def common_make_root_histogram_test():
x_list = list(range(0,10000))
w_list = []
for i in range( len(x_list) ):
x_list[i] = float(x_list[i])/10000.
w_list.append(x_list[i])
y_list = __sine_list(2., 2.*math.pi, x_list)
hist1 = common.make_root_histogram('hist sin(x)', y_list, 'hist of x', 100)
hist2 = common.make_root_histogram('hist sin(x)', y_list, 'hist of x', 50, x_list, 'hist of y', 50)
hist3 = common.make_root_histogram('hist sin(x)', y_list, 'hist of x', 10, x_list, 'hist of y', 10, w_list)
testpass = __test_root_hist(hist3,'hist sin(x)','hist of x','hist of y',common.min_max(y_list, w_list, rg.histo_margin)[0], common.min_max(y_list, w_list, rg.histo_margin)[1],
common.min_max(x_list, w_list, rg.histo_margin)[0], common.min_max(x_list, w_list, rg.histo_margin)[1],
rg.line_color, rg.line_style, rg.line_width, rg.fill_color, '')
hist4 = common.make_root_histogram('hist sin(x)', y_list, 'hist of x', 10, x_list, 'hist of y', 10, w_list, xmin=y_list[7500],xmax=y_list[2500],ymin=x_list[2500],ymax=x_list[7500],
line_color=1, line_style=2, line_width=3, fill_color=4, hist_title_string='title_string')
testpass = __test_root_hist(hist4,'hist sin(x)', 'hist of x', 'hist of y', y_list[7500], y_list[2500], x_list[2500], x_list[7500], 1, 2, 3, 4, 'title_string')
canvas1 = common.make_root_canvas('hist_test1')
hist1.Draw()
canvas2 = common.make_root_canvas('hist_test2')
hist2.Draw('cont1z')
canvas3 = common.make_root_canvas('hist_test3')
hist3.Draw('lego')
canvas4 = common.make_root_canvas('hist_test4')
hist4.Draw('lego')
if not testpass: return 'fail'
return 'pass'
def test_binomial(self):
"""
Check that we can generate a binomial distribution for event number
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'spill')
bunch_weights = []
for bunch in bunch_dict.values():
bunch_weights.append(bunch.bunch_weight())
canvas = common.make_root_canvas("")
hist = common.make_root_histogram("generated distribution", \
bunch_weights, "bunch weights", BIN_N+1, xmin=-0.5, xmax=BIN_N+0.5)
hist.Fill(0,
1000 - hist.GetSumOfWeights()) # xboa ignores empty spills
hist.Draw()
test_hist = ROOT.TH1D("test_hist", "reference distribution", BIN_N + 1,
-0.5,
BIN_N + 0.5) # pylint: disable = E1101, C0301
for i in range(BIN_N + 1):
test_hist.SetBinContent(i, \
(ROOT.TMath.BinomialI(BIN_P, BIN_N, i-1)- ROOT.TMath.BinomialI(BIN_P, BIN_N, i))*N_SPILLS) # pylint: disable = E1101, C0301
print i, test_hist.GetBinContent(i), hist.GetBinContent(i)
test_hist.SetLineStyle(2)
test_hist.Draw("SAME")
ks_value = test_hist.KolmogorovTest(hist)
canvas.Update()
canvas.Print(PLOT_DIR + "/binomial_distribution_test.png")
print "binomial ks_value", ks_value
self.assertGreater(ks_value, 1e-3)
def test_binomial(self):
"""
Check that we can generate a binomial distribution for event number
"""
# make a dict of bunches of xboa.Hits separated by event (spill) number
bunch_dict = Bunch.new_dict_from_read_builtin('maus_json_primary', \
BIN_SIM, 'spill')
bunch_weights = []
for bunch in bunch_dict.values():
bunch_weights.append(bunch.bunch_weight())
canvas = common.make_root_canvas("")
hist = common.make_root_histogram("generated distribution", \
bunch_weights, "bunch weights", BIN_N+1, xmin=-0.5, xmax=BIN_N+0.5)
hist.Fill(0, 1000-hist.GetSumOfWeights()) # xboa ignores empty spills
hist.Draw()
test_hist = ROOT.TH1D("test_hist", "reference distribution", BIN_N+1, -0.5, BIN_N+0.5) # pylint: disable = E1101, C0301
for i in range(BIN_N+1):
test_hist.SetBinContent(i, \
(ROOT.TMath.BinomialI(BIN_P, BIN_N, i-1)- ROOT.TMath.BinomialI(BIN_P, BIN_N, i))*N_SPILLS) # pylint: disable = E1101, C0301
print i, test_hist.GetBinContent(i), hist.GetBinContent(i)
test_hist.SetLineStyle(2)
test_hist.Draw("SAME")
ks_value = test_hist.KolmogorovTest(hist)
canvas.Update()
canvas.Print(PLOT_DIR+"/binomial_distribution_test.png")
print "binomial ks_value", ks_value
self.assertGreater(ks_value, 1e-3)
def plot_one_phi(row_data, da_key, axis1, axis2, max_n_points, variables):
global ROOT_OBJECTS
hit_data = row_data[da_key]
name = "phi_" + da_key + "_" + axis1 + " vs " + axis2
title = get_title(variables)
name += " " + title
canvas = None
tune_data = []
area_data = []
da_row = get_da_row(hit_data, max_n_points)
print("Plot on phi")
cholesky_canvas = common.make_root_canvas(name + "_cholesky")
cholesky_canvas.SetCanvasSize(1000, 1000)
delta = (da_row + 1) * 0.4 + 1.
