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 common_make_root_canvas_test():
testpass = True
canvas = common.make_root_canvas('canvas_name')
testpass &= canvas.GetName().find('canvas_name') > -1
canvas = common.make_root_canvas('canvas_name_2', 'canvas_title', bg_color=10, highlight_color=12, border_mode=1, frame_fill_color=2)
testpass &= __test_root_canvas(canvas, 'canvas_name_2', 'canvas_title', 10, 12, 1, 2)
if testpass: return 'pass'
return 'fail'
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 plot_alignment(data, plot_dir):
del data[0]
optimisation_set = set([opt_key(item) for item in data])
for opt in optimisation_set:
name = opt.replace("Coils", "")
results = [item for item in data if opt_key(item) == opt]
canvas = common.make_root_canvas("alignment - "+name)
n_list = range(len(results))
x_list = [item["axes"][opt]["x"]["value"] for item in results]
y_list = [item["axes"][opt]["y"]["value"] for item in results]
xp_list = [item["axes"][opt]["xp"]["value"]*1000. for item in results]
yp_list = [item["axes"][opt]["yp"]["value"]*1000. for item in results]
hist, zero_graph = common.make_root_graph("x residual",
[0, len(results)], "Iteration",
[0., 0.], "Residual [mm]")
hist, x_graph = common.make_root_graph("x alignment", n_list, "iteration", x_list, "misalignment [mm]")
hist, y_graph = common.make_root_graph("y alignment", n_list, "iteration", y_list, "misalignment [mm]")
hist, all_graph = common.make_root_graph("all alignment", n_list*2, "iteration", x_list+y_list, "misalignment [mm]")
hist.SetTitle(name+" alignment")
hist.Draw()
zero_graph.Draw("SAMEL")
x_graph.Draw("SAMEP")
x_graph.SetMarkerStyle(24)
x_graph.SetMarkerColor(4)
y_graph.Draw("SAMEP")
y_graph.SetMarkerStyle(24)
y_graph.SetMarkerColor(8)
canvas.Update()
for format in ["pdf", "png", "root"]:
canvas.Print(plot_dir+"/alignment_"+name+"."+format)
canvas = common.make_root_canvas("tilt - "+name)
hist, zero_graph = common.make_root_graph("x residual",
[0, len(results)], "Iteration",
[0., 0.], "Residual [mm]")
hist, xp_graph = common.make_root_graph("x alignment", n_list, "iteration", xp_list, "tilt [mrad]")
hist, yp_graph = common.make_root_graph("y alignment", n_list, "iteration", yp_list, "tilt [mrad]")
hist, all_graph = common.make_root_graph("all alignment", n_list*2, "iteration", xp_list+yp_list, "titl [mrad]")
hist.SetTitle(name+" tilt")
hist.Draw()
zero_graph.Draw("SAMEL")
xp_graph.Draw("SAMEP")
xp_graph.SetMarkerStyle(24)
xp_graph.SetMarkerColor(4)
yp_graph.Draw("SAMEP")
yp_graph.SetMarkerStyle(24)
yp_graph.SetMarkerColor(8)
canvas.Update()
for format in ["pdf", "png", "root"]:
canvas.Print(plot_dir+"/tilt_"+name+"."+format)
def pdf_ratio_plot(self, suffix):
data = self.amp.amplitudes[suffix]["ratio"]
name = "pdf_ratio_" + suffix
canvas = common.make_root_canvas(name)
canvas.SetName(name)
pdf_graph_stats = self.get_asymm_error_graph(data["corrected_pdf"],
data["pdf_stats_errors"],
style=20,
name="PDF Ratio stats")
pdf_graph_sys = self.get_asymm_error_graph(data["corrected_pdf"],
data["pdf_sum_errors"],
fill=ROOT.kGray,
name="PDF Ratio sys")
graph_list = [pdf_graph_stats, pdf_graph_sys]
draw_list = ["p", "2"]
hist = self.get_hist(graph_list,
self.get_suffix_label(suffix) + " Amplitude [mm]",
"Number Ratio")
hist.Draw()
for i, graph in enumerate(graph_list):
graph.Draw("SAME " + draw_list[i])
self.text_box(graph_list)
print "PDF"
print data["corrected_pdf"]
print data["pdf_stats_errors"]
print data["pdf_sum_errors"]
canvas.Update()
for a_format in ["eps", "pdf", "root", "png"]:
canvas.Print(self.plot_dir + canvas.GetName() + "." + a_format)
return canvas
def graph_peak_times(a_data, errors, colour, y_range, canvas=None, title=""):
hist, graph = common.make_root_graph("Volts",
a_data["time_list"],
"Time [ns]",
a_data["voltage_list"],
"Signal Voltage [V]",
ymin=y_range[0],
ymax=y_range[1])
peak_time_list = [x["x"] for x in errors]
peak_voltage_list = [x["y"] for x in errors]
peak_hist, peak_graph = common.make_root_graph("Peaks", peak_time_list,
"Time of peak [ns]",
peak_voltage_list,
"Peak voltage")
if canvas == None:
canvas = common.make_root_canvas(title + "bpm peaks")
canvas.Draw()
hist.Draw()
canvas.cd()
peak_graph.SetMarkerColor(colour)
peak_graph.SetMarkerStyle(4)
peak_graph.Draw("p")
graph.SetMarkerColor(colour)
graph.Draw("p")
canvas.Update()
return canvas, hist, graph, peak_hist, peak_graph
def matrix_plot(self, matrix, title, axis_1, axis_2):
"""
Plot the matrix entries as a TH2D
* matrix: matrix data to plot; list n_bins by n_bins
* title: the title of the hist (used in the file name also)
* axis_1: x axis title
* axis_2: y axis title
"""
bin_centre_list = self.amp.amplitudes["reco"]["all_upstream"][
"bin_centre_list"]
xmax = max(self.amp.bin_edge_list)
nbins = len(bin_centre_list)
matrix_hist = ROOT.TH2D(title, title + ";" + axis_1 + ";" + axis_2,
nbins, 0., xmax, nbins, 0., xmax)
for i in range(nbins):
for j in range(nbins):
matrix_hist.Fill(bin_centre_list[i], bin_centre_list[j],
matrix[i][j])
self.root_objects.append(matrix_hist)
canvas = common.make_root_canvas(title)
canvas.SetFrameFillColor(utilities.utilities.get_frame_fill())
matrix_hist.SetTitle(self.config_anal['name'])
matrix_hist.SetStats(False)
matrix_hist.Draw("COLZ")
title = title.replace(" ", "_")
for format in ["eps", "png", "root"]:
canvas.Print(self.plot_dir + title + "." + format)
def _draw_volumes_2d(self, x_range, z_range):
"""Make a 2D plot of position vs volume"""
canvas = common.make_root_canvas("Volume Name vs position")
hist, graph = common.make_root_graph("Volume Name vs position",
[1e9], "z [mm]",
[1e9], "x [mm]",
xmin=z_range[0], xmax=z_range[1],
ymin=x_range[0], ymax=x_range[1])
hist.Draw()
draw_list = sorted(self.volume_data.keys(), self._sort_cmp)
n_colors = 5
for i, vol in enumerate(draw_list):
data = self.volume_data[vol]
r_list = [data["r_min"], data["r_max"], data["r_max"],
data["r_min"], data["r_min"]]
z_list = [data["z_min"], data["z_min"], data["z_max"],
data["z_max"], data["z_min"]]
hist, graph = common.make_root_graph(vol,
z_list, "z [mm]",
r_list, "r [mm]", sort = False)
color = self.color(i % n_colors, n_colors)
graph.SetLineColor(color)
graph.Draw('l')
self._draw_volume_label(data["z_min"], vol, x_range, color)
canvas.Update()
for _format in self.formats:
canvas.Print(self.vol_2d+"."+_format)
def _draw_materials_2d(self, step_data, material_dict, x_range, z_range):
"""Make a 2D plot of position vs material in r, z"""
# we plot G4_AIR first, then G4_GALACTIC?
canvas = common.make_root_canvas("Material vs position")
hist, graph = common.make_root_graph("Material vs position", [1e9],
"z [mm]", [1e9],
"r [mm]",
xmin=z_range[0],
xmax=z_range[1],
ymin=x_range[0],
ymax=x_range[1])
hist.Draw()
draw_list = step_data.keys()
draw_list = self.move(draw_list, "G4_AIR", 0)
draw_list = self.move(draw_list, "G4_Galactic", 1)
graph_list = []
for material in draw_list:
x_list = [step["r"] for step in step_data[material]]
z_list = [step["z"] for step in step_data[material]]
hist, graph = common.make_root_graph(material, z_list, "z [mm]",
x_list, "r [mm]")
graph.SetMarkerColor(material_dict[material])
graph.SetMarkerStyle(7)
graph.SetLineColor(10)
graph.Draw('p')
graph_list.append(graph)
common.make_root_legend(canvas, graph_list)
canvas.Update()
for _format in self.formats:
canvas.Print(self.mat_2d + "." + _format)
def make_plot(self, data_list, da_row, name, x_name, y_name):
graph_list = []
for x_data, y_data in data_list:
i = len(graph_list)
hist, graph = common.make_root_graph(name + str(i), x_data, x_name,
y_data, y_name)
graph.SetMarkerStyle(7)
if i == da_row:
canvas = common.make_root_canvas(name)
hist.SetTitle(name)
hist.Draw()
elif i == 0 and i < da_row:
graph.SetMarkerColor(ROOT.kBlue)
elif i < da_row:
graph.SetMarkerColor(ROOT.kGreen)
else:
graph.SetMarkerColor(ROOT.kGray)
graph_list.append(graph)
canvas.cd()
canvas.Update()
for graph in graph_list[da_row+1:]+graph_list[1:da_row]+\
graph_list[0:1]+graph_list[da_row:da_row+1]:
graph.Draw("SAMEP")
name = name.replace(":", "")
name = name.replace(" ", "_")
for fmt in self.formats:
canvas.Print(self.plot_dir + "/" + name + "." + fmt)
def _draw_materials_2d(self, step_data, material_dict, x_range, z_range):
"""Make a 2D plot of position vs material in r, z"""
# we plot G4_AIR first, then G4_GALACTIC?
canvas = common.make_root_canvas("Material vs position")
hist, graph = common.make_root_graph("Material vs position",
[1e9], "z [mm]",
[1e9], "r [mm]",
xmin=z_range[0], xmax=z_range[1],
ymin=x_range[0], ymax=x_range[1])
hist.Draw()
draw_list = step_data.keys()
draw_list = self.move(draw_list, "G4_AIR", 0)
draw_list = self.move(draw_list, "G4_Galactic", 1)
graph_list = []
for material in draw_list:
x_list = [step["r"] for step in step_data[material]]
z_list = [step["z"] for step in step_data[material]]
hist, graph = common.make_root_graph(material,
z_list, "z [mm]",
x_list, "r [mm]")
graph.SetMarkerColor(material_dict[material])
graph.SetMarkerStyle(7)
graph.SetLineColor(10)
graph.Draw('p')
graph_list.append(graph)
common.make_root_legend(canvas, graph_list)
canvas.Update()
for _format in self.formats:
canvas.Print(self.mat_2d+"."+_format)
def plot_trajectory(item, line_color, canvas = None):
z_list = [hit["z"] for hit in item["seed"]]
x_list = [hit["x"] for hit in item["seed"]]
y_list = [hit["y"] for hit in item["seed"]]
r_list = [hit["r"] for hit in item["seed"]]
x_min, x_max = common.min_max(x_list+r_list+y_list)
title = "x: "+str(item["seed"][0]["x"])+" mm "+\
"p_{x}: "+str(item["seed"][0]["px"])+" MeV/c"
hist_r, graph_r = common.make_root_graph(title, z_list, "z [mm]", r_list, "Radius [mm]", ymin=0.0, ymax=250.)
