def test_special_case_numpy_functions():
'''
Test a couple of functions/methods that need special treatment.
'''
from brian2.units.unitsafefunctions import ravel, diagonal, trace, dot, where
quadratic_matrix = np.reshape(np.arange(9), (3, 3)) * mV
# Temporarily suppress warnings related to the matplotlib 1.3 bug
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Check that function and method do the same thing
assert_equal(ravel(quadratic_matrix), quadratic_matrix.ravel())
# Check that function gives the same result as on unitless arrays
assert_equal(np.asarray(ravel(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Check that the function gives the same results as the original numpy
# function
assert_equal(np.ravel(np.asarray(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Do the same checks for diagonal, trace and dot
assert_equal(diagonal(quadratic_matrix), quadratic_matrix.diagonal())
assert_equal(np.asarray(diagonal(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(np.diagonal(np.asarray(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(trace(quadratic_matrix), quadratic_matrix.trace())
assert_equal(np.asarray(trace(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(np.trace(np.asarray(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(dot(quadratic_matrix, quadratic_matrix),
quadratic_matrix.dot(quadratic_matrix))
assert_equal(np.asarray(dot(quadratic_matrix, quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(np.dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(np.asarray(quadratic_matrix.prod()),
np.asarray(quadratic_matrix).prod())
assert_equal(np.asarray(quadratic_matrix.prod(axis=0)),
np.asarray(quadratic_matrix).prod(axis=0))
# Check for correct units
if use_matplotlib_units_fix:
assert have_same_dimensions(1, ravel(quadratic_matrix))
else:
assert have_same_dimensions(quadratic_matrix, ravel(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, trace(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, diagonal(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix[0] ** 2,
dot(quadratic_matrix, quadratic_matrix))
assert have_same_dimensions(quadratic_matrix.prod(axis=0),
quadratic_matrix[0] ** quadratic_matrix.shape[0])
# check the where function
# pure numpy array
cond = [True, False, False]
ar1 = np.array([1, 2, 3])
ar2 = np.array([4, 5, 6])
assert_equal(np.where(cond), where(cond))
assert_equal(np.where(cond, ar1, ar2), where(cond, ar1, ar2))
# dimensionless quantity
assert_equal(np.where(cond, ar1, ar2),
np.asarray(where(cond, ar1 * mV/mV, ar2 * mV/mV)))
# quantity with dimensions
ar1 = ar1 * mV
ar2 = ar2 * mV
assert_equal(np.where(cond, np.asarray(ar1), np.asarray(ar2)),
np.asarray(where(cond, ar1, ar2)))
# Check some error cases
assert_raises(ValueError, lambda: where(cond, ar1))
assert_raises(TypeError, lambda: where(cond, ar1, ar1, ar2))
assert_raises(DimensionMismatchError, lambda: where(cond, ar1, ar1 / ms))
# Check setasflat (for numpy < 1.7)
if hasattr(Quantity, 'setasflat'):
a = np.arange(10) * mV
b = np.ones(10).reshape(5, 2) * volt
c = np.ones(10).reshape(5, 2) * second
assert_raises(DimensionMismatchError, lambda: a.setasflat(c))
a.setasflat(b)
assert_equal(a.flatten(), b.flatten())
# Check cumprod
a = np.arange(1, 10) * mV/mV
assert_equal(a.cumprod(), np.asarray(a).cumprod())
assert_raises(TypeError, lambda: (np.arange(1, 5)*mV).cumprod())
def test_special_case_numpy_functions():
'''
Test a couple of functions/methods that need special treatment.
