def main():
arguments = docopt(__doc__, version='Dragon Data Browser 0.1alpha')
folder = arguments["<folder>"]
xdata = dict()
ydata = dict()
for pix in range(num_channels):
xdata[pix] = list()
ydata[pix] = list()
for (dirpath, dirnames, filenames) in os.walk(folder):
for filename in filenames:
path = os.path.join(dirpath,filename)
if not path.endswith(".dat"):
continue
print path
generator = read(path)
nEvents = len(generator)
ratios = np.zeros((num_channels,nEvents))
pulsePositionsLow = np.zeros((num_channels,nEvents))
pulsePositionsHigh = np.zeros((num_channels,nEvents))
pulseMaxLow = np.zeros((num_channels,nEvents))
pulseMaxHigh = np.zeros((num_channels,nEvents))
for i,event in enumerate(generator):
for pix in range(num_channels):
dataLow = event.data["low"][pix].astype(float)
dataLow -= estimateBaseline(dataLow,0,20)
dataHigh = event.data["high"][pix].astype(float)
dataHigh -= estimateBaseline(dataHigh,0,20)
pulsePositionsLow[pix][i], pulseMaxLow[pix][i] = find_max_amplitude(dataLow,0,90)
pulsePositionsHigh[pix][i], pulseMaxHigh[pix][i] = find_max_amplitude(dataHigh,0,90)
ratios[pix][i] = float(pulseMaxHigh[pix][i])/pulseMaxLow[pix][i]
averagedRatios = np.zeros(num_channels)
averagedMaxHigh = np.zeros(num_channels)
averagedMaxLow = np.zeros(num_channels)
for pix in range(num_channels):
averagedRatios[pix] = np.mean(ratios[pix])
averagedMaxHigh[pix] = np.mean(pulseMaxHigh[pix])
averagedMaxLow[pix] = np.mean(pulseMaxLow[pix])
xdata[pix].append(averagedMaxHigh[pix])
ydata[pix].append(averagedRatios[pix])
for pix in range(num_channels):
plt.plot(xdata[pix],ydata[pix],'*')
plt.title("Pixel: {}".format(pix))
plt.show()
from profilehooks import profile
from dragonboard import read
read = profile(read)
l = read('./Ped444706_1.dat')