hist_x, graph_x = common.make_root_graph("x", z_list, "z [mm]", x_list, "Position [mm]")
hist_y, graph_y = common.make_root_graph("y", z_list, "z [mm]", y_list, "Position [mm]")
graph_r.SetLineColor(line_color)
graph_x.SetLineColor(line_color)
graph_x.SetLineStyle(3)
graph_y.SetLineColor(line_color)
graph_y.SetLineStyle(4)
if canvas == None:
canvas = common.make_root_canvas("trajectory")
#hist_r.SetTitle("x = "+str(x_list[0])+" px = "+str(item["seed"][0]["px"]))
hist_r.Draw()
canvas.cd()
graph_r.Draw("SAMEPL")
#graph_x.Draw("SAMEPL")
#graph_y.Draw("SAMEPL")
canvas.Update()
return canvas, graph_r
def graph_peak_magnitude(pos_peak_with_errors, neg_peak_with_errors, title):
peak_time_list, peak_voltage_list, peak_err_list = [], [], []
for i, v_pos in enumerate(pos_peak_with_errors):
di = 0
if i >= len(neg_peak_with_errors):
break
while neg_peak_with_errors[i+di]["x"] < pos_peak_with_errors[i]["x"]:
if i+di+1 >= len(neg_peak_with_errors):
break
di += 1
v_neg = neg_peak_with_errors[i+di]
v_0 = (v_pos["y"]+v_neg["y"])/2.
v_err = abs(v_0)*(v_pos["cov(x,y)"][1][1]/v_pos["y"]**2+v_neg["cov(x,y)"][1][1]/v_neg["y"]**2)**0.5
peak_voltage_list.append(v_0)
peak_err_list.append(v_err*1e3)
peak_time_list.append(v_pos["x"]*DT)
print " Peak voltage mean:", numpy.mean(peak_voltage_list[-20:-10]),
print "sigma:", numpy.std(peak_voltage_list[-20:-10])
canvas = common.make_root_canvas(title+" rf voltage")
canvas.Draw()
canvas.cd()
y_range = common.min_max(peak_voltage_list+peak_err_list)
hist, graph = common.make_root_graph("Peaks", peak_time_list, "Time of peak [ns]", peak_voltage_list, "Peak voltage [kV]", ymin = y_range[0], ymax = y_range[1])
hist.Draw()
graph.SetMarkerStyle(6)
graph.Draw("p")
hist, graph = common.make_root_graph("Errors", peak_time_list, "Time of peak [ns]", peak_err_list, "Error [V]", ymin = y_range[0], ymax = y_range[1])
graph.SetMarkerColor(2)
graph.SetMarkerStyle(6)
graph.Draw("p")
canvas.Update()
return canvas, hist, graph
def _draw_volumes_1d(self, z_range):
"""Make a 1D plot of position vs volume along the z axis"""
draw_list = sorted(self.volume_data.keys(), self._sort_cmp)
x_list = []
canvas = common.make_root_canvas("Volume Name vs z")
# set up axis labels
print "VOLUME PLOT", z_range
hist = ROOT.TH2D("name", "Volume Name vs z", 30000,
z_range[0], z_range[1],
len(draw_list)+1, -0.5, len(draw_list)+0.5)
common._common._hist_persistent.append(hist) #pylint: disable=W0212
for material in draw_list:
hist.Fill(z_range[0], material, 1.)
hist.SetBit(ROOT.TH1.kCanRebin)
hist.SetStats(0)
hist.LabelsDeflate("Y")
hist.LabelsOption("v")
hist.Draw()
# now draw graphs
for i, volume in enumerate(draw_list):
x_list = [self.volume_data[volume]["z_min"],
self.volume_data[volume]["z_max"]]
y_list = [i+0.1, i+0.1]
hist, graph = common.make_root_graph("", x_list, "z [mm]",
y_list, "volume index")
graph.SetLineColor(ROOT.TColor.GetColor(0.5, 0.5, 0.5))
graph.Draw('l')
if self.volume_data[volume]["r_min"] < 1e-9:
y_list = [i, i]
hist, graph = common.make_root_graph("", x_list, "z [mm]",
y_list, "material index")
graph.Draw('l')
canvas.Update()
for _format in self.formats:
canvas.Print(self.vol_1d+"."+_format)
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_z(item, line_color, do_square, canvas=None):
z_list = [hit["z"] for hit in item["seed"]]
if not do_square: #i.e. do triangle
area_list = [
abs(area / item["an_area_list"][0] - 1)
for area in item["an_area_list"]
]
else:
area_list = [
abs(sum(area_list) / sum(item["area_list_of_lists"][0]) - 1)
for area_list in item["area_list_of_lists"]
]
title = "x: "+str(item["seed"][0]["x"])+" mm "+\
"p_{x}: "+str(item["seed"][0]["px"])+" MeV/c"
hist, graph = common.make_root_graph(
title,
z_list,
"z [mm]",
area_list,
"Fractional change in simplex content",
ymin=1e-5,
ymax=100.)
