from __future__ import division, print_function, absolute_import
import os
from distutils.version import LooseVersion
import functools
import numpy as np
from numpy.testing import assert_
import pytest
import scipy.special as sc
__all__ = ['with_special_errors', 'assert_tol_equal', 'assert_func_equal',
'FuncData']
#------------------------------------------------------------------------------
# Check if a module is present to be used in tests
#------------------------------------------------------------------------------
class MissingModule(object):
def __init__(self, name):
self.name = name
def check_version(module, min_ver):
if type(module) == MissingModule:
return pytest.mark.skip(reason="{} is not installed".format(module.name))
return pytest.mark.skipif(LooseVersion(module.__version__) < LooseVersion(min_ver),
reason="{} version >= {} required".format(module.__name__, min_ver))
#------------------------------------------------------------------------------
# Enable convergence and loss of precision warnings -- turn off one by one
#------------------------------------------------------------------------------
def with_special_errors(func):
"""
Enable special function errors (such as underflow, overflow,
loss of precision, etc.)
"""
@functools.wraps(func)
def wrapper(*a, **kw):
with sc.errstate(all='raise'):
res = func(*a, **kw)
return res
return wrapper
#------------------------------------------------------------------------------
# Comparing function values at many data points at once, with helpful
#------------------------------------------------------------------------------
def assert_tol_equal(a, b, rtol=1e-7, atol=0, err_msg='', verbose=True):
"""Assert that `a` and `b` are equal to tolerance ``atol + rtol*abs(b)``"""
def compare(x, y):
return np.allclose(x, y, rtol=rtol, atol=atol)
a, b = np.asanyarray(a), np.asanyarray(b)
header = 'Not equal to tolerance rtol=%g, atol=%g' % (rtol, atol)
np.testing.utils.assert_array_compare(compare, a, b, err_msg=str(err_msg),
verbose=verbose, header=header)
#------------------------------------------------------------------------------
# Comparing function values at many data points at once, with helpful
# error reports
#------------------------------------------------------------------------------
def assert_func_equal(func, results, points, rtol=None, atol=None,
param_filter=None, knownfailure=None,
vectorized=True, dtype=None, nan_ok=False,
ignore_inf_sign=False, distinguish_nan_and_inf=True):
if hasattr(points, 'next'):
# it's a generator
points = list(points)
points = np.asarray(points)
if points.ndim == 1:
points = points[:,None]
nparams = points.shape[1]
if hasattr(results, '__name__'):
# function
data = points
result_columns = None
result_func = results
else:
# dataset
data = np.c_[points, results]
result_columns = list(range(nparams, data.shape[1]))
result_func = None
fdata = FuncData(func, data, list(range(nparams)),
result_columns=result_columns, result_func=result_func,
rtol=rtol, atol=atol, param_filter=param_filter,
knownfailure=knownfailure, nan_ok=nan_ok, vectorized=vectorized,
ignore_inf_sign=ignore_inf_sign,
distinguish_nan_and_inf=distinguish_nan_and_inf)
fdata.check()
class FuncData(object):
"""
Data set for checking a special function.
Parameters
----------
func : function
Function to test
filename : str
Input file name
param_columns : int or tuple of ints
Columns indices in which the parameters to `func` lie.
Can be imaginary integers to indicate that the parameter
should be cast to complex.
result_columns : int or tuple of ints, optional
Column indices for expected results from `func`.
result_func : callable, optional
Function to call to obtain results.
rtol : float, optional
Required relative tolerance. Default is 5*eps.
atol : float, optional
Required absolute tolerance. Default is 5*tiny.
param_filter : function, or tuple of functions/Nones, optional
Filter functions to exclude some parameter ranges.
If omitted, no filtering is done.
knownfailure : str, optional
Known failure error message to raise when the test is run.
If omitted, no exception is raised.
nan_ok : bool, optional
If nan is always an accepted result.
vectorized : bool, optional
Whether all functions passed in are vectorized.
ignore_inf_sign : bool, optional
Whether to ignore signs of infinities.
(Doesn't matter for complex-valued functions.)
distinguish_nan_and_inf : bool, optional
If True, treat numbers which contain nans or infs as as
equal. Sets ignore_inf_sign to be True.
"""
def __init__(self, func, data, param_columns, result_columns=None,
result_func=None, rtol=None, atol=None, param_filter=None,
knownfailure=None, dataname=None, nan_ok=False, vectorized=True,
ignore_inf_sign=False, distinguish_nan_and_inf=True):
self.func = func
self.data = data
self.dataname = dataname
if not hasattr(param_columns, '__len__'):
param_columns = (param_columns,)
self.param_columns = tuple(param_columns)
if result_columns is not None:
if not hasattr(result_columns, '__len__'):
result_columns = (result_columns,)
self.result_columns = tuple(result_columns)
if result_func is not None:
raise ValueError("Only result_func or result_columns should be provided")
elif result_func is not None:
self.result_columns = None
else:
raise ValueError("Either result_func or result_columns should be provided")
self.result_func = result_func
self.rtol = rtol
self.atol = atol
if not hasattr(param_filter, '__len__'):
param_filter = (param_filter,)
self.param_filter = param_filter
self.knownfailure = knownfailure
self.nan_ok = nan_ok
self.vectorized = vectorized
self.ignore_inf_sign = ignore_inf_sign
self.distinguish_nan_and_inf = distinguish_nan_and_inf
if not self.distinguish_nan_and_inf:
self.ignore_inf_sign = True
def get_tolerances(self, dtype):
if not np.issubdtype(dtype, np.inexact):
dtype = np.dtype(float)
info = np.finfo(dtype)
rtol, atol = self.rtol, self.atol
if rtol is None:
rtol = 5*info.eps
if atol is None:
atol = 5*info.tiny
return rtol, atol
def check(self, data=None, dtype=None):
"""Check the special function against the data."""
