import numpy as np
import matplotlib.pyplot as plt
from demod_filter import demod_filter
import code.common.filehandler as fh
import glob
samp_rate = 50e6 / 100. / 22. / 38.
df = demod_filter(25, 2.0)
col = 2
row = 14
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111)
inter = np.zeros(131072)
inter2 = np.zeros(131072)
files = glob.glob(
'/data/cryo/20151204/det_on_2000_triangle_scan_call_0.5mms_25hz0.011')
#files=glob.glob('/data/cryo/20151203/det_on_2000_overnight_0.5mms_25hz0.???')
#files=glob.glob('/data/cryo/20151204/det_on_2000_triangle_scan_0.5mms_25hz0.???')
for f in files:
print f
data = fh.get_mce_data(f, row_col=True)
tes = data[row, col, :] - np.mean(data[row, col, :])
tes2 = data[14, col, :] - np.mean(data[14, col, :])
d_filter, d_fft = df.demod(tes, samp_rate)
d_filter2, d_fft2 = df.demod(tes2, samp_rate)
min_filter = np.min(d_filter)
max_filter = np.max(d_filter)
import numpy as np
import matplotlib.pyplot as plt
from demod_filter import demod_filter
import code.common.filehandler as fh
import glob
from scipy import signal
import mce_data
samp_rate = 50e6 / 100. / 22. / 38.
df = demod_filter(25, 4)
col_best = 2
row_best = 14
num_points = 131072
nrows = 22
ncols = 4
c0 = [0, 1, 10, 11, 12, 15, 19, 21]
c1 = [1, 2, 5, 6, 7, 14, 15, 17, 18]
c2 = [7, 8, 10, 11, 12, 14]
c3 = [1, 2, 3, 7, 8, 9, 10, 11, 12, 13]
row_arrays = [c0, c1, c2, c3]
if 1 == 0:
data = mce_data.SmallMCEFile(
'/data/cryo/20151204/det_on_2000_triangle_scan_call_0.5mms_25hz0.0')
ctime_init = data.header['runfile_id']
demod_all = np.array([])
for i in np.arange(273):