Spectral Python 0.18

Changes

• Improved handling of ENVI header files:
  • If "header offset" is missing, assume the offset is 0.
  • Stricter check for "ENVI" in the first line of the file.
  • [Issue #42] More-informative exceptions are raised when a problem is
    encountered while reading and ENVI header.
  • [Issue #46] No longer defaulting band_quantity to "Wavelength".

Bug Fixes

• [Issue #38 and #39] The tostring method has been deprecated in newer
  versions of PIL/Pillow. Using tobytes now and falling back to tostring
  if it fails.
• [Issue #40] An exception was raised when trying to get a pixel's row/col
  by CTRL-SHIFT-clicking in the ND window display.
• [Issue #44] Matplotlib was being set to interactive mode even if no SPy
  windows were displayed. This would affect behavior of other code using
  Matplotlib. Interactive mode is now set only once the first display is
  requested.
• [Issue #49] GaussianClassifier and MahalanobisDistanceClassifier method
  classify_image was failing when applied to an object that was not a
  numpy.ndarray (e.g., a SpyFile or TransformedImage).

Spectral Python 0.17

New Features

• Functions map_class_ids and map_classes were added for mapping class
  indices between images.
• view_nd now accepts custom axis labels.
• ImageArray and SpyFile have asarray method that provides numpy
  array interface.

Changes

• ENVI header parameter names are case insensitive (converted to lower-case
  after being read).
• ImageArray objects have additional SpyFile methods/attributes and
  indexing behavior is now like SpyFile's.
• An exception is now raised when attempting to open or save an image in
  ENVI format with nonzero major or minor frame offsets (see issue #35).

Bug Fixes

• [Issue #27] ImageView.str failed if image did not have "bands" in
  metadata.

Spectral Python 0.16.2

Bug-Fix Release 0.16.2

0.16.2 Bug Fixes

• [Issue #19] Fixed handling of intereave keyword.
• [Issue #20] envi.save_image fails when interleave keyword is provided for
  single-band images.
• [Issue #21] offset keyword has not effect in envi.create_image.
• [Issue #22] setup.py fails if numpy is not already installed.
• [Issue #24] save_rgb fails when format keyword is given.
• [Issue #25] view_cube fails in HypercubeWindow.load_textures.
• [Issue #26] Too few colors in view_cube side faces.

0.16.1 Bug Fixes

• [Issue #18] Missing import statements for pixel iterators.

Release 0.16.0

New Features

• Adaptive Coherence/Cosine Estimator (ACE) target detector
• Pixel Purity Index (PPI)
• Adjustable linear color stretches (based on data limits or cumulative histogram)
• Ability to save ENVI Classification files (thanks to @ohspite)
• ImageView class has set_title method

Changes

• imshow image displays use a linear 2% color stretch by default (can
  override this in spectral.settings)
• Limited Python 3 compatibility (all functions except view_cube and view_nd) supported
• get_rgb handles bands with no variation (color set to min value)
• Modified view_nd to support change in PyOpenGL API

Bug Fixes

• [Issue #16] Incorrect handling of alternate file extensions in envi.save_image
• [Issue #11] Incorrect handling of unsigned byte data type in ENVI files
• [Issue #13] As of version 1.9, numpy no longer provides oldnumeric submodule

Spectral Python 0.16.1

Bug-Fix Release 0.16.1

0.16.1 Bug Fixes

• [Issue #18] Missing import statements for pixel iterators.

Release 0.16.0

New Features

• Adaptive Coherence/Cosine Estimator (ACE) target detector
• Pixel Purity Index (PPI)
• Adjustable linear color stretches (based on data limits or cumulative histogram)
• Ability to save ENVI Classification files (thanks to @ohspite)
• ImageView class has set_title method

Changes

• imshow image displays use a linear 2% color stretch by default (can
  override this in spectral.settings)
• Limited Python 3 compatibility (all functions except view_cube and view_nd) supported
• get_rgb handles bands with no variation (color set to min value)
• Modified view_nd to support change in PyOpenGL API

Bug Fixes

• [Issue #16] Incorrect handling of alternate file extensions in envi.save_image
• [Issue #11] Incorrect handling of unsigned byte data type in ENVI files
• [Issue #13] As of version 1.9, numpy no longer provides oldnumeric submodule

Spectral Python 0.16.0

New Features

• Adaptive Coherence/Cosine Estimator (ACE) target detector
• Pixel Purity Index (PPI)
• Adjustable linear color stretches (based on data limits or cumulative histogram)
• Ability to save ENVI Classification files (thanks to @ohspite)
• ImageView class has set_title method

Changes

• imshow image displays use a linear 2% color stretch by default (can
  override this in spectral.settings)
• Limited Python 3 compatibility (all functions except view_cube and view_nd) supported
• get_rgb handles bands with no variation (color set to min value)
• Modified view_nd to support change in PyOpenGL API

Bug Fixes

• [Issue #16] Incorrect handling of alternate file extensions in envi.save_image
• [Issue #11] Incorrect handling of unsigned byte data type in ENVI files
• [Issue #13] As of version 1.9, numpy no longer provides oldnumeric submodule

Spectral Python 0.15.0

Installing

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.

New Features

• Added Minimum Noise Fraction (mnf) algorithm (a.k.a., Noise-Adjusted
  Principal Components). An associated noise_from_diffs function enables
  estimation of image noise from a homogeneous region of the image.

Changes

• When calling envi.save_image, assume an ndarray with two dimensions is
  a single-band image (i.e., don't require an explicit third dimension).
• [Issue #9] All SpyFile subclass read methods now have an optional
  use_memmap argument to indicate whether the memmap interface should be
  used (vice direct file read) on a per-call basis. Default values are
  specific to the particular method and file interleave.

Bug Fixes

• [Issue #7] Handle recognize comment lines in ENVI headers and accept blank
  parameter values in the header. Thanks to Don March (http://ohspite.net)
• [Issue #2] Garbage results were being generated for several algorithms when
  a NaN value was present in the image data. Reasonable checks are now
  performed in several algorithms and an optional allow_nan argument (False
  by default) was added to calc_stats.
• [Issue #1] For images with more rows than columns, the row/col of the pixel
  under the mouse cursor did not display if the row index was greater than
  the image width.

Performance Improvements

• [Issue #5] Improved BilFile.read_bands performance. Thanks to Don March
  (http://ohspite.net)
• [Issue #8] Faster creation/display of RGB images for display. Thanks to
  Don March (http://ohspite.net)

Spectral Python 0.14

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.

Bug Fixes

• 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.

Performance Improvements

• 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.