def drawPtResponse(ax, hist, filename=""):
ax.set_xlabel(r"$p_{T,obs}\left[GeV\right]$") # Add x-axis labels for bottom row
ax.set_ylabel(r"$p_{T,true}\left[GeV\right]$") # Add x-axis labels for bottom row
# rplt.hist2d(hist, label = "Hist",norm=LogNorm(),colorbar=True)
rplt.hist2d(hist, label="Hist", norm=LogNorm())
ax.set_xlim([5, 150])
ax.set_ylim([5, 150])
def drawResponse(ax, hist):
ax.set_xlabel(r"$j_{T,obs}\left[GeV\right]$") # Add x-axis labels for bottom row
ax.set_ylabel(r"$j_{T,true}\left[GeV\right]$") # Add x-axis labels for bottom row
# rplt.hist2d(hist, label = "Hist",norm=LogNorm(),colorbar=True)
rplt.hist2d(hist, label="Hist")
ax.set_xlim([0.01, 10])
ax.set_ylim([0.01, 10])
def test_hist2d():
from rootpy.plotting import root2matplotlib as rplt
from matplotlib import pyplot
import numpy as np
if not hasattr(pyplot, 'hist2d'):
raise SkipTest("matplotlib is too old")
a = Hist2D(100, -3, 3, 100, 0, 6)
a.fill_array(
np.random.multivariate_normal(mean=(0, 3),
cov=[[1, .5], [.5, 1]],
size=(1000, )))
rplt.hist2d(a)
def test_hist2d():
from rootpy.plotting import root2matplotlib as rplt
from matplotlib import pyplot
import numpy as np
if not hasattr(pyplot, 'hist2d'):
raise SkipTest("matplotlib is too old")
a = Hist2D(100, -3, 3, 100, 0, 6)
a.fill_array(np.random.multivariate_normal(
mean=(0, 3),
cov=[[1, .5], [.5, 1]],
size=(1000,)))
rplt.hist2d(a)
def draw(hist, ncols=1, nrows=1, cell=1, fig=None, figsize=(10, 5)):
if fig is None:
fig = plt.figure(figsize=figsize, dpi=100, facecolor='white')
else:
plt.figure(fig.number)
subplot = fig.add_subplot(nrows, ncols, cell)
subplot.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
if isinstance(hist, r.TH2):
rplt.hist2d(hist, axes=subplot)
else:
rplt.errorbar([hist], xerr=False, emptybins=False)
# plt.show()
return fig
def draw(hist, ncols=1, nrows=1, cell=1, fig=None, figsize=goldenaspect(3), **kwargs):
if fig is None:
fig = plt.figure(figsize=figsize, dpi=1200, facecolor="white")
else:
plt.figure(fig.number)
subplot = fig.add_subplot(nrows, ncols, cell)
# subplot.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
# subplot.ticklabel_format(scilimits=(0, 0))
if isinstance(hist, R.TH2):
rplt.hist2d(hist, axes=subplot, **kwargs)
else:
rplt.errorbar([hist], xerr=False, emptybins=False, **kwargs)
# plt.show()
return fig
def draw2D(hist, title):
fig, axs = plt.subplots(1, 1, figsize=(10, 10), sharex=True, sharey=True)
# axs = axs.reshape(4)
ax = axs
ax.set_xlabel(r"$j_T$")
ax.set_ylabel(r"$p_{T,jet}$")
ax.set_xscale("log")
# ax.set_yscale('log')
# rplt.hist2d(hist,label="Hist",norm=PowerNorm(0.8),colorbar=True)
rplt.hist2d(hist, label="Hist", norm=LogNorm(), colorbar=True)
ax.set_xlim([0.01, 10])
ax.set_ylim([5, 100])
ax.text(
1,
7,
title,
fontsize=10,
bbox=dict(boxstyle="round", ec=(1.0, 0.5, 0.5), fc=(1.0, 0.8, 0.8), alpha=0.8),
)
plt.show()
def draw2DComparison(hists, titles):
fig, axs = plt.subplots(1, 2, figsize=(10, 10))
axs = axs.reshape(2)
for ax, h, title in zip(axs, hists, titles):
ax.set_xlabel(r"$j_T$")
ax.set_ylabel(r"$p_{T,jet}$")
ax.set_xscale("log")
# ax.set_yscale('log')
# rplt.hist2d(hist,label="Hist",norm=PowerNorm(0.8),colorbar=True)
rplt.hist2d(h, axes=ax, label=title, norm=LogNorm(), colorbar=True)
ax.set_xlim([0.01, 10])
ax.set_ylim([5, 100])
ax.text(
1,
7,
title,
fontsize=10,
bbox=dict(
boxstyle="round", ec=(1.0, 0.5, 0.5), fc=(1.0, 0.8, 0.8), alpha=0.8
),
)
plt.show()
def sdraw(hists, xlabel="", ylabel="", xlims=None, ylims=None, space=0):
nr, nc = 2, 3
fig = plt.figure(figsize=goldenaspect(6.5, nr, nc))
gs = gridspec.GridSpec(nr, nc, wspace=space, hspace=space, left=0.1, right=0.95, bottom=0.175)
for irow in range(0, nr):
for icol in range(0, nc):
hnum = 3 * irow + (icol + 1)
h = hists[hnum - 1]
ax = plt.subplot(gs[irow, icol])
rplt.hist2d(h, axes=ax) # , norm=LogNorm())
