def megaplot(ctsets,
studyname,
emisfops=None,
labels=["$10^9$", "$10^8$", "$10^7$", "$10^6$"],
axlabel='Primaries [nr]'):
if len(ctsets) == 4:
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
auger.plot_all_ranges_CTONLY(ax1, ctsets[0])
auger.plot_all_ranges_CTONLY(ax2, ctsets[1])
auger.plot_all_ranges_CTONLY(ax3, ctsets[2])
#auger.plot_all_ranges_CTONLY(ax4,ctsets[3])
if not 'Primaries' in axlabel:
ax1.set_title(labels[0])
ax2.set_title(labels[1])
ax3.set_title(labels[2])
#ax4.set_title(labels[3])
f.subplots_adjust(hspace=.5)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
#ax4.set_ylabel('')
f.savefig(studyname + '-' + typ + '-FOP.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# print 'FOP shift distributions'
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax1 = plt.axes(projection='3d')
# ax1.view_init(30, -50)
# for i,ctset in enumerate(ctsets):
# auger.plotfodiffdist(ax1,ctset,i,emisfops,labels,axlabel)
# if not emisfops == None:
# fopshifts=[]
# for fopset in emisfops:
# fopshifts.append( fopset[-1]-fopset[0] )
# ax1.set_xlim3d(np.mean(fopshifts)-20,np.mean(fopshifts)+20)
# if emisfops is not None and len(emisfops) == 1:
# ax1.set_title(studyname+', $Shift_{em}$ = '+str(emisfops[0][-1]-emisfops[0][0]), y=1.08)
# #plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
# fig.savefig(studyname+'-'+typ+'-FOP-shift.pdf')#, bbox_inches='tight')
# plt.close('all')
#############################################################################################
# print 'FOP FOW Contrast DE averages over 10e9 ctset'
f, (ax1, ax2, ax3) = plot.subplots(nrows=3,
ncols=1,
sharex=False,
sharey=False)
x = ctsets[0]['ct']['x']
y = ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm = 4 / 0.8
if 'ipnl' in ctsets[0]['name']:
mm = 8
falloff_pos, g_fwhm, contrast = auger.get_fop_fow_contrast(
x,
y,
plot='wut',
ax=ax1,
ax2=ax2,
ax3=ax3,
smooth=0.2,
filename=ctsets[0]['ct']['path'],
contrast_divisor=ctsets[0]['nprim'] * len(ctsets[0]['ct']['files']) *
mm,
fitlines=False)
print "NPRIM", ctsets[0]['nprim'], "NJOBS", len(
ctsets[0]['ct']['files']), "MM", mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
f.savefig(studyname + '-' + typ + '-FOW.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# results table
for ctset in ctsets:
res = [
typ, ctset['nprim'], ctset['ct']['fopmu'], ctset['ct']['fopsigma'],
g_fwhm, contrast, ctset['detyieldmu'], ctset['detyieldsigma']
]
if precolli:
res.extend(
[ctset['precollidetyieldmu'], ctset['precollidetyieldsigma']])
resultstable.append(res)
if pgexit and '3' in typ:
res[-2] = res[-2] * pgprod_ratio_3mev
elif pgexit and '1' in typ:
res[-2] = res[-2] * pgprod_ratio_1mev
#############################################################################################
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
print 'FOP distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i, ctset in enumerate(ctsets):
auger.plotfodist_CTONLY(ax1, ctset, i, emisfops, labels, axlabel)
if emisfops is not None and len(emisfops) == 1:
ax1.set_title(studyname + ', $CT_{FOP_{em}}$ = ' +
str(emisfops[0][0])[:5] + ', $RPCT_{FOP_{em}}$ = ' +
str(emisfops[0][1])[:5],
y=1.08)
#plt.legend()#shadow = True,frameon = True,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'lower right')
#plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
plt.savefig(studyname + '-' + typ +
'-FOP-dist.pdf') #, bbox_inches='tight')
plt.close('all')
def megaplot(ctsets,
studyname,
emisfops=None,
labels=["$10^9$", "$10^8$", "$10^7$", "$10^6$"],
axlabel='Primaries [nr]'):
# print 'FOP FOW Contrast DE averages over 10e9 ctset'
# removed 2nd deriv from plot
f, (ax1, ax2) = plot.subplots(nrows=2, ncols=1, sharex=False, sharey=False)
x = ctsets[0]['ct']['x']
y = ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm = 4 / 0.8
