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Commit 36189a3e authored by JX's avatar JX 😵
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Merge remote-tracking branch 'origin/develop'

parents dd26d370 27646bc4
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ObservationSim/MockObject/SpecDisperser/SpecDisperser.py → observation_sim/mock_objects/SpecDisperser/SpecDisperser.py
View file @ 36189a3e
...@@ -97,7 +97,6 @@ class SpecDisperser(object): ...@@ -97,7 +97,6 @@ class SpecDisperser(object):
self.grating_conf_file = conf self.grating_conf_file = conf
self.ignoreBeam = ignoreBeam self.ignoreBeam = ignoreBeam
def compute_spec_orders(self): def compute_spec_orders(self):
all_orders = OrderedDict() all_orders = OrderedDict()
...@@ -121,16 +120,18 @@ class SpecDisperser(object): ...@@ -121,16 +120,18 @@ class SpecDisperser(object):
ytrace_beam, lam_beam = self.grating_conf.get_beam_trace(x=self.xcenter, y=self.ycenter, dx=(dx + xoff), ytrace_beam, lam_beam = self.grating_conf.get_beam_trace(x=self.xcenter, y=self.ycenter, dx=(dx + xoff),
beam=beam) beam=beam)
### Account for pixel centering of the trace # Account for pixel centering of the trace
yfrac_beam = ytrace_beam - floor(ytrace_beam+0.5) yfrac_beam = ytrace_beam - floor(ytrace_beam+0.5)
ysens = lam_beam * 0 ysens = lam_beam * 0
lam_index = argsort(lam_beam) lam_index = argsort(lam_beam)
conf_sens = self.grating_conf.sens[beam] conf_sens = self.grating_conf.sens[beam]
lam_intep = np.linspace(self.band_start, self.band_end, int((self.band_end - self.band_start) / 0.1)) lam_intep = np.linspace(self.band_start, self.band_end, int(
(self.band_end - self.band_start) / 0.1))
thri = interpolate.interp1d(conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY']) thri = interpolate.interp1d(
conf_sens['WAVELENGTH'], conf_sens['SENSITIVITY'])
spci = interpolate.interp1d(self.spec['WAVELENGTH'], self.spec['FLUX']) spci = interpolate.interp1d(self.spec['WAVELENGTH'], self.spec['FLUX'])
beam_thr = thri(lam_intep) beam_thr = thri(lam_intep)
...@@ -138,7 +139,7 @@ class SpecDisperser(object): ...@@ -138,7 +139,7 @@ class SpecDisperser(object):
bean_thr_spec = beam_thr * spec_sample bean_thr_spec = beam_thr * spec_sample
###generate sensitivity file for aXe # generate sensitivity file for aXe
# ysensitivity = lam_beam * 0 # ysensitivity = lam_beam * 0
# #
# ysensitivity[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep, # ysensitivity[lam_index] = interp.interp_conserve_c(lam_beam[lam_index], lam_intep,
...@@ -155,7 +156,8 @@ class SpecDisperser(object): ...@@ -155,7 +156,8 @@ class SpecDisperser(object):
sensitivity_beam = ysens sensitivity_beam = ysens
len_spec_x = len(dx) len_spec_x = len(dx)
len_spec_y = int(abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1) len_spec_y = int(
abs(ceil(ytrace_beam[-1]) - floor(ytrace_beam[0])) + 1)
beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x) beam_sh = (self.img_sh[0] + len_spec_y, self.img_sh[1] + len_spec_x)
modelf = zeros(product(beam_sh), dtype=float) modelf = zeros(product(beam_sh), dtype=float)
...@@ -169,7 +171,7 @@ class SpecDisperser(object): ...@@ -169,7 +171,7 @@ class SpecDisperser(object):
dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5)) dypix = cast[int](np.floor(ytrace_beam - dyc[0] + x0[0] + 0.5))
frac_ids = yfrac_beam<0 frac_ids = yfrac_beam < 0
dypix[frac_ids] = dypix[frac_ids] - 1 dypix[frac_ids] = dypix[frac_ids] - 1
yfrac_beam[frac_ids] = 1+yfrac_beam[frac_ids] yfrac_beam[frac_ids] = 1+yfrac_beam[frac_ids]
...@@ -199,7 +201,8 @@ class SpecDisperser(object): ...@@ -199,7 +201,8 @@ class SpecDisperser(object):
else: else:
beam_flat = zeros([len(modelf), len(self.flat_cube)]) beam_flat = zeros([len(modelf), len(self.flat_cube)])
sub_flat_cube = zeros([len(self.flat_cube),beam_sh[0], beam_sh[1]]) sub_flat_cube = zeros(
[len(self.flat_cube), beam_sh[0], beam_sh[1]])
sub_flat_cube[0] = sub_flat_cube[0] + 1. sub_flat_cube[0] = sub_flat_cube[0] + 1.
overlap_flag = 1 overlap_flag = 1
...@@ -210,23 +213,28 @@ class SpecDisperser(object): ...@@ -210,23 +213,28 @@ class SpecDisperser(object):
sub_x_e = originOut_x + beam_sh[1] - 1 sub_x_e = originOut_x + beam_sh[1] - 1
beam_x_s = max(sub_x_s, 0) beam_x_s = max(sub_x_s, 0)
if beam_x_s > self.flat_cube[0].shape[1] - 1: overlap_flag = 0 if beam_x_s > self.flat_cube[0].shape[1] - 1:
overlap_flag = 0
if overlap_flag == 1: if overlap_flag == 1:
beam_x_e = min(sub_x_e, self.flat_cube[0].shape[1] - 1) beam_x_e = min(sub_x_e, self.flat_cube[0].shape[1] - 1)
if beam_x_e < 0: overlap_flag = 0 if beam_x_e < 0:
overlap_flag = 0
if overlap_flag == 1: if overlap_flag == 1:
beam_y_s = max(sub_y_s, 0) beam_y_s = max(sub_y_s, 0)
if beam_y_s > self.flat_cube[0].shape[0] - 1: overlap_flag = 0 if beam_y_s > self.flat_cube[0].shape[0] - 1:
overlap_flag = 0
if overlap_flag == 1: if overlap_flag == 1:
beam_y_e = min(sub_y_e, self.flat_cube[0].shape[0] - 1) beam_y_e = min(sub_y_e, self.flat_cube[0].shape[0] - 1)
if beam_y_e < 0: overlap_flag = 0 if beam_y_e < 0:
overlap_flag = 0
if overlap_flag == 1: if overlap_flag == 1:
sub_flat_cube[:,beam_y_s-originOut_y:beam_y_e-originOut_y+1,beam_x_s-originOut_x:beam_x_e-originOut_x+1] = self.flat_cube[:,beam_y_s:beam_y_e+1,beam_x_s:beam_x_e+1] sub_flat_cube[:, beam_y_s-originOut_y:beam_y_e-originOut_y+1, beam_x_s-originOut_x:beam_x_e -
originOut_x+1] = self.flat_cube[:, beam_y_s:beam_y_e+1, beam_x_s:beam_x_e+1]
for i in arange(0, len(self.flat_cube), 1): for i in arange(0, len(self.flat_cube), 1):
beam_flat[:,i] = sub_flat_cube[i].flatten() beam_flat[:, i] = sub_flat_cube[i].flatten()
# beam_flat = zeros([len(modelf), len(self.flat_cube)]) # beam_flat = zeros([len(modelf), len(self.flat_cube)])
# flat_sh = self.flat_cube[0].shape # flat_sh = self.flat_cube[0].shape
# for i in arange(0, beam_sh[0], 1): # for i in arange(0, beam_sh[0], 1):
...@@ -243,7 +251,8 @@ class SpecDisperser(object): ...@@ -243,7 +251,8 @@ class SpecDisperser(object):
flat_index[nonz], yfrac_beam[nonz], flat_index[nonz], yfrac_beam[nonz],
sensitivity_beam[nonz], sensitivity_beam[nonz],
modelf, x0, modelf, x0,
array(self.img_sh, dtype=int64), array(self.img_sh,
dtype=int64),
array(beam_sh, dtype=int64), array(beam_sh, dtype=int64),
beam_flat, beam_flat,
lam_beam[lam_index][nonz]) lam_beam[lam_index][nonz])
...@@ -254,13 +263,15 @@ class SpecDisperser(object): ...@@ -254,13 +263,15 @@ class SpecDisperser(object):
if self.isAlongY == 1: if self.isAlongY == 1:
model, _, _ = rotate90(array_orig=model, isClockwise=0) model, _, _ = rotate90(array_orig=model, isClockwise=0)
return model, originOut_x, originOut_y, dxpix, dypix, lam_beam,ysens return model, originOut_x, originOut_y, dxpix, dypix, lam_beam, ysens
def writerSensitivityFile(self, conffile='', beam='', w=None, sens=None): def writerSensitivityFile(self, conffile='', beam='', w=None, sens=None):
orders = {'A': '1st', 'B': '0st', 'C': '2st', 'D': '-1st', 'E': '-2st'} orders = {'A': '1st', 'B': '0st', 'C': '2st', 'D': '-1st', 'E': '-2st'}
sens_file_name = conffile[0:-5] + '_sensitivity_' + orders[beam] + '.fits' sens_file_name = conffile[0:-5] + \
'_sensitivity_' + orders[beam] + '.fits'
if not os.path.exists(sens_file_name) == True: if not os.path.exists(sens_file_name) == True:
senstivity_out = Table(array([w, sens]).T, names=('WAVELENGTH', 'SENSITIVITY')) senstivity_out = Table(