if canvas == None:
canvas = common.make_root_canvas("content growth vs z")
canvas.SetLogy()
hist.Draw()
canvas.cd()
graph.SetLineColor(line_color)
graph.Draw("SAMEPL")
canvas.Update()
return canvas, graph
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_acceptance(da_list, eigenspace_key):
global ERRORS_GRAPH, MIN_N_CELLS
plot_dir = "baseline/"
acceptance_list_0 = []
acceptance_list_1 = []
pphi_list = []
mean_acceptance_list_0 = []
mean_acceptance_list_1 = []
mean_pphi_list = []
means_0 = ROOT.TGraphErrors(len(da_list))
means_0.SetMarkerStyle(21)
means_0.SetMarkerColor(8)
means_0.SetLineColor(8)
means_1 = ROOT.TGraphErrors(len(da_list))
means_1.SetMarkerStyle(21)
means_1.SetMarkerColor(4)
means_1.SetLineColor(4)
ERRORS_GRAPH.append(means_0)
ERRORS_GRAPH.append(means_1)
for i, element in enumerate(da_list):
ekin = get_ekin(element)
acceptance_list = get_acceptance(element, eigenspace_key, 0)
if len(acceptance_list) < 5:
continue
acceptance_list_0 += acceptance_list
means_0.SetPoint(i, ekin, numpy.mean(acceptance_list))
means_0.SetPointError(i, 0., 0.) #numpy.std(acceptance_list))
acceptance_list = get_acceptance(element, eigenspace_key, 1)
acceptance_list_1 += acceptance_list
means_1.SetPoint(i, ekin, numpy.mean(acceptance_list))
means_1.SetPointError(i, 0., 0.) #numpy.std(acceptance_list))
pphi_list += [ekin for item in acceptance_list]
canvas = common.make_root_canvas("acceptance " + eigenspace_key)
canvas.Draw()
axes, graph = common.make_root_graph('axes',
pphi_list,
"E_{kin} [MeV]",
acceptance_list_0,
"A_{2D} [mm]",
ymin=-0.01,
ymax=20.)
axes.Draw()
da_eigenspace = {'da_1': 0, 'da_2': 1, 'da_3': 2}[eigenspace_key]
axes.SetTitle("Acceptance for eigenspace " + str(da_eigenspace) +
" requiring " + str(MIN_N_CELLS / CELL_STEP) + " turns")
for acceptance_list, color in [(acceptance_list_0, 8),
(acceptance_list_1, 4)]:
dummy, graph = common.make_root_graph('axes', pphi_list,
"P_{phi} [MeV/c]",
acceptance_list, "A_{2D} [mm]")
graph.SetMarkerStyle(7)
graph.SetMarkerColor(color)
graph.Draw('pSAME')
means_0.Draw('PSAME')
means_1.Draw('PSAME')
canvas.Update()
name = "acceptance_eigenspace_" + str(da_eigenspace)
for format in "png", "root", "pdf":
canvas.Print(plot_dir + name + "." + format)
def find_peak_errors_derivative(self, data, peak_list, delta_index, draw=False):
"""
Find the error on the peak estimation based on a linear fit to the
derivative
"""
derivative = self._get_derivative(data)
# we want to fit for x = my + c
# c is the peak
# chi2 gives estimate of fit errors
peak_with_errors = []
for peak in peak_list:
x_min = max(peak-delta_index, 0)
x_max = min(peak+delta_index+1, len(derivative))
x_list = range(x_min, x_max)
y_list = derivative[x_min:x_max]
graph = ROOT.TGraph(len(x_list))
for i in range(5):
self.fit.SetParameter(i, 0.)
for i in range(2, 5):
self.fit.FixParameter(i, 0.)
self.fit.SetRange(min(y_list), max(y_list))
for i, x in enumerate(x_list):
y = y_list[i]
graph.SetPoint(i, y, x) # note this is intentionally backwards
graph.Fit(self.fit, "NO")
if draw:
canvas = common.make_root_canvas("derivatives")
canvas.Draw()
canvas.cd()
hist, graph = common.make_root_graph("name", x_list, "", y_list, "")
hist.Draw()
graph.SetMarkerStyle(6)
graph.Draw('p')
canvas.Update()
canvas = common.make_root_canvas("values")
canvas.Draw()
canvas.cd()
hist, graph = common.make_root_graph("name", x_list, "", data[x_min:x_max], "")
hist.Draw()
graph.SetMarkerStyle(6)
graph.Draw('p')
canvas.Update()
peak_with_errors.append([self.fit.GetParameter(0),
self.fit.GetParError(0)])
return peak_with_errors
def plot_data_vs_delta(data, title, filter_lambdas, x_axis_lambda, x_axis_name, canvas_square = None, canvas_tri = None, line_color=1):
data = filter_data(data, filter_lambdas)
if len(data) == 0:
return canvas_square, canvas_tri
print "plot", title
delta_list = [x_axis_lambda(item) for item in data]
triangle_list = [abs(item["an_area_list"][-1]/item["an_area_list"][0]-1) for item in data]
square_list = [abs(sum(item["area_list_of_lists"][-1])/sum(item["area_list_of_lists"][0])-1) for item in data]
print triangle_list, delta_list
ymin=min(square_list+triangle_list)*0.1
ymax=max(square_list+triangle_list)*1.1
hist, graph = common.make_root_graph("step size "+str(data[0]['step_size'])+" mm",
delta_list, x_axis_name,
triangle_list, "Fractional change in simplex content",
xmin=min(delta_list)*0.9, ymin=ymin, ymax=ymax)
if canvas_tri == None:
canvas_tri = common.make_root_canvas("delta triangles - "+title)
canvas_tri.SetLogy()
hist.Draw()
hist.SetTitle(title)
canvas_tri.cd()
graph.SetLineColor(line_color)
graph.SetMarkerStyle(24)
graph.Draw("SAMEPL")
canvas_tri.Update()
