FSPL Data Class Family

class model.FSPL

Bases: PSPL

Finite-Source, Point-Lens models.

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_all_arrays_CI`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_all_arrays_amg`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_amplification`(t[, filt_idx, amp_arr])	Get an array of the photometric amplifications at the input times.
`get_astrometry`(t[, filt_idx])	Get the astrometry of the unresolved (observed) position of the lensed source at the input times.
`get_astrometry_unlensed`(t[, filt_idx])	Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing.
`get_centroid_shift`(t[, filt_idx])	Get the centroid shift (in mas) at the input times.
`get_chi2_astrometry`(t, 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, 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_lens_astrometry`(t[, filt_idx])	Get the astrometry for the foreground lens at the input times.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, amp_arr])	Get the photometry for each of the lensed source images.
`get_resolved_amplification`(t[, filt_idx])	Get the photometric amplification terms at a set of times, t for both the plus and minus images.
`get_resolved_astrometry`(t[, filt_idx])	Get the relative RA and Dec astrometry for each of the two source images, which we label plus and minus.
`get_source_astrometry_unlensed`(t[, filt_idx])	Get the astrometry of the source if the lens didn't exist.
`get_u`(t[, filt_idx])	Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.
`log_likely_astrometry`(t, 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, 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_photometry`(t, mag_obs, mag_err_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, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

cent
detJac
get_all_arrays
im_pos1

animate(tE, time_steps, frame_time, name, size, zoom, astrometry, filt_idx=0)

Produces animation of microlensing event.

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_all_arrays_CI(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_all_arrays_amg(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Adaptive mesh grid creates more boundary points around the source only when it enters the Einstein ring.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_amplification(t, filt_idx=0, amp_arr=None)

Get an array of the photometric amplifications at the input times.

Parameters:

tarray_like: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

get_astrometry(t, filt_idx=0)

Get the astrometry of the unresolved (observed) position of the lensed source at the input times. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

centroidnumpy array, dtype=float, shape = [len(t), 2]: The flux-weighted centroid of all lensed images from the source and any luminous lenses.

get_astrometry_unlensed(t, filt_idx=0)

Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed, flux-weighted centroid position of the source+lens in arcseconds.

get_centroid_shift(t, filt_idx=0)

Get the centroid shift (in mas) at the input times. The centroid shift is the difference between the lensed, unresolved position (with lensed source + lens light) and the unlensed, unresolved position (with unlensed source + lens light). The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the photometric filter or data set.

get_chi2_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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_lens_astrometry(t, filt_idx=0)

Get the astrometry for the foreground lens at the input times. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xLarray_like, dtype=float, shape = [len(t), 2]: Position of the lens on the sky (arcsec).

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, amp_arr=None)

Get the photometry for each of the lensed source images.

Parameters:

tarray_like: Array of times to model.
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:

amp_arrarray_like

Amplifications of each individual image at each time, i.e. amp_arr.shape = (len(t), number of images at each t).

This will over-ride t; but is more efficient when calculating both photometry and astrometry. If None, then just use t.

get_resolved_amplification(t, filt_idx=0)

Get the photometric amplification terms at a set of times, t for both the plus and minus images. The returned tuple has two entries: (A_plus, A_minus), each with len(t) arrays.

Parameters:

tarray_like: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

Anumpy array, dtype=float, shape = [len(t), [+/-]: The amplification for the + and - lensed images.

get_resolved_astrometry(t, filt_idx=0)

Get the relative RA and Dec astrometry for each of the two source images, which we label plus and minus. The returned tuple has two entries: (xS_plus, xS_minus), each with [len(t), 2] arrays where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

(xS_plus, xS_minus)tuple of numpy arrays

xS_plus is the vector position of the plus image in arcsec with shape = [len(t), 2]
xS_minus is the vector position of the plus image in arcsec with shape = [len(t), 2]

get_source_astrometry_unlensed(t, filt_idx=0)

