def test_imshow():
from rootpy.plotting import root2matplotlib as rplt
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=(1000,)))
rplt.imshow(a)
def test_imshow():
from rootpy.plotting import root2matplotlib as rplt
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=(1000, )))
rplt.imshow(a)
def test_imshow():
from rootpy.plotting import root2matplotlib as rplt
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=(1000,)))
rplt.imshow(a)
def test_imshow():
from rootpy.plotting import root2matplotlib as rplt
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=(1000, )))
rplt.imshow(a)
def make_jet_response_plot(input_file, response):
global output_folder, output_formats, suffix
jet_response_plot = asrootpy(input_file.Get(response))
x_limits = [0, 200]
y_limits = [0.8, 1.2]
if '_eta' in response:
x_limits = [-3, 3]
x_title = '$\eta$(reco jet)'
else:
x_title = '$p_{\mathrm{T}}$(reco jet) [GeV]'
y_title = '$p_{\mathrm{T}}$(reco jet)/$p_{\mathrm{T}}$(HLT jet)'
save_as_name = response
plt.figure(figsize=(20, 16), dpi=200, facecolor='white')
ax0 = plt.axes()
ax0.minorticks_on()
ax0.grid(True, 'major', linewidth=2)
ax0.grid(True, 'minor')
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
ax0.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax0.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax0.xaxis.labelpad = 12
ax0.yaxis.labelpad = 12
if '_prof' in response:
rplt.errorbar(jet_response_plot,
xerr=True,
emptybins=True,
axes=ax0,
marker='o',
ms=15,
mew=3,
lw=2)
else:
im = rplt.imshow(jet_response_plot, axes=ax0, cmap=cm.Blues)
#plt.colorbar(im)
ax0.set_xlim(x_limits)
ax0.set_ylim(y_limits)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.xlabel(x_title, CMS.x_axis_title)
plt.ylabel(y_title, CMS.y_axis_title)
plt.title(r'e+jets, CMS Preliminary, $\sqrt{s}$ = 8 TeV', CMS.title)
if '_prof' in response:
plt.legend(['data'],
numpoints=1,
loc='lower right',
prop=CMS.legend_properties)
plt.tight_layout()
for output_format in output_formats:
plt.savefig(output_folder + save_as_name + '_' + suffix + '.' +
output_format)
def make_scatter_plot( input_file, histogram, bin_edges, channel, variable, title ):
global output_folder, output_formats, options
scatter_plot = get_histogram_from_file( histogram, input_file )
# Finding max value in scatterplot for colourmap normalisation
max_bin = scatter_plot.GetMaximumBin()
max_bin_content = scatter_plot.GetBinContent(max_bin)
norm = mpl.colors.LogNorm(vmin = 1, vmax = int(max_bin_content+1))
# scatter_plot.Rebin2D( 5, 5 )
x_limits = [bin_edges[variable][0], bin_edges[variable][-1]]
y_limits = x_limits
x_title = 'Reconstructed $' + variables_latex[variable] + '$ [GeV]'
y_title = 'Generated $' + variables_latex[variable] + '$ [GeV]'
save_as_name = channel + '_' + variable + '_' + str(options.CoM) + 'TeV'
plt.figure( figsize = ( 20, 16 ), dpi = 200, facecolor = 'white' )
ax0 = plt.axes()
ax0.minorticks_on()
