def plot_factor_in_subtype(cad_factors, factor_id, factor_annotation,
clinical_annotation, clinical_var, out, stats_out):
factor_da = xr.open_dataset(cad_factors)['factors']
with open(factor_annotation) as f:
factor_index = only(
[i for i, v in yaml.load(f).items() if v['id'] == factor_id])
factor = factor_da.sel(factor=factor_index).load()
factor.name = factor_id
clin = xr.open_dataset(clinical_annotation)[clinical_var].load()
factor, clin = xr.align(factor[factor.notnull()], clin[clin.notnull()])
with plot.subplots(figsize=(3.5, 3.5)) as (fig, ax):
plot.boxplot(
clin,
factor,
title="",
xlabel=clin_display_names[clinical_var],
ylabel=f"Factor {factor_index+1}",
ax=ax,
)
fig.savefig(out, format='svg')
factor_by_clin = split_by(factor.values, clin.values)
h, p = scipy.stats.kruskal(*factor_by_clin.values())
with open(stats_out, 'w') as f:
f.write(f"h: {h:.6e}\n")
f.write(f"p: {p:.6e}\n")
def plot_fa_variance_explained(mri_features, out):
mri_data_set = xr.open_dataset(mri_features).load()
mri = read_mri(mri_data_set)
mri = adjust_scale(mri)
pca = sklearn.decomposition.PCA()
mri_a = (mri / mri.std('case')).values
pca.fit(mri_a)
total_var = np.sum(pca.explained_variance_)
with plot.subplots(3, 1, sharex=True, figsize=(3, 3)) as (fig, axs):
axs[0].plot(
range(1,
len(pca.explained_variance_) + 1),
100 * np.cumsum(pca.explained_variance_) / total_var,
clip_on=False,
zorder=100,
)
axs[0].set_ylim(top=100.0)
axs[0].set_ylabel("$\Sigma$EV (%)")
axs[1].plot(
range(1,
len(pca.explained_variance_) + 1),
100 * pca.explained_variance_ / total_var,
clip_on=False,
zorder=100,
)
axs[1].set_ylim(bottom=0.0)
axs[1].set_ylabel("EV (%)")
axs[2].plot(
range(2,
len(pca.explained_variance_) + 1),
100 *
abs(pca.explained_variance_[1:] - pca.explained_variance_[:-1]) /
total_var,
clip_on=False,
zorder=100,
)
axs[2].set_ylim(bottom=0.0)
axs[2].set_ylabel("$\Delta$EV (%)")
for ax in axs:
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_position(('outward', 10))
ax.axvline(7, ls='-', lw=0.5, color='grey')
axs[2].set_xlabel("Principal Component")
axs[2].set_xlim(1, len(pca.explained_variance_))
axs[2].set_xticks([1, 6, 11, 16, 21])
axs[2].set_xticks([7], minor=True)
axs[0].set_xticklabels('', minor=True)
axs[1].set_xticklabels('', minor=True)
axs[2].set_xticklabels(['7'], {'fontsize': 8}, minor=True)
fig.savefig(out, format="svg")
def plot_mri_cad_factor_correlation(mri_features, out):
mri_ds = xr.open_dataset(mri_features)
del mri_ds['Comment']
del mri_ds['MultiFocal']
mri = mri_ds.to_array('cad_feature', 'mri_cad_features')
mri_ds.close()
mri = mri.isel(case=np.where(mri.isnull().sum('cad_feature') == 0)[0])
mri = mri.transpose('case', 'cad_feature')
assert all(f.item() in feature_order for f in mri['cad_feature'].values)
mri = mri.reindex(cad_feature=feature_order)
cor = xr.DataArray(
data=np.corrcoef(mri.values, rowvar=False),
dims=('cad_feature', 'cad_feature'),
coords={'cad_feature': mri.coords['cad_feature']},
)
cor.name = 'correlation'
with plot.subplots(figsize=(3.5, 3.5)) as (fig, ax):
c = plot.heatmap(
cor,
mask=np.tri(cor.shape[0]) < 0.5,
aspect='equal',
xlabel="MR Feature",
ylabel="MR Feature",
xticklabels=[''] * cor.shape[0],
