def extract_spectra(helper, band, obsids):
caldb = helper.get_caldb()
master_obsid = obsids[0]
data = caldb.load_image((band, master_obsid),
item_type="combined_image")
ap = get_simple_aperture(helper, band, obsids)
s = ap.extract_spectra_simple(data)
caldb.store_dict((band, master_obsid),
item_type="ONED_SPEC_JSON",
data=dict(orders=ap.orders,
specs=s,
aperture_basename=ap.basename
))
def find_affine_transform(helper, band, obsids):
master_obsid = obsids[0]
caldb = helper.get_caldb()
orders = caldb.load_resource_for((band, master_obsid), "orders")["orders"]
ap = get_simple_aperture(helper, band, obsids, orders=orders)
item_path = caldb.query_item_path((band, master_obsid),
"IDENTIFIED_LINES_JSON")
from libs.identified_lines import IdentifiedLines
identified_lines_tgt = IdentifiedLines.load(item_path)
xy_list_tgt = identified_lines_tgt.get_xy_list_from_pixlist(ap)
from libs.echellogram import Echellogram
from libs.master_calib import load_ref_data
echellogram_data = load_ref_data(helper.config, band,
kind="ECHELLOGRAM_JSON")
echellogram = Echellogram.from_dict(echellogram_data)
xy_list_ref = identified_lines_tgt.get_xy_list_from_wvllist(echellogram)
assert len(xy_list_tgt) == len(xy_list_ref)
from libs.align_echellogram_thar import fit_affine_clip
affine_tr, mm = fit_affine_clip(np.array(xy_list_ref),
np.array(xy_list_tgt))
from libs.products import PipelineDict
d = PipelineDict(xy1f=xy_list_ref, xy2f=xy_list_tgt,
affine_tr_matrix=affine_tr.get_matrix(),
affine_tr_mask=mm)
caldb.store_dict((band, master_obsid),
item_type="ALIGNING_MATRIX_JSON",
data=d)
def save_figures(helper, band, obsids):
### THIS NEEDS TO BE REFACTORED!
caldb = helper.get_caldb()
master_obsid = obsids[0]
orders = caldb.load_resource_for((band, master_obsid), "orders")["orders"]
thar_filenames = helper.get_filenames(band, obsids)
thar_basename = os.path.splitext(os.path.basename(thar_filenames[0]))[0]
thar_master_obsid = obsids[0]
if 1: # make amp and order falt
ap = get_simple_aperture(helper, band, obsids,
orders=orders)
# from libs.storage_descriptions import ONED_SPEC_JSON_DESC
#orders = thar_products[ONED_SPEC_JSON_DESC]["orders"]
order_map = ap.make_order_map()
#slitpos_map = ap.make_slitpos_map()
# load flat on products
#flat_on_params_name = flaton_path.get_secondary_path("flat_on_params")
#flaton_products = PipelineProducts.load(flat_on_params_name)
from libs.storage_descriptions import (FLAT_NORMED_DESC,
FLAT_MASK_DESC)
flaton_db_name = helper.igr_path.get_section_filename_base("PRIMARY_CALIB_PATH",
"flat_on.db",
)
flaton_db = ProductDB(flaton_db_name)
flaton_basename = flaton_db.query(band, thar_master_obsid)
flaton_products = helper.igr_storage.load([FLAT_NORMED_DESC,
FLAT_MASK_DESC],
flaton_basename)
from libs.process_flat import make_order_flat, check_order_flat
order_flat_products = make_order_flat(flaton_products,
orders, order_map)
#fn = thar_path.get_secondary_path("orderflat")
#order_flat_products.save(fn, masterhdu=hdu)
hdu = pyfits.open(thar_filenames[0])[0]
helper.igr_storage.store(order_flat_products,
mastername=flaton_basename,
masterhdu=hdu)
flat_mask = helper.igr_storage.load1(FLAT_MASK_DESC,
flaton_basename)
order_map2 = ap.make_order_map(mask_top_bottom=True)
bias_mask = flat_mask.data & (order_map2 > 0)
pp = PipelineProducts("")
from libs.storage_descriptions import BIAS_MASK_DESC
pp.add(BIAS_MASK_DESC,
PipelineImageBase([], bias_mask))
helper.igr_storage.store(pp,
mastername=flaton_basename,
masterhdu=hdu)
if 1:
fig_list = check_order_flat(order_flat_products)
from libs.qa_helper import figlist_to_pngs
orderflat_figs = helper.igr_path.get_section_filename_base("QA_PATH",
"orderflat",
"orderflat_"+thar_basename)
figlist_to_pngs(orderflat_figs, fig_list)