if xlims is not None:
plt.xlim(xlims)
if ylims is not None:
plt.ylim(ylims)
if icol > 0:
ax.set_yticklabels("")
if irow + 1 < nr:
ax.set_xticklabels("")
fig.add_axes([0.06, 0.105, 0.94, 0.895], frameon=False)
plt.xticks([])
plt.yticks([])
plt.ylabel(ylabel)
plt.xlabel(xlabel)
return fig
0.005, r'pPb $\sqrt{s_{NN}} = 5.02 \mathrm{TeV}$'
'\n Full jets\n'
r'Anti-$k_T$, R=0.4'
'\nJet Cone',
fontsize=7
) #Add text to second subfigure, first parameters are coordinates in the drawn scale/units
for jets, label in zip((measJets, trueJets), ('Measured', 'True')):
rplt.errorbar(jets, xerr=False, emptybins=False, axes=ax1, label=label)
ax1.legend(loc='best')
ax1.grid(True) #Draw grid
ratio = measJets.Clone()
ratio.Divide(trueJets)
rplt.errorbar(ratio, xerr=False, emptybins=False, axes=ax2)
ax1.set_xlim([5, 500]) #Set x-axis limits
ax1.set_ylim([5e-12, 2e0]) #Set y-axis limits
rplt.hist2d(jetPtResponse, axes=ax3, label="Response", norm=LogNorm())
x1 = linspace(0, 500, 50)
y1 = linspace(0, 500, 50)
plt.plot(x1, y1, '-r')
ax3.set_xlim([5, 500])
ax3.set_ylim([5, 500])
ax3.set_yscale('log')
plt.tight_layout()
plt.subplots_adjust(wspace=0, hspace=0) #Set space between subfigures to 0
#plt.savefig(name,format='pdf') #Save figure
plt.show() #Draw figure on screen
def make_correlation_plot_from_file( channel, variable, fit_variables, CoM, title, x_title, y_title, x_limits, y_limits, rebin = 1, save_folder = 'plots/fitchecks/', save_as = ['pdf', 'png'] ):
# global b_tag_bin
parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"]
parameters_latex = []
for template in parameters:
parameters_latex.append(samples_latex[template])
input_file = open( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), "r" )
# cycle through the lines in the file
for line_number, line in enumerate( input_file ):
# for now, only make plots for the fits for the central measurement
if "central" in line:
# matrix we want begins 11 lines below the line with the measurement ("central")
line_number = line_number + 11
break
input_file.close()
#Note: For some reason, the fit outputs the correlation matrix with the templates in the following order:
#parameter1: QCD
#parameter2: SingleTop
#parameter3: TTJet
#parameter4: V+Jets
for variable_bin in variable_bins_ROOT[variable]:
weights = {}
if channel == 'electron':
#formula to calculate the number of lines below "central" to access in each loop
number_of_lines_down = (variable_bins_ROOT[variable].index( variable_bin ) * 12)
#Get QCD correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
if channel == 'muon':
#formula to calculate the number of lines below "central" to access in each bin loop
number_of_lines_down = ( len( variable_bins_ROOT [variable] ) * 12 ) + ( variable_bins_ROOT[variable].index( variable_bin ) * 12 )
#Get QCD correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down )
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 1 )
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 2 )
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline( "logs/01_%s_fit_%dTeV_%s.log" % ( variable, CoM, fit_variables ), line_number + number_of_lines_down + 3 )
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
#Create histogram
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin
histogram_properties.y_axis_title = y_title
histogram_properties.x_axis_title = x_title
histogram_properties.y_limits = y_limits
histogram_properties.x_limits = x_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
#initialise 2D histogram
a = Hist2D( 4, 0, 4, 4, 0, 4 )
#fill histogram
for i in range( len( parameters ) ):
for j in range( len( parameters ) ):
a.fill( float( i ), float( j ), float( weights["%s_%s" % ( parameters[i], parameters[j] )] ) )
# create figure
plt.figure( figsize = CMS.figsize, dpi = CMS.dpi, facecolor = CMS.facecolor )