if 'ipnl' in ctsets[0]['name']:
mm = 8
falloff_pos, g_fwhm, contrast = auger.get_fop_fow_contrast(
x,
y,
plot='wut',
ax=ax1,
ax2=ax2,
smooth=0.2,
filename=ctsets[0]['ct']['path'],
contrast_divisor=ctsets[0]['nprim'] * len(ctsets[0]['ct']['files']) *
mm,
fitlines=False)
# some cosmetics
maxyrounded = int(math.ceil(max(y) / 100.0)) * 100
ticks = np.arange(0, maxyrounded, 100)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 100, 200)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 200, 400)
ax1.set_yticks(ticks)
minor_locator = AutoMinorLocator(2)
minor_locator1 = AutoMinorLocator(2)
ax1.xaxis.set_minor_locator(minor_locator)
ax2.xaxis.set_minor_locator(minor_locator)
ax2.yaxis.set_minor_locator(minor_locator1)
print "NPRIM", ctsets[0]['nprim'], "NJOBS", len(
ctsets[0]['ct']['files']), "MM", mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
f.savefig(studyname + '-' + typ + str(ctsets[0]['nprim']) + '-FOW.pdf',
bbox_inches='tight')
plot.close('all')
#############################################################################################
# results table
for ctset in ctsets:
res = [
typ, ctset['nprim'], ctset['ct']['fopmu'], ctset['ct']['fopsigma'],
g_fwhm, contrast, ctset['detyieldmu'], ctset['detyieldsigma'],
ctset['detcount']
]
if ctset['nprim'] == 10**9:
if 'iba' in typ:
#res.append(pgprod_1mev_iba*ctset['nprim'])
res[-1] = res[-1] / (pgprod_1mev_iba * ctset['nprim'])
if 'ipnl' in typ:
#res.append(pgprod_1mev_ipnl*ctset['nprim'])
res[-1] = res[-1] / (pgprod_1mev_ipnl * ctset['nprim'])
else:
res[-1] = ''
if precolli:
res.extend(
[ctset['precollidetyieldmu'], ctset['precollidetyieldsigma']])
resultstable.append(res)
def megaplot(ctsets,
studyname,
emisfops=None,
labels=["$10^9$", "$10^8$", "$10^7$", "$10^6$"],
axlabel='Primaries [nr]'):
f, (ax1, ax2) = plot.subplots(nrows=2, ncols=1, sharex=False, sharey=False)
x = ctsets[0]['ct']['x']
y = ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm = 4 / 0.8
if 'ipnl' in ctsets[0]['name']:
mm = 8
falloff_pos, g_fwhm, contrast = auger.get_fop_fow_contrast(
x,
y,
plot='wut',
ax=ax1,
ax2=ax2,
smooth=0.2,
filename=ctsets[0]['ct']['path'],
contrast_divisor=ctsets[0]['nprim'] * len(ctsets[0]['ct']['files']) *
mm,
fitlines=False)
# some cosmetics
maxyrounded = int(math.ceil(max(y) / 100.0)) * 100
ticks = np.arange(0, maxyrounded, 100)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 100, 200)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 200, 400)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 400, 800)
ax1.set_yticks(ticks)
minor_locator = AutoMinorLocator(2)
minor_locator1 = AutoMinorLocator(2)
ax1.xaxis.set_minor_locator(minor_locator)
ax2.xaxis.set_minor_locator(minor_locator)
ax2.yaxis.set_minor_locator(minor_locator1)
if PHYSDET_PROFILE_IBA != None and 'iba' in ctsets[0]['name']:
ax1.scatter(x,
PHYSDET_PROFILE_IBA,
color='lightgrey',
marker="x",
clip_on=False)
if PHYSDET_PROFILE_IPNL != None and 'ipnl' in ctsets[0]['name']:
ax1.scatter(x,
PHYSDET_PROFILE_IPNL,
color='lightgrey',
marker="x",
clip_on=False)
print "NPRIM", ctsets[0]['nprim'], "NJOBS", len(
ctsets[0]['ct']['files']), "MM", mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
f.savefig(studyname + '-' + typ + str(ctsets[0]['nprim']) + '-FOW.pdf',
bbox_inches='tight')
plot.close('all')
#############################################################################################
# results table
for ctset in ctsets:
res = [
typ, ctset['nprim'], ctset['ct']['fopmu'], ctset['ct']['fopsigma'],
g_fwhm, contrast, ctset['detyieldmu'], ctset['detyieldsigma'],
ctset['detcount'], ctset['detcount']
]
if ctset['nprim'] == 10**9:
if 'iba' in typ:
#res.append(pgprod_1mev_iba*ctset['nprim'])
res[-1] = res[-1] / (pgprod_1mev_iba * ctset['nprim'])
if 'ipnl' in typ:
#res.append(pgprod_1mev_ipnl*ctset['nprim'])
res[-1] = res[-1] / (pgprod_1mev_ipnl * ctset['nprim'])
