/
cubes_e2e.py
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cubes_e2e.py
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# ------------------------------------------------------------------------------
# CUBES_E2E
# Simulate the spectral format of CUBES and estimate the SNR for an input spectrum
# Guido Cupani - INAF-OATs
# ------------------------------------------------------------------------------
from cubes_e2e_config import *
from astropy import units as au
from astropy.io import ascii, fits
from astropy.modeling.fitting import LevMarLSQFitter as lm
from astropy.modeling.functional_models import Gaussian1D, Gaussian2D, Moffat2D
from astropy.table import Table
import matplotlib
from matplotlib import gridspec
from matplotlib import pyplot as plt
plt.rcParams.update({'font.size': 14})
from matplotlib import patches
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
from scipy.interpolate import CubicSpline as cspline #interp1d
from scipy.interpolate import UnivariateSpline as uspline
from scipy.ndimage import gaussian_filter, interpolation
from scipy.special import expit
import sys
import warnings
warnings.filterwarnings('ignore', '.*output shape of zoom.*')
class CCD(object):
def __init__(self, psf, spec, xsize=ccd_xsize, ysize=ccd_ysize,
xbin=ccd_xbin, ybin=ccd_ybin,
pix_xsize=pix_xsize, pix_ysize=pix_ysize,
spat_scale=spat_scale, slice_n=slice_n, func=extr_func):
self.psf = psf
self.spec = spec
self.xsize = xsize/xbin
self.ysize = ysize/ybin
self.xbin = xbin
self.ybin = ybin
self.npix = xbin*ybin
self.pix_xsize = pix_xsize*xbin
self.pix_ysize = pix_ysize*ybin
self.spat_scale = spat_scale
self.func = func
#self.signal = np.zeros((int(self.ysize.value), int(self.xsize.value)))
def add_arms(self, n, wave_sampl, wave_d, wave_d_shift):
#self.arm_n = n
#s = int(self.xsize.value/(n*3-1))
#self.xcens = np.arange(s, self.xsize.value, 3*s)
self.xcens = [self.xsize.value//2]*n
self.signal = np.zeros((int(self.ysize.value), int(self.xsize.value), n))
self.dsignal = np.zeros((int(self.ysize.value), int(self.xsize.value), n))
self.noise = np.zeros((int(self.ysize.value), int(self.xsize.value), n))
#print(ccd_dark, ccd_gain, self.npix, self.spec.phot.texp)
self.dark = np.sqrt((ccd_dark*ccd_gain*self.npix*self.spec.phot.texp)\
.to(au.photon).value)
self.ron = (ccd_ron*ccd_gain).value
"""
if n == 3:
self.wmins = [305, 328, 355] * au.nm
self.wmaxs = [335, 361, 390] * au.nm
self.wmins_d = [300, 331.5, 358] * au.nm # Dichroich transition wavelengths
self.wmaxs_d = [331.5, 358, 400] * au.nm
elif n == 2:
self.wmins = [305, 343] * au.nm
self.wmaxs = [350, 390] * au.nm
self.wmins_d = [300, 347.5] * au.nm # Dichroich transition wavelengths
self.wmaxs_d = [347.5, 400] * au.nm
"""
#print(self.wmins)
#print(self.wmaxs)
#print(self.wmins_d)
#print(self.wmaxs_d)
if arm_n > 1:
wmax = wave_d[0]+wave_d_shift
wmin = wmax-self.ysize*wave_sampl[0]
#print(wmin)
dw = np.full(int(self.ysize.value), 0.5*(wmin.value+wmax.value))
for j in range(10):
wmin2 = wmax.value-np.sum(cspline(disp_wave.value, disp_sampl*ccd_ybin)(dw))
dw = np.linspace(wmin2, wmax.value, int(self.ysize.value))
wmin = wmin2*wmin.unit
#print(wmin)
self.wmins = np.array([wmin.to(au.nm).value])
self.wmaxs = np.array([wmax.to(au.nm).value])
self.wmins_d = np.array([wmin.to(au.nm).value])
self.wmaxs_d = np.array([wave_d[0].to(au.nm).value])
for i in range(len(wave_d)):
wmin = wave_d[i]-wave_d_shift
wmax = wmin+self.ysize*wave_sampl[i+1]
#print(wmax)
dw = np.full(int(self.ysize.value), 0.5*(wmin.value+wmax.value))
for j in range(10):
wmax2 = wmin.value+np.sum(cspline(disp_wave.value, disp_sampl*ccd_ybin)(dw))
dw = np.linspace(wmin.value, wmax2, int(self.ysize.value))
wmax = wmax2*wmax.unit
#print(wmax)
self.wmins = np.append(self.wmins, wmin.to(au.nm).value)
self.wmaxs = np.append(self.wmaxs, wmax.to(au.nm).value)
self.wmins_d = np.append(self.wmins_d, wave_d[i].to(au.nm).value)
try:
self.wmaxs_d = np.append(self.wmaxs_d, wave_d[i+1].to(au.nm).value)
except:
self.wmaxs_d = np.append(self.wmaxs_d, wmax.to(au.nm).value)
else:
wcen = 347.5*au.nm
wmin = wcen-self.ysize.value//2*self.ysize.unit*wave_sampl[0]
wmax = wcen+self.ysize.value//2*self.ysize.unit*wave_sampl[-1]
dwmin = np.full(int(self.ysize.value), wcen)
dwmax = np.full(int(self.ysize.value), wcen)
for j in range(10):
wmin2 = wcen.value-np.sum(cspline(disp_wave.value, disp_sampl*ccd_ybin)(dwmin))/2
wmax2 = wcen.value+np.sum(cspline(disp_wave.value, disp_sampl*ccd_ybin)(dwmax))/2
dwmin = np.linspace(wmin2, wcen.value, int(self.ysize.value))
