| Current File : //usr/lib64/python2.7/site-packages/numpy/lib/tests/test_function_base.py |
import warnings
import numpy as np
from numpy.testing import (
run_module_suite, TestCase, assert_, assert_equal,
assert_array_equal, assert_almost_equal, assert_array_almost_equal,
assert_raises, assert_allclose, assert_array_max_ulp
)
from numpy.random import rand
from numpy.lib import *
class TestAny(TestCase):
def test_basic(self):
y1 = [0, 0, 1, 0]
y2 = [0, 0, 0, 0]
y3 = [1, 0, 1, 0]
assert_(np.any(y1))
assert_(np.any(y3))
assert_(not np.any(y2))
def test_nd(self):
y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
assert_(np.any(y1))
assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
class TestAll(TestCase):
def test_basic(self):
y1 = [0, 1, 1, 0]
y2 = [0, 0, 0, 0]
y3 = [1, 1, 1, 1]
assert_(not np.all(y1))
assert_(np.all(y3))
assert_(not np.all(y2))
assert_(np.all(~np.array(y2)))
def test_nd(self):
y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
assert_(not np.all(y1))
assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
class TestCopy(TestCase):
def test_basic(self):
a = np.array([[1, 2], [3, 4]])
a_copy = np.copy(a)
assert_array_equal(a, a_copy)
a_copy[0, 0] = 10
assert_equal(a[0, 0], 1)
assert_equal(a_copy[0, 0], 10)
def test_order(self):
# It turns out that people rely on np.copy() preserving order by
# default; changing this broke scikit-learn:
# https://github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783
a = np.array([[1, 2], [3, 4]])
assert_(a.flags.c_contiguous)
assert_(not a.flags.f_contiguous)
a_fort = np.array([[1, 2], [3, 4]], order="F")
assert_(not a_fort.flags.c_contiguous)
assert_(a_fort.flags.f_contiguous)
a_copy = np.copy(a)
assert_(a_copy.flags.c_contiguous)
assert_(not a_copy.flags.f_contiguous)
a_fort_copy = np.copy(a_fort)
assert_(not a_fort_copy.flags.c_contiguous)
assert_(a_fort_copy.flags.f_contiguous)
class TestAverage(TestCase):
def test_basic(self):
y1 = np.array([1, 2, 3])
assert_(average(y1, axis=0) == 2.)
y2 = np.array([1., 2., 3.])
assert_(average(y2, axis=0) == 2.)
y3 = [0., 0., 0.]
assert_(average(y3, axis=0) == 0.)
y4 = np.ones((4, 4))
y4[0, 1] = 0
y4[1, 0] = 2
assert_almost_equal(y4.mean(0), average(y4, 0))
assert_almost_equal(y4.mean(1), average(y4, 1))
y5 = rand(5, 5)
assert_almost_equal(y5.mean(0), average(y5, 0))
assert_almost_equal(y5.mean(1), average(y5, 1))
y6 = np.matrix(rand(5, 5))
assert_array_equal(y6.mean(0), average(y6, 0))
def test_weights(self):
y = np.arange(10)
w = np.arange(10)
actual = average(y, weights=w)
desired = (np.arange(10) ** 2).sum()*1. / np.arange(10).sum()
assert_almost_equal(actual, desired)
y1 = np.array([[1, 2, 3], [4, 5, 6]])
w0 = [1, 2]
actual = average(y1, weights=w0, axis=0)
desired = np.array([3., 4., 5.])
assert_almost_equal(actual, desired)
w1 = [0, 0, 1]
actual = average(y1, weights=w1, axis=1)
desired = np.array([3., 6.])
assert_almost_equal(actual, desired)
# This should raise an error. Can we test for that ?
# assert_equal(average(y1, weights=w1), 9./2.)
# 2D Case
w2 = [[0, 0, 1], [0, 0, 2]]
desired = np.array([3., 6.])
assert_array_equal(average(y1, weights=w2, axis=1), desired)
assert_equal(average(y1, weights=w2), 5.)
def test_returned(self):
y = np.array([[1, 2, 3], [4, 5, 6]])
# No weights
avg, scl = average(y, returned=True)
assert_equal(scl, 6.)
avg, scl = average(y, 0, returned=True)
assert_array_equal(scl, np.array([2., 2., 2.]))
avg, scl = average(y, 1, returned=True)
assert_array_equal(scl, np.array([3., 3.]))
# With weights
w0 = [1, 2]
avg, scl = average(y, weights=w0, axis=0, returned=True)
assert_array_equal(scl, np.array([3., 3., 3.]))
w1 = [1, 2, 3]
avg, scl = average(y, weights=w1, axis=1, returned=True)
assert_array_equal(scl, np.array([6., 6.]))
w2 = [[0, 0, 1], [1, 2, 3]]
avg, scl = average(y, weights=w2, axis=1, returned=True)
assert_array_equal(scl, np.array([1., 6.]))
class TestSelect(TestCase):
def _select(self, cond, values, default=0):
output = []
for m in range(len(cond)):
output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
return output
def test_basic(self):
choices = [np.array([1, 2, 3]),
np.array([4, 5, 6]),
np.array([7, 8, 9])]
conditions = [np.array([0, 0, 0]),
np.array([0, 1, 0]),
np.array([0, 0, 1])]
assert_array_equal(select(conditions, choices, default=15),
self._select(conditions, choices, default=15))
assert_equal(len(choices), 3)
assert_equal(len(conditions), 3)
class TestInsert(TestCase):
def test_basic(self):
a = [1, 2, 3]
assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
assert_equal(insert(a, 1,[1,2,3]), [1, 1, 2, 3, 2, 3])
assert_equal(insert(a,[1,2,3],9),[1,9,2,9,3,9])
b = np.array([0, 1], dtype=np.float64)
assert_equal(insert(b, 0, b[0]), [0., 0., 1.])
