| Current File : //usr/lib64/python2.7/site-packages/numpy/core/tests/test_ufunc.py |
import sys
import numpy as np
from numpy.testing import *
import numpy.core.umath_tests as umt
from numpy.compat import asbytes
class TestUfunc(TestCase):
def test_pickle(self):
import pickle
assert pickle.loads(pickle.dumps(np.sin)) is np.sin
def test_pickle_withstring(self):
import pickle
astring = asbytes("cnumpy.core\n_ufunc_reconstruct\np0\n(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
assert pickle.loads(astring) is np.cos
def test_reduceat_shifting_sum(self) :
L = 6
x = np.arange(L)
idx = np.array(zip(np.arange(L-2), np.arange(L-2)+2)).ravel()
assert_array_equal(np.add.reduceat(x,idx)[::2], [1,3,5,7])
def test_generic_loops(self) :
"""Test generic loops.
The loops to be tested are:
PyUFunc_ff_f_As_dd_d
PyUFunc_ff_f
PyUFunc_dd_d
PyUFunc_gg_g
PyUFunc_FF_F_As_DD_D
PyUFunc_DD_D
PyUFunc_FF_F
PyUFunc_GG_G
PyUFunc_OO_O
PyUFunc_OO_O_method
PyUFunc_f_f_As_d_d
PyUFunc_d_d
PyUFunc_f_f
PyUFunc_g_g
PyUFunc_F_F_As_D_D
PyUFunc_F_F
PyUFunc_D_D
PyUFunc_G_G
PyUFunc_O_O
PyUFunc_O_O_method
PyUFunc_On_Om
Where:
f -- float
d -- double
g -- long double
F -- complex float
D -- complex double
G -- complex long double
O -- python object
It is difficult to assure that each of these loops is entered from the
Python level as the special cased loops are a moving target and the
corresponding types are architecture dependent. We probably need to
define C level testing ufuncs to get at them. For the time being, I've
just looked at the signatures registered in the build directory to find
relevant functions.
Fixme, currently untested:
PyUFunc_ff_f_As_dd_d
PyUFunc_FF_F_As_DD_D
PyUFunc_f_f_As_d_d
PyUFunc_F_F_As_D_D
PyUFunc_On_Om
"""
fone = np.exp
ftwo = lambda x,y : x**y
fone_val = 1
ftwo_val = 1
# check unary PyUFunc_f_f.
msg = "PyUFunc_f_f"
x = np.zeros(10, dtype=np.single)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_d_d.
msg = "PyUFunc_d_d"
x = np.zeros(10, dtype=np.double)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_g_g.
msg = "PyUFunc_g_g"
x = np.zeros(10, dtype=np.longdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_F_F.
msg = "PyUFunc_F_F"
x = np.zeros(10, dtype=np.csingle)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_D_D.
msg = "PyUFunc_D_D"
x = np.zeros(10, dtype=np.cdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_G_G.
msg = "PyUFunc_G_G"
x = np.zeros(10, dtype=np.clongdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check binary PyUFunc_ff_f.
msg = "PyUFunc_ff_f"
x = np.ones(10, dtype=np.single)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# check binary PyUFunc_dd_d.
msg = "PyUFunc_dd_d"
x = np.ones(10, dtype=np.double)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# check binary PyUFunc_gg_g.
msg = "PyUFunc_gg_g"
x = np.ones(10, dtype=np.longdouble)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# check binary PyUFunc_FF_F.
msg = "PyUFunc_FF_F"
x = np.ones(10, dtype=np.csingle)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# check binary PyUFunc_DD_D.
msg = "PyUFunc_DD_D"
x = np.ones(10, dtype=np.cdouble)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# check binary PyUFunc_GG_G.