dwmax = np.linspace(wcen.value, wmax2, int(self.ysize.value))
wmin = wmin2*wmin.unit
wmax = wmax2*wmax.unit
#print(wmin)
self.wmins = np.array([wmin.to(au.nm).value])
self.wmaxs = np.array([wmax.to(au.nm).value])
self.wmins_d = np.array([wmin.to(au.nm).value])
self.wmaxs_d = np.array([wmax.to(au.nm).value])
self.wmins_d[0] = 200
self.wmaxs_d[-1] = 500
self.wmins = self.wmins * au.nm
self.wmaxs = self.wmaxs * au.nm
self.wmins_d = self.wmins_d * au.nm
self.wmaxs_d = self.wmaxs_d * au.nm
#"""
self.mod_init = []
self.sl_cen = []
self.spec.m_d = []
self.spec.M_d = []
self.spec.arm_wave = []
self.spec.arm_targ = []
self.spec.tot_eff = self.tot_eff
self.targ_sum = 0
self.bckg_sum = 0
self.targ_prof = []
self.bckg_prof = []
self.targ_noise_max = []
self.bckg_noise_med = []
self.spec.fwhm = []
self.spec.resol = []
self.eff_wave = []
self.eff_adc = []
self.eff_slc = []
self.eff_dch = []
self.eff_spc = []
self.eff_grt = []
self.eff_ccd = []
self.eff_tel = []
self.eff_tot = []
for i, (x, m, M, m_d, M_d) in enumerate(zip(
self.xcens, self.wmins, self.wmaxs, self.wmins_d, self.wmaxs_d)):
self.sl_targ_prof = []
self.sl_bckg_prof = []
self.arm_counter = i
self.arm_range = np.logical_and(self.spec.wave.value>m.value,
self.spec.wave.value<M.value)
self.arm_wave = self.spec.wave[self.arm_range].value
self.arm_targ = self.spec.targ_conv[self.arm_range].value
xlength = int(slice_length/self.spat_scale/self.pix_xsize)
self.sl_hlength = xlength // 2
self.psf_xlength = int(np.ceil(self.psf.seeing/self.spat_scale
/self.pix_xsize))
xspan = xlength + int(slice_gap.value/self.xbin)
xshift = (slice_n*xspan+xlength)//2
self.add_slices(int(x), xshift, xspan, self.psf_xlength,
wmin=m.value, wmax=M.value, wmin_d=m_d.value,
wmax_d=M_d.value)
self.spec.m_d.append(m_d.value)
self.spec.M_d.append(M_d.value)
self.spec.arm_wave.append(self.arm_wave)
self.spec.arm_targ.append(self.arm_targ)
#sampl = (M-m)/self.ysize
#fwhm = [w/resol[i]/wave_sampl[i] for w in [m, M]]
#self.spec.fwhm = np.vstack((self.fwhm, self.arm_wave/resol[i]/wave_sampl[i]))
spl_sel = np.where(np.logical_and(disp_wave.value>np.min(self.arm_wave),
disp_wave.value<np.max(self.arm_wave)))[0]
if len(spl_sel)>1:
spl_wave = disp_wave[spl_sel]
spl_sampl = disp_sampl[spl_sel]
spl_resol = np.array(disp_resol)[spl_sel]
else:
spl_wave = disp_wave
spl_sampl = disp_sampl
spl_resol = np.array(disp_resol)
disp = cspline(spl_wave, spl_resol*spl_sampl*ccd_ybin)(self.arm_wave)
#self.spec.fwhm.append(self.arm_wave/resol[i]/wave_sampl[i])
self.spec.fwhm.append(self.arm_wave/disp)
self.spec.resol.append(cspline(spl_wave, spl_resol)(self.arm_wave))
self.targ_prof = np.append(self.targ_prof, self.sl_targ_prof)
self.bckg_prof = np.append(self.bckg_prof, self.sl_bckg_prof)
self.sl_targ_peak = self.sl_targ_prof[self.sl_targ_prof>99e-2*np.max(self.sl_targ_prof)]
self.sl_targ_prof = self.sl_targ_prof * au.ph
self.sl_bckg_prof = self.sl_bckg_prof * au.ph
self.targ_noise_max = np.append(self.targ_noise_max, np.sqrt(np.max(self.sl_targ_prof.value)))
self.bckg_noise_med = np.append(self.bckg_noise_med, np.sqrt(np.median(self.sl_bckg_prof.value)))
print("Slices projected onto arms. ")
self.targ_peak = self.targ_prof[self.targ_prof>99e-2*np.max(self.targ_prof)]
self.targ_prof = self.targ_prof * au.ph
self.bckg_prof = self.bckg_prof * au.ph
self.targ_sum = self.targ_sum * au.ph
self.bckg_sum = self.bckg_sum * au.ph
self.spec.targ_sum = self.targ_sum
self.targ_noise_max = self.targ_noise_max * au.ph/au.pixel
self.bckg_noise_med = self.bckg_noise_med * au.ph/au.pixel
"""
print("Flux on the CCD:")
print(" from target: %2.3e %s" % (self.targ_sum.value, self.targ_sum.unit))
print(" from background: %2.3e %s" % (self.bckg_sum.value, self.bckg_sum.unit))
print("Median noise on the CCD:")
print(" from target: %2.3e %s" % (np.median(np.sqrt(self.targ_peak)), self.targ_prof.unit/au.pixel))
print(" from background: %2.3e %s" % (np.median(np.sqrt(self.bckg_prof.value)), self.bckg_prof.unit/au.pixel))
print(" from dark current: %2.3e %s" % (self.dark, au.ph/au.pixel))
print(" from readout: %2.3e %s" % (self.ron, au.ph/au.pixel))
"""
#print(self.sl_targ_sum, self.sl_bckg_sum, self.sl_targ_sum+self.sl_bckg_sum)
#print(np.sum(self.signal))
#self.shot = np.sqrt(self.signal)
#self.noise = np.sqrt(self.shot**2 + self.dark**2 + self.ron**2)
#print("Median shot noise: %2.3e ph/pix" % np.nanmedian(self.shot[self.shot>0]))
#print("Dark noise: %2.3e ph/pix" % self.dark)
#print("Readout noise: %2.3e ph/pix" % self.ron)
#print("Median total noise: %2.3e ph/pix" % np.nanmedian(self.noise[self.shot>0]))
#self.noise_rand = np.random.normal(0., np.abs(self.noise), self.signal.shape)
self.image = np.round(self.signal + self.dsignal)
def add_slice(self, trace, trace_targ, trace_bckg, xcen, wmin, wmax, wmin_d, wmax_d):
wave_in = self.spec.wave.value
# Normalization
targ_sum = np.sum(self.spec.targ_conv)
bckg_sum = np.sum(self.spec.bckg_conv)
targ = self.spec.targ_conv[np.logical_and(wave_in>self.wmins[0].value, wave_in<self.wmaxs[-1].value)]
bckg = self.spec.bckg_conv[np.logical_and(wave_in>self.wmins[0].value, wave_in<self.wmaxs[-1].value)]
wave_red = wave_in[np.logical_and(wave_in>self.wmins[0].value, wave_in<self.wmaxs[-1].value)]
self.spec.targ_norm = targ/targ_sum #np.sum(targ)
self.spec.bckg_norm = bckg/bckg_sum #np.sum(bckg)
if wmin is not None and wmax is not None or 1==1:
#norm = self.spec.norm_conv[np.logical_and(
# self.spec.wave.value>wmin, self.spec.wave.value<wmax)]
"""
targ = self.spec.targ_conv[np.logical_and(
self.spec.wave.value>wmin, self.spec.wave.value<wmax)]
bckg = self.spec.bckg_conv[np.logical_and(
self.spec.wave.value>wmin, self.spec.wave.value<wmax)]
"""
targ = self.spec.targ_norm[np.logical_and(wave_red>wmin, wave_red<wmax)]
bckg = self.spec.bckg_norm[np.logical_and(wave_red>wmin, wave_red<wmax)]
wave = self.wave_grid(wmin, wmax)
else:
#norm = self.spec.norm_conv
targ = self.spec.targ_norm
bckg = self.spec.bckg_norm
sl_trace = self.rebin(trace, self.sl_hlength*2)
sl_trace_targ = self.rebin(trace_targ, self.sl_hlength*2)
sl_trace_bckg = self.rebin(trace_bckg, self.sl_hlength*2)
"""
sl_norm = self.rebin(norm.value, self.ysize.value)
sl_norm_bckg = np.ones(sl_norm.shape)
sl_norm_bckg = sl_norm_bckg/np.sum(sl_norm_bckg)
"""
sl_targ = self.rebin(targ.value, self.ysize.value)
sl_bckg = self.rebin(bckg.value, self.ysize.value)
#sl_targ = self.rebin(targ.value/np.sum(targ.value), self.ysize.value)
#sl_bckg = self.rebin(bckg.value/np.sum(bckg.value), self.ysize.value)
#"""
#print(targ.value/np.sum(targ.value))
#print(self.spec.targ_norm)
# Efficiency
if wmin_d is not None and wmax_d is not None:
#sl_norm = sl_norm * self.tot_eff(wave, wmin_d, wmax_d)
tot_eff = self.tot_eff(wave, wmin_d, wmax_d)
sl_targ = sl_targ * tot_eff
sl_bckg = sl_bckg * tot_eff
#signal = np.round(np.multiply.outer(sl_norm, sl_trace))
#signal = np.round(np.multiply.outer(sl_norm, sl_trace_targ)+np.multiply.outer(sl_norm_bckg, sl_trace_bckg))
sl_targ_prof = np.multiply.outer(sl_targ, sl_trace_targ)
sl_bckg_prof = np.multiply.outer(sl_bckg, sl_trace_bckg)
self.targ_sum += np.sum(sl_targ_prof)
self.bckg_sum += np.sum(sl_bckg_prof)
self.sl_targ_prof = np.append(self.sl_targ_prof, sl_targ_prof)
self.sl_bckg_prof = np.append(self.sl_bckg_prof, sl_bckg_prof)
signal = np.round(sl_targ_prof+sl_bckg_prof)
targ_noise = np.random.normal(0., 1., sl_targ_prof.shape)*np.sqrt(sl_targ_prof)
bckg_noise = np.random.normal(0., 1., sl_bckg_prof.shape)*np.sqrt(sl_bckg_prof)
dsignal = targ_noise+bckg_noise
noise = np.sqrt(targ_noise**2 + bckg_noise**2 + self.dark**2 + self.ron**2)
#print(np.mean(dsignal))
#self.signal[:,xcen-self.sl_hlength:xcen+self.sl_hlength] = signal
self.signal[:,xcen-self.sl_hlength:xcen+self.sl_hlength][:,:,self.arm_counter] = signal
self.dsignal[:,xcen-self.sl_hlength:xcen+self.sl_hlength][:,:,self.arm_counter] = dsignal
self.noise[:,xcen-self.sl_hlength:xcen+self.sl_hlength][:,:,self.arm_counter] = noise
#return sl_hlength, sl_trace, sl_norm, np.mean(signal)
return sl_trace, sl_targ, np.mean(signal)
def add_slices(self, xcen, xshift, xspan, psf_xlength, wmin, wmax, wmin_d,
wmax_d):
xcens = range(xcen-xshift, xcen+xshift, xspan)
for s, (c, t, t_t, t_b) in enumerate(zip(xcens[1:], self.psf.traces, self.psf.traces_targ, self.psf.traces_bckg)):
print("Projecting slice %i onto arm %i..." % (s, self.arm_counter), end='\r')
_, _, sl_msignal = self.add_slice(t, t_t, t_b, c, wmin, wmax, wmin_d, wmax_d)
self.mod_init.append(
Gaussian1D(amplitude=sl_msignal, mean=c, stddev=psf_xlength))
self.sl_cen.append(c)
def draw(self):
fig_p, self.ax_p = plt.subplots(figsize=(10,7))
self.ax_p.set_title("Photon balance (CCD)")
sl_v = np.array([self.spec.targ_tot.value, np.sum(self.psf.targ_slice.value),
np.sum(self.psf.z_targ.value)-np.sum(self.psf.targ_slice.value),
self.targ_sum.value, np.sum(self.psf.z_targ.value)-self.targ_sum.value])
sl_l = ["target: %2.3e %s" % (sl_v[0], self.spec.targ_tot.unit),
"on slit: %2.3e %s" % (sl_v[1], self.psf.z_targ.unit),
"off slit: %2.3e %s" % (sl_v[2], self.psf.z_targ.unit),