array([w, sens]).T, names=('WAVELENGTH', 'SENSITIVITY'))
senstivity_out.write(sens_file_name, format='fits') senstivity_out.write(sens_file_name, format='fits')
...@@ -284,7 +295,7 @@ class aXeConf(): ...@@ -284,7 +295,7 @@ class aXeConf():
self.conf_file = conf_file self.conf_file = conf_file
self.count_beam_orders() self.count_beam_orders()
## Global XOFF/YOFF offsets # Global XOFF/YOFF offsets
if 'XOFF' in self.conf.keys(): if 'XOFF' in self.conf.keys():
self.xoff = np.float(self.conf['XOFF']) self.xoff = np.float(self.conf['XOFF'])
else: else:
...@@ -310,11 +321,11 @@ class aXeConf(): ...@@ -310,11 +321,11 @@ class aXeConf():
conf = OrderedDict() conf = OrderedDict()
lines = open(conf_file).readlines() lines = open(conf_file).readlines()
for line in lines: for line in lines:
## empty / commented lines # empty / commented lines
if (line.startswith('#')) | (line.strip() == '') | ('"' in line): if (line.startswith('#')) | (line.strip() == '') | ('"' in line):
continue continue
## split the line, taking out ; and # comments # split the line, taking out ; and # comments
spl = line.split(';')[0].split('#')[0].split() spl = line.split(';')[0].split('#')[0].split()
param = spl[0] param = spl[0]
if len(spl) > 2: if len(spl) > 2:
...@@ -360,13 +371,14 @@ class aXeConf(): ...@@ -360,13 +371,14 @@ class aXeConf():
self.beams.append(beam) self.beams.append(beam)
self.dxlam[beam] = np.arange(self.conf['BEAM{0}'.format(beam)].min(), self.dxlam[beam] = np.arange(self.conf['BEAM{0}'.format(beam)].min(),
self.conf['BEAM{0}'.format(beam)].max(), dtype=int) self.conf['BEAM{0}'.format(beam)].max(), dtype=int)
self.nx[beam] = int(self.dxlam[beam].max() - self.dxlam[beam].min()) + 1 self.nx[beam] = int(self.dxlam[beam].max() -
self.dxlam[beam].min()) + 1
self.sens[beam] = Table.read( self.sens[beam] = Table.read(
'{0}/{1}'.format(os.path.dirname(self.conf_file), self.conf['SENSITIVITY_{0}'.format(beam)])) '{0}/{1}'.format(os.path.dirname(self.conf_file), self.conf['SENSITIVITY_{0}'.format(beam)]))
# self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH']) # self.sens[beam].wave = np.cast[np.double](self.sens[beam]['WAVELENGTH'])
# self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY']) # self.sens[beam].sens = np.cast[np.double](self.sens[beam]['SENSITIVITY'])
### Need doubles for interpolating functions # Need doubles for interpolating functions
for col in self.sens[beam].colnames: for col in self.sens[beam].colnames:
data = np.cast[np.double](self.sens[beam][col]) data = np.cast[np.double](self.sens[beam][col])
self.sens[beam].remove_column(col) self.sens[beam].remove_column(col)
...@@ -394,22 +406,22 @@ class aXeConf(): ...@@ -394,22 +406,22 @@ class aXeConf():
Evaluated field-dependent coefficients Evaluated field-dependent coefficients
""" """
## number of coefficients for a given polynomial order # number of coefficients for a given polynomial order
## 1:1, 2:3, 3:6, 4:10, order:order*(order+1)/2 # 1:1, 2:3, 3:6, 4:10, order:order*(order+1)/2
if isinstance(coeffs, float): if isinstance(coeffs, float):
order = 1 order = 1
else: else:
order = int(-1 + np.sqrt(1 + 8 * len(coeffs))) // 2 order = int(-1 + np.sqrt(1 + 8 * len(coeffs))) // 2
## Build polynomial terms array # Build polynomial terms array
## $a = a_0+a_1x_i+a_2y_i+a_3x_i^2+a_4x_iy_i+a_5yi^2+$ ... # $a = a_0+a_1x_i+a_2y_i+a_3x_i^2+a_4x_iy_i+a_5yi^2+$ ...
xy = [] xy = []
for p in range(order): for p in range(order):
for px in range(p + 1): for px in range(p + 1):
# print 'x**%d y**%d' %(p-px, px) # print 'x**%d y**%d' %(p-px, px)
xy.append(xi ** (p - px) * yi ** (px)) xy.append(xi ** (p - px) * yi ** (px))
## Evaluate the polynomial, allowing for N-dimensional inputs # Evaluate the polynomial, allowing for N-dimensional inputs
a = np.sum((np.array(xy).T * coeffs).T, axis=0) a = np.sum((np.array(xy).T * coeffs).T, axis=0)
return a return a
...@@ -445,26 +457,27 @@ class aXeConf(): ...@@ -445,26 +457,27 @@ class aXeConf():
.. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2]) .. math:: dp = (u \sqrt{1+u^2} + \mathrm{arcsinh}\ u) / (4\cdot \mathrm{DYDX}[2])
""" """
## dp is the arc length along the trace # dp is the arc length along the trace
## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ... # $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...
poly_order = len(dydx) - 1 poly_order = len(dydx) - 1
if (poly_order == 2): if (poly_order == 2):
if np.abs(np.unique(dydx[2])).max() == 0: if np.abs(np.unique(dydx[2])).max() == 0:
poly_order = 1 poly_order = 1
if poly_order == 0: ## dy=0 if poly_order == 0: # dy=0
dp = dx dp = dx
elif poly_order == 1: ## constant dy/dx elif poly_order == 1: # constant dy/dx
dp = np.sqrt(1 + dydx[1] ** 2) * (dx) dp = np.sqrt(1 + dydx[1] ** 2) * (dx)
elif poly_order == 2: ## quadratic trace elif poly_order == 2: # quadratic trace
u0 = dydx[1] + 2 * dydx[2] * (0) u0 = dydx[1] + 2 * dydx[2] * (0)
dp0 = (u0 * np.sqrt(1 + u0 ** 2) + np.arcsinh(u0)) / (4 * dydx[2]) dp0 = (u0 * np.sqrt(1 + u0 ** 2) + np.arcsinh(u0)) / (4 * dydx[2])
u = dydx[1] + 2 * dydx[2] * (dx) u = dydx[1] + 2 * dydx[2] * (dx)
dp = (u * np.sqrt(1 + u ** 2) + np.arcsinh(u)) / (4 * dydx[2]) - dp0 dp = (u * np.sqrt(1 + u ** 2) + np.arcsinh(u)) / \
(4 * dydx[2]) - dp0
else: else:
## high order shape, numerical integration along trace # high order shape, numerical integration along trace
## (this can be slow) # (this can be slow)
xmin = np.minimum((dx).min(), 0) xmin = np.minimum((dx).min(), 0)
xmax = np.maximum((dx).max(), 0) xmax = np.maximum((dx).max(), 0)
xfull = np.arange(xmin, xmax) xfull = np.arange(xmin, xmax)
...@@ -472,11 +485,12 @@ class aXeConf(): ...@@ -472,11 +485,12 @@ class aXeConf():
for i in range(1, poly_order): for i in range(1, poly_order):
dyfull += i * dydx[i] * (xfull - 0.5) ** (i - 1) dyfull += i * dydx[i] * (xfull - 0.5) ** (i - 1)
## Integrate from 0 to dx / -dx # Integrate from 0 to dx / -dx
dpfull = xfull * 0. dpfull = xfull * 0.
lt0 = xfull < 0 lt0 = xfull < 0
if lt0.sum() > 1: if lt0.sum() > 1:
dpfull[lt0] = np.cumsum(np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1] dpfull[lt0] = np.cumsum(
np.sqrt(1 + dyfull[lt0][::-1] ** 2))[::-1]
dpfull[lt0] *= -1 dpfull[lt0] *= -1
# #
...@@ -520,10 +534,12 @@ class aXeConf(): ...@@ -520,10 +534,12 @@ class aXeConf():
NORDER = self.orders[beam] + 1 NORDER = self.orders[beam] + 1
xi, yi = x - self.xoff, y - self.yoff xi, yi = x - self.xoff, y - self.yoff
xoff_beam = self.field_dependent(xi, yi, self.conf['XOFF_{0}'.format(beam)]) xoff_beam = self.field_dependent(
yoff_beam = self.field_dependent(xi, yi, self.conf['YOFF_{0}'.format(beam)]) xi, yi, self.conf['XOFF_{0}'.format(beam)])
yoff_beam = self.field_dependent(
xi, yi, self.conf['YOFF_{0}'.format(beam)])
## y offset of trace (DYDX) # y offset of trace (DYDX)
dydx = np.zeros(NORDER) # 0 #+1.e-80 dydx = np.zeros(NORDER) # 0 #+1.e-80
dydx = [0] * NORDER dydx = [0] * NORDER
...@@ -538,7 +554,7 @@ class aXeConf(): ...@@ -538,7 +554,7 @@ class aXeConf():
for i in range(NORDER): for i in range(NORDER):
dy += dydx[i] * (dx - xoff_beam) ** i dy += dydx[i] * (dx - xoff_beam) ** i
## wavelength solution # wavelength solution
dldp = np.zeros(NORDER) dldp = np.zeros(NORDER)
dldp = [0] * NORDER dldp = [0] * NORDER
...@@ -556,7 +572,7 @@ class aXeConf(): ...@@ -556,7 +572,7 @@ class aXeConf():