yticklabels=[
feature_display_names[f]
for f in cor.coords['cad_feature'].values
],
cbar=False,
ax=ax,
)
cax = fig.add_axes([.7, .5, .05, .25])
cbar = fig.colorbar(c, cax=cax)
cbar.set_ticks([-1.0, -0.5, 0.0, 0.5, 1.0])
cbar.ax.set_yticklabels(cbar.ax.get_yticklabels(), fontsize=9)
cbar.ax.set_title("Pearson Correlation", fontsize=10)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
fig.savefig(out, format="svg")
def plot_mri_cad_factors(cad_factors, out):
fa_dataset = xr.open_dataset(cad_factors).load()
assert all(f in feature_order for f in fa_dataset['cad_feature'].values)
fa_dataset = fa_dataset.reindex(cad_feature=feature_order)
with plot.subplots(figsize=(3.5, 3.5)) as (fig, ax):
plot.heatmap(
fa_dataset['loadings'].T,
aspect='equal',
xlabel="MRI Factor",
ylabel="MRI Feature",
yticklabels=[
feature_display_names[f]
for f in fa_dataset.coords['cad_feature'].values
],
zlabel="Loading",
ax=ax,
)
fig.savefig(out, format="svg")
def plot_factor_in_subtype(mri_features, feature_id, clinical_annotation,
clinical_var, out, stats_out):
mri_ds = xr.open_dataset(mri_features)
feature = mri_ds[feature_id]
clin = xr.open_dataset(clinical_annotation)[clinical_var].load()
feature, clin = xr.align(feature[feature.notnull()], clin[clin.notnull()])
with plot.subplots(figsize=(3.5, 3.5)) as (fig, ax):
plot.boxplot(
clin,
feature,
title="",
xlabel=clin_display_names[clinical_var],
ylabel=feature_display_names[feature_id],
ax=ax,
)
fig.savefig(out, format='svg')
feature_by_clin = split_by(feature.values, clin.values)
h, p = scipy.stats.kruskal(*feature_by_clin.values())
with open(stats_out, 'w') as f:
f.write(f"h: {h:.6e}\n")
f.write(f"p: {p:.6e}\n")
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')
#res = [3.13,2.18]
#yerr = [1.16,0.89]
labels = ["Iso-energy","Iso-depth"]
ind = np.arange(len(res))
margin = 0.2
width = (1.-2.*margin)
xdata = ind-width/2.
ax.bar(xdata, res, width, yerr=yerr,color='k',ecolor='k',lw=0,fill=False)#,capsize=5)#, markeredgewidth=1)
#ax.bar(xdata, res, width, yerr=yerr, xerr=0.03,color='k',ecolor='k',lw=0,fill=False)
ax.errorbar(xdata+width/2.,res,yerr=yerr,color='black',fmt='.',lw=1,capsize=5, markeredgewidth=1)
ax.xaxis.set_major_locator(mpl.ticker.FixedLocator(range(len(res))))
#ax.set_xticklabels(['']+labels, fontsize=8, rotation=15, ha='center')
ax.set_xticklabels(labels, fontsize=8, rotation=15, ha='center')
# MPS, KES, (ABC)
f, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plot.subplots(nrows=2, ncols=3, sharex=True, sharey=True)
#Dose & 2.47 & 1.60 & 6.40 & 4.07 & 6.40 & 6.40
ax1.axhline(2.47, xmin= 0, xmax=0.5 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax1.axhline(1.60, xmin= 0.5, xmax=1 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax2.axhline(6.40, xmin= 0, xmax=0.5 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax2.axhline(4.07, xmin= 0.5, xmax=1 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax3.axhline(6.40, xmin= 0, xmax=0.5 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax3.axhline(6.40, xmin= 0.5, xmax=1 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax4.axhline(2.47, xmin= 0, xmax=0.5 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax4.axhline(1.60, xmin= 0.5, xmax=1 , color='indianred', ls='-',lw=1)#, alpha=0.5)
