def get_variance_map2(a_plus_b, a_minus_b, bias_mask2, pix_mask, gain):
#variance0 = a_minus_b
#a_minus_b = a-b
msk = bias_mask2 | pix_mask | ~np.isfinite(a_minus_b)
from destriper import destriper
variance0 = destriper.get_destriped(a_minus_b,
msk,
pattern=64,
remove_vertical=False,
hori=False)
#variance0 = a_minus_b
# stsci_median cannot be used due to too many array error.
#ss = stsci_median([m1 for m1 in variance0],)
dd1 = np.ma.array(variance0, mask=msk)
ss = np.ma.median(dd1, axis=0)
variance_ = variance0.copy()
variance_[msk] = np.nan
st = np.nanstd(variance_)
st = np.nanstd(variance_[np.abs(variance_) < 3 * st])
variance_[np.abs(variance_ - ss) > 3 * st] = np.nan
import scipy.ndimage as ni
x_std = ni.median_filter(np.nanstd(variance_, axis=0), 11)
variance_map0 = np.zeros_like(variance_) + x_std**2
variance_map = variance_map0 + np.abs(
a_plus_b) / gain # add poison noise in ADU
return variance_map
def get_variance_map2(a_plus_b, a_minus_b, bias_mask2, pix_mask, gain):
#variance0 = a_minus_b
#a_minus_b = a-b
msk = bias_mask2 | pix_mask | ~np.isfinite(a_minus_b)
from destriper import destriper
variance0 = destriper.get_destriped(a_minus_b,
msk,
pattern=64,
remove_vertical=False,
hori=False)
#variance0 = a_minus_b
# stsci_median cannot be used due to too many array error.
#ss = stsci_median([m1 for m1 in variance0],)
dd1 = np.ma.array(variance0, mask=msk)
ss = np.ma.median(dd1, axis=0)
variance_ = variance0.copy()
variance_[msk] = np.nan
st = np.nanstd(variance_)
st = np.nanstd(variance_[np.abs(variance_) < 3*st])
variance_[np.abs(variance_-ss) > 3*st] = np.nan
import scipy.ndimage as ni
x_std = ni.median_filter(np.nanstd(variance_, axis=0), 11)
variance_map0 = np.zeros_like(variance_) + x_std**2
variance_map = variance_map0 + np.abs(a_plus_b)/gain # add poison noise in ADU
return variance_map
def make_combined_image_thar(helper, band, obsids):
"""
simple median combine with destripping. Suitable for sky.
"""
filenames, basename, master_obsid = helper.get_base_info(band, obsids)
hdu_list = [pyfits.open(fn)[0] for fn in filenames]
_data = stsci_median([hdu.data for hdu in hdu_list])
from destriper import destriper
data = destriper.get_destriped(_data)
return data
def get_1d_median_specs(fits_names, ap):
#hdu_list = [pyfits.open(fn)[0] for fn in fits_names]
from load_fits import load_fits_data
hdu_list = [load_fits_data(fn) for fn in fits_names]
_data = stsci_median([hdu.data for hdu in hdu_list])
from destriper import destriper
data = destriper.get_destriped(_data)
s = ap.extract_spectra_v2(data)
from storage_descriptions import (COMBINED_IMAGE_DESC, ONED_SPEC_JSON_DESC)
r = PipelineProducts("1d median specs")
r.add(COMBINED_IMAGE_DESC, PipelineImageBase([], data))
r.add(ONED_SPEC_JSON_DESC, PipelineDict(orders=ap.orders, specs=s))
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 get_1d_median_specs(fits_names, ap):
hdu_list = [pyfits.open(fn)[0] for fn in fits_names]
_data = stsci_median([hdu.data for hdu in hdu_list])
from destriper import destriper
data = destriper.get_destriped(_data)
s = ap.extract_spectra_v2(data)
from storage_descriptions import (COMBINED_IMAGE_DESC,
ONED_SPEC_JSON_DESC)
r = PipelineProducts("1d median specs")
r.add(COMBINED_IMAGE_DESC,
PipelineImage([], data))
r.add(ONED_SPEC_JSON_DESC,
PipelineDict(orders=ap.orders,
specs=s))
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
return hdus
# data_list = [hdu.data for hdu in hdu_list]
# return data_list
def get_combined_image(hdus): #, destripe=True):
# destripe=True):
data_list = [hdu.data for hdu in hdus]
from stsci_helper import stsci_median
im = stsci_median(data_list)
return im
if 0:
cards = []
if destripe:
from destriper import destriper
im = destriper.get_destriped(im)
cards.append(Card("HISTORY", "IGR: image destriped."))
return im, cards
hdus = [load_fits_data(fn_) for fn_ in filenames]
return hdus
# data_list = [hdu.data for hdu in hdu_list]
# return data_list
def get_combined_image(hdus): #, destripe=True):
# destripe=True):
data_list = [hdu.data for hdu in hdus]
from stsci_helper import stsci_median
im = stsci_median(data_list)
return im
if 0:
cards = []
if destripe:
from destriper import destriper
im = destriper.get_destriped(im)
cards.append(Card("HISTORY", "IGR: image destriped."))
return im, cards