# ## dp is the arc length along the trace # ## dp is the arc length along the trace
# ## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ... # ## $\lambda = dldp_0 + dldp_1 dp + dldp_2 dp^2$ ...
# if self.conf['DYDX_ORDER_%s' %(beam)] == 0: ## dy=0 # if self.conf['DYDX_ORDER_%s' %(beam)] == 0: ## dy=0
# dp = dx-xoff_beam # dp = dx-xoff_beam
# elif self.conf['DYDX_ORDER_%s' %(beam)] == 1: ## constant dy/dx # elif self.conf['DYDX_ORDER_%s' %(beam)] == 1: ## constant dy/dx
# dp = np.sqrt(1+dydx[1]**2)*(dx-xoff_beam) # dp = np.sqrt(1+dydx[1]**2)*(dx-xoff_beam)
# elif self.conf['DYDX_ORDER_%s' %(beam)] == 2: ## quadratic trace # elif self.conf['DYDX_ORDER_%s' %(beam)] == 2: ## quadratic trace
...@@ -573,7 +589,7 @@ class aXeConf(): ...@@ -573,7 +589,7 @@ class aXeConf():
# dyfull = 0 # dyfull = 0
# for i in range(1, NORDER): # for i in range(1, NORDER):
# dyfull += i*dydx[i]*(xfull-0.5)**(i-1) # dyfull += i*dydx[i]*(xfull-0.5)**(i-1)
# #
# ## Integrate from 0 to dx / -dx # ## Integrate from 0 to dx / -dx
# dpfull = xfull*0. # dpfull = xfull*0.
# lt0 = xfull <= 0 # lt0 = xfull <= 0
...@@ -584,10 +600,10 @@ class aXeConf(): ...@@ -584,10 +600,10 @@ class aXeConf():
# gt0 = xfull >= 0 # gt0 = xfull >= 0
# if gt0.sum() > 0: # if gt0.sum() > 0:
# dpfull[gt0] = np.cumsum(np.sqrt(1+dyfull[gt0]**2)) # dpfull[gt0] = np.cumsum(np.sqrt(1+dyfull[gt0]**2))
# #
# dp = np.interp(dx-xoff_beam, xfull, dpfull) # dp = np.interp(dx-xoff_beam, xfull, dpfull)
## Evaluate dldp # Evaluate dldp
lam = dp * 0. lam = dp * 0.
for i in range(NORDER): for i in range(NORDER):
lam += dldp[i] * dp ** i lam += dldp[i] * dp ** i
...@@ -619,7 +635,8 @@ class aXeConf(): ...@@ -619,7 +635,8 @@ class aXeConf():
if 'XOFF_{0}'.format(beam) not in self.conf.keys(): if 'XOFF_{0}'.format(beam) not in self.conf.keys():
continue continue
xoff = self.field_dependent(x0, x1, self.conf['XOFF_{0}'.format(beam)]) xoff = self.field_dependent(
x0, x1, self.conf['XOFF_{0}'.format(beam)])
dy, lam = self.get_beam_trace(x0, x1, dx=dx, beam=beam) dy, lam = self.get_beam_trace(x0, x1, dx=dx, beam=beam)
xlim = self.conf['BEAM{0}'.format(beam)] xlim = self.conf['BEAM{0}'.format(beam)]
ok = (dx >= xlim[0]) & (dx <= xlim[1]) ok = (dx >= xlim[0]) & (dx <= xlim[1])
...@@ -627,7 +644,8 @@ class aXeConf(): ...@@ -627,7 +644,8 @@ class aXeConf():
alpha=0.5, edgecolor='None') alpha=0.5, edgecolor='None')
plt.text(np.median(dx[ok]), np.median(dy[ok]) + 1, beam, plt.text(np.median(dx[ok]), np.median(dy[ok]) + 1, beam,
ha='center', va='center', fontsize=14) ha='center', va='center', fontsize=14)
print('Beam {0}, lambda=({1:.1f} - {2:.1f})'.format(beam, lam[ok].min(), lam[ok].max())) print('Beam {0}, lambda=({1:.1f} - {2:.1f})'.format(beam,
lam[ok].min(), lam[ok].max()))
plt.grid() plt.grid()
plt.xlabel(r'$\Delta x$') plt.xlabel(r'$\Delta x$')
...@@ -650,7 +668,7 @@ class aXeConf(): ...@@ -650,7 +668,7 @@ class aXeConf():
# Returns # Returns
# ------- # -------
# conf : `~grizli.grismconf.aXeConf` # conf : `~grizli.grismconf.aXeConf`
# Configuration file object. Runs `conf.get_beams()` to read the # Configuration file object. Runs `conf.get_beams()` to read the
# sensitivity curves. # sensitivity curves.
# """ # """
# conf = aXeConf(conf_file) # conf = aXeConf(conf_file)
......
ObservationSim/MockObject/Stamp.py → observation_sim/mock_objects/Stamp.py
View file @ 36189a3e
import os, sys import os
import sys
import numpy as np import numpy as np
import galsim import galsim
import astropy.constants as cons import astropy.constants as cons
from astropy.table import Table from astropy.table import Table
from scipy import interpolate from scipy import interpolate
from ObservationSim.MockObject.MockObject import MockObject from observation_sim.mock_objects.MockObject import MockObject
from ObservationSim.MockObject.SpecDisperser import SpecDisperser from observation_sim.mock_objects.SpecDisperser import SpecDisperser
from ObservationSim.MockObject._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG,convolveGaussXorders from observation_sim.mock_objects._util import eObs, integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, convolveGaussXorders
class Stamp(MockObject): class Stamp(MockObject):
def __init__(self, param, logger=None): def __init__(self, param, logger=None):
...@@ -22,17 +24,17 @@ class Stamp(MockObject): ...@@ -22,17 +24,17 @@ class Stamp(MockObject):
if nphotons_tot == None: if nphotons_tot == None:
nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime) nphotons_tot = self.getElectronFluxFilt(filt, tel, exptime)
try: try:
full = integrate_sed_bandpass(sed=self.sed, bandpass=filt.bandpass_full) full = integrate_sed_bandpass(
sed=self.sed, bandpass=filt.bandpass_full)
except Exception as e: except Exception as e:
print(e) print(e)
self.logger.error(e) self.logger.error(e)
return False return False
#nphotons_sum = 0 # nphotons_sum = 0
#photons_list = [] # photons_list = []
#xmax, ymax = 0, 0 # xmax, ymax = 0, 0
if self.getMagFilter(filt) <= 15: if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4 folding_threshold = 5.e-4
...@@ -72,17 +74,18 @@ class Stamp(MockObject): ...@@ -72,17 +74,18 @@ class Stamp(MockObject):
nphotons = ratio * nphotons_tot nphotons = ratio * nphotons_tot
else: else:
continue continue
#nphotons_sum += nphotons # nphotons_sum += nphotons
psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold) psf, pos_shear = psf_model.get_PSF(
chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
_gal = self.param['image'] _gal = self.param['image']
galImg= galsim.ImageF(_gal, scale=self.param['pixScale']) galImg = galsim.ImageF(_gal, scale=self.param['pixScale'])
gal_temp= galsim.InterpolatedImage(galImg) gal_temp = galsim.InterpolatedImage(galImg)
gal_temp= gal_temp.shear(gal_shear) gal_temp = gal_temp.shear(gal_shear)
gal_temp= gal_temp.withFlux(nphotons) gal_temp = gal_temp.withFlux(nphotons)
gal_temp= galsim.Convolve(psf, gal_temp) gal_temp = galsim.Convolve(psf, gal_temp)
if i == 0: if i == 0:
gal = gal_temp gal = gal_temp
...@@ -95,7 +98,8 @@ class Stamp(MockObject): ...@@ -95,7 +98,8 @@ class Stamp(MockObject):
return 2, pos_shear return 2, pos_shear
stamp.setCenter(x_nominal, y_nominal) stamp.setCenter(x_nominal, y_nominal)
bounds = stamp.bounds & galsim.BoundsI(0, chip.npix_x - 1, 0, chip.npix_y - 1) bounds = stamp.bounds & galsim.BoundsI(
0, chip.npix_x - 1, 0, chip.npix_y - 1)
if bounds.area() > 0: if bounds.area() > 0:
chip.img.setOrigin(0, 0) chip.img.setOrigin(0, 0)
...@@ -105,22 +109,22 @@ class Stamp(MockObject): ...@@ -105,22 +109,22 @@ class Stamp(MockObject):
del stamp del stamp
if is_updated == 0: if is_updated == 0:
print("fits obj %s missed"%(self.id)) print("fits obj %s missed" % (self.id))
if self.logger: if self.logger:
self.logger.info("fits obj %s missed"%(self.id)) self.logger.info("fits obj %s missed" % (self.id))
return 0, pos_shear return 0, pos_shear
return 1, pos_shear return 1, pos_shear
def drawObj_slitless(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0, def drawObj_slitless(self, tel, pos_img, psf_model, bandpass_list, filt, chip, nphotons_tot=None, g1=0, g2=0,
exptime=150., normFilter=None, grating_split_pos=3685, fd_shear=None): exptime=150., normFilter=None, grating_split_pos=3685, fd_shear=None):
if normFilter is not None: if normFilter is not None:
norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001 norm_thr_rang_ids = normFilter['SENSITIVITY'] > 0.001
sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed, sedNormFactor = getNormFactorForSpecWithABMAG(ABMag=self.param['mag_use_normal'], spectrum=self.sed,