else:
res[-1] = ''
if precolli:
res.extend(
[ctset['precollidetyieldmu'], ctset['precollidetyieldsigma']])
resultstable.append(res)
#############################################################################################
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
print 'FOP distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i, ctset in enumerate(ctsets):
auger.plotfodist_CTONLY(ax1, ctset, i, emisfops, labels, axlabel)
# plt.legend()#shadow = True,frameon = True,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'lower right')
# plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
plt.savefig(studyname + '-' + typ +
'-FOP-dist.pdf') #, bbox_inches='tight')
plt.close('all')
def megaplot(ctsets,
studyname,
emisfops=None,
labels=["$10^9$", "$10^8$", "$10^7$", "$10^6$"],
axlabel='Primaries [nr]'):
if len(ctsets) == 4:
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
auger.plot_all_ranges_CTONLY(ax1, ctsets[0])
auger.plot_all_ranges_CTONLY(ax2, ctsets[1])
auger.plot_all_ranges_CTONLY(ax3, ctsets[2])
#auger.plot_all_ranges_CTONLY(ax4,ctsets[3])
if not 'Primaries' in axlabel:
ax1.set_title(labels[0])
ax2.set_title(labels[1])
ax3.set_title(labels[2])
#ax4.set_title(labels[3])
f.subplots_adjust(hspace=.5)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
#ax4.set_ylabel('')
f.savefig(studyname + '-' + typ + '-FOP.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# print 'FOP shift distributions'
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax1 = plt.axes(projection='3d')
# ax1.view_init(30, -50)
# for i,ctset in enumerate(ctsets):
# auger.plotfodiffdist(ax1,ctset,i,emisfops,labels,axlabel)
# if not emisfops == None:
# fopshifts=[]
# for fopset in emisfops:
# fopshifts.append( fopset[-1]-fopset[0] )
# ax1.set_xlim3d(np.mean(fopshifts)-20,np.mean(fopshifts)+20)
# if emisfops is not None and len(emisfops) == 1:
# ax1.set_title(studyname+', $Shift_{em}$ = '+str(emisfops[0][-1]-emisfops[0][0]), y=1.08)
# #plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
# fig.savefig(studyname+'-'+typ+'-FOP-shift.pdf')#, bbox_inches='tight')
# plt.close('all')
#############################################################################################
# print 'FOP FOW Contrast DE averages over 10e9 ctset'
# removed 2nd deriv from plot
f, (ax1, ax2) = plot.subplots(nrows=2, ncols=1, sharex=False, sharey=False)
x = ctsets[0]['ct']['x'] # 0 should be one with nprim=1e9
y = ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm = 4 / 0.8
if 'ipnl' in ctsets[0]['name']:
mm = 8
falloff_pos, g_fwhm, contrast = auger.get_fop_fow_contrast(
x,
y,
plot='wut',
ax=ax1,
ax2=ax2,
smooth=0.2,
filename=ctsets[0]['ct']['path'],
contrast_divisor=ctsets[0]['nprim'] * len(ctsets[0]['ct']['files']) *
mm,
fitlines=False)
# some cosmetics
maxyrounded = int(math.ceil(max(y) / 100.0)) * 100
ticks = np.arange(0, maxyrounded, 100)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 100, 200)
if len(ticks) > 10:
ticks = np.arange(0, maxyrounded + 200, 400)
ax1.set_yticks(ticks)
minor_locator = AutoMinorLocator(2)
minor_locator1 = AutoMinorLocator(2)
ax1.xaxis.set_minor_locator(minor_locator)
ax2.xaxis.set_minor_locator(minor_locator)
ax2.yaxis.set_minor_locator(minor_locator1)
print "NPRIM", ctsets[0]['nprim'], "NJOBS", len(
ctsets[0]['ct']['files']), "MM", mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
f.savefig(studyname + '-' + typ + '-FOW.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# results table
print 'Computing pgprod ratios...'
pgprod_1mev = dump.count_ekine_in_phasespace(
"/home/brent/phd/art2_lyso_box/stage2_box15_docker/output/pgprod-worldframe.root",
1.)
pgprod_3mev = dump.count_ekine_in_phasespace(
"/home/brent/phd/art2_lyso_box/stage2_box15_docker/output/pgprod-worldframe.root",
3.)
print 'done.'
