You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
444 lines
15 KiB
444 lines
15 KiB
1 year ago
|
import timeit
|
||
|
from functools import reduce
|
||
|
|
||
|
import numpy as np
|
||
|
from numpy import float_
|
||
|
import numpy.core.fromnumeric as fromnumeric
|
||
|
|
||
|
from numpy.testing import build_err_msg
|
||
|
|
||
|
|
||
|
pi = np.pi
|
||
|
|
||
|
class ModuleTester:
|
||
|
def __init__(self, module):
|
||
|
self.module = module
|
||
|
self.allequal = module.allequal
|
||
|
self.arange = module.arange
|
||
|
self.array = module.array
|
||
|
self.concatenate = module.concatenate
|
||
|
self.count = module.count
|
||
|
self.equal = module.equal
|
||
|
self.filled = module.filled
|
||
|
self.getmask = module.getmask
|
||
|
self.getmaskarray = module.getmaskarray
|
||
|
self.id = id
|
||
|
self.inner = module.inner
|
||
|
self.make_mask = module.make_mask
|
||
|
self.masked = module.masked
|
||
|
self.masked_array = module.masked_array
|
||
|
self.masked_values = module.masked_values
|
||
|
self.mask_or = module.mask_or
|
||
|
self.nomask = module.nomask
|
||
|
self.ones = module.ones
|
||
|
self.outer = module.outer
|
||
|
self.repeat = module.repeat
|
||
|
self.resize = module.resize
|
||
|
self.sort = module.sort
|
||
|
self.take = module.take
|
||
|
self.transpose = module.transpose
|
||
|
self.zeros = module.zeros
|
||
|
self.MaskType = module.MaskType
|
||
|
try:
|
||
|
self.umath = module.umath
|
||
|
except AttributeError:
|
||
|
self.umath = module.core.umath
|
||
|
self.testnames = []
|
||
|
|
||
|
def assert_array_compare(self, comparison, x, y, err_msg='', header='',
|
||
|
fill_value=True):
|
||
|
"""
|
||
|
Assert that a comparison of two masked arrays is satisfied elementwise.
|
||
|
|
||
|
"""
|
||
|
xf = self.filled(x)
|
||
|
yf = self.filled(y)
|
||
|
m = self.mask_or(self.getmask(x), self.getmask(y))
|
||
|
|
||
|
x = self.filled(self.masked_array(xf, mask=m), fill_value)
|
||
|
y = self.filled(self.masked_array(yf, mask=m), fill_value)
|
||
|
if (x.dtype.char != "O"):
|
||
|
x = x.astype(float_)
|
||
|
if isinstance(x, np.ndarray) and x.size > 1:
|
||
|
x[np.isnan(x)] = 0
|
||
|
elif np.isnan(x):
|
||
|
x = 0
|
||
|
if (y.dtype.char != "O"):
|
||
|
y = y.astype(float_)
|
||
|
if isinstance(y, np.ndarray) and y.size > 1:
|
||
|
y[np.isnan(y)] = 0
|
||
|
elif np.isnan(y):
|
||
|
y = 0
|
||
|
try:
|
||
|
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
|
||
|
if not cond:
|
||
|
msg = build_err_msg([x, y],
|
||
|
err_msg
|
||
|
+ f'\n(shapes {x.shape}, {y.shape} mismatch)',
|
||
|
header=header,
|
||
|
names=('x', 'y'))
|
||
|
assert cond, msg
|
||
|
val = comparison(x, y)
|
||
|
if m is not self.nomask and fill_value:
|
||
|
val = self.masked_array(val, mask=m)
|
||
|
if isinstance(val, bool):
|
||
|
cond = val
|
||
|
reduced = [0]
|
||
|
else:
|
||
|
reduced = val.ravel()
|
||
|
cond = reduced.all()
|
||
|
reduced = reduced.tolist()
|
||
|
if not cond:
|
||
|
match = 100-100.0*reduced.count(1)/len(reduced)
|
||
|
msg = build_err_msg([x, y],
|
||
|
err_msg
|
||
|
+ '\n(mismatch %s%%)' % (match,),
|
||
|
header=header,
|
||
|
names=('x', 'y'))
|
||
|
assert cond, msg
|
||
|
except ValueError as e:
|
||
|
msg = build_err_msg([x, y], err_msg, header=header, names=('x', 'y'))
|
||
|
raise ValueError(msg) from e
|
||
|
|
||
|
def assert_array_equal(self, x, y, err_msg=''):
|
||
|
"""
|
||
|
Checks the elementwise equality of two masked arrays.