def test_multidim(self):
a = [[1, 1, 1]]
r = [[2, 2, 2],
[1, 1, 1]]
assert_equal(insert(a, 0, [2, 2, 2], axis=0), r)
assert_equal(insert(a, 0, 2, axis=0), r)
a = np.arange(4).reshape(2,2)
assert_equal(insert(a[:,:1], 1, a[:,1], axis=1), a)
assert_equal(insert(a[:1,:], 1, a[1,:], axis=0), a)
class TestAmax(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
assert_equal(np.amax(a), 10.0)
b = [[3, 6.0, 9.0],
[4, 10.0, 5.0],
[8, 3.0, 2.0]]
assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0])
assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0])
class TestAmin(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
assert_equal(np.amin(a), -5.0)
b = [[3, 6.0, 9.0],
[4, 10.0, 5.0],
[8, 3.0, 2.0]]
assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0])
assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0])
class TestPtp(TestCase):
def test_basic(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
assert_equal(np.ptp(a, axis=0), 15.0)
b = [[3, 6.0, 9.0],
[4, 10.0, 5.0],
[8, 3.0, 2.0]]
assert_equal(np.ptp(b, axis=0), [5.0, 7.0, 7.0])
assert_equal(np.ptp(b, axis= -1), [6.0, 6.0, 6.0])
class TestCumsum(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array([[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array([[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
class TestProd(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, prod, a)
self.assertRaises(ArithmeticError, prod, a2, 1)
self.assertRaises(ArithmeticError, prod, a)
else:
assert_equal(np.prod(a, axis=0), 26400)
assert_array_equal(np.prod(a2, axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(np.prod(a2, axis= -1),
np.array([24, 1890, 600], ctype))
class TestCumprod(TestCase):
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, cumprod, a)
self.assertRaises(ArithmeticError, cumprod, a2, 1)
self.assertRaises(ArithmeticError, cumprod, a)
else:
assert_array_equal(np.cumprod(a, axis= -1),
np.array([1, 2, 20, 220, 1320, 6600, 26400], ctype))
assert_array_equal(np.cumprod(a2, axis=0),
np.array([[ 1, 2, 3, 4], [ 5, 12, 21, 36],
[50, 36, 84, 180]], ctype))
assert_array_equal(np.cumprod(a2, axis= -1),
np.array([[ 1, 2, 6, 24], [ 5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
class TestDiff(TestCase):
def test_basic(self):
x = [1, 4, 6, 7, 12]
out = np.array([3, 2, 1, 5])
out2 = np.array([-1, -1, 4])
out3 = np.array([0, 5])
assert_array_equal(diff(x), out)
assert_array_equal(diff(x, n=2), out2)
assert_array_equal(diff(x, n=3), out3)
def test_nd(self):
x = 20 * rand(10, 20, 30)
out1 = x[:, :, 1:] - x[:, :, :-1]
out2 = out1[:, :, 1:] - out1[:, :, :-1]
out3 = x[1:, :, :] - x[:-1, :, :]
out4 = out3[1:, :, :] - out3[:-1, :, :]
assert_array_equal(diff(x), out1)
assert_array_equal(diff(x, n=2), out2)
assert_array_equal(diff(x, axis=0), out3)
assert_array_equal(diff(x, n=2, axis=0), out4)
class TestGradient(TestCase):
def test_basic(self):
v = [[1, 1], [3, 4]]
x = np.array(v)
dx = [np.array([[2., 3.], [2., 3.]]),
np.array([[0., 0.], [1., 1.]])]
assert_array_equal(gradient(x), dx)
assert_array_equal(gradient(v), dx)
def test_badargs(self):
# for 2D array, gradient can take 0, 1, or 2 extra args
x = np.array([[1, 1], [3, 4]])
assert_raises(SyntaxError, gradient, x, np.array([1., 1.]),
np.array([1., 1.]), np.array([1., 1.]))
def test_masked(self):
# Make sure that gradient supports subclasses like masked arrays
x = np.ma.array([[1, 1], [3, 4]])
assert_equal(type(gradient(x)[0]), type(x))
def test_datetime64(self):
# Make sure gradient() can handle special types like datetime64
x = np.array(['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12',
'1910-10-12', '1910-12-12', '1912-12-12'],
dtype='datetime64[D]')
dx = np.array([ -5, -3, 0, 31, 61, 396, 731], dtype='timedelta64[D]')
assert_array_equal(gradient(x), dx)
assert_(dx.dtype == np.dtype('timedelta64[D]'))
def test_timedelta64(self):
# Make sure gradient() can handle special types like timedelta64
x = np.array([-5, -3, 10, 12, 61, 321, 300], dtype='timedelta64[D]')
dx = np.array([ 2, 7, 7, 25, 154, 119, -21], dtype='timedelta64[D]')
assert_array_equal(gradient(x), dx)
assert_(dx.dtype == np.dtype('timedelta64[D]'))
class TestAngle(TestCase):
def test_basic(self):
x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2,
1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
y = angle(x)
yo = [np.arctan(3.0 / 1.0), np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0,
- np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)]
z = angle(x, deg=1)
zo = np.array(yo) * 180 / np.pi
assert_array_almost_equal(y, yo, 11)
assert_array_almost_equal(z, zo, 11)
class TestTrimZeros(TestCase):
""" only testing for integer splits.