ax5.axhline(6.40, xmin= 0, xmax=0.5 , color='indianred', ls='-',lw=1)#, alpha=0.5)
for i, spot in enumerate(data):
#if i == 61:
#dose3dCT.imdata = spot*mask
#dose3dCT.imdata = dose3dCT.imdata.astype(np.float32,copy=False)
#dose3dCT.saveas('TEST')
rmse.append(np.sqrt(np.mean(np.square(spot * mask))))
#rmse.append(np.sqrt(np.mean(np.square(spot))))
nprim.append(newspots[i][4])
E.append(newspots[i][1])
Eh, Ehbin = np.histogram(E, bins=30)
nprimh, nprimhbin = plot.getloghist(nprim, 30)
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
ax1.set_title('Dose Diff (nprim)')
ax1.set_xlabel('Number of Primaries')
ax1.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax1)
ax2.set_title('Dose Diff (E)')
ax2.set_xlabel('Energy [MeV]')
ax2.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax2)
ax3.set_title('Dose Diff (nprim)')
ax3.set_xlabel('Number of Primaries')
ax3.set_ylabel('Dose Diff [RMSE]')
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 fieldseries(df, fname):
assert (isinstance(df, pd.DataFrame))
sns.set_style(style="whitegrid")
sns.set(font_scale=1.1)
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
df = df.query(
"Setname=='5cm' or Setname=='10cm' or Setname=='15cm' or Setname=='20cm'"
)
# df.to_csv(fname+".tussendump.csv")
sns.lineplot(x="Distance to isoc [mm]",
y="Dose [cGy]",
hue="Setname",
data=df.query("Axis=='X'"),
style="Generator",
ax=ax1,
lw=1)
sns.lineplot(x="Distance to isoc [mm]",
y="Dose [cGy]",
hue="Setname",
data=df.query("Axis=='Y'"),
style="Generator",
ax=ax2,
lw=1)
sns.lineplot(x="Distance to isoc [mm]",
y="Dose [cGy]",
hue="Setname",
data=df.query("Axis=='ratY'"),
ax=ax3,
lw=1)
# sns.lineplot(x="Distance to isoc [mm]", y="Dose [cGy]", hue="Setname", data=df.query("Axis=='relY'"), style="Generator", ax=ax3, lw=1)
sns.lineplot(x="Distance to isoc [mm]",
y="Dose [cGy]",
hue="Setname",
data=df.query("Axis=='Z'"),
style="Generator",
ax=ax4,
lw=1)
ax1.set_xlim(-170, 170)
# ax2.set_xlim(-170,170)
ax3.set_xlim(-170, 170)
ax4.set_xlim(-170, 170)
# ax3.set_ylim(-.25,0.5)
ax3.set_ylim(0, 2)
ax1.set_title("X (BLD:Jaw)")
ax2.set_title("Y")
# ax3.set_title("reldiff X") #relY is relative y over X axis
ax3.set_title("ratio X") #relY is relative y over X axis
ax4.set_title("Z (BLD:MLC)")
ax3.set_ylabel("Relative Dose")
ax3.set_ylabel("Dose Ratio")
ax1.legend(title="Fieldsize",
loc='upper right',
bbox_to_anchor=(1.4, 1.),
frameon=False)
ax2.legend_.remove() #.legend().set_title('Fieldsize')
ax3.legend(title="Fieldsize",
loc='upper right',
bbox_to_anchor=(1.4, 1.),
frameon=False)
ax4.legend_.remove() #.legend().set_title('Fieldsize')
f.subplots_adjust(hspace=.6, wspace=.5)
f.savefig(fname + '.pdf', bbox_inches='tight')
f.savefig(fname + '.png', bbox_inches='tight', dpi=300)
all = dump.get2D(dosephs, ['X', 'Y'])
doseim_ = image.image(doseim, type='yield')
doseuncim_ = image.image(doseuncim, type='relunc')
doseuncim_.imdata = doseuncim_.imdata.squeeze() * 100.
dosetleim_ = image.image(dosetleim, type='yield')
dosetleuncim_ = image.image(dosetleuncim, type='relunc')
dosetleuncim_.imdata = dosetleuncim_.imdata.squeeze() * 100.