|
||
|
|
||
|
"""
|
||
|
self.assert_array_compare(self.equal, x, y, err_msg=err_msg,
|
||
|
header='Arrays are not equal')
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_0(self):
|
||
|
"""
|
||
|
Tests creation
|
||
|
|
||
|
"""
|
||
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
||
|
m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
||
|
xm = self.masked_array(x, mask=m)
|
||
|
xm[0]
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_1(self):
|
||
|
"""
|
||
|
Tests creation
|
||
|
|
||
|
"""
|
||
|
x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
|
||
|
y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
|
||
|
m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
||
|
m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
|
||
|
xm = self.masked_array(x, mask=m1)
|
||
|
ym = self.masked_array(y, mask=m2)
|
||
|
xf = np.where(m1, 1.e+20, x)
|
||
|
xm.set_fill_value(1.e+20)
|
||
|
|
||
|
assert((xm-ym).filled(0).any())
|
||
|
s = x.shape
|
||
|
assert(xm.size == reduce(lambda x, y:x*y, s))
|
||
|
assert(self.count(xm) == len(m1) - reduce(lambda x, y:x+y, m1))
|
||
|
|
||
|
for s in [(4, 3), (6, 2)]:
|
||
|
x.shape = s
|
||
|
y.shape = s
|
||
|
xm.shape = s
|
||
|
ym.shape = s
|
||
|
xf.shape = s
|
||
|
assert(self.count(xm) == len(m1) - reduce(lambda x, y:x+y, m1))
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_2(self):
|
||
|
"""
|
||
|
Tests conversions and indexing.
|
||
|
|
||
|
"""
|
||
|
x1 = np.array([1, 2, 4, 3])
|
||
|
x2 = self.array(x1, mask=[1, 0, 0, 0])
|
||
|
x3 = self.array(x1, mask=[0, 1, 0, 1])
|
||
|
x4 = self.array(x1)
|
||
|
# test conversion to strings, no errors
|
||
|
str(x2)
|
||
|
repr(x2)
|
||
|
# tests of indexing
|
||
|
assert type(x2[1]) is type(x1[1])
|
||
|
assert x1[1] == x2[1]
|
||
|
x1[2] = 9
|
||
|
x2[2] = 9
|
||
|
self.assert_array_equal(x1, x2)
|
||
|
x1[1:3] = 99
|
||
|
x2[1:3] = 99
|
||
|
x2[1] = self.masked
|
||
|
x2[1:3] = self.masked
|
||
|
x2[:] = x1
|
||
|
x2[1] = self.masked
|
||
|
x3[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
|
||
|
x4[:] = self.masked_array([1, 2, 3, 4], [0, 1, 1, 0])
|
||
|
x1 = np.arange(5)*1.0
|
||
|
x2 = self.masked_values(x1, 3.0)
|
||
|
x1 = self.array([1, 'hello', 2, 3], object)
|
||
|
x2 = np.array([1, 'hello', 2, 3], object)