"""
def test_basic(self):
a = np.array([0, 0, 1, 2, 3, 4, 0])
res = trim_zeros(a)
assert_array_equal(res, np.array([1, 2, 3, 4]))
def test_leading_skip(self):
a = np.array([0, 0, 1, 0, 2, 3, 4, 0])
res = trim_zeros(a)
assert_array_equal(res, np.array([1, 0, 2, 3, 4]))
def test_trailing_skip(self):
a = np.array([0, 0, 1, 0, 2, 3, 0, 4, 0])
res = trim_zeros(a)
assert_array_equal(res, np.array([1, 0, 2, 3, 0, 4]))
class TestExtins(TestCase):
def test_basic(self):
a = np.array([1, 3, 2, 1, 2, 3, 3])
b = extract(a > 1, a)
assert_array_equal(b, [3, 2, 2, 3, 3])
def test_place(self):
a = np.array([1, 4, 3, 2, 5, 8, 7])
place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])
def test_both(self):
a = rand(10)
mask = a > 0.5
ac = a.copy()
c = extract(mask, a)
place(a, mask, 0)
place(a, mask, c)
assert_array_equal(a, ac)
class TestVectorize(TestCase):
def test_simple(self):
def addsubtract(a, b):
if a > b:
return a - b
else:
return a + b
f = vectorize(addsubtract)
r = f([0, 3, 6, 9], [1, 3, 5, 7])
assert_array_equal(r, [1, 6, 1, 2])
def test_scalar(self):
def addsubtract(a, b):
if a > b:
return a - b
else:
return a + b
f = vectorize(addsubtract)
r = f([0, 3, 6, 9], 5)
assert_array_equal(r, [5, 8, 1, 4])
def test_large(self):
x = np.linspace(-3, 2, 10000)
f = vectorize(lambda x: x)
y = f(x)
assert_array_equal(y, x)
def test_ufunc(self):
import math
f = vectorize(math.cos)
args = np.array([0, 0.5*np.pi, np.pi, 1.5*np.pi, 2*np.pi])
r1 = f(args)
r2 = np.cos(args)
assert_array_equal(r1, r2)
def test_keywords(self):
import math
def foo(a, b=1):
return a + b
f = vectorize(foo)
args = np.array([1, 2, 3])
r1 = f(args)
r2 = np.array([2, 3, 4])
assert_array_equal(r1, r2)
r1 = f(args, 2)
r2 = np.array([3, 4, 5])
assert_array_equal(r1, r2)
def test_keywords_no_func_code(self):
# This needs to test a function that has keywords but
# no func_code attribute, since otherwise vectorize will
# inspect the func_code.
import random
try:
f = vectorize(random.randrange)
except:
raise AssertionError()
def test_keywords2_ticket_2100(self):
r"""Test kwarg support: enhancement ticket 2100"""
import math
def foo(a, b=1):
return a + b
f = vectorize(foo)
args = np.array([1, 2, 3])
r1 = f(a=args)
r2 = np.array([2, 3, 4])
assert_array_equal(r1, r2)
r1 = f(b=1, a=args)
assert_array_equal(r1, r2)
r1 = f(args, b=2)
r2 = np.array([3, 4, 5])
assert_array_equal(r1, r2)
def test_keywords3_ticket_2100(self):
"""Test excluded with mixed positional and kwargs: ticket 2100"""
def mypolyval(x, p):
_p = list(p)
res = _p.pop(0)
while _p:
res = res*x + _p.pop(0)
return res
vpolyval = np.vectorize(mypolyval, excluded=['p',1])
ans = [3, 6]
assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3]))
def test_keywords4_ticket_2100(self):
"""Test vectorizing function with no positional args."""
@vectorize
def f(**kw):
res = 1.0
for _k in kw:
res *= kw[_k]
return res
assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8])
def test_keywords5_ticket_2100(self):
"""Test vectorizing function with no kwargs args."""
@vectorize
def f(*v):
return np.prod(v)
assert_array_equal(f([1, 2], [3, 4]), [3, 8])
def test_coverage1_ticket_2100(self):
def foo():
return 1
f = vectorize(foo)
assert_array_equal(f(), 1)
def test_assigning_docstring(self):
def foo(x):
return x
doc = "Provided documentation"
f = vectorize(foo, doc=doc)
assert_equal(f.__doc__, doc)
def test_UnboundMethod_ticket_1156(self):
"""Regression test for issue 1156"""
class Foo:
b = 2
def bar(self, a):
return a**self.b
assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
np.arange(9)**2)
assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
np.arange(9)**2)
def test_execution_order_ticket_1487(self):
"""Regression test for dependence on execution order: issue 1487"""
f1 = vectorize(lambda x: x)
res1a = f1(np.arange(3))
res1b = f1(np.arange(0.1, 3))
f2 = vectorize(lambda x: x)
res2b = f2(np.arange(0.1, 3))
res2a = f2(np.arange(3))
assert_equal(res1a, res2a)
assert_equal(res1b, res2b)
def test_string_ticket_1892(self):
"""Test vectorization over strings: issue 1892."""
f = np.vectorize(lambda x:x)
s = '0123456789'*10
assert_equal(s, f(s))
#z = f(np.array([s,s]))
#assert_array_equal([s,s], f(s))
def test_cache(self):
"""Ensure that vectorized func called exactly once per argument."""
_calls = [0]
@vectorize
def f(x):
_calls[0] += 1
return x**2
f.cache = True
x = np.arange(5)
assert_array_equal(f(x), x*x)
assert_equal(_calls[0], len(x))
class TestDigitize(TestCase):
def test_forward(self):
x = np.arange(-6, 5)
bins = np.arange(-5, 5)
assert_array_equal(digitize(x, bins), np.arange(11))
def test_reverse(self):
x = np.arange(5, -6, -1)
bins = np.arange(5, -5, -1)
assert_array_equal(digitize(x, bins), np.arange(11))
def test_random(self):
x = rand(10)
bin = np.linspace(x.min(), x.max(), 10)
assert_(np.all(digitize(x, bin) != 0))
def test_right_basic(self):
x = [1, 5, 4, 10, 8, 11, 0]
bins = [1, 5, 10]
default_answer = [1, 2, 1, 3, 2, 3, 0]
assert_array_equal(digitize(x, bins), default_answer)
right_answer = [0, 1, 1, 2, 2, 3, 0]
assert_array_equal(digitize(x, bins, True), right_answer)
def test_right_open(self):
x = np.arange(-6, 5)
bins = np.arange(-6, 4)
assert_array_equal(digitize(x, bins, True), np.arange(11))
def test_right_open_reverse(self):
x = np.arange(5, -6, -1)
bins = np.arange(4, -6, -1)
assert_array_equal(digitize(x, bins, True), np.arange(11))
def test_right_open_random(self):
x = rand(10)
bins = np.linspace(x.min(), x.max(), 10)
assert_(np.all(digitize(x, bins, True) != 10))
class TestUnwrap(TestCase):
def test_simple(self):
#check that unwrap removes jumps greather that 2*pi
assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1])
#check that unwrap maintans continuity
assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi))
class TestFilterwindows(TestCase):
def test_hanning(self):
#check symmetry
w = hanning(10)
assert_array_almost_equal(w, flipud(w), 7)
#check known value
assert_almost_equal(np.sum(w, axis=0), 4.500, 4)
def test_hamming(self):
#check symmetry
w = hamming(10)
assert_array_almost_equal(w, flipud(w), 7)
#check known value
assert_almost_equal(np.sum(w, axis=0), 4.9400, 4)
def test_bartlett(self):
#check symmetry
w = bartlett(10)
assert_array_almost_equal(w, flipud(w), 7)
#check known value
assert_almost_equal(np.sum(w, axis=0), 4.4444, 4)
def test_blackman(self):
#check symmetry
w = blackman(10)
assert_array_almost_equal(w, flipud(w), 7)
#check known value
assert_almost_equal(np.sum(w, axis=0), 3.7800, 4)
class TestTrapz(TestCase):
def test_simple(self):
x = np.arange(-10, 10, .1)
r = trapz(np.exp(-.5*x**2) / np.sqrt(2*np.pi), dx=0.1)
#check integral of normal equals 1
assert_almost_equal(r, 1, 7)
def test_ndim(self):
x = np.linspace(0, 1, 3)
y = np.linspace(0, 2, 8)
z = np.linspace(0, 3, 13)
wx = np.ones_like(x) * (x[1] - x[0])
wx[0] /= 2
wx[-1] /= 2
wy = np.ones_like(y) * (y[1] - y[0])
wy[0] /= 2
wy[-1] /= 2
wz = np.ones_like(z) * (z[1] - z[0])
wz[0] /= 2
wz[-1] /= 2
q = x[:, None, None] + y[None, :, None] + z[None, None, :]
qx = (q * wx[:, None, None]).sum(axis=0)
qy = (q * wy[None, :, None]).sum(axis=1)
qz = (q * wz[None, None, :]).sum(axis=2)
# n-d `x`
r = trapz(q, x=x[:, None, None], axis=0)
assert_almost_equal(r, qx)
r = trapz(q, x=y[None, :, None], axis=1)
assert_almost_equal(r, qy)
r = trapz(q, x=z[None, None, :], axis=2)
assert_almost_equal(r, qz)
# 1-d `x`
r = trapz(q, x=x, axis=0)
assert_almost_equal(r, qx)
r = trapz(q, x=y, axis=1)
assert_almost_equal(r, qy)
r = trapz(q, x=z, axis=2)
assert_almost_equal(r, qz)
def test_masked(self):
#Testing that masked arrays behave as if the function is 0 where
#masked
x = np.arange(5)
y = x * x
mask = x == 2
ym = np.ma.array(y, mask=mask)
r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
assert_almost_equal(trapz(ym, x), r)
xm = np.ma.array(x, mask=mask)
assert_almost_equal(trapz(ym, xm), r)
xm = np.ma.array(x, mask=mask)
assert_almost_equal(trapz(y, xm), r)
def test_matrix(self):
#Test to make sure matrices give the same answer as ndarrays
x = np.linspace(0, 5)
y = x * x
r = trapz(y, x)
mx = np.matrix(x)
my = np.matrix(y)
mr = trapz(my, mx)
assert_almost_equal(mr, r)
class TestSinc(TestCase):
def test_simple(self):
assert_(sinc(0) == 1)
w = sinc(np.linspace(-1, 1, 100))
#check symmetry
assert_array_almost_equal(w, flipud(w), 7)
def test_array_like(self):
x = [0, 0.5]
y1 = sinc(np.array(x))
y2 = sinc(list(x))
y3 = sinc(tuple(x))
assert_array_equal(y1, y2)
assert_array_equal(y1, y3)
class TestHistogram(TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_simple(self):
n = 100
v = rand(n)
(a, b) = histogram(v)
#check if the sum of the bins equals the number of samples
assert_equal(np.sum(a, axis=0), n)
#check that the bin counts are evenly spaced when the data is from a
# linear function
(a, b) = histogram(np.linspace(0, 10, 100))
assert_array_equal(a, 10)
def test_one_bin(self):
# Ticket 632
hist, edges = histogram([1, 2, 3, 4], [1, 2])
assert_array_equal(hist, [2,])
assert_array_equal(edges, [1, 2])
assert_raises(ValueError, histogram, [1, 2], bins=0)
h, e = histogram([1, 2], bins=1)
assert_equal(h, np.array([2]))
assert_allclose(e, np.array([1., 2.]))
def test_normed(self):
# Check that the integral of the density equals 1.
n = 100
v = rand(n)
a, b = histogram(v, normed=True)
area = np.sum(a * diff(b))
assert_almost_equal(area, 1)
# Check with non-constant bin widths (buggy but backwards compatible)
v = np.arange(10)
bins = [0, 1, 5, 9, 10]
a, b = histogram(v, bins, normed=True)
area = np.sum(a * diff(b))
assert_almost_equal(area, 1)
def test_density(self):
# Check that the integral of the density equals 1.
n = 100
v = rand(n)
a, b = histogram(v, density=True)
area = np.sum(a * diff(b))
assert_almost_equal(area, 1)
# Check with non-constant bin widths
v = np.arange(10)
bins = [0, 1, 3, 6, 10]
a, b = histogram(v, bins, density=True)
assert_array_equal(a, .1)
assert_equal(np.sum(a*diff(b)), 1)
# Variale bin widths are especially useful to deal with
# infinities.
v = np.arange(10)
bins = [0, 1, 3, 6, np.inf]
a, b = histogram(v, bins, density=True)
assert_array_equal(a, [.1, .1, .1, 0.])
