def plotfuncs(mydata, corde_data, toplabel):
x = np.float64(mydata)
y = corde_data
del_data = x-y
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
# mt.hist(del_data, bins=20, range=(-1, 1), ax=ax)
mt.hist(del_data, bins=20, ax=ax)
mt.addlabel(toplabel=toplabel)
# z = np.polyfit(x, y, 1)
p2 = np.poly1d([1, 0])
# fig = plt.figure()
ax = fig.add_subplot(1, 2, 2)
ax.plot(x, y, '.', x, p2(x), 'r')
mt.addlabel(toplabel=toplabel)
# Create larger gridspec
outergs= gs.GridSpec(3,2)
# pdb.set_trace()
# ======================================
# Find spot size for each step
# ======================================
# mt.figure('Shot')
for i,img in enumerate(imgs):
# img=np.flipud(np.rot90(f[img[0]],3))
img=np.flipud(np.rot90(f[img[0]]))
ax=fig.add_subplot(outergs[i])
ax.imshow(img[350:600,xstart:xstop],aspect='auto',interpolation='none')
fig.add_subplot(ax)
plt.figure(fig.number)
outergs.tight_layout(fig,pad=5)
mt.addlabel(toplabel='$\Delta E$={}GeV'.format(stepvalues[i]),xlabel='x [px]',ylabel='y [px]')
# plt.show()
# Fit individual slices
popt,pcov,chisq_red[i] = mt.fitimageslice(
img,
res_x,
res_y,
(xstart,xstop),
(ystart,ystop),
plot=True
)
mt.addlabel(toplabel='Gaussian Fit to Spot Profile, $\Delta E$={}GeV'.format(stepvalues[i]),xlabel='x [m]',ylabel='Counts')
variance[i] = popt[2]
bact = setQS.set_QS_energy_ELANEX(stepvalues[i])
def results2pdf(path, local=False, verbose=False, loglevel=logging.CRITICAL):
# =====================================
# Regex-out the dataset number, date
# =====================================
dataset_string = re.search('E200_(\d+)', path).groups()[0]
date_string_tmp = re.search('\d{8}', path).group()
year = np.int(date_string_tmp[0:4])
month = np.int(date_string_tmp[4:6])
day = np.int(date_string_tmp[6:])
date = dt.date(year=year, month=month, day=day)
date_string = date.strftime('%b %-d, %Y')
# =====================================
# Load data
# =====================================
data = E200.E200_load_data(path, local=local, readonly=True)
processed_drill = data.wdrill.data.processed
scalars_drill = processed_drill.scalars
vectors_drill = processed_drill.vectors
arrays_drill = processed_drill.arrays
scalars_rdrill = data.rdrill.data.raw.scalars
# =====================================
# Helper function
# =====================================
def get_str(drill):
out_str = drill._hdf5
return drill, out_str
# =====================================
# get uids, energy axes, images, etc
# =====================================
valid = scalars_drill.ss_ELANEX_valid
uids = valid.UID[valid.dat is True]
# uids = uids[0:4]
valid_drill, valid_str = get_str(scalars_drill.ss_ELANEX_valid)
valid = E200.E200_api_getdat(valid_str, uids)
quadval_drill, quadval_str = get_str(scalars_rdrill.step_value)
quadval = E200.E200_api_getdat(quadval_str, uids)
emit_n_drill, emit_n_str = get_str(scalars_drill.ss_ELANEX_emit_n)
emit_n = E200.E200_api_getdat(emit_n_str, uids)
betastar_drill, betastar_str = get_str(scalars_drill.ss_ELANEX_betastar)
betastar = E200.E200_api_getdat(betastar_str, uids)
sstar_drill, sstar_str = get_str(scalars_drill.ss_ELANEX_sstar)
sstar = E200.E200_api_getdat(sstar_str, uids)
rect_drill, rect_str = get_str(vectors_drill.ss_ELANEX_rect)
rects = E200.E200_api_getdat(rect_str, uids)
eaxis_drill, eaxis_str = get_str(vectors_drill.ss_ELANEX_energy_axis)
eaxis = E200.E200_api_getdat(eaxis_str, uids)
variance_drill, variance_str = get_str(vectors_drill.ss_ELANEX_variance)
