import matplotlib.pyplot as plt
##############################################################################
# Import some extra special libraries from my own repo and do some other stuff
##############################################################################
# Office PC (eea) path
sys.path.insert(0, r"D:\Python\Local Repo\library")
# Surface Pro path
sys.path.insert(0, r"C:\Users\Philip\Documents\GitHub\latest-python\library")
np.set_printoptions(suppress=True)
import prd_plots
import prd_data_proc
cs = prd_plots.palette()
##############################################################################
# Do some stuff
##############################################################################
# load photon timing files generated by 'Generate photons.py'
T = 100
τ_decay = 10
τ_excite = 1
drive = 'cw '
# Office PC (eea) path
p0 = r'D:\Experimental Data\Python simulations (G5 A5)'\
r'\Single photon statistics\Data\20190320\\'\
+ drive + str(τ_decay) + 'ns, ' + \
str(τ_excite) + 'x exc, ' + \
def spec_seq_Gauss_fit_20190611(d, popt, idx_pk, roi, pk_lb, Ps):
print(d)
λs, ctss, lbs = prd_file_import.load_spec_dir(d)
prd_plots.ggplot()
cs = prd_plots.palette()
size = 4
colors = plt.cm.viridis(np.linspace(0, 1, len(λs[0:])))
fig1 = plt.figure('fig1', figsize=(size * np.sqrt(2), size))
ax1 = fig1.add_subplot(1, 1, 1)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('Wavelength shifted with excitation power')
ax1.set_ylabel('Counts')
ax1.set_title('Spectra with fits - ' + pk_lb)
fig1.tight_layout()
fig2 = plt.figure('fig2', figsize=(size * np.sqrt(2), size))
ax2 = fig2.add_subplot(1, 1, 1)
fig2.patch.set_facecolor(cs['mnk_dgrey'])
ax2.set_xlabel('Power (μW)')
ax2.set_ylabel('Fit amplitude')
ax2.set_title('Fit amplitude with power - ' + pk_lb)
fig2.tight_layout()
fig3 = plt.figure('fig3', figsize=(size * np.sqrt(2), size))
ax3 = fig3.add_subplot(1, 1, 1)
fig3.patch.set_facecolor(cs['mnk_dgrey'])
ax3.set_xlabel('Power (μW)')
ax3.set_ylabel('Peak location, (λ$_c$ - nm)')
ax3.set_title('Peak location with power - ' + pk_lb)
fig3.tight_layout()
fig4 = plt.figure('fig4', figsize=(size * np.sqrt(2), size))
ax4 = fig4.add_subplot(1, 1, 1)
fig4.patch.set_facecolor(cs['mnk_dgrey'])
ax4.set_xlabel('Power (P - μW)')
ax4.set_ylabel('Peak width (σ - nm)')
ax4.set_title('Peak widths with power - ' + pk_lb)
fig4.tight_layout()
μs = []
σs = []
As = []
for i0, val0 in enumerate(λs[0:]):
P = Ps[i0]
x = λs[i0]
y = ctss[i0]
x_roi = x[int(idx_pk - roi / 2):int(idx_pk + roi / 2)]
y_roi = y[int(idx_pk - roi / 2):int(idx_pk + roi / 2)]
x_fit = np.linspace(min(x_roi), max(x_roi), 1000)
popt, pcov = curve_fit(prd_maths.Gaussian_1D, x_roi, y_roi, p0=[popt])
As.append(popt[0])
μs.append(popt[1])
σs.append(popt[2])
# Final plots
prd_plots.ggplot()
colors = plt.cm.viridis(np.linspace(0, 1, len(λs)))
ax1.plot(x - popt[1] + i0,
y,
'--',
alpha=0.5,
color=colors[i0],
label='',
lw=0)
ax1.plot(x_roi - popt[1] + i0, y_roi, '.', c=colors[i0], alpha=0.3)
ax1.plot(x_fit - popt[1] + i0,
prd_maths.Gaussian_1D(x_fit - popt[1] + i0,
*(popt[0], i0, popt[2], popt[3])),
label='fit',
c=colors[i0],
lw=0.5)
ax1.set_xlim((x[idx_pk - int(0.3 * roi)], x[idx_pk + int(0.3 * roi)]))
ax1.set_xlim((-1, 12))
fig1.tight_layout()
ax2.plot(P, popt[0], 'o', c=colors[i0])
fig2.tight_layout()
ax3.plot(P, popt[1], 'o', c=colors[i0])
fig3.tight_layout()
ax4.plot(P, popt[2], 'o', c=colors[i0])
fig4.tight_layout()
plt.show()
# ax1.figure.savefig(pk_lb + ' ' + ax1.get_title() + ' dark' + '.png')
# ax2.figure.savefig(pk_lb + ' ' + ax2.get_title() + ' dark' + '.png')
# ax3.figure.savefig(pk_lb+ ax3.get_title() + ' dark' + '.png')
# ax4.figure.savefig(pk_lb + ' ' + ax4.get_title() + ' dark' + '.png')
prd_plots.PPT_save_2d(fig1, ax1, pk_lb + ' ' + ax1.get_title())
prd_plots.PPT_save_2d(fig2, ax2, pk_lb + ' ' + ax2.get_title())
prd_plots.PPT_save_2d(fig3, ax3, pk_lb + ' ' + ax3.get_title())
prd_plots.PPT_save_2d(fig4, ax4, pk_lb + ' ' + ax4.get_title())
ax1.cla()
ax2.cla()
ax3.cla()
ax4.cla()
return As, μs, σs