hist = common.make_root_histogram("", [-100.],
"u",
100, [-100.],
"u'",
100,
xmin=-delta,
xmax=+delta,
ymin=-delta,
ymax=+delta)
hist.Draw()
for i, hit_list in enumerate(hit_data[:da_row + 1]):
hit_list = [Hit.new_from_dict(hit_dict) for hit_dict in hit_list[1]]
finder = DPhiTuneFinder()
finder.u = [hit[axis1] for hit in hit_list[1:]]
finder.up = [hit[axis2] for hit in hit_list[1:]]
area_src = numpy.array([[hit[axis1], hit[axis2]] for hit in hit_list])
try:
finder.get_tune()
an_area = get_area(area_src)
except Exception:
print("Failed to unpack data for phi da plot")
continue
cholesky_canvas, hist, graph = finder.plot_cholesky_space(
cholesky_canvas, 1. + i * 0.2)
if i < da_row:
color = ROOT.TColor(10000 + len(ROOT_OBJECTS), 0.,
1. - i * 1. / da_row, 0.)
ROOT_OBJECTS.append(color)
graph.SetMarkerColor(ROOT.kGreen)
ROOT_OBJECTS += [hist, graph]
tune_data += copy.deepcopy(finder.dphi)
area_data += [an_area for i in finder.dphi]
print(" Area", an_area, end=' ')
print(" tune", numpy.mean(tune_data), "+/-", numpy.std(tune_data))
canvas = common.make_root_canvas(name)
canvas.Draw()
hist, graph = common.make_root_graph(name, area_data, "Amplitude [mm]",
tune_data, "Fractional tune")
ROOT_OBJECTS += [hist, graph]
hist.SetTitle(da_key)
hist.Draw()
graph.SetMarkerStyle(24)
graph.Draw("p same")
return canvas, cholesky_canvas
def test_apply_weights_bound(self):
test_bunch = Bunch()
for i in range(1000):
x = random.gauss(0., 10.**0.5)
y = random.gauss(0., 20.**0.5)
test_hit = Hit.new_from_dict({'x':x, 'y':y})
test_bunch.append(test_hit)
limit_ellipse = numpy.zeros([2, 2])
for i in range(2):
limit_ellipse[i, i] = 200.