variance = E200.E200_api_getdat(variance_str, uids)
beta_drill, beta_str = get_str(vectors_drill.ss_ELANEX_LLS_beta)
beta = E200.E200_api_getdat(beta_str, uids)
y_error_drill, y_error_str = get_str(vectors_drill.ss_ELANEX_LLS_y_error)
y_error = E200.E200_api_getdat(y_error_str, uids)
X_unweighted_drill, X_unweighted_str = get_str(arrays_drill.ss_ELANEX_LLS_X_unweighted)
X_unweighted = E200.E200_api_getdat(X_unweighted_str, uids)
selected_img_drill, selected_img_str = get_str(arrays_drill.ss_ELANEX_selected_img)
selected_img = E200.E200_api_getdat(selected_img_str, uids)
oimg_eaxis_drill, oimg_eaxis_str = get_str(vectors_drill.ss_ELANEX_oimg_eaxis)
oimg_eaxis = E200.E200_api_getdat(oimg_eaxis_str, uids)
elanex_drill, elanex_str = get_str(data.rdrill.data.raw.images.ELANEX)
images = E200.E200_load_images(elanex_str, uids)
E224_Probe_drill, E224_Probe_str = get_str(data.rdrill.data.raw.images.E224_Probe)
# =====================================
# Determine laser on shots
# =====================================
laseron = laser_on_off(E224_Probe_str, uids)
# =====================================
# Determine number of shots, pages
# =====================================
num_shots = len(valid)
slots_per_page = 5
pages = np.int(np.ceil(num_shots/np.float(slots_per_page)))
# =====================================
# Create filename, pdf, main title
# =====================================
now = dt.datetime.now()
filename = '{}_processed_{}-{:02d}-{:02d}_{:02d}{:02d}.pdf'.format(dataset_string, now.year, now.month, now.day, now.hour, now.minute)
pdf = PdfPages(filename)
main_title = 'Single Shot Emittance Analysis, Dataset {dataset}, {datestring}, Page {{}}/{totalpgs}'.format(
dataset = dataset_string,
datestring = date_string,
totalpgs = pages+1
)
layout_rect = [0, 0, 1, 0.95]
# =====================================
# Create overview
# =====================================
worksheet(data, pdf, main_title)
# =====================================
# Basic formatting options
# =====================================
linewidth = 1
fontsize = 7
# =====================================
# Create pages with slots_per_page
# number of analysis/page
# =====================================
for i in range(pages):
gs = gridspec.GridSpec(5, 3)
fig = plt.figure(figsize=(8.5, 11))
fig.suptitle(main_title.format(i+2))
# =====================================
# Add analysis to each slot
# =====================================
for j in range(slots_per_page):
indx = i*slots_per_page+j
print(indx)
# =====================================
# Only add if there are shots left
# =====================================
if indx < num_shots:
# =====================================
# Plot Energy
# =====================================
img_to_plot = np.fliplr(np.rot90(images.images[indx]))
uid = images.UID[indx]
ax = fig.add_subplot(gs[j, 0])
e_axis = oimg_eaxis.dat[indx]
e_axis = np.flipud(e_axis)
# =====================================
# Get energy, pixel ranges
# =====================================
# x_min = 1
x_max = img_to_plot.shape[0]
# x_range = x_max-x_min
# y_min = e_axis[-1]
# y_max = e_axis[0]
# y_max = img_to_plot.shape[1]
# y_range = y_max-y_min
# aspect = x_range/y_range * img_to_plot.shape[1]/img_to_plot.shape[0]
# aspect = np.power(np.float64(img_to_plot.shape[1])/np.float64(img_to_plot.shape[0]), 2)
# extent = (x_min, x_max, y_min, y_max)
# =====================================
# Show image
# =====================================
# ax.imshow(img_to_plot, extent=extent, aspect=aspect, origin='lower')
# ax.imshow(img_to_plot, aspect=aspect, origin='lower')
# img = mpl.image.NonUniformImage(ax)
# img.set_data(e_axis, mt.linspacestep(1, x_max), img_to_plot)
pixels = selected_img.dat[indx].flatten()