msg = "PyUFunc_GG_G"
x = np.ones(10, dtype=np.clongdouble)[0::2]
assert_almost_equal(ftwo(x,x), ftwo_val, err_msg=msg)
# class to use in testing object method loops
class foo(object):
def conjugate(self) :
return np.bool_(1)
def logical_xor(self, obj) :
return np.bool_(1)
# check unary PyUFunc_O_O
msg = "PyUFunc_O_O"
x = np.ones(10, dtype=np.object)[0::2]
assert_(np.all(np.abs(x) == 1), msg)
# check unary PyUFunc_O_O_method
msg = "PyUFunc_O_O_method"
x = np.zeros(10, dtype=np.object)[0::2]
for i in range(len(x)) :
x[i] = foo()
assert_(np.all(np.conjugate(x) == True), msg)
# check binary PyUFunc_OO_O
msg = "PyUFunc_OO_O"
x = np.ones(10, dtype=np.object)[0::2]
assert_(np.all(np.add(x,x) == 2), msg)
# check binary PyUFunc_OO_O_method
msg = "PyUFunc_OO_O_method"
x = np.zeros(10, dtype=np.object)[0::2]
for i in range(len(x)) :
x[i] = foo()
assert_(np.all(np.logical_xor(x,x)), msg)
# check PyUFunc_On_Om
# fixme -- I don't know how to do this yet
def test_all_ufunc(self) :
"""Try to check presence and results of all ufuncs.
The list of ufuncs comes from generate_umath.py and is as follows:
===== ==== ============= =============== ========================
done args function types notes
===== ==== ============= =============== ========================
n 1 conjugate nums + O
n 1 absolute nums + O complex -> real
n 1 negative nums + O
n 1 sign nums + O -> int
n 1 invert bool + ints + O flts raise an error
n 1 degrees real + M cmplx raise an error
n 1 radians real + M cmplx raise an error
n 1 arccos flts + M
n 1 arccosh flts + M
n 1 arcsin flts + M
n 1 arcsinh flts + M
n 1 arctan flts + M
n 1 arctanh flts + M
n 1 cos flts + M
n 1 sin flts + M
n 1 tan flts + M
n 1 cosh flts + M
n 1 sinh flts + M
n 1 tanh flts + M
n 1 exp flts + M
n 1 expm1 flts + M
n 1 log flts + M
n 1 log10 flts + M
n 1 log1p flts + M
n 1 sqrt flts + M real x < 0 raises error
n 1 ceil real + M
n 1 trunc real + M
n 1 floor real + M
n 1 fabs real + M
n 1 rint flts + M
n 1 isnan flts -> bool
n 1 isinf flts -> bool
n 1 isfinite flts -> bool
n 1 signbit real -> bool
n 1 modf real -> (frac, int)
n 1 logical_not bool + nums + M -> bool
n 2 left_shift ints + O flts raise an error
n 2 right_shift ints + O flts raise an error
n 2 add bool + nums + O boolean + is ||
n 2 subtract bool + nums + O boolean - is ^
n 2 multiply bool + nums + O boolean * is &
n 2 divide nums + O
n 2 floor_divide nums + O
n 2 true_divide nums + O bBhH -> f, iIlLqQ -> d
n 2 fmod nums + M
n 2 power nums + O
n 2 greater bool + nums + O -> bool
n 2 greater_equal bool + nums + O -> bool
n 2 less bool + nums + O -> bool
n 2 less_equal bool + nums + O -> bool
n 2 equal bool + nums + O -> bool
n 2 not_equal bool + nums + O -> bool
n 2 logical_and bool + nums + M -> bool
n 2 logical_or bool + nums + M -> bool
n 2 logical_xor bool + nums + M -> bool
n 2 maximum bool + nums + O
n 2 minimum bool + nums + O
n 2 bitwise_and bool + ints + O flts raise an error
n 2 bitwise_or bool + ints + O flts raise an error
n 2 bitwise_xor bool + ints + O flts raise an error
n 2 arctan2 real + M
n 2 remainder ints + real + O
n 2 hypot real + M
===== ==== ============= =============== ========================
Types other than those listed will be accepted, but they are cast to
the smallest compatible type for which the function is defined. The
casting rules are:
bool -> int8 -> float32
ints -> double
"""
pass
def test_signature(self):
# the arguments to test_signature are: nin, nout, core_signature
# pass
assert_equal(umt.test_signature(2,1,"(i),(i)->()"), 1)
# pass. empty core signature; treat as plain ufunc (with trivial core)
assert_equal(umt.test_signature(2,1,"(),()->()"), 0)
# in the following calls, a ValueError should be raised because
# of error in core signature
# error: extra parenthesis
msg = "core_sig: extra parenthesis"
try:
ret = umt.test_signature(2,1,"((i)),(i)->()")
assert_equal(ret, None, err_msg=msg)
except ValueError: None
# error: parenthesis matching
msg = "core_sig: parenthesis matching"
try:
ret = umt.test_signature(2,1,"(i),)i(->()")
assert_equal(ret, None, err_msg=msg)
except ValueError: None
