def residual_Q_D2_234(param):
'''Function which computes the residual (as sum of squares) comparing the
ratio of expt with the corresponding calculated ratios. The calculated
ratios are computed for given T.
Param : T
'''
TK = param
sosD2 = compute_series_para.sumofstate_D2(TK)
QJ_D2 = 4 # max J value of analyzed Q-bands
computed_D2 = compute_series_para.D2_Q1(TK, QJ_D2, sosD2)
# ------ D2 ------
#print(computed_D2)
computed_D2=computed_D2[:-2, :]
#print(computed_D2)
dataD2Q = dataD2Q4[:-2, :]
print(computed_D2.shape, dataD2Q.shape)
trueR_D2 = gen_intensity_mat(computed_D2, 2)
expt_D2 = gen_intensity_mat(dataD2Q, 0)
errD2_output = gen_weight(dataD2Q)
errorP = errD2_output
#np.savetxt("exptD2",clean_mat(expt_D2),fmt='%2.4f')
#np.savetxt("errD2",clean_mat(errorP),fmt='%2.4f')
calc_D2 = clean_mat(trueR_D2)
expt_D2 = clean_mat(expt_D2)
errorP = clean_mat(errorP)
# ----------------
diffD2 = expt_D2 - calc_D2
# scale by weights
#diffD2 = (np.multiply(errorP , diffD2))
# remove redundant terms
diffD2 = clean_mat(diffD2)
np.savetxt("diff_D2", diffD2,fmt='%2.4f')
# return the residual
E=np.sum(np.square(diffD2))
return E
def residual_quartic(param):
'''Function which computes the residual (as sum of squares) comparing the
ratio of expt to theoretical intensity ratio to the sensitivity profile
modelled as a line, ( 1+ c1*x + c2*x**2 + c3*x**3 + c4*x**4 )
param : T, c1, c2, c3, c4
'''
TK = param[0]
sosD2 = compute_series_para.sumofstate_D2(TK)
sosHD = compute_series_para.sumofstate_HD(TK)
sosH2 = compute_series_para.sumofstate_H2(TK)
computed_D2_o1s1 = compute_series_para.spectra_D2_o1s1(
TK, OJ_D2, SJ_D2, sosD2)
computed_D2_q1 = compute_series_para.D2_Q1(TK, QJ_D2, sosD2)
computed_HD_o1s1 = compute_series_para.spectra_HD_o1s1(
TK, OJ_HD, SJ_HD, sosHD)
computed_HD_q1 = compute_series_para.HD_Q1(TK, QJ_HD, sosHD)
computed_H2_o1 = compute_series_para.H2_O1(TK, OJ_H2, sosH2)
computed_H2_q1 = compute_series_para.H2_Q1(TK, QJ_H2, sosH2)
# --------------------------------------------------
# generate the matrix of ratios
trueR_D2_o1s1 = gen_intensity_mat(computed_D2_o1s1, 2)
expt_D2_o1s1 = gen_intensity_mat(dataD2_o1s1, 0)
trueR_D2_q1 = gen_intensity_mat(computed_D2_q1, 2)
expt_D2_q1 = gen_intensity_mat(dataD2_q1, 0)
# --------------------------------------------------
trueR_HD_o1s1 = gen_intensity_mat(computed_HD_o1s1, 2)
expt_HD_o1s1 = gen_intensity_mat(dataHD_o1s1, 0)
trueR_HD_q1 = gen_intensity_mat(computed_HD_q1, 2)
expt_HD_q1 = gen_intensity_mat(dataHD_q1, 0)
# --------------------------------------------------
trueR_H2_o1 = gen_intensity_mat(computed_H2_o1, 2)
expt_H2_o1 = gen_intensity_mat(dataH2_o1, 0)
trueR_H2_q1 = gen_intensity_mat(computed_H2_q1, 2)
expt_H2_q1 = gen_intensity_mat(dataH2_q1, 0)
