def fit_hitrans_pixel(s_dict, ref_pixel_dict):
"""
s_list : dict
ref_pixel_list : dict
"""
igr_sol_list = {}
from fit_gaussian import fit_gaussian_simple
for i, ref_pixels in ref_pixel_dict.items():
s_igr = s_dict[i]
xx = np.arange(len(s_igr))
sol_list = []
for ll in ref_pixels:
sol = fit_gaussian_simple(xx, s_igr, ll,
sigma_init=5, do_plot=False)
sigma_init = max(1, sol[0][1])
sol = fit_gaussian_simple(xx, s_igr,
sol[0][0]+ll-ll[0],
sigma_init=sigma_init, do_plot=False)
sol_list.append(sol)
pixel = [sol_[0][0] for sol_ in sol_list]
pixel_sigma = [sol_[0][1] for sol_ in sol_list]
igr_sol_list[i] = dict(pixel=pixel,
pixel_sigma=pixel_sigma)
return igr_sol_list
def fit_hitrans_pixel(s_dict, ref_pixel_dict):
"""
s_list : dict
ref_pixel_list : dict
"""
igr_sol_list = {}
from fit_gaussian import fit_gaussian_simple
for i, ref_pixels in ref_pixel_dict.items():
s_igr = s_dict[i]
xx = np.arange(len(s_igr))
sol_list = []
for ll in ref_pixels:
sol = fit_gaussian_simple(xx,
s_igr,
ll,
sigma_init=5,
do_plot=False)
sigma_init = max(1, sol[0][1])
sol = fit_gaussian_simple(xx,
s_igr,
sol[0][0] + ll - ll[0],
sigma_init=sigma_init,
do_plot=False)
sol_list.append(sol)
pixel = [sol_[0][0] for sol_ in sol_list]
pixel_sigma = [sol_[0][1] for sol_ in sol_list]
igr_sol_list[i] = dict(pixel=pixel, pixel_sigma=pixel_sigma)
return igr_sol_list
def reidentify(s, x_list, x=None, sigma_init=1.5):
"""
given spectrum s, try to reidentify lines as x_list as initial point.
x_list is shifted by delta_x if not 0.
sigma_init : a single value, or an iterator of single value.
"""
if x is None:
x = np.arange(len(s))
try:
sigma_iter = iter(sigma_init)
except TypeError:
sigma_iter = repeat(sigma_init)
fit_results = []
for lines_pixel, sigma in izip(x_list, sigma_iter):
sol_ = fit_gaussian_simple(x, s, lines_pixel,
sigma_init=sigma,
do_plot=False)
fit_results.append(sol_)
return fit_results
def reidentify(s, x_list, x=None, sigma_init=1.5):
"""
given spectrum s, try to reidentify lines as x_list as initial point.
x_list is shifted by delta_x if not 0.
"""
if x is None:
x = np.arange(len(s))
fit_results = []
for lines_pixel in x_list:
sol_ = fit_gaussian_simple(x, s, lines_pixel,
sigma_init=sigma_init,
do_plot=False)
fit_results.append(sol_)
return fit_results
def find_peaks(s, sigma=3, ax=None):
emission_feature_msk = find_feature_mask_simple(s, sigma=sigma, ax=ax)
import scipy.ndimage as ni
emission_feature_label, label_max = ni.label(emission_feature_msk)
com_list = ni.center_of_mass(s, emission_feature_label,
range(1, label_max + 1))
from fit_gaussian import fit_gaussian_simple
x = np.arange(len(s))
sol_list = []
for com in com_list:
sol = fit_gaussian_simple(x, s, com, sigma_init=2., do_plot=False)
sol_list.append(sol)
#center_list = np.array([sol[0][0] for sol in sol_list])
#width_list = np.array([sol[0][1] for sol in sol_list])
#height_list = np.array([sol[0][2] for sol in sol_list])
return [sol_[0] for sol_ in sol_list]
def find_peaks(s, sigma=3, ax=None):
emission_feature_msk = find_feature_mask_simple(s, sigma=sigma, ax=ax)
import scipy.ndimage as ni
emission_feature_label, label_max = ni.label(emission_feature_msk)
com_list = ni.center_of_mass(s, emission_feature_label,
range(1, label_max+1))
from fit_gaussian import fit_gaussian_simple
x = np.arange(len(s))
sol_list = []
for com in com_list:
sol = fit_gaussian_simple(x, s, com, sigma_init=2., do_plot=False)
sol_list.append(sol)
#center_list = np.array([sol[0][0] for sol in sol_list])
#width_list = np.array([sol[0][1] for sol in sol_list])
#height_list = np.array([sol[0][2] for sol in sol_list])
return [sol_[0] for sol_ in sol_list]
def reidentify(s, x_list, x=None, sigma_init=1.5):
"""
given spectrum s, try to reidentify lines as x_list as initial point.
x_list is shifted by delta_x if not 0.