"on CCD: %2.3e %s" % (sl_v[3], self.targ_sum.unit),
"off CCD: %2.3e %s" % (sl_v[4], self.targ_sum.unit)]
sl_c = ['C0', 'C0', 'C0', 'C0', 'C0']
p0 = self.ax_p.pie([sl_v[0]], colors=[sl_c[0]], startangle=90, radius=1,
wedgeprops=dict(width=0.2, edgecolor='w'))
p1 = self.ax_p.pie(sl_v[1:3], colors=sl_c[1:3], startangle=90, radius=0.8,
wedgeprops=dict(width=0.2, edgecolor='w'))
p2 = self.ax_p.pie(sl_v[3:], colors=sl_c[3:], autopct='%1.1f%%', startangle=90, radius=0.6,
wedgeprops=dict(width=0.2, edgecolor='w'))
p1[0][0].set_alpha(2/3)
p1[0][1].set_alpha(1/6)
p2[0][0].set_alpha(1/3)
p2[0][1].set_alpha(1/6)
self.ax_p.legend([p0[0][0],p1[0][0],p2[0][0]], [sl_l[0]]+sl_l[1::2])
fig_r, self.ax_r = plt.subplots(figsize=(5,5))
self.ax_r.set_title("Pixel size")
xsize = pix_xsize*ccd_xbin
ysize = pix_ysize*ccd_ybin
xreal = pix_xsize*ccd_xbin*spat_scale
yreal = np.mean(disp_sampl.value)*ccd_ybin*disp_sampl.unit*au.pixel
size = max(xsize.value, ysize.value)
self.ax_r.add_patch(patches.Rectangle((0,0), xsize.value, ysize.value, edgecolor='b', facecolor='b', alpha=0.3))
self.ax_r.set_xlim(-0.2*size, size*1.2)
self.ax_r.set_ylim(-0.2*size, size*1.2)
for x in np.arange(0.0, xsize.value, pix_xsize.value):
self.ax_r.axvline(x, 1/7, 1/7+5/7*ysize.value/size, linestyle=':')
for y in np.arange(0.0, ysize.value, pix_ysize.value):
self.ax_r.axhline(y, 1/7, 1/7+5/7*xsize.value/size, linestyle=':')
self.ax_r.text(0.5*xsize.value, -0.1*ysize.value,
'%3.1f %s = %3.1f %s' % (xsize.value, xsize.unit, xreal.value, xreal.unit), ha='center', va='center')
self.ax_r.text(-0.1*xsize.value, 0.5*ysize.value,
'%3.1f %s ~ %3.2e %s' % (ysize.value, ysize.unit, yreal.value, yreal.unit), ha='center', va='center',
rotation=90)
self.ax_r.set_axis_off()
fig_s, self.ax_s = plt.subplots(3, 1, figsize=(10,10), sharex=True)
self.ax_s[0].set_title("Resolution and sampling")
for i in range(arm_n):
self.ax_s[0].plot(self.spec.arm_wave[i], self.spec.resol[i], label='Arm %i' % i, color='C0', alpha=1-i/arm_n)
self.ax_s[0].get_xaxis().set_visible(False)
self.ax_s[0].set_ylabel('Resolution')
#print(disp_wave, np.min(self.spec.arm_wave[i]), np.max(self.spec.arm_wave[i]))
"""
arm_sel = np.where(np.logical_and(disp_wave.value>np.min(self.spec.arm_wave[i]),
disp_wave.value<np.max(self.spec.arm_wave[i])))
print(disp_wave.value, np.min(self.spec.arm_wave[i]), np.max(self.spec.arm_wave[i]), disp_wave[arm_sel])
try:
self.ax_s[1].plot(self.spec.arm_wave[i],
cspline(disp_wave[arm_sel], disp_sampl[arm_sel]*ccd_ybin)(self.spec.arm_wave[i]),
label='Arm %i' % i, color='C0', alpha=1-i/arm_n)
except:
self.ax_s[1].plot(self.spec.arm_wave[i],
cspline(disp_wave, disp_sampl*ccd_ybin)(self.spec.arm_wave[i]),
label='Arm %i' % i, color='C0', alpha=1-i/arm_n)
"""
spl_sel = np.where(np.logical_and(disp_wave.value>np.min(self.spec.arm_wave[i]),
disp_wave.value<np.max(self.spec.arm_wave[i])))[0]
if len(spl_sel)>1:
spl_wave = disp_wave[spl_sel]
spl_sampl = disp_sampl[spl_sel]
else:
spl_wave = disp_wave
spl_sampl = disp_sampl
self.ax_s[1].plot(self.spec.arm_wave[i], cspline(spl_wave, spl_sampl*ccd_ybin)(self.spec.arm_wave[i]),
label='Arm %i' % i, color='C0', alpha=1-i/arm_n)
self.ax_s[1].get_xaxis().set_visible(False)
self.ax_s[1].set_ylabel('Sampling (%s/%s)' % (au.nm, au.pixel))
self.ax_s[2].plot(self.spec.arm_wave[i], self.spec.fwhm[i], label='Arm %i' % i, color='C0', alpha=1-i/arm_n)
self.ax_s[2].set_xlabel('Wavelength (%s)' % au.nm)
self.ax_s[2].set_ylabel('FWHM (%s)' % au.pixel)
self.ax_s[2].axhline(2.0, linestyle=':')
self.ax_s[2].text(np.min(self.spec.arm_wave[0]), 2.0, "Nyquist limit", ha='left', va='bottom')
self.ax_s[2].legend()
fig_s.subplots_adjust(hspace=0)
fig_e, self.ax_e = plt.subplots(figsize=(10,7))
self.ax_e.set_title("Efficiency")
for i in range(0,arm_n*slice_n,slice_n): # Only one slice per arm is plotted, as they are the same efficiency-wise
self.ax_e.plot(self.eff_wave[i], self.eff_adc[i], label='ADC' if i==0 else '', color='C0', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_slc[i], label='Slicer' if i==0 else '', color='C1', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_dch[i], label='Dichroich' if i==0 else '', color='C2', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_spc[i], label='Spectrograph' if i==0 else '', color='C3', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_grt[i], label='Grating' if i==0 else '', color='C4', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_ccd[i], label='CCD' if i==0 else '', color='C5', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_tel[i], label='Telescope' if i==0 else '', color='C6', linestyle=':')
