def roiEnergyAnalysis(data):
'''Troubleshooting function, compares the observed sum energy in an ROI to the genEnergy'''
genEnergies = []
sumEnergies = []
pbar = progressbar("Processing event &count&:", len(data)+1)
pbar.start()
count = 0
for event in data:
genEnergy = event[2]['getpt'] * np.cosh(event[2]['geneta'])
for i in range(len(genEnergy)):
clustersIndices = np.compress(event[1]['ROI'] == i, event[1]['clusterID'], axis=0) #|Only take clusters corresponding to right ROI
clusterEnergies = []
for clusterID in clustersIndices: #|Only take hits corresponding to correct cluster
hits = np.compress(event[0]['clusterID'] == clusterID, event[0], axis=0)
energies = hits['en']
for energy in energies:
clusterEnergies.append(energy) #|Add the energy to the cluster energies
ROIEnergy = np.sum(clusterEnergies)
# Append to original lists
genEnergies.append(genEnergy[i])
sumEnergies.append(ROIEnergy)
pbar.update(count)
count += 1
pbar.finish()
# np.save("sums.npy", sumEnergies)
# np.save("gens.npy", genEnergies)
# Plot it
Plotter.sumEnergyVsGenEnergy(sumEnergies, genEnergies)
def energyResolution((directory, f, threadID)):
'''Multiprocessed function for analysing resolution as a function of energy.'''
smearingValue = 50
writer = Writer((0,2*threadID+2))
pbar = progressbar("Loading %s" % f, 1, fd=writer)
pbar.start()
pbar.update(0)
data = np.load(os.path.expanduser(directory+f))
data = timeSmearing(data, smearingValue)
pbar.finish()
name = "{0:22s} &count&: ".format(str(f))
# en = f.split("_")[-1].split(".")[0]
pbar = progressbar(name, len(data) + 1, fd=writer)
pbar.start()
diffs = vertexData(data, pbar=pbar, quiet=True)
pbar.finish()
outfile = os.path.expanduser("Data/Processed/50ps/50ps"+str(f[:-4]) + "_tVertexed.npy")
np.save(outfile, np.multiply(10,diffs))
def energyResolution((directory, f, threadID)):
'''Multiprocessed function for analysing resolution as a function of energy.'''
smearingValue = 50
writer = Writer((0, 2 * threadID + 2))
pbar = progressbar("Loading %s" % f, 1, fd=writer)
pbar.start()
pbar.update(0)
data = np.load(os.path.expanduser(directory + f))
data = timeSmearing(data, smearingValue)
pbar.finish()
name = "{0:22s} &count&: ".format(str(f))
en = f.split("_")[-1].split(".")[0]
pbar = progressbar(name, len(data) + 1, fd=writer)
pbar.start()
diffs = tVertexData(data, pbar=pbar, quiet=True)
pbar.finish()
outfile = os.path.expanduser("Data/Processed/50ps/50ps" + str(f[:-4]) +
"_tVertexed.npy")
np.save(outfile, np.multiply(10, diffs))
def singleThreadEnergyResolution(directory, files):
'''Singly-processed function for analysing resolution as a function of energy.'''
dataSets = []
for f in files:
print "Loading %s..." % f
dataSets.append(np.load(os.path.expanduser(directory+f)))
totalLength = 0
for entry in dataSets:
totalLength += len(entry)
for data in dataSets:
print "Processing %i of %i files..." % (dataSets.index(data), len(dataSets))
pbar = progressbar("Computing &count&:", totalLength + 1)
pbar.start()
diffs = vertexData(data, pbar=pbar, quiet=True)
np.save(directory+str(f[:-5]) + "_tVertexed.npy", np.multiply(10,diffs))
pbar.finish()
print "Finished; closing file %s.\n" % f
def timeSmearingTest():
'''Iterator function to run a resolution analysis on all of the data available.'''
data = np.load("Data/500GeVPhoton.npy")
res = 50
diffsList = np.array([])
smearingValues = np.array([])
num = 0
pbar = progressbar("Computing &count&:", res*len(data))
pbar.start()
for smearingVal in np.linspace(0,50,res):
data = np.load("Data/500GeVPhoton.npy") #|Quick and dirty reload, numpy was doing funky shit
diffs = vertexData(timeSmearing(data, smearingVal), runNumber=num, pbar=pbar, quiet=True)
diffsList = np.append(diffsList, diffs)
smearingValues = np.append(smearingValues, np.repeat(smearingVal, len(diffs)))
num += 1
pbar.finish()
np.save("diffs.npy", np.multiply(10,diffsList))
np.save("vals.npy", smearingValues)
Plotter.tVertexErrorHist2D(np.multiply(10,diffsList), smearingValues)
def singleThreadEnergyResolution(directory, files):
'''Singly-processed function for analysing resolution as a function of energy.'''
dataSets = []
for f in files:
print "Loading %s..." % f
dataSets.append(np.load(os.path.expanduser(directory + f)))
totalLength = 0
for entry in dataSets:
totalLength += len(entry)
for data in dataSets:
print "Processing %i of %i files..." % (dataSets.index(data),
len(dataSets))
pbar = progressbar("Computing &count&:", totalLength + 1)
pbar.start()
diffs = tVertexData(data, pbar=pbar, quiet=True)
np.save(directory + str(f[:-5]) + "_tVertexed.npy",
np.multiply(10, diffs))
pbar.finish()
print "Finished; closing file %s.\n" % f
def timeSmearingTest():
'''Iterator function to run a resolution analysis on all of the data available.'''
