Data Class Family

class model.PSPL

Bases: ABC

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_chi2_photometry`(t_obs, mag_obs, mag_err_obs)	Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, print_warning])	Get the predicted photomety at the specified times for the specified photometric filter or data set.
`log_likely_photometry`(t_obs, mag_obs, ...[, ...])	Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set.
`log_likely_photometry_each`(t_obs, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

animate(tE, time_steps, frame_time, name, size, zoom, astrometry)

Produces animation of microlensing event. This function takes the PSPL and makes an animation, the input variables are as follows

Parameters:

tE:

number of einstein crossings times before/after the peak you want the animation to plot: e.g tE = 2 => graph will go from -2 tE to 2 tE

time_steps:

number of time steps before/after peak, so total number of time steps will be 2 times this value

frame_time:

times in ms of each frame in the animation

name: string

the animation will be saved as name.html

size: list

[horizontal, vertical] cm’s

zoom:

# of einstein radii plotted in vertical direction

get_chi2_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and photometric data.

get_lnL_constant(err_obs)

Get the natural log of the constant normalization terms of the likelihood.

\[-0.5 * \ln{2 \pi \sigma_{obs}^2}\]

Parameters:

err_obsarray_like: List of the uncertainties.

Returns:

List of ln(likelihood constants).

get_photometry(t, filt_idx=0, print_warning=True)

Get the predicted photomety at the specified times for the specified photometric filter or data set.

Parameters:

tarray_like: List of times in MJD for the observations.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

mag_modelarray_like: Magnitude of the unresolved microlensing event at t.

Other Parameters:

print_warningbool, optional: Print a warning in the rare case that the magnitude exceeds a zeropoint of 30 and conversions result in NaN returned.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set. Note, this function returns the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Lfloat: ln(likelihood) summed over the photometric measurement

log_likely_photometry_each(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the natural log of the likelihood for the input photometric data in the specified filter or data sets. Note, this function returns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each photometric measurement.

class model.PSPL_Phot

Bases: PSPL

Contains methods for model a PSPL photometry only. This is a Data-type class in our hierarchy. It is abstract and should not be instantiated.

Attributes:

Available class variables that should be defined.
t0
tE
u0_amp
u0_E
u0_N
piE_E - valid only if parallax model
piE_N - valid only if parallax model
piE_amp
b_sff[#]
mag_src[#] – add in
mag_base[#] – add in
raL - if parallax model
decL - if parallax model

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_chi2_photometry`(t_obs, mag_obs, mag_err_obs)	Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, print_warning])	Get the predicted photomety at the specified times for the specified photometric filter or data set.
`log_likely_photometry`(t_obs, mag_obs, ...[, ...])	Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set.
`log_likely_photometry_each`(t_obs, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

get_astrometry
get_astrometry_unlensed
get_centroid_shift
get_lens_astrometry
get_resolved_amplification
get_resolved_astrometry
log_likely_astrometry

animate(tE, time_steps, frame_time, name, size, zoom, astrometry)

Produces animation of microlensing event. This function takes the PSPL and makes an animation, the input variables are as follows

Parameters:

tE:

number of einstein crossings times before/after the peak you want the animation to plot: e.g tE = 2 => graph will go from -2 tE to 2 tE

time_steps:

number of time steps before/after peak, so total number of time steps will be 2 times this value

frame_time:

times in ms of each frame in the animation

name: string

the animation will be saved as name.html

size: list

[horizontal, vertical] cm’s

zoom:

# of einstein radii plotted in vertical direction

get_chi2_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and photometric data.

get_lnL_constant(err_obs)

Get the natural log of the constant normalization terms of the likelihood.

\[-0.5 * \ln{2 \pi \sigma_{obs}^2}\]

Parameters:

err_obsarray_like: List of the uncertainties.

Returns:

List of ln(likelihood constants).

get_photometry(t, filt_idx=0, print_warning=True)

Get the predicted photomety at the specified times for the specified photometric filter or data set.

Parameters:

tarray_like: List of times in MJD for the observations.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

mag_modelarray_like: Magnitude of the unresolved microlensing event at t.