norm_thr=normFilter, norm_thr=normFilter,
sWave=np.floor(normFilter[norm_thr_rang_ids][0][0]), sWave=np.floor(
eWave=np.ceil(normFilter[norm_thr_rang_ids][-1][0])) normFilter[norm_thr_rang_ids][0][0]),
eWave=np.ceil(normFilter[norm_thr_rang_ids][-1][0]))
if sedNormFactor == 0: if sedNormFactor == 0:
return 2, None return 2, None
else: else:
...@@ -140,7 +144,6 @@ class Stamp(MockObject): ...@@ -140,7 +144,6 @@ class Stamp(MockObject):
chip_wcs_local = self.chip_wcs.local(self.real_pos) chip_wcs_local = self.chip_wcs.local(self.real_pos)
if self.getMagFilter(filt) <= 15: if self.getMagFilter(filt) <= 15:
folding_threshold = 5.e-4 folding_threshold = 5.e-4
else: else:
...@@ -150,7 +153,8 @@ class Stamp(MockObject): ...@@ -150,7 +153,8 @@ class Stamp(MockObject):
flat_cube = chip.flat_cube flat_cube = chip.flat_cube
xOrderSigPlus = {'A':1.3909419820029296,'B':1.4760376591236062,'C':4.035447379743442,'D':5.5684364343742825,'E':16.260021029735388} xOrderSigPlus = {'A': 1.3909419820029296, 'B': 1.4760376591236062,
'C': 4.035447379743442, 'D': 5.5684364343742825, 'E': 16.260021029735388}
grating_split_pos_chip = 0 + grating_split_pos grating_split_pos_chip = 0 + grating_split_pos
branges = np.zeros([len(bandpass_list), 2]) branges = np.zeros([len(bandpass_list), 2])
...@@ -174,9 +178,9 @@ class Stamp(MockObject): ...@@ -174,9 +178,9 @@ class Stamp(MockObject):
# psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold) # psf, pos_shear = psf_model.get_PSF(chip=chip, pos_img=pos_img, bandpass=bandpass, folding_threshold=folding_threshold)
_gal = self.param['image'] _gal = self.param['image']
galImg= galsim.ImageF(_gal, scale=self.param['pixScale']) galImg = galsim.ImageF(_gal, scale=self.param['pixScale'])
gal = galsim.InterpolatedImage(galImg) gal = galsim.InterpolatedImage(galImg)
# (TEST) Random knots # (TEST) Random knots
# knots = galsim.RandomKnots(npoints=100, profile=disk) # knots = galsim.RandomKnots(npoints=100, profile=disk)
...@@ -196,40 +200,43 @@ class Stamp(MockObject): ...@@ -196,40 +200,43 @@ class Stamp(MockObject):
# # if fd_shear is not None: # # if fd_shear is not None:
# # gal = gal.shear(fd_shear) # # gal = gal.shear(fd_shear)
starImg = gal.drawImage(wcs=chip_wcs_local, offset=offset,method = 'real_space') starImg = gal.drawImage(
wcs=chip_wcs_local, offset=offset, method='real_space')
origin_star = [y_nominal - (starImg.center.y - starImg.ymin), origin_star = [y_nominal - (starImg.center.y - starImg.ymin),
x_nominal - (starImg.center.x - starImg.xmin)] x_nominal - (starImg.center.x - starImg.xmin)]
starImg.setOrigin(0, 0) starImg.setOrigin(0, 0)
gal_origin = [origin_star[0], origin_star[1]] gal_origin = [origin_star[0], origin_star[1]]
gal_end = [origin_star[0] + starImg.array.shape[0] - 1, origin_star[1] + starImg.array.shape[1] - 1] gal_end = [origin_star[0] + starImg.array.shape[0] -
1, origin_star[1] + starImg.array.shape[1] - 1]
if gal_origin[1] < grating_split_pos_chip < gal_end[1]: if gal_origin[1] < grating_split_pos_chip < gal_end[1]:
subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1) subSlitPos = int(grating_split_pos_chip - gal_origin[1] + 1)
## part img disperse # part img disperse
subImg_p1 = starImg.array[:, 0:subSlitPos] subImg_p1 = starImg.array[:, 0:subSlitPos]
star_p1 = galsim.Image(subImg_p1) star_p1 = galsim.Image(subImg_p1)
star_p1.setOrigin(0, 0) star_p1.setOrigin(0, 0)
origin_p1 = origin_star origin_p1 = origin_star
xcenter_p1 = min(x_nominal,grating_split_pos_chip-1) - 0 xcenter_p1 = min(x_nominal, grating_split_pos_chip-1) - 0
ycenter_p1 = y_nominal-0 ycenter_p1 = y_nominal-0
sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1, sdp_p1 = SpecDisperser(orig_img=star_p1, xcenter=xcenter_p1,
ycenter=ycenter_p1, origin=origin_p1, ycenter=ycenter_p1, origin=origin_p1,
tar_spec=normalSED, tar_spec=normalSED,
band_start=brange[0], band_end=brange[1], band_start=brange[0], band_end=brange[1],
conf=chip.sls_conf[0], conf=chip.sls_conf[0],
isAlongY=0, isAlongY=0,
flat_cube=flat_cube) flat_cube=flat_cube)
# self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local) # self.addSLStoChipImage(sdp=sdp_p1, chip=chip, xOrderSigPlus = xOrderSigPlus, local_wcs=chip_wcs_local)
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local=[xcenter_p1, ycenter_p1], pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p1, chip=chip, pos_img_local=[xcenter_p1, ycenter_p1],
psf_model=psf_model, bandNo=i + 1, psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img) local_wcs=chip_wcs_local, pos_img=pos_img)
subImg_p2 = starImg.array[:, subSlitPos+1:starImg.array.shape[1]] subImg_p2 = starImg.array[:,
subSlitPos+1:starImg.array.shape[1]]
star_p2 = galsim.Image(subImg_p2) star_p2 = galsim.Image(subImg_p2)
star_p2.setOrigin(0, 0) star_p2.setOrigin(0, 0)
origin_p2 = [origin_star[0], grating_split_pos_chip] origin_p2 = [origin_star[0], grating_split_pos_chip]
...@@ -248,11 +255,11 @@ class Stamp(MockObject): ...@@ -248,11 +255,11 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2], pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp_p2, chip=chip, pos_img_local=[xcenter_p2, ycenter_p2],
psf_model=psf_model, bandNo=i + 1, psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img) local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp_p1 del sdp_p1
del sdp_p2 del sdp_p2
elif grating_split_pos_chip<=gal_origin[1]: elif grating_split_pos_chip <= gal_origin[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0, sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star, ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED, tar_spec=normalSED,
...@@ -264,9 +271,9 @@ class Stamp(MockObject): ...@@ -264,9 +271,9 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal], pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img) local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp del sdp
elif grating_split_pos_chip>=gal_end[1]: elif grating_split_pos_chip >= gal_end[1]:
sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0, sdp = SpecDisperser(orig_img=starImg, xcenter=x_nominal - 0,
ycenter=y_nominal - 0, origin=origin_star, ycenter=y_nominal - 0, origin=origin_star,
tar_spec=normalSED, tar_spec=normalSED,
...@@ -278,7 +285,7 @@ class Stamp(MockObject): ...@@ -278,7 +285,7 @@ class Stamp(MockObject):
pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal], pos_shear = self.addSLStoChipImageWithPSF(sdp=sdp, chip=chip, pos_img_local=[x_nominal, y_nominal],
psf_model=psf_model, bandNo=i + 1, psf_model=psf_model, bandNo=i + 1,
grating_split_pos=grating_split_pos, grating_split_pos=grating_split_pos,
local_wcs=chip_wcs_local, pos_img = pos_img) local_wcs=chip_wcs_local, pos_img=pos_img)
del sdp del sdp
# print(self.y_nominal, starImg.center.y, starImg.ymin) # print(self.y_nominal, starImg.center.y, starImg.ymin)
......
ObservationSim/MockObject/Star.py → observation_sim/mock_objects/Star.py
View file @ 36189a3e
...@@ -6,8 +6,8 @@ import astropy.constants as cons ...@@ -6,8 +6,8 @@ import astropy.constants as cons
from astropy.table import Table from astropy.table import Table
from scipy import interpolate from scipy import interpolate
from ObservationSim.MockObject._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, tag_sed from observation_sim.mock_objects._util import integrate_sed_bandpass, getNormFactorForSpecWithABMAG, getObservedSED, getABMAG, tag_sed
from ObservationSim.MockObject.MockObject import MockObject from observation_sim.mock_objects.MockObject import MockObject
class Star(MockObject): class Star(MockObject):
......
