bins=bin_N,
alpha=0.5,
facecolor=cs['ggred'],
edgecolor=cs['ggred'],
label='Probabilistic',
density=False)
plt.plot(bin_centres, h1, '.', color=cs['ggblue'])
plt.savefig('hist.svg')
plt.title('count rate = ' +
str(np.round(1e3 * ((γs_det2 + γs_det1) / t_clk), 2)) + ' Mcps')
plt.tight_layout()
fig1 = plt.figure('fig1', figsize=(plot_size * np.sqrt(2), plot_size))
ax1 = fig1.add_subplot(1, 1, 1)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('τ (ns)')
ax1.set_ylabel('g$^2$(τ)')
# ax1.set_yscale('log')
plt.plot(bin_centres, y2, '.', color=cs['ggblue'])
plt.savefig('g2s.svg')
plt.title('count rate = ' +
str(np.round(1e3 * ((γs_det2 + γs_det1) / t_clk), 2)) + ' Mcps')
plt.plot(x3 + t_excite, 0.8 * y3)
plt.tight_layout()
plt.show()
prd_plots.PPT_save_2d(fig1, ax1, p0 + '-g2.png')
prd_plots.PPT_save_2d(fig2, ax2, p0 + '-hist.png')
# prd_plots.PPT_save_2d(fig1, ax1, p0 + '-g2 log.png')
# prd_plots.PPT_save_2d(fig2, ax2, p0 + '-hist log.png')
# import x/y data from filename (v0)
ts, cts = load_HH(v0)
# set plot parameters
ggplot()
size = 4
# begin populating figure
fig2 = plt.figure('fig2', figsize=(size * np.sqrt(2), size))
ax2 = fig2.add_subplot(111)
fig2.patch.set_facecolor(cs['mnk_dgrey'])
ax2.set_xlabel('Δt (ns)')
ax2.set_ylabel('counts')
t_shift = 93
plt.plot(ts - t_shift, cts, lw=0.5,
alpha=0.4, color=cs['ggdred'], label=lb)
plt.plot(ts - t_shift, cts, 'o', lw=0.5,
alpha=1, color=cs['gglred'], markersize=2)
plt.xlim((-t_shift, 500))
plt.ylim((0, np.max(cts)))
plt.tight_layout()
# show figure (pop up)
plt.show()
# generate filename
plot_file_name = lb
# save figure
prd_plots.PPT_save_2d(fig2, ax2, 'plot')
ax2.get_xaxis().set_visible(False)
fig2.tight_layout()
# img plot ###################################################################
t_ax = thrs
fq_ax = np.flip(fs)
size = 4
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('time, hrs')
ax4.set_ylabel('frequency, Hz')
im4 = plt.imshow(
np.flipud(np.transpose(dBs_tf_R)),
cmap='magma',
aspect='auto',
extent=[np.min(t_ax),
np.max(t_ax),
np.min(fq_ax), (np.max(fq_ax))])
divider = make_axes_locatable(ax4)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb4 = fig4.colorbar(im4, cax=cax)
plt.show()
# save plots #################################################################
os.chdir(dirpath)
prd_plots.PPT_save_2d(fig1, ax1, 'integrated spectrum')
prd_plots.PPT_save_2d(fig2, ax2, 'time series')
prd_plots.PPT_save_2d_im(fig4, ax4, cb4, 'spectral timeline')
global_cps0 = np.mean(global_cps0)
global_cps1 = np.mean(global_cps1)
##############################################################################
# Plot some figures
##############################################################################
# plot_path = r"C:\Users\Phil\Documents\GitHub\plots"
prd_plots.ggplot()
# xy plot ####################################################################
size = 4
fig1 = plt.figure('fig1', figsize=(size * np.sqrt(2), size))
ax1 = fig1.add_subplot(111)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('x axis')
ax1.set_ylabel('y axis')
plt.hist(dt0_ns,
bins=200,
range=(0, 150),
edgecolor=cs['mnk_dgrey'],
alpha=0.8)
plt.hist(dt1_ns,
bins=200,
range=(0, 150),
edgecolor=cs['mnk_dgrey'],
alpha=0.8)
fig1.tight_layout()
plt.show()
# save figure
prd_plots.PPT_save_2d(fig1, ax1, 'hist_ts')
# θs are the divergence angles of the beam at each point
θs = np.array(ps)[:, 1]
##############################################################################
# Plot the outputted waists
##############################################################################
# Scale values for appropriate plotting
prd_plots.ggplot()
plot_path = r"C:\local files\Python\Plots"
zs = 1e-3 * np.array(zs)
ws = 1e0 * ws
xs = 1e0 * xs
fig1 = plt.figure('fig1')
ax1 = fig1.add_subplot(1, 1, 1)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('optical axis (km)')
ax1.set_ylabel('y axis - beam waist (m)')
plt.plot(zs, ws, '-', c=cs['ggred'], label='Gaussian Beam')