hist, edges = np.histogram(pixels, bins=50)
new_edges = edges[1:]
max_count = np.max(new_edges[hist > 50])
img = mt.NonUniformImage(e_axis, mt.linspacestep(1, x_max), img_to_plot, ax=ax)
img.set_clim([0, max_count])
mt.addlabel(axes = ax, ylabel='Pixels', xlabel='Energy [GeV]')
cb = fig.colorbar(img)
ax.tick_params(labelsize = fontsize)
cb.ax.tick_params(labelsize = fontsize)
ax.get_figure().tight_layout()
# =====================================
# Get rectangle info
# =====================================
thisrect = rects.dat[indx]
# =====================================
# Retool y to energy scale
# =====================================
# x = thisrect[0]
# y = thisrect[1]
# width = thisrect[2]
# height=thisrect[3]
myrect = mt.qt.Rectangle(*thisrect)
y0 = myrect.x0
y1 = myrect.x1
temp = np.flipud(e_axis)
e_y0 = temp[np.int(np.round(y0))]
e_y1 = temp[np.int(np.round(y1))]
e_width = e_y1-e_y0
# rect = mt.qt.Rectangle(x, e_y0, e_width, height, axes=ax, alpha=1, fill=False)
rect = mt.qt.Rectangle(e_y0, y, e_width, height, axes=ax, alpha=1, fill=False)
ax.add_patch(rect.get_rect())
mt.graphics.less_labels(ax, y_fraction=1)
mt.graphics.axesfontsize(ax, fontsize)
# =====================================
# Plot Fit
# =====================================
figlabel = 'Butterfly Fit'
ax = fig.add_subplot(gs[j, 1])
ax0 = bt.plotfit(
eaxis.dat[indx],
variance.dat[indx],
beta.dat[indx],
X_unweighted.dat[indx],
top = 'Emittance and Beam Parameter Fit',
figlabel = figlabel,
bottom = 'Energy [GeV]',
axes = ax,
error = y_error.dat[indx],
linewidth = linewidth,
fontsize = fontsize,
markersize = 2
)
mt.graphics.less_labels(ax0, y_fraction=1)
mt.graphics.axesfontsize(ax0, fontsize)
ax0.get_figure().tight_layout()
# =====================================
# Add numbers
# =====================================
ax = fig.add_subplot(gs[j, 2])
# Calculate pinch energy
size = np.dot(X_unweighted.dat[indx], beta.dat[indx])
argmin = np.argmin(size)
pinch_energy = eaxis.dat[indx][argmin]
if laseron[indx]:
laser_status = 'On'
else:
laser_status = 'Off'
table_begin = r'''
\begin{{tabular}}{{ll}}
\toprule
Date & {} \\
UID & {:0.0f} \\
Quad Offset [GeV] & {:0.3f} \\
\midrule Pinch Energy [GeV] & {:0.3f} \\
Norm Emit [mm-mrad] & {:0.3f} \\
Beta* [m] & {:0.3f} \\
S* [m] & {:0.3f} \\
Laser & {} \\
\bottomrule
\end{{tabular}}'''
table_begin = table_begin.format(
date_string,
uid,
quadval.dat[indx],
pinch_energy,
emit_n.dat[indx]*1e6,
betastar.dat[indx],
sstar.dat[indx],
laser_status
)
table_begin = re.sub('\\n', ' ', table_begin)
# table_begin = r'''\begin{tabular}{ c | c | c | c } & col1 & col2 & col3 \\\hline row1 & 11 & 12 & 13 \\\hline row2 & 21 & 22 & 23 \\\hline row3 & 31 & 32 & 33 \end{tabular}'''
ax.text(-0.1, 0.25, table_begin, fontsize=fontsize)
ax.set_frame_on(False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_figure().tight_layout()
gs.tight_layout(fig, rect=layout_rect)
pdf.savefig(fig)
plt.close()
pdf.close()
command = 'open {}'.format(filename)
subprocess.call(shlex.split(command))
def coversheet(data, pdf, main_title):
gs = gridspec.GridSpec(3, 2)
fig = plt.figure(figsize=(8.5, 11))
fig.suptitle(main_title.format(1))