Get the astrometry of the source if the lens didn’t exist. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed positions of the source in arcseconds.

get_u(t, filt_idx=0)

Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

uarray, float, shape = [len(t), 2]: Separation vector in East, North on the sky in units of theta_E.

log_likely_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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.FSPL_Phot

Bases: FSPL

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_all_arrays_CI`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_all_arrays_amg`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_amplification`(t[, filt_idx, amp_arr])	Get an array of the photometric amplifications at the input times.
`get_astrometry`(t[, filt_idx])	Get the astrometry of the unresolved (observed) position of the lensed source at the input times.
`get_astrometry_unlensed`(t[, filt_idx])	Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing.
`get_centroid_shift`(t[, filt_idx])	Get the centroid shift (in mas) at the input times.
`get_chi2_astrometry`(t, 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, 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_lens_astrometry`(t[, filt_idx])	Get the astrometry for the foreground lens at the input times.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, amp_arr])	Get the photometry for each of the lensed source images.
`get_resolved_amplification`(t[, filt_idx])	Get the photometric amplification terms at a set of times, t for both the plus and minus images.
`get_resolved_astrometry`(t[, filt_idx])	Get the relative RA and Dec astrometry for each of the two source images, which we label plus and minus.
`get_resolved_astrometry_outline`(t[, filt_idx])	Get the delta-x, delta-y astrometry for each of the two lensed source images and all the associated outline points for each.
`get_source_astrometry_unlensed`(t[, filt_idx])	Get the astrometry of the source if the lens didn't exist.
`get_u`(t[, filt_idx])	Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.
`get_u_outline`(t[, filt_idx])	Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector for each point of the source outline.
`log_likely_astrometry`(t, 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, 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_photometry`(t, mag_obs, mag_err_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, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

cent
detJac
get_all_arrays
im_pos1

animate(tE, time_steps, frame_time, name, size, zoom, astrometry, filt_idx=0)

Produces animation of microlensing event.

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_all_arrays_CI(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_all_arrays_amg(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Adaptive mesh grid creates more boundary points around the source only when it enters the Einstein ring.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_amplification(t, filt_idx=0, amp_arr=None)

Get an array of the photometric amplifications at the input times.

Parameters:

tarray_like: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

get_astrometry(t, filt_idx=0)

Get the astrometry of the unresolved (observed) position of the lensed source at the input times. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

centroidnumpy array, dtype=float, shape = [len(t), 2]: The flux-weighted centroid of all lensed images from the source and any luminous lenses.

get_astrometry_unlensed(t, filt_idx=0)

Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed, flux-weighted centroid position of the source+lens in arcseconds.

get_centroid_shift(t, filt_idx=0)

Get the centroid shift (in mas) at the input times. The centroid shift is the difference between the lensed, unresolved position (with lensed source + lens light) and the unlensed, unresolved position (with unlensed source + lens light). The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the photometric filter or data set.

get_chi2_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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_lens_astrometry(t, filt_idx=0)

Get the astrometry for the foreground lens at the input times. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xLarray_like, dtype=float, shape = [len(t), 2]: Position of the lens on the sky (arcsec).

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, amp_arr=None)

Get the photometry for each of the lensed source images.

Parameters:

tarray_like: Array of times to model.
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:

amp_arrarray_like

Amplifications of each individual image at each time, i.e. amp_arr.shape = (len(t), number of images at each t).

This will over-ride t; but is more efficient when calculating both photometry and astrometry. If None, then just use t.

get_resolved_amplification(t, filt_idx=0)

Get the photometric amplification terms at a set of times, t for both the plus and minus images. The returned tuple has two entries: (A_plus, A_minus), each with len(t) arrays.

Parameters:

tarray_like: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

Anumpy array, dtype=float, shape = [len(t), [+/-]: The amplification for the + and - lensed images.

get_resolved_astrometry(t, filt_idx=0)

Get the relative RA and Dec astrometry for each of the two source images, which we label plus and minus. The returned tuple has two entries: (xS_plus, xS_minus), each with [len(t), 2] arrays where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

(xS_plus, xS_minus)tuple of numpy arrays

xS_plus is the vector position of the plus image in arcsec with shape = [len(t), 2]
xS_minus is the vector position of the plus image in arcsec with shape = [len(t), 2]

get_resolved_astrometry_outline(t, filt_idx=0)

Get the delta-x, delta-y astrometry for each of the two lensed source images and all the associated outline points for each. The two lensed source images are labeled plus and minus.

These are relative offsets from the lens (at origin) in units of thetaE. (xS - xL) / thetaE

Returns:

u_lensed_resolved_outlinenumpy array: Vector position of the plus and minus images. shape = [len(t), self.n_outline, [+,-], [E,N]] where the last axis contains East and North positions.

get_source_astrometry_unlensed(t, filt_idx=0)

Get the astrometry of the source if the lens didn’t exist. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed positions of the source in arcseconds.

get_u(t, filt_idx=0)

Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

uarray, float, shape = [len(t), 2]: Separation vector in East, North on the sky in units of theta_E.

get_u_outline(t, filt_idx=0)

Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector for each point of the source outline. Positions are on the plane of the sky in units of theta_E.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

uarray, float, shape = [len(t), n_outline, [E, N]]: Separation vector in East, North on the sky in units of theta_E.

log_likely_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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.FSPL_PhotAstrom

Bases: FSPL, PSPL_PhotAstrom

Contains methods for model FSPL 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)
radiusS
n
b_sff[#]
mag_src[#]
mag_base[#]
raL - if parallax model
decL - if parallax model

Methods

`animate`(crossings, time_steps, frame_time, ...)	Produces animation of microlensing event.
`get_all_arrays_CI`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_all_arrays_amg`(t[, filt_idx])	Obtain the image and amplitude arrays for each t.
`get_amplification`(t[, filt_idx, amp_arr])	Get an array of the photometric amplifications at the input times.
`get_astrometry`(t[, image_arr, amp_arr, filt_idx])	Position of the observed (unresolved) source position in arcsec.
`get_astrometry_outline_unlensed`(t[, filt_idx])	Get the astrometry of the source outline if the lens didn't exist.
`get_astrometry_unlensed`(t[, filt_idx])	Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing.
`get_centroid_shift`(t[, filt_idx, image_arr, ...])	Parallax: Get the centroid shift (in mas) for a list of observation times (in MJD).
`get_chi2_astrometry`(t, 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, 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_lens_astrometry`(t[, filt_idx])	Get the astrometry for the foreground lens at the input times.
`get_lnL_constant`(err_obs)	Get the natural log of the constant normalization terms of the likelihood.
`get_photometry`(t[, filt_idx, amp_arr])	Get the photometry for the combined source images.
`get_resolved_amplification`(t[, filt_idx, ...])	Get the photometric amplification term at a set of times, t for both the plus and minus images.
`get_resolved_astrometry`(t[, image_arr, ...])	Position of the observed (lensed) source position on the sky.
`get_resolved_astrometry_outline`(t[, filt_idx])	Get the x, y astrometry for each of the two lensed source images and all the associated outline points for each.
`get_resolved_photometry`(t[, filt_idx, amp_arr])	Get the photometry for each of the lensed source images.
`get_resolved_shift_outline`(t[, filt_idx])	Get the astrometric microlensing shift of each point in the source outline for each of the multiple lensed images.
`get_source_astrometry_unlensed`(t[, filt_idx])	Get the astrometry of the source if the lens didn't exist.
`get_u`(t[, filt_idx])	Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.
`get_u_outline`(t[, filt_idx])	Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector for each point of the source outline.
`log_likely_astrometry`(t, 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, 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_photometry`(t, mag_obs, mag_err_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, mag_obs, ...)	Get the natural log of the likelihood for the input photometric data in the specified filter or data sets.

cent
detJac
get_all_arrays
im_pos1

animate(crossings, time_steps, frame_time, name, size, zoom, astrometry, filt_idx=0)

Produces animation of microlensing event.

