astrobase/astrokep.py
def get_centroid_offsets(lcd, t_ing_egr, oot_buffer_time=0.1, sample_factor=3):
    '''After running `detrend_centroid`, this gets positions of centroids during
    transits, and outside of transits.

    These positions can then be used in a false positive analysis.

    This routine requires knowing the ingress and egress times for every
    transit of interest within the quarter this routine is being called for.
    There is currently no astrobase routine that automates this for periodic
    transits (it must be done in a calling routine).

    To get out of transit centroids, this routine takes points outside of the
    "buffer" set by `oot_buffer_time`, sampling 3x as many points on either
    side of the transit as are in the transit (or however many are specified by
    `sample_factor`).

    Parameters
    ----------

    lcd : lcdict
        An `lcdict` generated by the `read_kepler_fitslc` function. We assume
        that the `detrend_centroid` function has been run on this `lcdict`.

    t_ing_egr : list of tuples
        This is of the form::

            [(ingress time of i^th transit, egress time of i^th transit)]

        for i the transit number index in this quarter (starts at zero at the
        beginning of every quarter). Assumes units of BJD.

    oot_buffer_time : float
        Number of days away from ingress and egress times to begin sampling "out
        of transit" centroid points. The number of out of transit points to take
        per transit is 3x the number of points in transit.

    sample_factor : float
        The size of out of transit window from which to sample.

    Returns
    -------

    dict
        This is a dictionary keyed by transit number (i.e., the same index as
        `t_ing_egr`), where each key contains the following value::

            {'ctd_x_in_tra':ctd_x_in_tra,
             'ctd_y_in_tra':ctd_y_in_tra,
             'ctd_x_oot':ctd_x_oot,
             'ctd_y_oot':ctd_y_oot,
             'npts_in_tra':len(ctd_x_in_tra),
             'npts_oot':len(ctd_x_oot),
             'in_tra_times':in_tra_times,
             'oot_times':oot_times}

    '''

    # NOTE:
    # Bryson+ (2013) gives a more complicated and more correct approach to this
    # problem, computing offsets relative to positions defined on the SKY. This
    # requires using a Kepler focal plane geometry model. I don't have that
    # model, or know how to get it. So I use a simpler approach.

    qnum = int(np.unique(lcd['quarter']))
    LOGINFO('Getting centroid offsets (qnum: {:d})...'.format(qnum))
    # Kepler pixel scale, cf.
    # https://keplerscience.arc.nasa.gov/the-kepler-space-telescope.html
    arcsec_per_px = 3.98

    # Get the residuals (units: pixel offset).
    times = lcd['ctd_dtr']['times']
    ctd_resid_x = lcd['ctd_dtr']['ctd_x'] - lcd['ctd_dtr']['fit_ctd_x']
    ctd_resid_y = lcd['ctd_dtr']['ctd_y'] - lcd['ctd_dtr']['fit_ctd_y']

    # Return results in "centroid dictionary" (has keys of transit number).
    cd = {}
    for ix,(t_ing,t_egr) in enumerate(t_ing_egr):

        # We have in-transit times as input.
        in_tra_times = times[(times > t_ing) & (times < t_egr)]

        # Compute out of transit times on either side of the in-transit times.
        transit_dur = t_egr - t_ing
        oot_window_len = sample_factor * transit_dur

        oot_before = times[
            (times < (t_ing-oot_buffer_time)) &
            (times > (t_ing-oot_buffer_time-oot_window_len))
        ]
        oot_after = times[
            (times > (t_egr+oot_buffer_time)) &
            (times < (t_egr+oot_buffer_time+oot_window_len))
        ]

        oot_times = npconcatenate([oot_before, oot_after])

        mask_tra = npin1d(times, in_tra_times)
        mask_oot = npin1d(times, oot_times)

        # Convert to units of arcseconds.
        ctd_x_in_tra = ctd_resid_x[mask_tra]*arcsec_per_px
        ctd_y_in_tra = ctd_resid_y[mask_tra]*arcsec_per_px
        ctd_x_oot = ctd_resid_x[mask_oot]*arcsec_per_px
        ctd_y_oot = ctd_resid_y[mask_oot]*arcsec_per_px

        cd[ix] = {'ctd_x_in_tra':ctd_x_in_tra,
                  'ctd_y_in_tra':ctd_y_in_tra,
                  'ctd_x_oot':ctd_x_oot,
                  'ctd_y_oot':ctd_y_oot,
                  'npts_in_tra':len(ctd_x_in_tra),
                  'npts_oot':len(ctd_x_oot),
                  'in_tra_times':in_tra_times,
                  'oot_times':oot_times}

    LOGINFO('Got centroid offsets (qnum: {:d}).'.format(qnum))

    return cd
def get_centroid_offsets(lcd, t_ing_egr, oot_buffer_time=0.1, sample_factor=3):
    '''After running `detrend_centroid`, this gets positions of centroids during
    transits, and outside of transits.