|
||
|
# check that no error occurs.
|
||
|
x1[1]
|
||
|
x2[1]
|
||
|
assert x1[1:1].shape == (0,)
|
||
|
# Tests copy-size
|
||
|
n = [0, 0, 1, 0, 0]
|
||
|
m = self.make_mask(n)
|
||
|
m2 = self.make_mask(m)
|
||
|
assert(m is m2)
|
||
|
m3 = self.make_mask(m, copy=1)
|
||
|
assert(m is not m3)
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_3(self):
|
||
|
"""
|
||
|
Tests resize/repeat
|
||
|
|
||
|
"""
|
||
|
x4 = self.arange(4)
|
||
|
x4[2] = self.masked
|
||
|
y4 = self.resize(x4, (8,))
|
||
|
assert self.allequal(self.concatenate([x4, x4]), y4)
|
||
|
assert self.allequal(self.getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])
|
||
|
y5 = self.repeat(x4, (2, 2, 2, 2), axis=0)
|
||
|
self.assert_array_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3])
|
||
|
y6 = self.repeat(x4, 2, axis=0)
|
||
|
assert self.allequal(y5, y6)
|
||
|
y7 = x4.repeat((2, 2, 2, 2), axis=0)
|
||
|
assert self.allequal(y5, y7)
|
||
|
y8 = x4.repeat(2, 0)
|
||
|
assert self.allequal(y5, y8)
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_4(self):
|
||
|
"""
|
||
|
Test of take, transpose, inner, outer products.
|
||
|
|
||
|
"""
|
||
|
x = self.arange(24)
|
||
|
y = np.arange(24)
|
||
|
x[5:6] = self.masked
|
||
|
x = x.reshape(2, 3, 4)
|
||
|
y = y.reshape(2, 3, 4)
|
||
|
assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
|
||
|
assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
|
||
|
assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
|
||
|
self.inner(x, y))
|
||
|
assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
|
||
|
self.outer(x, y))
|
||
|
y = self.array(['abc', 1, 'def', 2, 3], object)
|
||
|
y[2] = self.masked
|
||
|
t = self.take(y, [0, 3, 4])
|
||
|
assert t[0] == 'abc'
|
||
|
assert t[1] == 2
|
||
|
assert t[2] == 3
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_5(self):
|
||
|
"""
|
||
|
Tests inplace w/ scalar
|
||
|
|
||
|
"""
|
||
|
x = self.arange(10)
|
||
|
y = self.arange(10)
|
||
|
xm = self.arange(10)
|
||
|
xm[2] = self.masked
|
||
|
x += 1
|
||
|
assert self.allequal(x, y+1)
|
||
|
xm += 1
|
||
|
assert self.allequal(xm, y+1)
|
||
|
|
||
|
x = self.arange(10)
|
||
|
xm = self.arange(10)
|
||
|
xm[2] = self.masked
|
||
|
x -= 1
|
||
|
assert self.allequal(x, y-1)
|
||
|
xm -= 1
|
||
|
assert self.allequal(xm, y-1)
|
||
|
|
||
|
x = self.arange(10)*1.0
|
||
|
xm = self.arange(10)*1.0
|
||
|
xm[2] = self.masked
|
||
|
x *= 2.0
|
||
|
assert self.allequal(x, y*2)
|
||
|
xm *= 2.0
|
||
|
assert self.allequal(xm, y*2)
|
||
|
|
||
|
x = self.arange(10)*2
|
||
|
xm = self.arange(10)*2
|
||
|
xm[2] = self.masked
|
||
|
x /= 2
|
||
|
assert self.allequal(x, y)
|
||
|
xm /= 2
|
||
|
assert self.allequal(xm, y)
|
||
|
|
||
|
x = self.arange(10)*1.0
|
||
|
xm = self.arange(10)*1.0
|
||
|
xm[2] = self.masked
|
||
|
x /= 2.0
|
||
|
assert self.allequal(x, y/2.0)
|
||
|
xm /= self.arange(10)
|
||
|
self.assert_array_equal(xm, self.ones((10,)))
|
||
|
|
||
|
x = self.arange(10).astype(float_)
|
||
|
xm = self.arange(10)
|
||
|
xm[2] = self.masked
|
||
|
x += 1.
|
||
|
assert self.allequal(x, y + 1.)
