def find_offset_old(self,datafile, nonlinmin, nonlinmax, exclude, threshold):
'''find_offset is used to determine the systematic offset present
in the experimental setup that causes data to not be symmetric
about zero input angle. It reads in the output of laserBench and
returns the offset (in degrees)'''
input_a, output_a = np.loadtxt(datafile,usecols=(0,1),unpack=True)
for e in exclude:
did = np.where(input_a == e)
output_a = np.delete(output_a, did)
input_a = np.delete(input_a, did)
pidx = np.where(input_a > nonlinmax)
nidx = np.where(input_a < nonlinmin)
in_a = np.append(input_a[nidx],input_a[pidx])
out_a = np.append(-1*output_a[nidx],output_a[pidx])
error = np.zeros(in_a.size)+1
b = 1000.
offset = 0.
while abs(b) > threshold:
m, b = ADE.fit_line(in_a,out_a,error)
offset += b
in_a += b
return offset
def ratio_test(t_file,data_path):
T = thePot(t_file)
fits_list = glob(data_path+'/*.FIT')
volts = np.array([])
counts = np.array([])
for fits in fits_list:
print fits,
HDU = pyfits.open(fits)[0]
exptime = HDU.header['EXPTIME']
timestr = HDU.header['TIME-OBS']
obstime = np.float(timestr[6:])\
+ np.float(timestr[3:5])*60.\
+ np.float(timestr[0:2])*3600.
voltage = T.get_voltage(obstime,obstime+exptime,'V')
r, sb, e = ADE.fast_annulize(HDU.data,300)
r *= 0.024
flux = np.cumsum(sb)
rate = flux/exptime
ADU = np.interp(54/10.,r,rate)
print voltage, ADU
volts = np.append(volts,voltage)
counts = np.append(counts,ADU)
fit = ADE.fit_line(volts,counts,np.ones(counts.shape))
print fit
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(volts,counts,'.')
ax.set_xlabel('Voltage')
ax.set_ylabel('ADU/s')
fig.show()
def find_offset(self,datafile, nonlinmin, nonlinmax, exclude, threshold):
input_a, output_a = np.loadtxt(datafile,usecols=(0,1),unpack=True)
for e in exclude:
did = np.where(input_a == e)
output_a = np.delete(output_a, did)
input_a = np.delete(input_a, did)
pidx = np.where(input_a > nonlinmax)
nidx = np.where(input_a < nonlinmin)
in_a = np.append(input_a[nidx],input_a[pidx])
out_a = np.append(-1*output_a[nidx],output_a[pidx])
e = np.zeros(in_a.size)+1
b = 1000.
offset = 0.
while abs(b) > threshold:
m, b = ADE.fit_line(in_a,out_a,e)
offset += b
in_a += b
return offset
