SciPy 0.7.1 Release Notes

SciPy 0.7.1 is a bug-fix release with no new features compared to 0.7.0.

Bugs fixed:

  • Several fixes in Matlab file IO


Bugs fixed:

  • Work around a failure with Python 2.6


Memory leak in lfilter have been fixed, as well as support for array object

Bugs fixed:

  • #880, #925: lfilter fixes
  • #871: bicgstab fails on Win32


Bugs fixed:


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.

Also, when scipy.special.errprint(1) has been enabled, warning messages are now issued as Python warnings instead of printing them to stderr.


  • 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 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.