hist, graph = common.make_root_graph("step size "+str(data[0]['step_size'])+" mm",
delta_list, x_axis_name,
square_list, "Fractional change in hypercube content",
xmin=min(delta_list)*0.9, ymin=ymin, ymax=ymax)
hist.SetTitle(title)
if canvas_square == None:
canvas_square = common.make_root_canvas("delta triangles - "+title)
canvas_square.SetLogy()
hist.Draw()
canvas_square.cd()
graph.SetLineColor(line_color)
graph.SetMarkerStyle(24)
graph.Draw("SAMEPL")
canvas_square.Update()
return canvas_square, canvas_tri
def plot_fields(data, plot_dir, foil_probe, foil_var, axis_name):
bump_fields = {}
bump_position = get_bump_position(data, foil_probe, foil_var)
for bump in data:
for key in bump["bump_fields"]:
if key not in bump_fields:
bump_fields[key] = []
bump_fields[key].append(bump["bump_fields"][key])
print("bump_fields = {")
for key in bump_fields:
print(" \"" + key + "\" :", bump_fields[key], ",")
print("}")
canvas = common.make_root_canvas("fields")
all_fields = []
for key in bump_fields:
all_fields += bump_fields[key]
delta = 0.2
max_pos = (1. + delta) * max(bump_position) - delta * min(bump_position)
hist, graph = common.make_root_graph(
"fields",
bump_position * len(bump_fields) + [max_pos], axis_name,
all_fields + [0.], "Dipole field [T]")
hist.Draw()
color = [
ROOT.kBlue, ROOT.kGreen + 2, ROOT.kRed, ROOT.kYellow + 2,
ROOT.kCyan + 1
]
marker = [20, 24, 21, 25]
leg_list = []
index = 0
marker_index = 0
for key, field in sorted(bump_fields.items()):
name = key.replace("__", "")
name = name.replace("_", " ")
hist, graph = common.make_root_graph(name, bump_position, axis_name,
field, "Dipole field [T]")
graph.SetMarkerStyle(marker[marker_index])
graph.SetMarkerColor(color[index])
graph.SetLineColor(color[index])
graph.SetLineStyle(color[index])
graph.Draw("lpsame")
leg_list.append(graph)
index += 1
if index == len(color):
index = 0
marker_index += 1
leg = common.make_root_legend(canvas, leg_list)
leg.SetBorderSize(1)
leg.SetX1NDC(0.75)
leg.SetX2NDC(0.9)
leg.SetY1NDC(0.5)
leg.SetY2NDC(0.9)
leg.Draw()
canvas.Update()
for format in ["png"]:
canvas.Print(plot_dir + "/bump_fields." + format)
def make_global_plot(self, min_n_points, plot_dir):
axis_candidates = utilities.get_substitutions_axis(
self.data, "substitutions")
my_axes_x = dict([(key, []) for key in axis_candidates])
da = {'y_da': [], 'x_da': []}
for item in self.data:
for da_key in da:
if da_key not in list(item.keys()):
print("Did not find", da_key, "in keys", list(item.keys()),
"... skipping")
continue
da[da_key].append(
self.calculate_da(item, da_key, min_n_points) * 1e3)
print(da_key, da[da_key][-1])
for ax_key in my_axes_x:
my_axes_x[ax_key].append(item['substitutions'][ax_key])
for ax_key in my_axes_x:
ordinate = my_axes_x[ax_key]
x_name = utilities.sub_to_name(ax_key)
canvas_name = x_name + ' vs da'
canvas = common.make_root_canvas(canvas_name)
canvas.SetLogy()
x_range = [min(ordinate), max(ordinate)]
y_range = [
min(da['x_da'] + da['y_da']),
max(da['x_da'] + da['y_da'])
]
y_range[0] = max(y_range[0] / 2, 0.01)
hist, graph = common.make_root_graph('axes',
x_range,
x_name,
y_range,
"DA [#pi #mum]",
ymin=y_range[0],
ymax=y_range[1] * 3)
hist.Draw()
if len(da['x_da']) == len(ordinate):
hist_x, graph_x = common.make_root_graph(
'horizontal da', ordinate, "", da['x_da'], "")
graph_x.SetMarkerStyle(24)
graph_x.SetMarkerColor(2)
graph_x.Draw("SAME P")
if len(da['y_da']) == len(ordinate):
hist_y, graph_y = common.make_root_graph(
'vertical da', ordinate, "", da['y_da'], "")
graph_y.SetMarkerStyle(26)
graph_y.SetMarkerColor(4)
graph_y.Draw("SAME P")
#legend = common.make_root_legend(canvas, [graph_x, graph_y])
#legend.Draw()
canvas.Update()
canvas_name = canvas_name.replace(" ", "_")
for format in "eps", "png", "root":
canvas.Print(plot_dir + "/" + canvas_name + "." + format)
return
def plot_gauss(self, x_list, gauss_list):
canvas = common.make_root_canvas("gauss")
canvas.Draw()
canvas.SetLogy()
hist, graph = common.make_root_graph("gauss", x_list, "position", gauss_list, "Gauss")
hist.Draw()
graph.Draw('l')
fit_function = ROOT.TF1("fit", "gaus")
graph.Fit(fit_function)
canvas.Update()
return fit_function
def plot_one_da(self, row_data, da_key, axis1, axis2, max_n_points,
variables, acceptance):
hit_data = row_data[da_key]
name = da_key + "_" + axis1 + " vs " + axis2
title = self.get_title(variables)
name += " " + title
canvas = None
da_row = self.get_da_row(hit_data, max_n_points)
graph_list = []
acceptance_graph = None
units = {
'x': 'mm',
'y': 'mm',
'px': 'MeV/c',
'py': 'MeV/c',
"x'": 'rad',
"y'": 'rad'
}
for i, hit_list in enumerate(hit_data):
hit_list = [
Hit.new_from_dict(hit_dict) for hit_dict in hit_list[1]
