def Fit(model_file, obs_file, plot):
obs_values = tb.readtxt(obs_file,
['wg_lines', 'flux_lines', 'sflux_lines'])
model_values = tb.readtxt(model_file,
['wg_lines', 'flux_lines', 'sflux_lines'])
Lm, Lo = Match(model_values[0], obs_values[0])
if plot is True:
fig, ax1 = pl.subplots()
pl.xlabel('obs')
pl.ylabel('template')
ax1.plot(Lo, Lm, 'r^')
degree = 2
if (len(Lm) <= degree):
degree = int(len(Lm) - 1)
print "Changed degree to : ", degree
fit_coef = np.polyfit(Lo, Lm, deg=degree)
points = np.linspace(100, 1500, 100)
dispersion = np.polyval(fit_coef, Lo)
dispersion2 = np.polyval(fit_coef, points)
if plot is True:
ax1.plot(points, dispersion2, color='k')
fig, ax1 = pl.subplots()
ax1.plot(Lo, Lm - dispersion, 'r^')
pl.axhline(y=0., linewidth=1, color='k')
pl.xlabel('template')
pl.ylabel('residuals')
print 'rms residuals = ', np.std(Lm - dispersion)
return fit_coef, Lm
def BuildDefectMap(self):
xsize = int(np.round(self.x_end - self.x_start, 3))
ysize = int(np.round(self.y_end - self.y_start, 3))
defect_map = np.zeros([ysize, xsize])
if self.orientation == 'y':
defect_map = defect_map.T
xsize = ysize
'''Bad S/N for mono-> switch to broadband flat'''
#fits, wgth = tb.readtxt(self.monochro_flat_list, ['fits', 'wavelength'])
#path = self.monochro_flat_path
fits, wgth = tb.readtxt(self.masterflat_list, ['fits', 'wavelength'])
out = zip(*sorted(zip(wgth, fits)))
length = len(out[0])
if length == 1:
ref1 = instru.telinst(self.masterflat_path + out[1][0])
defect_map = Flip((ref1.Image(self.masterflat_path + out[1][0]))\
[self.y_start : self.y_end, self.x_start : self.x_end], flip=self.flip)
elif length > 1:
defect_map = self.SyntheticMap(defect_map, length, out, xsize)
if self.orientation == 'y':
defect_map = defect_map.T # To come back to the footprint
return defect_map
def BuildSyntheticFlat(self):
''' monitoring photodiode, R is in (mA/W)'''
wg, responsivity, qe =\
tb.readfile(self.spectrum.monitoring_photodiode_file ,\
['wavelength', 'responsivity','QE'])
xsize = self.spectrum.x_end - self.spectrum.x_start
ysize = self.spectrum.y_end - self.spectrum.y_start
syntheticflat = np.zeros([ysize, xsize])
pointsX = []
pointsY = []
values = []
fits, wgth = tb.readtxt(self.monochro_flat_list,
['fits', 'wavelength'])
for img, w in zip(fits, wgth):
pixel = self.dispersion.Wavelength2Pixel(w)
# ! pixel is relative to the object-> translate with respect to frame
pixel = self.dispersion.RefPos2FramePos(pixel)
flat = instru.telinst(self.monochro_flat_path + img)
head = flat.header
iphtot = head['IPHTOT']
siphtot = head['SIPHTOT']
image_data = tb.Flip((flat.Image(self.monochro_flat_path + img))\
[self.spectrum.y_start:self.spectrum.y_end, self.spectrum.x_start:self.spectrum.x_end], flip=self.spectrum.flip)
which_amp = flat.IsInAmp(self.spectrum.y_start,
self.spectrum.x_start,
Trimmed=True)
low = head['LOW' + str(which_amp)]
high = head['HIGH' + str(which_amp)]
'''remove 5s outliers'''
if (self.spectrum.orientation == 'x'):
signal = tb.Flip((image_data[:, int(pixel)])[(image_data[:, int(pixel)] > low)\
& (image_data[:, int(pixel)] < high)],flip=self.spectrum.flip)
if (self.spectrum.orientation == 'y'):
signal = tb.Flip((image_data[int(pixel),:])[(image_data[int(pixel),:] > low)\
& (image_data[int(pixel),:] < high)],flip=self.spectrum.flip)
respinterp = interp.griddata(wg, responsivity, w)
signal = np.mean(signal) / (iphtot / (respinterp))
'''Convert flux into nb of e_ because CCD counts e-, then to QE'''
logging.info('calib : ' + str(w) + str(signal))
logging.debug('Normalizing flat ' + img + ' by photodiode QE')