    These positions can then be used in a false positive analysis.

    This routine requires knowing the ingress and egress times for every
    transit of interest within the quarter this routine is being called for.
    There is currently no astrobase routine that automates this for periodic
    transits (it must be done in a calling routine).

    To get out of transit centroids, this routine takes points outside of the
    "buffer" set by `oot_buffer_time`, sampling 3x as many points on either
    side of the transit as are in the transit (or however many are specified by
    `sample_factor`).

    Parameters
    ----------

    lcd : lcdict
        An `lcdict` generated by the `read_kepler_fitslc` function. We assume
        that the `detrend_centroid` function has been run on this `lcdict`.

    t_ing_egr : list of tuples
        This is of the form::

            [(ingress time of i^th transit, egress time of i^th transit)]

        for i the transit number index in this quarter (starts at zero at the
        beginning of every quarter). Assumes units of BJD.

    oot_buffer_time : float
        Number of days away from ingress and egress times to begin sampling "out
        of transit" centroid points. The number of out of transit points to take
        per transit is 3x the number of points in transit.

    sample_factor : float
        The size of out of transit window from which to sample.

    Returns
    -------

    dict
        This is a dictionary keyed by transit number (i.e., the same index as
        `t_ing_egr`), where each key contains the following value::

            {'ctd_x_in_tra':ctd_x_in_tra,
             'ctd_y_in_tra':ctd_y_in_tra,
             'ctd_x_oot':ctd_x_oot,
             'ctd_y_oot':ctd_y_oot,
             'npts_in_tra':len(ctd_x_in_tra),
             'npts_oot':len(ctd_x_oot),
             'in_tra_times':in_tra_times,
             'oot_times':oot_times}

    '''

    # NOTE:
    # Bryson+ (2013) gives a more complicated and more correct approach to this
    # problem, computing offsets relative to positions defined on the SKY. This
    # requires using a Kepler focal plane geometry model. I don't have that
    # model, or know how to get it. So I use a simpler approach.

    qnum = int(np.unique(lcd['quarter']))
    LOGINFO('Getting centroid offsets (qnum: {:d})...'.format(qnum))
    # Kepler pixel scale, cf.
    # https://keplerscience.arc.nasa.gov/the-kepler-space-telescope.html
    arcsec_per_px = 3.98

    # Get the residuals (units: pixel offset).
    times = lcd['ctd_dtr']['times']
    ctd_resid_x = lcd['ctd_dtr']['ctd_x'] - lcd['ctd_dtr']['fit_ctd_x']
    ctd_resid_y = lcd['ctd_dtr']['ctd_y'] - lcd['ctd_dtr']['fit_ctd_y']

    # Return results in "centroid dictionary" (has keys of transit number).
    cd = {}
    for ix,(t_ing,t_egr) in enumerate(t_ing_egr):

        # We have in-transit times as input.
        in_tra_times = times[(times > t_ing) & (times < t_egr)]

        # Compute out of transit times on either side of the in-transit times.
        transit_dur = t_egr - t_ing
        oot_window_len = sample_factor * transit_dur

        oot_before = times[
            (times < (t_ing-oot_buffer_time)) &
            (times > (t_ing-oot_buffer_time-oot_window_len))
        ]
        oot_after = times[
            (times > (t_egr+oot_buffer_time)) &
            (times < (t_egr+oot_buffer_time+oot_window_len))
        ]

        oot_times = npconcatenate([oot_before, oot_after])

        mask_tra = npin1d(times, in_tra_times)
        mask_oot = npin1d(times, oot_times)

        # Convert to units of arcseconds.
        ctd_x_in_tra = ctd_resid_x[mask_tra]*arcsec_per_px
        ctd_y_in_tra = ctd_resid_y[mask_tra]*arcsec_per_px
        ctd_x_oot = ctd_resid_x[mask_oot]*arcsec_per_px
        ctd_y_oot = ctd_resid_y[mask_oot]*arcsec_per_px

        cd[ix] = {'ctd_x_in_tra':ctd_x_in_tra,
                  'ctd_y_in_tra':ctd_y_in_tra,
                  'ctd_x_oot':ctd_x_oot,
                  'ctd_y_oot':ctd_y_oot,
                  'npts_in_tra':len(ctd_x_in_tra),
                  'npts_oot':len(ctd_x_oot),
                  'in_tra_times':in_tra_times,
                  'oot_times':oot_times}