bound = BoundingEllipse(limit_ellipse, numpy.zeros([2]), 50)
my_weights = VoronoiWeighting(['x', 'y'],
numpy.array([[20., 0.],[0., 20.]]),
voronoi_bound = bound)
print("Plotting weights", datetime.datetime.now())
canvas, hist = test_bunch.root_histogram('x', 'mm', 'y', 'mm')
hist.Draw("COLZ")
canvas.Update()
print('Covariances ["x", "y"] before\n', test_bunch.covariance_matrix(['x', 'y']))
print("Applying weights", datetime.datetime.now())
my_weights.apply_weights(test_bunch, False)
print("Plotting tesselation", datetime.datetime.now())
#my_weights.plot_two_d_projection(['x', 'y'], 'weight')
my_weights.plot_two_d_projection(['x', 'y'], 'weight')
my_weights.plot_two_d_projection(['x', 'y'], 'content')
print("Plotting weights", datetime.datetime.now())
canvas, hist = test_bunch.root_histogram('x', 'mm', 'y', 'mm')
hist.Draw("COLZ")
canvas.Update()
test_bunch.root_histogram('local_weight', nx_bins=100)
canvas = common.make_root_canvas('local_weight')
hist = common.make_root_histogram('local_weight', test_bunch.list_get_hit_variable(['local_weight'])[0], 'local_weight', 100)
hist.Draw()
canvas.Update()
canvas = common.make_root_canvas('content')
hist = common.make_root_histogram('content', my_weights.tile_content_list, 'content', 100)
hist.Draw()
canvas.Update()
print('Covariances ["x", "y"] after\n', test_bunch.covariance_matrix(['x', 'y']))
def plot_areas(self, areas_us, areas_ds):
assert(len(areas_us) == len(areas_ds))
# remove None items from the list (failed to calculate area)
areas = zip(areas_us, areas_ds)
areas = [item for item in areas if item[0] != None and item[1] != None]
areas_us = [item[0] for item in areas]
areas_ds = [item[1] for item in areas]
# remove residuals
areas_res = [item[1]-item[0] for item in areas]
for i in range(3):#len(areas_us)):
print i, str(round(areas_us[i])).ljust(10), str(round(areas_ds[i])).ljust(10), areas_res[i]
# plots
canvas = common.make_root_canvas("content")
n_bins = 100# max(int((len(bunch_us)/10)**0.5), 10)
hist_us = common.make_root_histogram("Upstream", areas_us, "cell content [mm MeV/c]", n_bins, xmin=-5000.0, xmax=5000.0)
hist_us.SetLineColor(2)
hist_us.Draw()
hist_ds = common.make_root_histogram("Downstream", areas_ds, "cell content [mm MeV/c]", n_bins, xmin=-5000.0, xmax=5000.0)
hist_ds.SetLineColor(4)
hist_ds.Draw("SAME")
hist_res = common.make_root_histogram("Residuals", areas_res, "cell content [mm MeV/c]", n_bins)
hist_res.Draw("SAME")
def plot_areas(self, areas_us, areas_ds):
assert (len(areas_us) == len(areas_ds))
# remove None items from the list (failed to calculate area)
areas = zip(areas_us, areas_ds)
areas = [item for item in areas if item[0] != None and item[1] != None]
areas_us = [item[0] for item in areas]
areas_ds = [item[1] for item in areas]
# remove residuals
areas_res = [item[1] - item[0] for item in areas]
for i in range(3): #len(areas_us)):
print i, str(round(areas_us[i])).ljust(10), str(round(
areas_ds[i])).ljust(10), areas_res[i]
# plots
canvas = common.make_root_canvas("content")
n_bins = 100 # max(int((len(bunch_us)/10)**0.5), 10)
hist_us = common.make_root_histogram("Upstream",
areas_us,
"cell content [mm MeV/c]",
n_bins,
xmin=-5000.0,
xmax=5000.0)
hist_us.SetLineColor(2)
hist_us.Draw()
hist_ds = common.make_root_histogram("Downstream",
areas_ds,
"cell content [mm MeV/c]",
n_bins,
xmin=-5000.0,
xmax=5000.0)
hist_ds.SetLineColor(4)
hist_ds.Draw("SAME")
hist_res = common.make_root_histogram("Residuals", areas_res,
"cell content [mm MeV/c]",
n_bins)
hist_res.Draw("SAME")
def test_sft_accuracy(self):
# test slow fourier transform
canvas = common.make_root_canvas("SFT Residuals")
hist = common.make_root_histogram('', [],
'Residuals',
1000, [],
'',
1000,
xmin=1.,
xmax=1000.0,
ymin=0.0,
ymax=1.0)
hist.Draw()
for i in range(3, 4):
truth_list, sft_list, fft_list, n_list = [], [], [], []
tune = i * 0.1
for n in [5, 10, 20, 50, 100, 200, 500, 1000]:
co, tracking = matrix(tune * 2. * math.pi, n)
fft = FFTTuneFinder()
fft.run_tracking('x', 10.0, co, tracking)
tune_sft, tune_fft, tune_sft_hanning = 0., 0., 0.