# --------------------------------------------------
# take ratio of expt to calculated
I_D2_q1 = np.divide(expt_D2_q1, trueR_D2_q1)
I_D2_o1s1 = np.divide(expt_D2_o1s1, trueR_D2_o1s1)
I_HD_q1 = np.divide(expt_HD_q1, trueR_HD_q1)
I_HD_o1s1 = np.divide(expt_HD_o1s1, trueR_HD_o1s1)
I_H2_q1 = np.divide(expt_H2_q1, trueR_H2_q1)
I_H2_o1 = np.divide(expt_H2_o1, trueR_H2_o1)
# remove redundant elements
I_D2_q1 = clean_mat(I_D2_q1)
I_HD_q1 = clean_mat(I_HD_q1)
I_H2_q1 = clean_mat(I_H2_q1)
I_D2_o1s1 = clean_mat(I_D2_o1s1)
I_HD_o1s1 = clean_mat(I_HD_o1s1)
I_H2_o1 = clean_mat(I_H2_o1)
# --------------------------------------------------
# generate sensitivity matrix using true data
sD2_q1 = gen_s_quartic(computed_D2_q1, param)
sHD_q1 = gen_s_quartic(computed_HD_q1, param)
sH2_q1 = gen_s_quartic(computed_H2_q1, param)
sD2_o1s1 = gen_s_quartic(computed_D2_o1s1, param)
sHD_o1s1 = gen_s_quartic(computed_HD_o1s1, param)
sH2_o1 = gen_s_quartic(computed_H2_o1, param)
# --------------------------------------------------
eD2_q1 = (np.multiply(1, I_D2_q1) - sD2_q1)
eHD_q1 = (np.multiply(1, I_HD_q1) - sHD_q1)
eH2_q1 = (np.multiply(1, I_H2_q1) - sH2_q1)
eD2_o1s1 = (np.multiply(weight, I_D2_o1s1) - sD2_o1s1)
eHD_o1s1 = (np.multiply(weight, I_HD_o1s1) - sHD_o1s1)
eH2_o1 = (np.multiply(1, I_H2_o1) - sH2_o1)
# --------------------------------------------------
eD2_o1s1 = clean_mat(eD2_o1s1)
eD2_q1 = clean_mat(eD2_q1)
eHD_o1s1 = clean_mat(eHD_o1s1)
eHD_q1 = clean_mat(eHD_q1)
eH2_q1 = clean_mat(eH2_q1)
eH2_o1 = clean_mat(eH2_o1)
# --------------------------------------------------
E = np.sum(np.abs(eD2_q1)) + np.sum(np.abs(eHD_q1)) \
+ np.sum(np.abs(eH2_q1)) + np.sum(np.abs(eD2_o1s1)) \
+ np.sum(np.abs(eHD_o1s1)) + + np.sum(np.abs(eH2_o1))
return (E)
wMat_H2 = 1
# checks for input done here
# generate calculated data for the entered J values
sosD2 = compute_series_para.sumofstate_D2(299)
sosHD = compute_series_para.sumofstate_HD(299)
sosH2 = compute_series_para.sumofstate_H2(299)
print(sosD2, sosHD, sosH2)
# ----------------------------------------
computed_D2_o1s1 = compute_series_para.spectra_D2_o1s1(299, OJ_D2, SJ_D2,
sosD2)
computed_D2_q1 = compute_series_para.D2_Q1(299, QJ_D2, sosD2)
computed_HD_o1s1 = compute_series_para.spectra_HD_o1s1(299, OJ_HD, SJ_HD,
sosHD)
computed_HD_q1 = compute_series_para.HD_Q1(299, QJ_HD, sosHD)
computed_H2_o1 = compute_series_para.H2_O1(299, OJ_H2, sosH2)
computed_H2_q1 = compute_series_para.H2_Q1(299, QJ_H2, sosH2)
# ----------------------------------------
# checks for dimension match done here
if (computed_D2_o1s1.shape[0] != dataD2_o1s1.shape[0]):
print('D2 : Dimension of input data does not match with the calculated\
spectra. Check input expt data or the J-indices entered.')
sys.exit("\tError: Quitting.")