SciPy 0.7.1 Release Notes¶
SciPy 0.7.1 is a bug-fix release with no new features compared to 0.7.0.
Memory leak in lfilter have been fixed, as well as support for array object
- #880, #925: lfilter fixes
- #871: bicgstab fails on Win32
- #883: scipy.io.mmread with scipy.sparse.lil_matrix broken
- lil_matrix and csc_matrix reject now unexpected sequences, cf. http://thread.gmane.org/gmane.comp.python.scientific.user/19996
Several bugs of varying severity were fixed in the special functions:
- #503, #640: iv: problems at large arguments fixed by new implementation
- #623: jv: fix errors at large arguments
- #679: struve: fix wrong output for v < 0
- #803: pbdv produces invalid output
- #804: lqmn: fix crashes on some input
- #823: betainc: fix documentation
- #834: exp1 strange behavior near negative integer values
- #852: jn_zeros: more accurate results for large s, also in jnp/yn/ynp_zeros
- #853: jv, yv, iv: invalid results for non-integer v < 0, complex x
- #854: jv, yv, iv, kv: return nan more consistently when out-of-domain
- #927: ellipj: fix segfault on Windows
- #946: ellpj: fix segfault on Mac OS X/python 2.6 combination.
- ive, jve, yve, kv, kve: with real-valued input, return nan for out-of-domain instead of returning only the real part of the result.
scipy.special.errprint(1) has been enabled, warning
messages are now issued as Python warnings instead of printing them to
- linregress, mannwhitneyu, describe: errors fixed
- kstwobign, norm, expon, exponweib, exponpow, frechet, genexpon, rdist, truncexpon, planck: improvements to numerical accuracy in distributions
Windows binaries for python 2.6¶
python 2.6 binaries for windows are now included. The binary for python 2.5 requires numpy 1.2.0 or above, and the one for python 2.6 requires numpy 1.3.0 or above.
Universal build for scipy¶
Mac OS X binary installer is now a proper universal build, and does not depend on gfortran anymore (libgfortran is statically linked). The python 2.5 version of scipy requires numpy 1.2.0 or above, the python 2.6 version requires numpy 1.3.0 or above.