PandasHDFStoreBig -- fastest for a medium or large number of frames
PandasHDFStoreSingleNode -- optimizes HDF queries that access multiple frames (advanced)

Writing your own interface¶

Trackpy implements a generic interface that could be used to store and retrieve particle tracking data in any file format. We hope that it can make it easier for researchers who use different file formats to exchange data. Any in-house format could be accessed using the same simple interface demonstrated above.

At present, the interface is implemented only for HDF5 files. To extend it to any format, write a class subclassing trackpy.FramewiseData. This custom class must implement the methods put, get, close, and __iter__ and the properties max_frame and t_column. Refer to the built-in classes in framewise_data.py for examples to work from.

on-disk#