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 plot_aggregate_fill_overlayed(beam, fill_list):
""" 'beam' must be iterable: (1,), (2,), or (1, 2) """
fig, axes = plt.subplots(2, sharex=True, sharey=True)
for b in beam:
ls = '--' if b == 2 and len(beam) == 2 else '-'
for nbr in fill_list:
fill = Fill(nbr, beam=b)
c = np.random.rand(3)
axes[0].plot(*fill.blm_ir3(), color=c, alpha=0.3, linestyle=ls)
axes[1].plot(*fill.blm_ir7(), color=c, alpha=0.3, linestyle=ls)
aggr = aggregate_fill(b, fill_list)
axes[0].plot(*aggr.blm_ir3(),
color='black',
label='IR3 Aggregate B{}'.format(b),
zorder=5,
linestyle=ls)
axes[1].plot(*aggr.blm_ir7(),
color='black',
label='IR7 Aggregate B{}'.format(b),
zorder=4,
linestyle=ls)
axes[0].set_xlim(aggr.blm_ir3().x[aggr.OML_period()] +
np.array([-5, +120]))
axes[0].set_ylabel("Losses (Gy/s)")
axes[1].set_ylabel("Losses (Gy/s)")
for ax in axes:
ax.set_yscale("log")
ax.set_xlabel("t (s)")
ax.legend(loc="upper right")
fig.suptitle("Overlay plot (beam {})".format(",".join(map(str, beam))))
# plt.title("Overlay plot (beam {})".format(beam))
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()