lc_correction package

lc_correction.compute module

lc_correction.compute.CHINR_THRESHOLD = 2

max threshold for chinr

lc_correction.compute.DISTANCE_THRESHOLD = 1.4

max threshold for distnr

lc_correction.compute.SCORE_THRESHOLD = 0.4

max threshold for sgscore

lc_correction.compute.SHARPNR_MAX = 0.1

max value for sharpnr

lc_correction.compute.SHARPNR_MIN = -0.13

min value for sharpnr

lc_correction.compute.ZERO_MAG = 100.0

default value for zero magnitude (a big value!)

lc_correction.compute.apply_correction(candidate)

Correction function for a set of detections

Parameters:candidate (pd.DataFrame) – A dataframe with detections of a candidate. Returns:Wrapper for correction for magnitude, sigma and sigma_ext Return type:tuple

Example:

(m_corr, s_corr, s_corr_ext) = correction(a, b, c, d, e)

lc_correction.compute.apply_correction_df(df)

Correction function for a set of detections with the same object id and filter id. Use with pd.DataFrame.apply(this)

Parameters:df (pd.DataFrame) – A dataframe with detections of a candidate. Returns:A pandas dataframe with detections corrected Return type:pd.DataFrame

Example:

corrected = detections.groupby(["objectId", "fid"]).apply(apply_correction_df)

lc_correction.compute.apply_mag_stats(df, distnr=None, distpsnr1=None, sgscore1=None, chinr=None, sharpnr=None)

Parameters:

• df (pd.DataFrame) – A dataframe with corrected detections of a candidate.
• distnr (float) –
• distpsnr1 (float) –
• sgscore1 (float) –
• chinr (float) –
• sharpnr (float) –

Returns:

A pandas dataframe with magnitude statistics

Return type:

pd.DataFrame

lc_correction.compute.apply_object_stats_df(corrected, magstats, step_name=None)

Parameters:

• corrected (pd.DataFrame) – A dataframe with corrected detections.
• magstats (pd.DataFrame) – A dataframe with magnitude statistics.
• step_name (string) –

Returns:

Object statistics in a dataframe

Return type:

pd.DataFrame

lc_correction.compute.apply_objstats_from_correction(df)

Parameters:df (pd.DataFrame) – A dataframe with corrected detections of a candidate. Returns:A pandas series with statistics of an object Return type:pd.Series

lc_correction.compute.apply_objstats_from_magstats(df)

Parameters:df (pd.DataFrame) – A dataframe with magnitude statistics. Returns:A pandas series with statistics of an object Return type:pd.Series

lc_correction.compute.correction(magnr, magpsf, sigmagnr, sigmapsf, isdiffpos, oid=None)

Correction function. Implement of correction formula.

Parameters:

• magnr (float) – Magnitude of nearest source in reference image PSF-catalog within 30 arcsec [mag]
• magpsf (float) – Magnitude from PSF-fit photometry [mag]
• sigmagnr (float) – 1-sigma uncertainty in magnr within 30 arcsec [mag]
• sigmapsf (float) – 1-sigma uncertainty in magpsf [mag]
• isdiffpos (int) – 1 => candidate is from positive (sci minus ref) subtraction; 0 => candidate is from negative (ref minus sci) subtraction

Returns:

Correction for magnitude, sigma and sigma_ext

Return type:

tuple

Example:

(m_corr, s_corr, s_corr_ext) = correction(a, b, c, d, e)

lc_correction.compute.dmdt(magpsf_first, sigmapsf_first, nd_diffmaglim, mjd_first, nd_mjd)

Calculate dm/dt

Parameters:

• magpsf_first (float) –
• sigmapsf_first (float) –
• nd_diffmaglim (float) –
• mjd_first (float) –
• nd_mjd (float) –

Returns:

dm_sigma, dt, dmsigdt

Return type:

tuple

Example:

dm_sigma, dt, dmsigdt = dmdt(magpsf_first,
                             sigmapsf_first,
                             nd.diffmaglim,
                             mjd_first,
                             nd.mjd)

lc_correction.compute.do_dmdt(nd, magstats, dt_min=0.5)

Parameters:

• nd (pd.DataFrame) – A dataframe with non detections.
• magstats (pd.DataFrame) – A dataframe with magnitude statistics.
• dt_min (float) –

Returns:

Compute of dmdt of an object

Return type:

pd.Series

lc_correction.compute.do_dmdt_df(magstats, non_dets)

Parameters:

• magstats (pd.DataFrame) – A dataframe with magnitude statistics.
• non_dets (pd.DataFrame) – A dataframe with non detections.

Returns:

Compute of dmdt of an object in a dataframe

Return type:

pd.DataFrame

lc_correction.compute.is_dubious(corrected, isdiffpos, corr_magstats)

Get if object is dubious

Parameters:

• corrected (bool) –
• isdiffpos (bool) –
• corr_magstats (bool) –

Returns:

if the object is dubious

Return type:

bool

lc_correction.compute.is_stellar(nearZTF, nearPS1, stellarPS1, stellarZTF)

Get if object is stellar

Parameters:

• nearZTF (bool) –
• nearPS1 (bool) –
• stellarPS1 (bool) –
• stellarZTF (bool) –

Returns:

if the object is stellar

Return type:

bool

lc_correction.compute.near_stellar(first_distnr, first_distpsnr1, first_sgscore1, first_chinr, first_sharpnr)

Get if object is near stellar

Parameters:

• first_distnr (float) – Distance to nearest source in reference image PSF-catalog within 30 arcsec [pixels]
• first_distpsnr1 (float) – Distance of closest source from PS1 catalog; if exists within 30 arcsec [arcsec]
• first_sgscore1 (float) – Star/Galaxy score of closest source from PS1 catalog 0 <= sgscore <= 1 where closer to 1 implies higher likelihood of being a star
• first_chinr (float) – DAOPhot chi parameter of nearest source in reference image PSF-catalog within 30 arcsec
• first_sharpnr (float) – DAOPhot sharp parameter of nearest source in reference image PSF-catalog within 30 arcsec

Returns:

if the object is near stellar

Return type:

tuple

lc_correction.helpers module

lc_correction.helpers.get_clean_corrected(df, step_name=None)

lc_correction.helpers.get_data_quality(df)

lc_correction.helpers.get_ps1_ztf(df)

lc_correction.helpers.get_ss_ztf(df)