if (self.spectrum.plot):
if (self.spectrum.orientation == 'x'):
syntheticflat[:, int(pixel)] = signal
if (self.spectrum.orientation == 'y'):
syntheticflat[int(pixel), :] = signal
for i in range(int(ysize)):
pointsX.append(int(pixel))
pointsY.append(i)
values.append(signal)
values = values / max(values)
''' Build the synthetic flat'''
points = np.vstack((np.array(pointsY), np.array(pointsX))).T
values = (np.array(values)).T
grid_x, grid_y = np.mgrid[0:ysize, 0:xsize]
interp_map = griddata(points,
values, (grid_x, grid_y),
method='linear')
'''Control plots'''
if (self.spectrum.plot):
self.ControlPlot2(values, syntheticflat, interp_map)
return interp_map
import os, sys
import pylab as pl
import toolbox as tb
import numpy as np
from croaks import NTuple
if __name__ == "__main__":
narg = len(sys.argv)
if narg < 2:
print "joinlist.py file1 file2"
print "join points"
print
file1 = sys.argv[1]
file2 = sys.argv[2]
data1 = tb.readtxt(file1, ['img', 'wght', 'FLUX_AUTO'])
data2 = tb.readtxt(file2, ['FILENAME', 'WAVELENGTH', 'SPOT_0_FLUX'])
data1 = np.array(data1).transpose()
data2 = np.array(data2).transpose()
#data1 = data1[data1[:, 0].argsort()]
#data2 = data2[data2[:, 0].argsort()]
#parsing img number
for i, item2 in enumerate(data2):
img = ((item2[0].split("\\")[-1]).strip('.fits').split('_'))[-1]
data2[i, 0] = img
print i, data2[i, 0]
#sys.exit()
img = []
wght = []
for obj in objects:
for filt in filters:
colors = iter(cm.rainbow(np.linspace(0, 2, 200)))
print obj, filt
fig, axarr = pl.subplots(2, sharex=True)
nb = 0
for file in files:
split = re.split('[_.]', file)
Object = split[1]
Filter = split[2]
img = re.split('/', split[0])[-1]
if ((obj == Object) and (filt == Filter)):
nb += 1
#value, keys = tb.readlist(file)
values = tb.readtxt(
file,
['pixel', 'aper_flux', 'center', 'psf_flux', 'sigma'])
pl.ylabel('aper_flux / psf_flux')
pl.xlabel('pixel')
title = str(obj) + ' ' + str(filt)
print 'title :', title, img
pl.title(title)
color = next(colors)
axarr[0].plot(
values[0],
values[1],
color=color,
linewidth=1,
#linestyle='None',
drawstyle='steps',
default=None)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = grabargs()
file1 = args.file
plot = args.plot
name = args.value
site = args.site
method = args.distrib
MIN = args.min
MAX = args.max
print MIN, MAX
[hour, jd, value] = tb.readtxt(file1, ['entry', 'jd', 'value'])
base = 24. # if sampling is hourly
if plot:
fig = pl.figure(1)
pl.plot(hour / base, value, label=site + ' 2017')
pl.xlabel('time (days)')
pl.ylabel(name)
pl.legend()
value = value[(value < MAX) & (value > MIN)]
fig = pl.figure(2)
n, bins, patches = pl.hist(value,
50,
normed=True,
color='k',
label='amp ' + str(ampli) + ' g = ' + str(round(g, 2)) +
' (e/ADU)')
#pl.plot(meanflat,b*meanflat + c,'b') # show linear part
#pl.plot(meanflat,( meanflat/b + (1/(1+meanflat*c))), 'r')
pl.xlabel('flux (ADU)')
pl.ylabel('variance')
pl.legend(loc='upper left')
pl.title('Quadratc fit of the PTC')
out = [float(ampli), GAIN_HEADER, g, sg, c, c_error, a, a_error, KN]
return out
if __name__ == '__main__':
args = grabargs()
file = args.img
flux_min = 0.0
flux_max = 58000.0 * 2. # it's a pair!
values = tb.readtxt(file, ['ampl', 'mean_sum', 'rms_diff'])
values = np.array(values).transpose()
amps = ('11', '12', '21', '22')
colors = iter(cm.rainbow(np.linspace(0, 1, 4)))
for amp in amps:
ptc = values[(values[:, 0] == int(amp)) & (values[:, 1] >= flux_min) &
(values[:, 1] <= flux_max)]
meanflat = ptc[:, 1]
variance = ptc[:, 2]**2
determine_gain(meanflat, variance, amp, colors)
pl.show()