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_6(self):
|
||
|
"""
|
||
|
Tests inplace w/ array
|
||
|
|
||
|
"""
|
||
|
x = self.arange(10, dtype=float_)
|
||
|
y = self.arange(10)
|
||
|
xm = self.arange(10, dtype=float_)
|
||
|
xm[2] = self.masked
|
||
|
m = xm.mask
|
||
|
a = self.arange(10, dtype=float_)
|
||
|
a[-1] = self.masked
|
||
|
x += a
|
||
|
xm += a
|
||
|
assert self.allequal(x, y+a)
|
||
|
assert self.allequal(xm, y+a)
|
||
|
assert self.allequal(xm.mask, self.mask_or(m, a.mask))
|
||
|
|
||
|
x = self.arange(10, dtype=float_)
|
||
|
xm = self.arange(10, dtype=float_)
|
||
|
xm[2] = self.masked
|
||
|
m = xm.mask
|
||
|
a = self.arange(10, dtype=float_)
|
||
|
a[-1] = self.masked
|
||
|
x -= a
|
||
|
xm -= a
|
||
|
assert self.allequal(x, y-a)
|
||
|
assert self.allequal(xm, y-a)
|
||
|
assert self.allequal(xm.mask, self.mask_or(m, a.mask))
|
||
|
|
||
|
x = self.arange(10, dtype=float_)
|
||
|
xm = self.arange(10, dtype=float_)
|
||
|
xm[2] = self.masked
|
||
|
m = xm.mask
|
||
|
a = self.arange(10, dtype=float_)
|
||
|
a[-1] = self.masked
|
||
|
x *= a
|
||
|
xm *= a
|
||
|
assert self.allequal(x, y*a)
|
||
|
assert self.allequal(xm, y*a)
|
||
|
assert self.allequal(xm.mask, self.mask_or(m, a.mask))
|
||
|
|
||
|
x = self.arange(10, dtype=float_)
|
||
|
xm = self.arange(10, dtype=float_)
|
||
|
xm[2] = self.masked
|
||
|
m = xm.mask
|
||
|
a = self.arange(10, dtype=float_)
|
||
|
a[-1] = self.masked
|
||
|
x /= a
|
||
|
xm /= a
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_7(self):
|
||
|
"Tests ufunc"
|
||
|
d = (self.array([1.0, 0, -1, pi/2]*2, mask=[0, 1]+[0]*6),
|
||
|
self.array([1.0, 0, -1, pi/2]*2, mask=[1, 0]+[0]*6),)
|
||
|
for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
|
||
|
# 'sin', 'cos', 'tan',
|
||
|
# 'arcsin', 'arccos', 'arctan',
|
||
|
# 'sinh', 'cosh', 'tanh',
|
||
|
# 'arcsinh',
|
||
|
# 'arccosh',
|
||
|
# 'arctanh',
|
||
|
# 'absolute', 'fabs', 'negative',
|
||
|
# # 'nonzero', 'around',
|
||
|
# 'floor', 'ceil',
|
||
|
# # 'sometrue', 'alltrue',
|
||
|
# 'logical_not',
|
||
|
# 'add', 'subtract', 'multiply',
|
||
|
# 'divide', 'true_divide', 'floor_divide',
|
||
|
# 'remainder', 'fmod', 'hypot', 'arctan2',
|
||
|
# 'equal', 'not_equal', 'less_equal', 'greater_equal',
|
||
|
# 'less', 'greater',
|
||
|
# 'logical_and', 'logical_or', 'logical_xor',
|
||
|
]:
|
||
|
try:
|
||
|
uf = getattr(self.umath, f)
|
||
|
except AttributeError:
|
||
|
uf = getattr(fromnumeric, f)
|
||
|
mf = getattr(self.module, f)
|
||
|
args = d[:uf.nin]
|
||
|
ur = uf(*args)
|
||
|
mr = mf(*args)
|
||
|
self.assert_array_equal(ur.filled(0), mr.filled(0), f)
|
||
|
self.assert_array_equal(ur._mask, mr._mask)
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_99(self):
|
||
|
# test average
|
||
|
ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
|
||
|
self.assert_array_equal(2.0, self.average(ott, axis=0))
|
||
|
self.assert_array_equal(2.0, self.average(ott, weights=[1., 1., 2., 1.]))