ax = fig.add_subplot(gs[0, 0])
# data=E200.E200_load_data('nas/nas-li20-pm00/E200/2014/20140629/E200_13537', local=True, readonly=True)
dats = E200.E200_api_getdat(data.wdrill.data.processed.scalars.ss_ELANEX_valid._hdf5)
uids = dats.UID[dats.dat is True]
emit_dats = E200.E200_api_getdat(data.wdrill.data.processed.scalars.ss_ELANEX_emit_n._hdf5, UID=uids)
imgstr = data.rdrill.data.raw.images.E224_Probe._hdf5
laseron = laser_on_off(imgstr, uids)
if not np.any(laseron is True):
laser_on_exist = False
logger.log(level=loggerlevel, msg='No laser on shots')
else:
laser_on_exist = True
if not np.any(laseron is False):
laser_off_exist = False
logger.log(level=loggerlevel, msg='No laser off shots')
else:
laser_off_exist = True
# =====================================
# Create figure if no axes specified
# =====================================
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# =====================================
# Get Numbers
# =====================================
norm_emit_on = emit_dats.dat[laseron == 1]*1e6
if len(norm_emit_on) > 0:
avg_norm_emit_on = '{:0.3f}'.format(np.mean(norm_emit_on))
std_norm_emit_on = '{:0.3f}'.format(np.std(norm_emit_on))
else:
avg_norm_emit_on = 'N/A'
std_norm_emit_on = 'N/A'
norm_emit_off = emit_dats.dat[laseron == 0]*1e6
# ipdb.set_trace()
if len(norm_emit_off) > 0:
avg_norm_emit_off = '{:0.3f}'.format(np.mean(norm_emit_off))
std_norm_emit_off = '{:0.3f}'.format(np.std(norm_emit_off))
else:
avg_norm_emit_off = '(N/A)'
std_norm_emit_off = '(N/A)'
# =====================================
# Plot logic
# =====================================
if laser_on_exist and laser_off_exist:
# out_plot = ax.plot(norm_emit_on, '.-', norm_emit_off, '.-')
ax.legend(['Laser On', 'Laser Off'], fontsize=fontsize)
mt.addlabel(toplabel = 'Effect of Laser on Emittance', axes=ax)
elif laser_on_exist and laser_off_exist is False:
# out_plot = ax.plot(norm_emit_on, '.-')
ax.legend(['Laser On'], fontsize=fontsize)
toplabel = 'Effect of Laser on Emittance\nNO LASER OFF DATA'
ax.set_title(toplabel, color='red')
elif laser_on_exist is False and laser_off_exist:
# out_plot = ax.plot(norm_emit_off, '.-')
ax.legend(['Laser On'], fontsize=fontsize)
toplabel = 'Effect of Laser on Emittance\nNO LASER ON DATA'
ax.set_title(toplabel, color='red')
else:
raise NotImplementedError('Check laser on/off arrays')
mt.addlabel(
axes = ax,
xlabel = 'Unsorted Shots',
ylabel = 'Norm. Emittance [mm-mrad]')
# fig.tight_layout()
#
# plt.show()
#
# fig.savefig('NormEmit_Laser_On_Off_13537.jpg')
mt.graphics.axesfontsize(ax, fontsize=7)
ax = fig.add_subplot(gs[0, 1])
table_begin = r'''
\begin{{tabular}}{{ll}}
\toprule
\multicolumn{{2}}{{c}}{{Laser On}} \\
\midrule
Average [mm-mrad] & {} \\
Std. Dev. [mm-mrad] & {} \\
\toprule
\multicolumn{{2}}{{c}}{{Laser Off}} \\
\midrule
Average [mm-mrad] & {} \\
Std. Dev. [mm-mrad] & {} \\
\bottomrule
\end{{tabular}}'''
table_begin = table_begin.format(
avg_norm_emit_on,
std_norm_emit_on,
avg_norm_emit_off,
std_norm_emit_off
)
table_begin = re.sub('\\n', ' ', table_begin)
ax.text(0.5, 1, table_begin, fontsize=fontsize, ha='center', va='top')
ax.set_frame_on(False)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_figure().tight_layout()
gs.tight_layout(fig, rect=layout_rect)
pdf.savefig(fig)
return pdf