# error: incomplete signature. letters outside of parenthesis are ignored
msg = "core_sig: incomplete signature"
try:
ret = umt.test_signature(2,1,"(i),->()")
assert_equal(ret, None, err_msg=msg)
except ValueError: None
# error: incomplete signature. 2 output arguments are specified
msg = "core_sig: incomplete signature"
try:
ret = umt.test_signature(2,2,"(i),(i)->()")
assert_equal(ret, None, err_msg=msg)
except ValueError: None
# more complicated names for variables
assert_equal(umt.test_signature(2,1,"(i1,i2),(J_1)->(_kAB)"),1)
def test_get_signature(self):
assert_equal(umt.inner1d.signature, "(i),(i)->()")
def test_forced_sig(self):
a = 0.5*np.arange(3,dtype='f8')
assert_equal(np.add(a,0.5), [0.5, 1, 1.5])
assert_equal(np.add(a,0.5,sig='i',casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a,0.5,sig='ii->i',casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a,0.5,sig=('i4',),casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a,0.5,sig=('i4','i4','i4'),
casting='unsafe'), [0, 0, 1])
b = np.zeros((3,),dtype='f8')
np.add(a,0.5,out=b)
assert_equal(b, [0.5, 1, 1.5])
b[:] = 0
np.add(a,0.5,sig='i',out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a,0.5,sig='ii->i',out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a,0.5,sig=('i4',),out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a,0.5,sig=('i4','i4','i4'),out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
def test_inner1d(self):
a = np.arange(6).reshape((2,3))
assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1))
def test_broadcast(self):
msg = "broadcast"
a = np.arange(4).reshape((2,1,2))
b = np.arange(4).reshape((1,2,2))
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
msg = "extend & broadcast loop dimensions"
b = np.arange(4).reshape((2,2))
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
msg = "broadcast in core dimensions"
a = np.arange(8).reshape((4,2))
b = np.arange(4).reshape((4,1))
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
msg = "extend & broadcast core and loop dimensions"
a = np.arange(8).reshape((4,2))
b = np.array(7)
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
msg = "broadcast should fail"
a = np.arange(2).reshape((2,1,1))
b = np.arange(3).reshape((3,1,1))
try:
ret = umt.inner1d(a,b)
assert_equal(ret, None, err_msg=msg)
except ValueError: None
def test_type_cast(self):
msg = "type cast"
a = np.arange(6, dtype='short').reshape((2,3))
assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)
msg = "type cast on one argument"
a = np.arange(6).reshape((2,3))
b = a+0.1
assert_array_almost_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1),
err_msg=msg)
def test_endian(self):
msg = "big endian"
a = np.arange(6, dtype='>i4').reshape((2,3))
assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)
msg = "little endian"
a = np.arange(6, dtype='<i4').reshape((2,3))
assert_array_equal(umt.inner1d(a,a), np.sum(a*a,axis=-1), err_msg=msg)
# Output should always be native-endian
Ba = np.arange(1, dtype='>f8')
La = np.arange(1, dtype='<f8')
assert_equal((Ba+Ba).dtype, np.dtype('f8'))
assert_equal((Ba+La).dtype, np.dtype('f8'))
assert_equal((La+Ba).dtype, np.dtype('f8'))
assert_equal((La+La).dtype, np.dtype('f8'))
assert_equal(np.absolute(La).dtype, np.dtype('f8'))
assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
assert_equal(np.negative(La).dtype, np.dtype('f8'))
assert_equal(np.negative(Ba).dtype, np.dtype('f8'))
def test_incontiguous_array(self):
msg = "incontiguous memory layout of array"
x = np.arange(64).reshape((2,2,2,2,2,2))
a = x[:,0,:,0,:,0]
b = x[:,1,:,1,:,1]
a[0,0,0] = -1
msg2 = "make sure it references to the original array"
assert_equal(x[0,0,0,0,0,0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
x = np.arange(24).reshape(2,3,4)
a = x.T
b = x.T
a[0,0,0] = -1
assert_equal(x[0,0,0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a,b), np.sum(a*b,axis=-1), err_msg=msg)
def test_output_argument(self):
msg = "output argument"
a = np.arange(12).reshape((2,3,2))
b = np.arange(4).reshape((2,1,2)) + 1
c = np.zeros((2,3),dtype='int')
umt.inner1d(a,b,c)
assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a,b,out=c)
assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
msg = "output argument with type cast"
c = np.zeros((2,3),dtype='int16')
umt.inner1d(a,b,c)
assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a,b,out=c)
assert_array_equal(c, np.sum(a*b,axis=-1), err_msg=msg)
msg = "output argument with incontiguous layout"
c = np.zeros((2,3,4),dtype='int16')
umt.inner1d(a,b,c[...,0])
assert_array_equal(c[...,0], np.sum(a*b,axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a,b,out=c[...,0])
assert_array_equal(c[...,0], np.sum(a*b,axis=-1), err_msg=msg)
def test_innerwt(self):
a = np.arange(6).reshape((2,3))
b = np.arange(10,16).reshape((2,3))
w = np.arange(20,26).reshape((2,3))
assert_array_equal(umt.innerwt(a,b,w), np.sum(a*b*w,axis=-1))
a = np.arange(100,124).reshape((2,3,4))
b = np.arange(200,224).reshape((2,3,4))
w = np.arange(300,324).reshape((2,3,4))
assert_array_equal(umt.innerwt(a,b,w), np.sum(a*b*w,axis=-1))
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def compare_matrix_multiply_results(self, tp):
d1 = np.array(rand(2,3,4), dtype=tp)
d2 = np.array(rand(2,3,4), dtype=tp)
msg = "matrix multiply on type %s" % d1.dtype.name
def permute_n(n):
if n == 1:
return ([0],)
ret = ()
base = permute_n(n-1)
for perm in base:
for i in xrange(n):
new = perm + [n-1]
new[n-1] = new[i]
new[i] = n-1
ret += (new,)
return ret
def slice_n(n):
if n == 0:
return ((),)
ret = ()
base = slice_n(n-1)
for sl in base:
ret += (sl+(slice(None),),)
ret += (sl+(slice(0,1),),)
return ret
def broadcastable(s1,s2):
return s1 == s2 or s1 == 1 or s2 == 1
permute_3 = permute_n(3)
slice_3 = slice_n(3) + ((slice(None,None,-1),)*3,)
ref = True
for p1 in permute_3:
for p2 in permute_3:
for s1 in slice_3:
for s2 in slice_3:
a1 = d1.transpose(p1)[s1]
a2 = d2.transpose(p2)[s2]
ref = ref and a1.base != None
ref = ref and a2.base != None
if broadcastable(a1.shape[-1], a2.shape[-2]) and \
broadcastable(a1.shape[0], a2.shape[0]):
assert_array_almost_equal(
umt.matrix_multiply(a1,a2),
np.sum(a2[...,np.newaxis].swapaxes(-3,-1) *
a1[...,np.newaxis,:], axis=-1),
err_msg = msg+' %s %s' % (str(a1.shape),
str(a2.shape)))
assert_equal(ref, True, err_msg="reference check")
def test_object_logical(self):
a = np.array([3, None, True, False, "test", ""], dtype=object)
assert_equal(np.logical_or(a, None),
np.array([x or None for x in a], dtype=object))
assert_equal(np.logical_or(a, True),
np.array([x or True for x in a], dtype=object))
assert_equal(np.logical_or(a, 12),
np.array([x or 12 for x in a], dtype=object))
assert_equal(np.logical_or(a, "blah"),
np.array([x or "blah" for x in a], dtype=object))
assert_equal(np.logical_and(a, None),
np.array([x and None for x in a], dtype=object))
assert_equal(np.logical_and(a, True),
np.array([x and True for x in a], dtype=object))
assert_equal(np.logical_and(a, 12),
np.array([x and 12 for x in a], dtype=object))
assert_equal(np.logical_and(a, "blah"),
np.array([x and "blah" for x in a], dtype=object))
assert_equal(np.logical_not(a),
np.array([not x for x in a], dtype=object))
assert_equal(np.logical_or.reduce(a), 3)
assert_equal(np.logical_and.reduce(a), None)
def test_object_array_reduction(self):
# Reductions on object arrays
a = np.array(['a', 'b', 'c'], dtype=object)
assert_equal(np.sum(a), 'abc')
assert_equal(np.max(a), 'c')
assert_equal(np.min(a), 'a')
a = np.array([True, False, True], dtype=object)
assert_equal(np.sum(a), 2)
assert_equal(np.prod(a), 0)
assert_equal(np.any(a), True)
assert_equal(np.all(a), False)
assert_equal(np.max(a), True)
assert_equal(np.min(a), False)
def test_zerosize_reduction(self):
# Test with default dtype and object dtype
for a in [[], np.array([], dtype=object)]:
assert_equal(np.sum(a), 0)
assert_equal(np.prod(a), 1)
assert_equal(np.any(a), False)
assert_equal(np.all(a), True)
assert_raises(ValueError, np.max, a)
assert_raises(ValueError, np.min, a)
def test_axis_out_of_bounds(self):
a = np.array([False, False])
assert_raises(ValueError, a.all, axis=1)
a = np.array([False, False])
assert_raises(ValueError, a.all, axis=-2)
a = np.array([False, False])
assert_raises(ValueError, a.any, axis=1)
a = np.array([False, False])
assert_raises(ValueError, a.any, axis=-2)