# ax0.grid( True, 'major', linewidth = 2 )
# ax0.grid( True, 'minor' )
plt.tick_params( **CMS.axis_label_major )
plt.tick_params( **CMS.axis_label_minor )
ax0.xaxis.labelpad = 12
ax0.yaxis.labelpad = 12
im = rplt.imshow( scatter_plot, axes = ax0, cmap = my_cmap, norm=norm )
colorbar = plt.colorbar( im )
colorbar.ax.tick_params( **CMS.axis_label_major )
# draw lines at bin edges values
for edge in bin_edges[variable]:
# do not inclue first and last values
if ( edge != bin_edges[variable][0] ) and ( edge != bin_edges[variable][-1] ):
plt.axvline( x = edge, color = 'red', linewidth = 4, alpha = 0.5 )
plt.axhline( y = edge, color = 'red', linewidth = 4, alpha = 0.5 )
ax0.set_xlim( x_limits )
ax0.set_ylim( y_limits )
plt.tick_params( **CMS.axis_label_major )
plt.tick_params( **CMS.axis_label_minor )
plt.xlabel( x_title, CMS.x_axis_title )
plt.ylabel( y_title, CMS.y_axis_title )
plt.title( title, CMS.title )
plt.tight_layout()
for output_format in output_formats:
plt.savefig( output_folder + save_as_name + '.' + output_format )
def make_jet_response_plot(input_file, response):
global output_folder, output_formats, suffix
jet_response_plot = asrootpy(input_file.Get(response))
x_limits = [0, 200]
y_limits = [0.8, 1.2]
if '_eta' in response:
x_limits = [-3, 3]
x_title = '$\eta$(reco jet)'
else:
x_title = '$p_{\mathrm{T}}$(reco jet) [GeV]'
y_title = '$p_{\mathrm{T}}$(reco jet)/$p_{\mathrm{T}}$(HLT jet)'
save_as_name = response
plt.figure(figsize=(20, 16), dpi=200, facecolor='white')
ax0 = plt.axes()
ax0.minorticks_on()
ax0.grid(True, 'major', linewidth=2)
ax0.grid(True, 'minor')
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
ax0.xaxis.set_major_formatter(FormatStrFormatter('%d'))
ax0.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax0.xaxis.labelpad = 12
ax0.yaxis.labelpad = 12
if '_prof' in response:
rplt.errorbar(jet_response_plot, xerr=True, emptybins=True, axes=ax0)
else:
im = rplt.imshow(jet_response_plot, axes=ax0, cmap = cm.Blues)
#plt.colorbar(im)
ax0.set_xlim(x_limits)
ax0.set_ylim(y_limits)
plt.tick_params(**CMS.axis_label_major)
plt.tick_params(**CMS.axis_label_minor)
plt.xlabel(x_title, CMS.x_axis_title)
plt.ylabel(y_title, CMS.y_axis_title)
plt.title(r'e+jets, CMS Preliminary, $\sqrt{s}$ = 8 TeV', CMS.title)
if '_prof' in response:
plt.legend(['data'], numpoints=1, loc='lower right', prop=CMS.legend_properties)
plt.tight_layout()
for output_format in output_formats:
plt.savefig(output_folder + save_as_name + '_' + suffix + '.' + output_format)
def draw_interp_subplot(ax, limit, x_spec, y_spec, z_spec, triang = None, xi = None, yi = None, zi = None, cont = None, title = "", show_grids = False, masked_region = None):
if isinstance(limit,r.TH2):
img = rplt.imshow(limit, ax, norm=mpl.colors.LogNorm(), vmin = z_spec.ax_min , vmax = z_spec.ax_max)
elif isinstance(limit,np.ma.core.MaskedArray):
dx = (xi[2][2]-xi[1][1])
xi_n = xi - dx/2.
dy = (yi[2][2]-yi[1][1])
yi_n = yi - dy/2.
img = ax.pcolor(xi_n,yi_n,limit, vmin = z_spec.ax_min, vmax = z_spec.ax_max, norm=mpl.colors.LogNorm())
else:
log.error("Argument 'limit' has wrong type: %s"%type(limit))
sys.exit()
ax.set_xlim(x_spec.limits)
ax.set_xlabel(x_spec.label, fontsize = 45)
ax.set_ylim(y_spec.limits)
ax.set_ylabel(y_spec.label, fontsize = 45)
ax.set_title(title)
if not xi==None and not yi==None and not zi==None and cont == None:
c = ax.contour(xi, yi, zi, colors='black')
ax.clabel(c, inline=1, fontsize=15)
if not xi==None and not yi==None and not zi==None:
if masked_region == None:
c = ax.contour(xi, yi, zi, levels = cont, colors='black', extrapolate = True, interp = True)
else:
zi[masked_region] = np.ma.masked
c = ax.contour(xi, yi, zi, levels = cont, colors='black', extrapolate = True, interp = True)
c.levels = [format_clabel(val) for val in c.levels ]
fmt = r'%r'
ax.clabel(c, fontsize=18, fmt = fmt)
if show_grids:
if triang:
ax.triplot(triang, 'ko-')
if (not xi == None):
ax.plot(xi_n, yi_n, 'k-', alpha=0.5)
if (not yi == None):
ax.plot(xi_n.T, yi_n.T, 'k-', alpha=0.5)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = plt.colorbar(img, cax=cax)
cbar.set_label(z_spec.label,fontsize=45)
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')
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')
def mega_plot(hist_2D, ax=None, range_2d=None, yrange_px=None, xrange_py=None,
details=None, fit=False, exclude_peak=True, plot_fit=False, dphi_extra=None):
# start with a rectangular Figure
if ax:
fig = plt.gcf()
else:
fig = plt.figure(1, figsize=(8, 10))
#ax = plt.axes()
ax = plt.axes([0.18, 0.18, .64, .64], axisbg='w')
#ax_debug = plt.axes([0,0,1,1], axisbg='r')
ax.axis('off')
frame = 0.0
fibo = 1.681
heat_x = heat_y = 1.0 / fibo
cbar_dist = 0.01
extra_left_legend = 0.08 # may over extend to right but not left...
xpro_x, xpro_y = heat_x, 1.0 - 1.0 / fibo
cbar_x, cbar_y = 0.04, heat_y
ypro_x, ypro_y = xpro_y - cbar_x - cbar_dist, heat_y
rect_2d = [frame, frame, heat_x, heat_y]
rect_histx = [frame, frame + heat_y, xpro_x, xpro_y]
rect_histy = [frame + heat_x, frame, ypro_x, ypro_y]
rect_cbar = [frame + heat_x + ypro_x + cbar_dist, frame, cbar_x, cbar_y]
rect_legend = [frame + heat_x + extra_left_legend, frame + heat_y,
ypro_x + cbar_dist + cbar_x, xpro_y]
# collect plotted lines for legend
lns = list()
ax_2d = _add_subplot_axes(ax, rect_2d)
ax_2d.set_xlabel(r'$\Delta\varphi$')
ax_2d.set_ylabel(r'$\Delta\eta$')
if isinstance(range_2d, (list, tuple)):
im = rplt.imshow(hist_2D, vmin=range_2d[0], vmax=range_2d[1])
else:
im = rplt.imshow(hist_2D)
# x projection:
axHistx = _add_subplot_axes(ax, rect_histx)
if exclude_peak:
lable = '$\Delta \phi$ projection\nexcl. peak'
else:
lable = '$\Delta \phi$ projection'
_plot_x_projection(hist_2D, yrange_px=yrange_px, lns=lns,
lable=lable, ax=axHistx, exclude_peak=exclude_peak,
plot_fit=plot_fit, dphi_extra=dphi_extra)
# y axis projection
sub_py = project_onto_deta_with_sections(hist_2D)
axHisty = _add_subplot_axes(ax, rect_histy)
axHisty.set_ylim(bottom=ax_2d.get_ylim()[0], top=ax_2d.get_ylim()[1])
# cannot use rplt since x and y are swapped
mini, maxi = 99, -99
for hist in sub_py:
mini = hist.GetMinimum() if hist.GetMinimum() < mini else mini
maxi = hist.GetMaximum() if hist.GetMaximum() > maxi else maxi
lns.append(plt.errorbar(x=list(hist.y()), y=list(hist.x()),
xerr=np.array([list(hist.yerrh()), list(hist.yerrl())]),
label=hist.GetTitle()))
if isinstance(xrange_py, (list, tuple)):
axHisty.set_xlim(xrange_py)
else:
dist = (maxi - mini)*0.1
axHisty.set_xlim([mini-dist, maxi+dist])
axHisty.set_xlabel(r'$\frac{1}{N_{tr}}\frac{dN_{as}}{d\Delta \eta}$ [rad$^{-1}$]',
labelpad=0)
# fix labels and ticks
nullfmt = NullFormatter() # no labels
axHistx.xaxis.set_major_formatter(nullfmt)
axHistx.yaxis.set_major_locator(MaxNLocator(prune='lower', nbins=4))
axHistx.xaxis.set_minor_locator(AutoMinorLocator())
axHistx.yaxis.set_minor_locator(AutoMinorLocator())
axHisty.yaxis.set_major_formatter(nullfmt)
axHisty.xaxis.set_minor_locator(AutoMinorLocator())
axHisty.yaxis.set_minor_locator(AutoMinorLocator())
axHisty.xaxis.set_major_locator(MaxNLocator(prune='lower', nbins=4))
# get lables for the py projection and rotate them
axHisty.set_xticklabels(axHisty.get_xticks(),
rotation=35, ha='right')
#plt.setp(axHisty.get_xmajorticklabels(), rotation=35)
# colorbar axes
cbar_ax = _add_subplot_axes(ax, rect_cbar)
cb = fig.colorbar(im, cax=cbar_ax, )
cb.set_label(r'$\frac{1}{N_{tr}}\frac{dN^{2}_{as}}{d\Delta \eta d\Delta \varphi}$[rad$^{-1}$]',) # labelpad=0 rotation=270)
# make the legend axes:
ax_leg = _add_subplot_axes(ax, rect_legend)
labels = [l.get_label() for l in lns]
#ax_leg.xaxis.set_major_formatter(nullfmt)
#ax_leg.yaxis.set_major_formatter(nullfmt)
if details:
ax_leg.text(0.05, 0.75, details)
ax_leg.legend(lns, labels, numpoints=1, loc=10)#loc=(.03, -0.03))
ax_leg.axis('off')
# write title at center of plot
#ax.text(0.5, 1.1, hist_2D.title, fontsize=12, transform=axHistx.transAxes,
# horizontalalignment='center', verticalalignment='center')
ax.set_title(hist_2D.title)
return fig
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')
#if not ROOT.gROOT.IsBatch():
# plt.show()
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()
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()
plt.clf()
plt.close('all')
if (MCSample,norm, label, firstInCombinedSample) in samplestooverlay:
alpha = 0.7
linestyle = 'solid'
levels = [ round_to_1( MCScales[MCSample]*NEvent[MCSample]* tmpfactor ) for tmpfactor in [ 0.0001,0.001,0.01,0.1] ]
if colornorm==0:
colornorm = matplotlib.colors.Normalize(vmin = 0 , vmax = 100, clip = False)
# plt.grid()
# # ims[MCSample] = rplt.imshow(a,axes=OverlayAxes, norm = colornorm, cmap = sns.blend_palette(["ghostwhite", colorpal[iSample]], as_cmap=True) , alpha=0.25 )
ims[MCSample] = rplt.imshow (corrhist,axes=OverlayAxes, norm = colornorm, cmap = sns.blend_palette(["ghostwhite", colorpal[iSample]], as_cmap=True) , alpha=0.25 )
ctr[MCSample] = rplt.contour(corrhist,axes=OverlayAxes, linewidth=10, alpha=alpha, linestyles=linestyle , colors = matplotlib.colors.rgb2hex(colorpal[iSample]) ) #, locator=ticker.FixedLocator(levels) )
plt.clabel(ctr[MCSample], inline=0, inline_spacing=-1, fontsize=10,fmt='%1.0f')
OverlayAxes.annotate(r"\textbf{\textit{ATLAS}} Internal", xy=(0.65, 0.05), xycoords='axes fraction', fontweight='bold', fontsize=10)
pass
OverlayAxes.set_xlim(var1limits[:2])
OverlayAxes.set_ylim(var2limits[:2])
tmpRectangle = {}
for iSample, (MCSample,norm, label, tmp) in enumerate(samplestodraw) :
tmpRectangle[MCSample] = Rectangle((0, 0), 1, 1, fc=matplotlib.colors.rgb2hex(colorpal[iSample]) )
OverlayAxes.legend([tmpRectangle[x] for (x,y,z,x1) in samplestooverlay ], [z for (x,y,z,x1) in samplestooverlay], loc="best" , borderaxespad=3)
OverlayAxes.set_xlabel(var1label)