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SciPy Reference Guide, Release 0.8.dev
Numerous efficiency improvements to format conversions and sparse matrix arithmetic have been made. Finally, this
release contains numerous bugfixes.
2.1.7 Statistics package
Statistical functions for masked arrays have been added, and are accessible through scipy.stats.mstats. The
functions are similar to their counterparts in scipy.stats but they have not yet been verified for identical interfaces
and algorithms.
Several bugs were fixed for statistical functions, of those, kstest and percentileofscore gained new keyword
arguments.
Added deprecation warning for mean, median, var, std, cov, and corrcoef. These functions should
be replaced by their numpy counterparts. Note, however, that some of the default options differ between the
scipy.stats and numpy versions of these functions.
Numerous bug fixes to stats.distributions: all generic methods now work correctly, several methods in
individual distributions were corrected. However, a few issues remain with higher moments (skew, kurtosis)
and entropy. The maximum likelihood estimator, fit, does not work out-of-the-box for some distributions - in
some cases, starting values have to be carefully chosen, in other cases, the generic implementation of the maximum
likelihood method might not be the numerically appropriate estimation method.
We expect more bugfixes, increases in numerical precision and enhancements in the next release of scipy.
2.1.8 Reworking of IO package
The IO code in both NumPy and SciPy is being extensively reworked. NumPy will be where basic code for reading
and writing NumPy arrays is located, while SciPy will house file readers and writers for various data formats (data,
audio, video, images, matlab, etc.).
Several functions in scipy.io have been deprecated and will be removed in the 0.8.0 release including
npfile, save, load, create_module, create_shelf, objload, objsave, fopen, read_array,
write_array, fread, fwrite, bswap, packbits, unpackbits, and convert_objectarray. Some
of these functions have been replaced by NumPy’s raw reading and writing capabilities, memory-mapping capabilities,
or array methods. Others have been moved from SciPy to NumPy, since basic array reading and writing capability
is now handled by NumPy.
The Matlab (TM) file readers/writers have a number of improvements:
• default version 5
• v5 writers for structures, cell arrays, and objects
• v5 readers/writers for function handles and 64-bit integers
• new struct_as_record keyword argument to loadmat, which loads struct arrays in matlab as record arrays in
numpy
• string arrays have dtype=’U...’ instead of dtype=object
• loadmat no longer squeezes singleton dimensions, i.e. squeeze_me=False by default
2.1.9 New Hierarchical Clustering module
This module adds new hierarchical clustering functionality to the scipy.cluster package. The function interfaces
are similar to the functions provided MATLAB(TM)’s Statistics Toolbox to help facilitate easier migration to
126 Chapter 2. Release Notes