Data Class Family

class model.BSPL

Bases: PSPL

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_amplification`(t[, filt_idx])	Parallax: Get the photometric amplification term at a set of times, t.
`get_resolved_amplification`(t)	Parallax: Get the photometric amplification term at a set of times, t for both the plus and minus images.
`get_u`(t)	Parameters

get_chi2_photometry
get_lnL_constant
get_photometry
log_likely_photometry
log_likely_photometry_each

get_u(t)

Parameters

tnumpy array: Times at which to evaluate the source-lens separation.

Returns

unumpy array: Shape = [len(t), 2 sources, 2 directions on sky]

get_resolved_amplification(t)

Parallax: 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

Returns

A_resolvednumpy array: [shape = len(t), len(sources), 2]

Notes

For each time t and each source, we have:

A_plus is the amplification for the plus image.
A_minus is the amplification for the minus image.

In other words,

xS[0, 0, 0] returns the amplification of the first source’s “plus” image at the first time.

Similarly,

xS[0, 0, 1] returns the amplification of the first source’s “minus” image at the first time.

get_amplification(t, filt_idx=0)

Parallax: Get the photometric amplification term at a set of times, t.

Note that this is a convenience function that combines amplifications from multiple sources. The returned amplification is

..math:

A = (f1 * A1 + f2 * A2) / (f1 + f2)

where the fluxes are the intrinsic source flux in the specified filter.

Parameters

t: Array of times in MJD.DDD

Returns

Anumpy array: Array of combined amplifications in the specified filter.

Shape = [len(t)]

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

class model.BSPL_Phot

Bases: BSPL, PSPL_Phot

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

Attributes

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_amplification`(t[, filt_idx])	Parallax: Get the photometric amplification term at a set of times, t.
`get_astrometry`(t[, ast_filt_idx])	Position of the observed (unresolved) source position in Einstein radii.
`get_astrometry_unlensed`(t[, ast_filt_idx])	Get the astrometry of the source if the lens didn't exist.
`get_resolved_amplification`(t)	Parallax: Get the photometric amplification term at a set of times, t for both the plus and minus images.
`get_resolved_astrometry`(t)	Position of the observed source position in Einstein radii.
`get_resolved_astrometry_unlensed`(t)	Get the astrometry of the source if the lens didn't exist.
`get_u`(t)	Parameters

get_centroid_shift
get_chi2_photometry
get_lens_astrometry
get_lnL_constant
get_photometry
log_likely_astrometry
log_likely_photometry
log_likely_photometry_each

get_resolved_astrometry_unlensed(t)

Get the astrometry of the source if the lens didn’t exist. Note, this is a photometry only model, so units are in Einstein radii.

Returns

xS_resolved_unlensednumpy array, [shape = len(t_obs), N_sources, 2]: The unlensed positions of the sources in Einstein radii.
In other words,: xS[0, 0, :] returns the 2D sky position of the first source at the first time.
Similarly,: xS[0, 1, :] returns the 2D sky position of the second source at the first time.

get_astrometry_unlensed(t, ast_filt_idx=0)

Get the astrometry of the source if the lens didn’t exist. Note, this is a photometry only model, so units are in Einstein radii.

Returns

xS_unlensednumpy array, dtype=float, shape = len(t_obs) x 2: The unlensed positions of the source in Einstein radii.

get_resolved_astrometry(t)

Position of the observed source position in Einstein radii.

Parameters

tarray_like, shape = [N_times]: Array of times to model.

Returns

model_posarray_like. shape = [N_times, N_images, 2]: Array of vector positions of the centroid at each t_obs.

get_astrometry(t, ast_filt_idx=0)

Position of the observed (unresolved) source position in Einstein radii.

Parameters

t: array_like: Array of times to model.

Returns

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

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_amplification(t, filt_idx=0)

Parallax: Get the photometric amplification term at a set of times, t.

Note that this is a convenience function that combines amplifications from multiple sources. The returned amplification is

..math:

A = (f1 * A1 + f2 * A2) / (f1 + f2)

where the fluxes are the intrinsic source flux in the specified filter.

Parameters

t: Array of times in MJD.DDD

Returns

Anumpy array: Array of combined amplifications in the specified filter.

Shape = [len(t)]

get_resolved_amplification(t)

Parallax: 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

Returns

A_resolvednumpy array: [shape = len(t), len(sources), 2]

Notes

For each time t and each source, we have:

A_plus is the amplification for the plus image.
A_minus is the amplification for the minus image.

