def _container(context, N, cls):
import numpy
Nn = int(N)
spectra = []
def func(x, y, t):
Delta = 10.0
omega = 2.0 * 3.14159 / 20.0
gamma = 1.0 / 100.0
data = numpy.zeros((len(x), len(y)))
for i_x in range(len(x)):
data[i_x, :] = numpy.exp(-((x[i_x]+y)/Delta)**2)* \
numpy.cos(omega*t)*numpy.exp(-t/gamma)
return data
time = cls(0.0, Nn, 2.0)
xrange = qr.ValueAxis(-50.0, 100, 1.0)
yrange = qr.ValueAxis(-50.0, 100, 1.0)
cont = qr.TwoDSpectrumContainer()
cont.use_indexing_type(time)
for k_n in range(Nn):
tt = time.data[k_n]
data = func(xrange.data, yrange.data, tt)
spect = qr.TwoDSpectrum()
spect.set_resolution("off")
spect.set_data_flag("total")
#spect.set_data(data)
spect._add_data(data, dtype="total")
spect.set_axis_1(xrange)
spect.set_axis_3(yrange)
spectra.append(spect)
cont.set_spectrum(spect, tt)
context.container = cont
def step_given_7(context, N, step):
"""
And I have a ValueAxis of lenght {N} starting from zero with certain {step}
"""
length = int(N)
step = float(step)
vaxis = qr.ValueAxis(0.0, length, step)
context.vaxis = vaxis
context.length = length
context.step = step
def step_then_28(context):
"""
And the TwoDSpectrum container will be indexed by ValueAxis with frequencies corresponding to the original ValueAxis
"""
fft = context.new_container
oldaxis = context.container.axis
axis = fft.axis
freq = (2.0 * numpy.pi) * numpy.fft.fftfreq(oldaxis.length, oldaxis.step)
freq = numpy.fft.fftshift(freq)
# this has to be done to get desired precision
vax = qr.ValueAxis(freq[0], len(freq), freq[1] - freq[0])
#
# Asserting the value axis has the right values
#
numpy.testing.assert_allclose(axis.data, vax.data)
def get_peak_cross_sections(V, twod_p):
cut_long = twod_p.get_cut_along_line(V[0], V[1])
cut_short1 = twod_p.get_cut_along_line(V[3], V[2])
cut_short2 = twod_p.get_cut_along_line(V[2], V[3])
data1 = np.array(cut_short1.data)
data2 = np.flip(np.array(cut_short2.data))
data3 = np.hstack((data1, data2))
shift = cut_short1.axis.data[1] + cut_short1.axis.data[-1]
cut_short2.axis.data = cut_short2.axis.data + shift
new_axis = np.hstack((cut_short1.axis.data, cut_short2.axis.data))
qr_axis = qr.ValueAxis(0.0, len(new_axis), new_axis[1])
short_final = qr.DFunction(qr_axis, data3)
data_max = (np.amax(twod_p.data))/2
long_p_1 = next(x for x, val in enumerate(cut_long.data) if val > (data_max))
for_lp2 = np.flip(cut_long.data)
long_p_2 = next(x for x, val in enumerate(for_lp2) if val > (data_max))
long_width = cut_long.axis.data[-long_p_2-1] - cut_long.axis.data[long_p_1]
short_p_1 = next(x for x, val in enumerate(short_final.data) if val > (data_max))
for_sp2 = np.flip(short_final.data)
short_p_2 = next(x for x, val in enumerate(for_sp2) if val > (data_max))
short_width = short_final.axis.data[-short_p_2-1] - short_final.axis.data[short_p_1]
fig, ax = plt.subplots(2)
ax[0].plot(cut_long.axis.data, cut_long.data)
ax[0].set_title('long')
ax[1].plot(short_final.axis.data, short_final.data)
ax[1].set_title('short')
plt.show()
return short_width, long_width
t2_save_pathways = INP.t2_save_pathways #[50.0, 100.0, 200.0, 300.0]
#
# Here we construct a path through parameters space
#
center = INP.center #600.0
step = INP.step #2.0
max_available_fwhm = INP.max_available_fwhm #100.0
how_many_fwhm = INP.how_many_fwhm #2
#step = 2
Ns_d = int(2.0 * how_many_fwhm * max_available_fwhm / step) # 50
Ns_u = int(2.0 * how_many_fwhm * max_available_fwhm / step) # 50
vax = qr.ValueAxis(center - Ns_d * step, Ns_d + Ns_u + 1, step)
trimer = INP.trimer
use_trimer = trimer["useit"]
trimer_disorder = False # trimer["disorder"]
#if use_trimer:
# vax2 = qr.ValueAxis(trimer["center2"]-Ns_d*step, Ns_d+Ns_u+1, step)
print("\nSummary of simulation parameters\n")
print("Energy gap values:")
print("Minimal gap =", vax.min)
print("Maximum gap =", vax.max)
#if use_trimer and trimer_disorder:
# print("Minimal gap (2nd dim) =", vax2.min)
# print("Maximum gap (2nd dim) =", vax2.max)
print("Number of steps =", vax.length)
spects.append(sp)
#print("dE =", dE)
de_min = cont.get_spectrum(0).get_log_params()["dE"]
de2 = cont.get_spectrum(1).get_log_params()["dE"]
de_step = de2 - de_min
de_N = cont.length()
print("de_min:", de_min)
print("de_step:", de_step)
print("de_N", de_N)
#
# These are the values of energy gap that the spectra represent
#
des = qr.ValueAxis(de_min, de_N, de_step)
#print(des.data)
#
# parameters of the disorder
#
width_dis = numpy.zeros(2, dtype=qr.REAL)
width_dis[0] = INP.width_dis[0] #50.0 # cm-1
width_dis[1] = INP.width_dis[1] #50.0 # cm-1
how_many_fwhm = INP.how_many_fwhm
#
# Average values of the monomer energies
#
if INP.use_default_values:
