resultsList = []
boxes = return_boxes("%s/broad_sweep/" % rootDirec)
for box in boxes:
broadFiles = return_files("%s/broad_sweep" % rootDirec, box)
lowFiles = return_files("%s/low_intensity" % rootDirec, box)
for j in range(len(lowFiles)):
broadVals = check_data(plot_ipw.read_scope_scan(broadFiles[j]))
lowVals = check_data(plot_ipw.read_scope_scan(lowFiles[j]))
# Creat plots
photonVsPIN_broad = ROOT.TGraphErrors()
photonVsIPW_broad = ROOT.TGraphErrors()
photonVsPIN_low = ROOT.TGraphErrors()
photonVsIPW_low = ROOT.TGraphErrors()
print len(broadVals), len(lowVals), broadFiles[j], lowFiles[j]
for i in range(len(broadVals)):
photonBroad = plot_ipw.get_photons(broadVals[i]["area"], 0.5)
photonErrBroad = plot_ipw.get_photons(broadVals[i]["area_err"],
0.5)
# Fill plots with data
# Note: Data points are returned as mean and stdev(rms)
# for fitting, uncertainties should be given as standard error.
photonVsPIN_broad.SetPoint(i, broadVals[i]["pin"], photonBroad)
photonVsPIN_broad.SetPointError(
i, broadVals[i]["pin_err"] / np.sqrt(100),
photonErrBroad / np.sqrt(100))
#photonVsPIN_broad.SetPointError(i,0,photonErrBroad/np.sqrt(1))
photonVsIPW_broad.SetPoint(i, broadVals[i]["ipw"], photonBroad)
photonVsIPW_broad.SetPointError(i, 0,
photonErrBroad / np.sqrt(100))
if i < len(lowVals):
channelDate = []
ratios = []
for directory in directoryArray:
PINValues = []
PhotonValues = []
PINErrors = []
PhotonErrors = []
lowFiles = glob.glob(directory + "/*.dat")
dict1 = createDictionaryFromFile(lowFiles[0])
channel, date1 = getDateAndChannel(lowFiles[0])
dict2 = createDictionaryFromFile(lowFiles[1])
channel, date2 = getDateAndChannel(lowFiles[1])
#max_index = returnFirstZeroIndex(dict1["area"],dict2["area"])
PhotonValues.append([plot_ipw.get_photons(x, 0.7) for x in dict1["area"]])
PhotonValues.append([plot_ipw.get_photons(x, 0.7) for x in dict2["area"]])
max_index = returnFirstLessThanValueIndex(PhotonValues[0], PhotonValues[1],
10000)
del PhotonValues[:]
PhotonValues.append(
[plot_ipw.get_photons(x, 0.7) for x in dict1["area"][:max_index]])
PhotonValues.append(
[plot_ipw.get_photons(x, 0.7) for x in dict2["area"][:max_index]])
relative_photon_error_1 = np.divide(dict1["area_error"][:max_index],
dict1["area"][:max_index])
relative_photon_error_2 = np.divide(dict2["area_error"][:max_index],
dict2["area"][:max_index])
relative_ratio_error = np.sqrt(
np.power(relative_photon_error_1, 2) +
np.power(relative_photon_error_2, 2))
if firstIter:
for key in row.keys():
file1Dict[key] = [float(row[key])]
firstIter = False
else:
for key in row.keys():
file1Dict[key].append(float(row[key]))
reader = csv.DictReader(file2,delimiter="\t")
firstIter = True
for row in reader:
if firstIter:
for key in row.keys():
file2Dict[key] = [float(row[key])]
firstIter = False
else:
for key in row.keys():
file2Dict[key].append(float(row[key]))
photonCounts1 = plot_ipw.get_photons(file1Dict["AREA"],0.7)
photonCountsError1 = plot_ipw.get_photons(file1Dict["AREA Error"],0.7)
photonCounts2 = plot_ipw.get_photons(file2Dict["AREA"],0.7)
photonCountsError2 = plot_ipw.get_photons(file2Dict["AREA Error"],0.7)
plt.errorbar(file1Dict["#PWIDTH"],np.subtract(photonCounts2,photonCounts1),yerr=np.sqrt(np.power(photonCountsError2,2)+np.power(photonCountsError1,2)))
plt.show()
resultsList = []
boxes = return_boxes("%s/broad_sweep/"%rootDirec)
for box in boxes:
broadFiles = return_files("%s/broad_sweep"%rootDirec, box)
lowFiles = return_files("%s/low_intensity"%rootDirec, box)
for j in range(len(lowFiles)):
broadVals = check_data(plot_ipw.read_scope_scan(broadFiles[j]))
lowVals = check_data(plot_ipw.read_scope_scan(lowFiles[j]))
# Creat plots
photonVsPIN_broad = ROOT.TGraphErrors()
photonVsIPW_broad = ROOT.TGraphErrors()
photonVsPIN_low = ROOT.TGraphErrors()
photonVsIPW_low = ROOT.TGraphErrors()
print len(broadVals), len(lowVals), broadFiles[j], lowFiles[j]
for i in range(len(broadVals)):
photonBroad = plot_ipw.get_photons(broadVals[i]["area"], 0.5)
photonErrBroad = plot_ipw.get_photons(broadVals[i]["area_err"], 0.5)
# Fill plots with data
# Note: Data points are returned as mean and stdev(rms)
# for fitting, uncertainties should be given as standard error.
photonVsPIN_broad.SetPoint(i,broadVals[i]["pin"],photonBroad)
photonVsPIN_broad.SetPointError(i,broadVals[i]["pin_err"]/np.sqrt(100),photonErrBroad/np.sqrt(100))
#photonVsPIN_broad.SetPointError(i,0,photonErrBroad/np.sqrt(1))
photonVsIPW_broad.SetPoint(i,broadVals[i]["ipw"],photonBroad)
photonVsIPW_broad.SetPointError(i,0,photonErrBroad/np.sqrt(100))
if i < len(lowVals):
photonLow = plot_ipw.get_photons(lowVals[i]["area"], 0.7)
photonErrLow = plot_ipw.get_photons(lowVals[i]["area_err"], 0.7)
photonVsPIN_low.SetPoint(i,lowVals[i]["pin"],photonLow)
photonVsPIN_low.SetPointError(i,lowVals[i]["pin_err"]/np.sqrt(100),photonErrLow/np.sqrt(100))
resultsList = []
boxes = return_boxes("%s/low_intensity/"%rootDirec)
for box in boxes:
#lowFiles = return_all_files("%s/low_intensity"%rootDirec, box)
lowFiles = return_files("%s/low_intensity"%rootDirec, box)
for j in range(len(lowFiles)):
lowVals = check_data(plot_ipw.read_scope_scan(lowFiles[j]))
# Creat plots
photonVsPIN_low = ROOT.TGraphErrors()
photonVsIPW_low = ROOT.TGraphErrors()
print len(lowVals), lowFiles[j]
for i in range(len(lowVals)):
# Fill plots with data
# Note: Data points are returned as mean and stdev(rms)
# for fitting, uncertainties should be given as standard error.
photonLow = plot_ipw.get_photons(lowVals[i]["area"], 0.7)
photonErrLow = plot_ipw.get_photons(lowVals[i]["area_err"], 0.7)
photonVsPIN_low.SetPoint(i,lowVals[i]["pin"],photonLow)
photonVsPIN_low.SetPointError(i,lowVals[i]["pin_err"]/np.sqrt(100),photonErrLow/np.sqrt(100))
#photonVsPIN_broad.SetPointError(i,0,photonErrBroad/np.sqrt(1))
photonVsIPW_low.SetPoint(i,lowVals[i]["ipw"],photonLow)
photonVsIPW_low.SetPointError(i,0,photonErrLow/np.sqrt(100))
# Add titles, labels and styling
logical_channel = (int(lowFiles[j][-33:-31])-1)*8 + int(lowFiles[j][-26:-24])
time_str = lowFiles[j][-16:-4]
photonVsPIN_low.SetName("Chan%02d_PIN_%s"%(logical_channel, time_str))
photonVsPIN_low.GetXaxis().SetTitle("PIN reading (16 bit)")
photonVsPIN_low.GetYaxis().SetTitle("No. photons")
channelDate = []
ratios = []
for directory in directoryArray:
PINValues = []
PhotonValues = []
PINErrors = []
PhotonErrors = []
lowFiles = glob.glob(directory+"/*.dat")
dict1 = createDictionaryFromFile(lowFiles[0])
channel, date1 = getDateAndChannel(lowFiles[0])
dict2 = createDictionaryFromFile(lowFiles[1])
channel, date2 = getDateAndChannel(lowFiles[1])
#max_index = returnFirstZeroIndex(dict1["area"],dict2["area"])
PhotonValues.append([plot_ipw.get_photons(x,0.7) for x in dict1["area"]])
PhotonValues.append([plot_ipw.get_photons(x,0.7) for x in dict2["area"]])
max_index = returnFirstLessThanValueIndex(PhotonValues[0],PhotonValues[1],10000)
del PhotonValues[:]