self.ax_e.plot(self.eff_wave[i], self.eff_tot[i], label='Total' if i==0 else '', color='C0')
self.ax_e.plot(self.eff_wave[i], self.eff_tot[i]*cspline(self.spec.wave, self.spec.atmo_ex)(self.eff_wave[i]),
label='Total including extinction' if i==0 else '', color='C0', linestyle='--')
self.ax_e.scatter(eff_wave, eff_adc, color='C0')
self.ax_e.scatter(eff_wave, eff_slc, color='C1')
self.ax_e.scatter(eff_wave, eff_dch, color='C2')
self.ax_e.scatter(eff_wave, eff_spc, color='C3')
self.ax_e.scatter(eff_wave, eff_grt, color='C4')
self.ax_e.scatter(eff_wave, eff_ccd, color='C5')
self.ax_e.scatter(eff_wave, eff_tel, color='C6')
self.ax_e.set_xlabel('Wavelength (%s)' % au.nm)
self.ax_e.set_ylabel('Fractional throughput')
self.ax_e.legend()
fig_b, self.ax_b = plt.subplots(figsize=(7,7))
self.ax_b.set_title("Noise breakdown")
x = np.arange(arm_n)
self.ax_b.bar(x-0.3, self.targ_noise_max.value, width=0.2, label='Target (maximum)')
self.ax_b.bar(x-0.1, self.bckg_noise_med.value, width=0.2, label='Background (median)')
self.ax_b.bar(x+0.1, self.dark, width=0.2, label='Dark')
self.ax_b.bar(x+0.3, self.ron, width=0.2, label='Read-out')
self.ax_b.set_xlabel('Arm')
self.ax_b.set_ylabel('Noise (%s)' % self.targ_noise_max.unit)
self.ax_b.set_xticks(range(arm_n))
self.ax_b.legend()
#self.ax_b.set_xlabels(range(arm_n)+1)
image = np.zeros(self.image.shape)
thres = np.infty
image[self.image > thres] = thres
image[self.image < thres] = self.image[self.image < thres]
for i in range(arm_n):
#if fig is None:
fig, self.ax = plt.subplots(figsize=(8,8))
divider = make_axes_locatable(self.ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
im = self.ax.imshow(image[:,:,i], vmin=0)
self.ax.set_title('CCD')
self.ax.set_xlabel('X')
self.ax.set_ylabel('Y')
self.ax.text(0.025, 0.025, "Total: %1.3e %s"
% (np.sum(self.signal), au.photon),
ha='left', va='bottom', color='white',
transform=self.ax.transAxes)
cax.xaxis.set_label_position('top')
cax.set_xlabel(au.ph)
fig.colorbar(im, cax=cax, orientation='vertical')
def extr_arms(self, n, slice_n):
wave_snr = np.arange(self.wmins[0].value, self.wmaxs[-1].value, snr_sampl.value)
self.spec.wave_snr = wave_snr
for a in range(n):
wave_extr = self.wave_grid(self.wmins[a], self.wmaxs[a])
flux_extr = 0
err_extr = 0
b = []
for s in range(slice_n):
print("Extracting slice %i from arm %i..." % (s, a), end='\r')
i = a*slice_n+s
x = range(self.sl_cen[i]-self.sl_hlength,
self.sl_cen[i]+self.sl_hlength)
s_extr = np.empty(int(self.ysize.value))
n_extr = np.empty(int(self.ysize.value))
for p in range(int(self.ysize.value)):
y = self.image[p, self.sl_cen[i]-self.sl_hlength:
self.sl_cen[i]+self.sl_hlength, a]
b1 = np.median(self.image[p, self.sl_cen[i]-self.sl_hlength+1:
self.sl_cen[i]-self.sl_hlength+6, a])
b2 = np.median(self.image[p, self.sl_cen[i]+self.sl_hlength-6:
self.sl_cen[i]+self.sl_hlength-1, a])
b.append(0.5*(b1+b2))
y = y - b[-1]
dy = self.noise[p, self.sl_cen[i]-self.sl_hlength:
self.sl_cen[i]+self.sl_hlength, a]
s_extr[p], n_extr[p] = getattr(self, 'extr_'+self.func)\
(y, dy=dy, mod=self.mod_init[i], x=x, p=p)
flux_extr += s_extr
err_extr = np.sqrt(err_extr**2 + n_extr**2)
#print(flux_extr, err_extr)
dw = (wave_extr[2:]-wave_extr[:-2])*0.5
dw = np.append(dw[:1], dw)
dw = np.append(dw, dw[-1:])
#print(np.mean(b), np.median(b))
flux_extr = flux_extr / dw
err_extr = err_extr / dw
"""
print("Median error of extraction: %2.3e" % np.nanmedian(err_extr))
flux_window = flux_extr#[3000//ccd_xbin:3100//ccd_ybin]
print("RMS of extraction: %2.3e"
% np.sqrt(np.nanmean(np.square(
flux_window-np.nanmean(flux_window)))))
"""
#wave_snr = wave_extr[::snr_sampl]
snr_extr = flux_extr/err_extr
snr_extr[np.where(np.isnan(snr_extr))] = 0
snr_extr[np.where(np.isinf(snr_extr))] = 0
snr_spl = cspline(wave_extr, snr_extr)(wave_snr)
snr_spl[wave_snr<self.wmins[a].value] = 0.0
snr_spl[wave_snr>self.wmaxs[a].value] = 0.0
if a == 0:
self.spec.snr = snr_spl
"""
line.set_label('Extracted')
"""
else:
self.spec.snr = np.sqrt(self.spec.snr**2+snr_spl**2)
#snr = uspline(wave_extr, snr_extr)(wave_snr)
if a==0:
self.spec.wave_extr = np.array(wave_extr)
self.spec.flux_extr = np.array(flux_extr)
self.spec.err_extr = np.array(err_extr)
else:
self.spec.wave_extr = np.vstack((self.spec.wave_extr, wave_extr.value))
self.spec.flux_extr = np.vstack((self.spec.flux_extr, flux_extr.value))
self.spec.err_extr = np.vstack((self.spec.err_extr, err_extr.value))
"""
