def get_transitions(Yg, Ye, ve, vg, cnt_freqs, df=100e6, T=1, Jmax=1):
Jg_arr = np.arange(0, Jmax + 1)
Je_arr = np.arange(0, Jmax + 1)
w = get_doppler(T, 100 * c * cnt_freq)
nus = np.linspace(100 * c * (cnt_freq) - df, 100 * c * (cnt_freq) + df,
2000)
spectrum_P = np.zeros(len(nus))
spectrum_Q = np.zeros(len(nus))
spectrum_R = np.zeros(len(nus))
f_P = []
f_Q = []
f_R = []
for Jg in Jg_arr:
for Je in Je_arr:
# only dipole transitions
eng = 100 * c * (energy(Ye, ve, Je) - energy(Yg, vg, Jg))
# apply population of ground states
A = get_pop(Jg, 100 * c * Yg[1][0], T)
if Je - Jg == -1:
spectrum_P += gauss(nus, eng, A, w)
f_P.append(eng)
#plt.plot( 2 * [(eng - 100*c*cnt_freq) / 1e9], [-0.1,0.0], 'r' )
if Je - Jg == 0:
spectrum_Q += gauss(nus, eng, A, w)
f_Q.append(eng)
#plt.plot( 2 * [(eng - 100*c*cnt_freq) / 1e9], [-0.1,0.0], 'g' )
if Je - Jg == +1:
spectrum_R += gauss(nus, eng, A, w)
f_R.append(eng)
#plt.plot( 2 * [(eng - 100*c*cnt_freq) / 1e9], [-0.1,0.0], 'b' )
f_P = np.array(f_P) - 100 * c * cnt_freq
f_Q = np.array(f_Q) - 100 * c * cnt_freq
f_R = np.array(f_R) - 100 * c * cnt_freq
nus = nus - 100 * c * cnt_freqs
return (nus, [spectrum_P, spectrum_Q, spectrum_R], [f_P, f_Q, f_R])
def fcn2min(params, x, data, Yg35, Ye35, plot_fit = False):
a = params['a']
Trot = params['Trot']
Tcoll = params['Tcoll']
x0 = params['x0']
Ye = Ye35
Yg = Yg35
vg = 0
ve = 0
Jmax = 10
cnt_freq = 3*382.11035e12/100.0/c
Jg_arr = np.arange(0, Jmax+1)
w = get_doppler(Tcoll, 100*c*cnt_freq)
nus = x*1e6 - x0 + 100 * c * cnt_freq # in Hz
df = 1e9
if plot_fit == False:
xfit = nus
else:
xfit = np.linspace(np.min(nus) - df, np.max(nus) + df, 500)
spectrum_Q = np.zeros(len(xfit))
for Jg in Jg_arr:
Je = Jg
eng = 100*c*(energy(Ye, ve, Je) - energy(Yg, vg, Jg))
# apply population of ground states
A = get_pop(Jg, 100 * c * np.abs(Yg[1][0]), Trot)
spectrum_Q += gauss(xfit, eng, A, w)
y_th = a * spectrum_Q # (0.76 * spectrum35 + 0.24 * spectrum37)
if plot_fit == False:
return y_th - data
else:
return ((xfit - 100*c*cnt_freq + x0)/1e6, y_th)