Other Parameters:

print_warningbool, optional: Print a warning in the rare case that the magnitude exceeds a zeropoint of 30 and conversions result in NaN returned.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set. Note, this function returns the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Lfloat: ln(likelihood) summed over the photometric measurement

log_likely_photometry_each(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the natural log of the likelihood for the input photometric data in the specified filter or data sets. Note, this function returns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each photometric measurement.

class model.PSPL_PhotAstrom

Bases: PSPL

Contains methods for model a PSPL photometry + astrometry. This is a Data-type class in our hierarchy. It is abstract and should not be instantiated.

Attributes:

Available class variables that should be defined.
t0
tE
u0_amp
u0_E
u0_N
beta
piE_E - valid only if parallax model
piE_N - valid only if parallax model
piE_amp
mL
thetaE_amp
thetaE_E
thetaE_N
xS0_E
xS0_N
xL0_E
xL0_N
muS_E
muS_N
muL_E
muL_N
muRel_E
muRel_N
muRel_amp
piS
piL
dL
dS
dL_dS (dL over dS)
b_sff[#]
mag_src[#] – add in
mag_base[#] – add in
raL - if parallax model
decL - if parallax model

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_chi2_astrometry`(t_obs, x_obs, y_obs, ...)	Get the chi^2 value for this model given input astrometry data and uncertainties for the specified astrometric data set.
`get_chi2_photometry`(t_obs, mag_obs, mag_err_obs)	Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, print_warning])	Get the predicted photomety at the specified times for the specified photometric filter or data set.
`log_likely_astrometry`(t_obs, x_obs, y_obs, ...)	Get the summed natural log of the likelihood for the input astrometric data in the specified filter or data sets.
`log_likely_astrometry_each`(t_obs, x_obs, ...)	Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets.
`log_likely_photometry`(t_obs, mag_obs, ...[, ...])	Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set.
`log_likely_photometry_each`(t_obs, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

animate(tE, time_steps, frame_time, name, size, zoom, astrometry)

Produces animation of microlensing event. This function takes the PSPL and makes an animation, the input variables are as follows

Parameters:

tE:

number of einstein crossings times before/after the peak you want the animation to plot: e.g tE = 2 => graph will go from -2 tE to 2 tE

time_steps:

number of time steps before/after peak, so total number of time steps will be 2 times this value

frame_time:

times in ms of each frame in the animation

name: string

the animation will be saved as name.html

size: list

[horizontal, vertical] cm’s

zoom:

# of einstein radii plotted in vertical direction

get_chi2_astrometry(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the chi^2 value for this model given input astrometry data and uncertainties for the specified astrometric data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: The index of the astrometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and astrometric data.

get_chi2_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and photometric data.

get_lnL_constant(err_obs)

Get the natural log of the constant normalization terms of the likelihood.

\[-0.5 * \ln{2 \pi \sigma_{obs}^2}\]

Parameters:

err_obsarray_like: List of the uncertainties.

Returns:

List of ln(likelihood constants).

get_photometry(t, filt_idx=0, print_warning=True)

Get the predicted photomety at the specified times for the specified photometric filter or data set.

Parameters:

tarray_like: List of times in MJD for the observations.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

mag_modelarray_like: Magnitude of the unresolved microlensing event at t.

Other Parameters:

print_warningbool, optional: Print a warning in the rare case that the magnitude exceeds a zeropoint of 30 and conversions result in NaN returned.

log_likely_astrometry(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the summed natural log of the likelihood for the input astrometric data in the specified filter or data sets. Note, this function returns the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

ln_Lfloat: The ln(likelihood) summed over all astrometric measurements.

log_likely_astrometry_each(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets. Note, this function eturns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each astrometric measurement.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set. Note, this function returns the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Lfloat: ln(likelihood) summed over the photometric measurement

log_likely_photometry_each(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the natural log of the likelihood for the input photometric data in the specified filter or data sets. Note, this function returns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each photometric measurement.

class model.PSPL_Astrom

Bases: PSPL

Contains methods for model a PSPL photometry + astrometry. This is a Data-type class in our hierarchy. It is abstract and should not be instantiated.

