Exemplo n.º 1
0
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)
Exemplo n.º 2
0
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)
Exemplo n.º 3
0
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)
Exemplo n.º 4
0
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)
Exemplo n.º 6
0
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)  
Exemplo n.º 8
0
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)
Exemplo n.º 9
0
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')
Exemplo n.º 11
0
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()
Exemplo n.º 13
0
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()
Exemplo n.º 14
0
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)