]
x_data = [hit[axis1] for hit in hit_list]
y_data = [hit[axis2] for hit in hit_list]
axis1_label = axis1 + " [" + units[axis1] + "]"
axis2_label = axis2 + " [" + units[axis2] + "]"
hist, graph = common.make_root_graph(name + " " + str(i), x_data,
axis1_label, y_data,
axis2_label)
graph.SetMarkerStyle(7)
if i == da_row:
canvas = common.make_root_canvas(name)
hist.SetTitle(title)
hist.Draw()
elif i < da_row:
graph.SetMarkerColor(ROOT.kGreen)
else:
graph.SetMarkerColor(ROOT.kGray)
graph_list.append(graph)
pz = hit_list[0]['pz']
mass = common.pdg_pid_to_mass[2212]
if axis2 == "px" or axis2 == "py":
geom_acceptance = acceptance * mass #mm mrad normalised -> MeV/c
elif axis2 == "x'" or axis2 == "y'":
geom_acceptance = acceptance * mass / pz
#if i == 3:
# acceptance_graph = self.plot_acceptance(geom_acceptance, x_data, y_data, axis2)
for graph in graph_list:
graph.Draw("SAMEP")
if acceptance_graph != None:
acceptance_graph.Draw("SAME C")
canvas.Update()
return 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_heat(data, plane = None, canvas = None):
if plane != None:
print "Doing plane", plane
data = filter_data(data, [lambda item: item["an_area_list"][-1] != None],)
data = filter_data(data, [lambda item: item["seed"][-1]["r"] < 1e3],)
triangle_list = []
for item in data:
if plane == None:
triangle_list.append(abs(item["an_area_list"][-1]/item["an_area_list"][0]-1))
else:
if plane < len(item["an_area_list"]):
triangle_list.append(abs(item["an_area_list"][plane]/item["an_area_list"][0]-1))
else:
triangle_list.append(1e9)
x_list = [item["seed"][0]["x"] for item in data]
px_list = [item["seed"][0]["px"] for item in data]
x_min, x_max, n_x_bins = get_axis(x_list)
px_min, px_max, n_px_bins = get_axis(px_list)
if canvas == None:
canvas = common.make_root_canvas("heating map")
if plane != None:
canvas.SetCanvasSize(700, 1000)
canvas.Divide(1, 2)
if plane != None:
canvas.cd(2)
get_b_field(canvas.GetPad(2), z = item["seed"][plane]["z"])
canvas.cd(1)
canvas.GetPad(1).SetLogz()
hist = ROOT.TH2D("heating map", ";x [mm];p_{x} [MeV/c]", n_x_bins, x_min, x_max, n_px_bins, px_min, px_max)
hist.SetStats(False)
z_min, z_max = 1e-4, 100.
hist.GetZaxis().SetRangeUser(z_min, z_max)
canvas.Draw()
for i, content in enumerate(triangle_list):
bin = hist.FindBin(x_list[i], px_list[i])
if content < z_min:
content = z_min*1.00001
if content > z_max:
content = z_max*0.99999
hist.SetBinContent(bin, content)
hist.Draw("COLZ")
root_list.append(hist)
canvas.SetLogz()
canvas.Update()
if plane == None:
canvas.Print("plots/heat_map.png")
else:
name = str(plane).rjust(4, '0')
canvas.Print("plots/heat_map_"+name+".gif")
return canvas
def cdf_plot(self, suffix):
us_data = self.amp.amplitudes[suffix]["all_upstream"]
ds_data = self.amp.amplitudes[suffix]["all_downstream"]
name = "amplitude_cdf_" + suffix
canvas = common.make_root_canvas(name)
canvas.SetName(name)
upstream_graph = self.get_asymm_error_graph(
us_data["corrected_cdf"],
us_data["cdf_stats_errors"],
style=24,
color=self.us_color,
name="Upstream CDF stats",
centred=False)
downstream_graph = self.get_asymm_error_graph(
ds_data["corrected_cdf"],
ds_data["cdf_stats_errors"],
style=26,
color=self.ds_color,
name="Downstream CDF stats",
centred=False)
upstream_graph_sys = self.get_asymm_error_graph(
us_data["corrected_cdf"],
us_data["cdf_sum_errors"],
fill=self.us_color,
name="Upstream CDF sys",
centred=False)
downstream_graph_sys = self.get_asymm_error_graph(
ds_data["corrected_cdf"],
ds_data["cdf_sum_errors"],
fill=self.ds_color,
name="Downstream CDF sys",
centred=False)
graph_list = [
upstream_graph, downstream_graph, upstream_graph_sys,
downstream_graph_sys
]
draw_list = ["p", "p", "2", "2"]
hist = self.get_hist(graph_list,
self.get_suffix_label(suffix) + " Amplitude [mm]",
"Cumulative Number")
hist.Draw()
for i, graph in enumerate(graph_list):
graph.Draw("SAME " + draw_list[i])
self.text_box(graph_list)
canvas.Update()
for a_format in ["eps", "pdf", "root", "png"]:
canvas.Print(self.plot_dir + "amplitude_cdf_" + suffix + "." +
a_format)
return canvas
def graph_peak_errors(error_list, colour, canvas = None, title=""):
peak_time_list = [x["x"]*DT for x in error_list] # 0.2 ns per count
peak_error_list = [(x["cov(x,y)"][0][0]**0.5)*DT for x in error_list]
hist, graph = common.make_root_graph("Peaks", peak_time_list, "Time of peak [ns]", peak_error_list, "Estimated error on peak [ns]")
if canvas == None:
canvas = common.make_root_canvas(title+" errors")
canvas.Draw()
hist.Draw()
canvas.cd()
graph.SetMarkerStyle(6)
graph.SetMarkerColor(colour)
graph.Draw("p")
canvas.Update()
return canvas, hist, graph
def common_make_root_ellipse_function_test():
