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from numpy.testing import *
import numpy as np
from numpy import ( array, ones, r_, mgrid, unravel_index, zeros, where,
ndenumerate, fill_diagonal, diag_indices,
diag_indices_from, s_, index_exp )
class TestUnravelIndex(TestCase):
def test_basic(self):
assert unravel_index(2,(2,2)) == (1,0)
assert unravel_index(254,(17,94)) == (2, 66)
assert_raises(ValueError, unravel_index, 4,(2,2))
class TestGrid(TestCase):
def test_basic(self):
a = mgrid[-1:1:10j]
b = mgrid[-1:1:0.1]
assert(a.shape == (10,))
assert(b.shape == (20,))
assert(a[0] == -1)
assert_almost_equal(a[-1],1)
assert(b[0] == -1)
assert_almost_equal(b[1]-b[0],0.1,11)
assert_almost_equal(b[-1],b[0]+19*0.1,11)
assert_almost_equal(a[1]-a[0],2.0/9.0,11)
def test_linspace_equivalence(self):
y,st = np.linspace(2,10,retstep=1)
assert_almost_equal(st,8/49.0)
assert_array_almost_equal(y,mgrid[2:10:50j],13)
def test_nd(self):
c = mgrid[-1:1:10j,-2:2:10j]
d = mgrid[-1:1:0.1,-2:2:0.2]
assert(c.shape == (2,10,10))
assert(d.shape == (2,20,20))
assert_array_equal(c[0][0,:],-ones(10,'d'))
assert_array_equal(c[1][:,0],-2*ones(10,'d'))
assert_array_almost_equal(c[0][-1,:],ones(10,'d'),11)
assert_array_almost_equal(c[1][:,-1],2*ones(10,'d'),11)
assert_array_almost_equal(d[0,1,:]-d[0,0,:], 0.1*ones(20,'d'),11)
assert_array_almost_equal(d[1,:,1]-d[1,:,0], 0.2*ones(20,'d'),11)
class TestConcatenator(TestCase):
def test_1d(self):
assert_array_equal(r_[1,2,3,4,5,6],array([1,2,3,4,5,6]))
b = ones(5)
c = r_[b,0,0,b]
assert_array_equal(c,[1,1,1,1,1,0,0,1,1,1,1,1])
def test_mixed_type(self):
g = r_[10.1, 1:10]
assert(g.dtype == 'f8')
def test_more_mixed_type(self):
g = r_[-10.1, array([1]), array([2,3,4]), 10.0]
assert(g.dtype == 'f8')
def test_2d(self):
b = rand(5,5)
c = rand(5,5)
d = r_['1',b,c] # append columns
assert(d.shape == (5,10))
assert_array_equal(d[:,:5],b)
assert_array_equal(d[:,5:],c)
d = r_[b,c]
assert(d.shape == (10,5))
assert_array_equal(d[:5,:],b)
assert_array_equal(d[5:,:],c)
class TestNdenumerate(TestCase):
def test_basic(self):
a = array([[1,2], [3,4]])
assert_equal(list(ndenumerate(a)),
[((0,0), 1), ((0,1), 2), ((1,0), 3), ((1,1), 4)])
class TestIndexExpression(TestCase):
def test_regression_1(self):
# ticket #1196
a = np.arange(2)
assert_equal(a[:-1], a[s_[:-1]])
assert_equal(a[:-1], a[index_exp[:-1]])
def test_simple_1(self):
a = np.random.rand(4,5,6)
assert_equal(a[:,:3,[1,2]], a[index_exp[:,:3,[1,2]]])
assert_equal(a[:,:3,[1,2]], a[s_[:,:3,[1,2]]])
def test_fill_diagonal():
a = zeros((3, 3),int)
fill_diagonal(a, 5)
yield (assert_array_equal, a,
array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]]))
# The same function can operate on a 4-d array:
a = zeros((3, 3, 3, 3), int)
fill_diagonal(a, 4)
i = array([0, 1, 2])
yield (assert_equal, where(a != 0), (i, i, i, i))
def test_diag_indices():
di = diag_indices(4)
a = array([[1, 2, 3, 4],
[5, 6, 7, 8],
[9, 10, 11, 12],
[13, 14, 15, 16]])
a[di] = 100
yield (assert_array_equal, a,
array([[100, 2, 3, 4],
[ 5, 100, 7, 8],
[ 9, 10, 100, 12],
[ 13, 14, 15, 100]]))
# Now, we create indices to manipulate a 3-d array:
d3 = diag_indices(2, 3)
# And use it to set the diagonal of a zeros array to 1:
a = zeros((2, 2, 2),int)
a[d3] = 1
yield (assert_array_equal, a,
array([[[1, 0],
[0, 0]],
[[0, 0],
[0, 1]]]) )
def test_diag_indices_from():
x = np.random.random((4, 4))
r, c = diag_indices_from(x)
assert_array_equal(r, np.arange(4))
assert_array_equal(c, np.arange(4))
if __name__ == "__main__":
run_module_suite()
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