def make_flatoff_hotpixmap(self, sigma_clip1=100, sigma_clip2=10,
medfilter_size=None,
destripe=True):
flat_off_cards = []
flat_off = stsci_median(self.data_list)
if destripe:
flat_offs = destriper.get_destriped(flat_off)
flat_off_cards.append(Card(("HISTORY", "IGR: image destriped.")))
hotpix_mask = bp.badpixel_mask(flat_offs,
sigma_clip1=sigma_clip1,
sigma_clip2=sigma_clip2,
medfilter_size=medfilter_size)
bg_std = flat_offs[~hotpix_mask].std()
flat_off_cards.append(Card(("BG_STD", bg_std,
"IGR: stddev of combined flat")))
flat_off_image = PipelineImageBase(flat_off_cards,
flat_offs)
hotpix_mask_image = PipelineImageBase([],
hotpix_mask)
from storage_descriptions import (FLAT_OFF_DESC,
FLATOFF_JSON_DESC,
HOTPIX_MASK_DESC)
r = PipelineProducts("flat off products")
r.add(FLAT_OFF_DESC, flat_off_image)
r.add(HOTPIX_MASK_DESC,
hotpix_mask_image)
r.add(FLATOFF_JSON_DESC,
PipelineDict(bg_std=bg_std))
return r
def make_flatoff_hotpixmap(self,
sigma_clip1=100,
sigma_clip2=10,
medfilter_size=None,
destripe=True):
flat_off_cards = []
flat_off = stsci_median(self.data_list)
if destripe:
flat_offs = destriper.get_destriped(flat_off)
flat_off_cards.append(Card(("HISTORY", "IGR: image destriped.")))
hotpix_mask = bp.badpixel_mask(flat_offs,
sigma_clip1=sigma_clip1,
sigma_clip2=sigma_clip2,
medfilter_size=medfilter_size)
bg_std = flat_offs[~hotpix_mask].std()
flat_off_cards.append(
Card(("BG_STD", bg_std, "IGR: stddev of combined flat")))
flat_off_image = PipelineImageBase(flat_off_cards, flat_offs)
hotpix_mask_image = PipelineImageBase([], hotpix_mask)
from storage_descriptions import (FLAT_OFF_DESC, FLATOFF_JSON_DESC,
HOTPIX_MASK_DESC)
r = PipelineProducts("flat off products")
r.add(FLAT_OFF_DESC, flat_off_image)
r.add(HOTPIX_MASK_DESC, hotpix_mask_image)
r.add(FLATOFF_JSON_DESC, PipelineDict(bg_std=bg_std))
return r
def process_flat(ondata_list, offdata_list):
from stsci_helper import stsci_median
return_object = {}
flat_on = stsci_median(ondata_list)
flat_off = stsci_median(offdata_list)
bpix_mask = bp.badpixel_mask(flat_off, sigma_clip1=100)
bg_std = flat_off[~bpix_mask].std()
flat_on_off = flat_on - flat_off
norm_factor = get_flat_normalization(flat_on_off, bg_std, bpix_mask)
flat_norm = flat_on_off / norm_factor
bg_std_norm = bg_std / norm_factor
flat_bpix = flat_norm.astype("d", copy=True)
flat_bpix[bpix_mask] = np.nan
flat_mask = get_flat_mask(flat_bpix, bg_std_norm, sigma=5)
return_object["flat_on_off"] = flat_on_off
return_object["flat_norm_factor"] = norm_factor
return_object["flat_normed"] = flat_norm
return_object["flat_bpix_mask"] = bpix_mask
return_object["bg_std"] = bg_std
return_object["bg_std_normed"] = bg_std_norm
return_object["flat_mask"] = flat_mask
flat_deriv_ = get_y_derivativemap(flat_norm,
flat_bpix,
bg_std_norm,
max_sep_order=150,
pad=50,
flat_mask=flat_mask)
flat_deriv, flat_deriv_pos_msk, flat_deriv_neg_msk = flat_deriv_[
"data"], flat_deriv_["pos_mask"], flat_deriv_["neg_mask"]
return_object["flat_deriv"] = flat_deriv
return_object["flat_deriv_pos_mask"] = flat_deriv_pos_msk
