def solve_Ab(A, b, squeeze=True):
AA, bb = make_same_dims(A, b)
x = np.linalg.solve(AA, bb)
result = hfarray(x, dims=bb.dims)
if squeeze:
result = result.squeeze()
return result
def delay(freq, var):
r"""Berakna grupploptid :math:`\tau=-\frac{d\varphi}{d\omega}` for data
Invariabler
*freq*
frekvenser som *var* galler for
*var*
komplexa data som skall anvandas i berakningen
Resultat (f,tau)
*f*
frekvenserna som motsvarar mittpunkter i *freq*
*tau*
beraknad grupploptid med hjalp av mittpunkts approximation for
derivatan
>>> x=hfarray(np.arange(0,10.))
>>> y=hfarray(np.exp(-2j*np.pi*x/10))
>>> f,tau=delay(x,y)
>>> f
hfarray([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
>>> tau
hfarray([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
"""
freq, var = make_same_dims(freq, var)
f_mean = (freq[:-1] + freq[1:]) / 2
domega = (2 * pi * (freq[:-1] - freq[1:]))
dfi = angle(var[1:] / var[:-1])
return (f_mean, dfi / domega)
def delay(freq, var):
r"""Berakna grupploptid :math:`\tau=-\frac{d\varphi}{d\omega}` for data
Invariabler
*freq*
frekvenser som *var* galler for
*var*
komplexa data som skall anvandas i berakningen
Resultat (f,tau)
*f*
frekvenserna som motsvarar mittpunkter i *freq*
*tau*
beraknad grupploptid med hjalp av mittpunkts approximation for
derivatan
>>> x=hfarray(np.arange(0,10.))
>>> y=hfarray(np.exp(-2j*np.pi*x/10))
>>> f,tau=delay(x,y)
>>> f
hfarray([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5])
>>> tau
hfarray([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
"""
freq, var = make_same_dims(freq, var)
f_mean = (freq[:-1] + freq[1:]) / 2
domega = (2 * pi * (freq[:-1] - freq[1:]))
dfi = angle(var[1:] / var[:-1])
return (f_mean, dfi / domega)
def solve_Ab(A, b, squeeze=True):
AA, bb = make_same_dims(A, b)
x = np.linalg.solve(AA, bb)
result = hfarray(x, dims=bb.dims)
if squeeze:
result = result.squeeze()
return result
def matrix_multiply(a, b):
"""Multiply arrays of matrices.
a and b are hfarrays containing dimensions DimMatrix_i and DimMatrix_j.
Matrix multiplication is done by broadcasting the other dimensions first.
"""
A, B = make_same_dims(a, b)
res = np.einsum("...ij,...jk->...ik", A, B)
return hfarray(res, dims=A.dims)
def matrix_multiply(a, b):
"""Multiply arrays of matrices.
a and b are hfarrays containing dimensions DimMatrix_i and DimMatrix_j.
Matrix multiplication is done by broadcasting the other dimensions first.
"""
A, B = make_same_dims(a, b)
res = np.einsum("...ij,...jk->...ik", A, B)
return hfarray(res, dims=A.dims)
def test_4(self):
a, b = aobj.make_same_dims(self.a, np.array(self.a))
self.assertEqual(a.dims, b.dims)
self.assertEqual(a.dims, (ds.DiagAxis("f", 3), ))
def test_3(self):
b, a = aobj.make_same_dims(self.b, self.a)
self.assertEqual(a.dims, b.dims)
self.assertEqual(a.dims, (ds.DiagAxis("f", 3), ds.DiagAxis("p", 3),))
def test_2(self):
a, b = aobj.make_same_dims(self.a, self.b)
self.assertEqual(a.dims, b.dims)
self.assertEqual(a.dims, (ds.DiagAxis("f", 3), ds.DiagAxis("p", 3),))
def test_1(self):
a, b = aobj.make_same_dims(self.a, self.a)
self.assertEqual(a.dims, b.dims)