In other words,

xS[0, 0, 0] returns the amplification of the first source’s “plus” image at the first time.

Similarly,

xS[0, 0, 1] returns the amplification of the first source’s “minus” image at the first time.

get_u(t)

Parameters

tnumpy array: Times at which to evaluate the source-lens separation.

Returns

unumpy array: Shape = [len(t), 2 sources, 2 directions on sky]

class model.BSPL_PhotAstrom

Bases: BSPL, PSPL_PhotAstrom

Methods

`animate`(tE, time_steps, frame_time, name, ...)	Produces animation of microlensing event.
`get_amplification`(t[, filt_idx])	Parallax: Get the photometric amplification term at a set of times, t.
`get_astrometry`(t[, ast_filt_idx])	Parallax: Get unresolved astrometry for binary source, point lens.
`get_astrometry_unlensed`(t[, ast_filt_idx])	Get the astrometry of the sources if the lens didn't exist.
`get_centroid_shift`(t[, ast_filt_idx])	Parallax: Get the centroid shift (in mas) for a list of observation times (in MJD).
`get_resolved_amplification`(t)	Parallax: Get the photometric amplification term at a set of times, t for both the plus and minus images.
`get_resolved_astrometry`(t)	Parallax: For each source, get the x, y astrometry for the two lensed source images.
`get_resolved_astrometry_unlensed`(t)	Get the astrometry of the source if the lens didn't exist.
`get_u`(t)	Parameters

get_chi2_astrometry
get_chi2_photometry
get_lnL_constant
get_photometry
log_likely_astrometry
log_likely_astrometry_each
log_likely_photometry
log_likely_photometry_each

get_resolved_astrometry_unlensed(t)

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

Returns

xS_resolved_unlensednumpy array, [shape = len(t_obs), N_sources, 2]: The unlensed positions of the sources in arcseconds.
In other words,: xS[0, 0, :] returns the 2D sky position of the first source at the first time.
Similarly,: xS[0, 1, :] returns the 2D sky position of the second source at the first time.

get_astrometry_unlensed(t, ast_filt_idx=0)

Get the astrometry of the sources if the lens didn’t exist.

Returns

xS_unlensednumpy array, dtype=float, shape = len(t_obs) x 2 directions: The unlensed positions of the combined sources in arcseconds.

Shape = [len(t), 2 directions]

get_resolved_astrometry(t)

Parallax: For each source, get the x, y astrometry for the two lensed source images. For each source, we label the two images as plus and minus.

Returns

xS_resolvednumpy array: [shape = len(t), len(sources), 2, 2]

Notes

For each time t and each source, we have:

xS_plus is the vector position of the plus image.
xS_minus is the vector position of the minus image.

In other words,

xS[0, 0, 0, :] returns the 2D sky position of the first source’s “plus” image at the first time.

Similarly,

xS[0, 0, 1, :] returns the 2D sky position of the first source’s “minus” image at the first time.

get_astrometry(t, ast_filt_idx=0)

Parallax: Get unresolved astrometry for binary source, point lens.

Parameters

t:: Array of times in MJD.DDD

Returns

xS_lensed: Returns flux-weighted average of lensed source positions.

get_centroid_shift(t, ast_filt_idx=0)

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

Returns

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

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_amplification(t, filt_idx=0)

Parallax: Get the photometric amplification term at a set of times, t.

Note that this is a convenience function that combines amplifications from multiple sources. The returned amplification is

..math:

A = (f1 * A1 + f2 * A2) / (f1 + f2)

where the fluxes are the intrinsic source flux in the specified filter.

Parameters

t: Array of times in MJD.DDD

Returns

Anumpy array: Array of combined amplifications in the specified filter.

Shape = [len(t)]

get_resolved_amplification(t)

Parallax: 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

Returns

A_resolvednumpy array: [shape = len(t), len(sources), 2]

Notes

For each time t and each source, we have:

A_plus is the amplification for the plus image.
A_minus is the amplification for the minus image.

In other words,

xS[0, 0, 0] returns the amplification of the first source’s “plus” image at the first time.

Similarly,

xS[0, 0, 1] returns the amplification of the first source’s “minus” image at the first time.

get_u(t)

Parameters

tnumpy array: Times at which to evaluate the source-lens separation.

Returns

unumpy array: Shape = [len(t), 2 sources, 2 directions on sky]