try:
tune_fft, tune_sft, tune_sft_hanning = self._ft_test(fft)
except ValueError:
# statistics are low enough that no primary peaks can be
# found
sys.excepthook(*sys.exc_info())
if tune > 0.5:
tune_sft = 1 - tune_sft
tune_fft = 1 - tune_fft
#print "truth", tune, "sft", tune_sft,
#print "fft", tune_fft, "hanning", tune_sft_hanning
truth_list.append(tune)
sft_list.append(tune_sft)
fft_list.append(tune_fft)
n_list.append(n)
residual_list = []
for i, n in enumerate(n_list):
if fft_list[i] - truth_list[i] != 0.:
residual_list.append(abs(sft_list[i]-truth_list[i])\
/abs(fft_list[i]-truth_list[i]))
hist, graph = common.make_root_graph("SFT residuals", n_list,
"Number of points",
residual_list, "Residuals")
graph.Draw('L')
canvas.SetLogx()
canvas.Update()
def peak_hist(peak_list):
canvas = common.make_root_canvas("projected peaks")
hist = common.make_root_histogram("projected peaks",
peak_list,
"#deltat [ns]",
200,
xmin=0,
xmax=630.)
bins = []
for bin_index in range(1, hist.GetNbinsX() + 1):
bins.append(hist.GetBinContent(bin_index))
smoothing = xboa.algorithms.smoothing.GaussianSmoothing(2., 3, True)
smoothed = smoothing.smooth(bins)
finder = xboa.algorithms.peak_finder.WindowPeakFinder(10, -1, 1)
peak_graph_indices, peak_x, peak_y = [], [], []
peaks = []
for peak in finder.find_peaks(smoothed):
a_peak = {
"peak": hist.GetBinCenter(peak + 1),
"magnitude": bins[peak],
"err": 0.
}
if a_peak["magnitude"] < 0.5 * max(smoothed):
continue
peak_graph_indices.append(peak)
for bin in range(peak, 0, -1) + range(len(bins) - 1, peak, -1):
if smoothed[bin] < smoothed[peak] / 2.:
peak_graph_indices.append(bin)
a_peak["lower_bound"] = hist.GetBinCenter(bin + 1)
break
for bin in range(peak, len(bins), +1) + range(0, peak, 1):
if smoothed[bin] < smoothed[peak] / 2.:
peak_graph_indices.append(bin)
a_peak["upper_bound"] = hist.GetBinCenter(bin + 1)
break
peaks.append(a_peak)
for peak_graph_index in peak_graph_indices:
peak_x.append(hist.GetBinCenter(peak_graph_index + 1))
peak_y.append(smoothed[peak_graph_index])
dummy, graph = xboa.common.make_root_graph("peaks", peak_x, "", peak_y, "")
graph.SetMarkerStyle(24)
graph.SetMarkerColor(2)
canvas.Draw()
hist.Draw()
graph.Draw('p')
canvas.Update()
return peaks, canvas
def get_period(data):
rf_peak_indices = data["RF"]["peak_indices"]
rf_peak_list = [data["RF"]["time_list"][i] / 1e-9 for i in rf_peak_indices]
rf_peak_deltas_list = [None] * (len(rf_peak_list) - 1)
for i, rf_time in enumerate(rf_peak_list[0:-1]):
rf_next_time = rf_peak_list[i + 1]
rf_peak_deltas_list[i] = rf_next_time - rf_time
canvas = common.make_root_canvas("rf_period")
canvas.Draw()
hist = common.make_root_histogram("times", rf_peak_deltas_list,
"RF period [ns]", 100)
hist.Draw()
hist.SetStats(True)
canvas.Update()
canvas.Draw()
return numpy.mean(rf_peak_deltas_list), rf_peak_list[0], canvas
def plot(plot_dir, data_list_pairs):
tmp_data = []
for i, data in enumerate(data_list_pairs):
pos_peak = numpy.mean(
[x["y"] for x in data["RF"]["pos_peak_with_errors"]])
neg_peak = numpy.mean(
[-x["y"] for x in data["RF"]["neg_peak_with_errors"]])
voltage = (pos_peak - neg_peak)
data["rf_peak_to_peak_voltage"] = voltage
rf_list = ["0.2", "0.32", "0.46", "0.62", "0.81", "3.01", "7.18"]
if True or str(round(voltage, 2)) in rf_list:
print "Using", voltage
tmp_data.append(data)
else:
print "Not using", voltage
data_list_pairs = tmp_data
peak_delta_canvas = common.make_root_canvas("delta rf vs bpm")
peak_delta_canvas.Draw()
peak_delta_hist_canvas = common.make_root_canvas("delta rf vs bpm - hist")
peak_delta_hist_canvas.Draw()
graph_list = []
hist_list = []
dt_mean_list = []
dt_err_list = []
measured_voltage_list = []
max_y = -1e9
min_y = 1e9