unc_axis = np.linspace(0, 50, 50)
f, ((ax1, ax2, ax3), (ax4, ax5,
ax6)) = plot.subplots(nrows=2,
ncols=3,
sharex=False,
sharey=False) #,figsize=(28,10))
#f.subplots_adjust(hspace=.5)
#f.subplots_adjust(wspace=.5)
f.suptitle('Runtime: ' + runtime + 's', fontsize=10)
ax1.set_title("PhS, X,Y")
ax2.set_title("Dose")
ax3.set_title("Dose TLE")
ax4.set_title("PhS, rel. unc")
ax5.set_title("Dose, rel. unc")
ax6.set_title("Dose TLE, rel. unc")
# BIN EDGES!
xbins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
ybins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
'Ekine',
ParticleName='gamma',
Primary=True,
Transmission=False)
phot_other_E = dump.get1D(rootfile,
'Ekine',
ParticleName='gamma',
Primary=False)
nonphot_E = dump.get1D(rootfile, 'Ekine', ParticleName='not_gamma')
partname = dump.get1D(rootfile, 'ParticleName')
f, ((ax1, ax2, ax3, ax4, ax5), (ax6, ax7, ax8, ax9, ax10),
(ax11, ax12, ax13,
ax14, ax15)) = plot.subplots(nrows=3,
ncols=5,
sharex=False,
sharey=False) #,figsize=(28,10))
f.subplots_adjust(hspace=.5)
f.subplots_adjust(wspace=.5)
ax1.set_title("Total Counts")
ax2.set_title("Prim Trans")
ax3.set_title("Prim Scat")
ax4.set_title("Secon Phot")
ax5.set_title("Nonphot")
# BIN EDGES!
xbins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
ybins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
#electronprod = glob.glob(indir+"/**/epid-entry.root")[0]
all = dump.get2D(dosephs,['X','Y'])
doseim_ = image.image(doseim,type='yield')
doseuncim_ = image.image(doseuncim,type='relunc')
doseuncim_.imdata = doseuncim_.imdata.squeeze()*100.
dosetleim_ = image.image(dosetleim,type='yield')
dosetleuncim_ = image.image(dosetleuncim,type='relunc')
dosetleuncim_.imdata = dosetleuncim_.imdata.squeeze()*100.
unc_axis = np.linspace(0,50,50)
f, ((ax1 ,ax2, ax3 ),(ax4, ax5, ax6))= plot.subplots(nrows=2, ncols=3, sharex=False, sharey=False)#,figsize=(28,10))
#f.subplots_adjust(hspace=.5)
#f.subplots_adjust(wspace=.5)
f.suptitle('Runtime: '+runtime+'s', fontsize=10)
ax1.set_title("PhS, X,Y")
ax2.set_title("Dose")
ax3.set_title("Dose TLE")
ax4.set_title("PhS, rel. unc")
ax5.set_title("Dose, rel. unc")
ax6.set_title("Dose TLE, rel. unc")
# BIN EDGES!
xbins = np.linspace(-410/2.,410/2.,256+1)
ybins = np.linspace(-410/2.,410/2.,256+1)
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')
#!/usr/bin/env python
import plot,numpy as np
### plot loss of PGs
f, ax = plot.subplots(nrows=1, ncols=1)
# numbers = [4.00E+10,2.00E+08,4.60E+07,1.80E+06,3.40E+04,1.60E+04,4E+03]
protons = [2.00E+08,0,0,0,0,0]
pgs = [0,4.60E+07,1.80E+06,3.40E+04,1.60E+04,8E+03]
pgs = [0,4.60E+07,1.80E+06,3.40E+04,3.40E+04*.66,3.40E+04*.66*.66]
ax.set_ylim([5e3,5e8])
ind = np.arange(len(pgs))
margin = 0.2
width = (1.-2.*margin)
xdata = ind- width/2.
labels = ["Primaries","PGs Exiting patient","Solid angle detector","Post-collimator","Detector Efficiency ?","Reconstruction Eff. ?"]
p1 = ax.bar(xdata, protons, width, color='k',lw=0)
p2 = ax.bar(xdata, pgs, width, color='r',bottom=protons,lw=0)
#ax.set_xticklabels(labels, fontsize=8, rotation=15, ha='left')
ax.set_xticklabels(['']+labels, fontsize=8, rotation=15, ha='center')
ax.legend((p1[0], p2[0]), ('Protons', 'Prompt Gammas'),frameon=False)
ax.set_ylabel('Counts')
#ax.set_title('Progressive loss of Prompt Gammas')
ax.set_yscale('log')
ax.set_ylim([5e3,5e8])
f.savefig('lossofpgs.pdf', bbox_inches='tight')
f.savefig('lossofpgs.png', bbox_inches='tight',dpi=300)
plot.close('all')
) + glob.glob(indir + "/**/epiddose-tle-Dose.mhd")
imuncs = glob.glob(
indir + "/**/epiddose-trans-tle-Dose-Uncertainty.mhd") + glob.glob(
indir + "/**/epiddose-nontrans-tle-Dose-Uncertainty.mhd"
) + glob.glob(indir + "/**/epiddose-tle-Dose-Uncertainty.mhd")
print imyields
labels = ['nontransmission', 'transmission', 'total dose'
] #(non) transmission swapped, because IT IS KILLED BY THE ACTOR!!!!
