def runTest(self):
fp = lambda e: mpmath.matrix([[e, e], [e, e]])
rk.use_mpmath_types()
cal = rk.get_asym_calc(cu.rydbergs, [0, 0])
dmat = rk.get_dmat_from_continuous(rk.Smat, fp, cal, 1., 5., 10,
"dum4")
self.assertEqual(nw.mode, nw.mode_mpmath)
self.assertEqual(nw.dps, nw.dps_default_mpmath)
for mat in dmat.values():
nw.shape(mat) # Will get an exception if types are wrong.
def runTest(self):
data = {}
data[1.] = mpmath.matrix([[1., 1.], [1., 1.]])
data[2.] = mpmath.matrix([[2., 2.], [2., 2.]])
rk.use_mpmath_types()
cal = rk.get_asym_calc(cu.rydbergs, [0, 0])
dmat = rk.get_dmat_from_discrete(rk.Smat, data, cal, "dum2")
self.assertEqual(nw.mode, nw.mode_mpmath)
self.assertEqual(nw.dps, nw.dps_default_mpmath)
for mat in dmat.values():
nw.shape(mat) # Will get an exception if types are wrong.
def _check_coeff_input(enes, smatdata, asymcalc):
first_shape = nw.shape(smatdata[enes[0]])
excStr = ""
if first_shape[0] == 0 or first_shape[0] != first_shape[1]:
excStr = "Bad Input: Matrix not square"
if first_shape[0] != asymcalc.get_number_channels():
excStr = "Bad Input: Inconsistent channel specification"
if first_shape != nw.shape(smatdata[enes[-1]]):
excStr = "Bad Input: S-matrices have difference shapes"
if len(smatdata) < 2:
excStr = "Bad Input: Not enough Data"
if len(smatdata) < 2:
excStr = "Bad Input: Specified fit size too small"
if len(smatdata) > len(smatdata):
excStr = "Bad Input: Specified fitsize larger than number of data"
if len(smatdata) % len(smatdata) != 0:
excStr = "Bad Input: Num of data is not a multiple of the fit size"
if len(smatdata) % 2 != 0:
excStr = "Bad Input: Number of data not even"
if len(smatdata) % 2 != 0:
excStr = "Bad Input: Fit size not even"
if excStr != "":
raise ParSmatException(excStr)
def _calculate_coefficients(enes, smatdata, asymcalc):
num_data = len(smatdata)
num_poly_terms = num_data / 2
num_coeffs = num_poly_terms + 1
num_channels = nw.shape(smatdata[enes[0]])[0]
alphas = _initialise_coefficients(num_coeffs, num_channels)
betas = _initialise_coefficients(num_coeffs, num_channels)
for j in range(num_channels):
sys_mat = nw.matrix(
_get_sys_mat_init(num_data, num_channels, num_poly_terms))
res_vec = nw.matrix(_get_res_vec_init(num_data, num_channels))
for i in range(num_channels):
ei = 0
for ene in enes:
for ti in range(
num_poly_terms
): #We have two indices ci (coefficient) and ti (term). We know the first term in the poly expansion so num_coeffs = num_poly_terms + 1
exp = ti + 1
for k in range(num_channels):
if k == i:
alpha_coeff = _primary_alpha(
smatdata, asymcalc, i, j, ene, exp)
beta_coeff = _primary_beta(smatdata, asymcalc, i,
j, ene, exp)
else:
alpha_coeff = _secondary_alpha(
smatdata, asymcalc, i, j, k, ene, exp)
beta_coeff = _secondary_beta(
smatdata, asymcalc, i, j, k, ene, exp)
sys_mat[
_row(num_data, i, ei),
_alpha_index(num_poly_terms, k, ti)] = alpha_coeff
sys_mat[_row(num_data, i, ei),
_beta_index(num_poly_terms, num_channels, k, ti
)] = beta_coeff
res_vec[_row(num_data, i, ei),
0] = _result(smatdata, i, j, ene)
ei += 1
coeff_vec = nw.lin_solve(sys_mat, res_vec)
_copy_column_coeffs(alphas, betas, coeff_vec, num_poly_terms,
num_channels, num_coeffs, j)
return alphas, betas
def valshape(self):
return nw.shape(self[self.keys()[0]])