Exemple #1
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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
Exemple #2
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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
Exemple #3
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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
Exemple #4
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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
Exemple #5
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    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
Exemple #6
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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
Exemple #7
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    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
Exemple #8
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    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
Exemple #9
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    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