"""
if x is None:
x = np.arange(len(s))
fit_results = []
for lines_pixel in x_list:
sol_ = fit_gaussian_simple(x,
s,
lines_pixel,
sigma_init=sigma_init,
do_plot=False)
fit_results.append(sol_)
return fit_results
def bootstrap(utdate):
igrins_orders = {}
igrins_orders["H"] = range(99, 122)
igrins_orders["K"] = range(72, 94)
hitran = Hitran()
from hitran_igrins import order as hitrans_detected
from hitran_igrins import ig_order as hitrans_ig_detected
from fit_gaussian import fit_gaussian_simple
band = "K"
import json
s_list = json.load(open("arc_spec_sky_%s_%s.json" % (band, utdate)))
wvl_sol = json.load(open("ecfit/wvl_sol_ohlines_%s_%s.json" \
% (band, utdate)))
# emasure line coordinates from hitran data
hitran_wvl, hitran_s = hitran.load_cires()
hitran_sol_list = []
for i in range(5):
# fit hitran data
sol_list = []
dwvl_list = []
for ll in hitrans_detected[i]:
sol = fit_gaussian_simple(hitran_wvl, hitran_s,
ll,
sigma_init=7e-5, do_plot=False)
dwvl = np.array(ll)-ll[0]
sol = fit_gaussian_simple(hitran_wvl, hitran_s,
sol[0][0]+dwvl,
sigma_init=7e-5, do_plot=False)
dwvl_list.append(dwvl)
sol_list.append(sol)
wavelength = [sol_[0][0] for sol_ in sol_list]
wavelength_grouped = [list(sol_[0][0]+dwvl_) for dwvl_, sol_ \
in zip(dwvl_list, sol_list)]
wavelength_sigma = [sol_[0][1] for sol_ in sol_list]
hitran_sol_list.append(dict(wavelength=wavelength,
wavelength_grouped=wavelength_grouped,
wavelength_sigma=wavelength_sigma))
hitran_sol_dict = dict(zip(igrins_orders["K"], hitran_sol_list))
# now get the pixel corrdinates from observed data.
med_filter = hitran.get_median_filtered_spec
s_list_m = [med_filter(wvl_sol[i], s_list[i]) for i in range(5)]
s_list_dict = dict(zip(igrins_orders[band],
s_list_m))
ref_pixel_dict = dict((igrins_orders[band][i], r) for i, r \
in hitrans_ig_detected.items())
igr_sol = fit_hitrans_pixel(s_list_dict, ref_pixel_dict)
# merge tow data
sol_sol = {}
for i in igr_sol:
s0 = dict()
s0.update(hitran_sol_dict[i])
s0.update(igr_sol[i])
sol_sol[i] = s0
if 1:
json.dump(sol_sol,
open("hitran_bootstrap_%s_%s.json" % (band, utdate), "w"))
def bootstrap(utdate):
igrins_orders = {}
igrins_orders["H"] = range(99, 122)
igrins_orders["K"] = range(72, 94)
hitran = Hitran()
from hitran_igrins import order as hitrans_detected
from hitran_igrins import ig_order as hitrans_ig_detected
from fit_gaussian import fit_gaussian_simple
band = "K"
import json
s_list = json.load(open("arc_spec_sky_%s_%s.json" % (band, utdate)))
wvl_sol = json.load(open("ecfit/wvl_sol_ohlines_%s_%s.json" \
% (band, utdate)))
# emasure line coordinates from hitran data
hitran_wvl, hitran_s = hitran.load_cires()
hitran_sol_list = []
for i in range(5):
# fit hitran data
sol_list = []
dwvl_list = []
for ll in hitrans_detected[i]:
sol = fit_gaussian_simple(hitran_wvl,
hitran_s,
ll,
sigma_init=7e-5,
do_plot=False)
dwvl = np.array(ll) - ll[0]
sol = fit_gaussian_simple(hitran_wvl,
hitran_s,
sol[0][0] + dwvl,
sigma_init=7e-5,
do_plot=False)
dwvl_list.append(dwvl)
sol_list.append(sol)
wavelength = [sol_[0][0] for sol_ in sol_list]
wavelength_grouped = [list(sol_[0][0]+dwvl_) for dwvl_, sol_ \
in zip(dwvl_list, sol_list)]
wavelength_sigma = [sol_[0][1] for sol_ in sol_list]
hitran_sol_list.append(
dict(wavelength=wavelength,
wavelength_grouped=wavelength_grouped,
wavelength_sigma=wavelength_sigma))
hitran_sol_dict = dict(zip(igrins_orders["K"], hitran_sol_list))
# now get the pixel corrdinates from observed data.
med_filter = hitran.get_median_filtered_spec
s_list_m = [med_filter(wvl_sol[i], s_list[i]) for i in range(5)]
s_list_dict = dict(zip(igrins_orders[band], s_list_m))
ref_pixel_dict = dict((igrins_orders[band][i], r) for i, r \
in hitrans_ig_detected.items())
igr_sol = fit_hitrans_pixel(s_list_dict, ref_pixel_dict)
# merge tow data
sol_sol = {}
for i in igr_sol:
s0 = dict()
s0.update(hitran_sol_dict[i])
s0.update(igr_sol[i])
sol_sol[i] = s0
if 1:
json.dump(sol_sol,
open("hitran_bootstrap_%s_%s.json" % (band, utdate), "w"))
