60 lines
3.0 KiB
Python
60 lines
3.0 KiB
Python
import numpy as np
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import platform
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from os import path
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import sys
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import pytest
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from ctypes import c_longlong, c_double, c_float, c_int, cast, pointer, POINTER
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from numpy.testing import assert_array_max_ulp
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from numpy.core._multiarray_umath import __cpu_features__
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IS_AVX = __cpu_features__.get('AVX512F', False) or \
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(__cpu_features__.get('FMA3', False) and __cpu_features__.get('AVX2', False))
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runtest = sys.platform.startswith('linux') and IS_AVX
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platform_skip = pytest.mark.skipif(not runtest,
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reason="avoid testing inconsistent platform "
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"library implementations")
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# convert string to hex function taken from:
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# https://stackoverflow.com/questions/1592158/convert-hex-to-float #
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def convert(s, datatype="np.float32"):
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i = int(s, 16) # convert from hex to a Python int
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if (datatype == "np.float64"):
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cp = pointer(c_longlong(i)) # make this into a c long long integer
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fp = cast(cp, POINTER(c_double)) # cast the int pointer to a double pointer
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else:
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cp = pointer(c_int(i)) # make this into a c integer
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fp = cast(cp, POINTER(c_float)) # cast the int pointer to a float pointer
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return fp.contents.value # dereference the pointer, get the float
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str_to_float = np.vectorize(convert)
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files = ['umath-validation-set-exp',
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'umath-validation-set-log',
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'umath-validation-set-sin',
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'umath-validation-set-cos']
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class TestAccuracy:
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@platform_skip
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def test_validate_transcendentals(self):
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with np.errstate(all='ignore'):
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for filename in files:
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data_dir = path.join(path.dirname(__file__), 'data')
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filepath = path.join(data_dir, filename)
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with open(filepath) as fid:
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file_without_comments = (r for r in fid if not r[0] in ('$', '#'))
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data = np.genfromtxt(file_without_comments,
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dtype=('|S39','|S39','|S39',int),
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names=('type','input','output','ulperr'),
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delimiter=',',
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skip_header=1)
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npfunc = getattr(np, filename.split('-')[3])
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for datatype in np.unique(data['type']):
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data_subset = data[data['type'] == datatype]
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inval = np.array(str_to_float(data_subset['input'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))
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outval = np.array(str_to_float(data_subset['output'].astype(str), data_subset['type'].astype(str)), dtype=eval(datatype))
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perm = np.random.permutation(len(inval))
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inval = inval[perm]
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outval = outval[perm]
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maxulperr = data_subset['ulperr'].max()
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assert_array_max_ulp(npfunc(inval), outval, maxulperr)
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