# make subplot(?)
fig, ax = plt.subplots( nrows = 1, ncols = 1 )
rplt.hist2d( a )
plt.subplots_adjust( right = 0.8 )
#Set labels and formats for titles and axes
plt.ylabel( histogram_properties.y_axis_title )
plt.xlabel( histogram_properties.x_axis_title )
plt.title( histogram_properties.title )
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
ax.set_xticklabels( parameters_latex )
ax.set_yticklabels( parameters_latex )
ax.set_xticks( [0.5, 1.5, 2.5, 3.5] )
ax.set_yticks( [0.5, 1.5, 2.5, 3.5] )
plt.setp( ax.get_xticklabels(), visible = True )
plt.setp( ax.get_yticklabels(), visible = True )
#create and draw colour bar to the right of the main plot
im = rplt.imshow( a, axes = ax, vmin = -1.0, vmax = 1.0 )
#set location and dimensions (left, lower, width, height)
cbar_ax = fig.add_axes( [0.85, 0.10, 0.05, 0.8] )
fig.colorbar( im, cax = cbar_ax )
for xpoint in range( len( parameters ) ):
for ypoint in range( len( parameters ) ):
correlation_value = weights["%s_%s" % ( parameters[xpoint], parameters[ypoint] )]
ax.annotate( correlation_value, xy = ( xpoint + 0.5, ypoint + 0.5 ), ha = 'center', va = 'center', bbox = dict( fc = 'white', ec = 'none' ) )
for save in save_as:
plt.savefig( save_folder + histogram_properties.name + '.' + save )
plt.close(fig)
plt.close('all')
print __doc__
import ROOT
from matplotlib import pyplot as plt
from rootpy.plotting import root2matplotlib as rplt
from rootpy.plotting import Hist2D
import numpy as np
a = Hist2D(100, -3, 3, 100, 0, 6)
a.fill_array(
np.random.multivariate_normal(mean=(0, 3),
cov=np.arange(4).reshape(2, 2),
size=(1E6, )))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
ax1.set_title('hist2d')
rplt.hist2d(a, axes=ax1)
ax2.set_title('imshow')
im = rplt.imshow(a, axes=ax2)
ax3.set_title('contour')
rplt.contour(a, axes=ax3)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
if not ROOT.gROOT.IsBatch():
plt.show()
data_combo = np.stack(
(data_integral_1, # The pulse integral on y axis
data_pulseheight_1), # The pulseheight on x axis
axis=-1) # To get the direction correct
a = rootpy.plotting.Hist2D(100, 0, 100000, 100, 0, 4500)
a.fill_array(data_combo)
canvas = rootpy.plotting.Canvas(width = 800, height = 600)
a.Draw("colz")
rootpy.interactive.wait()
fig = plt.figure(station)
root2matplotlib.hist2d(a)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
fig.savefig(
'hist2d.pdf', # Name of the file
bbox_inches='tight')
plt.close(fig)
print "####### I'm Done Bitches! #######"
################################## FOOTER ##################################
# Combine the detector data
data_combo = np.stack(
(data_integral_detector, # The pulse integral on y axis
data_pulseheight_detector), # The pulseheight on x axis
axis=-1) # To get the direction correct
# Initiate a 2D histogram (ROOT style)
histogram_combo_detector = rootpy.plotting.Hist2D(100, 0, 150000, 100, 0, 4500)
# Fill the Histogram
histogram_combo_detector.fill_array(data_combo)
# Plot the histogram with logarithmic colors in correct place
if detector == 0:
root2matplotlib.hist2d(histogram_combo_detector, norm=LogNorm(), axes=ax1)
elif detector == 1:
root2matplotlib.hist2d(histogram_combo_detector, norm=LogNorm(), axes=ax2)
elif detector == 2:
root2matplotlib.hist2d(histogram_combo_detector, norm=LogNorm(), axes=ax3)
elif detector == 3:
root2matplotlib.hist2d(histogram_combo_detector, norm=LogNorm(), axes=ax4)
# Save the file
figure_combo.savefig(
'pmt_saturation_s%d.pdf' %station) # Name of the file
