def get_mV_image(raw):
im = Image.Image(raw)
im.set_channel_groups((1,4))
im.FPN_correction(rowstart=0,rowstop=2999,target=0)
im.RTN_correction(False,colstart=0,colstop=3999,target=0)
im.multiply(-1/4000.*1200) # conversion to Vt (minus because DN = rst-sig = Vpix-Vt-DAC)
return im
#rows = 3136
#columns = 4352
rows = 3000
columns = 4352
#roi = (68,3135,0,3999)
col_chan = 16
n_adc = 6
col_start = n_adc * columns / 8
col_stop = col_start + columns / 8
PTNtoplot = []
for raw in [raw0, raw1]:
im0 = Image.Image(raw[1], rows=rows, columns=columns)
im1 = Image.Image(raw[2], rows=rows, columns=columns)
im0.set_ROI(roi)
im1.set_ROI(roi)
im1.subtract(im0)
arr = im1.get_array()
# to use to split adc:
#arr = arr[:,col_start:col_stop]
arr = arr[:, 0:4178]
arr_split = arr[:, 0::col_chan]
for i in range(1, col_chan):
arr_split = numpy.hstack((arr_split, arr[:, i::col_chan]))
raw0 = "F:\\INVISAGE\\PC6\\Column_response_loss\\Data\\20150821\\64A2\\image_64A2_20MHz_12dB_skew95.raw"
if bdark:
dark0 = "F:\\INVISAGE\\PC6\\Column_response_loss\\Data\\20150728\\Q8A752W03#31-A1\\Dark\\Dark_0000.raw"
rows = 3128
columns = 4352
#rows = 3128
#columns = 768
#columns = 1280
num_channels = 4
num_columns = 64
num_cds = 16 #1st CDS skipped
im0 = Image.Image(raw0, rows=rows, columns=columns)
if bdark:
dark0 = Image.Image(dark0, rows=rows, columns=columns)
im0.subtract(dark0)
im0.set_channel_groups((1, num_channels))
R_cds = numpy.zeros((num_channels, num_cds))
R_col = numpy.zeros((num_channels, num_columns))
gain_col = numpy.zeros((num_channels, num_columns))
for i_chan in range(num_channels):
#for i_chan in range(1):
profile_ch = im0.get_profile(axis=0, i_channel_group=i_chan)
#profile_ch = profile_ch[num_columns:]
bRTN=bRTN,
rtn_colstart=rtn_colstart,
rtn_colstop=rtn_colstop,
directory_suffix=directory_suffix,
bsave=False,
bfullresolution=bfullresolution,
blog=blog)
# cross-check on Ric's xtalk heatmap (horizontal)
bxcheck = False
if bxcheck:
file_heatmap = "F:\\INVISAGE\\PC3\\Crosstalk\\Data\\from_Ric\\GrOverR.txt"
f_heatmap = open(file_heatmap)
lines_heatmap = f_heatmap.readlines()
val = []
for i in range(len(lines_heatmap)):
val.append(lines_heatmap[i].split(',')[:-1])
arr = numpy.array(val).astype('float32')
print "Heatmap shape:", arr.shape
im_heatmap = Image.Image(arr)
im_heatmap.plot_image(minval=0.3,
maxval=0.5,
bsave=True,
filename=os.path.join(directory,
'Ric_xtalk.png'))
xmin = 6
#xmin = 0
xmax = 10
#xmax = 9
channel_groups = (1,4)
#channel_groups = (1,1)
num_channels = channel_groups[0] * channel_groups[1]
bswap = False
Npixels = 10 # number of pixel histogram to plot for each channel
PTN_min = 10.0
PTN_max = 10.5
im0 = Image.Image(raw0[0])
im0.set_channel_groups(channel_groups)
im0.set_ROI(roi)
tmp_mean = []
tmp_mean_row = []
tmp_mean_column = []
tmp_rms = []
tmp_rms_row = []
tmp_rms_column = []
for i_channel_group in range(num_channels):
tmp_mean.append(im0.zeroarray(bROI=True,channel=i_channel_group))
tmp_mean_row.append(numpy.zeros(tmp_mean[-1].shape[0]))
tmp_mean_column.append(numpy.zeros(tmp_mean[-1].shape[1]))
tmp_rms.append(im0.zeroarray(bROI=True,channel=i_channel_group))
tmp_rms_row.append(numpy.zeros(tmp_rms[-1].shape[0]))