def histogram_max_abort_gap_before_OML(fills):
max_ag = {1: [], 2: []}
for nbr in fills:
for beam in (1, 2):
fill = Fill(nbr, beam=beam)
OML_start = fill.OML_period()[0]
max_ag[beam].append(fill.abort_gap().y[:OML_start].max())
bins = np.arange(0, 2e10, 2e9)
fig, ax = plt.subplots()
ax.hist([max_ag[1], max_ag[2]], bins=bins, label=["Beam 1", "Beam 2"])
ax.legend(loc="upper right")
ax.set_ylabel("Fill count")
ax.set_xlabel("Abort gap intensity [$ 10^9 $]")
ax.xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{0:.1f}".format(x / 1e9)))
plt.title("Max abort gap intensity before start of ramp")
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()