else:
run, part, path, numwaves, startrow = sys.argv[1:6]
if not path.endswith('/'):
path += '/'
numwaves = int(numwaves)
startrow = int(startrow)
length = 3500
#for i in range(4,len(sys.argv[4:])): #PARSE ARGS LIKE IN ca_analysis.py
name = 'Run_' + str(run) + '_' + str(part) + '.bin'
data = fr.raw(path + name, length=length, numwaves=numwaves, row=startrow)
bd, ch = 4, 3
data = pd.single_pixel(data, bd, ch)
wo.baseline_restore(data, pretrigger=600)
tbins = np.linspace(0, length - 1, length)
#Making trap to plot on top of Waveform
rise, top, fall = 400, 70, 1050
trapar = np.zeros(len(tbins))
wo.trap(trapar, rise, top, fall)
#
emin, emax = 100., 10000.
fs = 15
for i in range(len(data)):
convolution = signal.fftconvolve(data[i]['wave'],
if rank > 0:
print('Starting on ' + name)
chunk = int((os.stat(run).st_size - 8) / 7033)
chunk = int(chunk / (size - 1))
row = (rank - 1) * chunk
numwaves = chunk
if rank == size - 1:
numwaves = chunk + int(
(os.stat(run).st_size - 8) / 7033) % (size - 1)
data = fr.raw(run, length=3500, row=row, numwaves=numwaves)
numwaves = len(data)
wo.baseline_restore(data, 600)
length = 3500
t = np.arange(length)
out = np.zeros(length)
trap = np.zeros(length)
fall = 0
maxouts = 0
omega = 2 * np.pi / length
pars = []
beg = time.time()
sys.stdout.flush()
for i in range(numwaves):
if i % int(numwaves / 100) == 0 and rank == 1:
string = 'Done with {:0.2f}%'.format(100 * i / numwaves)
sys.stdout.write(string)
row=row + i * piece)
writebuffer[0:piece + rem]['result'], writebuffer[
0:piece +
rem]['evID'], writebuffer[0:piece + rem][
'board'], writebuffer[0:piece + rem][
'channel'], writebuffer[0:piece + rem][
'timestamp'], writebuffer[
0:piece +
rem]['requesttime'] = data[
'result'], data['evID'], data[
'board'], data[
'channel'], data[
'timestamp'], data[
'requesttime']
wo.baseline_restore(
data, 600
) #restores baseline and performs necessary preformatting of the data (data & 16383...)
#smooth_wave= signal.filtfilt(b,a,data['wave'])
wo.maxes(waves=data['wave'],
startpoint=500,
wavelength=length,
maxamps=maxamps[0:piece + rem],
maxlocs=maxamps[0:piece + rem])
wo.rises(data['wave'], maxamps[0:piece + rem],
maxamps[0:piece + rem],
risetimes[0:piece + rem])
writebuffer[0:piece +
rem]['risetime'] = risetimes[0:piece +
rem]
if fitting == 1:
tot = chunk + rem
data = fr.raw(inpath + name,
length=length,
numwaves=tot,
row=i * chunk)
for j in range(len(timestamps)):
x = data[pd.land(
pd.land(data['timestamp'] == timestamps[j]['timestamp'],
data['board'] == timestamps[j]['board']),
data['channel'] == timestamps[j]['channel'])]
count += len(x)
if len(x) > 0:
# print x['board'],x['channel'],x['timestamp']
temp[count - 1] = x
print(temp['board'])
wo.baseline_restore(temp, 600)
tbins = np.arange(3500)
#for i in range(len(temp)):
with PdfPages('fallwaves.pdf') as pdf:
for j in range(int(portion), len(timestamps)):
# print temp[temp['timestamp']==timestamps[j]['timestamp']]['wave'][0]
if timestamps[j]['board'] > 2:
plt.plot(
tbins,
temp[temp['timestamp'] == timestamps[j]
['timestamp']]['wave'][0],
label='falltime = ' + str(timestamps[j]['falltime']) + '\n ' +
pd.pixel(timestamps[j]['board'], timestamps[j]['channel']))
plt.legend(fontsize=20)
plt.tight_layout()
pdf.savefig()