delta_hist_min = 1e9
delta_hist_max = -1e9
for i, data in enumerate(data_list_pairs):
"""
voltage_str = "V="+str(round(data["rf_peak_to_peak_voltage"], 2))
print "Plotting measured "+voltage_str
formats = ["png", "root"]
times = data["RF"]["time_list"]
rf_voltage_list = data["RF"]["voltage_list"]
rf_peak_error_list = data["RF"]["pos_peak_with_errors"]
rf_peak_list = [x["x"] for x in rf_peak_error_list]
rf_period = sum([rf_peak_list[j+1] - rf_peak_list[j] for j, index in enumerate(rf_peak_list[:-1])])
rf_period = DT*rf_period/float(len(rf_peak_list))
rf_frequency = 1./rf_period
print " RF Period", rf_period, "Frequency", rf_frequency
print " Plotting RF magnitude and error"
canvas_rf_v, hist_rf_v, graph_rf_v = graph_peak_magnitude(data["RF"]["pos_peak_with_errors"], data["RF"]["neg_peak_with_errors"], title=voltage_str)
for format in formats:
canvas_rf_v.Print(plot_dir+voltage_str+"_rf_voltage."+format)
print " Plotting peak finding errors"
canvas_err, hist_err, graph_err = graph_peak_errors(data["signal"]["peak_with_errors"], 4, title=voltage_str)
graph_peak_errors(data["RF"]["pos_peak_with_errors"], 2, canvas_err)
for format in formats:
canvas_err.Print(plot_dir+voltage_str+"_peak_errors."+format)
print " Plotting peak to peak distance"
print " signal",
canvas_pp, hist_pp, graph_pp = graph_delta_times(data["signal"]["peak_with_errors"], 4, title=voltage_str)
print " RF",
graph_delta_times(data["RF"]["pos_peak_with_errors"], 2, canvas_pp)
for format in formats:
canvas_pp.Print(plot_dir+voltage_str+"_peak_deltas."+format)
print " Plotting data"
y_range = common.min_max(data["RF"]["voltage_list"]+data["signal"]["voltage_list"]+data["dc_signal"]["voltage_list"])
canvas, hist, graph, peak_hist, peak_graph = graph_peak_times(data["RF"], data["RF"]["pos_peak_with_errors"], 2, y_range, title=voltage_str)
bpm_peak_indices = data["signal"]["peak_indices"]
bpm_voltage_list = data["signal"]["voltage_list"]
bpm_peak_list = [index for index in bpm_peak_indices]
graph_peak_times(data["signal"], data["signal"]["peak_with_errors"], 4, y_range, canvas)
graph_peak_times(data["dc_signal"], [{"x":0, "y":0}], 6, y_range, canvas)
canvas.Update()
for format in formats:
canvas.Print(plot_dir+voltage_str+"_signals."+format)
"""
print " Plotting distance between bpm and rf peaks"
peak_delta_canvas.cd()
peak_time_list = data["peak_time_list"]
peak_delta_list = data["peak_delta_list"]
hist, graph = common.make_root_graph(voltage_str, peak_time_list,
"time [ns]", peak_delta_list,
"dt [ns]")
min_y = min([hist.GetYaxis().GetXmin(), min_y])
max_y = max([hist.GetYaxis().GetXmax(), max_y])
if len(data_list_pairs) > 1:
hist.Draw()
color_fraction = float(i) / float(len(data_list_pairs) - 1)
else:
color_fraction = 1
color = ROOT.TColor.GetColor(color_fraction, 1. - color_fraction, 0)
graph.SetMarkerColor(color)
graph.SetLineColor(color)
graph.SetMarkerStyle(6)
graph_list.append(graph)
peak_delta_hist_canvas.cd()
filtered_list = []
for j, peak in enumerate(peak_delta_list):
if peak_time_list[j] > 30.e3 and \
peak_time_list[j] < 40.e3:
filtered_list.append(peak)
if len(filtered_list) > 0:
dt_mean_list.append(max(filtered_list))
#dt_err_list.append(numpy.std(filtered_list)/len(filtered_list)**0.5)
measured_voltage_list.append(data["rf_peak_to_peak_voltage"])
hist = common.make_root_histogram(voltage_str, filtered_list,
"dt [ns]", 4)
delta_hist_min = min([hist.GetXaxis().GetXmin(), delta_hist_min])
delta_hist_max = max([hist.GetXaxis().GetXmax(), delta_hist_max])
hist.SetLineColor(color)
hist_list.append(hist)
peak_delta_canvas.cd()
hist, graph = common.make_root_graph(voltage_str,
peak_time_list,
"t [ns]",
peak_delta_list,
"dt [ns]",
ymin=0.,
ymax=700.)
