import sys
import os
from numpy.testing import Tester
sys.path.insert(0, os.path.abspath('sharedmem'))
import sharedmem
from sys import argv
tester = Tester()
result = tester.test(extra_argv=['-w', 'tests'] + argv[1:])
if not result:
raise Exception("Test Failed")
if func is None:
func = getattr(m2, func_name)
func.module_name = m2_name
else:
func.module_name = m1_name
if force_clapack and m1 is flapack:
func2 = getattr(m2, func_name, None)
if func2 is not None:
import new
exec(_colmajor_func_template % {'func_name': func_name})
func = new.function(func_code, {'clapack_func': func2},
func_name)
func.module_name = m2_name
func.__doc__ = func2.__doc__
func.prefix = required_prefix
func.dtypechar = dtypechar
funcs.append(func)
return tuple(funcs)
_colmajor_func_template = '''\
def %(func_name)s(*args,**kws):
if "rowmajor" not in kws:
kws["rowmajor"] = 0
return clapack_func(*args,**kws)
func_code = %(func_name)s.func_code
'''
from numpy.testing import Tester
test = Tester().test
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""DataLad aims to expose (scientific) data available online as a unified data
distribution with the convenience of git-annex repositories as a backend."""
from .version import __version__
from datalad.log import lgr
lgr.debug("Importing the rest of datalad.__init__")
from .config import ConfigManager
cfg = ConfigManager()
# be friendly on systems with ancient numpy -- no tests, but at least
# importable
try:
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench
del Tester
except ImportError:
def test(*args, **kwargs):
lgr.warning('Need numpy >= 1.2 for datalad.tests(). Nothing is done')
test.__test__ = False
# Following fixtures are necessary at the top level __init__ for fixtures which
# would cover all **/tests and not just datalad/tests/
def setup_package():
import os
# To overcome pybuild overriding HOME but us possibly wanting our
# own HOME where we pre-setup git for testing (name, email)
def test(*a, **kw):
extra_argv = kw.pop('extra_argv', ())
extra_argv = extra_argv + tests[1:]
kw['extra_argv'] = extra_argv
from numpy.testing import Tester
return Tester(tests[0]).test(*a, **kw)
simplex containing a given point, and barycentric coordinate
computations.
Functions
---------
.. autosummary::
:toctree: generated/
tsearch
distance_matrix
minkowski_distance
minkowski_distance_p
"""
from __future__ import division, print_function, absolute_import
from .kdtree import *
from .ckdtree import *
from .qhull import *
from ._plotutils import *
__all__ = [s for s in dir() if not s.startswith('_')]
__all__ += ['distance']
from . import distance
from numpy.testing import Tester
test = Tester().test
bench = Tester().bench
for i in range(10):
b.bench_run(np.random.uniform(0, 10, 2))
b.print_results()
def bench_beale(self):
s = funcs.Beale()
# print "checking gradient", scipy.optimize.check_grad(s.fun, s.der, np.array([1.1, -2.3]))
b = _BenchOptimizers("Beale's function",
fun=s.fun,
der=s.der,
hess=None)
for i in range(10):
b.bench_run(np.random.uniform(0, 10, 2))
b.print_results()
def bench_LJ(self):
s = funcs.LJ()
# print "checking gradient", scipy.optimize.check_grad(s.get_energy, s.get_gradient, np.random.uniform(-2,2,3*4))
natoms = 4
b = _BenchOptimizers("%d atom Lennard Jones potential" % (natoms),
fun=s.fun,
der=s.der,
hess=None)
for i in range(10):
b.bench_run(np.random.uniform(-2, 2, natoms * 3))
b.print_results()
if __name__ == "__main__":
Tester().bench(extra_argv=dict())
def tests():
Tester(testing).test(verbose=10)
import pkg_resources
from .info import __doc__
from .core import *
from . import extrapolation, limits, step_generators
from numpy.testing import Tester
try:
__version__ = pkg_resources.get_distribution(__name__).version
except pkg_resources.DistributionNotFound:
__version__ = 'unknown'
test = Tester(raise_warnings="release").test
tm_dense = tm_end - tm_start
# Optionally use the sparse conjugate gradient solver.
if sparse_is_active:
tm_start = time.clock()
for i in range(repeats):
x_sparse, info = sparse.linalg.cg(P_sparse, b)
tm_end = time.clock()
tm_sparse = tm_end - tm_start
# Check that the solutions are close to each other.
if dense_is_active and sparse_is_active:
assert_allclose(x_dense, x_sparse, rtol=1e-4)
# Write the rows.
shape = (n * n, n * n)
if dense_is_active:
print(fmt % (shape, repeats, 'dense solve', tm_dense))
if sparse_is_active:
print(fmt % (shape, repeats, 'sparse cg', tm_sparse))
dense_is_active = (tm_dense < 5)
sparse_is_active = (tm_sparse < 5)
print()
if __name__ == '__main__':
Tester().bench()
from scipy.version import version as __version__
from scipy._lib._version import NumpyVersion as _NumpyVersion
if _NumpyVersion(__numpy_version__) < '1.8.2':
import warnings
warnings.warn(
"Numpy 1.8.2 or above is recommended for this version of "
"scipy (detected version %s)" % __numpy_version__, UserWarning)
del _NumpyVersion
from scipy._lib._ccallback import LowLevelCallable
from numpy.testing import Tester
def test(*a, **kw):
# Nose never recurses into directories with underscores prefix, so we
# need to list those explicitly. Note that numpy.testing.Tester inserts
# the top-level package path determined from __file__ to argv unconditionally,
# so we only need to add the part that is not otherwise recursed into.
import os
underscore_modules = ['_lib', '_build_utils']
base_dir = os.path.abspath(os.path.dirname(__file__))
underscore_paths = [
os.path.join(base_dir, name) for name in underscore_modules
]
kw['extra_argv'] = list(kw.get('extra_argv', [])) + underscore_paths
return test._tester.test(*a, **kw)
test._tester = Tester()
test.__doc__ = test._tester.test.__doc__
test.__test__ = False # Prevent nose from treating test() as a test
def test(level=1, verbosity=1):
from numpy.testing import Tester
return Tester().test(level, verbosity)
if sys.platform != 'cli':
import scalarmath
from function_base import *
from machar import *
from getlimits import *
from shape_base import *
del nt
from fromnumeric import amax as max, amin as min, round_ as round
from numeric import absolute as abs
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += fromnumeric.__all__
__all__ += rec.__all__
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += machar.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
from numpy.testing import Tester
test = Tester(__file__).test
bench = Tester(__file__).bench