data = np.load("Data/500GeVPhoton.npy")
res = 50
diffsList = np.array([])
smearingValues = np.array([])
num = 0
pbar = progressbar("Computing &count&:", res * len(data))
pbar.start()
for smearingVal in np.linspace(0, 50, res):
data = np.load("Data/500GeVPhoton.npy"
) #|Quick and dirty reload, numpy was doing funky shit
diffs = tVertexData(timeSmearing(data, smearingVal),
runNumber=num,
pbar=pbar,
quiet=True)
diffsList = np.append(diffsList, diffs)
smearingValues = np.append(smearingValues,
np.repeat(smearingVal, len(diffs)))
num += 1
pbar.finish()
np.save("diffs.npy", np.multiply(10, diffsList))
np.save("vals.npy", smearingValues)
Plotter.tVertexErrorHist2D(np.multiply(10, diffsList), smearingValues)
def showerAnimator(
hits, eventNo, title, clusterID=-1, delete=False, frames=30, endLoop=0, projections=True, transparency=False
):
"""
Usage: showerAnimator(recordedHits, eventToAnalyse, plotTitle, clusterToAnalyse=None,
deleteFramesOnFinish=False, enableMatplotlibTransparency=False)
Plots an animated 5D (x,y,z,t,energy) graph over time and renders it to an animated gif.
If you want to make plots like this, I've also created a cleaned-up version of this code
and put it on GitHub as an easily-callable package. (github.com/bencbartlett/Animator5D)
"""
# Set up figure
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
import matplotlib.cm as cm
# Parse data for proper cluster ID and existence of timing data
if clusterID >= 0:
hits = np.extract(
np.logical_and(hits["clusterID"] == clusterID, hits["t"] > 0), hits
) # |Select only the rechits corresponding to the given cluster and with time data
else:
hits = np.extract(hits["t"] > 0, hits) # |Only include timing data points
# Further data parsing
hits = np.sort(hits, order=["tofT"])
xh, yh, zh, cth, Eh = hits["x"], hits["y"], hits["z"], hits["tofT"], hits["en"] # |Separating hits
maxEh = np.max(Eh)
xmin, xmax = min(xh), max(xh) # |Get and set limits
ymin, ymax = min(yh), max(yh)
zmin, zmax = min(zh), max(zh)
xc = np.mean([xmin, xmax]) # |x and y centroids, so we can keep scale proportional on all axes
yc = np.mean([ymin, ymax])
zd = zmax - zmin
# Set limits and labels
ax.set_zlim(
xc - zd / 2, xc + zd / 2
) # |Not a mistake, the reversal of the xyz pairing is so that z appears horizontally on the plot.
ax.set_ylim(yc - zd / 2, yc + zd / 2)
ax.set_xlim(zmin, zmax)
ax.set_zlabel("x (cm)") # |Label stuff
ax.set_ylabel("y (cm)")
ax.set_xlabel("Beamline (cm)")
# Clear out existing crap
path = "Plots/ShowerAnimations/" + title + "/Event" + str(eventNo)
if os.path.exists(path): # |Remove previous crap
shutil.rmtree(path)
os.makedirs(path + "/frames")
else:
os.makedirs(path + "/frames")
# Set up animation
count = 1
t = t0 = np.min(cth)
maxt = np.max(cth)
tstep = (maxt - t) / float(frames) # |Time step in ns
title = ax.text2D(0.3, 1.0, "Shower simulation", transform=ax.transAxes, size="large")
colorcal = ax.scatter([0, 0], [0, 0], [0, 0], c=[0, maxEh + 1], cmap=cm.jet)
cbar = fig.colorbar(colorcal, shrink=0.7)
pbar = progressbar("Rendering &count& frames:", int((maxt - t0) / tstep) + 1)
cbar.set_label("Energy (MIPs)")
pbar.start()
# Render frames
while t <= np.max(maxt):
mask = np.logical_and(t < cth, cth <= (t + tstep)) # |What to plot in this time step
xplt = np.extract(mask, xh)
yplt = np.extract(mask, yh)
zplt = np.extract(mask, zh)
Eplt = np.extract(mask, Eh)
txt = ax.text2D(0.1, 0.9, "$t=%.3f$ns" % t, transform=ax.transAxes)
cx = np.ones_like(xplt) * ax.get_zlim3d()[0] # |Again, not a typo with mixing x and z
cy = np.ones_like(yplt) * ax.get_ylim3d()[1]
cz = np.ones_like(zplt) * ax.get_xlim3d()[0]
mark = ax.scatter(
zplt, yplt, xplt, c=Eplt, cmap=cm.jet, vmin=0, vmax=maxEh, s=100 * Eplt / maxEh, marker=",", lw=1
)
if projections:
ax.scatter(
zplt, yplt, cx, c="#444444", marker=",", lw=0, s=100 * Eplt / maxEh, alpha=0.3
) # |Plot the projections
ax.scatter(zplt, cy, xplt, c="#444444", marker=",", lw=0, s=100 * Eplt / maxEh, alpha=0.3)
ax.scatter(cz, yplt, xplt, c="#444444", marker=",", lw=0, s=100 * Eplt / maxEh, alpha=0.3)
if not transparency:
mark.set_edgecolors = (
mark.set_facecolors
) = lambda *args: None # |Super-hacky way to disable transparency in the 3D plot, makes it cleaner to read.
plt.draw()
filename = path + "/frames/" + str(count).zfill(3) + ".gif"
plt.savefig(filename) # |Save the frame
txt.remove()
pbar.update(count)
count += 1
t += tstep
pbar.finish()
if endLoop:
print "Copying tail frames..."
for i in xrange(endLoop):
shutil.copyfile(filename, filename[:-7] + str(count).zfill(3) + ".gif")
count += 1
# Combine frames to animated gif
print "Combining frames..."
import subprocess
args = [
"convert",
"-delay",
".1",
"-loop",
"0",
path + "/frames/*.gif",
path + "/Shower.gif",
] # |This part requires ImageMagick to function
print "Saved to path " + str(os.path.abspath(path)) + "/Shower.gif"
subprocess.check_call(args)
# Delete frames if told to do so
if delete:
shutil.rmtree(path + "/frames")
def layerVarianceAnalysis(
data, eventNo, title, mode=None, clusterID=0, rng=None, endLoop=0, numEvents=1, delete=False, cumulative=False
):
"""
Plots an evolving 2D histogram by layer of a hit.
Warning: running this on cumulative mode for very large datasets can make you run out of RAM.
"""
# Parse data for proper cluster ID and existence of timing data
if not cumulative:
hits = np.extract(
data[eventNo][0]["clusterID"] == clusterID, data[eventNo][0]
) # |Select only the rechits corresponding to the given cluster
# Further data parsing
xh, yh, zh, Eh = hits["x"], hits["y"], hits["z"], hits["en"] # |Separating hits
center = [data[eventNo][1]["eta"][clusterID], data[eventNo][1]["phi"][clusterID]]
eta, phi = XYZtoEtaPhi([xh, yh, zh])
layers = hits["layerID"]
if mode == "log":
repeats = np.ceil(np.log(Eh)).astype(int)
etaw = np.repeat(eta, repeats) # |Energy-weighted eta and phi values
phiw = np.repeat(phi, repeats)
layersw = np.repeat(layers, repeats)
elif mode == "linear":
repeats = np.ceil(Eh).astype(int)
etaw = np.repeat(eta, repeats) # |Energy-weighted eta and phi values
phiw = np.repeat(phi, repeats)
layersw = np.repeat(layers, repeats)
elif mode == "flat":
etaw, phiw, layersw = eta, phi, layers
else:
center = [0, 0]
eta = np.array([])
phi = np.array([])
etaw = np.array([])
phiw = np.array([])
layers = np.array([])
layersw = np.array([])
Eh = np.array([])
k = 0
pbar = progressbar("Processing &count& events:", len(data) + 1)
pbar.start()
for event in data:
hits = np.extract(
event[0]["clusterID"] == clusterID, event[0]
) # |Select only the rechits corresponding to the given cluster
# Further data parsing
xh, yh, zh, EhE = hits["x"], hits["y"], hits["z"], hits["en"] # |Separating hits
etaE, phiE = XYZtoEtaPhi([xh, yh, zh])
layersE = hits["layerID"]
for i in range(len(hits)):
clusterID = hits[i]["clusterID"]
etaE[i] -= event[1]["eta"][clusterID]
phiE[i] -= event[1]["phi"][clusterID]
if phiE[i] > 3.0:
phiE[i] -= 2 * np.pi # |This fixes some modular issues we were having, with -epsilon = 2pi-epsilon
elif phiE[i] < -3.0:
phiE[i] += 2 * np.pi
if mode == "log":
repeats = np.ceil(np.log(EhE)).astype(int)
etawE = np.repeat(etaE, repeats) # |Energy-weighted eta and phi values
phiwE = np.repeat(phiE, repeats)
layerswE = np.repeat(layersE, repeats)
elif mode == "linear":
repeats = np.ceil(EhE).astype(int)
etawE = np.repeat(etaE, repeats) # |Energy-weighted eta and phi values
phiwE = np.repeat(phiE, repeats)
layerswE = np.repeat(layersE, repeats)
elif mode == "flat":
etawE, phiwE, layerswE = etaE, phiE, layersE
eta = np.append(eta, etaE)
phi = np.append(phi, phiE)
etaw = np.append(etaw, etawE)
phiw = np.append(phiw, phiwE)
layers = np.append(layers, layersE)
layersw = np.append(layersw, layerswE)
Eh = np.append(Eh, EhE)
k += 1
pbar.update(k)
pbar.finish()