fracties_totaal = []
fracties_isocentrum = []
f, axes = plot.subplots(nrows=len(imyields) + 1,
ncols=4,
sharex=False,
sharey=False) #,figsize=(28,10))
for axrow, yim, uim, label in zip(axes, imyields, imuncs, labels):
print yim, uim, label
yim_ = image.image(yim, type='yield')
uim_ = image.image(
uim, type='relunc'
) #the doseactor outputs relative uncertainties as per Chetty, see GateImageWithStatistic
#uim_.applymask(mask90pc)
uim_.imdata = uim_.imdata.squeeze() * 100. #fractions to percentages
fracties_isocentrum.append(yim_.getcenter().mean())
fracties_totaal.append(yim_.imdata.sum())
axrow[0].imshow(yim_.imdata.squeeze(), extent=[0, 41, 0, 41], cmap='gray')
axrow[0].set_title(label + '\n$\sum$ Dose: ' +
plot.sn(fracties_totaal[-1]))
imyields = glob.glob(indir+"/**/epiddose-trans-Dose.mhd")+glob.glob(indir+"/**/epiddose-nontrans-Dose.mhd")+glob.glob(indir+"/**/epiddose-Dose.mhd")
imuncs = glob.glob(indir+"/**/epiddose-trans-Dose-Uncertainty.mhd")+glob.glob(indir+"/**/epiddose-nontrans-Dose-Uncertainty.mhd")+glob.glob(indir+"/**/epiddose-Dose-Uncertainty.mhd")
if args.tle:
outname = 'epid_tle_plot.pdf'
imyields = glob.glob(indir+"/**/epiddose-trans-tle-Dose.mhd")+glob.glob(indir+"/**/epiddose-nontrans-tle-Dose.mhd")+glob.glob(indir+"/**/epiddose-tle-Dose.mhd")
imuncs = glob.glob(indir+"/**/epiddose-trans-tle-Dose-Uncertainty.mhd")+glob.glob(indir+"/**/epiddose-nontrans-tle-Dose-Uncertainty.mhd")+glob.glob(indir+"/**/epiddose-tle-Dose-Uncertainty.mhd")
print imyields
labels = ['nontransmission','transmission','total dose'] #(non) transmission swapped, because IT IS KILLED BY THE ACTOR!!!!
fracties_totaal = []
fracties_isocentrum = []
f, axes = plot.subplots(nrows=len(imyields)+1, ncols=4, sharex=False, sharey=False)#,figsize=(28,10))
for axrow,yim,uim,label in zip(axes,imyields,imuncs,labels):
print yim,uim,label
yim_ = image.image(yim,type='yield')
uim_ = image.image(uim,type='relunc') #the doseactor outputs relative uncertainties as per Chetty, see GateImageWithStatistic
#uim_.applymask(mask90pc)
uim_.imdata = uim_.imdata.squeeze()*100. #fractions to percentages
fracties_isocentrum.append( yim_.getcenter().mean() )
fracties_totaal.append( yim_.imdata.sum() )
axrow[0].imshow( yim_.imdata.squeeze() , extent = [0,41,0,41], cmap='gray')
axrow[0].set_title(label + '\n$\sum$ Dose: '+ plot.sn(fracties_totaal[-1]))
axrow[1].set_title(label + ' Profile')
axrow[1].plot(*yim_.getprofile('y'), color = 'steelblue' , label='x')
axrow[1].plot(*yim_.getprofile('x'), color = 'indianred' , label='y')
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')
from collections import Counter
clrs=plot.colors
#plot depth dose of some particles.