linet, = self.spec.ax_snr.plot(wave_snr, snr, c='black')
if a == 0:
line.set_label('Extracted')
linet.set_label('SNR')
self.spec.ax_snr.text(self.wmaxs[2].value, 0,
"Median SNR: %2.1f" % np.median(snr),
ha='right', va='bottom')
self.spec.ax_snr.legend(loc=2)
"""
"""
linet, = self.spec.ax_snr.plot(wave_snr, snr, linestyle='--', c='black')
linet.set_label('SNR')
self.spec.ax_snr.text(0.99, 0.92,
"Median SNR: %2.1f" % np.median(snr),
ha='right', va='top',
transform=self.spec.ax_snr.transAxes)
self.spec.ax_snr.legend(loc=2, fontsize=8)
"""
print("Slices extracted from arms. ")
#print(len(self.spec.flux_extr))
self.spec.flux_extr = self.spec.flux_extr * au.ph
#flux_final = self.spec.flux_extr
#print(self.spec.flux_extr.value, self.wmaxs.value, self.wmins.value)
if arm_n > 1:
self.spec.flux_final_tot = np.sum([np.sum(f)/len(f) * (M-m) for f, M, m
in zip(self.spec.flux_extr.value, self.wmaxs.value, self.wmins.value)])
else:
self.spec.flux_final_tot = np.sum(self.spec.flux_extr.value)/len(self.spec.flux_extr.value) \
* (self.wmaxs.value-self.wmins.value)
self.spec.flux_final_tot = self.spec.flux_final_tot * au.ph
"""
print("Flux extracted: ")
print(" from target: %2.3e %s" % (flux_final_tot, flux_final.unit))
"""
def extr_sum(self, y, dy, **kwargs):
sel = np.s_[self.sl_hlength-self.psf_xlength
:self.sl_hlength+self.psf_xlength]
ysel = y[sel]
dysel = dy[sel]
s = np.sum(ysel)
n = np.sqrt(np.sum(dysel**2))
if np.isnan(s) or np.isnan(n) or np.isinf(s) or np.isinf(n) \
or np.abs(s) > 1e30 or np.abs(n) > 1e30:
s = 0
n = 1
return s, n
def extr_opt(self, y, dy, mod, x, p):
mod_fit = lm()(mod, x, y)(x)
mod_fit[mod_fit < 1e-3] = 0
if np.sum(mod_fit*dy) > 0 and not np.isnan(mod_fit).any():
mod_norm = mod_fit/np.sum(mod_fit)
#print(mod_fit)
w = dy>0
s = np.sum(mod_norm[w]*y[w]/dy[w]**2)/np.sum(mod_norm[w]**2/dy[w]**2)
n = np.sqrt(np.sum(mod_norm[w])/np.sum(mod_norm[w]**2/dy[w]**2))
else:
s = 0
n = 1
if np.isnan(s) or np.isnan(n) or np.isinf(s) or np.isinf(n) \
or np.abs(s) > 1e30 or np.abs(n) > 1e30:
s = 0
n = 1
return s, n
def rebin(self, arr, length):
# Adapted from http://www.bdnyc.org/2012/03/rebin-numpy-arrays-in-python/
#pix_length = length/self.spat_scale/self.pix_xsize
# Good for now, but need to find more sophisticated solution
zoom_factor = length / arr.shape[0]
new = interpolation.zoom(arr, zoom_factor)
if np.sum(new) != 0:
return new/np.sum(new)*np.sum(arr)
else:
return new
def tot_eff(self, wave, wmin_d, wmax_d, fact=2):
dch_shape = expit(fact*(wave-wmin_d))*expit(fact*(wmax_d-wave))
i = self.arm_counter
adc = cspline(eff_wave, eff_adc)(wave)
slc = cspline(eff_wave, eff_slc)(wave)
dch = cspline(eff_wave, eff_dch)(wave) * dch_shape
spc = cspline(eff_wave, eff_spc)(wave)
grt = cspline(eff_wave, eff_grt)(wave)
ccd = cspline(eff_wave, eff_ccd)(wave)
tel = cspline(eff_wave, eff_tel)(wave)
tot = adc * slc * dch * spc * grt * ccd * tel
#adc = eff_adc[i]
#slc = eff_slc[i]
#dch = dch_shape * dch_spl
#spc = eff_spc[i]
#grt = eff_grt[i]
#ccd = eff_ccd[i]
#tel = eff_tel[i]
#return dch_shape
self.eff_wave.append(wave)
#print(self.eff_wave)
self.eff_adc.append(adc)
self.eff_slc.append(slc)
self.eff_dch.append(dch)
self.eff_spc.append(spc)
self.eff_grt.append(grt)
self.eff_ccd.append(ccd)
self.eff_tel.append(tel)
#print(self.eff_dch)
self.eff_tot.append(tot)
return tot
def wave_grid(self, wmin, wmax):
return np.linspace(wmin, wmax, int(self.ysize.value))
class Photons(object):
def __init__(self, targ_mag=targ_mag, bckg_mag=bckg_mag, area=area,
texp=texp):
self.targ_mag = targ_mag
self.bckg_mag = bckg_mag
self.area = area #(400*au.cm)**2 * np.pi
self.texp = texp
"""
if mag_syst == 'Vega':
self.wave_ref = wave_U
self.flux_ref = flux_U
if mag_syst == 'AB':
self.wave_ref = wave_u
self.flux_ref = flux_u
"""
self.wave_ref = globals()['wave_ref_'+mag_syst][mag_band]
self.flux_ref = globals()['flux_ref_'+mag_syst][mag_band]
try:
data_band = ascii.read('database/phot_%s.dat' % mag_band)
self.wave_band = data_band['col1'] * au.nm
if mag_band in ['J', 'H', 'K']:
self.wave_band = self.wave_band*1e3
self.dwave_band = self.wave_band[1]-self.wave_band[0]
self.flux_band = data_band['col2']
self.flux_band = self.flux_band/np.sum(self.flux_band)*self.dwave_band
#print(np.sum(self.flux_band))
#plt.plot(self.wave_band, self.flux_band)
#plt.show()
except:
pass
f = self.flux_ref * self.area * texp # Flux density @ 555.6 nm, V = 0
self.targ = f * pow(10, -0.4*self.targ_mag)
self.bckg = f * pow(10, -0.4*self.bckg_mag) / au.arcsec**2
self.atmo()
print("Photons collected.")