plt.plot(zs, -ws, '-', c=cs['ggred'])
plt.plot(zs, xs, '-', c=cs['ggblue'], label='Raytrace')
plt.plot(zs, -xs, '-', c=cs['ggblue'])
ax1.legend(loc='upper right', fancybox=True, framealpha=1)
plt.tight_layout()
plt.show()
# Saving plots
plot_file_name = plot_path + r'\Gauss and Ray tracing Propagation.png'
ax1.legend(loc='upper left', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
prd_plots.PPT_save_2d(fig1, ax1, plot_file_name)
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')
# # label='Probabilistic')
# # ax3.plot(hist_time, hist[0] * np.exp(-(hist_time / τ)), label='Analytic')
# ax3.legend(loc='upper right', fancybox=True, framealpha=0.5)
# plt.savefig('logplot.svg')
# plt.title('τs_rel')
# plt.tight_layout()
# plt.xlim(0, 1000)
plt.show()
# ax2.legend(loc='upper right', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
# ax1.legend(loc='upper right', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
# prd_plots.PPT_save_2d(fig1, ax1, 'plot.png')
prd_plots.PPT_save_2d(fig2, ax2, 'log plot.png')
# barchart time plot
# fig3 = plt.figure('fig3', figsize=(6 * np.sqrt(2), 3))
# ax3 = fig3.add_subplot(1, 1, 1)
# fig3.patch.set_facecolor(cs['mnk_dgrey'])
# ax3.set_xlabel('Time')
# ax3.set_ylabel('#')
# plt.bar(τs_abs[0:15], np.ones(len(τs_abs[0:15])), 2,
# edgecolor=cs['mnk_dgrey'], label='all counts')
# plt.tight_layout()
# plt.xlim(0, 250)
# ax3.legend(loc='upper right', fancybox=True, framealpha=0.5)
# plt.savefig('barchart.svg')
# fig4 = plt.figure('fig4', figsize=(6 * np.sqrt(2), 3))
# Do some stuff
##############################################################################
# Specify results directory and change working directory to this location
p0 = (r"C:\local files\Experimental Data\G4 L12 Rennishaw\20191101\spec")
os.chdir(p0)
# Generate list of relevant data files and sort them chronologically
datafiles = glob.glob(p0 + r'\*.txt')
# Initialise lists of datasets
sz = 5
prd_plots.ggplot()
fig1 = plt.figure('fig1', figsize=(sz * np.sqrt(2), sz))
ax1 = fig1.add_subplot(1, 1, 1)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('Wavelength (λ) / nm')
ax1.set_ylabel('Spectral signal (arb)')
for i0, val0 in enumerate(datafiles[0:]):
data = np.genfromtxt(val0)
λ = data[:, 0]
cts = data[:, 1]
ax1.plot(1e9 * λ, cts)
plt.xlim((550, 775))
plt.ylim((0, 1250))
plt.show()
ax1.figure.savefig('plot_name' + 'dark.svg')
prd_plots.PPT_save_2d(fig1, ax1, 'fig.svg')
ts.append(np.mean(t))
Ids.append(Id)
# save some of the series data
data_name = 'Power series.dat'
data = np.array(Ps)
header = "Powers"
np.savetxt(data_name, da, header=header)
# Perform fit on series data
x_fit = np.linspace(Ids[0], Ids[-1], 1000)
m = Ps[-1] / Ids[-1]
popt, pcov = curve_fit(prd_maths.straight_line,
Ids, Ps, p0=[m, 0])
m_str = str(np.round(popt[0], 2))
c_str = str(np.round(popt[1], 2))
fit_lb = 'y = ' + m_str + 'x ' + c_str
# plot series data
ax3.plot(x_fit, prd_maths.straight_line(x_fit, *popt),
c=cs['ggred'],
label=fit_lb)
ax3.legend(loc='upper left', fancybox=True, framealpha=1)
plt.show()
# save series plots
ax3.legend(loc='upper left', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
prd_plots.PPT_save_2d(fig1, ax1, 'time series.png')
prd_plots.PPT_save_2d(fig2, ax2, 'histogram.png')
prd_plots.PPT_save_2d(fig3, ax3, 'scatter.png')
# plot_path = r"C:\Users\Philip\Documents\GitHub\plots"
plot_path = r"D:\Python\Plots"
###### image plot ############################################################
# fig1 = plt.figure('fig1', figsize=(5, 5))
# ax1 = fig1.add_subplot(1, 1, 1)
# fig1.patch.set_facecolor(cs['mnk_dgrey'])
# ax1.set_xlabel('x axis')
# ax1.set_ylabel('y axis')
# plt.imshow(im, extent=prd.extents(x) + prd.extents(y))
###### xy plot ###############################################################
size = 4
fig2 = plt.figure('fig2', figsize=(size * np.sqrt(2), size))
ax2 = fig2.add_subplot(111)
fig2.patch.set_facecolor(cs['mnk_dgrey'])
ax2.set_xlabel('10ET reading (mV)')
ax2.set_ylabel('DUT reading (kct/s)')