# Close the figure
plt.close(figure_combo)
# Now we go to the next detector
#import ROOT
import matplotlib.pyplot as plt
from rootpy.plotting import root2matplotlib
import rootpy.plotting
import numpy as np
a = rootpy.plotting.Hist2D(100, -3, 3, 100, 0, 6)
a.fill_array(np.random.multivariate_normal(
mean=(0, 3),
cov=np.arange(4).reshape(2, 2),
size=(1E6,)))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
ax1.set_title('hist2d')
root2matplotlib.hist2d(a, axes=ax1)
ax2.set_title('imshow')
im = root2matplotlib.imshow(a, axes=ax2)
ax3.set_title('contour')
root2matplotlib.contour(a, axes=ax3)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
fig.savefig(
'hist2d.pdf', # Name of the file
bbox_inches='tight')
def main():
print('Number of arguments: ', len(sys.argv), 'arguments.')
print('Argument list:', str(sys.argv))
filename = sys.argv[1]
print("Input file: ")
print(filename)
MC_FullJets_R04 = dataset("FullR04",
NFIN=0,
range=(0, 9),
filename=filename,
directory='AliJJetJtTask/AliJJetJtHistManager',
color=2,
style=24,
rebin=1)
response, jetPt = MC_FullJets_R04.get2DHist(
'TrackJtCorrBin',
dir='AliJJetJtTask/AliJJetJtMCHistManager',
jetpt=True)
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
linear = ROOT.TF1("linear", "x", 0, 10)
linear.SetLineColor(1)
linear.SetLineWidth(2)
linear.SetLineStyle(2)
x = linspace(0, 10, 50)
y = linspace(0, 10, 50)
#response[1].Draw('colz')
#linear.Draw('Same')
ij = 3
rplt.hist2d(response[ij],
label=MC_FullJets_R04.name(),
norm=LogNorm(),
colorbar=True)
ax = plt.gca()
ax.set_xlim([0, 4])
ax.set_ylim([0, 4])
ax.set_xlabel(
r'$j_{T,true}\left[GeV\right]$') #Add x-axis labels for bottom row
ax.set_ylabel(
r'$j_{T,obs}\left[GeV\right]$') #Add x-axis labels for bottom row
plt.plot(x, y, '-r')
ax.text(0.5,
2.8,
d['system'] + '\n' + d['jettype'] + '\n' + d['jetalg'] +
'\n Jet Cone \n' + r'$p_{{T,\mathrm{{jet}}}}$:'
'\n'
r' {:02d}-{:02d} GeV'.format(jetPt[ij][0], jetPt[ij][1]),
fontsize=10,
bbox=dict(boxstyle="round",
ec=(1., 0.5, 0.5),
fc=(1., 0.8, 0.8),
alpha=0.8))
plt.savefig("PythonFigures/ResponseMatrixNFin00JetPt03.pdf",
format='pdf') #Save figure
d['system'] = r'pPb MC$\sqrt{s_{NN}} = 5.02 \mathrm{TeV}$'
fig, axs = defs.makegrid(4,
2,
xlog=False,
ylog=False,
d=d,
shareY=True,
xtitle=r'$j_{T,true}\left[GeV\right]$',
ytitle=r'$j_{T,obs}\left[GeV\right]$')
axs = axs.reshape(8)
axs[1].text(0.5,
5,
d['system'] + '\n' + d['jettype'] + '\n' + d['jetalg'] +
'\n Jet Cone',
fontsize=7,
bbox=dict(boxstyle="round",
ec=(1., 0.5, 0.5),
fc=(1., 0.8, 0.8),
alpha=0.7))
for r, pT, ax, i in zip(response[1:], jetPt[1:], axs, range(0, 9)):
rplt.hist2d(r, axes=ax, label=MC_FullJets_R04.name(), norm=LogNorm())
x1 = linspace(0, 10, 50)
y1 = linspace(0, 10, 50)
plt.plot(x1, y1, '-r')
ax.text(3.5,
0.5,
r'$p_{{T,\mathrm{{jet}}}}$:'
'\n'
r' {:02d}-{:02d} GeV'.format(pT[0], pT[1]),
bbox=dict(boxstyle="round",
ec=(1., 0.5, 0.5),
fc=(1., 0.8, 0.8),
alpha=0.7))
ax.set_xlim([0, 6.5]) #Set x-axis limits
ax.set_ylim([0, 6.5]) #Set y-axis limits
axs[0].legend(loc='lower left')
#plt.savefig("PythonFigures/MixedFullJetsR04JetConeJtSignal.pdf",format='pdf') #Save figure
plt.savefig("PythonFigures/ResponseMatrixNFin00.pdf",
format='pdf') #Save figure
plt.show() #Draw figure on screen
def saskia_plot_pmt(data_file_name, station_number):
# If the plot exist we skip the plotting
if os.path.isfile('./img/pmt_saturation_s%d.pdf' %station_number):
# Say if the plot is present
print "PMT saturation histogram already present for station %d" % station_number