# Taken from a bug report from N. Becker on the numpy-discussion
# mailing list Aug. 6, 2010.
counts, dmy = np.histogram([1, 2, 3, 4], [0.5, 1.5, np.inf], density=True)
assert_equal(counts, [.25, 0])
def test_outliers(self):
# Check that outliers are not tallied
a = np.arange(10) + .5
# Lower outliers
h, b = histogram(a, range=[0, 9])
assert_equal(h.sum(), 9)
# Upper outliers
h, b = histogram(a, range=[1, 10])
assert_equal(h.sum(), 9)
# Normalization
h, b = histogram(a, range=[1, 9], normed=True)
assert_equal((h * diff(b)).sum(), 1)
# Weights
w = np.arange(10) + .5
h, b = histogram(a, range=[1, 9], weights=w, normed=True)
assert_equal((h * diff(b)).sum(), 1)
h, b = histogram(a, bins=8, range=[1, 9], weights=w)
assert_equal(h, w[1:-1])
def test_type(self):
# Check the type of the returned histogram
a = np.arange(10) + .5
h, b = histogram(a)
assert_(issubdtype(h.dtype, int))
h, b = histogram(a, normed=True)
assert_(issubdtype(h.dtype, float))
h, b = histogram(a, weights=np.ones(10, int))
assert_(issubdtype(h.dtype, int))
h, b = histogram(a, weights=np.ones(10, float))
assert_(issubdtype(h.dtype, float))
def test_weights(self):
v = rand(100)
w = np.ones(100) * 5
a, b = histogram(v)
na, nb = histogram(v, normed=True)
wa, wb = histogram(v, weights=w)
nwa, nwb = histogram(v, weights=w, normed=True)
assert_array_almost_equal(a * 5, wa)
assert_array_almost_equal(na, nwa)
# Check weights are properly applied.
v = np.linspace(0, 10, 10)
w = np.concatenate((np.zeros(5), np.ones(5)))
wa, wb = histogram(v, bins=np.arange(11), weights=w)
assert_array_almost_equal(wa, w)
# Check with integer weights
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
assert_array_equal(wa, [4, 5, 0, 1])
wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4)
# Check weights with non-uniform bin widths
a, b = histogram(np.arange(9), [0, 1, 3, 6, 10], \
weights=[2, 1, 1, 1, 1, 1, 1, 1, 1], density=True)
assert_almost_equal(a, [.2, .1, .1, .075])
def test_empty(self):
a, b = histogram([], bins=([0, 1]))
assert_array_equal(a, np.array([0]))
assert_array_equal(b, np.array([0, 1]))
class TestHistogramdd(TestCase):
def test_simple(self):
x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5], \
[.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
H, edges = histogramdd(x, (2, 3, 3), range=[[-1, 1], [0, 3], [0, 3]])
answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]], [[0, 1, 0], [0, 0, 1],
[0, 0, 1]]])
assert_array_equal(H, answer)
# Check normalization
ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
H, edges = histogramdd(x, bins=ed, normed=True)
assert_(np.all(H == answer / 12.))
# Check that H has the correct shape.
H, edges = histogramdd(x, (2, 3, 4), range=[[-1, 1], [0, 3], [0, 4]],
normed=True)
answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]], [[0, 1, 0, 0],
[0, 0, 1, 0], [0, 0, 1, 0]]])
assert_array_almost_equal(H, answer / 6., 4)
# Check that a sequence of arrays is accepted and H has the correct
# shape.
z = [np.squeeze(y) for y in split(x, 3, axis=1)]
H, edges = histogramdd(z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
answer = np.array([[[0, 0], [0, 0], [0, 0]],
[[0, 1], [0, 0], [1, 0]],
[[0, 1], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0]]])
assert_array_equal(H, answer)
Z = np.zeros((5, 5, 5))
Z[range(5), range(5), range(5)] = 1.
H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
assert_array_equal(H, Z)
def test_shape_3d(self):
# All possible permutations for bins of different lengths in 3D.
bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
(4, 5, 6))
r = rand(10, 3)
for b in bins:
H, edges = histogramdd(r, b)
assert_(H.shape == b)
def test_shape_4d(self):
# All possible permutations for bins of different lengths in 4D.
bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
(5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
(7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
(4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
(6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
(5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))
r = rand(10, 4)
for b in bins:
H, edges = histogramdd(r, b)
assert_(H.shape == b)
def test_weights(self):
v = rand(100, 2)
hist, edges = histogramdd(v)
n_hist, edges = histogramdd(v, normed=True)
w_hist, edges = histogramdd(v, weights=np.ones(100))
assert_array_equal(w_hist, hist)
w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True)
assert_array_equal(w_hist, n_hist)
w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
assert_array_equal(w_hist, 2 * hist)
def test_identical_samples(self):
x = np.zeros((10, 2), int)
hist, edges = histogramdd(x, bins=2)
assert_array_equal(edges[0], np.array([-0.5, 0. , 0.5]))
def test_empty(self):
a, b = histogramdd([[], []], bins=([0, 1], [0, 1]))
assert_array_max_ulp(a, np.array([[ 0.]]))
a, b = np.histogramdd([[], [], []], bins=2)
assert_array_max_ulp(a, np.zeros((2, 2, 2)))
def test_bins_errors(self):
"""There are two ways to specify bins. Check for the right errors when
mixing those."""
