def aggregate_fill(beam, fill_list=[], from_cache=False):
""" Create an aggregate fill of the fill ids. If reading from cache,
the fill_list can be empty. """
if not fill_list and not from_cache:
raise Exception(
"'fill_list' can't be empty if not to read from cache'")
elif not beam in (1, 2):
raise Exception("'beam' must be 1 or 2, is '{}'".format(beam))
fill = Fill(nbr=-beam, beam=beam, fetch=False)
if from_cache:
fill.load_cache()
return fill
fills = []
for nbr in fill_list:
_fill = Fill(nbr)
_fill.beta_coll_merge()
fills.append(_fill)
# This is cumbersome to refactor... TODO
var = [
"synch_coll_b{}".format(beam), "beta_coll_b{}".format(beam), "energy",
"intensity_b{}".format(beam)
]
for v in var:
merged = merge_variable(fills, v)
fill.data[v] = merged["average"]
return fill
def plot_aggregate_fill(beam, fill_list):
# We can't reuse the 'aggregate_fill' function above, as we want to plot more
# than for a normal fill (min, max, average)
fills = []
for nbr in fill_list:
fill = Fill(nbr, beam=beam)
fill.beta_coll_merge()
fills.append(fill)
var = [
"synch_coll_b{}".format(beam), "beta_coll_b{}".format(beam), "energy"
]
merged = {}
for v in var:
merged[v] = merge_variable(fills, v)
fig, ax = plt.subplots()
ax.set_yscale("log")
e_ax = ax.twinx()
v = "synch_coll_b{}".format(beam)
ax.plot(*merged[v]["average"], zorder=10, color='red', label='OML')
ax.fill_between(*merged[v]["max"],
merged[v]["min"].y,
color='red',
alpha='0.3')
v = "beta_coll_b{}".format(beam)
ax.plot(*merged[v]["average"], zorder=9, color='green', label='TL')
ax.fill_between(*merged[v]["max"],
merged[v]["min"].y,
color='green',
alpha='0.2')
e_ax.plot(*merged["energy"]["average"],
zorder=5,
color='black',
label='energy')
e_ax.set_ylabel("Reference energy (GeV)")
ax.set_xlabel("t (s)")
ax.set_ylabel("Gy/s")
ax.legend(loc="upper right")
ax.set_xlim(fills[0].blm_ir3().x[fills[0].OML_period()] +
np.array([-5, +120]))
# ax.axvspan(0.0, 13.55, facecolor='b', zorder=0, alpha=0.1)
plt.title("Aggregate fill 2016 (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()