def atmo(self):
data = fits.open('database/atmoexan.fits')[1].data
self.atmo_wave = data['LAMBDA']*0.1 * au.nm
self.atmo_ex = data['LA_SILLA']
class PSF(object):
def __init__(self, spec, seeing=seeing, slice_width=slice_width,
xsize=slice_length, ysize=slice_length, sampl=psf_sampl,
func=psf_func):
#self.phot = phot
self.spec = spec
self.seeing = seeing
self.slice_width = slice_width
self.xsize = xsize
self.ysize = ysize
self.area = xsize * ysize
self.sampl = sampl
self.func = func
self.rects = []
x = np.linspace(-xsize.value/2, xsize.value/2, int(sampl.value))
y = np.linspace(-ysize.value/2, ysize.value/2, int(sampl.value))
self.x, self.y = np.meshgrid(x, y)
getattr(self, func)() # Apply the chosen function for the PSF
self.z_norm = self.z/np.sum(self.z)
""" Deprecated
self.bckg = np.ones(self.spec.wave.shape) \
* self.spec.phot.bckg * self.area
self.bckg_int = np.sum(self.bckg)/len(self.bckg) \
* (self.spec.wmax-self.spec.wmin)
self.z = self.z/np.sum(self.z) # Normalize the counts
self.z_targ = self.z * self.spec.targ_int # Counts from the target
self.z_bckg = np.full(self.z.shape, self.bckg_int.value) / self.z.size \
* self.bckg_int.unit # Counts from the background
self.z = self.z_targ + self.z_bckg # Total counts
"""
self.z_norm_a = np.ones(self.z.shape)/self.z.size
self.z_targ = self.z_norm * self.spec.targ_tot # Counts from the target
self.z_bckg = self.z_norm_a * self.spec.bckg_tot*self.area # Counts from the background
self.z = self.z_targ + self.z_bckg
self.spec.z_targ = self.z_targ
#print("Flux within the field:")
#print(" from target: %2.3e %s" % (np.sum(self.z_targ.value), self.z_targ.unit))
#print(" from background: %2.3e %s" % (np.sum(self.z_bckg.value), self.z_bckg.unit))
def add_slice(self, cen=None): # Central pixel of the slice
width = self.slice_width
length = self.ysize
if cen == None:
cen = (0, 0)
hwidth = width.value / 2
hlength = length.value / 2
rect = patches.Rectangle((cen[0]-hwidth, cen[1]-hlength), width.value,
length.value, edgecolor='r', facecolor='none')
self.rects.append(rect)
# In this way, fractions of pixels in the slices are counted
ones = np.ones(self.x.shape)
self.pix_xsize = self.xsize / self.sampl
self.pix_ysize = self.ysize / self.sampl
left_dist = (self.x-cen[0]+hwidth) / self.pix_xsize.value
right_dist = (cen[0]+hwidth-self.x) / self.pix_xsize.value
down_dist = (self.y-cen[1]+hlength) / self.pix_ysize.value
up_dist = (cen[1]+hlength-self.y) / self.pix_ysize.value
# This mask gives half weight to the edge pixels, to avoid superposition issues
mask_left = np.maximum(-ones, np.minimum(ones, left_dist))*0.5+0.5
mask_right = np.maximum(-ones, np.minimum(ones, right_dist))*0.5+0.5
mask_down = np.maximum(-ones, np.minimum(ones, down_dist))*0.5+0.5
mask_up = np.maximum(-ones, np.minimum(ones, up_dist))*0.5+0.5
mask = mask_left*mask_right*mask_down*mask_up
cut = np.asarray(mask_down*mask_up>0).nonzero()
mask_z = self.z * mask
mask_z_targ = self.z_targ * mask
mask_z_bckg = self.z_bckg * mask
cut_shape = (int(len(cut[0])/mask_z.shape[1]), mask_z.shape[1])
cut_z = mask_z[cut].reshape(cut_shape)
cut_z_targ = mask_z_targ[cut].reshape(cut_shape)
cut_z_bckg = mask_z_bckg[cut].reshape(cut_shape)
flux = np.sum(mask_z)
flux_targ = np.sum(mask_z_targ)
flux_bckg = np.sum(mask_z_bckg)
trace = np.sum(cut_z, axis=1)
trace_targ = np.sum(cut_z_targ, axis=1)
trace_bckg = np.sum(cut_z_bckg, axis=1)
return flux, flux_targ, flux_bckg, trace, trace_targ, trace_bckg
def add_slices(self, n=slice_n, cen=None):
width = self.slice_width
length = self.ysize
if cen == None:
cen = (0,0)
hwidth = width.value / 2
hlength = length.value / 2
shift = ((n+1)*width.value)/2
cens = np.arange(cen[0]-shift, cen[0]+shift, width.value)
self.flux_slice = 0.
self.flux_targ_slice = 0.
self.flux_bckg_slice = 0.
