matrixscreener 0.4.0

Python API for Leica LAS AF MatrixScreener

This is a python module for interfacing with Leica LAS AF MatrixScreener. It can read experiments and communicate with the microscope over network.

The module can be used to:

• stitch wells from an experiment exported with the LAS AF Data Exporter

• batch compress images lossless

• programmatically select slides/wells/fields/images given by attributes like

  • slide (S)

  • well position (U, V)

  • field position (X, Y)

  • z-stack position (Z)

  • channel (C)

• read experiment data from OME-XML

The module is developed on Mac OS X, but should work on Linux and Windows too. If you find any bugs, please report them as an issue on github. Pull request are also welcome.

Features

• Access experiment as a python object

• Compress to PNGs without loosing precision, metadata or colormap

• ImageJ stitching

• Communicate with microscope over CAM TCP/IP socket

Install

pip install matrixscreener

Examples

stitch experiment

import matrixscreener
# create short hand
Experiment = matrixscreener.experiment.Experiment

# path should contain AditionalData and slide--S*
scan = Experiment('path/to/experiment')

print(matrixscreener.imagej._bin) # default is for fiji on mac os x
matrixscreener.imagej._bin = '/path/to/imagej'

# if path is omitted, experiment path is used for output files
stitched_images = experiment.stitch('/path/to/output/files/')

stitch specific well

from matrixscreener import experiment

# path should contain AditionalData and slide--S*
stitched_images = experiment.stitch('/path/to/well')

do stuff on all images

from matrixscreener import experiment

scan = experiment.Experiment('path/to/experiment--')

for image in scan.images:
    do stuff...

do stuff on specific wells/fields

from matrixscreener import experiment

# select specific parts
selected_wells = [well for well in scan.wells if 'U00' in well]
for well in selected_wells:
    do stuff...

def condition(path):
    x_above = experiment.attribute(path, 'X') > 1
    x_below = experiment.attribute(path, 'X') < 5
    return x_above and x_below

selected_fields = [field for field in scan.fields if condition(field)]
for field in selected_fields:
    do stuff..

subtract data

from matrixscreener.experiment import attribute

# get all channels
channels = [attribute(image, 'C') for image in scan.images]
min_ch, max_ch = min(channels), max(channels)

communicate with microscope

from matrixscreener.cam import CAM

cam = CAM()   # initiate and connect, default localhost:8895

# some commands are created as short hands
# start matrix scan
response = cam.start_scan()
print(response)

# but you could also create your own command with a list of tuples
command = [('cmd', 'enableall'),
           ('value', 'true')]
response = cam.send(command)
print(response)

# or even send it as a bytes string (note the b)
command = b'/cmd:enableall /value:true'
response = cam.send(command)
print(response)

batch lossless compress of experiment

import matrixscreener as ms

e = ms.experiment.Experiment('/path/to/experiment')
pngs = ms.experiment.compress(e.images)
print(pngs)

See also this notebook.

Dependencies

• ImageJ with Grid stitching plugin (fiji is recommended)

Develop

git clone https://github.com/arve0/matrixscreener.git
cd matrixscreener
# hack
./setup.py install

Testing

pip install tox
tox

API Reference

All commands should be documented in docstrings in numpy format.

API reference is available online, can be read with pydoc or any editor/repl that does autocomplete with docstrings.

In example:

pydoc matrixscreener
pydoc matrixscreener.cam
pydoc matrixscreener.experiment
pydoc matrixscreener.imagej