# print "Saving array..."
# np.save("Data/etaPhiProcessed.npy", [eta, phi, etaw, phiw, layers, layersw, Eh])
# eta, phi, etaw, phiw, layers, layersw, Eh = np.load("Data/etaPhiProcessed.npy")
# center = [0,0]
# Set plot ranges
if rng == None:
pltrange = np.array([(np.min(eta), np.max(eta)), (np.min(phi), np.max(phi))])
else:
pltrange = rng
# Clear out existing crap
path = "Plots/LayerHitAnimations/" + title + "/Event" + str(eventNo)
if os.path.exists(path): # |Remove previous crap
shutil.rmtree(path)
os.makedirs(path + "/frames")
else:
os.makedirs(path + "/frames")
# Set up animation
minlayer = 1 # |Minimum layer
maxlayer = 30 # |Maximum layer
count = minlayer
pbar = progressbar("Rendering &count& frames:", maxlayer - minlayer + 1)
pbar.start()
# Render frames
while count <= maxlayer:
etapltw = np.extract(layersw == count, etaw)
phipltw = np.extract(layersw == count, phiw)
etaplt = np.extract(layers == count, eta)
phiplt = np.extract(layers == count, phi)
# Eplt = np.extract(layers == count, Eh)
filename = path + "/frames/" + str(count).zfill(3) + ".gif"
if len(etaplt) > 0:
layerVarianceFrame(
[etapltw, phipltw],
[etaplt, phiplt],
count,
center,
rng=pltrange,
numEvents=numEvents,
maxE=np.max(Eh),
xbins=50,
ybins=50,
saveAs=filename,
)
pbar.update(count)
count += 1
pbar.finish()
# Render tail if needed:
if endLoop:
print "Copying tail frames..."
for i in xrange(endLoop):
shutil.copyfile(filename, filename[:-7] + str(count).zfill(3) + ".gif")
count += 1
# Combine frames to animated gif
print "Combining frames..."
import subprocess
args = [
"convert",
"-delay",
"25",
"-loop",
"0",
path + "/frames/*.gif",
path + "/Shower.gif",
] # |This part requires ImageMagick to function
print "Saved to path " + str(os.path.abspath(path)) + "/Shower.gif"
subprocess.check_call(args)
# Delete frames if told to do so
if delete:
shutil.rmtree(path + "/frames")
def process((f, outdir, showProgress, threadID, quiet)):
'''
Usage: process(fileName, writeDirectory)
Takes a HGCROI-formatted .root file and converts it to a list of structured numpy arrays.
The format is as follows:
List of events
For each event, array for various data structures (RecHits, Clusters, etc.).
Unfortunately, there is no way to do recursive recarrays, so we use the following
organisation method:
eventArray[0]=RecHits
eventArray[1]=Clusters
eventArray[2]=ROIs
eventArray[2]=Vertices
eventArray[3]=GenVertex
For each data structure array, recarray of properties
For example:
(Index 0 = Event 0)
(Index 0 = RecHits)
'x' -> [1.23, 4.25, ...]
'y' -> [5.24, 6.42, ...]
...
'clusterID' -> [1, 2, 1, 1, ...]
(Index 1 = Clusters)
'centerX' -> [3.21, 2.56, ...]
...
...
(Index 1 = Event 1)
(Index 0 = RecHits)
...
(Index 1 = Clusters)
...
...
...
So, for example, to access the array of x positions in the RecHits of the 7th event,
you would use:
xpos = Data[7][0]['x']
'''
if not quiet: print "Processing file: " + str(f) + "..."
outArray = []
fIn = ROOT.TFile.Open(f)
tree = fIn.Get('analysis/HGC')
numentries = tree.GetEntries()
if showProgress:
if threadID:
writer = Writer((0, 2*threadID+2))
string = "{0:22s} &count&: ".format(str(f))
pbar = progressbar(string, numentries + 1, fd=writer)
else:
pbar = progressbar("Processing &count& events from %s"%str(f), numentries + 1)
pbar.start()
for i in xrange(0, numentries):
tree.GetEntry(i)
eventArray = []
names = ""
# RecHits
RecHits = True #|Store rechits
x = [] #|Position of the rechit
y = []
z = []
t = []
tofT = [] #|Time-of-flight corrected time data
en = [] #|Energy
clusterID = [] #|What cluster the hit belongs to
detID = []
layerID = []
isIn3x3 = [] #|Is in 3x3 grid from center of energy
isIn5x5 = [] #|Is in 5x5 grid from center of energy
isIn7x7 = [] #|Is in 7x7 grid from center of energy
# Clusters
Clusters = True #|Store clusters
center_x_ = [] #|Center of the cluster (energy weighted)
center_y_ = []
center_z_ = []
axis_x_ = [] #|Direction the cluster is pointing (looking back at the beamline from the cluster)
axis_y_ = []
axis_z_ = []
ev_1_ = [] #|Eigenvalues from principal component analysis
ev_2_ = []
ev_3_ = []
clusteren = [] #|Energy
clustereta = [] #|Eta
clusterphi = [] #|Phi
slfClustID = [] #|Self-referencing cluster ID
clusterroi = [] #|ROI ID for the cluster
# ROIs
ROIs = True
roiID = [] #|Self-referencing ROI ID
roieta = [] #|Energy-weighted eta
roiphi = [] #|Energy-weighted phi
roipt = [] #|Energy-weighted pt
roimass = []
roiarea = []
roigenpt = []
roigeneta = []
roigenphi = []
roigenmass = []
roigenarea = []
roistablex = []
roistabley = []
roistablez = []
roistablID = []
# Vertices
Vertices = False #|Store vertices, won't work for photon gun
vertex_x_ = [] #|Reconstructed vertex location using tracker
vertex_y_ = []
vertex_z_ = []
# Generated vertices ("true" vertices)
GenVert = True #|Store generated vertices
gen_x_ = [] #|Actual vertex location from simulated event
gen_y_ = []
gen_z_ = []
if RecHits:
for hit in tree.RecHits: #|Modify properties you want to extract at will.
