322 lines
12 KiB
Python
322 lines
12 KiB
Python
import os
|
|
import functools
|
|
import operator
|
|
from scipy._lib import _pep440
|
|
|
|
import numpy as np
|
|
from numpy.testing import assert_
|
|
import pytest
|
|
|
|
import scipy.special as sc
|
|
|
|
__all__ = ['with_special_errors', 'assert_func_equal', 'FuncData']
|
|
|
|
|
|
#------------------------------------------------------------------------------
|
|
# Check if a module is present to be used in tests
|
|
#------------------------------------------------------------------------------
|
|
|
|
class MissingModule:
|
|
def __init__(self, name):
|
|
self.name = name
|
|
|
|
|
|
def check_version(module, min_ver):
|
|
if type(module) == MissingModule:
|
|
return pytest.mark.skip(reason=f"{module.name} is not installed")
|
|
return pytest.mark.skipif(
|
|
_pep440.parse(module.__version__) < _pep440.Version(min_ver),
|
|
reason=f"{module.__name__} version >= {min_ver} required"
|
|
)
|
|
|
|
|
|
#------------------------------------------------------------------------------
|
|
# 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
|
|
# 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:
|
|
"""
|
|
Data set for checking a special function.
|
|
|
|
Parameters
|
|
----------
|
|
func : function
|
|
Function to test
|
|
data : numpy array
|
|
columnar data to use for testing
|
|
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
|
|
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:
|
|
message = "Only result_func or result_columns should be provided"
|
|
raise ValueError(message)
|
|
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, dtypes=None):
|
|
"""Check the special function against the data."""
|
|
__tracebackhide__ = operator.methodcaller(
|
|
'errisinstance', AssertionError
|
|
)
|
|
|
|
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 idx, j in enumerate(self.param_columns):
|
|
if np.iscomplexobj(j):
|
|
j = int(j.imag)
|
|
params.append(data[:,j].astype(complex))
|
|
elif dtypes and idx < len(dtypes):
|
|
params.append(data[:, j].astype(dtypes[idx]))
|
|
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)
|
|
|
|
with np.errstate(all='ignore'):
|
|
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
|
|
|
|
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[bad_j].max())
|
|
msg.append("Max |rdiff|: %g" % rdiff[bad_j].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.nonzero(bad_j)[0]:
|
|
j = int(j)
|
|
def fmt(x):
|
|
return '%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(f"{a} => {b} != {c} (rdiff {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 {}{}: {}>".format(self.func.__name__, is_complex,
|
|
os.path.basename(self.dataname))
|
|
else:
|
|
return f"<Data for {self.func.__name__}{is_complex}>"
|