ObservationSim/MockObject/__init__.py → observation_sim/mock_objects/__init__.py
View file @ 36189a3e
...@@ -5,5 +5,3 @@ from .Quasar import Quasar ...@@ -5,5 +5,3 @@ from .Quasar import Quasar
from .Star import Star from .Star import Star
from .Stamp import Stamp from .Stamp import Stamp
from .FlatLED import FlatLED from .FlatLED import FlatLED
# from .SkybackgroundMap import *
# from .CosmicRay import CosmicRay
ObservationSim/MockObject/_util.py → observation_sim/mock_objects/_util.py
View file @ 36189a3e
...@@ -7,16 +7,19 @@ import galsim ...@@ -7,16 +7,19 @@ import galsim
VC_A = 2.99792458e+18 # speed of light: A/s VC_A = 2.99792458e+18 # speed of light: A/s
VC_M = 2.99792458e+8 # speed of light: m/s VC_M = 2.99792458e+8 # speed of light: m/s
H_PLANK = 6.626196e-27 # Plank constant: erg s H_PLANK = 6.626196e-27 # Plank constant: erg s
def comoving_dist(z, om_m=0.3111, om_L=0.6889, h=0.6766):
def comoving_dist(z, om_m=0.3111, om_L=0.6889, h=0.6766):
# Return comving distance in pc # Return comving distance in pc
H0 = h*100. # km / (s Mpc) H0 = h*100. # km / (s Mpc)
def dist_int(z): def dist_int(z):
return 1./np.sqrt(om_m*(1.+z)**3 + om_L) return 1./np.sqrt(om_m*(1.+z)**3 + om_L)
res, err = integrate.quad(dist_int, 0., z) res, err = integrate.quad(dist_int, 0., z)
return [res * (VC_M/1e3/H0) * 1e6, err * (VC_M/1e3/H0) * 1e6] return [res * (VC_M/1e3/H0) * 1e6, err * (VC_M/1e3/H0) * 1e6]
def magToFlux(mag): def magToFlux(mag):
""" """
flux of a given AB magnitude flux of a given AB magnitude
...@@ -30,6 +33,7 @@ def magToFlux(mag): ...@@ -30,6 +33,7 @@ def magToFlux(mag):
flux = 10**(-0.4*(mag+48.6)) flux = 10**(-0.4*(mag+48.6))
return flux return flux
def extAv(nav, seed=1212123): def extAv(nav, seed=1212123):
""" """
Generate random intrinsic extinction Av Generate random intrinsic extinction Av
...@@ -39,7 +43,7 @@ def extAv(nav, seed=1212123): ...@@ -39,7 +43,7 @@ def extAv(nav, seed=1212123):
tau = 0.4 tau = 0.4
peak, a = 0.1, 0.5 peak, a = 0.1, 0.5
b = a*(tau-peak) b = a*(tau-peak)
pav = lambda av: (a*av+b)*np.exp(-av/tau) def pav(av): return (a*av+b)*np.exp(-av/tau)
avmin, avmax = 0., 3. avmin, avmax = 0., 3.
avs = np.linspace(avmin, avmax, int((avmax-avmin)/0.001)+1) avs = np.linspace(avmin, avmax, int((avmax-avmin)/0.001)+1)
norm = np.trapz(pav(avs), avs) norm = np.trapz(pav(avs), avs)
...@@ -66,18 +70,20 @@ def seds(sedlistn, seddir="./", unit="A"): ...@@ -66,18 +70,20 @@ def seds(sedlistn, seddir="./", unit="A"):
reds = {} reds = {}
sedlist = seddir + sedlistn sedlist = seddir + sedlistn
sedn = open(sedlist).read().splitlines() sedn = open(sedlist).read().splitlines()
sedtype = range(1,len(sedn)+1) sedtype = range(1, len(sedn)+1)
for i in range(len(sedn)): for i in range(len(sedn)):
xxx = sedn[i].split() xxx = sedn[i].split()
isedn = seddir+xxx[0] isedn = seddir+xxx[0]
itype = sedtype[i] itype = sedtype[i]
ised = np.loadtxt(isedn) ised = np.loadtxt(isedn)
if unit=="nm": ised[:,0] *= 10.0 if unit == "nm":
ised[:, 0] *= 10.0
seds[itype] = ised seds[itype] = ised
reds[itype] = int(xxx[1]) reds[itype] = int(xxx[1])
return seds, reds return seds, reds
def sed_assign(phz, btt, rng): def sed_assign(phz, btt, rng):
""" """
assign SED template to a galaxy. assign SED template to a galaxy.
...@@ -106,6 +112,8 @@ def sed_assign(phz, btt, rng): ...@@ -106,6 +112,8 @@ def sed_assign(phz, btt, rng):
return sedtype return sedtype
########################################### ###########################################
def tflux(filt, sed, redshift=0.0, av=0.0, redden=0): def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
""" """
calculate the theoretical SED for given filter set and template calculate the theoretical SED for given filter set and template
...@@ -130,44 +138,47 @@ def tflux(filt, sed, redshift=0.0, av=0.0, redden=0): ...@@ -130,44 +138,47 @@ def tflux(filt, sed, redshift=0.0, av=0.0, redden=0):
SED in observed frame SED in observed frame
""" """
z = redshift + 1.0 z = redshift + 1.0
sw, sf = sed[:,0], sed[:,1] sw, sf = sed[:, 0], sed[:, 1]
# reddening # reddening
sf = reddening(sw, sf, av=av, model=redden) sf = reddening(sw, sf, av=av, model=redden)
sw, sf = sw*z, sf*(z**3) sw, sf = sw*z, sf*(z**3)
# lyman forest correction # lyman forest correction
sf = lyman_forest(sw, sf, redshift) sf = lyman_forest(sw, sf, redshift)
sedxx = (sw.copy(), sf.copy()) sedxx = (sw.copy(), sf.copy())
sw = VC_A/sw sw = VC_A/sw
sf = sf*(VC_A/sw**2) # convert flux unit to erg/s/cm^s/Hz sf = sf*(VC_A/sw**2) # convert flux unit to erg/s/cm^s/Hz
sw, sf = sw[::-1], sf[::-1] sw, sf = sw[::-1], sf[::-1]
sfun = interp1d(sw, sf, kind='linear') sfun = interp1d(sw, sf, kind='linear')
fwave, fresp = filt[:,0], filt[:,1] fwave, fresp = filt[:, 0], filt[:, 1]
fwave = VC_A/fwave fwave = VC_A/fwave
fwave, fresp = fwave[::-1], fresp[::-1] fwave, fresp = fwave[::-1], fresp[::-1]
tflux = sfun(fwave) tflux = sfun(fwave)
zpflux = 3.631*1.0e-20 zpflux = 3.631*1.0e-20
tflux = np.trapz(tflux*fresp/fwave,fwave)/np.trapz(zpflux*fresp/fwave,fwave) tflux = np.trapz(tflux*fresp/fwave, fwave) / \
#tflux = np.trapz(tflux*fresp,fwave)/np.trapz(zpflux*fresp,fwave) np.trapz(zpflux*fresp/fwave, fwave)
# tflux = np.trapz(tflux*fresp,fwave)/np.trapz(zpflux*fresp,fwave)
return tflux, sedxx return tflux, sedxx
########################################### ###########################################
def lyman_forest(wavelen, flux, z): def lyman_forest(wavelen, flux, z):
""" """
Compute the Lyman forest mean absorption of an input spectrum, Compute the Lyman forest mean absorption of an input spectrum,
according to D_A and D_B evolution from Madau (1995). according to D_A and D_B evolution from Madau (1995).
The waveeln and flux are in observed frame The waveeln and flux are in observed frame
""" """
if z<=0: if z <= 0:
flux0 = flux flux0 = flux
else: else:
nw = 200 nw = 200
istep = np.linspace(0,nw-1,nw) istep = np.linspace(0, nw-1, nw)
w1a, w2a = 1050.0*(1.0+z), 1170.0*(1.0+z) w1a, w2a = 1050.0*(1.0+z), 1170.0*(1.0+z)
w1b, w2b = 920.0*(1.0+z), 1015.0*(1.0+z) w1b, w2b = 920.0*(1.0+z), 1015.0*(1.0+z)
wstepa = (w2a-w1a)/float(nw) wstepa = (w2a-w1a)/float(nw)
...@@ -177,20 +188,25 @@ def lyman_forest(wavelen, flux, z): ...@@ -177,20 +188,25 @@ def lyman_forest(wavelen, flux, z):
ptaua = np.exp(-3.6e-03*(wtempa/1216.0)**3.46) ptaua = np.exp(-3.6e-03*(wtempa/1216.0)**3.46)
wtempb = w1b + istep*wstepb wtempb = w1b + istep*wstepb
ptaub = np.exp(-1.7e-3*(wtempb/1026.0)**3.46\ ptaub = np.exp(-1.7e-3*(wtempb/1026.0)**3.46