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_all_arrays_CI(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_all_arrays_amg(t, filt_idx=0)

Obtain the image and amplitude arrays for each t. These arrays contain the positions for each point in the outline for each lensed image.

Adaptive mesh grid creates more boundary points around the source only when it enters the Einstein ring.

Parameters:

tarray_like: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

imagesarray_like: Array/tuple of positions of each lensed image at each t. Shape = [len(t), n_images=2, [E,N]] The last axis contains East and North positions on the sky in arcseconds.
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [len(t), n_images=2]

Notes

The algorithm uses Green’s theorem to change an area integral of the image of the source into a path integral around the outline. For the amplification, we perform a first-order contour integral to approximate the area, and include a second-order parabolic correction. For the centroid calculation, we perform only the first-order contour integral with no second-order parabolic correction. Equations for the contour integrals come from Bozza et al. (2021).

get_amplification(t, filt_idx=0, amp_arr=None)

Get an array of the photometric amplifications at the input times.

Parameters:

tarray_like: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

get_astrometry(t, image_arr=None, amp_arr=None, filt_idx=0)

Position of the observed (unresolved) source position in arcsec.

Parameters:

tarray_like: Array of times to model.

Returns:

model_posarray_like: Array of vector positions of the centroid at each t.

Other Parameters:

image_arrarray_like: Array of complex image positions at each t, i.e. image_arr.shape = (len(t), number of images at each t). Each value in this array is complex (real = north component, imaginary = east component)
amp_arrarray_like: Array of magnifications of each images. Same shape as image_arr.
filt_idxint: The filter index (def=0).

get_astrometry_outline_unlensed(t, filt_idx=0)

Get the astrometry of the source outline if the lens didn’t exist.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), self.n_outline, 2]: The unlensed positions of the source outline points in arcseconds. The source outline is described by a list of points along the circumference of the circular source. The last axis contains East/North positions.

get_astrometry_unlensed(t, filt_idx=0)

Get the unresolved astrometry of the combined source and lens if there was no gravitational lensing. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed, flux-weighted centroid position of the source+lens in arcseconds.

get_centroid_shift(t, filt_idx=0, image_arr=None, amp_arr=None)

Parallax: Get the centroid shift (in mas) for a list of observation times (in MJD).

Returns the flux-weighted centroid of all the sources lensed images.

Parameters:

t:: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

centroid_shiftnumpy array: [shape = len(t), 2] in milliarcseoncds

get_chi2_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=0)

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

Parameters:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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_lens_astrometry(t, filt_idx=0)

Get the astrometry for the foreground lens at the input times. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xLarray_like, dtype=float, shape = [len(t), 2]: Position of the lens on the sky (arcsec).

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, amp_arr=None)

Get the photometry for the combined source images.

Parameters:

tarray_like: Array of times to model.

Returns:

mag_modelarray_like: Magnitude of the centroid at t.

Other Parameters:

amp_arrarray_like

Amplifications of each individual image at each time, i.e. amp_arr.shape = (len(t), number of images at each t).

This will over-ride t; but is more efficient when calculating both photometry and astrometry. If None, then just use t.

filt_idxint

The filter index (def=0).

get_resolved_amplification(t, filt_idx=0, amp_arr=None)

Get the photometric amplification term at a set of times, t for both the plus and minus images.

Parameters:

t: Array of times in MJD.DDD
filt_idxint, optional: Index of the astrometric filter or data set.
Returns
_______
amp_arrarray_like: Array/tuple of amplification of each lensed image at each t. Shape = [n_images=2, len(t)]

get_resolved_astrometry(t, image_arr=None, amp_arr=None, filt_idx=0)

Position of the observed (lensed) source position on the sky.