# If there is no plot we make it
else:
# Get event data
event_data = data_file_name.get_node(
'/s%d' %station, # From the group (/s..station..)
'events') # Get the node with events
# Get the pulseheight from all events
data_phs = event_data.col('pulseheights') # col takes all data from events (this improves the speed)
# Get the integral from all events
data_ints = event_data.col('integrals') # col takes all data from events
# Make a figure so it can be closed
figure_combo, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex = 'col', sharey = 'row')
# Setting the plot titles
ax1.set_title('Detector 1')
ax2.set_title('Detector 2')
ax3.set_title('Detector 3')
ax4.set_title('Detector 4')
# Setting the plot labels
ax1.set_ylabel('Pulseheight [ADC]')
ax3.set_ylabel('Pulseheight [ADC]')
ax3.set_xlabel('Pulse integral [ADC.ns]')
ax4.set_xlabel('Pulse integral [ADC.ns]')
# Now we plot the data of every detector
for detector in range(0,4):
# Select the detector data
data_ph_detector = data_phs[:,detector]
data_int_detector = data_ints[:,detector]
# Combine the detector data
data_combo = np.stack(
(data_int_detector, # The pulse integral on y axis
data_ph_detector), # The pulseheight on x axis
axis=-1) # To get the direction correct
# Initiate a 2D histogram (ROOT style)
histo_combo_detector = rootpy.plotting.Hist2D(100, 0, 150000, 100, 0, 4500)
# Fill the Histogram
histo_combo_detector.fill_array(data_combo)
# Plot the histogram with logarithmic colors in correct place
if detector == 0:
root2matplotlib.hist2d(histo_combo_detector, norm=LogNorm(), axes=ax1)
elif detector == 1:
root2matplotlib.hist2d(histo_combo_detector, norm=LogNorm(), axes=ax2)
elif detector == 2:
root2matplotlib.hist2d(histo_combo_detector, norm=LogNorm(), axes=ax3)
elif detector == 3:
root2matplotlib.hist2d(histo_combo_detector, norm=LogNorm(), axes=ax4)
# Save the file
figure_combo.savefig(
'./img/pmt_saturation_s%d.pdf' %station_number) # Name of the file
# Close the figure
plt.close(figure_combo)
# Now we go to the next detector
detector +1
print(__doc__)
import ROOT
from matplotlib import pyplot as plt
from rootpy.plotting import root2matplotlib as rplt
from rootpy.plotting import Hist2D
import numpy as np
a = Hist2D(100, -3, 3, 100, 0, 6)
a.fill_array(np.random.multivariate_normal(
mean=(0, 3),
cov=[[1, .5], [.5, 1]],
size=(1E6,)))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
ax1.set_title('hist2d')
rplt.hist2d(a, axes=ax1)
ax2.set_title('imshow')
im = rplt.imshow(a, axes=ax2)
ax3.set_title('contour')
rplt.contour(a, axes=ax3)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
if not ROOT.gROOT.IsBatch():
plt.show()
data_combo = np.stack(
(data_integral_1, # The pulse integral on y axis
data_pulseheight_1), # The pulseheight on x axis
axis=-1) # To get the direction correct
a = rootpy.plotting.Hist2D(100, 0, 150000, 100, 0, 5000)
a.fill_array(data_combo)
canvas = rootpy.plotting.Canvas(width = 800, height = 600)
a.Draw("colz")
wait()
fig = plt.figure(station)
im = root2matplotlib.hist2d(a, norm=LogNorm())
#cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
#fig.colorbar(im)
fig.savefig(
'hist2d.pdf', # Name of the file
bbox_inches='tight')
plt.close(fig)
print "####### I'm Done Bitches! #######"
################################## FOOTER ##################################
"""
def make_correlation_plot_from_file(channel,
variable,
fit_variables,
CoM,
title,
x_title,
y_title,
x_limits,
y_limits,
rebin=1,