    LOGINFO('Got centroid offsets (qnum: {:d}).'.format(qnum))

    return cd
def get_centroid_offsets ( lcd , t_ing_egr , oot_buffer_time = 0.1 , sample_factor = 3 ) : # NOTE: # Bryson+ (2013) gives a more complicated and more correct approach to this # problem, computing offsets relative to positions defined on the SKY. This # requires using a Kepler focal plane geometry model. I don't have that # model, or know how to get it. So I use a simpler approach. qnum = int ( np . unique ( lcd [ 'quarter' ] ) ) LOGINFO ( 'Getting centroid offsets (qnum: {:d})...' . format ( qnum ) ) # Kepler pixel scale, cf. # https://keplerscience.arc.nasa.gov/the-kepler-space-telescope.html arcsec_per_px = 3.98 # Get the residuals (units: pixel offset). times = lcd [ 'ctd_dtr' ] [ 'times' ] ctd_resid_x = lcd [ 'ctd_dtr' ] [ 'ctd_x' ] - lcd [ 'ctd_dtr' ] [ 'fit_ctd_x' ] ctd_resid_y = lcd [ 'ctd_dtr' ] [ 'ctd_y' ] - lcd [ 'ctd_dtr' ] [ 'fit_ctd_y' ] # Return results in "centroid dictionary" (has keys of transit number). cd = { } for ix , ( t_ing , t_egr ) in enumerate ( t_ing_egr ) : # We have in-transit times as input. in_tra_times = times [ ( times > t_ing ) & ( times < t_egr ) ] # Compute out of transit times on either side of the in-transit times. transit_dur = t_egr - t_ing oot_window_len = sample_factor * transit_dur oot_before = times [ ( times < ( t_ing - oot_buffer_time ) ) & ( times > ( t_ing - oot_buffer_time - oot_window_len ) ) ] oot_after = times [ ( times > ( t_egr + oot_buffer_time ) ) & ( times < ( t_egr + oot_buffer_time + oot_window_len ) ) ] oot_times = npconcatenate ( [ oot_before , oot_after ] ) mask_tra = npin1d ( times , in_tra_times ) mask_oot = npin1d ( times , oot_times ) # Convert to units of arcseconds. ctd_x_in_tra = ctd_resid_x [ mask_tra ] * arcsec_per_px ctd_y_in_tra = ctd_resid_y [ mask_tra ] * arcsec_per_px ctd_x_oot = ctd_resid_x [ mask_oot ] * arcsec_per_px ctd_y_oot = ctd_resid_y [ mask_oot ] * arcsec_per_px cd [ ix ] = { 'ctd_x_in_tra' : ctd_x_in_tra , 'ctd_y_in_tra' : ctd_y_in_tra , 'ctd_x_oot' : ctd_x_oot , 'ctd_y_oot' : ctd_y_oot , 'npts_in_tra' : len ( ctd_x_in_tra ) , 'npts_oot' : len ( ctd_x_oot ) , 'in_tra_times' : in_tra_times , 'oot_times' : oot_times } LOGINFO ( 'Got centroid offsets (qnum: {:d}).' . format ( qnum ) ) return cd
After running `detrend_centroid`, this gets positions of centroids during
    transits, and outside of transits.

    These positions can then be used in a false positive analysis.

    This routine requires knowing the ingress and egress times for every
    transit of interest within the quarter this routine is being called for.
    There is currently no astrobase routine that automates this for periodic
    transits (it must be done in a calling routine).

    To get out of transit centroids, this routine takes points outside of the
    "buffer" set by `oot_buffer_time`, sampling 3x as many points on either
    side of the transit as are in the transit (or however many are specified by
    `sample_factor`).

    Parameters
    ----------

    lcd : lcdict
        An `lcdict` generated by the `read_kepler_fitslc` function. We assume
        that the `detrend_centroid` function has been run on this `lcdict`.

    t_ing_egr : list of tuples
        This is of the form::

            [(ingress time of i^th transit, egress time of i^th transit)]

        for i the transit number index in this quarter (starts at zero at the
        beginning of every quarter). Assumes units of BJD.

    oot_buffer_time : float
        Number of days away from ingress and egress times to begin sampling "out
        of transit" centroid points. The number of out of transit points to take
        per transit is 3x the number of points in transit.

    sample_factor : float
        The size of out of transit window from which to sample.

    Returns
    -------

    dict
        This is a dictionary keyed by transit number (i.e., the same index as
        `t_ing_egr`), where each key contains the following value::

            {'ctd_x_in_tra':ctd_x_in_tra,
             'ctd_y_in_tra':ctd_y_in_tra,
             'ctd_x_oot':ctd_x_oot,
             'ctd_y_oot':ctd_y_oot,
             'npts_in_tra':len(ctd_x_in_tra),
             'npts_oot':len(ctd_x_oot),
             'in_tra_times':in_tra_times,
             'oot_times':oot_times}
After running detrend_centroid this gets positions of centroids during transits and outside of transits .