Parameters:

tarray_like, shape = [N_times]: Array of times to model.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

model_posarray_like. shape = [N_times, N_images=2, 2]: Array of vector positions of the centroid at each t. Last axis contains East/North positions.

Other Parameters:

image_arrarray_like: Array of complex image positions at each t, i.e. image_arr.shape = (len(t), number of images at each t, [E,N]).
amp_arrarray_like: Array of magnifications of each images. Same shape as image_arr.

get_resolved_astrometry_outline(t, filt_idx=0)

Get the x, y astrometry for each of the two lensed source images and all the associated outline points for each. The two lensed source images are labeled plus and minus.

These are actual positions on the sky.

Returns:

xS_lensed_resolved_outlinenumpy array: Vector position of the plus and minus images in arcsec. shape = [len(t), self.n_outline, [+,-], [E,N]] where the last axis contains East and North positions.

get_resolved_photometry(t, filt_idx=0, amp_arr=None)

Get the photometry for each of the lensed source images. Implement with no blending (since we don’t support different blendings for the different images).

Parameters:

tarray_like: Array of times to model.

Returns:

mag_modelarray_like: Magnitude of each lensed image centroid at t. Shape = [2, len(t)]

Other Parameters:

amp_arrarray_like

Amplifications of each individual image and each outline point for that image at each time, i.e. amp_arr.shape = (len(t), self.n_outline, number of images at each t).

This will over-ride t; but is more efficient when calculating both photometry and astrometry. If None, then just use t.

filt_idxint

The filter index (def=0).

get_resolved_shift_outline(t, filt_idx=0)

Get the astrometric microlensing shift of each point in the source outline for each of the multiple lensed images. These are the positional offsets between the source image outline and the lens position. No impact from the lens flux is included.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xSL_lensed_resolvedarray, float, shape = [len(t), n_outline, [+, -], [E, N]]: Relative astrometric position of the plus and minus image in East, North w.r.t. the lens in units of milli-arcseconds.

get_source_astrometry_unlensed(t, filt_idx=0)

Get the astrometry of the source if the lens didn’t exist. The returned array is in arcsec and has a shape of [len(t), 2] where the second dimension includes [RA, Dec] positions in arcsec.

Parameters:

tarray_like: Time (in MJD).
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

xS_unlensednumpy array, dtype=float, shape = [len(t), 2]: The unlensed positions of the source in arcseconds.

get_u(t, filt_idx=0)

Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector on the plane of the sky in units of theta_E.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

uarray, float, shape = [len(t), 2]: Separation vector in East, North on the sky in units of theta_E.

get_u_outline(t, filt_idx=0)

Get the separation vector, vec{u}(t), which is the unlensed source - lens separation vector for each point of the source outline. Positions are on the plane of the sky in units of theta_E.

Parameters:

tarray, float: Times in MJD at which to evaluate the separation.
filt_idxint, optional: Index of the astrometric filter or data set.

Returns:

uarray, float, shape = [len(t), n_outline, [E, N]]: Separation vector in East, North on the sky in units of theta_E.

log_likely_astrometry(t, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, x_obs, y_obs, x_err_obs, y_err_obs, 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:

tarray_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.
y_obsarray_like: List of relative Dec. astrometric positions on the sky in arcsec. Length must match t.
x_err_obsarray_like: List of relative R.A. astrometric positional errors on the sky in arcsec. Length must match t.
y_err_obsarray_like: List of relative Dec. astrometric positional errors on the sky in arcsec. Length must match t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
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, mag_obs, mag_err_obs, filt_idx=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:

tarray_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.
mag_err_obsarray_like: List of observed photometric uncertainties of the microlensing event in magnitudes. Length must be the same as t.
filt_idxint, optional: Index of the photometric filter or data set.

Returns:

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