hist.Draw() # redraw hist to get axes right
for graph in graph_list:
graph.Draw("p")
legend = common.make_root_legend(peak_delta_canvas, graph_list)
legend.Draw()
peak_delta_canvas.Update()
for format in formats:
peak_delta_canvas.Print(plot_dir + "bpm_to_rf_deltas." + format)
peak_delta_hist_canvas.cd()
print delta_hist_min, delta_hist_max
hist = common.make_root_histogram(voltage_str, [-1],
"dt [ns]",
1000, [-1],
"Counts",
1000,
xmin=delta_hist_min,
xmax=delta_hist_max,
ymin=1e-2,
ymax=20.)
hist.Draw() # redraw hist to get axes right
for a_hist in hist_list:
a_hist.Draw("same")
legend = common.make_root_legend(peak_delta_hist_canvas, hist_list)
legend.Draw()
peak_delta_hist_canvas.Update()
for format in formats:
peak_delta_hist_canvas.Print(plot_dir + "bpm_to_rf_deltas_hist." +
format)
if len(dt_mean_list) < 2:
return
phase_mean_list = [
2. * math.pi * dt / rf_period for dt in dt_mean_list[1:]
]
measured_voltage_list = measured_voltage_list[1:]
for popper in []:
phase_mean_list.pop(popper)
phase_err_list.pop(popper)
measured_voltage_list.pop(popper)
synchronous_phase_canvas = common.make_root_canvas(
"Synchronous phase vs voltage")
hist, graph = common.make_root_graph("Synchronous phase vs voltage",
phase_mean_list, "#phi [rad]",
measured_voltage_list, "V [au]")
graph = ROOT.TGraph(len(phase_mean_list))
graph.SetMarkerStyle(4)
for i in range(len(phase_mean_list)):
graph.SetPoint(i, phase_mean_list[i], measured_voltage_list[i])
print phase_mean_list
print measured_voltage_list
hist.Draw()
graph.Draw('p')
print "Doing unweighted fit"
fit_unweighted = ROOT.TF1("fit", "[0]/sin([1]+x)")
fit_unweighted.SetParameter(0, 0.1)
fit_unweighted.SetParameter(1, 0.)
fit_unweighted.SetLineColor(4)
graph.Fit(fit_unweighted, "EX0")
fit_unweighted.Draw("SAME")
synchronous_phase_canvas.Update()
for format in formats:
synchronous_phase_canvas.Print(
plot_dir + "synchronous_phase_vs_voltage_unweighted." + format)
def amplitude_scatter_plot(self, suffix):
"""
Make a scatter plot of upstream versus downstream data
"""
amp_dict_ds = self.amp.amplitudes[suffix]["amplitude_dict_downstream"]
amp_dict_us = self.amp.amplitudes[suffix]["amplitude_dict_upstream"]
amp_list_us = []
amp_list_ds = []
amp_list_delta = []
suffix_label = self.get_suffix_label(suffix)
for key, amp_ds in amp_dict_ds.iteritems():
try:
amp_us = amp_dict_us[key]
except KeyError:
sys.excepthook(*sys.exc_info())
continue
amp_delta = amp_us - amp_ds
amp_list_us.append(amp_us)
amp_list_ds.append(amp_ds)
amp_list_delta.append(amp_delta)
canvas = common.make_root_canvas("amplitude_residuals")
canvas.Draw()
n_points = min(len(amp_list_us),
10000) # no more than 10k points in the scatter
hist, graph = common.make_root_graph(
"delta amplitude scatter",
amp_list_us,
"US " + suffix_label + " Amplitude [mm]",
amp_list_delta,
suffix_label + " US Amplitude - DS Amplitude [mm]",
xmin=0.,
xmax=100.,
ymin=-50.,
ymax=50.)
