--- /dev/null
+#!/usr/bin/env python
+
+from pylab import *
+
+from glob import glob
+
+
+cal_values = []
+cal_outs = []
+
+N_RUNS = 8
+sample_runs = randint(48, size=(N_RUNS,))
+N_PLOTS = 4
+N_COLS = 4
+nrun = 0
+for infile in sorted(glob('*.npz')):
+ d = np.load(infile)
+
+ #print infile, d.keys()
+
+ guesses = d['guesses']
+ results = d['results']
+
+ d.close() # close file descriptor
+
+ if 1:
+ for i in range(N_PLOTS):
+ if nrun not in sample_runs:
+ continue
+ g = guesses[:, 0, i]
+ r = 1e3*(2.5/2/2**13)*results[:, 0, i]
+ #print g
+ #print r
+
+ subplot(N_PLOTS, N_COLS, N_COLS*i+1)
+ plot(g, '-')
+ ylim((0, 127))
+ ylabel('Channel %02i-A' % i)
+
+ subplot(N_PLOTS, N_COLS, N_COLS*i+2)
+ plot(r, '-')
+
+ subplot(N_PLOTS, N_COLS, 1)
+ title('calibration value')
+
+ subplot(N_PLOTS, N_COLS, 2)
+ title('Vout (mV)')
+
+ subplot(N_PLOTS, N_COLS, 3)
+ title('Cal histogram')
+
+ subplot(N_PLOTS, N_COLS, 4)
+ title('Vout (mV) histogram')
+
+ cal_values.append(guesses[-1, :, :])
+ cal_outs.append(results[-1, :, :])
+ nrun += 1
+
+cv = array(cal_values)
+co = array(cal_outs)
+
+for i in range(N_PLOTS):
+ subplot(N_PLOTS, N_COLS, N_COLS*i+3)
+ data = cv[:, 0, i]
+ hist(data, bins=range(128), align='left')
+ xlim((data.min()-1, data.max()+1))
+ xlim((0, 127))
+
+ subplot(N_PLOTS, N_COLS, N_COLS*i+4)
+ data = 1e3*(2.5/2/2**13)*co[:, 0, i]
+ dm = max((abs(data.min()), abs(data.max())))
+ print dm
+ dm = 200
+ hist(data, bins=100, range=(-dm, dm), align='left')
+ xlim((-dm, dm))
+
+
+print sample_runs