def test_scalar_reduction(self):
# The functions 'sum', 'prod', etc allow specifying axis=0
# even for scalars
assert_equal(np.sum(3, axis=0), 3)
assert_equal(np.prod(3.5, axis=0), 3.5)
assert_equal(np.any(True, axis=0), True)
assert_equal(np.all(False, axis=0), False)
assert_equal(np.max(3, axis=0), 3)
assert_equal(np.min(2.5, axis=0), 2.5)
# Make sure that scalars are coming out from this operation
assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32)
def test_casting_out_param(self):
# Test that it's possible to do casts on output
a = np.ones((200,100), np.int64)
b = np.ones((200,100), np.int64)
c = np.ones((200,100), np.float64)
np.add(a, b, out=c)
assert_equal(c, 2)
a = np.zeros(65536)
b = np.zeros(65536, dtype=np.float32)
np.subtract(a, 0, out=b)
assert_equal(b, 0)
def test_where_param(self):
# Test that the where= ufunc parameter works with regular arrays
a = np.arange(7)
b = np.ones(7)
c = np.zeros(7)
np.add(a, b, out=c, where=(a % 2 == 1))
assert_equal(c, [0,2,0,4,0,6,0])
a = np.arange(4).reshape(2,2) + 2
np.power(a, [2,3], out=a, where=[[0,1],[1,0]])
assert_equal(a, [[2, 27], [16, 5]])
# Broadcasting the where= parameter
np.subtract(a, 2, out=a, where=[True,False])
assert_equal(a, [[0, 27], [14, 5]])
def test_where_param_buffer_output(self):
# This test is temporarily skipped because it requires
# adding masking features to the nditer to work properly
# With casting on output
a = np.ones(10, np.int64)
b = np.ones(10, np.int64)
c = 1.5 * np.ones(10, np.float64)
np.add(a, b, out=c, where=[1,0,0,1,0,0,1,1,1,0])
assert_equal(c, [2,1.5,1.5,2,1.5,1.5,2,2,2,1.5])
def check_identityless_reduction(self, a):
# np.minimum.reduce is a identityless reduction
# Verify that it sees the zero at various positions
a[...] = 1
a[1,0,0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0,1)), [0,1,1,1])
assert_equal(np.minimum.reduce(a, axis=(0,2)), [0,1,1])
assert_equal(np.minimum.reduce(a, axis=(1,2)), [1,0])
assert_equal(np.minimum.reduce(a, axis=0),
[[0,1,1,1], [1,1,1,1], [1,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1,1,1,1], [0,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1,1,1], [0,1,1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0,1,0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0,1)), [0,1,1,1])
assert_equal(np.minimum.reduce(a, axis=(0,2)), [1,0,1])
assert_equal(np.minimum.reduce(a, axis=(1,2)), [0,1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1,1,1,1], [0,1,1,1], [1,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[0,1,1,1], [1,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1,0,1], [1,1,1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0,0,1] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0,1)), [1,0,1,1])
assert_equal(np.minimum.reduce(a, axis=(0,2)), [0,1,1])
assert_equal(np.minimum.reduce(a, axis=(1,2)), [0,1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1,0,1,1], [1,1,1,1], [1,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1,0,1,1], [1,1,1,1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[0,1,1], [1,1,1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
def test_identityless_reduction_corder(self):
a = np.empty((2,3,4), order='C')
self.check_identityless_reduction(a)
def test_identityless_reduction_forder(self):
a = np.empty((2,3,4), order='F')
self.check_identityless_reduction(a)
def test_identityless_reduction_otherorder(self):
a = np.empty((2,4,3), order='C').swapaxes(1,2)
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig(self):
a = np.empty((3,5,4), order='C').swapaxes(1,2)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig_unaligned(self):
a = np.empty((3*4*5*8 + 1,), dtype='i1')
a = a[1:].view(dtype='f8')
a.shape = (3,4,5)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_nonreorderable(self):
a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]])
res = np.divide.reduce(a, axis=0)
assert_equal(res, [8.0, 4.0, 8.0])
res = np.divide.reduce(a, axis=1)
assert_equal(res, [2.0, 8.0])
res = np.divide.reduce(a, axis=())
assert_equal(res, a)
assert_raises(ValueError, np.divide.reduce, a, axis=(0,1))
def test_reduce_zero_axis(self):
