def check_offset(highFile, lowFile, plot=False):
highData, lowData = dataSet(), dataSet()
highData.parseFile(highFile)
lowData.parseFile(lowFile)
channels = np.asarray(highData.findData("channel").getData())
low_channels = np.asarray(lowData.findData("channel").getData())
for channel in channels:
if np.where(low_channels == channel)[0]:
cutoff_high = find_parabola_min(highData, np.where(channels == channel)[0])
cutoff_low = find_parabola_min(lowData, np.where(low_channels == channel)[0])
print channel, cutoff_high, cutoff_low, (cutoff_low - cutoff_high)
if plot == True:
plot_curves(highData, lowData, channel)
def flagBadFits(dataFile):
fileData = dataSet()
fileData.parseFile(dataFile)
ipw_ChiSq = np.asarray(fileData.findData("ipwChi2").getData())
pin_ChiSq = np.asarray(fileData.findData("pinChi2").getData())
meanChiIPW = np.mean(ipw_ChiSq)
stdChiIPW = np.std(ipw_ChiSq)
meanChiPIN = np.nanmean(pin_ChiSq)
stdChiPIN = np.nanstd(pin_ChiSq)
for i in range(len(ipw_ChiSq)):
if ipw_ChiSq[i] > meanChiIPW+stdChiIPW:
print "Channel %d failed IPW fit test: " %(i+1)
if pin_ChiSq[i] > meanChiPIN+stdChiPIN:
print "Channel %d failed pin fit test: " %(i+1)
from channelPlot import data
from channelPlot import dataSet
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
fileData = dataSet()
fileData.parseFile("results/resultsOverview.csv")
ipw_p0 = np.asarray(fileData.findData("ipw_p0").getData())
ipw_p1 = np.asarray(fileData.findData("ipw_p1").getData())
ipw_p2 = np.asarray(fileData.findData("ipw_p2").getData())
ipw_p0_mean = np.mean(ipw_p0)
ipw_p1_mean = np.mean(ipw_p1)
ipw_p2_mean = np.mean(ipw_p2)
x = np.linspace(0000, 19000, 18000)
total_F = np.zeros((len(ipw_p0), len(x)), dtype=np.float64)
for i in range(len(ipw_p0)):
trial_F = ipw_p0[i] + (ipw_p1[i] * x) + (ipw_p2[i] * x**2)
minIndex = np.argmin(trial_F)
minY = trial_F[minIndex]
minX = x[minIndex]
offset = minX - 5000
shiftUpwards = 0
if minY > 1000:
print "Channel %d is above 1000 photons minimum" % (i + 1)
if minY < 0:
shiftUpwards = -minY
trial_F_shifted = ipw_p0[i] + shiftUpwards + (
ipw_p1[i] * (x + offset)) + (ipw_p2[i] * (x + offset)**2)
from channelPlot import data
from channelPlot import dataSet
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
fileData = dataSet()
fileData.parseFile("results/resultsOverview.csv")
ipw_p0 = np.asarray(fileData.findData("ipw_p0").getData())
ipw_p1 = np.asarray(fileData.findData("ipw_p1").getData())
ipw_p2 = np.asarray(fileData.findData("ipw_p2").getData())
ipw_p0_mean = np.mean(ipw_p0)
ipw_p1_mean = np.mean(ipw_p1)
ipw_p2_mean = np.mean(ipw_p2)
x = np.linspace(0000,19000,18000)
total_F = np.zeros((len(ipw_p0),len(x)),dtype=np.float64)
for i in range(len(ipw_p0)):
trial_F = ipw_p0[i]+(ipw_p1[i]*x)+(ipw_p2[i]*x**2)
minIndex = np.argmin(trial_F)
minY = trial_F[minIndex]
minX = x[minIndex]
offset = minX-5000
shiftUpwards = 0
if minY>1000:
print "Channel %d is above 1000 photons minimum" % (i+1)
if minY<0:
shiftUpwards = -minY
trial_F_shifted = ipw_p0[i]+shiftUpwards+(ipw_p1[i]*(x+offset))+(ipw_p2[i]*(x+offset)**2)
plt.plot(x,trial_F_shifted,label="Channel %d shifted" %(i+1))
