def test_contour(): 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.contour(a)
def test_contour(): 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.contour(a)
def test_contour(): 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.contour(a)
def test_contour(): 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.contour(a)
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.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) OverlayAxes.set_ylabel(var2label)