testpass = True
# worth checking by eye - the ellipse should be tilted forwards (i.e. +ve
# correlation); and should roughly fill the canvas. If one sets correlation
# to 0., y-intercept at x=0., 200. and x-intercept at y=0., 2.
ell_cov = numpy.array([[10000., 0.], [0., 1.]])
ell_mean = [100., 1.]
canv = common.make_root_canvas("ellipse_function_test_0")
func = common.make_root_ellipse_function(ell_mean, ell_cov, contours=[1.], xmin=-10., xmax=210., ymin=-0.1, ymax=2.1)
func.Draw()
canv.Update()
for value in [(0., 1.), (200., 1.), (100., 0.), (100., 2.)]:
testpass = testpass and (func.Eval(*value)-1.) < common.float_tolerance
if not testpass:
print('FAILED ON ', value, func.Eval(*value))
ell_cov = numpy.array([[3, 1], [1, 2]])
ell_mean = [1, 1]
canv = common.make_root_canvas("ellipse_function_test_1")
func = common.make_root_ellipse_function(ell_mean, ell_cov)
func.Draw()
canv.Update()
ell_cov = numpy.array([[1, 0.99], [0.99, 1]])
ell_mean = [1, 1]
canv = common.make_root_canvas("ellipse_function_test_2")
func = common.make_root_ellipse_function(ell_mean, ell_cov, [1., 0.08], 1-3, 1+3, 1-2, 1+2)
func.SetLineColor(4)
func.Draw()
canv.Update()
# note this looks grotty - blame ROOT
testpass = testpass and (func.GetContourLevel(1)-0.08) < common.float_tolerance \
and (func.GetContourLevel(0)-1.) < common.float_tolerance
if testpass:
return 'pass'
return 'fail'
def graph_delta_times(error_list, colour, canvas = None, title=""):
peak_time_list = [x["x"]*DT for x in error_list] # 0.2 ns per count
peak_delta_list = [t1-peak_time_list[i] for i, t1 in enumerate(peak_time_list[1:])]
hist, graph = common.make_root_graph("Peaks", peak_time_list[0:-1], "Time of peak [ns]", peak_delta_list, "Time between peaks [ns]")
mean_sigma_cut(peak_delta_list)
if canvas == None:
canvas = common.make_root_canvas(title+"bpm signal")
canvas.Draw()
hist.Draw()
canvas.cd()
graph.SetMarkerStyle(6)
graph.SetMarkerColor(colour)
graph.Draw("p")
canvas.Update()
return canvas, hist, graph
def plot_signal(self, title):
"""
Plot the FFT frequency spectrum
- title, string used as a title in FFT plots
Returns tuple of TCanvas, TH2, TGraph
"""
canvas = common.make_root_canvas(title)
x_list = range(len(self.u))
hist, graph = common.make_root_graph(title, x_list, 'count', self.u,
'signal')
hist.SetTitle(title)
hist.Draw()
graph.Draw('l')
canvas.Update()
return canvas, hist, graph
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 graph_peak_times(a_data, errors, colour, y_range, canvas = None, title=""):
hist, graph = common.make_root_graph("Volts", a_data["time_list"], "Time [ns]", a_data["voltage_list"], "Signal Voltage [V]", ymin=y_range[0], ymax=y_range[1])
peak_time_list = [x["x"] for x in errors]
peak_voltage_list = [x["y"] for x in errors]
peak_hist, peak_graph = common.make_root_graph("Peaks", peak_time_list, "Time of peak [ns]", peak_voltage_list, "Peak voltage")
if canvas == None:
canvas = common.make_root_canvas(title+"bpm peaks")
canvas.Draw()
hist.Draw()
canvas.cd()
peak_graph.SetMarkerColor(colour)
peak_graph.SetMarkerStyle(4)
peak_graph.Draw("p")
graph.SetMarkerColor(colour)
graph.Draw("p")
canvas.Update()
return canvas, hist, graph, peak_hist, peak_graph
def plot_phase_space(self, title):
"""
Plot the phase space for the fourier transform
- title, string used as a title in FFT plots
Returns tuple of TCanvas, TH2, TGraph
"""
# NEEDS TEST
canvas = common.make_root_canvas(title)
print(len(self.u), len(self.up))
hist, graph = common.make_root_graph(title, self.u, 'displacement',
self.up, 'divergence')
hist.SetTitle(title)
hist.Draw()
graph.SetMarkerStyle(26)
graph.Draw('p')
canvas.Update()
return canvas, hist, graph
def _draw_materials_1d(self, step_data, z_range):
"""Make a 1D plot of position vs material along the z axis"""
# we plot G4_AIR first, then G4_GALACTIC?
draw_list = step_data.keys()
draw_list = self.move(draw_list, "G4_AIR", 0)
draw_list = self.move(draw_list, "G4_Galactic", 1)
black_x_list, black_y_list = [], []
grey_x_list, grey_y_list = [], []
canvas = common.make_root_canvas("Material vs z")
hist = ROOT.TH2D("name", "Material vs z", 30000, z_range[0],
z_range[1],
len(draw_list) + 1, -0.5,
len(draw_list) + 0.5)
common._common._hist_persistent.append(hist) #pylint: disable=W0212
for material in draw_list:
hist.Fill(z_range[0], material, 1.)
