def new(self, event=None, with_dialog=True, title="Math Inspector"):
if with_dialog:
if not messagebox.askokcancel("MathInspector",
"Are you sure you want to start a new file? Any unsaved data will be lost."):
return
plot.close()
self.app.title(title)
object_keys = list(self.app.objects.store.keys())
module_keys = list(self.app.modules.store.keys())
for i in object_keys:
del self.app.objects[i]
for j in module_keys:
del self.app.modules[j]
children = self.app.modules.get_children()
for k in children:
self.app.modules.delete(k)
self.app.modules.rootfolder = None
self.app.modules.stop_observer()
self.app.console.clear()
self.app.node.zoom = 1
self.app.console.prompt.history.clear()
self.app.horizontal_panel.sashpos(0,0)
self.app.vertical_panel.sashpos(0,0)
self.app.console.do_greet()
a = plot.plot2dhist( ax3, phot_scatter['X'], phot_scatter['Y'], xbins=xbins,ybins=ybins, log=True)
a = plot.plot2dhist( ax4, phot_other['X'], phot_other['Y'], xbins=xbins,ybins=ybins, log=True)
a = plot.plot2dhist( ax5, nonphot['X'], nonphot['Y'], xbins=xbins,ybins=ybins, log=True)
#,norm=matplotlib.colors.LogNorm(),vmin=1e0,vmax=1e2)
plot.plot1dhist(ax6,all_E,count=True)
plot.plot1dhist(ax7,phot_trans_E,count=True)
plot.plot1dhist(ax8,phot_scatter_E,count=True)
plot.plot1dhist(ax9,phot_other_E,count=True)
ax9.set_xlim(0,2)
plot.plot1dhist(ax10,nonphot_E,count=True)
d = {
'e-':'Electron',
'e+':'Positron',
'gamma':'Photon'
}
plot.plotbar(ax14,partname,relabel=d,log=True)
plot.plotbar(ax15,['Total Counts' for i in range(len(all['X']))]+['Prim Trans' for i in range(len(phot_trans['X']))]+['Prim Scat' for i in range(len(phot_scatter['X']))]+['Secon Phot' for i in range(len(phot_other['X']))],rotation=30)
ax15.text(0.05, 0.95, 'Trans/Total: '+str( len(phot_trans['X'])/float(len(all['X'])) ) , ha='left', va='center', transform=ax15.transAxes)
#cb = f.colorbar(a, ax=(ax1,ax2,ax3,ax4))#, aspect=50)
#cb.outline.set_visible(False)
#cb.set_label('Hits', rotation=270)
f.savefig(rootfile+'.pdf', bbox_inches='tight')
plot.close('all')
'fromclus-sorted/layers-beide/run.7coj',
'fromclus-sorted/layers-beide/run.ptEe',
typ,
manualshift=volume_offset),
firstcolor='steelblue',
secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax6,
auger.getctset(
979533764,
'fromclus-sorted/layers-beide/run.8aMd',
'fromclus-sorted/layers-beide/run.wM5z',
typ,
manualshift=volume_offset),
firstcolor='seagreen',
secondcolor=None)
ax6.set_title('MPS, Spot C,\nyield: ' + plot.sn(yield_av), fontsize=8)
ax4.xaxis.set_visible(False)
ax5.xaxis.set_visible(False)
ax6.xaxis.set_visible(False)
ax5.yaxis.set_visible(False)
ax6.yaxis.set_visible(False)
ax2.yaxis.set_visible(False)
ax3.yaxis.set_visible(False)
f.subplots_adjust(hspace=0.3)
f.savefig('layerprofiles.results.pdf', bbox_inches='tight')
plot.close('all')
def megaplot(ctsets,
studyname,
emisfops=None,
labels=["$10^9$", "$10^8$", "$10^7$", "$10^6$"],
axlabel='Primaries [nr]'):
# if emisfops is not None:
# for emisfop in emisfops:
# emisfop[0]+=15.863
# emisfop[1]+=15.863
# print 'FOP shift all overlaid'
if len(ctsets) == 4:
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
auger.plot_all_ranges(ax1, ctsets[0])
auger.plot_all_ranges(ax2, ctsets[1])
auger.plot_all_ranges(ax3, ctsets[2])
auger.plot_all_ranges(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 distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i, ctset in enumerate(ctsets):
auger.plotfodist(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]'):
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')
f, (ax1,ax2,ax3) = plot.subplots(nrows=3, ncols=1, sharex=False, sharey=False)
x=ctsets[0]['rpct']['x']
y=ctsets[0]['ct']['av']
