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"""
SciPy: A scientific computing package for Python
================================================
Documentation is available in the docstrings and
online at http://docs.scipy.org.
Contents
--------
SciPy imports all the functions from the NumPy namespace, and in
addition provides:
Subpackages
-----------
::
odr --- Orthogonal Distance Regression [*]
misc --- Various utilities that don't have
another home.
cluster --- Vector Quantization / Kmeans [*]
fftpack --- Discrete Fourier Transform algorithms
[*]
io --- Data input and output [*]
sparse.linalg.eigen.lobpcg --- Locally Optimal Block Preconditioned
Conjugate Gradient Method (LOBPCG) [*]
special --- Airy Functions [*]
lib.blas --- Wrappers to BLAS library [*]
sparse.linalg.eigen --- Sparse Eigenvalue Solvers [*]
stats --- Statistical Functions [*]
lib --- Python wrappers to external libraries
[*]
lib.lapack --- Wrappers to LAPACK library [*]
maxentropy --- Routines for fitting maximum entropy
models [*]
integrate --- Integration routines [*]
ndimage --- n-dimensional image package [*]
linalg --- Linear algebra routines [*]
spatial --- Spatial data structures and algorithms
[*]
interpolate --- Interpolation Tools [*]
sparse.linalg --- Sparse Linear Algebra [*]
sparse.linalg.dsolve.umfpack --- :Interface to the UMFPACK library: [*]
sparse.linalg.dsolve --- Linear Solvers [*]
optimize --- Optimization Tools [*]
sparse.linalg.eigen.arpack --- Eigenvalue solver using iterative
methods. [*]
signal --- Signal Processing Tools [*]
sparse --- Sparse Matrices [*]
[*] - using a package requires explicit import
Global symbols from subpackages
-------------------------------
::
misc --> info, factorial, factorial2, factorialk,
comb, who, lena, central_diff_weights,
derivative, pade, source
fftpack --> fft, fftn, fft2, ifft, ifft2, ifftn,
fftshift, ifftshift, fftfreq
stats --> find_repeats
linalg.dsolve.umfpack --> UmfpackContext
Utility tools
-------------
::
test --- Run scipy unittests
show_config --- Show scipy build configuration
show_numpy_config --- Show numpy build configuration
__version__ --- Scipy version string
__numpy_version__ --- Numpy version string
"""
__all__ = ['test']
from numpy import show_config as show_numpy_config
if show_numpy_config is None:
raise ImportError("Cannot import scipy when running from numpy source directory.")
from numpy import __version__ as __numpy_version__
# Import numpy symbols to scipy name space
import numpy as _num
from numpy import oldnumeric
from numpy import *
from numpy.random import rand, randn
from numpy.fft import fft, ifft
from numpy.lib.scimath import *
# Emit a warning if numpy is too old
majver, minver = [float(i) for i in _num.version.version.split('.')[:2]]
if majver < 1 or (majver == 1 and minver < 5):
import warnings
warnings.warn("Numpy 1.5.0 or above is recommended for this version of " \
"scipy (detected version %s)" % _num.version.version,
UserWarning)
__all__ += ['oldnumeric']+_num.__all__
__all__ += ['randn', 'rand', 'fft', 'ifft']
del _num
# Remove the linalg imported from numpy so that the scipy.linalg package can be
# imported.
del linalg
__all__.remove('linalg')
# We first need to detect if we're being called as part of the scipy
# setup procedure itself in a reliable manner.
try:
__SCIPY_SETUP__
except NameError:
__SCIPY_SETUP__ = False
if __SCIPY_SETUP__:
import sys as _sys
_sys.stderr.write('Running from scipy source directory.\n')
del _sys
else:
try:
from scipy.__config__ import show as show_config
except ImportError:
msg = """Error importing scipy: you cannot import scipy while
being in scipy source directory; please exit the scipy source
tree first, and relaunch your python intepreter."""
raise ImportError(msg)
from scipy.version import version as __version__
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench
|