x .append(hit.x_) #|Loops over files, extracting the xyzt data from rechits in each file and saving each rechit array to a .npy file for later use.
y .append(hit.y_)
z .append(hit.z_)
t .append(hit.t_)
tofT .append(hit.t_ + np.sqrt(hit.x_**2 + hit.y_**2 + hit.z_**2)/c)
en .append(hit.en_)
clusterID.append(hit.clustId_)
detID .append(hit.detId_)
layerID .append(hit.layerId_)
isIn3x3 .append(hit.isIn3x3_)
isIn5x5 .append(hit.isIn5x5_)
isIn7x7 .append(hit.isIn7x7_)
recHitsArray = np.core.records.fromarrays([x, y, z, t, en, tofT, clusterID,
detID, layerID, isIn3x3, isIn5x5, isIn7x7],
names = 'x,y,z,t,en,tofT,clusterID,\
detID,layerID,isIn3x3,isIn5x5,isIn7x7') #|Form rechit array
eventArray.append(recHitsArray) #|Append to event array
names += 'RecHits' #|Add to names list
else:
eventArray.append([]) #|This is to keep the index of the arrays the same
clusterindex = 0
if Clusters:
for cluster in tree.Clusters:
center_x_ .append(cluster.center_x_)
center_y_ .append(cluster.center_y_)
center_z_ .append(cluster.center_z_)
axis_x_ .append(cluster.axis_x_)
axis_y_ .append(cluster.axis_y_)
axis_z_ .append(cluster.axis_z_)
ev_1_ .append(cluster.ev_1_)
ev_2_ .append(cluster.ev_2_)
ev_3_ .append(cluster.ev_3_)
clusteren .append(cluster.en_)
clustereta.append(cluster.eta_)
clusterphi.append(cluster.phi_)
slfClustID.append(clusterindex)
clusterroi.append(cluster.roiidx_)
clusterindex += 1
clusterArray = np.core.records.fromarrays([center_x_, center_y_, center_z_,
axis_x_, axis_y_, axis_z_,
ev_1_, ev_2_, ev_3_, clusteren, clustereta,
clusterphi, slfClustID, clusterroi],
names = 'centerX,centerY,centerZ,\
axisX,axisY,axisZ,ev1,ev2,ev3,en,\
eta,phi,clusterID,ROI') #|Form array for clusters
eventArray.append(clusterArray) #|Append to event array
names += ',Clusters'
else:
eventArray.append([]) #|This is to keep the index of the arrays the same
ROIindex = 0
if ROIs:
for ROI in tree.ROIs:
roiID .append(ROIindex)
roipt .append(ROI.pt_)
roieta .append(ROI.eta_)
roiphi .append(ROI.phi_)
roimass .append(ROI.mass_)
roiarea .append(ROI.area_)
roigenpt .append(ROI.genpt_)
roigeneta .append(ROI.geneta_)
roigenphi .append(ROI.genphi_)
roigenmass.append(ROI.genmass_)
roigenarea.append(ROI.genarea_)
roistablex.append(ROI.stablex_)
roistabley.append(ROI.stabley_)
roistablez.append(ROI.stablez_)
roistablID.append(ROI.stableid_)
ROIindex +=1
ROIArray = np.core.records.fromarrays([roiID, roipt, roieta, roiphi, roimass, roiarea,
roigenpt, roigeneta, roigenphi, roigenmass,
roigenarea, roistablex, roistabley, roistablez,
roistablID],
names = 'roiID,pt,eta,phi,mass,area,getpt,\
geneta,genphi,genarea,stablex,stabley,\
stablez,stableID')
eventArray.append(ROIArray)
names += ',ROIs'
else:
eventArray.append([])
if Vertices:
for vertex in tree.Vertices:
vertex_x_.append(vertex.x_)
vertex_y_.append(vertex.y_)
vertex_z_.append(vertex.z_)
vertexArray = np.core.records.fromarrays([vertex_x_, vertex_y_, vertex_z_],
names='x,y,z')
eventArray.append(vertexArray) #|Vertices array
names += ',Vertices'
else:
eventArray.append([]) #|This is to keep the index of the arrays the same
if GenVert:
try:
gen_x_.append(tree.GenVertex.X()) #|GenVertex is not iterable like the other classes, since there is only one per event.
gen_y_.append(tree.GenVertex.Y())
gen_z_.append(tree.GenVertex.Z())
except AttributeError: #|Some sets use TLorentzVectors, some use TVector3. For some unfathomable reason, these have different capitalizations.
gen_x_.append(tree.GenVertex.x()) #|GenVertex is not iterable like the other classes, since there is only one per event.
gen_y_.append(tree.GenVertex.y())
gen_z_.append(tree.GenVertex.z())
genVertexArray = np.core.records.fromarrays([gen_x_, gen_y_, gen_z_],
names='x,y,z')
eventArray.append(genVertexArray) #|Generated vertices array
names += ',GenVertex'
else:
eventArray.append([]) #|This is to keep the index of the arrays the same
# Combine arrays for single event and append to outArray
outArray.append(eventArray) #|Converts to a 2D numpy structured array indexed by event number
if showProgress: pbar.update(i) #|Update progressbar
if (not showProgress) and (i % 100 == 0) and (not quiet):
print "Thread %i >> Processed %i of %i events." % (threadID, i, numentries) #|In the case this is being multiprocessed, progressbar doesn't work well, so just print the stuff.