Attributes:

Available class variables that should be defined.
t0
tE
u0_amp
u0_E
u0_N
beta
piE_E - valid only if parallax model
piE_N - valid only if parallax model
piE_amp
mL
thetaE_amp
thetaE_E
thetaE_N
xS0_E
xS0_N
xL0_E
xL0_N
muS_E
muS_N
muL_E
muL_N
muRel_E
muRel_N
muRel_amp
piS
piL
dL
dS
dL_dS (dL over dS)
b_sff[#]
raL - if parallax model
decL - if parallax model

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_chi2_astrometry`(t_obs, x_obs, y_obs, ...)	Get the chi^2 value for this model given input astrometry data and uncertainties for the specified astrometric data set.
`get_chi2_photometry`(t_obs, mag_obs, mag_err_obs)	Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t_obs[, filt_idx, print_warning])	Get the predicted photomety at the specified times for the specified photometric filter or data set.
`log_likely_astrometry`(t_obs, x_obs, y_obs, ...)	Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets.
`log_likely_astrometry_each`(t_obs, x_obs, ...)	Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets.
`log_likely_photometry`(t_obs, mag_obs, ...[, ...])	Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set.
`log_likely_photometry_each`(t_obs, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

animate(tE, time_steps, frame_time, name, size, zoom, astrometry)

Produces animation of microlensing event. This function takes the PSPL and makes an animation, the input variables are as follows

Parameters:

tE:

number of einstein crossings times before/after the peak you want the animation to plot: e.g tE = 2 => graph will go from -2 tE to 2 tE

time_steps:

number of time steps before/after peak, so total number of time steps will be 2 times this value

frame_time:

times in ms of each frame in the animation

name: string

the animation will be saved as name.html

size: list

[horizontal, vertical] cm’s

zoom:

# of einstein radii plotted in vertical direction

get_chi2_astrometry(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the chi^2 value for this model given input astrometry data and uncertainties for the specified astrometric data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: The index of the astrometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and astrometric data.

get_chi2_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get chi^2 values for the model and input photometric data in the specified photometric filter or data set.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

chi2array_like: List of chi^2 values from the model and photometric data.

get_lnL_constant(err_obs)

Get the natural log of the constant normalization terms of the likelihood.

\[-0.5 * \ln{2 \pi \sigma_{obs}^2}\]

Parameters:

err_obsarray_like: List of the uncertainties.

Returns:

List of ln(likelihood constants).

get_photometry(t_obs, filt_idx=0, print_warning=True)

Get the predicted photomety at the specified times for the specified photometric filter or data set.

Parameters:

tarray_like: List of times in MJD for the observations.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

mag_modelarray_like: Magnitude of the unresolved microlensing event at t.

Other Parameters:

print_warningbool, optional: Print a warning in the rare case that the magnitude exceeds a zeropoint of 30 and conversions result in NaN returned.

log_likely_astrometry(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets. Note, this function eturns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each astrometric measurement.

log_likely_astrometry_each(t_obs, x_obs, y_obs, x_err_obs, y_err_obs, ast_filt_idx=0)

Get the natural log of the likelihood for the input astrometric data in the specified filter or data sets. Note, this function eturns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
x_obsarray_like: List of relative R.A. astrometric positions on the sky in arcsec. Length must match t_obs.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t_obs.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t_obs.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t_obs.
ast_filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each astrometric measurement.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the summed natural log of the likelihood for the input photometric data for the specified filter or data set. Note, this function returns the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Lfloat: ln(likelihood) summed over the photometric measurement

log_likely_photometry_each(t_obs, mag_obs, mag_err_obs, filt_index=0)

Get the natural log of the likelihood for the input photometric data in the specified filter or data sets. Note, this function returns a list and it is the full ln(likelihood), including the normalization constant.

Parameters:

t_obsarray_like: List of times in MJD for the observations.
mag_obsarray_like: List of observed photometric measurements of the microlensing event in magnitudes. Length must be the same as t_obs.
mag_obs_errarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t_obs.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

ln_Larray_like: List of ln(likelihood) for each photometric measurement.