save_folder='plots/fitchecks/',
save_as=['pdf', 'png']):
# global b_tag_bin
parameters = ["TTJet", "SingleTop", "V+Jets", "QCD"]
parameters_latex = []
for template in parameters:
parameters_latex.append(samples_latex[template])
input_file = open(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables), "r")
# cycle through the lines in the file
for line_number, line in enumerate(input_file):
# for now, only make plots for the fits for the central measurement
if "central" in line:
# matrix we want begins 11 lines below the line with the measurement ("central")
line_number = line_number + 11
break
input_file.close()
#Note: For some reason, the fit outputs the correlation matrix with the templates in the following order:
#parameter1: QCD
#parameter2: SingleTop
#parameter3: TTJet
#parameter4: V+Jets
for variable_bin in variable_bins_ROOT[variable]:
weights = {}
if channel == 'electron':
#formula to calculate the number of lines below "central" to access in each loop
number_of_lines_down = (
variable_bins_ROOT[variable].index(variable_bin) * 12)
#Get QCD correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down)
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 1)
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 2)
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 3)
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
if channel == 'muon':
#formula to calculate the number of lines below "central" to access in each bin loop
number_of_lines_down = (len(variable_bins_ROOT[variable]) * 12) + (
variable_bins_ROOT[variable].index(variable_bin) * 12)
#Get QCD correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down)
weights["QCD_QCD"] = matrix_line.split()[2]
weights["QCD_SingleTop"] = matrix_line.split()[3]
weights["QCD_TTJet"] = matrix_line.split()[4]
weights["QCD_V+Jets"] = matrix_line.split()[5]
#Get SingleTop correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 1)
weights["SingleTop_QCD"] = matrix_line.split()[2]
weights["SingleTop_SingleTop"] = matrix_line.split()[3]
weights["SingleTop_TTJet"] = matrix_line.split()[4]
weights["SingleTop_V+Jets"] = matrix_line.split()[5]
#Get TTJet correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 2)
weights["TTJet_QCD"] = matrix_line.split()[2]
weights["TTJet_SingleTop"] = matrix_line.split()[3]
weights["TTJet_TTJet"] = matrix_line.split()[4]
weights["TTJet_V+Jets"] = matrix_line.split()[5]
#Get V+Jets correlations
matrix_line = linecache.getline(
"logs/01_%s_fit_%dTeV_%s.log" % (variable, CoM, fit_variables),
line_number + number_of_lines_down + 3)
weights["V+Jets_QCD"] = matrix_line.split()[2]
weights["V+Jets_SingleTop"] = matrix_line.split()[3]
weights["V+Jets_TTJet"] = matrix_line.split()[4]
weights["V+Jets_V+Jets"] = matrix_line.split()[5]
#Create histogram
histogram_properties = Histogram_properties()
histogram_properties.title = title
histogram_properties.name = 'Correlations_' + channel + '_' + variable + '_' + variable_bin
histogram_properties.y_axis_title = y_title
histogram_properties.x_axis_title = x_title
histogram_properties.y_limits = y_limits
histogram_properties.x_limits = x_limits
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
#initialise 2D histogram
a = Hist2D(4, 0, 4, 4, 0, 4)
#fill histogram
for i in range(len(parameters)):
for j in range(len(parameters)):
a.fill(
float(i), float(j),
float(weights["%s_%s" % (parameters[i], parameters[j])]))
# create figure
plt.figure(figsize=CMS.figsize, dpi=CMS.dpi, facecolor=CMS.facecolor)
