def db_datedir(datedir_list, ABS, cursor):
for datedir in datedir_list:
print datedir
iv_files = glob.glob(datedir + '/iv_1?????????')
det_ivs = []
for iv_file in iv_files:
ctime = iv_file.split('v_')[1]
print iv_file
try:
db_bias = np.genfromtxt(iv_file + '.bias', skiprows=1)
rec_biases = fh.IV_data_to_arr(iv_file + '.out', 'rec_biases')
bias_volts = rec_biases[:,
0] * ABS.db_DAC_volts / 2**ABS.db_bits / ABS.Rdetb
data = fh.get_mce_data(iv_file, row_col=True)
for col in np.arange(ABS.num_cols):
Rsh = ABS.Rshunt_cols[col]
target_bias = bias_volts[ABS.bias_lines[col]]
for row in np.arange(ABS.num_rows):
# print 'col:', col,' row: ', row
data_tes = data[row, col, :]
if np.median(np.diff(
data_tes[0:ABS.index_normal])) > 0:
data_tes = data_tes * -1
clean = Clean()
data_tes = clean.data_fix_jumps(data_tes)
# tes=iv.IV(data=data_tes,db_bias=db_bias,Rsh=Rsh,filtgain=ABS.filtgain,Rfb=ABS.Rfb,Rdetb=ABS.Rdetb, index_normal=ABS.index_normal, dac_bits=ABS.dac_bits, M_ratio=ABS.M_ratio, db_bits=ABS.db_bits, fb_DAC_volts=ABS.fb_DAC_volts, db_DAC_volts=ABS.db_DAC_volts)
# Rn=np.median(tes.Rtes[0:ABS.index_normal])
# Psat=tes.calc_Psat_inter(Rn,PRn_min=0.75,PRn_max=0.85)
# fit=tes.fit_params()
# det=(int(ctime),int(col),int(row),Rsh,Rn,Psat,target_bias,fit[0],fit[1],fit[2],fit[3],fit[4],fit[5],fit[6])
# det_ivs.append(det)
except:
print sys.exc_info()
break
def db_datedir(datedir_list, ABS, cursor):
for datedir in datedir_list:
print datedir
iv_files=glob.glob(datedir+'/iv_1?????????')
det_ivs=[]
for iv_file in iv_files:
ctime=iv_file.split('v_')[1]
ind_miss=np.where(missing==int(ctime))[0]
print ind_miss
if len(ind_miss) > 0:
print iv_file
# try:
db_bias=np.genfromtxt(iv_file+'.bias',skiprows=1)
rec_biases=fh.IV_data_to_arr(iv_file+'.out','rec_biases')
bias_volts=rec_biases[:,0]*ABS.db_DAC_volts/2**ABS.db_bits/ABS.Rdetb
data=fh.get_mce_data(iv_file,row_col=True)
print "first col loop start"
for col in np.arange(ABS.num_cols):
Rsh=ABS.Rshunt_cols[col]
target_bias=bias_volts[ABS.bias_lines[col]]
for row in np.arange(ABS.num_rows):
# print 'col:', col,' row: ', row
data_tes=data[row,col,:]
if np.median(np.diff(data_tes[0:ABS.index_normal])) > 0:
data_tes=data_tes*-1
clean=Clean()
data_tes=clean.data_fix_jumps(data_tes)
tes=iv.IV(data=data_tes,db_bias=db_bias,Rsh=Rsh,filtgain=ABS.filtgain,Rfb=ABS.Rfb,Rdetb=ABS.Rdetb, index_normal=ABS.index_normal, dac_bits=ABS.dac_bits, M_ratio=ABS.M_ratio, db_bits=ABS.db_bits, fb_DAC_volts=ABS.fb_DAC_volts, db_DAC_volts=ABS.db_DAC_volts)
Rn=np.median(tes.Rtes[0:ABS.index_normal])
Psat=tes.calc_Psat_inter(Rn,PRn_min=0.75,PRn_max=0.85)
fit=tes.fit_params()
det=(int(ctime),int(col),int(row),Rsh,Rn,Psat,target_bias,fit[0],fit[1],fit[2],fit[3],fit[4],fit[5],fit[6])
det_ivs.append(det)
# except:
# print sys.exc_info()
# break
cursor.executemany('''INSERT INTO ivs(ctime,col,row,rsh,rn,psat,target_bias,fit0,fit1,fit2,fit3,fit4,fit5,fit6) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?)''',det_ivs)
db.commit()
ax1=fig.add_subplot(211)