hist.SetTitle(self.config_anal['name'])
hist.Draw()
graph.Draw("P")
canvas.Update()
for format in ["eps", "png", "root"]:
canvas.Print(self.plot_dir + "amplitude_delta_" + suffix +
"_scatter." + format)
canvas = common.make_root_canvas("delta_amplitude_hist")
canvas.Draw()
canvas.SetFrameFillColor(utilities.utilities.get_frame_fill())
hist = common.make_root_histogram("delta amplitude hist",
amp_list_us,
suffix_label + " US Amplitude [mm]",
100,
amp_list_delta,
suffix_label +
" US Amplitude - DS Amplitude [mm]",
100,
xmin=0.,
xmax=100.,
ymin=-50.,
ymax=50.)
hist.SetTitle(self.config_anal['name'])
hist.Draw("COLZ")
canvas.Update()
for format in ["eps", "png", "root"]:
canvas.Print(self.plot_dir + "amplitude_delta_" + suffix +
"_hist." + format)
def plot(plot_dir, data_list_pairs):
tmp_data = []
for i, data in enumerate(data_list_pairs):
pos_peak = numpy.mean([x["y"] for x in data["RF"]["pos_peak_with_errors"]])
neg_peak = numpy.mean([-x["y"] for x in data["RF"]["neg_peak_with_errors"]])
voltage = (pos_peak-neg_peak)
data["rf_peak_to_peak_voltage"] = voltage
rf_list = ["0.2", "0.32", "0.46", "0.62", "0.81", "3.01", "7.18"]
if True or str(round(voltage, 2)) in rf_list:
print "Using", voltage
tmp_data.append(data)
else:
print "Not using", voltage
data_list_pairs = tmp_data
peak_delta_canvas = common.make_root_canvas("delta rf vs bpm")
peak_delta_canvas.Draw()
peak_delta_hist_canvas = common.make_root_canvas("delta rf vs bpm - hist")
peak_delta_hist_canvas.Draw()
graph_list = []
hist_list = []
dt_mean_list = []
dt_err_list = []
measured_voltage_list = []
max_y = -1e9
min_y = 1e9
delta_hist_min = 1e9
delta_hist_max = -1e9
for i, data in enumerate(data_list_pairs):
"""
voltage_str = "V="+str(round(data["rf_peak_to_peak_voltage"], 2))
print "Plotting measured "+voltage_str
formats = ["png", "root"]
times = data["RF"]["time_list"]
rf_voltage_list = data["RF"]["voltage_list"]
rf_peak_error_list = data["RF"]["pos_peak_with_errors"]
rf_peak_list = [x["x"] for x in rf_peak_error_list]
rf_period = sum([rf_peak_list[j+1] - rf_peak_list[j] for j, index in enumerate(rf_peak_list[:-1])])
rf_period = DT*rf_period/float(len(rf_peak_list))
rf_frequency = 1./rf_period
print " RF Period", rf_period, "Frequency", rf_frequency
print " Plotting RF magnitude and error"
canvas_rf_v, hist_rf_v, graph_rf_v = graph_peak_magnitude(data["RF"]["pos_peak_with_errors"], data["RF"]["neg_peak_with_errors"], title=voltage_str)
for format in formats:
canvas_rf_v.Print(plot_dir+voltage_str+"_rf_voltage."+format)
print " Plotting peak finding errors"
canvas_err, hist_err, graph_err = graph_peak_errors(data["signal"]["peak_with_errors"], 4, title=voltage_str)
graph_peak_errors(data["RF"]["pos_peak_with_errors"], 2, canvas_err)
for format in formats:
canvas_err.Print(plot_dir+voltage_str+"_peak_errors."+format)
print " Plotting peak to peak distance"
print " signal",
canvas_pp, hist_pp, graph_pp = graph_delta_times(data["signal"]["peak_with_errors"], 4, title=voltage_str)
print " RF",
graph_delta_times(data["RF"]["pos_peak_with_errors"], 2, canvas_pp)
for format in formats:
canvas_pp.Print(plot_dir+voltage_str+"_peak_deltas."+format)
print " Plotting data"
y_range = common.min_max(data["RF"]["voltage_list"]+data["signal"]["voltage_list"]+data["dc_signal"]["voltage_list"])
canvas, hist, graph, peak_hist, peak_graph = graph_peak_times(data["RF"], data["RF"]["pos_peak_with_errors"], 2, y_range, title=voltage_str)
bpm_peak_indices = data["signal"]["peak_indices"]
bpm_voltage_list = data["signal"]["voltage_list"]
bpm_peak_list = [index for index in bpm_peak_indices]
