header = "Powers, Gaussian Amplitudes, Gaussian centres, Gaussian widths"
np.savetxt(data_name, data, header=header)
print(len(fit_data))
prd_plots.ggplot()
size = 4
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 (λ - nm)')
ax1.set_ylabel('Counts')
ax1.set_title('Labelled spectrum with fits')
ax1.plot(xs0, ys0, '.', markersize=2,
alpha=0.5, color=cs['gglred'], label='')
pk_ys = [ys0[i] for i in pk_idxs]
for i0, val0 in enumerate(fit_data):
pk_x = fit_data[i0][1][1]
pk_y = prd_maths.Gaussian_1D(pk_x,
fit_data[i0][0][1],
fit_data[i0][1][1],
fit_data[i0][2][1])
ax1.plot(pk_x, pk_y, '.',
mfc=cs['ggblue'],
mec=cs['ggblue'],
label='peak ' + str(i0))
ax1.text(pk_x, pk_y, ' peak ' + str(i0))
fig1.tight_layout()
plt.show()
prd_plots.PPT_save_2d(fig1, ax1, 'peak labels.png')
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])
# Final plots
prd_plots.ggplot()
colors = plt.cm.viridis(np.linspace(0, 1, len(λs)))
ax1.plot(x - popt[2], y, '--', alpha=0.5,
color=colors[i1],
label='',
lw=0)
plt.plot(x_roi - popt[2], y_roi, '.',
c=colors[i1],
alpha=0.3)
plt.plot(x_fit - popt[2], prd_maths.Gaussian_1D(
x_fit - popt[2], *popt),
label='fit',
c=colors[i1],
lw=0.5)
# plt.xlim((x[idx_pk - int(0.3 * roi)], x[idx_pk + int(0.3 * roi)]))
ax1.set_title('spectra with fits')
fig1.tight_layout()
plt.show()
ax1.figure.savefig('spectra with fits dark' + '.png')
prd_plots.PPT_save_2d(fig1, ax1, 'spectra with fits')
fig2.patch.set_facecolor(cs['mnk_dgrey'])
ax2.set_xlabel('Wavelength (λ) / nm')
ax2.set_ylabel('Residuals')
x = λ
y = cts
# Final plots
prd_plots.ggplot()
ax1.plot(x_roi, y_roi, '.',
label='data',
color=cs['ggred'],
alpha=1)
ax1.plot(x_fit, prd_maths.Gaussian_1D(
x_fit, *popt_G),
color=cs['ggblue'],
label='Gaussian fit',
lw=0.5)
ax1.plot(x_fit, prd_maths.Lorentzian_1D(
x_fit, *popt_L),
color=cs['ggpurple'],
label='Lorentzian fit',
lw=0.5)
ax1.plot(x_fit, prd_maths.Voigt_1D(
x_fit, *popt_V),
color=cs['ggyellow'],
label='Voigt fit',
lw=0.5)
ax2.plot(x, y - prd_maths.Gaussian_1D(
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