source: fact/tools/pyscripts/sandbox/dneise/gain/gain.py@ 13421

#!/usr/bin/python -tti
#
# 

from pyfact   import RawData
from drs_spikes import DRSSpikes        
from fir_filter import CFD
from extractor import ZeroXing
from fir_filter import SlidingAverage

from plotters import Plotter

import sys
import cPickle
import matplotlib.pyplot as plt
import numpy as np

data_filename = 'data/20111017_010.fits.gz'
calib_filename = 'data/20111017_007.drs.fits.gz'
out_filename = 'test.pkl'

run = RawData(data_filename, calib_filename, return_dict = True, do_calibration=True)
despike = DRSSpikes()
smooth = SlidingAverage(8)
cfd = CFD()
find = ZeroXing()

#plt.ion()

peak_list = []
        
p = Plotter('data[0]')


#fig = plt.figure()
#plt.grid(True)

stupid_list = []

for event in run:
    print event['event_id'].value
    data = event['acal_data']
    data = despike(data)
    data = smooth(data)
    filtered = cfd(data)
    filtered = smooth(filtered)
    peak_positions = find(data)
    
    # find peak heights
    # and get rid of too small peaks
#    good_peaks = []
#    for idx,pixel in enumerate(peak_positions):
#        good_peaks.append([])
#        for peak_pos in pixel:
#            peak_height = data[idx][int(peak_pos)]
#            stupid_list.append(peak_height)
#            if peak_height > 4.0:
#                good_peaks[-1].append((peak_pos,peak_height)) 
#
#    plt.hist(np.array(stupid_list))

    p((data[0],filtered[0]))
    print 'peak_positions:', map(int, peak_positions[0])
    
    peak_pussies = map(int, peak_positions[0])
    peak_h = []
    for i in peak_pussies:
        peak_h.append(data[0][i-8])


    pp = Plotter('peaks', x=peak_pussies)
    pp(peak_h)
    

    ret = raw_input('quit?')
    if ret=='q':
        break

#    peak_list.append( good_peaks )


#output = open(out_filename, 'wb')
#cPickle.dump(data_filename, output, -1)
#cPickle.dump(calib_filename, output, -1)
#cPickle.dump(peak_list, output, -1)