# Finish up and save array to file
if showProgress: pbar.finish()
filename = str(f[:-5]) + ".npy" #|Replace .root with .npy
filename = filename.split("/")[-1] #|Removes directory prefixes
filepath = outdir+filename
if not quiet: print "Writing file " + os.path.abspath(filepath) + "..."
np.save(filepath, outArray)
if not quiet: print "Processing complete.\n"
def process((f, outdir, showProgress, threadID, quiet)):
'''
Usage: process(fileName, writeDirectory)
Takes a HGCROI-formatted .root file and converts it to a list of structured numpy arrays.
The format is as follows:
List of events
For each event, array for various data structures (RecHits, Clusters, etc.).
Unfortunately, there is no way to do recursive recarrays, so we use the following
organisation method:
eventArray[0]=RecHits
eventArray[1]=Clusters
eventArray[2]=ROIs
eventArray[2]=Vertices
eventArray[3]=GenVertex
For each data structure array, recarray of properties
For example:
(Index 0 = Event 0)
(Index 0 = RecHits)
'x' -> [1.23, 4.25, ...]
'y' -> [5.24, 6.42, ...]
...
'clusterID' -> [1, 2, 1, 1, ...]
(Index 1 = Clusters)
'centerX' -> [3.21, 2.56, ...]
...
...
(Index 1 = Event 1)
(Index 0 = RecHits)
...
(Index 1 = Clusters)
...
...
...
So, for example, to access the array of x positions in the RecHits of the 7th event,
you would use:
xpos = Data[7][0]['x']
'''
if not quiet: print "Processing file: " + str(f) + "..."
outArray = []
fIn = ROOT.TFile.Open(f)
tree = fIn.Get('analysis/HGC')
numentries = tree.GetEntries()
if showProgress:
if threadID:
writer = Writer((0, 2 * threadID + 2))
string = "{0:22s} &count&: ".format(str(f))
pbar = progressbar(string, numentries + 1, fd=writer)
else:
pbar = progressbar("Processing &count& events from %s" % str(f),
numentries + 1)
pbar.start()
for i in xrange(0, numentries):
tree.GetEntry(i)
eventArray = []
names = ""
# RecHits
RecHits = True #|Store rechits
x = [] #|Position of the rechit
y = []
z = []
t = []
tofT = [] #|Time-of-flight corrected time data
en = [] #|Energy
clusterID = [] #|What cluster the hit belongs to
detID = []
layerID = []
isIn3x3 = [] #|Is in 3x3 grid from center of energy
isIn5x5 = [] #|Is in 5x5 grid from center of energy
isIn7x7 = [] #|Is in 7x7 grid from center of energy
# Clusters
Clusters = True #|Store clusters
center_x_ = [] #|Center of the cluster (energy weighted)
center_y_ = []
center_z_ = []
axis_x_ = [
] #|Direction the cluster is pointing (looking back at the beamline from the cluster)
axis_y_ = []
axis_z_ = []
ev_1_ = [] #|Eigenvalues from principal component analysis
ev_2_ = []
ev_3_ = []
clusteren = [] #|Energy
clustereta = [] #|Eta
clusterphi = [] #|Phi
slfClustID = [] #|Self-referencing cluster ID
clusterroi = [] #|ROI ID for the cluster
# ROIs
ROIs = True
roiID = [] #|Self-referencing ROI ID
roieta = [] #|Energy-weighted eta
roiphi = [] #|Energy-weighted phi
roipt = [] #|Energy-weighted pt
roimass = []
roiarea = []
roigenpt = []
roigeneta = []
roigenphi = []
roigenmass = []
roigenarea = []
roistablex = []
roistabley = []
roistablez = []
roistablID = []
# Vertices
Vertices = False #|Store vertices, won't work for photon gun
vertex_x_ = [] #|Reconstructed vertex location using tracker
vertex_y_ = []
vertex_z_ = []
# Generated vertices ("true" vertices)
GenVert = True #|Store generated vertices
gen_x_ = [] #|Actual vertex location from simulated event
gen_y_ = []
gen_z_ = []
if RecHits:
for hit in tree.RecHits: #|Modify properties you want to extract at will.
x.append(
hit.x_
) #|Loops over files, extracting the xyzt data from rechits in each file and saving each rechit array to a .npy file for later use.
