def histogram_crossover_point(file):
""" Histogram on what durations OML dominate the transversal losses """
fills = fills_from_file(file, "OML")
durations = []
outliers = []
for nbr in fills:
fill = Fill(nbr)
crossover = fill.crossover_point()
if crossover['t'] > 40:
outliers.append(nbr)
else:
durations.append(crossover["t"])
draw_histogram("Duration OML > transversal losses from '{}'".format(file),
durations, 1, 'Duration (s) after spike with OML > TM',
'Count')
return outliers
def bar_graph_crossover_point(file):
""" Bar graph displaying the 'time till max spike' + 'time where OML > TM' for all fills in file """
fills = fills_from_file(file, "OML")
oml_dom_duration = []
time_till_spike = []
total_duration = []
odd = []
for nbr in fills:
fill = Fill(nbr)
crossover = fill.crossover_point()
oml = fill.blm_ir3()
ispike = np.argmax(oml.y)
tspike = oml.x[ispike]
time_till_spike.append(tspike)
oml_dom_duration.append(crossover["t"] - tspike)
total_duration.append(crossover['t'])
if crossover['t'] > 40 or tspike > 12: # why am I using this?
odd.append(nbr)
print("odd looking fills: ", odd)
fig, ax = plt.subplots()
ax.bar(range(len(oml_dom_duration)),
time_till_spike,
label='Time to max peak (s)')
ax.bar(range(len(oml_dom_duration)),
oml_dom_duration,
bottom=time_till_spike,
label='Crossover point (s)')
ax.legend(loc="upper right")
ax.set_xlabel("Fill nbr")
ax.set_ylabel("Duration (s)")
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()