graph_peak_times(data["signal"], data["signal"]["peak_with_errors"], 4, y_range, canvas)
graph_peak_times(data["dc_signal"], [{"x":0, "y":0}], 6, y_range, canvas)
canvas.Update()
for format in formats:
canvas.Print(plot_dir+voltage_str+"_signals."+format)
"""
print " Plotting distance between bpm and rf peaks"
peak_delta_canvas.cd()
peak_time_list = data["peak_time_list"]
peak_delta_list = data["peak_delta_list"]
hist, graph = common.make_root_graph(voltage_str, peak_time_list, "time [ns]", peak_delta_list, "dt [ns]")
min_y = min([hist.GetYaxis().GetXmin(), min_y])
max_y = max([hist.GetYaxis().GetXmax(), max_y])
if len(data_list_pairs) > 1:
hist.Draw()
color_fraction = float(i)/float(len(data_list_pairs)-1)
else:
color_fraction = 1
color = ROOT.TColor.GetColor(color_fraction, 1.-color_fraction, 0)
graph.SetMarkerColor(color)
graph.SetLineColor(color)
graph.SetMarkerStyle(6)
graph_list.append(graph)
peak_delta_hist_canvas.cd()
filtered_list = []
for j, peak in enumerate(peak_delta_list):
if peak_time_list[j] > 30.e3 and \
peak_time_list[j] < 40.e3:
filtered_list.append(peak)
if len(filtered_list) > 0:
dt_mean_list.append(max(filtered_list))
#dt_err_list.append(numpy.std(filtered_list)/len(filtered_list)**0.5)
measured_voltage_list.append(data["rf_peak_to_peak_voltage"])
hist = common.make_root_histogram(voltage_str, filtered_list, "dt [ns]", 4)
delta_hist_min = min([hist.GetXaxis().GetXmin(), delta_hist_min])
delta_hist_max = max([hist.GetXaxis().GetXmax(), delta_hist_max])
hist.SetLineColor(color)
hist_list.append(hist)
peak_delta_canvas.cd()
hist, graph = common.make_root_graph(voltage_str, peak_time_list, "t [ns]", peak_delta_list, "dt [ns]", ymin=0., ymax=700.)
hist.Draw() # redraw hist to get axes right
for graph in graph_list:
graph.Draw("p")
legend = common.make_root_legend(peak_delta_canvas, graph_list)
legend.Draw()
peak_delta_canvas.Update()
for format in formats:
peak_delta_canvas.Print(plot_dir+"bpm_to_rf_deltas."+format)
peak_delta_hist_canvas.cd()
print delta_hist_min, delta_hist_max
hist = common.make_root_histogram(voltage_str, [-1], "dt [ns]", 1000, [-1], "Counts", 1000, xmin=delta_hist_min, xmax=delta_hist_max, ymin=1e-2, ymax=20.)
hist.Draw() # redraw hist to get axes right
for a_hist in hist_list:
a_hist.Draw("same")
legend = common.make_root_legend(peak_delta_hist_canvas, hist_list)
legend.Draw()
peak_delta_hist_canvas.Update()
for format in formats:
peak_delta_hist_canvas.Print(plot_dir+"bpm_to_rf_deltas_hist."+format)
if len(dt_mean_list) < 2:
return
phase_mean_list = [2.*math.pi*dt/rf_period for dt in dt_mean_list[1:]]
measured_voltage_list = measured_voltage_list[1:]
for popper in []:
phase_mean_list.pop(popper)
phase_err_list.pop(popper)
measured_voltage_list.pop(popper)
synchronous_phase_canvas = common.make_root_canvas("Synchronous phase vs voltage")
hist, graph = common.make_root_graph("Synchronous phase vs voltage", phase_mean_list, "#phi [rad]", measured_voltage_list, "V [au]")
graph = ROOT.TGraph(len(phase_mean_list))
graph.SetMarkerStyle(4)
for i in range(len(phase_mean_list)):
graph.SetPoint(i, phase_mean_list[i], measured_voltage_list[i])
print phase_mean_list
print measured_voltage_list
hist.Draw()
graph.Draw('p')
print "Doing unweighted fit"
fit_unweighted = ROOT.TF1("fit", "[0]/sin([1]+x)")
fit_unweighted.SetParameter(0, 0.1)
fit_unweighted.SetParameter(1, 0.)
fit_unweighted.SetLineColor(4)
graph.Fit(fit_unweighted, "EX0")
fit_unweighted.Draw("SAME")
synchronous_phase_canvas.Update()
for format in formats:
synchronous_phase_canvas.Print(plot_dir+"synchronous_phase_vs_voltage_unweighted."+format)