y.append(hit.y_)
z.append(hit.z_)
t.append(hit.t_)
tofT.append(hit.t_ +
np.sqrt(hit.x_**2 + hit.y_**2 + hit.z_**2) / c)
en.append(hit.en_)
clusterID.append(hit.clustId_)
detID.append(hit.detId_)
layerID.append(hit.layerId_)
isIn3x3.append(hit.isIn3x3_)
isIn5x5.append(hit.isIn5x5_)
isIn7x7.append(hit.isIn7x7_)
recHitsArray = np.core.records.fromarrays(
[
x, y, z, t, en, tofT, clusterID, detID, layerID, isIn3x3,
isIn5x5, isIn7x7
],
names='x,y,z,t,en,tofT,clusterID,\
detID,layerID,isIn3x3,isIn5x5,isIn7x7') #|Form rechit array
eventArray.append(recHitsArray) #|Append to event array
names += 'RecHits' #|Add to names list
else:
eventArray.append(
[]) #|This is to keep the index of the arrays the same
clusterindex = 0
if Clusters:
for cluster in tree.Clusters:
center_x_.append(cluster.center_x_)
center_y_.append(cluster.center_y_)
center_z_.append(cluster.center_z_)
axis_x_.append(cluster.axis_x_)
axis_y_.append(cluster.axis_y_)
axis_z_.append(cluster.axis_z_)
ev_1_.append(cluster.ev_1_)
ev_2_.append(cluster.ev_2_)
ev_3_.append(cluster.ev_3_)
clusteren.append(cluster.en_)
clustereta.append(cluster.eta_)
clusterphi.append(cluster.phi_)
slfClustID.append(clusterindex)
clusterroi.append(cluster.roiidx_)
clusterindex += 1
clusterArray = np.core.records.fromarrays(
[
center_x_, center_y_, center_z_, axis_x_, axis_y_, axis_z_,
ev_1_, ev_2_, ev_3_, clusteren, clustereta, clusterphi,
slfClustID, clusterroi
],
names='centerX,centerY,centerZ,\
axisX,axisY,axisZ,ev1,ev2,ev3,en,\
eta,phi,clusterID,ROI') #|Form array for clusters
eventArray.append(clusterArray) #|Append to event array
names += ',Clusters'
else:
eventArray.append(
[]) #|This is to keep the index of the arrays the same
ROIindex = 0
if ROIs:
for ROI in tree.ROIs:
roiID.append(ROIindex)
roipt.append(ROI.pt_)
roieta.append(ROI.eta_)
roiphi.append(ROI.phi_)
roimass.append(ROI.mass_)
roiarea.append(ROI.area_)
roigenpt.append(ROI.genpt_)
roigeneta.append(ROI.geneta_)
roigenphi.append(ROI.genphi_)
roigenmass.append(ROI.genmass_)
roigenarea.append(ROI.genarea_)
roistablex.append(ROI.stablex_)
roistabley.append(ROI.stabley_)
roistablez.append(ROI.stablez_)
roistablID.append(ROI.stableid_)
ROIindex += 1
ROIArray = np.core.records.fromarrays(
[
roiID, roipt, roieta, roiphi, roimass, roiarea, roigenpt,
roigeneta, roigenphi, roigenmass, roigenarea, roistablex,
roistabley, roistablez, roistablID
],
names='roiID,pt,eta,phi,mass,area,getpt,\
geneta,getphi,genarea,stablex,stabley,\
stablez,stableID')
eventArray.append(ROIArray)
names += ',ROIs'
else:
eventArray.append([])
if Vertices:
for vertex in tree.Vertices:
vertex_x_.append(vertex.x_)
vertex_y_.append(vertex.y_)
vertex_z_.append(vertex.z_)
vertexArray = np.core.records.fromarrays(
[vertex_x_, vertex_y_, vertex_z_], names='x,y,z')
eventArray.append(vertexArray) #|Vertices array
names += ',Vertices'
else:
eventArray.append(
[]) #|This is to keep the index of the arrays the same
if GenVert:
try:
gen_x_.append(
tree.GenVertex.X()
) #|GenVertex is not iterable like the other classes, since there is only one per event.
gen_y_.append(tree.GenVertex.Y())
gen_z_.append(tree.GenVertex.Z())
except AttributeError: #|Some sets use TLorentzVectors, some use TVector3. For some unfathomable reason, these have different capitalizations.
gen_x_.append(
tree.GenVertex.x()
) #|GenVertex is not iterable like the other classes, since there is only one per event.
gen_y_.append(tree.GenVertex.y())
gen_z_.append(tree.GenVertex.z())
genVertexArray = np.core.records.fromarrays(
[gen_x_, gen_y_, gen_z_], names='x,y,z')
eventArray.append(genVertexArray) #|Generated vertices array
names += ',GenVertex'
else:
eventArray.append(
[]) #|This is to keep the index of the arrays the same
# Combine arrays for single event and append to outArray
outArray.append(
eventArray
) #|Converts to a 2D numpy structured array indexed by event number
if showProgress: pbar.update(i) #|Update progressbar
if (not showProgress) and (i % 100 == 0) and (not quiet):
print "Thread %i >> Processed %i of %i events." % (
threadNo, i, numentries
) #|In the case this is being multiprocessed, progressbar doesn't work well, so just print the stuff.
# Finish up and save array to file
if showProgress: pbar.finish()
filename = str(f[:-5]) + ".npy" #|Replace .root with .npy
filename = filename.split("/")[-1] #|Removes directory prefixes
filepath = outdir + filename
if not quiet: print "Writing file " + os.path.abspath(filepath) + "..."
np.save(filepath, outArray)
if not quiet: print "Processing complete.\n"
def showerAnimator(hits,
eventNo,
title,
clusterID=-1,
delete=False,
frames=30,
endLoop=0,
projections=True,
transparency=False):
'''
Usage: showerAnimator(recordedHits, plotTitle, eventToAnalyse, clusterToAnalyse=None,
deleteFramesOnFinish=False, enableMatplotlibTransparency=False)
Plots an animated 5D (x,y,z,t,energy) graph over time and renders it to an animated gif.