x = np.arange(8).reshape(2, 4)
assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5])
assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1])
assert_raises(ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]])
assert_raises(ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]]))
def test_inf_edges(self):
"""Test using +/-inf bin edges works. See #1788."""
olderr = np.seterr(invalid='ignore')
try:
x = np.arange(6).reshape(3, 2)
expected = np.array([[1, 0], [0, 1], [0, 1]])
h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
assert_allclose(h, expected)
h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
assert_allclose(h, expected)
h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
assert_allclose(h, expected)
finally:
np.seterr(**olderr)
class TestUnique(TestCase):
def test_simple(self):
x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0])
assert_(np.all(unique(x) == [0, 1, 2, 3, 4]))
assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1]))
x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))
class TestCheckFinite(TestCase):
def test_simple(self):
a = [1, 2, 3]
b = [1, 2, np.inf]
c = [1, 2, np.nan]
np.lib.asarray_chkfinite(a)
assert_raises(ValueError, np.lib.asarray_chkfinite, b)
assert_raises(ValueError, np.lib.asarray_chkfinite, c)
def test_dtype_order(self):
"""Regression test for missing dtype and order arguments"""
a = [1, 2, 3]
a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64)
assert_(a.dtype == np.float64)
class TestNaNFuncts(TestCase):
def setUp(self):
self.A = np.array([[[ np.nan, 0.01319214, 0.01620964],
[ 0.11704017, np.nan, 0.75157887],
[ 0.28333658, 0.1630199 , np.nan ]],
[[ 0.59541557, np.nan, 0.37910852],
[ np.nan, 0.87964135, np.nan ],
[ 0.70543747, np.nan, 0.34306596]],
[[ 0.72687499, 0.91084584, np.nan ],
[ 0.84386844, 0.38944762, 0.23913896],
[ np.nan, 0.37068164, 0.33850425]]])
def test_nansum(self):
assert_almost_equal(nansum(self.A), 8.0664079100000006)
assert_almost_equal(nansum(self.A, 0),
np.array([[ 1.32229056, 0.92403798, 0.39531816],
[ 0.96090861, 1.26908897, 0.99071783],
[ 0.98877405, 0.53370154, 0.68157021]]))
assert_almost_equal(nansum(self.A, 1),
np.array([[ 0.40037675, 0.17621204, 0.76778851],
[ 1.30085304, 0.87964135, 0.72217448],
[ 1.57074343, 1.6709751 , 0.57764321]]))
assert_almost_equal(nansum(self.A, 2),
np.array([[ 0.02940178, 0.86861904, 0.44635648],
[ 0.97452409, 0.87964135, 1.04850343],
[ 1.63772083, 1.47245502, 0.70918589]]))
def test_nanmin(self):
assert_almost_equal(nanmin(self.A), 0.01319214)
assert_almost_equal(nanmin(self.A, 0),
np.array([[ 0.59541557, 0.01319214, 0.01620964],
[ 0.11704017, 0.38944762, 0.23913896],
[ 0.28333658, 0.1630199 , 0.33850425]]))
assert_almost_equal(nanmin(self.A, 1),
np.array([[ 0.11704017, 0.01319214, 0.01620964],
[ 0.59541557, 0.87964135, 0.34306596],
[ 0.72687499, 0.37068164, 0.23913896]]))
assert_almost_equal(nanmin(self.A, 2),
np.array([[ 0.01319214, 0.11704017, 0.1630199 ],
[ 0.37910852, 0.87964135, 0.34306596],
[ 0.72687499, 0.23913896, 0.33850425]]))
assert_(np.isnan(nanmin([np.nan, np.nan])))
def test_nanargmin(self):
assert_almost_equal(nanargmin(self.A), 1)
assert_almost_equal(nanargmin(self.A, 0),
np.array([[1, 0, 0],
[0, 2, 2],
[0, 0, 2]]))
assert_almost_equal(nanargmin(self.A, 1),
np.array([[1, 0, 0],
[0, 1, 2],
[0, 2, 1]]))
assert_almost_equal(nanargmin(self.A, 2),
np.array([[1, 0, 1],
[2, 1, 2],
[0, 2, 2]]))
def test_nanmax(self):
assert_almost_equal(nanmax(self.A), 0.91084584000000002)
assert_almost_equal(nanmax(self.A, 0),
np.array([[ 0.72687499, 0.91084584, 0.37910852],
[ 0.84386844, 0.87964135, 0.75157887],
[ 0.70543747, 0.37068164, 0.34306596]]))
assert_almost_equal(nanmax(self.A, 1),
np.array([[ 0.28333658, 0.1630199 , 0.75157887],
[ 0.70543747, 0.87964135, 0.37910852],
[ 0.84386844, 0.91084584, 0.33850425]]))
assert_almost_equal(nanmax(self.A, 2),
np.array([[ 0.01620964, 0.75157887, 0.28333658],
[ 0.59541557, 0.87964135, 0.70543747],
[ 0.91084584, 0.84386844, 0.37068164]]))
assert_(np.isnan(nanmax([np.nan, np.nan])))
def test_nanmin_allnan_on_axis(self):
assert_array_equal(np.isnan(nanmin([[np.nan] * 2] * 3, axis=1)),
[True, True, True])
def test_nanmin_masked(self):
a = np.ma.fix_invalid([[2, 1, 3, np.nan], [5, 2, 3, np.nan]])
ctrl_mask = a._mask.copy()
test = np.nanmin(a, axis=1)
assert_equal(test, [1, 2])
assert_equal(a._mask, ctrl_mask)
assert_equal(np.isinf(a), np.zeros((2, 4), dtype=bool))
class TestNanFunctsIntTypes(TestCase):
int_types = (
np.int8, np.int16, np.int32, np.int64, np.uint8,
np.uint16, np.uint32, np.uint64)
def setUp(self, *args, **kwargs):
self.A = np.array([127, 39, 93, 87, 46])
def integer_arrays(self):
for dtype in self.int_types:
yield self.A.astype(dtype)
def test_nanmin(self):
min_value = min(self.A)
for A in self.integer_arrays():
assert_equal(nanmin(A), min_value)
def test_nanmax(self):
max_value = max(self.A)
for A in self.integer_arrays():
assert_equal(nanmax(A), max_value)
def test_nanargmin(self):
min_arg = np.argmin(self.A)
for A in self.integer_arrays():
assert_equal(nanargmin(A), min_arg)
def test_nanargmax(self):
max_arg = np.argmax(self.A)
for A in self.integer_arrays():
assert_equal(nanargmax(A), max_arg)
class TestCorrCoef(TestCase):
A = np.array([[ 0.15391142, 0.18045767, 0.14197213],
[ 0.70461506, 0.96474128, 0.27906989],
[ 0.9297531 , 0.32296769, 0.19267156]])
B = np.array([[ 0.10377691, 0.5417086 , 0.49807457],
[ 0.82872117, 0.77801674, 0.39226705],
[ 0.9314666 , 0.66800209, 0.03538394]])
res1 = np.array([[ 1. , 0.9379533 , -0.04931983],
[ 0.9379533 , 1. , 0.30007991],
[-0.04931983, 0.30007991, 1. ]])
res2 = np.array([[ 1. , 0.9379533 , -0.04931983,
0.30151751, 0.66318558, 0.51532523],
[ 0.9379533 , 1. , 0.30007991,
- 0.04781421, 0.88157256, 0.78052386],
[-0.04931983, 0.30007991, 1. ,
- 0.96717111, 0.71483595, 0.83053601],
[ 0.30151751, -0.04781421, -0.96717111,
1. , -0.51366032, -0.66173113],
[ 0.66318558, 0.88157256, 0.71483595,
- 0.51366032, 1. , 0.98317823],
[ 0.51532523, 0.78052386, 0.83053601,
- 0.66173113, 0.98317823, 1. ]])
def test_simple(self):
assert_almost_equal(corrcoef(self.A), self.res1)
assert_almost_equal(corrcoef(self.A, self.B), self.res2)
def test_ddof(self):
assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
def test_empty(self):
assert_equal(corrcoef(np.array([])).size, 0)
assert_equal(corrcoef(np.array([]).reshape(0, 2)).shape, (0, 2))
class TestCov(TestCase):
def test_basic(self):
x = np.array([[0, 2], [1, 1], [2, 0]]).T
assert_allclose(np.cov(x), np.array([[ 1., -1.], [-1., 1.]]))
def test_empty(self):
assert_equal(cov(np.array([])).size, 0)
assert_equal(cov(np.array([]).reshape(0, 2)).shape, (0, 2))
class Test_i0(TestCase):
def test_simple(self):
assert_almost_equal(i0(0.5), np.array(1.0634833707413234))
A = np.array([ 0.49842636, 0.6969809 , 0.22011976, 0.0155549])
assert_almost_equal(i0(A),
np.array([ 1.06307822, 1.12518299, 1.01214991, 1.00006049]))
B = np.array([[ 0.827002 , 0.99959078],
[ 0.89694769, 0.39298162],
[ 0.37954418, 0.05206293],
[ 0.36465447, 0.72446427],
[ 0.48164949, 0.50324519]])
assert_almost_equal(i0(B),
np.array([[ 1.17843223, 1.26583466],
[ 1.21147086, 1.0389829 ],
[ 1.03633899, 1.00067775],
[ 1.03352052, 1.13557954],
[ 1.0588429 , 1.06432317]]))
class TestKaiser(TestCase):
def test_simple(self):
assert_almost_equal(kaiser(0, 1.0), np.array([]))
assert_(np.isfinite(kaiser(1, 1.0)))
assert_almost_equal(kaiser(2, 1.0), np.array([ 0.78984831, 0.78984831]))
assert_almost_equal(kaiser(5, 1.0),
np.array([ 0.78984831, 0.94503323, 1. ,
0.94503323, 0.78984831]))
assert_almost_equal(kaiser(5, 1.56789),
np.array([ 0.58285404, 0.88409679, 1. ,
0.88409679, 0.58285404]))
def test_int_beta(self):
kaiser(3, 4)
class TestMsort(TestCase):
def test_simple(self):
A = np.array([[ 0.44567325, 0.79115165, 0.5490053 ],
[ 0.36844147, 0.37325583, 0.96098397],
[ 0.64864341, 0.52929049, 0.39172155]])
assert_almost_equal(msort(A),
np.array([[ 0.36844147, 0.37325583, 0.39172155],
[ 0.44567325, 0.52929049, 0.5490053 ],
[ 0.64864341, 0.79115165, 0.96098397]]))
class TestMeshgrid(TestCase):
def test_simple(self):
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
assert_(np.all(X == np.array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3],
[1, 2, 3]])))
assert_(np.all(Y == np.array([[4, 4, 4],
[5, 5, 5],
[6, 6, 6],
[7, 7, 7]])))
def test_single_input(self):
assert_raises(ValueError, meshgrid, np.arange(5))
def test_indexing(self):
x = [1, 2, 3]
y = [4, 5, 6, 7]
[X, Y] = meshgrid(x, y, indexing='ij')
assert_(np.all(X == np.array([[1, 1, 1, 1],
[2, 2, 2, 2],
[3, 3, 3, 3]])))
assert_(np.all(Y == np.array([[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 7]])))
# Test expected shapes:
z = [8, 9]
assert_(meshgrid(x, y)[0].shape == (4, 3))
assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4))
assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2))
assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2))
assert_raises(ValueError, meshgrid, x, y, indexing='notvalid')
def test_sparse(self):
[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True)
assert_(np.all(X == np.array([[1, 2, 3]])))
assert_(np.all(Y == np.array([[4], [5], [6], [7]])))
class TestPiecewise(TestCase):
def test_simple(self):
# Condition is single bool list
x = piecewise([0, 0], [True, False], [1])
assert_array_equal(x, [1, 0])
# List of conditions: single bool list
x = piecewise([0, 0], [[True, False]], [1])
