def histogram_intensity(file):
fills = fills_from_file(file, "OML")
intensities = []
for nbr in fills:
fill = Fill(nbr, fetch=False)
fill.fetch()
intensities.append(max(fill.intensity().y))
draw_histogram('Intensity for {}'.format(file), intensities, 1e13,
"Intensity", "Count")
def comp_blm_ir3_vs_intensity(file):
fills = fills_from_file(file, "OML")
intensity = []
mean_loss = []
max_loss = []
discarded = 0
for nbr in fills:
fill = Fill(nbr, False)
fill.fetch()
smin, smax = fill.OML_period()
ssubset = fill.blm_ir3().y[smin:smax]
maxint = max(fill.intensity().y)
if maxint < 1.8e14:
discarded += 1
continue
mean_loss.append(np.mean(ssubset))
max_loss.append(max(ssubset))
intensity.append(maxint)
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122, sharey=ax1)
ax1.set_xlabel("Mean momentum (IR3) TCP")
ax1.set_ylabel("Intensity")
ax1.scatter(mean_loss, intensity, color='b', label='mean')
ax1.set_xlim([0, 1.1 * max(mean_loss)])
ax1.set_ylim([1.5e14, 1.1 * max(intensity)])
ax1.legend(loc="lower right")
ax2.set_xlabel("Max momentum (IR3) TCP")
ax2.set_ylabel("Intensity")
ax2.scatter(max_loss, intensity, color='r', label='max')
ax2.set_xlim([0, 1.1 * max(max_loss)])
ax2.legend(loc="lower right")
percent_used = int(
round(float(len(intensity)) / (len(intensity) + discarded) * 100))
fig.suptitle(
"Intensity vs OML for {} (only intenities > 1.8e14, {}% of total)\n".
format(file, percent_used))
plt.show()
def comp_blm_ir3_vs_abort_gap(file):
fills = fills_from_file(file, "OML")
abort_gap = []
average_loss = []
max_loss = []
for nbr in fills:
fill = Fill(nbr, False)
fill.fetch()
smin, smax = fill.OML_period()
# Only looking until t_co instead -- will not affect max
smax = fill.crossover_point()['i']
tmax = fill.blm_ir3().x[smax]
tmin = fill.blm_ir3().x[smin]
# tmax = find_crossover_point(fill)['t']
ag_average = moving_average(fill.abort_gap().y, 5)
agmin = fill.abort_gap().index_for_time(tmin)
agmax = fill.abort_gap().index_for_time(tmax)
ssubset = fill.blm_ir3().y[smin:smax]
average_loss.append(np.average(ssubset))
max_loss.append(max(ssubset))
abort_gap.append(ag_average[agmin] - ag_average[agmax])
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122, sharey=ax1)
# fig1, ax1 = plt.subplots()
ax1.set_xlabel("Average BLM")
ax1.set_ylabel("∆ abort gap intensity")
ax1.scatter(average_loss, abort_gap, color='b', label='average')
ax1.set_xlim([0, 1.1 * max(average_loss)])
ax1.set_ylim([0, 1.1 * max(abort_gap)])
xval = [0, 1]
slope, intercept, r_value, p_value, std_err = stats.linregress(
average_loss, abort_gap)
print("Average fit")
print(
"\tk ={:>10.3E}\n\tm ={:>10.3E}\n\tr ={:>10.7f}\n\tp ={:>10.3E}\n\te^2={:>10.3E}"
.format(slope, intercept, r_value, p_value, std_err))
yfit = [slope * x + intercept for x in xval]
ax1.plot(xval, yfit, color='gray')
ax1.legend(loc="lower right")
# fig2, ax2 = plt.subplots()
ax2.set_xlabel("Max BLM")
ax2.scatter(max_loss, abort_gap, color='r', label='max')
ax2.set_xlim([0, 1.1 * max(max_loss)])
ax2.legend(loc="lower right")
slope, intercept, r_value, p_value, std_err = stats.linregress(
max_loss, abort_gap)
print("Max fit")
print(
"\tk ={:>10.3E}\n\tm ={:>10.3E}\n\tr ={:>10.7f}\n\tp ={:>10.3E}\n\te^2={:>10.3E}"
.format(slope, intercept, r_value, p_value, std_err))
yfit = [slope * x + intercept for x in xval]
ax2.plot(xval, yfit, color='gray')
fig.suptitle(
"Correlation between abort gap intensity and BLM signal for TCP in IR3"
)
plt.show()