auger.get_fow(x,y,plot='wut',ax=ax1,ax2=ax2,ax3=ax3,smooth=0.2,filename=ctsets[0]['ct']['path']) #since high statistics, no smoothing needed
# ax1.set_title('FOP = '+str(falloff_pos), fontsize=8)
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')
def megaplot(ctsets,studyname,emisfops=None,labels=["$10^9$","$10^8$","$10^7$","$10^6$"],axlabel='Primaries [nr]'):
if emisfops is not None:
for emisfop in emisfops:
emisfop[0]+=15.863
emisfop[1]+=15.863
print 'FOP shift all overlaid'
if len(ctsets) == 4:
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
auger.plot_all_ranges(ax1,ctsets[0])
auger.plot_all_ranges(ax2,ctsets[1])
auger.plot_all_ranges(ax3,ctsets[2])
auger.plot_all_ranges(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 distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
for i,ctset in enumerate(ctsets):
auger.plotfodist(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'
# 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 emisfops is not None:
#for emisfop in emisfops:
#emisfop[0]+=15.863
#emisfop[1]+=15.863
print 'FOP shift all overlaid'
#if len(ctsets) == 3:
#f, (ax1,ax2,ax3) = plot.subplots(nrows=1, ncols=3, sharex=False, sharey=False)
#auger.plot_all_ranges(ax1,ctsets[0])
#auger.plot_all_ranges(ax2,ctsets[1])
#auger.plot_all_ranges(ax3,ctsets[2])
#f.savefig(studyname+'-'+typ+'-FOP.pdf', bbox_inches='tight')
#plot.close('all')
#f.subplots_adjust(hspace=.5)
#ax2.set_ylabel('')
#ax3.set_ylabel('')
if len(ctsets) > 2:
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
ax1.set_xlabel('')
ax2.set_xlabel('')
ax2.set_ylabel('')
ax4.set_ylabel('')
elif len(ctsets) == 2:
f, (ax1,ax2) = plot.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
ax2.set_xlabel('')
f.subplots_adjust(hspace=0.7,wspace=0.5)
auger.plot_all_ranges(ax1,ctsets[0])
auger.plot_all_ranges(ax2,ctsets[1])
if len(ctsets) > 2:
try:
auger.plot_all_ranges(ax3,ctsets[2])
except:
ax3.axis('off')
try:
auger.plot_all_ranges(ax4,ctsets[3])
except:
ax4.axis('off')
#auger.plot_single_range(ax1,ctsets[0])
#auger.plot_single_range(ax2,ctsets[1])
#auger.plot_single_range(ax3,ctsets[2])
#auger.plot_single_range(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])
#if 'waterbox' in studyname:
#import matplotlib
#matplotlib.pyplot.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
#f.subplots_adjust(wspace=.3)
#x0 = []
#y1 = []
#y1std = []
#y2 = []
#y2std = []
#for ct in ctsets:
#x0.append(1./sqrt(ct['nprim']))
#y1.append(ct['ct']['fopsigma'])
#y2.append(ct['rpct']['fopsigma'])
#y1std.append( ct['ct']['fopsigma']/sqrt(2*(len(ct['ct']['falloff'])-1)) )
#y2std.append( ct['rpct']['fopsigma']/sqrt(2*(len(ct['rpct']['falloff'])-1)) )
#plot.plot_errorbands(ax4,x0,y1,y1std,color='steelblue')
#plot.plot_errorbands(ax4,x0,y2,y2std,color='indianred')
##ax4.set_xlim(x[0],x[-1])
##ax4.set_ylim(y[0],y[-1])
#ax4.set_title('CNR check', fontsize=8)
#ax4.set_xlabel('1/sqrt(N$_{prim}$)', fontsize=8)
##ax4.set_ylabel('sqrt(background)/signal', fontsize=8)
#ax4.set_ylabel('$\sigma_{FOP} [mm]$', fontsize=8)
#plot.texax(ax4)
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)
tmpprim=0
center=0
for ctset in ctsets:
if ctset['nprim'] > tmpprim:
tmpprim = ctset['nprim']
center= ctset['fodiffmu']
for i,ctset in enumerate(ctsets):
auger.plotfodiffdist(ax1,ctset,i,emisfops,labels,axlabel,center)
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)
pass
#fig.suptitle('FOP shifts', fontsize=10)
#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 distributions'
fig = plt.figure()
ax1 = plt.axes(projection='3d')
ax1.view_init(30, -50)
tmpprim=0
center=0
for ctset in ctsets:
if ctset['nprim'] > tmpprim:
tmpprim = ctset['nprim']
center= ( ctset['ct']['fopmu'] + ctset['rpct']['fopmu'] ) /2.
