def histogram_spike_duration(file):
fills = fills_from_file(file, "OML")
outliers = []
durations = []
for nbr in fills:
fill = Fill(nbr)
start, end = fill.OML_period()
d = fill.blm_ir7().x[end] - fill.blm_ir7().x[start]
if d < 70 or d > 300:
outliers.append(nbr)
durations.append(d)
draw_histogram('Spike duration for {}'.format(file), durations, 10,
'Seconds', 'Count')
return outliers
def histogram_intensity_lost_during_OML(fills, beam):
agg_fill = aggregate_fill(beam=beam, from_cache=True)
t_agg = agg_fill.blm_ir3().x[agg_fill.OML_period()]
lost_agg_period = np.empty(len(fills))
lost_fill_period = np.empty(len(fills))
total_lost_during_ramp = np.empty(len(fills))
for i, nbr in enumerate(fills):
fill = Fill(nbr)
a_index = fill.intensity().index_for_time(t_agg)
int_agg = fill.intensity().y[a_index]
lost_agg_period[i] = int_agg[0] - int_agg[1]
f_index = fill.intensity().index_for_time(
fill.blm_ir3().x[fill.OML_period()])
int_fill = fill.intensity().y[f_index]
lost_fill_period[i] = int_fill[0] - int_fill[1]
total_lost_during_ramp[i] = fill.intensity().y[0] - fill.intensity(
).y[-1]
fig, ax = plt.subplots()
ax.hist((lost_agg_period, lost_fill_period),
label=("Aggregate OML period", "Fill OML period"),
edgecolor='white')
# ax.hist((lost_agg_period,), label=("Aggregate OML period",) , edgecolor='white')
ax.legend(loc='upper right')
ax.xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{0:.2f}".format(x * 100.0)))
ax.set_xlabel("Intensity lost (%)")
ax.set_ylabel("# fills")
plt.title(
"Intensity during off-momentum loss peak at start of ramp (beam {})".
format(beam))
# ax.hist(lost_fill_period, edgecolor='white')
fig2, ax2 = plt.subplots()
ax2.hist(total_lost_during_ramp, edgecolor='white')
ax2.xaxis.set_major_formatter(
FuncFormatter(lambda x, pos: "{0:.2f}".format(x * 100.0)))
ax2.set_xlabel("Intensity lost (%)")
ax2.set_ylabel("# fills")
plt.title("Total intensity lost during whole ramp (beam {})".format(beam))
plt.show()
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 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()
def comp_blm_ir3_vs_ir7(file):
fills = fills_from_file(file, "OML")
ok = 0
notok = 0
sdata = {
'max': [],
'mean': [],
}
bdata = {'max': [], 'mean': []}
for nbr in fills:
fill = Fill(nbr)
fill.beta_coll_merge()
smin, smax = fill.OML_period()
tmin, tmax = fill.blm_ir3().x[[smin, smax]]
bmin = fill.blm_ir7().index_for_time(tmin)
bmax = fill.blm_ir7().index_for_time(tmax)
bsubset = fill.blm_ir7().y[bmin:bmax]
ssubset = fill.blm_ir3().y[smin:smax]
sdata['max'].append(max(ssubset))
sdata['mean'].append(np.mean(ssubset))
bdata['max'].append(max(bsubset))
bdata['mean'].append(np.mean(bsubset))
fig, ax = plt.subplots()
ax.set_xlabel("Synchrotron (IR3) TCP")
ax.set_ylabel("Betatron (IR7) TCPs")
ax.scatter(sdata['max'], bdata['max'], color='r')
slope, intercept, r_value, p_value, std_err = stats.linregress(
sdata['max'], bdata['max'])
# print(slope, intercept, r_value, p_value, std_err)
xval = [0, 1]
max_yval = [slope * x + intercept for x in xval]
ax.plot(xval, max_yval, color='r', label='max')
ax.scatter(sdata['mean'], bdata['mean'], color='b')
slope, intercept, r_value, p_value, std_err = stats.linregress(
sdata['mean'], bdata['mean'])
# print(slope, intercept, r_value, p_value, std_err)
mean_yval = [slope * x + intercept for x in xval]
ax.plot(xval, mean_yval, color='b', label='mean')
ax.plot([0, 1], [0, 1], color='black', label='delimiter')
for v in ['max', 'mean']:
count = 0
for i, sd in enumerate(sdata[v]):
if bdata[v][i] > sd:
count += 1
print(v, "over: ", count,
"({}%)".format(int(float(count) / len(sdata[v]) * 100)))
plt.title(
'Losses due to synchrotron vs betatron oscillations\n for {}'.format(
file))
ax.legend(loc='upper right')
ax.set_ylim([0, 0.5])
ax.set_xlim([0, 0.5])
plt.show()