ax2.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='o',
elinewidth=0.5, ecolor=cs['gglred'],
alpha=1, color=cs['ggdred'], label='')
# ax2.grid(False)
plt.tight_layout()
plt.show()
plot_file_name = plot_path + r'\plot1.png'
prd_plots.PPT_save_2d(fig2, ax2, plot_file_name)
label='Gaussian residuals',
lw=0.5)
ax2.plot(x, y - prd_maths.Lorentzian_1D(
x, *popt_L), '.-',
color=cs['ggpurple'],
label='Lorentzian residuals',
lw=0.5)
ax2.plot(x, y - prd_maths.Voigt_1D(
x, *popt_V), '.-',
color=cs['ggyellow'],
label='Voigt residuals',
lw=0.5)
ax1.set_xlim((x[idx_pk - int(0.3 * roi)], x[idx_pk + int(0.3 * roi)]))
ax2.set_xlim((x[idx_pk - int(0.3 * roi)], x[idx_pk + int(0.3 * roi)]))
ax2.set_ylim((-1000, 1000))
ax1.legend(loc='upper right', fancybox=True, framealpha=0.5)
ax2.legend(loc='upper right', fancybox=True, framealpha=0.5)
ax1.set_title('spectrum with fits')
ax2.set_title('residuals')
fig1.tight_layout()
fig2.tight_layout()
plt.show()
ax1.figure.savefig('spectrum with fits dark' + '.png')
ax1.legend(loc='upper left', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
ax2.legend(loc='upper left', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
prd_plots.PPT_save_2d(fig1, ax1, 'spectrum with fits 30mA')
prd_plots.PPT_save_2d(fig2, ax2, 'residuals 30mA')
##############################################################################
V0 = 100
BO_rate = 0.009
days = 50
Vs = []
Vt = V0
for i0, j0 in enumerate(np.arange(days)):
Vt = Vt - 0.009 * Vt
Vs.append(Vt)
print('day ', i0, ', V = ', np.round(Vt, 2), 'l')
##############################################################################
# Plot some figures
##############################################################################
prd_plots.ggplot()
###
scale = 5
fig1 = plt.figure('fig1', figsize=(scale * np.sqrt(2), scale * 1))
ax1 = fig1.add_subplot(1, 1, 1)
fig1.patch.set_facecolor(cs['mnk_dgrey'])
ax1.set_xlabel('Time, days')
ax1.set_ylabel('Volume (l)')
plt.plot(np.arange(days), Vs, 'o:', label='He lost forever')
plt.tight_layout()
ax1.legend(loc='upper right', fancybox=True, framealpha=0.5)
ax1.legend(loc='upper right', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
plt.show()
prd_plots.PPT_save_2d(fig1, ax1, 'plot1.png')
prd_plots.ggplot()
# histograms
x1 = τs_HBT
x1 = [i for i in x1 if i <= 100]
x1 = [i for i in x1 if i >= -100]
bin_N = 201
hist, bins = np.histogram(x1, bins=bin_N)
hist_time = np.linspace(bins[0] + (bins[1] - bins[0]) / 2,
bins[-2] + (bins[-1] - bins[-2]) / 2, 1000)
fig2 = plt.figure('fig2', figsize=(3 * np.sqrt(2), 3))
ax2 = fig2.add_subplot(1, 1, 1)
fig2.patch.set_facecolor(cs['mnk_dgrey'])
ax2.set_xlabel('Time')
ax2.set_ylabel('#')
plt.hist(x1,
bins=bin_N,
alpha=0.5,
facecolor=cs['ggred'],
edgecolor=cs['mnk_dgrey'],
label='Probabilistic')
plt.savefig('logplot.svg')
plt.title('τs_HBT')
plt.tight_layout()
# plt.ylim(0, 1.5 * np.mean(hist))
plt.show()
prd_plots.PPT_save_2d(fig2, ax2, f4 + '.png')
for i0, val0 in enumerate(datafiles):
print(val0)
data = np.genfromtxt(datafiles[i0])
Ps = (data[:, 0])
Ps_2fit = (data[0:idx_lims[i0], 0])
Ps_range = np.linspace(np.min(Ps_2fit), np.max(Ps_2fit), 100)
As = (data[:, 1])
As_2fit = (data[0:idx_lims[i0], 1])
popt, pcov = curve_fit(prd_maths.Monomial, Ps_2fit, As_2fit, p0=[100, 2])
ax1.plot(Ps_2fit,
As_2fit,
'o',
mec=colors[10 * i0],
mfc=colors[10 * i0 + 2])
ax1.plot(Ps, As, '.', c=colors[10 * i0 + 4], lw=0.5)
ax1.plot(Ps_range,
prd_maths.Monomial(Ps_range, *popt),
c=colors[10 * i0 + 6],
label='Peak ' + str(i0) + ' m = ' + str(np.round(popt[1], 2)))
ax1.legend(loc='lower right', fancybox=True, framealpha=1)
ax1.set_title('Power saturation fits')
plt.tight_layout()
plt.show()
ax1.figure.savefig('Psat fits dark' + '.png')
ax1.legend(loc='upper left', fancybox=True, facecolor=(1.0, 1.0, 1.0, 0.0))
prd_plots.PPT_save_2d(fig1, ax1, 'Psat fits')
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')
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
