print '1..3'
di = np.arange(1001, dtype='u8')
do = bin_data(di, 10)
good = len(do) == 100 and all(do['counts'][0:100] == 10)
result(good, 'basic binning')
# Check that partial bins are thrown away
di = np.arange(1000, 1555, dtype='u8')
do = bin_data(di, 100)
good = len(do) == 5 \
and all(do['counts'] == 100) \
and all(do['time'] == np.arange(1000,1500,100))
result(good, 'partial bins')
di = np.array([0, 100, 110], dtype='u8')
do = bin_data(di, 10)
good = len(do) == 11 \
and do['counts'][0] == 1 \
and all(do['counts'][1:9] == 0) \
and do['counts'][10] == 1
result(good, 'bin_photons zero bins test')
di = np.array([200, 300, 400, 500], dtype='u8')
do = bin_photons(di, 100, 0, 1000)
good = np.all(do['count'] == [0, 0, 1, 1, 1, 1, 0, 0, 0, 0]) \
and np.all(do['start_t'] == [0, 100, 200, 300, 400, 500, 600, 700, 800, 900])
result(good, 'bin_photons start_t/end_t')
print('1..3')
di = np.arange(1001, dtype='u8')
do = bin_data(di, 10)
good = len(do) == 100 and all(do['counts'][0:100] == 10)
result(good, 'basic binning')
# Check that partial bins are thrown away
di = np.arange(1000, 1555, dtype='u8')
do = bin_data(di, 100)
good = len(do) == 5 \
and all(do['counts'] == 100) \
and all(do['time'] == np.arange(1000,1500,100))
result(good, 'partial bins')
di = np.array([0, 100, 110], dtype='u8')
do = bin_data(di, 10)
good = len(do) == 11 \
and do['counts'][0] == 1 \
and all(do['counts'][1:9] == 0) \
and do['counts'][10] == 1
result(good, 'bin_photons zero bins test')
di = np.array([200, 300, 400, 500], dtype='u8')
do = bin_photons(di, 100, 0, 1000)
good = np.all(do['count'] == [0, 0, 1, 1, 1, 1, 0, 0, 0, 0]) \
and np.all(do['start_t'] == [0, 100, 200, 300, 400, 500, 600, 700, 800, 900])
result(good, 'bin_photons start_t/end_t')
#!/usr/bin/python
import matplotlib.pyplot as pl
import numpy as np
from photon_tools.bin_photons import bin_photons
jiffy = 1/128e6 # seconds
width = 1e-3 # seconds
spans = np.genfromtxt('spans')
points = np.genfromtxt('points')
ts = np.fromfile('2011-07-04-run_001.strobe1.times', dtype='u8')
dts = ts[1:] - ts[:-1]
bins = bin_photons(ts, width / jiffy)
pl.subplot(211)
pl.xlabel(r'Time (s)')
pl.ylabel(r'Rate')
pl.plot(jiffy*bins['start_t'], bins['count']/width, '+')
for (a,b) in spans:
pl.axvspan(jiffy*ts[a], jiffy*ts[b], color='g', alpha=0.3)
pl.xlim(jiffy*ts[0], jiffy*ts[0] + 2)
pl.subplot(212)
pl.semilogy(jiffy*ts[:len(points)], points[:,2], '+')
pl.axhline(2, color='g')
pl.ylabel(r'$\beta$')
pl.xlim(jiffy*ts[0], jiffy*ts[0] + 2)
pl.show()
return runs.reshape(-1, 2) - [0,1]
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('file', nargs='+', help='Timestamp files')
parser.add_argument('-w', '--bin-width', type=float, default=0.1,
help='Bin width in seconds')
parser.add_argument('-b', '--buffer', type=float, default=0.2,
help='Amount of time before and after burst to drop')
parser.add_argument('-t', '--threshold', type=float, default=10,
help='Threshold (multiple of the median count')
args = parser.parse_args()
for fname in args.file:
f = read_photons.open(fname)
bins = bin_photons(f.channel(0), bin_width=args.bin_width / f.jiffy, include_zeros=False)
counts = bins['count']
t = bins['start_t']
thresh = args.threshold * np.median(counts)
bursts = np.array(find_runs(counts > thresh))
starts = t[bursts[:,0]] - args.buffer / f.jiffy
ends = t[bursts[:,1]] + args.buffer / f.jiffy
print('%s: Found %d bursts above threshold of %f / bin' % (fname, len(bursts), thresh))
pl.clf()
pl.plot(t * f.jiffy, counts, '+')
for start,end in zip(starts, ends):
pl.axvspan(start * f.jiffy, end * f.jiffy, alpha=0.3, color='k')
pl.savefig(fname+'-bursts.png')
