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)
