[Scipy-svn] r6676 - trunk/doc/release

scipy-svn@scip... scipy-svn@scip...
Sat Sep 4 18:32:20 CDT 2010

Author: ptvirtan
Date: 2010-09-04 18:32:19 -0500 (Sat, 04 Sep 2010)
New Revision: 6676

DOC: release: copy 0.8.0 release notes from the 0.8.x branch

Modified: trunk/doc/release/0.8.0-notes.rst
--- trunk/doc/release/0.8.0-notes.rst	2010-09-04 22:53:52 UTC (rev 6675)
+++ trunk/doc/release/0.8.0-notes.rst	2010-09-04 23:32:19 UTC (rev 6676)
@@ -4,7 +4,7 @@
 .. contents::
-SciPy 0.8.0 is the culmination of XXX months of hard work. It contains
+SciPy 0.8.0 is the culmination of 17 months of hard work. It contains
 many new features, numerous bug-fixes, improved test coverage and
 better documentation.  There have been a number of deprecations and
 API changes in this release, which are documented below.  All users
@@ -12,16 +12,14 @@
 of bug-fixes and optimizations.  Moreover, our development attention
 will now shift to bug-fix releases on the 0.8.x branch, and on adding
 new features on the development trunk.  This release requires Python
-2.4 - 2.6 and NumPy 1.3 or greater.
+2.4 - 2.6 and NumPy 1.4.1 or greater.
 Please note that SciPy is still considered to have "Beta" status, as
 we work toward a SciPy 1.0.0 release.  The 1.0.0 release will mark a
 major milestone in the development of SciPy, after which changing the
 package structure or API will be much more difficult.  Whilst these
 pre-1.0 releases are considered to have "Beta" status, we are
-committed to making them as bug-free as possible.  For example, in
-addition to fixing numerous bugs in this release, we have also doubled
-the number of unit tests since the last release.
+committed to making them as bug-free as possible.  
 However, until the 1.0 release, we are aggressively reviewing and
 refining the functionality, organization, and interface. This is being
@@ -36,8 +34,9 @@
 Python 3 compatibility is planned and is currently technically
 feasible, since Numpy has been ported. However, since the Python 3
-compatible Numpy 2.0 has not been released yet, support for Python 3
-in Scipy might not yet be included in Scipy 0.8.
+compatible Numpy 1.5 has not been released yet, support for Python 3
+in Scipy is not yet included in Scipy 0.8.  SciPy 0.9, planned for fall 
+2010, will very likely include experimental support for Python 3.
 Major documentation improvements
@@ -63,6 +62,7 @@
 Additional deprecations
 * linalg: The function `solveh_banded` currently returns a tuple containing
   the Cholesky factorization and the solution to the linear system.  In
   SciPy 0.9, the return value will be just the solution.
@@ -75,6 +75,7 @@
 * Passing the coefficients of a polynomial as the argument `f0` to
   `signal.chirp` is deprecated.  Use the function `signal.sweep_poly`
+* The `io.recaster` module has been deprecated and will be removed in 0.9.0.
 New features
@@ -83,8 +84,7 @@
 New realtransforms have been added, namely dct and idct for Discrete Cosine
-Transform; type I, II and III are available, for both single and double
+Transform; type I, II and III are available.
 Single precision support for fft functions (scipy.fftpack)
@@ -92,6 +92,10 @@
 fft functions can now handle single precision inputs as well: fft(x) will
 return a single precision array if x is single precision.
+At the moment, for FFT sizes that are not composites of 2, 3, and 5, the
+transform is computed internally in double precision to avoid rounding error in
 Correlation functions now implement the usual definition (scipy.signal)
@@ -106,6 +110,7 @@
 Additions and modification to LTI functions (scipy.signal)
 * The functions `impulse2` and `step2` were added to `scipy.signal`.
   They use the function `scipy.signal.lsim2` to compute the impulse and
   step response of a system, respectively.
@@ -114,6 +119,7 @@
 Improved waveform generators (scipy.signal)
 Several improvements to the `chirp` function in `scipy.signal` were made:
 * The waveform generated when `method="logarithmic"` was corrected; it
@@ -129,12 +135,29 @@
 New functions and other changes in scipy.linalg
 The functions `cho_solve_banded`, `circulant`, `companion`, `hadamard` and
 `leslie` were added to `scipy.linalg`.
 The function `block_diag` was enhanced to accept scalar and 1D arguments,
 along with the usual 2D arguments.
+New function and changes in scipy.optimize
+The `curve_fit` function has been added; it takes a function and uses
+non-linear least squares to fit that to the provided data.
+The `leastsq` and `fsolve` functions now return an array of size one instead of
+a scalar when solving for a single parameter.
+New sparse least squares solver
+The `lsqr` function was added to `scipy.sparse`.  `This routine
+<http://www.stanford.edu/group/SOL/software/lsqr.html>`_ finds a
+least-squares solution to a large, sparse, linear system of equations.
 ARPACK-based sparse SVD
@@ -144,6 +167,7 @@
 Alternative behavior available for `scipy.constants.find`
 The keyword argument `disp` was added to the function `scipy.constants.find`,
 with the default value `True`.  When `disp` is `True`, the behavior is the
 same as in Scipy version 0.7.  When `False`, the function returns the list of
@@ -159,6 +183,7 @@
 Faster matlab file reader and default behavior change
 We've rewritten the matlab file reader in Cython and it should now read
 matlab files at around the same speed that Matlab does.
@@ -179,23 +204,40 @@
 of this change; for now we suggest using the ``oned_as='row'`` keyword
 argument to `scipy.io.savemat` and friends.
+Faster evaluation of orthogonal polynomials
-Improvements to scipy.stats
+Values of orthogonal polynomials can be evaluated with new vectorized functions
+in `scipy.special`: `eval_legendre`, `eval_chebyt`, `eval_chebyu`,
+`eval_chebyc`, `eval_chebys`, `eval_jacobi`, `eval_laguerre`,
+`eval_genlaguerre`, `eval_hermite`, `eval_hermitenorm`,
+`eval_gegenbauer`, `eval_sh_legendre`, `eval_sh_chebyt`,
+`eval_sh_chebyu`, `eval_sh_jacobi`. This is faster than constructing the
+full coefficient representation of the polynomials, which was previously the
+only available way.
-* addition of mvsdist function which returns distribution objects
-  providing full information about mean, variance, and standard deviation
-  of a data-set
-* addition of 'median', 'mean', 'std', 'var', 'interval', 'logpdf', 
-              'logcdf', 'logsf', 'expect'
-* addition of 'fit_loc_scale' (deprecation of 'est_loc_scale')
-* improvement to 'fit' method of distribution objects so that sub-classes
-  can add a _fitstart method to fix the starting position of the arguments. 
-  Also, some parameters can be fixed and the data-fitting proceed over the
-  remaining free parameters using f0..fn and floc and fscale keywords to the
-  fit function. 
+Note that the previous orthogonal polynomial routines will now also invoke this
+feature, when possible.
+Lambert W function
+`scipy.special.lambertw` can now be used for evaluating the Lambert W
+Improved hypergeometric 2F1 function
+Implementation of `scipy.special.hyp2f1` for real parameters was revised.
+The new version should produce accurate values for all real parameters.
+More flexible interface for Radial basis function interpolation
+The `scipy.interpolate.Rbf` class now accepts a callable as input for the
+"function" argument, in addition to the built-in radial basis functions which
+can be selected with a string argument.
 Removed features
@@ -203,4 +245,19 @@
 The module `scipy.misc.limits` was removed.
+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` are 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.

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