-1.2e-3*(wtempb/972.50)**3.46\ - 1.2e-3*(wtempb/972.50)**3.46
-9.3e-4*(wtempb/950.00)**3.46) - 9.3e-4*(wtempb/950.00)**3.46)
da = (1.0/(120.0*(1.0+z)))*np.trapz(ptaua, wtempa) da = (1.0/(120.0*(1.0+z)))*np.trapz(ptaua, wtempa)
db = (1.0/(95.0*(1.0+z)))*np.trapz(ptaub, wtempb) db = (1.0/(95.0*(1.0+z)))*np.trapz(ptaub, wtempb)
if da>1.0: da=1.0 if da > 1.0:
if db>1.0: db=1.0 da = 1.0
if da<0.0: da=0.0 if db > 1.0:
if db<0.0: db=0.0 db = 1.0
if da < 0.0:
da = 0.0
if db < 0.0:
db = 0.0
flux0 = flux.copy() flux0 = flux.copy()
id0 = wavelen<=1026.0*(1.0+z) id0 = wavelen <= 1026.0*(1.0+z)
id1 = np.logical_and(wavelen<1216.0*(1.0+z),wavelen>=1026.0*(1.0+z)) id1 = np.logical_and(wavelen < 1216.0*(1.0+z),
wavelen >= 1026.0*(1.0+z))
flux0[id0] = db*flux[id0] flux0[id0] = db*flux[id0]
flux0[id1] = da*flux[id1] flux0[id1] = da*flux[id1]
...@@ -220,128 +236,131 @@ def reddening(sw, sf, av=0.0, model=0): ...@@ -220,128 +236,131 @@ def reddening(sw, sf, av=0.0, model=0):
Return: Return:
reddening-corrected flux or observed flux reddening-corrected flux or observed flux
""" """
if model==0 or av==0.0: if model == 0 or av == 0.0:
flux=sf flux = sf
elif model==1: # Allen (1976) for the Milky Way elif model == 1: # Allen (1976) for the Milky Way
lambda0 = np.array([1000, 1110, 1250, 1430, 1670, \ lambda0 = np.array([1000, 1110, 1250, 1430, 1670,
2000, 2220, 2500, 2850, 3330, \ 2000, 2220, 2500, 2850, 3330,
3650, 4000, 4400, 5000, 5530, \ 3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float) 6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([4.20, 3.70, 3.30, 3.00, 2.70, \ kR = np.array([4.20, 3.70, 3.30, 3.00, 2.70,
2.80, 2.90, 2.30, 1.97, 1.69, \ 2.80, 2.90, 2.30, 1.97, 1.69,
1.58, 1.45, 1.32, 1.13, 1.00, \ 1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float) 0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1) ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw) A_lambda = av*ext0(sw)
A_lambda[A_lambda<0.0] = 0.0 A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda) flux = sf*10**(-0.4*A_lambda)
elif model==2: # Seaton (1979) fit by Fitzpatrick (1986) for the Milky Way elif model == 2: # Seaton (1979) fit by Fitzpatrick (1986) for the Milky Way
Rv=3.1 Rv = 3.1
al0, ga, c1, c2, c3, c4 = 4.595, 1.051, -0.38, 0.74, 3.96, 0.26 al0, ga, c1, c2, c3, c4 = 4.595, 1.051, -0.38, 0.74, 3.96, 0.26
ff11 = __red(1100.0,al0,ga,c1,c2,c3,c4) ff11 = __red(1100.0, al0, ga, c1, c2, c3, c4)
ff12 = __red(1200.0,al0,ga,c1,c2,c3,c4) ff12 = __red(1200.0, al0, ga, c1, c2, c3, c4)
slope=(ff12-ff11)/100.0 slope = (ff12-ff11)/100.0
lambda0 = np.array([3650, 4000, 4400, 5000, 5530, \ lambda0 = np.array([3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float) 6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([1.58, 1.45, 1.32, 1.13, 1.00, \ kR = np.array([1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float) 0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
fun = interp1d(lambda0, kR, kind='linear') fun = interp1d(lambda0, kR, kind='linear')
sw0 = sw[sw<1200.0] sw0 = sw[sw < 1200.0]
A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0 A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0
sw1 = sw[np.logical_and(sw>=1200.0, sw<=3650.0)] sw1 = sw[np.logical_and(sw >= 1200.0, sw <= 3650.0)]
ff = __red(sw1,al0,ga,c1,c2,c3,c4) ff = __red(sw1, al0, ga, c1, c2, c3, c4)
A_lambda1 = ff/Rv+1.0 A_lambda1 = ff/Rv+1.0
sw2 = sw[np.logical_and(sw>3650.0, sw<=100000.0)] sw2 = sw[np.logical_and(sw > 3650.0, sw <= 100000.0)]
A_lambda2 = fun(sw2) A_lambda2 = fun(sw2)
A_lambda3 = sw[sw>100000.0]*0.0 A_lambda3 = sw[sw > 100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0,A_lambda1,A_lambda2,A_lambda3]) A_lambda = av*np.hstack([A_lambda0, A_lambda1, A_lambda2, A_lambda3])
A_lambda[A_lambda<0.0] = 0.0 A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda) flux = sf*10**(-0.4*A_lambda)
elif model==3: # Fitzpatrick (1986) for the Large Magellanic Cloud (LMC) elif model == 3: # Fitzpatrick (1986) for the Large Magellanic Cloud (LMC)
Rv=3.1 Rv = 3.1
al0, ga, c1, c2, c3, c4 = 4.608, 0.994, -0.69, 0.89, 2.55, 0.50 al0, ga, c1, c2, c3, c4 = 4.608, 0.994, -0.69, 0.89, 2.55, 0.50
ff11 = __red(1100.0,al0,ga,c1,c2,c3,c4) ff11 = __red(1100.0, al0, ga, c1, c2, c3, c4)
ff12 = __red(1200.0,al0,ga,c1,c2,c3,c4) ff12 = __red(1200.0, al0, ga, c1, c2, c3, c4)
slope=(ff12-ff11)/100.0 slope = (ff12-ff11)/100.0
lambda0 = np.array([3330, 3650, 4000, 4400, 5000, 5530, \ lambda0 = np.array([3330, 3650, 4000, 4400, 5000, 5530,
6700, 9000, 10000, 20000, 100000], dtype=float) 6700, 9000, 10000, 20000, 100000], dtype=float)
kR = np.array([1.682, 1.58, 1.45, 1.32, 1.13, 1.00, \ kR = np.array([1.682, 1.58, 1.45, 1.32, 1.13, 1.00,
0.74, 0.46, 0.38, 0.11, 0.00],dtype=float) 0.74, 0.46, 0.38, 0.11, 0.00], dtype=float)
fun = interp1d(lambda0, kR, kind='linear') fun = interp1d(lambda0, kR, kind='linear')
sw0 = sw[sw<1200.0] sw0 = sw[sw < 1200.0]
A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0 A_lambda0 = (ff11+(sw0-1100.0)*slope)/Rv+1.0
sw1 = sw[np.logical_and(sw>=1200.0, sw<=3330.0)] sw1 = sw[np.logical_and(sw >= 1200.0, sw <= 3330.0)]
ff = __red(sw1,al0,ga,c1,c2,c3,c4) ff = __red(sw1, al0, ga, c1, c2, c3, c4)
A_lambda1 = ff/Rv+1.0 A_lambda1 = ff/Rv+1.0
sw2 = sw[np.logical_and(sw>3330.0, sw<=100000.0)] sw2 = sw[np.logical_and(sw > 3330.0, sw <= 100000.0)]
A_lambda2 = fun(sw2) A_lambda2 = fun(sw2)
A_lambda3 = sw[sw>100000.0]*0.0 A_lambda3 = sw[sw > 100000.0]*0.0
A_lambda = av*np.hstack([A_lambda0,A_lambda1,A_lambda2,A_lambda3]) A_lambda = av*np.hstack([A_lambda0, A_lambda1, A_lambda2, A_lambda3])
A_lambda[A_lambda<0.0] = 0.0 A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda) flux = sf*10**(-0.4*A_lambda)
elif model==4: # Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC) # Prevot et al (1984) and Bouchet (1985) for the Small Magellanic Cloud (SMC)
elif model == 4:
Rv = 2.72 Rv = 2.72
lambda0 = np.array([1275, 1330, 1385, 1435, 1490, 1545, \ lambda0 = np.array([1275, 1330, 1385, 1435, 1490, 1545,
1595, 1647, 1700, 1755, 1810, 1860, \ 1595, 1647, 1700, 1755, 1810, 1860,
1910, 2000, 2115, 2220, 2335, 2445, \ 1910, 2000, 2115, 2220, 2335, 2445,
2550, 2665, 2778, 2890, 2995, 3105, \ 2550, 2665, 2778, 2890, 2995, 3105,
3704, 4255, 5291, 12500, 16500, 22000], dtype=float) 3704, 4255, 5291, 12500, 16500, 22000], dtype=float)
kR = np.array([13.54, 12.52, 11.51, 10.80, 9.84, 9.28, \ kR = np.array([13.54, 12.52, 11.51, 10.80, 9.84, 9.28,
9.06, 8.49, 8.01, 7.71, 7.17, 6.90, 6.76, \ 9.06, 8.49, 8.01, 7.71, 7.17, 6.90, 6.76,