for ctset in ctsets:
res = [
typ, ctset['nprim'], ctset['ct']['fopmu'], ctset['ct']['fopsigma'],
g_fwhm, contrast, ctset['detyieldmu'], ctset['detyieldsigma'],
ctset['detcount']
]
if ctset['nprim'] == 10**9:
if pgexit and '3' in typ:
res[-1] = res[-1] / pgprod_3mev
elif pgexit and '1' in typ:
res[-1] = res[-1] / pgprod_1mev
else:
res[-1] = ''
if precolli:
res.extend(
[ctset['precollidetyieldmu'], ctset['precollidetyieldsigma']])
resultstable.append(res)
#############################################################################################
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
print 'FOP distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i, ctset in enumerate(ctsets):
auger.plotfodist_CTONLY(ax1, ctset, i, emisfops, labels, axlabel)
if emisfops is not None and len(emisfops) == 1:
ax1.set_title(studyname + ', $CT_{FOP_{em}}$ = ' +
str(emisfops[0][0])[:5] + ', $RPCT_{FOP_{em}}$ = ' +
str(emisfops[0][1])[:5],
y=1.08)
#plt.legend()#shadow = True,frameon = True,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'lower right')
#plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
plt.savefig(studyname + '-' + typ +
'-FOP-dist.pdf') #, bbox_inches='tight')
plt.close('all')
def megaplot(ctsets,studyname,emisfops=None,labels=["$10^9$","$10^8$","$10^7$","$10^6$"],axlabel='Primaries [nr]'):
if len(ctsets) == 4:
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
auger.plot_all_ranges_CTONLY(ax1,ctsets[0])
auger.plot_all_ranges_CTONLY(ax2,ctsets[1])
auger.plot_all_ranges_CTONLY(ax3,ctsets[2])
auger.plot_all_ranges_CTONLY(ax4,ctsets[3])
if not 'Primaries' in axlabel:
ax1.set_title(labels[0])
ax2.set_title(labels[1])
ax3.set_title(labels[2])
ax4.set_title(labels[3])
f.subplots_adjust(hspace=.5)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
ax4.set_ylabel('')
f.savefig(studyname+'-'+typ+'-FOP.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
# print 'FOP shift distributions'
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax1 = plt.axes(projection='3d')
# ax1.view_init(30, -50)
# for i,ctset in enumerate(ctsets):
# auger.plotfodiffdist(ax1,ctset,i,emisfops,labels,axlabel)
# if not emisfops == None:
# fopshifts=[]
# for fopset in emisfops:
# fopshifts.append( fopset[-1]-fopset[0] )
# ax1.set_xlim3d(np.mean(fopshifts)-20,np.mean(fopshifts)+20)
# if emisfops is not None and len(emisfops) == 1:
# ax1.set_title(studyname+', $Shift_{em}$ = '+str(emisfops[0][-1]-emisfops[0][0]), y=1.08)
# #plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
# fig.savefig(studyname+'-'+typ+'-FOP-shift.pdf')#, bbox_inches='tight')
# plt.close('all')
#############################################################################################
# print 'FOP FOW Contrast DE averages over 10e9 ctset'
f, (ax1,ax2,ax3) = plot.subplots(nrows=3, ncols=1, sharex=False, sharey=False)
x=ctsets[0]['ct']['x']
y=ctsets[0]['ct']['av']
if 'iba' in ctsets[0]['name']: # ctset['name'] == typ
mm=4/0.8
if 'ipnl' in ctsets[0]['name']:
mm=8
falloff_pos,g_fwhm,contrast = auger.get_fop_fow_contrast(x,y,plot='wut',ax=ax1,ax2=ax2,ax3=ax3,smooth=0.2,filename=ctsets[0]['ct']['path'],contrast_divisor=ctsets[0]['nprim']*len(ctsets[0]['ct']['files'])*mm)
print "NPRIM", ctsets[0]['nprim'],"NJOBS",len(ctsets[0]['ct']['files']),"MM",mm
#gebruiken deze falloff_pos niet. we doen contrast en fow over de average van 50 batches wegens smoothe curve. daardoor geen sigma
res=[typ,ctsets[0]['ct']['fopmu'],ctsets[0]['ct']['fopsigma'],g_fwhm,contrast,ctsets[0]['detyieldmu'],ctsets[0]['detyieldsigma']]
resultstable.append(res)
f.savefig(studyname+'-'+typ+'-FOW.pdf', bbox_inches='tight')
plot.close('all')
#############################################################################################
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
print 'FOP distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i,ctset in enumerate(ctsets):
auger.plotfodist_CTONLY(ax1,ctset,i,emisfops,labels,axlabel)
if emisfops is not None and len(emisfops) == 1:
ax1.set_title(studyname+', $CT_{FOP_{em}}$ = '+str(emisfops[0][0])[:5]+', $RPCT_{FOP_{em}}$ = '+str(emisfops[0][1])[:5], y=1.08)
#plt.legend()#shadow = True,frameon = True,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'lower right')
#plt.tight_layout(rect = [-0.1, 0.0, 1.0, 1.1])#L,B,R,T
plt.savefig(studyname+'-'+typ+'-FOP-dist.pdf')#, bbox_inches='tight')
plt.close('all')