If you want to make plots like this, I've also created a cleaned-up version of this code
and put it on GitHub as an easily-callable package. (github.com/bencbartlett/Animator5D)
'''
# Set up figure
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
import matplotlib.cm as cm
# Parse data for proper cluster ID and existence of timing data
if clusterID >= 0:
hits = np.extract(
np.logical_and(hits['clusterID'] == clusterID, hits['t'] > 0), hits
) #|Select only the rechits corresponding to the given cluster and with time data
else:
hits = np.extract(hits['t'] > 0,
hits) #|Only include timing data points
# Further data parsing
hits = np.sort(hits, order=['tofT'])
xh, yh, zh, th, cth, Eh = hits['x'], hits['y'], hits['z'], hits['t'], hits[
'tofT'], hits['en'] #|Separating hits
maxEh = np.max(Eh)
normEh = Eh / maxEh
xmin, xmax = min(xh), max(xh) #|Get and set limits
ymin, ymax = min(yh), max(yh)
zmin, zmax = min(zh), max(zh)
xc = np.mean([
xmin, xmax
]) #|x and y centroids, so we can keep scale proportional on all axes
yc = np.mean([ymin, ymax])
zd = zmax - zmin
# Set limits and labels
ax.set_zlim(
xc - zd / 2, xc + zd / 2
) #|Not a mistake, the reversal of the xyz pairing is so that z appears horizontally on the plot.
ax.set_ylim(yc - zd / 2, yc + zd / 2)
ax.set_xlim(zmin, zmax)
ax.set_zlabel('x (cm)') #|Label stuff
ax.set_ylabel('y (cm)')
ax.set_xlabel('Beamline (cm)')
# Clear out existing crap
path = "Plots/ShowerAnimations/" + title + "/Event" + str(eventNo)
if os.path.exists(path): #|Remove previous crap
shutil.rmtree(path)
os.makedirs(path + "/frames")
else:
os.makedirs(path + "/frames")
# Set up animation
count = 1
t = t0 = np.min(cth)
maxt = np.max(cth)
tstep = (maxt - t) / float(frames) #|Time step in ns
title = ax.text2D(.3,
1.0,
'Shower simulation',
transform=ax.transAxes,
size='large')
colorcal = ax.scatter([0, 0], [0, 0], [0, 0],
c=[0, maxEh + 1],
cmap=cm.rainbow)
cbar = fig.colorbar(colorcal, shrink=.7)
pbar = progressbar("Rendering &count& frames:",
int((maxt - t0) / tstep) + 1)
cbar.set_label("Energy (MIPs)")
pbar.start()
# Render frames
while t <= np.max(maxt):
mask = np.logical_and(t < cth, cth <=
(t + tstep)) #|What to plot in this time step
xplt = np.extract(mask, xh)
yplt = np.extract(mask, yh)
zplt = np.extract(mask, zh)
Eplt = np.extract(mask, Eh)
txt = ax.text2D(0.1, 0.9, '$t=%.3f$ns' % t, transform=ax.transAxes)
cx = np.ones_like(xplt) * ax.get_zlim3d()[
0] #|Again, not a typo with mixing x and z
cy = np.ones_like(yplt) * ax.get_ylim3d()[1]
cz = np.ones_like(zplt) * ax.get_xlim3d()[0]
mark = ax.scatter(zplt, yplt, xplt, c=Eplt, cmap=cm.rainbow, vmin=0, vmax=maxEh, \
s=100*Eplt/maxEh, marker=',', lw=1)
if projections:
ax.scatter(zplt,
yplt,
cx,
c='#444444',
marker=',',
lw=0,
s=100 * Eplt / maxEh,
alpha=0.3) #|Plot the projections
ax.scatter(zplt,
cy,
xplt,
c='#444444',
marker=',',
lw=0,
s=100 * Eplt / maxEh,
alpha=0.3)
ax.scatter(cz,
yplt,
xplt,
c='#444444',
marker=',',
lw=0,
s=100 * Eplt / maxEh,
alpha=0.3)
if not transparency:
mark.set_edgecolors = mark.set_facecolors = lambda *args: None #|Super-hacky way to disable transparency in the 3D plot, makes it cleaner to read.
plt.draw()
filename = path + "/frames/" + str(count).zfill(3) + ".gif"
plt.savefig(filename) #|Save the frame
txt.remove()
pbar.update(count)
count += 1
t += tstep
pbar.finish()
if endLoop: print "Copying tail frames..."
for i in xrange(endLoop):
shutil.copyfile(filename, filename[:-7] + str(count).zfill(3) + ".gif")
count += 1
# Combine frames to animated gif
print "Combining frames..."
import subprocess
args = ([
'convert', '-delay', '.1', '-loop', '0', path + "/frames/*.gif",
path + "/Shower.gif"
]) #|This part requires ImageMagick to function
print "Saved to path " + str(os.path.abspath(path)) + "/Shower.gif"
subprocess.check_call(args)
# Delete frames if told to do so
if delete:
shutil.rmtree(path + "/frames")