PhotonValues.append([plot_ipw.get_photons(x,0.7) for x in dict1["area"][:max_index]])
PhotonValues.append([plot_ipw.get_photons(x,0.7) for x in dict2["area"][:max_index]])
relative_photon_error_1 = np.divide(dict1["area_error"][:max_index],dict1["area"][:max_index])
relative_photon_error_2 = np.divide(dict2["area_error"][:max_index],dict2["area"][:max_index])
relative_ratio_error = np.sqrt(np.power(relative_photon_error_1,2)+np.power(relative_photon_error_2,2))
ratio_photon = (np.divide(dict1["area"][:max_index],dict2["area"][:max_index]))
mean_ratio_photon = np.mean(ratio_photon)
print "The ratio of photon count between "+str(lowFiles[0])+" and "+str(lowFiles[1])+" is "+str(mean_ratio_photon)
plt.errorbar(dict1["ipw"][:max_index],ratio_photon,yerr=np.multiply(ratio_photon,relative_ratio_error),label=str(channel+" "+date1+"/"+date2))
PINValues.append(dict1["PIN"][:max_index])
PINValues.append(dict2["PIN"][:max_index])
PINErrors.append(dict1["PIN_error"][:max_index])
resultsList = []
boxes = return_boxes("%s/low_intensity/" % rootDirec)
for box in boxes:
#lowFiles = return_all_files("%s/low_intensity"%rootDirec, box)
lowFiles = return_files("%s/low_intensity" % rootDirec, box)
for j in range(len(lowFiles)):
lowVals = check_data(plot_ipw.read_scope_scan(lowFiles[j]))
# Creat plots
photonVsPIN_low = ROOT.TGraphErrors()
photonVsIPW_low = ROOT.TGraphErrors()
print len(lowVals), lowFiles[j]
for i in range(len(lowVals)):
# Fill plots with data
# Note: Data points are returned as mean and stdev(rms)
# for fitting, uncertainties should be given as standard error.
photonLow = plot_ipw.get_photons(lowVals[i]["area"], 0.7)
photonErrLow = plot_ipw.get_photons(lowVals[i]["area_err"],
0.7)
photonVsPIN_low.SetPoint(i, lowVals[i]["pin"], photonLow)
photonVsPIN_low.SetPointError(
i, lowVals[i]["pin_err"] / np.sqrt(100),
photonErrLow / np.sqrt(100))
#photonVsPIN_broad.SetPointError(i,0,photonErrBroad/np.sqrt(1))
photonVsIPW_low.SetPoint(i, lowVals[i]["ipw"], photonLow)
photonVsIPW_low.SetPointError(i, 0,
photonErrLow / np.sqrt(100))
# Add titles, labels and styling
logical_channel = (int(lowFiles[j][-33:-31]) - 1) * 8 + int(
firstIter = False
else:
for key in row.keys():
file1Dict[key].append(float(row[key]))
reader = csv.DictReader(file2, delimiter="\t")
firstIter = True
for row in reader:
if firstIter:
for key in row.keys():
file2Dict[key] = [float(row[key])]
firstIter = False
else:
for key in row.keys():
file2Dict[key].append(float(row[key]))
photonCounts1 = plot_ipw.get_photons(file1Dict["AREA"], 0.7)
photonCountsError1 = plot_ipw.get_photons(file1Dict["AREA Error"], 0.7)
photonCounts2 = plot_ipw.get_photons(file2Dict["AREA"], 0.7)
photonCountsError2 = plot_ipw.get_photons(file2Dict["AREA Error"], 0.7)
plt.errorbar(file1Dict["#PWIDTH"],
np.subtract(photonCounts2, photonCounts1),
yerr=np.sqrt(
np.power(photonCountsError2, 2) +
np.power(photonCountsError1, 2)))
plt.show()