ax2=fig.add_subplot(212)
fig=plt.figure(3)
ax4=fig.add_subplot(111)
#ax5=fig.add_subplot(312)
#ax6=fig.add_subplot(313)
Psat=np.zeros(len(temp))
i=0
for filename in filenames:
print filename
data=fh.get_mce_data(filename,row_col=True)
data_tes=data[row,col,:]*args.polarity
clean=Clean()
data_tes=clean.data_fix_jumps(data_tes)
db_bias=np.genfromtxt(filename+'.bias',skiprows=1)
if args.ABS:
tes=iv.IV(data_tes,db_bias,Rsh,filtgain=1311,Rfb=7080,Rdetb=582)
else:
tes=iv.IV(data_tes,db_bias,Rsh)
Ites,Vtes,Rtes,Ptes,Ibias=tes.IV_calib(index_normal)
Ibias_target=tes.detbias_dac_to_Ibias(target_bias)
if Ibias_target > 0:
index_target=np.where(Ibias < Ibias_target)[0][0]
print Ibias[index_target],Ibias_target
print 'Rtes target=',Rtes[index_target]
else:
index_target=0
resp=tes.resp(index_normal)
from matplotlib.backends.backend_pdf import PdfPages
parser = argparse.ArgumentParser(
description=
'analyses CLASS test array iv, picks optimal bias for each group',
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
fromfile_prefix_chars='@')
parser.add_argument('filename', type=str, help='I-V file to analyze')
parser.add_argument('--plot',
action='store_true',
help='plot I-V raw data and results for all detectors')
args = parser.parse_args()
filename = args.filename
test = param()
clean = Clean(thres_jump=test.thres_jump)
def find_optimal_bias_DAC(PRn_group, DAC_bias_group, PRn_low, PRn_high,
optimal_DAC_default):
DAC_bias_group_Rn_cut = []
for i in np.arange(len(DAC_bias_group)):
bias_set = DAC_bias_group[i]
PRn_set = PRn_group[i]
bias_set_cut = bias_set[np.where(
np.logical_and(PRn_set > PRn_low, PRn_set < PRn_high))]
DAC_bias_group_Rn_cut.append(bias_set_cut)
all_bias = np.concatenate(DAC_bias_group_Rn_cut, axis=0)
if len(all_bias) == 0:
all_bias = [optimal_DAC_default]
import matplotlib.pyplot as plt
import iv
import filehandler as fh
from Qband_param import Qband_param
from clean_timestream import Clean
import argparse
from matplotlib.backends.backend_pdf import PdfPages
parser=argparse.ArgumentParser(description= 'analyses CLASS Qband array iv, picks optimal bias for each group', formatter_class=argparse.ArgumentDefaultsHelpFormatter,fromfile_prefix_chars='@')
parser.add_argument('filename', type=str,help='I-V file to analyze')
parser.add_argument('--plot', action='store_true', help='plot I-V raw data and results for all detectors')
args=parser.parse_args()
filename=args.filename
Qband=Qband_param()
clean=Clean(thres_jump=Qband.thres_jump)
def find_optimal_bias_DAC(PRn_group,DAC_bias_group, PRn_low, PRn_high, optimal_DAC_default):
DAC_bias_group_Rn_cut=[]
for i in np.arange(len(DAC_bias_group)):
bias_set=DAC_bias_group[i]
PRn_set=PRn_group[i]
bias_set_cut=bias_set[np.where(np.logical_and(PRn_set > PRn_low, PRn_set < PRn_high))]
DAC_bias_group_Rn_cut.append(bias_set_cut)
all_bias=np.concatenate(DAC_bias_group_Rn_cut,axis=0)
if len(all_bias) == 0:
all_bias=[optimal_DAC_default]
bias_count=np.bincount(np.int0(all_bias))
common_biases=np.where(bias_count == np.max(bias_count))[0]