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2.1.4 Building SciPy
SciPy Reference Guide, Release 0.8.dev
Support for NumScons has been added. NumScons is a tentative new build system for NumPy/SciPy, using SCons at
its core.
SCons is a next-generation build system, intended to replace the venerable Make with the integrated functionality
of autoconf/automake and ccache. Scons is written in Python and its configuration files are Python scripts.
NumScons is meant to replace NumPy’s custom version of distutils providing more advanced functionality, such
as autoconf, improved fortran support, more tools, and support for numpy.distutils/scons cooperation.
2.1.5 Sandbox Removed
While porting SciPy to NumPy in 2005, several packages and modules were moved into scipy.sandbox. The
sandbox was a staging ground for packages that were undergoing rapid development and whose APIs were in flux. It
was also a place where broken code could live. The sandbox has served its purpose well, but was starting to create
confusion. Thus scipy.sandbox was removed. Most of the code was moved into scipy, some code was made
into a scikit, and the remaining code was just deleted, as the functionality had been replaced by other code.
2.1.6 Sparse Matrices
Sparse matrices have seen extensive improvements. There is now support for integer dtypes such int8, uint32, etc.
Two new sparse formats were added:
• new class dia_matrix : the sparse DIAgonal format
• new class bsr_matrix : the Block CSR format
Several new sparse matrix construction functions were added:
• sparse.kron : sparse Kronecker product
• sparse.bmat : sparse version of numpy.bmat
• sparse.vstack : sparse version of numpy.vstack
• sparse.hstack : sparse version of numpy.hstack
Extraction of submatrices and nonzero values have been added:
• sparse.tril : extract lower triangle
• sparse.triu : extract upper triangle
• sparse.find : nonzero values and their indices
csr_matrix and csc_matrix now support slicing and fancy indexing (e.g., A[1:3, 4:7] and
A[[3,2,6,8],:]). Conversions among all sparse formats are now possible:
• using member functions such as .tocsr() and .tolil()
• using the .asformat() member function, e.g. A.asformat(’csr’)
• using constructors A = lil_matrix([[1,2]]); B = csr_matrix(A)
All sparse constructors now accept dense matrices and lists of lists. For example:
• A = csr_matrix( rand(3,3) ) and B = lil_matrix( [[1,2],[3,4]] )
The handling of diagonals in the spdiags function has been changed. It now agrees with the MATLAB(TM) function
of the same name.
2.1. SciPy 0.7.0 Release Notes 125