Release Spectral Python 0.14 · spectralpython/spectral

To install, uncompress the archive, cd into the unpacked directory, and type

python setup.py install

Instead of downloading the archive, the latest release of the software can be automatically downloaded and installed using pip:

pip install -U spectral

For information on package dependencies, see the web site.

Changes

Attempt to use Pillow fork of PIL, if available, rather than older PIL.
view_cube now uses common color scale limits on all side faces.
When creating an AsterDatabase instance, directories in the SPECTRAL_DATA
environment variable are search for the specified file (after the current
directory).
spectral.imshow accepts an optional fignum argument to render to an
existing figure.
Class labels in a spectral.imshow window can be reassigned even when class
labels were not provided in the function call (all pixels will start with
class 0).
File spectral/algorithms/perceptron.py can be used independently of the
rest of the package.

Front and left sides of the image cube displayed by view_cube were
mirrored left-right. Cube aspect ratio was being computed incorrectly for
non-square images. These bugs were introduced by a recent release.
Global covariance was not being scaled properly in the
MahalanobisDistanceClassifier. Mathematically, it does not affect results
and did not affect results on the test data but for large covariance with
many classes, it could have cause rounding/truncation that would affect
results.
PerceptronClassifier constructor was failing due to recent changes in
base class code. Unit tests have been added to ensure it continues to work
properly.

PerceptronClassifier is roughly an order of magnitude faster due to better
use of numpy. Inputs are now scaled and weights are initialized withing the
data limits, which usually results in fewer iterations for convergence.