def test_mean_ndarray(self):
for j, (x, xa) in enumerate([(self.x, self.xa), (self.xc, self.xca)]):
ave = 0.0
for val in x:
ave += val
ave = ave/float(len(x))
for m in [function.mean(xa)]:
if j == 0:
self.assertTrue(equivalent(value(m), ave.x))
self.assertTrue(equivalent(uncertainty(m), ave.u))
else:
self.assertTrue(equivalent_complex(value(m), ave.x))
self.assertTrue(equivalent(uncertainty(m).real, ave.u.real))
self.assertTrue(equivalent(uncertainty(m).imag, ave.u.imag))
xa = xa.reshape(2, 3)
for m in [function.mean(xa)]:
if j == 0:
self.assertTrue(equivalent(value(m), ave.x))
self.assertTrue(equivalent(uncertainty(m), ave.u))
else:
self.assertTrue(equivalent_complex(value(m), ave.x))
self.assertTrue(equivalent(uncertainty(m).real, ave.u.real))
self.assertTrue(equivalent(uncertainty(m).imag, ave.u.imag))
for m in [function.mean(xa, axis=0)]:
aves = [(x[0] + x[3])/2.0, (x[1] + x[4])/2.0, (x[2] + x[5])/2.0]
for idx in range(3):
if j == 0:
self.assertTrue(equivalent(m[idx].x, aves[idx].x))
self.assertTrue(equivalent(m[idx].u, aves[idx].u))
else:
self.assertTrue(equivalent_complex(m[idx].x, aves[idx].x))
self.assertTrue(equivalent(m[idx].u.real, aves[idx].u.real))
self.assertTrue(equivalent(m[idx].u.imag, aves[idx].u.imag))
for m in [function.mean(xa, axis=1)]:
aves = [(x[0] + x[1] + x[2])/3.0, (x[3] + x[4] + x[5])/3.0]
for idx in range(2):
if j == 0:
self.assertTrue(equivalent(m[idx].x, aves[idx].x))
self.assertTrue(equivalent(m[idx].u, aves[idx].u))
else:
self.assertTrue(equivalent_complex(m[idx].x, aves[idx].x))
self.assertTrue(equivalent(m[idx].u.real, aves[idx].u.real))
self.assertTrue(equivalent(m[idx].u.imag, aves[idx].u.imag))
def _isinf(number):
val = value(number)
if isinstance(val, Real):
return isinf(val)
elif isinstance(val, Complex):
return cisinf(val)
else:
raise TypeError('cannot calculate isinf of type {}'.format(type(number)))
def value(self):
"""The result of :func:`~.core.value` for each element in the array.
**Example**::
>>> a = la.uarray([0.57, ureal(0.45, 0.12), ucomplex(1.1+0.68j, 0.19)])
>>> a.value()
uarray([0.57, 0.45, (1.1+0.68j)])
:rtype: :class:`UncertainArray`
"""
# Note: in the future we might allow different `dtype` values.
# However, this needs some thought. Should `dtype=float`
# return complex numbers as a pair of reals, for example?
# What are the most likely use-cases?
# :param dtype: The data type of the returned array.
# :type dtype: :class:`numpy.dtype`
arr, itemset, iterator = self._create_empty(dtype=None)
for i, item in enumerate(iterator):
itemset(i, value(item))
return UncertainArray(arr)
def test_sum_ndarray(self):
# 1D array
xlist = [ureal(i, i*0.1) for i in range(100)]
xarray = uarray(xlist)
self.assertTrue(xarray.shape == (100,))
b = 0
for x in xlist:
b += x
for a in [function.sum(xarray)]:
self.assertTrue(equivalent(value(a), b.x))
self.assertTrue(equivalent(uncertainty(a), b.u))
# 3D array
xlist = [[[ureal(i*j*k, i*j*k*0.1) for k in range(1, 5)] for j in range(7, 10)] for i in range(3, 9)]
xarray = uarray(xlist)
self.assertTrue(xarray.shape == (6, 3, 4))
axis_none = 0.0
axis_0 = [[0.0 for i in range(4)] for j in range(3)]
axis_1 = [[0.0 for i in range(4)] for j in range(6)]
axis_2 = [[0.0 for i in range(3)] for j in range(6)]
for i in range(6):
for j in range(3):
for k in range(4):
_value = xlist[i][j][k]
axis_none += _value
axis_0[j][k] += _value
axis_1[i][k] += _value
axis_2[i][j] += _value
# axis=None
for a in [function.sum(xarray)]:
self.assertTrue(equivalent(value(a), axis_none.x))
self.assertTrue(equivalent(uncertainty(a), axis_none.u))
# axis=0
m, n = len(axis_0), len(axis_0[0])
for a in [function.sum(xarray, axis=0)]:
self.assertTrue(a.shape == (m, n))
for j in range(m):
for k in range(n):
self.assertTrue(equivalent(a[j, k].x, axis_0[j][k].x))
self.assertTrue(equivalent(a[j, k].u, axis_0[j][k].u))
# axis=1
m, n = len(axis_1), len(axis_1[0])
for a in [function.sum(xarray, axis=1)]:
self.assertTrue(a.shape == (m, n))
for i in range(m):
for k in range(n):
self.assertTrue(equivalent(a[i, k].x, axis_1[i][k].x))
self.assertTrue(equivalent(a[i, k].u, axis_1[i][k].u))
# axis=2
m, n = len(axis_2), len(axis_2[0])
for a in [function.sum(xarray, axis=2)]:
self.assertTrue(a.shape == (m, n))
for i in range(m):
for j in range(n):
self.assertTrue(equivalent(a[i, j].x, axis_2[i][j].x))
self.assertTrue(equivalent(a[i, j].u, axis_2[i][j].u))