# If we have a n x m array and do a reduction with axis=1, then we are
# doing n reductions, and each reduction takes an m-element array. For
# a reduction operation without an identity, then:
# n > 0, m > 0: fine
# n = 0, m > 0: fine, doing 0 reductions of m-element arrays
# n > 0, m = 0: can't reduce a 0-element array, ValueError
# n = 0, m = 0: can't reduce a 0-element array, ValueError (for
# consistency with the above case)
# This test doesn't actually look at return values, it just checks to
# make sure that error we get an error in exactly those cases where we
# expect one, and assumes the calculations themselves are done
# correctly.
def ok(f, *args, **kwargs):
f(*args, **kwargs)
def err(f, *args, **kwargs):
assert_raises(ValueError, f, *args, **kwargs)
def t(expect, func, n, m):
expect(func, np.zeros((n, m)), axis=1)
expect(func, np.zeros((m, n)), axis=0)
expect(func, np.zeros((n // 2, n // 2, m)), axis=2)
expect(func, np.zeros((n // 2, m, n // 2)), axis=1)
expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2))
expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2))
expect(func, np.zeros((m // 3, m // 3, m // 3,
n // 2, n //2)),
axis=(0, 1, 2))
# Check what happens if the inner (resp. outer) dimensions are a
# mix of zero and non-zero:
expect(func, np.zeros((10, m, n)), axis=(0, 1))
expect(func, np.zeros((10, n, m)), axis=(0, 2))
expect(func, np.zeros((m, 10, n)), axis=0)
expect(func, np.zeros((10, m, n)), axis=1)
expect(func, np.zeros((10, n, m)), axis=2)
# np.maximum is just an arbitrary ufunc with no reduction identity
assert_equal(np.maximum.identity, None)
t(ok, np.maximum.reduce, 30, 30)
t(ok, np.maximum.reduce, 0, 30)
t(err, np.maximum.reduce, 30, 0)
t(err, np.maximum.reduce, 0, 0)
err(np.maximum.reduce, [])
np.maximum.reduce(np.zeros((0, 0)), axis=())
# all of the combinations are fine for a reduction that has an
# identity
t(ok, np.add.reduce, 30, 30)
t(ok, np.add.reduce, 0, 30)
t(ok, np.add.reduce, 30, 0)
t(ok, np.add.reduce, 0, 0)
np.add.reduce([])
np.add.reduce(np.zeros((0, 0)), axis=())
# OTOH, accumulate always makes sense for any combination of n and m,
# because it maps an m-element array to an m-element array. These
# tests are simpler because accumulate doesn't accept multiple axes.
for uf in (np.maximum, np.add):
uf.accumulate(np.zeros((30, 0)), axis=0)
uf.accumulate(np.zeros((0, 30)), axis=0)
uf.accumulate(np.zeros((30, 30)), axis=0)
uf.accumulate(np.zeros((0, 0)), axis=0)
def test_safe_casting(self):
# In old versions of numpy, in-place operations used the 'unsafe'
# casting rules. In some future version, 'same_kind' will become the
# default.
a = np.array([1, 2, 3], dtype=int)
# Non-in-place addition is fine
assert_array_equal(assert_no_warnings(np.add, a, 1.1),
[2.1, 3.1, 4.1])
assert_warns(DeprecationWarning, np.add, a, 1.1, out=a)
assert_array_equal(a, [2, 3, 4])
def add_inplace(a, b):
a += b
assert_warns(DeprecationWarning, add_inplace, a, 1.1)
assert_array_equal(a, [3, 4, 5])
# Make sure that explicitly overriding the warning is allowed:
assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe")
assert_array_equal(a, [4, 5, 6])
if __name__ == "__main__":
run_module_suite()