|
||
|
result, wts = self.average(ott, weights=[1., 1., 2., 1.], returned=1)
|
||
|
self.assert_array_equal(2.0, result)
|
||
|
assert(wts == 4.0)
|
||
|
ott[:] = self.masked
|
||
|
assert(self.average(ott, axis=0) is self.masked)
|
||
|
ott = self.array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
|
||
|
ott = ott.reshape(2, 2)
|
||
|
ott[:, 1] = self.masked
|
||
|
self.assert_array_equal(self.average(ott, axis=0), [2.0, 0.0])
|
||
|
assert(self.average(ott, axis=1)[0] is self.masked)
|
||
|
self.assert_array_equal([2., 0.], self.average(ott, axis=0))
|
||
|
result, wts = self.average(ott, axis=0, returned=1)
|
||
|
self.assert_array_equal(wts, [1., 0.])
|
||
|
w1 = [0, 1, 1, 1, 1, 0]
|
||
|
w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
|
||
|
x = self.arange(6)
|
||
|
self.assert_array_equal(self.average(x, axis=0), 2.5)
|
||
|
self.assert_array_equal(self.average(x, axis=0, weights=w1), 2.5)
|
||
|
y = self.array([self.arange(6), 2.0*self.arange(6)])
|
||
|
self.assert_array_equal(self.average(y, None), np.add.reduce(np.arange(6))*3./12.)
|
||
|
self.assert_array_equal(self.average(y, axis=0), np.arange(6) * 3./2.)
|
||
|
self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0])
|
||
|
self.assert_array_equal(self.average(y, None, weights=w2), 20./6.)
|
||
|
self.assert_array_equal(self.average(y, axis=0, weights=w2), [0., 1., 2., 3., 4., 10.])
|
||
|
self.assert_array_equal(self.average(y, axis=1), [self.average(x, axis=0), self.average(x, axis=0) * 2.0])
|
||
|
m1 = self.zeros(6)
|
||
|
m2 = [0, 0, 1, 1, 0, 0]
|
||
|
m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
|
||
|
m4 = self.ones(6)
|
||
|
m5 = [0, 1, 1, 1, 1, 1]
|
||
|
self.assert_array_equal(self.average(self.masked_array(x, m1), axis=0), 2.5)
|
||
|
self.assert_array_equal(self.average(self.masked_array(x, m2), axis=0), 2.5)
|
||
|
self.assert_array_equal(self.average(self.masked_array(x, m5), axis=0), 0.0)
|
||
|
self.assert_array_equal(self.count(self.average(self.masked_array(x, m4), axis=0)), 0)
|
||
|
z = self.masked_array(y, m3)
|
||
|
self.assert_array_equal(self.average(z, None), 20./6.)
|
||
|
self.assert_array_equal(self.average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
|
||
|
self.assert_array_equal(self.average(z, axis=1), [2.5, 5.0])
|
||
|
self.assert_array_equal(self.average(z, axis=0, weights=w2), [0., 1., 99., 99., 4.0, 10.0])
|
||
|
|
||
|
@np.errstate(all='ignore')
|
||
|
def test_A(self):
|
||
|
x = self.arange(24)
|
||
|
x[5:6] = self.masked
|
||
|
x = x.reshape(2, 3, 4)
|
||
|
|
||
|
|
||
|
if __name__ == '__main__':
|
||
|
setup_base = ("from __main__ import ModuleTester \n"
|
||
|
"import numpy\n"
|
||
|
"tester = ModuleTester(module)\n")
|
||
|
setup_cur = "import numpy.ma.core as module\n" + setup_base
|
||
|
(nrepeat, nloop) = (10, 10)
|
||
|
|
||
|
for i in range(1, 8):
|
||
|
func = 'tester.test_%i()' % i
|
||
|
cur = timeit.Timer(func, setup_cur).repeat(nrepeat, nloop*10)
|
||
|
cur = np.sort(cur)
|
||
|
print("#%i" % i + 50*'.')
|
||
|
print(eval("ModuleTester.test_%i.__doc__" % i))
|
||
|
print(f'core_current : {cur[0]:.3f} - {cur[1]:.3f}')
|