# make subplot(?)
fig, ax = plt.subplots(nrows=1, ncols=1)
rplt.hist2d(a)
plt.subplots_adjust(right=0.8)
#Set labels and formats for titles and axes
plt.ylabel(histogram_properties.y_axis_title)
plt.xlabel(histogram_properties.x_axis_title)
plt.title(histogram_properties.title)
x_limits = histogram_properties.x_limits
y_limits = histogram_properties.y_limits
ax.set_xticklabels(parameters_latex)
ax.set_yticklabels(parameters_latex)
ax.set_xticks([0.5, 1.5, 2.5, 3.5])
ax.set_yticks([0.5, 1.5, 2.5, 3.5])
plt.setp(ax.get_xticklabels(), visible=True)
plt.setp(ax.get_yticklabels(), visible=True)
#create and draw colour bar to the right of the main plot
im = rplt.imshow(a, axes=ax, vmin=-1.0, vmax=1.0)
#set location and dimensions (left, lower, width, height)
cbar_ax = fig.add_axes([0.85, 0.10, 0.05, 0.8])
fig.colorbar(im, cax=cbar_ax)
for xpoint in range(len(parameters)):
for ypoint in range(len(parameters)):
correlation_value = weights["%s_%s" % (parameters[xpoint],
parameters[ypoint])]
ax.annotate(correlation_value,
xy=(xpoint + 0.5, ypoint + 0.5),
ha='center',
va='center',
bbox=dict(fc='white', ec='none'))
for save in save_as:
plt.savefig(save_folder + histogram_properties.name + '.' + save)
plt.close(fig)
plt.close('all')
#h.SetMaximum(0.5*h.GetMaximum())
# <codecell>
fgaus = r.TF1('fgaus', 'gaus', 0, 0.3)
nr, nc = 2, 3
fig = figure(figsize=goldenaspect(6.5,nr,nc))
gs = gridspec.GridSpec(nr, nc, wspace=0, hspace=0, left=0.1, right=0.95, bottom=0.175)
for irow in range(0,nr):
for icol in range(0,nc):
hnum = 3*irow+(icol+1)
h2 = hs_ec_oVi[hnum-1]
hx = h2.ProjectionX()
hx.Fit(fgaus, 'N0', '', 0.01, 0.035)
ax = subplot(gs[irow, icol])
rplt.hist2d(h2, axes=ax, norm=LogNorm())
X = linspace(0, 0.1, 100)
fscale = fgaus.GetParameter(0)/(0.9*0.3)
plot(X, [fgaus.Eval(x)/fscale for x in X],
'k-', linewidth=0.5)
cutoff = fgaus.GetParameter(1)+5*fgaus.GetParameter(2)
#plot([0.13,cutoff], [0,cutoff], 'r-')
#plot([cutoff,cutoff], [cutoff,0.3], 'r-')
plot([cutoff,cutoff], [0,0.3], 'r-')
# text(0.9, 0.85, 'sector %d'%hnum, fontsize=12, color='black', transform=ax.transAxes,
# verticalalignment='top', horizontalalignment='right', bbox=dict(facecolor='gray', pad=5))
print('%d,%.3f' % (hnum, cutoff))
# annotate('(%.3f, %.3f) GeV'%(cutoff,cutoff), xy=(cutoff, cutoff),
# xytext=(+0.075, +0.2), fontsize=8,
# arrowprops=dict(arrowstyle="->",
# connectionstyle="arc3,rad=.2"),