for i,ctset in enumerate(ctsets):
auger.plotfodist(ax1,ctset,i,emisfops,labels,axlabel,center)
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
#fig.suptitle('FOP distributions', fontsize=10)
plt.savefig(studyname+'-'+typ+'-FOP-dist.pdf')#, bbox_inches='tight')
plt.close('all')
def save(self, file=AUTOSAVE_PATH, quit_app=False):
if file is None or file not in (AUTOSAVE_PATH, self.app.modules.rootfolder):
file = filedialog.asksaveasfilename(defaultextension="")
if not file: return
files = self.app.modules.files("file")
folders = self.app.modules.files("folder")
is_rootfolder = file != AUTOSAVE_PATH and len(files + folders) > 0
if file != AUTOSAVE_PATH:
self.app.title(os.path.basename(file))
if is_rootfolder:
# need to clear prev files
# check in modified
self._copy_project_files(file)
objects = {}
modules = []
file_locals = self.app.modules.locals
for j in self.app.modules.store:
if not self.app.modules.has_tag(j, "local"):
modules.append({ "alias": j, "name": self.app.modules[j].__name__})
# modules = [{ "alias": j, "name": self.app.modules[j].__name__} for j in self.app.modules.store]
functions = {}
methods = {}
custom_fn = {}
for name in self.app.objects:
objects[name] = self.app.objects.store[name]
itemdata = {}
for i in self.app.node:
item = self.app.node[i]
itemdata[i] = {
"name": item.name,
"position": item.position(),
"connection": item.connection,
"coord": item.position(),
"args": { j: item.args[j] for j in item.args if j != "<value>" },
"kwargs": { k: item.kwargs[k] for k in item.kwargs },
"opts": item.opts.store,
}
plot_config = plot.config()
try:
help_geometry = help.browser.geometry()
except:
help_geometry = help.geometry
data = [{
"name": "app_title",
"value": self.app.title()
},{
"name": "zoom",
"value": self.app.node.zoom
},{
"name": "geometry",
"value": self.app.geometry() if self.app.geometry()[:3] != "1x1" else "1280x720"
},
{
"name": "horizontal_panel_sash",
"value": self.app.horizontal_panel.sashpos(0)
},{
"name": "vertical_panel_sash",
"value": self.app.vertical_panel.sashpos(0)
},
{
"name": "rootfolder",
"value": self.app.modules.rootfolder
},{
"name": "modules",
"value": modules
},{
"name": "file_locals",
"value": file_locals
},{
"name": "objects",
"value": objects
},{
"name": "itemdata",
"value": itemdata
},{
"name": "files",
"value": files
},{
"name": "folders",
"value": self.app.modules.files("folder")
},{
"name": "disabled_files",
"value": self.app.modules.files("disabled", True)
},{
"name": "plot_window_size",
"value": plot_config["size"] if plot_config else None
},{
"name": "plot_window_position",
"value": plot_config["window_pos"] if plot_config else None
},{
"name": "plot2d_options",
"value": { i: plot.OPTIONS_2D[i] for i in ["show_grid", "show_range"] }
},{
"name": "plot3d_options",
"value": { i: plot.OPTIONS_3D[i] for i in ["show_grid"] }
},{
"name": "plot_window_position",
"value": os.environ["SDL_VIDEO_WINDOW_POS"] if "SDL_VIDEO_WINDOW_POS" in os.environ else None
},{
"name": "help_geometry",
"value": help_geometry
},{
"name": "objects_order",
"value": self.app.objects.order()
},{
"name": "objects_expanded",
"value": self.app.objects.expanded()
},{
"name": "console_history",
"value": self.app.console.prompt.history.cmds
},{
"name": "side_select",
"value": self.app.side_view.select()
},{
"name": "module_order",
"value": self.app.modules.order()
},{
"name": "module_expanded",
"value": self.app.modules.expanded()
}]
name, ext = name_ext(file)
if is_rootfolder:
filepath = os.path.join(file, os.path.basename(file) + ".math")
else:
filepath = os.path.join(os.path.dirname(file), name + ".math")
with open(filepath, "wb") as output:
cloudpickle.dump(data, output)
if quit_app:
plot.close()
self.app.quit()
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')
f, (ax1, ax2, ax3) = plot.subplots(nrows=3,
ncols=1,
sharex=False,
sharey=False)
x = ctsets[0]['rpct']['x']
y = ctsets[0]['ct']['av']
auger.get_fow(x,
y,
plot='wut',
ax=ax1,
ax2=ax2,
ax3=ax3,
smooth=0.2,
filename=ctsets[0]['ct']
['path']) #since high statistics, no smoothing needed
# ax1.set_title('FOP = '+str(falloff_pos), fontsize=8)
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')
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')