# ===================================== # Plot Emittance # ===================================== y = emit_n*1e6 fig = plt.figure() ax = fig.add_subplot(1, 1, 1) line = ax.plot(result, y, 'o-', linewidth=linewidth) toplabel = 'Ionization-Injected Witness Emittance' windowlabel = toplabel xlabel = 'Result #' ylabel = 'Emittance [mm-mrad]' mt.addlabel(fig=fig, axes=ax, windowlabel=windowlabel, toplabel=toplabel, xlabel=xlabel, ylabel=ylabel) fig.tight_layout() mt.graphics.savefig(filename=toplabel, path=figpath) # ===================================== # Plot Spot size # ===================================== y = np.sqrt(emit*betastar)*1e6 fig = plt.figure() ax = fig.add_subplot(1, 1, 1) line = ax.plot(result, y, 'o-', linewidth=linewidth) windowlabel = 'Spot Size'
yy = yy[shot17.ystart:shot17.ystop] xx = xx[shot17.xstart:shot17.xstop]*shot17.res*1e6/np.sqrt(2) xx = xx-np.mean(xx) vmax = 1600 p1 = ax.pcolormesh(xx, yy, shot17.img.transpose(), vmax=vmax, cmap=cm.RdBu_r) # p1 = ax.contourf(xx, yy, shot17.img.transpose(), vmax=1500, cmap=cm.Blues) cbar = fig.colorbar(p1) p2 = ax.contour(xx, yy, shot17.img.transpose(), levels=np.linspace(0, vmax, 6), colors='Black', linewidths=linewidth) ax.axhspan(ymin=yy[36*5], ymax=yy[37*5], color='Red', fill=False, linewidth=4) ax.axis([xx.min(), xx.max(), yy.min(), yy.max()]) toplabel = 'Ionization-Injected Witness Profile at Lanex' windowlabel = 'Lanex' mt.addlabel(fig=fig, axes=ax, windowlabel=windowlabel, toplabel=toplabel, ylabel='Energy [GeV]', xlabel='Horizontal Axis $x$ [$\mu$m]', cb=cbar, clabel='Counts') mt.graphics.less_labels(ax, x_fraction=1) mt.graphics.axesfontsize(ax, fontsize) fig.tight_layout() mt.graphics.savefig(filename=toplabel, path=figpath) # ===================================== # Plot Gauss # ===================================== gfit = shot17.gaussfits[36] fig2 = plt.figure() ax2 = fig2.add_subplot(1, 1, 1)
# y_scorde, energy_scorde = ce.Energy_Axis_CMOS_FAR(13438, 1.5)
else:
raise NotImplementedError('Camera name is not available: {}'.format(camname))
energy_scorde = np.flipud(energy_scorde)
# ======================================
# Get my energy
# ======================================
rf = data.read_file
data_rf = rf['data']
# myenergy, myenergy_approx = E200.eaxis(y=y_scorde, uid=uid_scorde[0], camname=camname, hdf5_data=data_rf, E0=20.35, etay=0, etapy=0)
myenergy_approx = E200.Energy_Axis(camname=camname, hdf5_data=data_rf, uid=uid_scorde[0]).energy(ypx=y_scorde)
fig_en = plt.figure()
mygs = gs.GridSpec(1, 1)
ax1 = fig_en.add_subplot(mygs[0])
plots = ax1.plot(y_scorde, energy_scorde, 'b', y_scorde, myenergy_approx, 'r')
# plots=ax1.plot(y_scorde, myenergy_approx, 'g')
# plots=ax1.plot(y_scorde, energy_scorde, 'b')
# plots[0].set_label('Sebastien')
# plots[1].set_label('Joel')
# ax1.legend()
# ax1.set_yscale('log')
mt.addlabel(toplabel=camname, xlabel='Pixels', ylabel='Energy [GeV]', axes=ax1)
plt.show()
def calcR12(E):
beamline.gamma = E/emass
return beamline.R[0, 1]
R11 = beamline.R[0, 0]
R12 = beamline.R[0, 1]
R11p = derivative(calcR11, val)
R12p = derivative(calcR12, val)
Eres2[i] = val - (R11*R11p*twiss.beta**2+R12*R12p)/(R11p**2*twiss.beta**2 + R12p**2)
print('Energy should be {}'.format(Eres2[i]))
fig = plt.figure()
plt.plot(Escan, Eres, '.-')
mt.addlabel('Pinch Energy Proportional to Imaging Energy', 'Image Energy', 'Pinch Energy')
pp.savefig(fig)
pp.close()
for i, val in enumerate(Escan):
Eres3[i] = betafromE(20.35, val)
Eres4[i] = alphafromE(20.35, val)
plt.figure()
plt.plot(Escan, Eres3, '.-')
plt.figure()
plt.plot(Escan, Eres4, '.-')
plt.tight_layout()
# plt.show()