if self.knownfailure:
pytest.xfail(reason=self.knownfailure)
if data is None:
data = self.data
if dtype is None:
dtype = data.dtype
else:
data = data.astype(dtype)
rtol, atol = self.get_tolerances(dtype)
# Apply given filter functions
if self.param_filter:
param_mask = np.ones((data.shape[0],), np.bool_)
for j, filter in zip(self.param_columns, self.param_filter):
if filter:
param_mask &= list(filter(data[:,j]))
data = data[param_mask]
# Pick parameters from the correct columns
params = []
for j in self.param_columns:
if np.iscomplexobj(j):
j = int(j.imag)
params.append(data[:,j].astype(complex))
else:
params.append(data[:,j])
# Helper for evaluating results
def eval_func_at_params(func, skip_mask=None):
if self.vectorized:
got = func(*params)
else:
got = []
for j in range(len(params[0])):
if skip_mask is not None and skip_mask[j]:
got.append(np.nan)
continue
got.append(func(*tuple([params[i][j] for i in range(len(params))])))
got = np.asarray(got)
if not isinstance(got, tuple):
got = (got,)
return got
# Evaluate function to be tested
got = eval_func_at_params(self.func)
# Grab the correct results
if self.result_columns is not None:
# Correct results passed in with the data
wanted = tuple([data[:,icol] for icol in self.result_columns])
else:
# Function producing correct results passed in
skip_mask = None
if self.nan_ok and len(got) == 1:
# Don't spend time evaluating what doesn't need to be evaluated
skip_mask = np.isnan(got[0])
wanted = eval_func_at_params(self.result_func, skip_mask=skip_mask)
# Check the validity of each output returned
assert_(len(got) == len(wanted))
for output_num, (x, y) in enumerate(zip(got, wanted)):
if np.issubdtype(x.dtype, np.complexfloating) or self.ignore_inf_sign:
pinf_x = np.isinf(x)
pinf_y = np.isinf(y)
minf_x = np.isinf(x)
minf_y = np.isinf(y)
else:
pinf_x = np.isposinf(x)
pinf_y = np.isposinf(y)
minf_x = np.isneginf(x)
minf_y = np.isneginf(y)
nan_x = np.isnan(x)
nan_y = np.isnan(y)
olderr = np.seterr(all='ignore')
try:
abs_y = np.absolute(y)
abs_y[~np.isfinite(abs_y)] = 0
diff = np.absolute(x - y)
diff[~np.isfinite(diff)] = 0
rdiff = diff / np.absolute(y)
rdiff[~np.isfinite(rdiff)] = 0
finally:
np.seterr(**olderr)
tol_mask = (diff <= atol + rtol*abs_y)
pinf_mask = (pinf_x == pinf_y)
minf_mask = (minf_x == minf_y)
nan_mask = (nan_x == nan_y)
bad_j = ~(tol_mask & pinf_mask & minf_mask & nan_mask)
point_count = bad_j.size
if self.nan_ok:
bad_j &= ~nan_x
bad_j &= ~nan_y
point_count -= (nan_x | nan_y).sum()
if not self.distinguish_nan_and_inf and not self.nan_ok:
# If nan's are okay we've already covered all these cases
inf_x = np.isinf(x)
inf_y = np.isinf(y)
both_nonfinite = (inf_x & nan_y) | (nan_x & inf_y)
bad_j &= ~both_nonfinite
point_count -= both_nonfinite.sum()
if np.any(bad_j):
# Some bad results: inform what, where, and how bad
msg = [""]
msg.append("Max |adiff|: %g" % diff.max())
msg.append("Max |rdiff|: %g" % rdiff.max())
msg.append("Bad results (%d out of %d) for the following points (in output %d):"
% (np.sum(bad_j), point_count, output_num,))
for j in np.where(bad_j)[0]:
j = int(j)
fmt = lambda x: "%30s" % np.array2string(x[j], precision=18)
a = " ".join(map(fmt, params))
b = " ".join(map(fmt, got))
c = " ".join(map(fmt, wanted))
d = fmt(rdiff)
msg.append("%s => %s != %s (rdiff %s)" % (a, b, c, d))
assert_(False, "\n".join(msg))
def __repr__(self):
"""Pretty-printing, esp. for Nose output"""
if np.any(list(map(np.iscomplexobj, self.param_columns))):
is_complex = " (complex)"
else:
is_complex = ""
if self.dataname:
return "<Data for %s%s: %s>" % (self.func.__name__, is_complex,
os.path.basename(self.dataname))
else:
return "<Data for %s%s>" % (self.func.__name__, is_complex)