return_object["flat_deriv_neg_mask"] = flat_deriv_neg_msk
ny, nx = flat_deriv.shape
cent_bottom_list = identify_horizontal_line(flat_deriv,
flat_deriv_pos_msk,
pad=50,
bg_std=bg_std_norm)
cent_up_list = identify_horizontal_line(-flat_deriv,
flat_deriv_neg_msk,
pad=50,
bg_std=bg_std_norm)
if 1: # chevyshev
_ = trace_centroids_chevyshev(cent_bottom_list,
cent_up_list,
domain=[0, 2048],
ref_x=nx / 2)
if 0:
order = 5
func_fitter = get_line_fiiter(order)
_ = trace_centroids(cent_bottom_list,
cent_up_list,
func_fitter=func_fitter,
ref_x=nx / 2)
bottom_up_solutions, centroid_bottom_up_list = _
if 0:
plot_solutions(flat_norm, centroid_bottom_up_list, bottom_up_solutions)
return_object["cent_up_list"] = cent_up_list
return_object["cent_bottom_list"] = cent_bottom_list
return_object["bottomup_solutions"] = bottom_up_solutions
return_object["bottomup_centroids"] = centroid_bottom_up_list
return return_object
def process_flat(ondata_list, offdata_list):
from stsci_helper import stsci_median
return_object = {}
flat_on = stsci_median(ondata_list)
flat_off = stsci_median(offdata_list)
bpix_mask = bp.badpixel_mask(flat_off, sigma_clip1=100)
bg_std = flat_off[~bpix_mask].std()
flat_on_off = flat_on - flat_off
norm_factor = get_flat_normalization(flat_on_off,
bg_std, bpix_mask)
flat_norm = flat_on_off / norm_factor
bg_std_norm = bg_std/norm_factor
flat_bpix = flat_norm.astype("d", copy=True)
flat_bpix[bpix_mask] = np.nan
flat_mask = get_flat_mask(flat_bpix, bg_std_norm, sigma=5)
return_object["flat_on_off"] = flat_on_off
return_object["flat_norm_factor"] = norm_factor
return_object["flat_normed"] = flat_norm
return_object["flat_bpix_mask"] = bpix_mask
return_object["bg_std"] = bg_std
return_object["bg_std_normed"] = bg_std_norm
return_object["flat_mask"] = flat_mask
flat_deriv_ = get_y_derivativemap(flat_norm, flat_bpix,
bg_std_norm,
max_sep_order=150, pad=50,
flat_mask=flat_mask)
flat_deriv, flat_deriv_pos_msk, flat_deriv_neg_msk = flat_deriv_["data"], flat_deriv_["pos_mask"], flat_deriv_["neg_mask"]
return_object["flat_deriv"] = flat_deriv
return_object["flat_deriv_pos_mask"] = flat_deriv_pos_msk
return_object["flat_deriv_neg_mask"] = flat_deriv_neg_msk
ny, nx = flat_deriv.shape
cent_bottom_list = identify_horizontal_line(flat_deriv,
flat_deriv_pos_msk,
pad=50,
bg_std=bg_std_norm)
cent_up_list = identify_horizontal_line(-flat_deriv,
flat_deriv_neg_msk,
pad=50,
bg_std=bg_std_norm)
if 1: # chevyshev
_ = trace_centroids_chevyshev(cent_bottom_list,
cent_up_list,
domain=[0, 2048],
ref_x=nx/2)
if 0:
order = 5
func_fitter = get_line_fiiter(order)
_ = trace_centroids(cent_bottom_list,
cent_up_list,
func_fitter=func_fitter,
ref_x=nx/2)
bottom_up_solutions, centroid_bottom_up_list = _
if 0:
plot_solutions(flat_norm,
centroid_bottom_up_list,
bottom_up_solutions)
return_object["cent_up_list"] = cent_up_list
return_object["cent_bottom_list"] = cent_bottom_list
return_object["bottomup_solutions"] = bottom_up_solutions
return_object["bottomup_centroids"] = centroid_bottom_up_list
return return_object