files = {
'Electron (20 MeV)':'output/dose-e--20-Dose.root',
'Photon (6 MeV)':'output/dose-gamma-6-Dose.root',
'Helium (700 MeV)':'output/dose-alpha-700-Dose.root',
'Proton (177 MeV)':'output/dose-proton-177-Dose.root',
'Pion^{+} (84 MeV)':'output/dose-pi+-84-Dose.root',
'Carbon (4 Gev)':'output/dose-ion-4000-Dose.root'
}
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
for index,(key,val) in enumerate(files.iteritems()):
data = dump.thist2np(val)['histo']
maxi=max(data)
plot.plot([x/maxi for x in data], label=key)
ax1.set_ylabel('Dose (normalized)')
ax1.set_xlabel('Depth (mm)')
ax1.set_title('Dose as function of depth for some particles')
plot.texax(ax1)
ax1.legend(loc='upper right', bbox_to_anchor=(1.3, 1.),frameon=False)
f.savefig('depthdose.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 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]'):
# 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)
#!/usr/bin/env python
import argparse, numpy as np, matplotlib, dump, plot
parser = argparse.ArgumentParser(description='Plot 2D hist from ROOT TTree.')
parser.add_argument('files', nargs='*')
args = parser.parse_args()
rootfiles = args.files
for rootfile in rootfiles:
all = dump.get2D(rootfile, ['X', 'Y'])
f, (ax1, ax2) = plot.subplots(nrows=1, ncols=2, sharex=False,
sharey=False) #,figsize=(28,10))
f.subplots_adjust(hspace=.5)
f.subplots_adjust(wspace=.5)
ax1.set_title("X,Y")
ax2.set_title("unc histo")
# BIN EDGES!
xbins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
ybins = np.linspace(-410 / 2., 410 / 2., 256 + 1)
counts = plot.plot2dhist(ax1,
all['X'],
all['Y'],
xbins=xbins,
ybins=ybins,
log=True)
return nr[:3]+'\%'
def plotprof(ax,xax,emit,dete,name, **kwargs):
if name == 'CT':
color='steelblue'
elif name == 'RPCT':
color='indianred'
else:
color='black'
ax.step(xax,emit, color=color,lw=1., alpha=1, label=name+', yield: '+yld(emit), where='mid')
#ax.step(xax,dete, color=color,lw=1., alpha=0.5, label=name+' PSF', where='mid')
return ax
###########################################################################################################
f, (ax1,ax2) = plot.subplots(nrows=1, ncols=2, sharex=True, sharey=False)
plotprof(ax1,pgsrc_ct_x,pgelay.imdata,detprof_pgelay,'CT')
plotprof(ax1,pgsrc_ct_x,rppgelay.imdata,detprof_rppgelay,'RPCT')
ax1.set_title('Iso-energy layer, PG shift: '+str(rppgelay_fo-pgelay_fo)[:4]+' mm', fontsize=10)
ax1.legend(frameon = False,loc='upper left')
ax1.set_xlim(-80,60)
ax1.set_ylim(0,0.003)
ax1.set_ylabel('Cumulative PG emission per proton')
plot.texax(ax1)
plotprof(ax2,pgsrc_ct_x,pggeolay.imdata,detprof_pggeolay,'CT')
plotprof(ax2,pgsrc_ct_x,rppggeolay.imdata,detprof_rppggeolay,'RPCT')
ax2.set_title('Iso-depth layer, PG shift: '+str(rppggeolay_fo-pggeolay_fo)[:4]+' mm', fontsize=10)
ax2.legend(frameon = False,loc='upper left')
#ax2.set_xlim(-80,70)
#!/usr/bin/env python
import plot, numpy as np, auger, image, rtplan
from scipy.ndimage.filters import gaussian_filter
###########################################################################################################
#volume_offset=-141.59+7.96#spot sources
volume_offset = 150 - 141.59 + 7.96 #spot sources
###########################################################################################################