for i, c in enumerate(cens[1:]):
print("Designing slice %i on field..." % i, end='\r')
flux, flux_targ, flux_bckg, trace, trace_targ, trace_bckg = self.add_slice((c, cen[1]))
self.flux_slice += flux
self.flux_targ_slice += flux_targ
self.flux_bckg_slice += flux_bckg
if i == 0:
self.fluxes = np.array([flux.value])
self.fluxes_targ = np.array([flux_targ.value])
self.fluxes_bckg = np.array([flux_bckg.value])
self.traces = [trace]
self.traces_targ = [trace_targ]
self.traces_bckg = [trace_bckg]
else:
self.fluxes = np.append(self.fluxes, flux.value)
self.fluxes_targ = np.append(self.fluxes_targ, flux_targ.value)
self.fluxes_bckg = np.append(self.fluxes_bckg, flux_bckg.value)
self.traces = np.vstack((self.traces, trace.value))
self.traces_targ = np.vstack((self.traces_targ, trace_targ.value))
self.traces_bckg = np.vstack((self.traces_bckg, trace_bckg.value))
print("Slices designed on field. ")
self.slice_area = min((n * width-self.pix_xsize*au.pixel) * (length-self.pix_ysize*au.pixel),
(n * width-self.pix_xsize*au.pixel) * (self.ysize-self.pix_ysize*au.pixel))
self.slice_area = min(n*width*length, n*width*self.ysize)
#self.bckg_slice = self.bckg_int * self.slice_area/self.area
self.bckg_slice = self.flux_bckg_slice
self.targ_slice = self.flux_slice-self.bckg_slice
self.spec.targ_slice = self.targ_slice
self.losses = 1-(self.flux_targ_slice.value)/np.sum(self.z_targ.value)
#print("Flux within the slit:")
#print(" from target: %2.3e %s (losses: %2.1f%%)" \
# % (np.sum(self.targ_slice.value), self.targ_slice.unit, 100*self.losses))
#print(" from background: %2.3e %s" % (np.sum(self.bckg_slice.value), self.bckg_slice.unit))
# Update spectrum with flux into slices and background
#self.spec.targ_loss = self.spec.targ*(1-self.losses)
if self.func == 'gaussian':#or 1==0:
"""
self.spec.targ_conv = gaussian_filter(
self.spec.targ_loss, self.sigma.value)*self.spec.targ_loss.unit
self.spec.norm_conv = gaussian_filter(
self.spec.norm, self.sigma.value)*self.spec.targ_loss.unit
"""
self.spec.targ_conv = gaussian_filter(
self.spec.targ_ext, self.sigma.value)*self.spec.targ_ext.unit
self.spec.bckg_conv = gaussian_filter(
self.spec.bckg_ext, self.sigma.value)*self.spec.bckg_ext.unit
else:
"""
self.spec.targ_conv = self.spec.targ_loss
self.spec.norm_conv = self.spec.norm
"""
self.spec.targ_conv = self.spec.targ_ext#*(1-self.losses)
self.spec.bckg_conv = self.spec.bckg_ext
def draw(self):
"""
fig_p, self.ax_p = plt.subplots(1, 3, figsize=(15,5))
self.ax_p[1].set_title("Photon balance (field)")
sl_v0 = [np.sum(self.z_targ.value), np.sum(self.z_bckg.value)]
sl_l0 = ["target\n%2.3e %s" % (sl_v0[0], self.z_targ.unit),
"background\n%2.3e %s" % (sl_v0[1], self.z_bckg.unit)]
sl_c0 = ['C0', 'C1']
p0 = self.ax_p[0].pie(sl_v0, labels=sl_l0, colors=sl_c0, autopct='%1.1f%%', startangle=90, radius=1,
wedgeprops=dict(width=0.2, edgecolor='w'))
sl_v1 = [np.sum(self.targ_slice.value), np.sum(self.z_targ.value)-np.sum(self.targ_slice.value)]
sl_l1 = ["target in slit\n%2.3e %s" % (sl_v1[0], self.z_targ.unit),
"target off slit\n%2.3e %s" % (sl_v1[1], self.z_bckg.unit)]
sl_c1 = ['C0', 'C0']
p1 = self.ax_p[1].pie(sl_v1, labels=sl_l1, colors=sl_c1, autopct='%1.1f%%', startangle=90, radius=1,
wedgeprops=dict(width=0.2, edgecolor='w'))
p1[0][1].set_alpha(1/3)
sl_v2 = [np.sum(self.bckg_slice.value), np.sum(self.z_bckg.value)-np.sum(self.bckg_slice.value)]
sl_l2 = ["background in slit\n%2.3e %s" % (sl_v2[0], self.z_targ.unit),
"background off slit\n%2.3e %s" % (sl_v2[1], self.z_bckg.unit)]
sl_c2 = ['C1', 'C1']
p2 = self.ax_p[2].pie(sl_v2, labels=sl_l2, colors=sl_c2, autopct='%1.1f%%', startangle=90, radius=1,
wedgeprops=dict(width=0.2, edgecolor='w'))
p2[0][1].set_alpha(1/3)
"""
fig_p, self.ax_p = plt.subplots(figsize=(10,7))
self.ax_p.set_title("Photon balance (slit)")
sl_v = np.array([self.spec.targ_tot.value, np.sum(self.targ_slice.value),
np.sum(self.z_targ.value)-np.sum(self.targ_slice.value)])
sl_l = ["total: %2.3e %s" % (sl_v[0], self.spec.targ_tot.unit),
"on slit: %2.3e %s" % (sl_v[1], self.z_targ.unit),
"off slit: %2.3e %s" % (sl_v[2], self.z_targ.unit)]
sl_c = ['C0', 'C0', 'C0']
p0 = self.ax_p.pie([sl_v[0]], colors=[sl_c[0]], startangle=90, radius=1,
wedgeprops=dict(width=0.2, edgecolor='w'))
p1 = self.ax_p.pie(sl_v[1:], colors=sl_c[1:], autopct='%1.1f%%', startangle=90, radius=0.8,