'''
from brian2.units.unitsafefunctions import ravel, diagonal, trace, dot, where
quadratic_matrix = np.reshape(np.arange(9), (3, 3)) * mV
# Temporarily suppress warnings related to the matplotlib 1.3 bug
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Check that function and method do the same thing
assert_equal(ravel(quadratic_matrix), quadratic_matrix.ravel())
# Check that function gives the same result as on unitless arrays
assert_equal(np.asarray(ravel(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Check that the function gives the same results as the original numpy
# function
assert_equal(np.ravel(np.asarray(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Do the same checks for diagonal, trace and dot
assert_equal(diagonal(quadratic_matrix), quadratic_matrix.diagonal())
assert_equal(np.asarray(diagonal(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(np.diagonal(np.asarray(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(trace(quadratic_matrix), quadratic_matrix.trace())
assert_equal(np.asarray(trace(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(np.trace(np.asarray(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(dot(quadratic_matrix, quadratic_matrix),
quadratic_matrix.dot(quadratic_matrix))
assert_equal(
np.asarray(dot(quadratic_matrix, quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(
np.dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(np.asarray(quadratic_matrix.prod()),
np.asarray(quadratic_matrix).prod())
assert_equal(np.asarray(quadratic_matrix.prod(axis=0)),
np.asarray(quadratic_matrix).prod(axis=0))
# Check for correct units
if use_matplotlib_units_fix:
assert have_same_dimensions(1, ravel(quadratic_matrix))
else:
assert have_same_dimensions(quadratic_matrix, ravel(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, trace(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, diagonal(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix[0]**2,
dot(quadratic_matrix, quadratic_matrix))
assert have_same_dimensions(quadratic_matrix.prod(axis=0),
quadratic_matrix[0]**quadratic_matrix.shape[0])
# check the where function
# pure numpy array
cond = [True, False, False]
ar1 = np.array([1, 2, 3])
ar2 = np.array([4, 5, 6])
assert_equal(np.where(cond), where(cond))
assert_equal(np.where(cond, ar1, ar2), where(cond, ar1, ar2))
# dimensionless quantity
assert_equal(np.where(cond, ar1, ar2),
np.asarray(where(cond, ar1 * mV / mV, ar2 * mV / mV)))
# quantity with dimensions
ar1 = ar1 * mV
ar2 = ar2 * mV
assert_equal(np.where(cond, np.asarray(ar1), np.asarray(ar2)),
np.asarray(where(cond, ar1, ar2)))
# Check some error cases
with pytest.raises(ValueError):
where(cond, ar1)
with pytest.raises(TypeError):
where(cond, ar1, ar1, ar2)
with pytest.raises(DimensionMismatchError):
where(cond, ar1, ar1 / ms)
# Check setasflat (for numpy < 1.7)
if hasattr(Quantity, 'setasflat'):
a = np.arange(10) * mV
b = np.ones(10).reshape(5, 2) * volt
c = np.ones(10).reshape(5, 2) * second
with pytest.raises(DimensionMismatchError):
a.setasflat(c)
a.setasflat(b)
assert_equal(a.flatten(), b.flatten())
# Check cumprod
a = np.arange(1, 10) * mV / mV
assert_equal(a.cumprod(), np.asarray(a).cumprod())
with pytest.raises(TypeError):
(np.arange(1, 5) * mV).cumprod()
def test_special_case_numpy_functions():
'''
Test a couple of functions/methods that need special treatment.
'''
from brian2.units.unitsafefunctions import ravel, diagonal, trace, dot, where
quadratic_matrix = np.reshape(np.arange(9), (3, 3)) * mV
# Check that function and method do the same thing
assert_equal(ravel(quadratic_matrix), quadratic_matrix.ravel())
# Check that function gives the same result as on unitless arrays
assert_equal(np.asarray(ravel(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Check that the function gives the same results as the original numpy
# function
assert_equal(np.ravel(np.asarray(quadratic_matrix)),
ravel(np.asarray(quadratic_matrix)))
# Do the same checks for diagonal, trace and dot
assert_equal(diagonal(quadratic_matrix), quadratic_matrix.diagonal())
assert_equal(np.asarray(diagonal(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(np.diagonal(np.asarray(quadratic_matrix)),
diagonal(np.asarray(quadratic_matrix)))
assert_equal(trace(quadratic_matrix), quadratic_matrix.trace())
assert_equal(np.asarray(trace(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(np.trace(np.asarray(quadratic_matrix)),
trace(np.asarray(quadratic_matrix)))
assert_equal(dot(quadratic_matrix, quadratic_matrix),
quadratic_matrix.dot(quadratic_matrix))
assert_equal(np.asarray(dot(quadratic_matrix, quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(np.dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)),
dot(np.asarray(quadratic_matrix), np.asarray(quadratic_matrix)))
assert_equal(np.asarray(quadratic_matrix.prod()),
np.asarray(quadratic_matrix).prod())
assert_equal(np.asarray(quadratic_matrix.prod(axis=0)),
np.asarray(quadratic_matrix).prod(axis=0))
# Check for correct units
assert have_same_dimensions(quadratic_matrix, ravel(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, trace(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix, diagonal(quadratic_matrix))
assert have_same_dimensions(quadratic_matrix[0] ** 2,
dot(quadratic_matrix, quadratic_matrix))
assert have_same_dimensions(quadratic_matrix.prod(axis=0),
quadratic_matrix[0] ** quadratic_matrix.shape[0])
# check the where function
# pure numpy array
cond = [True, False, False]
ar1 = np.array([1, 2, 3])
ar2 = np.array([4, 5, 6])
assert_equal(np.where(cond), where(cond))
assert_equal(np.where(cond, ar1, ar2), where(cond, ar1, ar2))
# dimensionless quantity
assert_equal(np.where(cond, ar1, ar2),
np.asarray(where(cond, ar1 * mV/mV, ar2 * mV/mV)))
# quantity with dimensions
ar1 = ar1 * mV
ar2 = ar2 * mV
assert_equal(np.where(cond, np.asarray(ar1), np.asarray(ar2)),
np.asarray(where(cond, ar1, ar2)))
# Check some error cases
assert_raises(ValueError, lambda: where(cond, ar1))
assert_raises(TypeError, lambda: where(cond, ar1, ar1, ar2))
assert_raises(DimensionMismatchError, lambda: where(cond, ar1, ar1 / ms))