# BOTTOM ROW IBA
f, ((ax4, ax5, ax6), (ax1, ax2, ax3)) = plot.subplots(nrows=2,
ncols=3,
sharex=True,
sharey=False)
typ = 'iba-auger-notof-3.root'
yield_av = auger.plot_all_ranges_2(ax1,
auger.getctset(
840964615,
'fromclus-sorted/layers-beide/run.e8ai',
'fromclus-sorted/layers-beide/run.96ra',
typ,
manualshift=volume_offset),
firstcolor='steelblue',
secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax1,
auger.getctset(
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')
#typ='ipnl-auger-notof-1.root'
#typ='iba-auger-tof-3.root'
typ='iba-auger-notof-3.root'
#nieuwe sums. distal layer
#22 , 4 ,
#4 : 65802598.6018 , 7 : 88235302.6706 , 10 : 67298112.2064 , 43 : 64073566.2641 ,
ctset_4 = auger.getctset(65802598,'run.4Zh8','run.8Yoh',typ)
ctset_7 = auger.getctset(88235302,'run.MjnN','run.a5rv',typ)
ctset_10 = auger.getctset(67298112,'run.XXMy','run.btzm',typ)#XXMy + 3x HyIv
ctset_43 = auger.getctset(64073566,'run.iohj','run.hDu1',typ)
ctset_sum_0 = auger.sumctset('sum',ctset_4,ctset_7,ctset_10,ctset_43)
f, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plot.subplots(nrows=2, ncols=3, sharex=False, sharey=False)
auger.plotrange(ax1,ctset_4)
auger.plotrange(ax2,ctset_7)
auger.plotrange(ax3,ctset_10)
auger.plotrange(ax4,ctset_43)
ax5.axis('off')
auger.plotrange(ax6,ctset_sum_0)
f.savefig(typ+'-spotsums-0.pdf', bbox_inches='tight')
plot.close('all')
#nieuwe sums. distal-1 layer
#18 , 5 ,
#6 : 58325030.5789 , 9 : 65802598.6018 , 28 : 54600663.4 , 32 : 66406212.2432 , 39 : 42230305.0377 ,
#!/usr/bin/env python
import seaborn as sns,pandas as pd,plot
df = pd.read_csv("results.tsv",sep='\t',nrows=6,skiprows=[1])
print df
sns.set_style(style="whitegrid")
sns.set(font_scale=1.1)
f, ax1 = plot.subplots(nrows=1, ncols=1)
sns.lineplot(x="nprim", y="fopsigma", hue="typ", markers=True, data=df, ax=ax1)
ax1.set_title("Fall-off Retrieval position (FRP)")
ax1.set_ylabel("FRP (mm)")
ax1.set_xlabel("Number of primary protons")
ax1.semilogx()
new_labels = ['Camera', 'MPS', 'KES']
for t, l in zip(ax1.get_legend().texts, new_labels): t.set_text(l)
f.savefig('amv_frp_plot.pdf', bbox_inches='tight')
MSW=[]
for spot in rtplan.spots:
MSW.append(spot[-1])
#spot_weight_mean = np.median(MSW)
spot_weight_mean = np.mean(MSW)
nrfields=len(rtplan.fields)
fontsize=6
mpl.rcParams['xtick.labelsize'] = fontsize
mpl.rcParams['ytick.labelsize'] = fontsize
mpl.rcParams['axes.titlesize'] = fontsize
mpl.rcParams['axes.labelsize'] = fontsize
plotke = plot.subplots(nrows=2, ncols=nrfields, sharex='col', sharey='row')
plotke[0].get_axes()[0].annotate('Total nr. of primaries: '+plot.sn(rtplan.TotalMetersetWeight)+'. Mean spot weight: '+plot.sn(spot_weight_mean), (0.5, 0.975), xycoords='figure fraction', ha='center', fontsize=fontsize)
if nrfields == 1:
spotaxes = [plotke[-1][0]] #0 is eerste kolom
layaxes = [plotke[-1][1]] #0 is eerste kolom
spotloop= [spotdata[0]]
layloop = [layerdata[0]]
else:
spotaxes = plotke[-1][0] #0 is eerste kolom
layaxes = plotke[-1][1] #0 is eerste kolom
spotloop = spotdata
layloop = layerdata
first = True
layer_min_y = 1e10
#!/usr/bin/env python
import argparse,numpy as np,matplotlib,dump,plot
parser = argparse.ArgumentParser(description='Plot 2D hist from ROOT TTree.')