6.38, 5.85, 5.30, 4.53, 4.24, 3.91, 3.49, \ 6.38, 5.85, 5.30, 4.53, 4.24, 3.91, 3.49,
3.15, 3.00, 2.65, 2.29, 1.81, 1.00, 0.00, \ 3.15, 3.00, 2.65, 2.29, 1.81, 1.00, 0.00,
-2.02, -2.36, -2.47],dtype=float) -2.02, -2.36, -2.47], dtype=float)
kR = kR/Rv+1.0 kR = kR/Rv+1.0
ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1) ext0 = InterpolatedUnivariateSpline(lambda0, kR, k=1)
A_lambda = av*ext0(sw) A_lambda = av*ext0(sw)
A_lambda[A_lambda<0.0] = 0.0 A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda) flux = sf*10**(-0.4*A_lambda)
elif model==5: # Calzetti et al (2000) for starburst galaxies elif model == 5: # Calzetti et al (2000) for starburst galaxies
Rv = 4.05 Rv = 4.05
sw = sw*1.0e-04 #wavelength in microns sw = sw*1.0e-04 # wavelength in microns
fun1 = lambda x: 2.659*(-2.156+1.509/x-0.198/x**2+0.011/x**3)+Rv def fun1(x): return 2.659*(-2.156+1.509/x-0.198/x**2+0.011/x**3)+Rv
fun2 = lambda x: 2.659*(-1.857+1.040/x)+Rv def fun2(x): return 2.659*(-1.857+1.040/x)+Rv
ff11, ff12 = fun1(0.11), fun1(0.12) ff11, ff12 = fun1(0.11), fun1(0.12)
slope1=(ff12-ff11)/0.01 slope1 = (ff12-ff11)/0.01
ff99, ff100 = fun2(2.19), fun2(2.2) ff99, ff100 = fun2(2.19), fun2(2.2)
slope2=(ff100-ff99)/0.01 slope2 = (ff100-ff99)/0.01
sw0 = sw[sw<0.12] sw0 = sw[sw < 0.12]
sw1 = sw[np.logical_and(sw>=0.12, sw<=0.63)] sw1 = sw[np.logical_and(sw >= 0.12, sw <= 0.63)]
sw2 = sw[np.logical_and(sw>0.63, sw<=2.2)] sw2 = sw[np.logical_and(sw > 0.63, sw <= 2.2)]
sw3 = sw[sw>2.2] sw3 = sw[sw > 2.2]
k_lambda0 = ff11+(sw0-0.11)*slope1 k_lambda0 = ff11+(sw0-0.11)*slope1
k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2) k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2)
k_lambda3 = ff99+(sw3-2.19)*slope2 k_lambda3 = ff99+(sw3-2.19)*slope2
A_lambda = av*np.hstack([k_lambda0,k_lambda1,k_lambda2,k_lambda3])/Rv A_lambda = av*np.hstack([k_lambda0, k_lambda1,
A_lambda[A_lambda<0.0] = 0.0 k_lambda2, k_lambda3])/Rv
A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda) flux = sf*10**(-0.4*A_lambda)
elif model==6: # Reddy et al (2015) for satr forming galaxies elif model == 6: # Reddy et al (2015) for satr forming galaxies
Rv = 2.505 Rv = 2.505
sw = sw*1.0e-04 sw = sw*1.0e-04
fun1 = lambda x: -5.726+4.004/x-0.525/x**2+0.029/x**3+Rv def fun1(x): return -5.726+4.004/x-0.525/x**2+0.029/x**3+Rv
fun2 = lambda x: -2.672-0.010/x+1.532/x**2-0.412/x**3+Rv def fun2(x): return -2.672-0.010/x+1.532/x**2-0.412/x**3+Rv
ff11, ff12 = fun1(0.14), fun1(0.15) ff11, ff12 = fun1(0.14), fun1(0.15)
slope1=(ff12-ff11)/0.01 slope1 = (ff12-ff11)/0.01
ff99, ff100 = fun2(2.84), fun2(2.85) ff99, ff100 = fun2(2.84), fun2(2.85)
slope2=(ff100-ff99)/0.01 slope2 = (ff100-ff99)/0.01
sw0 = sw[sw<0.15] sw0 = sw[sw < 0.15]
sw1 = sw[np.logical_and(sw>=0.15, sw<0.60)] sw1 = sw[np.logical_and(sw >= 0.15, sw < 0.60)]
sw2 = sw[np.logical_and(sw>=0.60, sw<2.85)] sw2 = sw[np.logical_and(sw >= 0.60, sw < 2.85)]
sw3 = sw[sw>=2.85] sw3 = sw[sw >= 2.85]
k_lambda0 = ff11+(sw0-0.14)*slope1 k_lambda0 = ff11+(sw0-0.14)*slope1
k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2) k_lambda1, k_lambda2 = fun1(sw1), fun2(sw2)
k_lambda3 = ff99+(sw3-2.84)*slope2 k_lambda3 = ff99+(sw3-2.84)*slope2
A_lambda = av*np.hstack([k_lambda0,k_lambda1,k_lambda2,k_lambda3])/Rv A_lambda = av*np.hstack([k_lambda0, k_lambda1,
A_lambda[A_lambda<0.0] = 0.0 k_lambda2, k_lambda3])/Rv
flux = sf*10**(-0.4*A_lambda) A_lambda[A_lambda < 0.0] = 0.0
flux = sf*10**(-0.4*A_lambda)
else: else:
raise ValueError("!!! Please select a proper reddening model") raise ValueError("!!! Please select a proper reddening model")
...@@ -349,25 +368,30 @@ def reddening(sw, sf, av=0.0, model=0): ...@@ -349,25 +368,30 @@ def reddening(sw, sf, av=0.0, model=0):
return flux return flux
########################################### ###########################################
def __red(alan,al0,ga,c1,c2,c3,c4):
fun1 = lambda x: c3/(((x-(al0**2/x))**2)+ga*ga)
fun2 = lambda x,cc: cc*(0.539*((x-5.9)**2)+0.0564*((x-5.9)**3))
fun = lambda x,cc: c1+c2*x+fun1(x)+fun2(x,cc)
ala = alan*1.0e-04 #wavelength in microns def __red(alan, al0, ga, c1, c2, c3, c4):
def fun1(x): return c3/(((x-(al0**2/x))**2)+ga*ga)
def fun2(x, cc): return cc*(0.539*((x-5.9)**2)+0.0564*((x-5.9)**3))
def fun(x, cc): return c1+c2*x+fun1(x)+fun2(x, cc)
ala = alan*1.0e-04 # wavelength in microns
p = 1.0/ala p = 1.0/ala
if np.size(p)>1: if np.size(p) > 1:
p1, p2 = p[p>=5.9], p[p<5.9] p1, p2 = p[p >= 5.9], p[p < 5.9]
ff = np.append(fun(p1,c4), fun(p2,0.0)) ff = np.append(fun(p1, c4), fun(p2, 0.0))
elif np.size(p)==1: elif np.size(p) == 1:
if p<5.9: c4 = 0.0 if p < 5.9:
c4 = 0.0
ff = fun(p, c4) ff = fun(p, c4)
else: else:
return return
return ff return ff
########################################### ###########################################
def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0): def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0):
# load the filters # load the filters
...@@ -378,19 +402,23 @@ def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0): ...@@ -378,19 +402,23 @@ def sed2mag(mag_i, sedCat, filter_list, redshift=0.0, av=0.0, redden=0):
if filter_list[k].filter_type == 'i': if filter_list[k].filter_type == 'i':
nid = k nid = k
bandpass = filter_list[k].bandpass_full bandpass = filter_list[k].bandpass_full
ktrans = np.transpose(np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)])) ktrans = np.transpose(
aflux[k], isedObs = tflux(ktrans, sedCat, redshift=redshift, av=av, redden=redden) np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)]))
aflux[k], isedObs = tflux(
ktrans, sedCat, redshift=redshift, av=av, redden=redden)
# normalize to i-band # normalize to i-band
aflux = aflux / aflux[nid] aflux = aflux / aflux[nid]
# magnitudes in all filters # magnitudes in all filters
amag = -2.5*np.log10(aflux) + mag_i amag = -2.5*np.log10(aflux) + mag_i
spec = galsim.LookupTable(x=np.array(isedObs[0]), f=np.array(isedObs[1]), interpolant='nearest') spec = galsim.LookupTable(x=np.array(isedObs[0]), f=np.array(
isedObs[1]), interpolant='nearest')
isedObs = galsim.SED(spec, wave_type='A', flux_type='1', fast=False) isedObs = galsim.SED(spec, wave_type='A', flux_type='1', fast=False)
return amag, isedObs return amag, isedObs
def eObs(e1,e2,g1,g2):
def eObs(e1, e2, g1, g2):
""" """
Calculate the sheared (observed) ellipticity using the Calculate the sheared (observed) ellipticity using the
intrinsic ellipticity and cosmic shear components. intrinsic ellipticity and cosmic shear components.