assert_array_equal(x, [1, 0])
# Conditions is single bool array
x = piecewise([0, 0], np.array([True, False]), [1])
assert_array_equal(x, [1, 0])
# Condition is single int array
x = piecewise([0, 0], np.array([1, 0]), [1])
assert_array_equal(x, [1, 0])
# List of conditions: int array
x = piecewise([0, 0], [np.array([1, 0])], [1])
assert_array_equal(x, [1, 0])
x = piecewise([0, 0], [[False, True]], [lambda x:-1])
assert_array_equal(x, [0, -1])
x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
assert_array_equal(x, [3, 4])
def test_default(self):
# No value specified for x[1], should be 0
x = piecewise([1, 2], [True, False], [2])
assert_array_equal(x, [2, 0])
# Should set x[1] to 3
x = piecewise([1, 2], [True, False], [2, 3])
assert_array_equal(x, [2, 3])
def test_0d(self):
x = np.array(3)
y = piecewise(x, x > 3, [4, 0])
assert_(y.ndim == 0)
assert_(y == 0)
class TestBincount(TestCase):
def test_simple(self):
y = np.bincount(np.arange(4))
assert_array_equal(y, np.ones(4))
def test_simple2(self):
y = np.bincount(np.array([1, 5, 2, 4, 1]))
assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))
def test_simple_weight(self):
x = np.arange(4)
w = np.array([0.2, 0.3, 0.5, 0.1])
y = np.bincount(x, w)
assert_array_equal(y, w)
def test_simple_weight2(self):
x = np.array([1, 2, 4, 5, 2])
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
y = np.bincount(x, w)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))
def test_with_minlength(self):
x = np.array([0, 1, 0, 1, 1])
y = np.bincount(x, minlength=3)
assert_array_equal(y, np.array([2, 3, 0]))
def test_with_minlength_smaller_than_maxvalue(self):
x = np.array([0, 1, 1, 2, 2, 3, 3])
y = np.bincount(x, minlength=2)
assert_array_equal(y, np.array([1, 2, 2, 2]))
def test_with_minlength_and_weights(self):
x = np.array([1, 2, 4, 5, 2])
w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
y = np.bincount(x, w, 8)
assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))
def test_empty(self):
x = np.array([], dtype=int)
y = np.bincount(x)
assert_array_equal(x, y)
def test_empty_with_minlength(self):
x = np.array([], dtype=int)
y = np.bincount(x, minlength=5)
assert_array_equal(y, np.zeros(5, dtype=int))
class TestInterp(TestCase):
def test_exceptions(self):
assert_raises(ValueError, interp, 0, [], [])
assert_raises(ValueError, interp, 0, [0], [1, 2])
def test_basic(self):
x = np.linspace(0, 1, 5)
y = np.linspace(0, 1, 5)
x0 = np.linspace(0, 1, 50)
assert_almost_equal(np.interp(x0, x, y), x0)
def test_right_left_behavior(self):
assert_equal(interp([-1, 0, 1], [0], [1]), [1, 1, 1])
assert_equal(interp([-1, 0, 1], [0], [1], left=0), [0, 1, 1])
assert_equal(interp([-1, 0, 1], [0], [1], right=0), [1, 1, 0])
assert_equal(interp([-1, 0, 1], [0], [1], left=0, right=0), [0, 1, 0])
def test_scalar_interpolation_point(self):
x = np.linspace(0, 1, 5)
y = np.linspace(0, 1, 5)
x0 = 0
assert_almost_equal(np.interp(x0, x, y), x0)
x0 = .3
assert_almost_equal(np.interp(x0, x, y), x0)
x0 = np.float32(.3)
assert_almost_equal(np.interp(x0, x, y), x0)
x0 = np.float64(.3)
assert_almost_equal(np.interp(x0, x, y), x0)
def test_zero_dimensional_interpolation_point(self):
x = np.linspace(0, 1, 5)
y = np.linspace(0, 1, 5)
x0 = np.array(.3)
assert_almost_equal(np.interp(x0, x, y), x0)
x0 = np.array(.3, dtype=object)
assert_almost_equal(np.interp(x0, x, y), .3)
def test_if_len_x_is_small(self):
xp = np.arange(0, 1000, 0.0001)
fp = np.sin(xp)
assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)
def compare_results(res, desired):
for i in range(len(desired)):
assert_array_equal(res[i], desired[i])
def test_percentile_list():
assert_equal(np.percentile([1, 2, 3], 0), 1)
def test_percentile_out():
x = np.array([1, 2, 3])
y = np.zeros((3,))
p = (1, 2, 3)
np.percentile(x, p, out=y)
assert_equal(y, np.percentile(x, p))
x = np.array([[1, 2, 3],
[4, 5, 6]])
y = np.zeros((3, 3))
np.percentile(x, p, axis=0, out=y)
assert_equal(y, np.percentile(x, p, axis=0))
y = np.zeros((3, 2))
np.percentile(x, p, axis=1, out=y)
assert_equal(y, np.percentile(x, p, axis=1))
def test_median():
a0 = np.array(1)
a1 = np.arange(2)
a2 = np.arange(6).reshape(2, 3)
assert_allclose(np.median(a0), 1)
assert_allclose(np.median(a1), 0.5)
assert_allclose(np.median(a2), 2.5)
assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5])
assert_allclose(np.median(a2, axis=1), [1, 4])
class TestAdd_newdoc_ufunc(TestCase):
def test_ufunc_arg(self):
assert_raises(TypeError, add_newdoc_ufunc, 2, "blah")
assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah")
def test_string_arg(self):
assert_raises(TypeError, add_newdoc_ufunc, np.add, 3)
if __name__ == "__main__":
run_module_suite()