Python has a growing ecosystem of scientific packages around numpy

Numpy provides basic data types (arrays, matrices).
Packages provide intelligence.

The wider ecosystem

Multiple packages act together

Mahotas can rely on pre-existing functionality

1. An image type (numpy array).
2. Types to hold computed data (numpy array again).
3. Plotting & displaying (matplotlib).
4. Machine learning (sklearn or milk).

Modularity is good software engineering

• Improvements to one package benefit all.
• Separation of concerns.

Consistency also helps human users

• Single type for many uses.
• Many simple operations can be done in numpy.
• Same basic conventions.
• No copying/conversion of data between packages.

What can mahotas do for you?

• Image loading & writing
  (including formats like LSM or STK).
• Image filtering (morphological, Gaussian, &c)
• Feature computation (Haralick, LBPs, SURF, &c)
• Most functions work in 3D (or even 4D, 5D, up to 32D).
• Many utility functions (total is over 100 functions)

Mahotas' internal code is in C++

• It is also heavily optimized
• The same is true of other scientific packages
  (Or they use C or Fortran)

Mahotas is general purpose, but was developed for bioimage informatics

Original goal:
to support the Murphy Lab pipeline with Python.

Has since grown.

A worked out example of using mahotas to classify some images

1. Load images
2. Compute features
3. Use machine learning

I'm going to use a
subcellular determination problem as an example.

Example of cytoplasmic class

Example of nuclear class

Example of code (I): imports & init

from glob import glob
import numpy as np
import mahotas as mh
                    

images = glob('nuclear/*dna.tiff') + glob('cytoplasmic/*dna.tiff')
labels = []
features = []
                    

Example of code (II): compute features

for im in images:
    protein = mh.imread(im.replace('dna', 'protein'))
    features.append(mh.features.haralick(protein).mean(0))
    labels.append('nuclear' in im)

features = np.array(features)
labels = np.array(labels)
                    

Example of code (III): call sklearn

from sklearn import cross_validation
from sklearn.linear_model.logistic import LogisticRegression
scores = cross_validation.cross_val_score(
    LogisticRegression(), features, labels, cv=5)
print("Logistic regression accuracy: {:%}".format(scores.mean()))
                    

Example of code (IV): call milk

import milk
cmat,_ = milk.nfoldcrossvalidation(features, labels)

acc = cmat.trace()/float(cmat.sum())

print("SVM accuracy: {:%}".format(acc))
                    

Let's See A Live Demonstration...

If you're coming from Matlab, Spyder will look familiar

Basic packages are stable, others are expanding

Mahotas releases...

• Version 1.0.3 October 6th
• Version 1.0.2. July 10th
• Version 1.0.1. July 9th
• Version 1.0. May 21st
• Version 0.99. April 22nd
• Version 0.9.7. February 3rd
• Version 0.9.6. December 2nd
• ...

People are using it

Documentation is extensive

• All functions are extensively documented.
• Tutorials illustrate higher level thinking.
• Online documentation is up-to-date.

Mahotas has unit tests with automated testing

• Unit tests check for code quality and regression.
• Continuous integration (with travis).
• There are no known bugs in a release.

Summary

• The Python Numpy-based ecosystem is powerful and flexible.
• Mahotas is the cog that was built for bioimage informatics.
• There is a lot of development activity.
• All the packages mentioned are open source (MIT-style).
• Commercial support is available.

For more information

• http://mahotas.rtfd.org/
• http://github.com/luispedro/mahotas
• Mahotas paper
• Pythonvision mailing list
• Email me: luis@luispedro.org
• Talk to me in person

Thank You

This presentation is available at http://bit.ly/eubias-mahotas