parser.add_argument('files', nargs='*')
args = parser.parse_args()
rootfiles = args.files
for rootfile in rootfiles:
all = dump.scaletreefast(rootfile,['X','Y'])
f, ((ax1 ,ax2), (ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)#,figsize=(28,10))
f.subplots_adjust(hspace=.5,wspace=.5)
ax1.set_title("Positions")
ax2.set_title("Cutout")
panelsize = 410. #mm
numpix = 256
pixsize = panelsize/numpix #mm
xbins = np.linspace(-panelsize/2.,panelsize/2.,numpix+1)
ybins = xbins
xbins2 = plot.chopcentral(xbins,5)
ybins2 = xbins2
xbins3 = np.linspace(-5,5,10+1)
ybins3 = xbins3
xbins4 = np.linspace(-10,0,10+1)
ybins4 = xbins4
#!/usr/bin/env python
import image,numpy as np,auger,plot,dump
from scipy.ndimage.filters import gaussian_filter
np.random.seed(65983146)
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
#spot 29 CT
filen = 'run.pMSA/output.2391740/ipnl-auger-tof-1.root'
auger.testfit(filen,1e9,ax1)
filen = 'run.EPjL/output.2391621/ipnl-auger-tof-1.root'
auger.testfit(filen,1e7,ax2)
filen = 'run.pMSA/output.2391740/iba-auger-notof-3.root'
auger.testfit(filen,1e9,ax3)
filen = 'run.EPjL/output.2391621/iba-auger-notof-3.root'
auger.testfit(filen,1e7,ax4)
#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('')
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]'):
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')
for rootfile in rootfiles:
all = dump.get2D(rootfile,['X','Y'])
phot_trans = dump.get2D(rootfile,['X','Y'],ParticleName='gamma',Primary=True,Transmission=True) #transmission implies primary and gamma but anyway
phot_scatter = dump.get2D(rootfile,['X','Y'],ParticleName='gamma',Primary=True,Transmission=False)
phot_other = dump.get2D(rootfile,['X','Y'],ParticleName='gamma',Primary=False)
nonphot = dump.get2D(rootfile,['X','Y'],ParticleName='not_gamma')
all_E = dump.get1D(rootfile,'Ekine')
phot_trans_E = dump.get1D(rootfile,'Ekine',ParticleName='gamma',Primary=True,Transmission=True)
phot_scatter_E = dump.get1D(rootfile,'Ekine',ParticleName='gamma',Primary=True,Transmission=False)
phot_other_E = dump.get1D(rootfile,'Ekine',ParticleName='gamma',Primary=False)
nonphot_E = dump.get1D(rootfile,'Ekine',ParticleName='not_gamma')
partname = dump.get1D(rootfile,'ParticleName')
f, ((ax1 ,ax2 ,ax3 ,ax4, ax5),(ax6, ax7 ,ax8, ax9 ,ax10),(ax11 ,ax12, ax13, ax14, ax15))= plot.subplots(nrows=3, ncols=5, sharex=False, sharey=False)#,figsize=(28,10))
f.subplots_adjust(hspace=.5)
f.subplots_adjust(wspace=.5)
ax1.set_title("Total Counts")
ax2.set_title("Prim Trans")
ax3.set_title("Prim Scat")
ax4.set_title("Secon Phot")
ax5.set_title("Nonphot")
# BIN EDGES!
xbins = np.linspace(-410/2.,410/2.,256+1)
ybins = np.linspace(-410/2.,410/2.,256+1)
a = plot.plot2dhist( ax1, all['X'], all['Y'], xbins=xbins,ybins=ybins, log=True)
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) == 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')
#!/usr/bin/env python3
import numpy as np, matplotlib as mpl, plot, sys, re
clrs = plot.colors
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
x = [1, 2, 3, 7]
cpu = [174.116, 124.691, 128.150, 133.497]
gpu = [133.161, 102.820, 94.058, 90.751]
#cpu_cost = ['CPU (Threadripper 1920X)']*657
#gpu_cost = ['GPU (Quadro M6000 12GB)']*2406
#ratio_cost = ['Ratio']*2406
ax1.set_title('GPUMCD runtimes')
ax1.set_xlabel('Streams')
ax1.set_ylabel('Runtime [s]')
ax1.plot(x, cpu, color=clrs[0], lw=1, label='CPU (2x XeonE5-2630v3)')
ax1.plot(x, gpu, color=clrs[1], lw=1, label='GPU (1x TitanXp)')
ax2 = ax1.twinx()
ax2.plot(x, [i / j * 100. - 100. for i, j in zip(cpu, gpu)],
color=clrs[2],
lw=1,
label='GPU speed advantage')
ax2.set_ylabel('Speed up (+%)')
ax1.legend(loc='upper left', frameon=False)
ax2.legend(loc='upper right', bbox_to_anchor=(1., 1.), frameon=False)
ax1.set_ylim(bottom=0.)
#ax9.set_title('Cost')
