xl, yl, zl = get_ordered_line_data(d_x_wvl)
# xl : pixel
# yl : order
# zl : wvl * order
x_domain = [0, 2047]
orders_band = igrins_orders[band]
#orders = igrins_orders[band]
y_domain = [orders_band[0]-2, orders_band[-1]+2]
p = fit_2dspec(xl, yl, zl, x_degree=4, y_degree=3,
x_domain=x_domain, y_domain=y_domain)
if 0:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl, yl, zl, p, orders_band, d_x_wvl)
fig.tight_layout()
xx = np.arange(2048)
wvl_sol = []
for o in orders_band:
oo = np.empty_like(xx)
oo.fill(o)
wvl = p(xx, oo) / o
wvl_sol.append(list(wvl))
if 1:
json.dump(wvl_sol,
open("wvl_sol_phase0_%s_%s.json" % \
(band, igrins_log.date), "w"))
x_domain = [0, 2047]
orders_band = igrins_orders[band]
#orders = igrins_orders[band]
y_domain = [orders_band[0] - 2, orders_band[-1] + 2]
p = fit_2dspec(xl,
yl,
zl,
x_degree=4,
y_degree=3,
x_domain=x_domain,
y_domain=y_domain)
if 0:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl, yl, zl, p, orders_band, d_x_wvl)
fig.tight_layout()
xx = np.arange(2048)
wvl_sol = []
for o in orders_band:
oo = np.empty_like(xx)
oo.fill(o)
wvl = p(xx, oo) / o
wvl_sol.append(list(wvl))
if 1:
json.dump(wvl_sol,
open("wvl_sol_phase0_%s_%s.json" % \
(band, igrins_log.date), "w"))
di_list = [len(reidentified_lines_map[k][0]) for k in keys]
endi_list = np.add.accumulate(di_list)
filter_mask = [m[endi-di:endi] for di, endi in zip(di_list, endi_list)]
#from itertools import compress
# _ = [list(compress(indices, mm)) for indices, mm \
# in zip(line_indices_list, filter_mask)]
# line_indices_list_filtered = _
reidentified_lines_ = [reidentified_lines_map[k] for k in keys]
_ = [(v[0][mm], v[1][mm]) for v, mm \
in zip(reidentified_lines_, filter_mask)]
reidentified_lines_map_filtered = dict(zip(igrins_orders[band], _))
if 1:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl, yl, zl, p,
orders_band,
reidentified_lines_map)
fig.tight_layout()
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl[m], yl[m], zl[m], p,
orders_band,
reidentified_lines_map_filtered)
fig.tight_layout()
# filter out the line indices not well fit by the surface
keys = reidentified_lines_map.keys()
di_list = [len(reidentified_lines_map[k][0]) for k in keys]
endi_list = np.add.accumulate(di_list)
filter_mask = [m[endi - di:endi] for di, endi in zip(di_list, endi_list)]
#from itertools import compress
# _ = [list(compress(indices, mm)) for indices, mm \
# in zip(line_indices_list, filter_mask)]
# line_indices_list_filtered = _
reidentified_lines_ = [reidentified_lines_map[k] for k in keys]
_ = [(v[0][mm], v[1][mm]) for v, mm \
in zip(reidentified_lines_, filter_mask)]
reidentified_lines_map_filtered = dict(zip(igrins_orders[band], _))
if 1:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl, yl, zl, p, orders_band, reidentified_lines_map)
fig.tight_layout()
fig = plt.figure(figsize=(12, 7))
check_fit(fig, xl[m], yl[m], zl[m], p, orders_band,
reidentified_lines_map_filtered)
fig.tight_layout()