...@@ -424,7 +452,7 @@ def eObs(e1,e2,g1,g2): ...@@ -424,7 +452,7 @@ def eObs(e1,e2,g1,g2):
e = complex(e1[i], e2[i]) e = complex(e1[i], e2[i])
g = complex(g1[i], g2[i]) g = complex(g1[i], g2[i])
e, gg = abs(e), abs(g) e, gg = abs(e), abs(g)
if gg<=1.0: if gg <= 1.0:
tt = e + g tt = e + g
bb = 1.0 + e*g.conjugate() bb = 1.0 + e*g.conjugate()
eobs = tt/bb eobs = tt/bb
...@@ -432,27 +460,34 @@ def eObs(e1,e2,g1,g2): ...@@ -432,27 +460,34 @@ def eObs(e1,e2,g1,g2):
tt = 1.0 + g*e.conjugate() tt = 1.0 + g*e.conjugate()
bb = e.conjugate() + g.conjugate() bb = e.conjugate() + g.conjugate()
eobs = tt/bb eobs = tt/bb
# derive the orientation # derive the orientation
dd = 0.5*np.arctan(abs(eobs.imag/eobs.real))*180.0/np.pi dd = 0.5*np.arctan(abs(eobs.imag/eobs.real))*180.0/np.pi
if eobs.imag>0 and eobs.real>0: dd = dd if eobs.imag > 0 and eobs.real > 0:
if eobs.imag>0 and eobs.real<0: dd = 90.0 - dd dd = dd
if eobs.imag<0 and eobs.real>0: dd = 0.0 - dd if eobs.imag > 0 and eobs.real < 0:
if eobs.imag<0 and eobs.real<0: dd = dd - 90.0 dd = 90.0 - dd
if eobs.imag < 0 and eobs.real > 0:
dd = 0.0 - dd
if eobs.imag < 0 and eobs.real < 0:
dd = dd - 90.0
e1obs += [eobs.real] e1obs += [eobs.real]
e2obs += [eobs.imag] e2obs += [eobs.imag]
eeobs += [abs(eobs)] eeobs += [abs(eobs)]
theta += [dd] theta += [dd]
e1obs,e2obs,eeobs,theta = np.array(e1obs),np.array(e2obs),np.array(eeobs),np.array(theta) e1obs, e2obs, eeobs, theta = np.array(e1obs), np.array(
if nobj == 1: e1obs,e2obs,eeobs,theta = e1obs[0],e2obs[0],eeobs[0],theta[0] e2obs), np.array(eeobs), np.array(theta)
if nobj == 1:
e1obs, e2obs, eeobs, theta = e1obs[0], e2obs[0], eeobs[0], theta[0]
return e1obs, e2obs, eeobs, theta return e1obs, e2obs, eeobs, theta
def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0): def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0):
z = redshift + 1.0 z = redshift + 1.0
sw, sf = sedCat[:,0], sedCat[:,1] sw, sf = sedCat[:, 0], sedCat[:, 1]
# reddening # reddening
sf = reddening(sw, sf, av=av, model=redden) sf = reddening(sw, sf, av=av, model=redden)
# sw, sf = sw*z, sf*(z**3) # sw, sf = sw*z, sf*(z**3)
...@@ -464,28 +499,33 @@ def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0): ...@@ -464,28 +499,33 @@ def getObservedSED(sedCat, redshift=0.0, av=0.0, redden=0):
isedObs = (sw.copy(), sf.copy()) isedObs = (sw.copy(), sf.copy())
return isedObs return isedObs
def integrate_sed_bandpass(sed, bandpass): def integrate_sed_bandpass(sed, bandpass):
wave = np.linspace(bandpass.blue_limit, bandpass.red_limit, 1000) # in nm wave = np.linspace(bandpass.blue_limit, bandpass.red_limit, 1000) # in nm
flux_normalized = sed(wave)*bandpass(wave) flux_normalized = sed(wave)*bandpass(wave)
# print('in integrate_sed_bandpass', bandpass.blue_limit, bandpass.red_limit) # print('in integrate_sed_bandpass', bandpass.blue_limit, bandpass.red_limit)
int_flux = np.trapz(y=flux_normalized, x=wave) * 10. # convert to photons s-1 m-2 A-1 int_flux = np.trapz(y=flux_normalized, x=wave) * \
10. # convert to photons s-1 m-2 A-1
return int_flux return int_flux
def getABMAG(interFlux, bandpass): def getABMAG(interFlux, bandpass):
throughtput = Table(np.array(np.array([bandpass.wave_list*10.0, bandpass.func(bandpass.wave_list)])).T, names=(['WAVELENGTH', 'SENSITIVITY'])) throughtput = Table(np.array(np.array([bandpass.wave_list*10.0, bandpass.func(
bandpass.wave_list)])).T, names=(['WAVELENGTH', 'SENSITIVITY']))
sWave = bandpass.blue_limit*10.0 sWave = bandpass.blue_limit*10.0
eWave = bandpass.red_limit*10.0 eWave = bandpass.red_limit*10.0
# print('in getABMAG', sWave, eWave) # print('in getABMAG', sWave, eWave)
ABMAG_zero = getABMagAverageVal( ABMAG_zero = getABMagAverageVal(
ABmag=0, ABmag=0,
norm_thr=throughtput, norm_thr=throughtput,
sWave=sWave, sWave=sWave,
eWave=eWave) eWave=eWave)
flux_ave = interFlux / (eWave-sWave) flux_ave = interFlux / (eWave-sWave)
ABMAG_spec = -2.5 * np.log10(flux_ave/ABMAG_zero) ABMAG_spec = -2.5 * np.log10(flux_ave/ABMAG_zero)
return ABMAG_spec return ABMAG_spec
def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250):
def getABMagAverageVal(ABmag=20., norm_thr=None, sWave=6840, eWave=8250):
""" """
norm_thr: astropy.table, 2 colum, 'WAVELENGTH', 'SENSITIVITY' norm_thr: astropy.table, 2 colum, 'WAVELENGTH', 'SENSITIVITY'
...@@ -496,15 +536,16 @@ def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250): ...@@ -496,15 +536,16 @@ def getABMagAverageVal(ABmag=20.,norm_thr=None, sWave=6840, eWave=8250):
inverseLambda = norm_thr['SENSITIVITY']/norm_thr['WAVELENGTH'] inverseLambda = norm_thr['SENSITIVITY']/norm_thr['WAVELENGTH']
norm_thr_i = interpolate.interp1d(norm_thr['WAVELENGTH'], inverseLambda) norm_thr_i = interpolate.interp1d(norm_thr['WAVELENGTH'], inverseLambda)
x = np.linspace(sWave,eWave, int(eWave)-int(sWave)+1) x = np.linspace(sWave, eWave, int(eWave)-int(sWave)+1)
y = norm_thr_i(x) y = norm_thr_i(x)
AverageLamdaInverse = np.trapz(y,x)/(eWave-sWave) AverageLamdaInverse = np.trapz(y, x)/(eWave-sWave)
norm = 54798696332.52474 * pow(10.0, -0.4 * ABmag) * AverageLamdaInverse norm = 54798696332.52474 * pow(10.0, -0.4 * ABmag) * AverageLamdaInverse
# print('AverageLamdaInverse = ', AverageLamdaInverse) # print('AverageLamdaInverse = ', AverageLamdaInverse)
# print('norm = ', norm) # print('norm = ', norm)
return norm return norm
def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave=6840, eWave=8250): def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave=6840, eWave=8250):
""" """
Use AB magnitude system (zero point, fv = 3631 janskys) in the normal band(norm_thr) normalize the spectrum by inpute ABMag Use AB magnitude system (zero point, fv = 3631 janskys) in the normal band(norm_thr) normalize the spectrum by inpute ABMag
...@@ -520,17 +561,19 @@ def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave ...@@ -520,17 +561,19 @@ def getNormFactorForSpecWithABMAG(ABMag=20., spectrum=None, norm_thr=None, sWave
the normalization factor flux of AB system(fix a band and magnitude ) /the flux of inpute spectrum(fix a band) the normalization factor flux of AB system(fix a band and magnitude ) /the flux of inpute spectrum(fix a band)
""" """
spectrumi = interpolate.interp1d(spectrum['WAVELENGTH'], spectrum['FLUX']) spectrumi = interpolate.interp1d(spectrum['WAVELENGTH'], spectrum['FLUX'])
norm_thri = interpolate.interp1d(norm_thr['WAVELENGTH'], norm_thr['SENSITIVITY']) norm_thri = interpolate.interp1d(
norm_thr['WAVELENGTH'], norm_thr['SENSITIVITY'])
x = np.linspace(sWave,eWave, int(eWave)-int(sWave)+1) x = np.linspace(sWave, eWave, int(eWave)-int(sWave)+1)
y_spec = spectrumi(x) y_spec = spectrumi(x)
y_thr = norm_thri(x) y_thr = norm_thri(x)
y = y_spec*y_thr y = y_spec*y_thr
specAve = np.trapz(y,x)/(eWave-sWave) specAve = np.trapz(y, x)/(eWave-sWave)
norm = getABMagAverageVal(ABmag=ABMag, norm_thr=norm_thr, sWave=sWave, eWave=eWave) norm = getABMagAverageVal(
ABmag=ABMag, norm_thr=norm_thr, sWave=sWave, eWave=eWave)
if specAve == 0: if specAve == 0:
return 0 return 0
...@@ -551,12 +594,14 @@ def tag_sed(h5file, model_tag, teff=5000, logg=2, feh=0): ...@@ -551,12 +594,14 @@ def tag_sed(h5file, model_tag, teff=5000, logg=2, feh=0):
close_feh = 99 close_feh = 99
else: else:
close_feh = feh_grid[np.argmin(np.abs(feh_grid - feh))] close_feh = feh_grid[np.argmin(np.abs(feh_grid - feh))]
path = model_tag_str + f"_teff_{close_teff:.1f}_logg_{close_logg:.2f}_feh_{close_feh:.1f}" path = model_tag_str + \
f"_teff_{close_teff:.1f}_logg_{close_logg:.2f}_feh_{close_feh:.1f}"
wave = np.array(h5file["wave"][model_tag_str][()]).ravel() wave = np.array(h5file["wave"][model_tag_str][()]).ravel()
flux = np.array(h5file["sed"][path][()]).ravel() flux = np.array(h5file["sed"][path][()]).ravel()
return path, wave, flux return path, wave, flux
def convolveGaussXorders(img=None, sigma = 1):
def convolveGaussXorders(img=None, sigma=1):
from astropy.modeling.models import Gaussian2D from astropy.modeling.models import Gaussian2D
from scipy import signal from scipy import signal
offset = int(np.ceil(sigma*10)) offset = int(np.ceil(sigma*10))
...@@ -571,14 +616,13 @@ def convolveGaussXorders(img=None, sigma = 1): ...@@ -571,14 +616,13 @@ def convolveGaussXorders(img=None, sigma = 1):
convImg = signal.fftconvolve(img, psf, mode='full', axes=None) convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
return convImg, offset return convImg, offset
def convolveImg(img=None, psf = None):
def convolveImg(img=None, psf=None):
from astropy.modeling.models import Gaussian2D from astropy.modeling.models import Gaussian2D
from scipy import signal from scipy import signal
convImg = signal.fftconvolve(img, psf, mode='full', axes=None) convImg = signal.fftconvolve(img, psf, mode='full', axes=None)
offset_x = int(psf.shape[1]/2. + 0.5) - 1 offset_x = int(psf.shape[1]/2. + 0.5) - 1
offset_y = int(psf.shape[0]/2. + 0.5) - 1 offset_y = int(psf.shape[0]/2. + 0.5) - 1
offset = [offset_x,offset_y] offset = [offset_x, offset_y]
return convImg, offset return convImg, offset
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