hist.SetBit(ROOT.TH1.kCanRebin)
hist.SetStats(0)
hist.LabelsDeflate("Y")
hist.LabelsOption("v")
hist.Draw()
for i, material in enumerate(draw_list):
grey_x_list += [i + 0.1 for step in step_data[material]]
grey_y_list += [step["z"] for step in step_data[material]]
black_x_list += [i for step in step_data[material] \
if step["r"] < 1e-9]
black_y_list += [step["z"] for step in step_data[material] \
if step["r"] < 1e-9]
if len(grey_x_list) == 0:
return
hist, graph = common.make_root_graph("", grey_y_list, "z [mm]",
grey_x_list, "material index")
graph.SetMarkerColor(ROOT.TColor.GetColor(0.5, 0.5, 0.5))
graph.SetMarkerStyle(7)
graph.Draw('p')
if len(black_x_list) != 0:
hist, graph = common.make_root_graph("", black_y_list, "z [mm]",
black_x_list,
"material index")
graph.SetMarkerStyle(7)
graph.Draw('p')
canvas.Update()
for _format in self.formats:
canvas.Print(self.mat_1d + "." + _format)
def common_make_root_multigraph_test():
canvas = common.make_root_canvas('multigraph_test')
x_list_of_lists = []
y_list_of_lists = []
for i in range(2):
x_list_of_lists.append(list(range(0,50)))
for i in range( len(x_list_of_lists[-1]) ):
x_list_of_lists[-1][i] /= 10.
x_list_of_lists.append(list(range(0,100)))
for i in range( len(x_list_of_lists[-1]) ):
x_list_of_lists[-1][i] /= 20.
for i in range( len(x_list_of_lists) ):
y_list_of_lists.append(__sine_list(i+1, i+1, x_list_of_lists[i]))
(hist,list_of_graphs) = common.make_root_multigraph('test', x_list_of_lists, 'x [rad]', y_list_of_lists, 'a*sin(b*x)')
hist.Draw()
for graph in list_of_graphs:
graph.Draw()
return 'pass' # a test that can't fail - excellent!
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 _draw_materials_1d(self, step_data, z_range):
"""Make a 1D plot of position vs material along the z axis"""
# we plot G4_AIR first, then G4_GALACTIC?
draw_list = step_data.keys()
draw_list = self.move(draw_list, "G4_AIR", 0)
draw_list = self.move(draw_list, "G4_Galactic", 1)
black_x_list, black_y_list = [], []
grey_x_list, grey_y_list = [], []
canvas = common.make_root_canvas("Material vs z")
hist = ROOT.TH2D("name", "Material vs z", 30000, z_range[0], z_range[1],
len(draw_list)+1, -0.5, len(draw_list)+0.5)
common._common._hist_persistent.append(hist) #pylint: disable=W0212
for material in draw_list:
hist.Fill(z_range[0], material, 1.)
hist.SetBit(ROOT.TH1.kCanRebin)
hist.SetStats(0)
hist.LabelsDeflate("Y")
hist.LabelsOption("v")
hist.Draw()
for i, material in enumerate(draw_list):
grey_x_list += [i+0.1 for step in step_data[material]]
grey_y_list += [step["z"] for step in step_data[material]]
black_x_list += [i for step in step_data[material] \
if step["r"] < 1e-9]
black_y_list += [step["z"] for step in step_data[material] \
if step["r"] < 1e-9]
if len(grey_x_list) == 0:
return
hist, graph = common.make_root_graph("", grey_y_list, "z [mm]",
grey_x_list, "material index")
graph.SetMarkerColor(ROOT.TColor.GetColor(0.5, 0.5, 0.5))
graph.SetMarkerStyle(7)
graph.Draw('p')
if len(black_x_list) != 0:
hist, graph = common.make_root_graph("", black_y_list, "z [mm]",
black_x_list, "material index")
graph.SetMarkerStyle(7)
graph.Draw('p')
canvas.Update()
for _format in self.formats:
canvas.Print(self.mat_1d+"."+_format)
def plot_z(item, line_color, do_square, canvas = None):
z_list = [hit["z"] for hit in item["seed"]]
if not do_square: #i.e. do triangle
area_list = [abs(area/item["an_area_list"][0]-1) for area in item["an_area_list"]]
else:
area_list = [abs(sum(area_list)/sum(item["area_list_of_lists"][0])-1) for area_list in item["area_list_of_lists"]]
title = "x: "+str(item["seed"][0]["x"])+" mm "+\
"p_{x}: "+str(item["seed"][0]["px"])+" MeV/c"
hist, graph = common.make_root_graph(title,
z_list, "z [mm]",
area_list, "Fractional change in simplex content",
ymin = 1e-5, ymax=100.
)
if canvas == None:
canvas = common.make_root_canvas("content growth vs z")
canvas.SetLogy()
hist.Draw()
canvas.cd()
graph.SetLineColor(line_color)
graph.Draw("SAMEPL")
canvas.Update()
return canvas, graph
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(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)
