Residuals

class salter.Residuals(residuals=None, buffer_duration=0.15, params=None, phases=None)

Bases: object

Transit light curve residuals.

Methods Summary

anderson(attrs1, attrs2)	k-sample Anderson test from anderson_ksamp.
from_rms(times, residuals, star, planet[, …])	Load residuals from an rms simulation.
from_transits(transits, params[, …])	Load transit residuals from a list of TransitLightCurve objects.
ks(attrs1, attrs2)	Two-sample KS test from ks_2samp
plot()	Generate a quick plot of the transit residuals.
ttest(attrs1, attrs2)	Independent two-sample T test from ttest_ind.

Methods Documentation

anderson(attrs1, attrs2)

k-sample Anderson test from anderson_ksamp.

Parameters:

attrs1 : list of attributes

List of conditions in first sample

attrs2 : list of attributes

List of conditions in second sample

Returns:

sig : float

significance level (see anderson_ksamp)

Examples

>>> import numpy as np
>>> import batman
>>> from salter import LightCurve
>>> # Create example transiting planet properties
>>> params = batman.TransitParams()
>>> params.t0 = 0.5
>>> params.rp = 0.1
>>> params.per = 1
>>> params.duration = 0.3
>>> params.inc = 90
>>> params.w = 90
>>> params.ecc = 0
>>> params.a = 10
>>> params.limb_dark = 'quadratic'
>>> params.u = [0.2, 0.1]
>>> # Create example transit light curves:
>>> transits = [LightCurve(times=i + np.linspace(0, 1, 500),
>>>                        fluxes=np.random.randn(500),
>>>                        params=params) for i in range(10)]
>>> r = Residuals(transits, params)
>>> # How significant is the difference between the distributions of the fluxes in and out-of-transit?
>>> r.anderson('out_of_transit', 'in_transit')
1.1428634099527666
>>> # How significant is the difference between the distributions of the in-transit fluxes before and after midtransit?
>>> r.anderson(['in_transit', 'before_midtransit'], ['in_transit', 'after_midtransit'])
0.2792395871784852

classmethod from_rms(times, residuals, star, planet, buffer_duration=0.15)

Load residuals from an rms simulation.

Parameters:

times : ndarray

Times of each flux

residuals : ndarray

Flux residual measurements

star : Star

Stellar properties

planet : Planet

Transiting planet parameters

buffer_duration : float

fraction of transit duration to ignore centered on ingress and egress.

classmethod from_transits(transits, params, buffer_duration=0.15)

Load transit residuals from a list of TransitLightCurve objects.

Parameters:

transits : list of (or single) TransitLightCurve objects

list of transits

params : TransitParams()

transiting planet parameters

buffer_duration : float

fraction of transit duration to ignore centered on ingress and egress.

ks(attrs1, attrs2)

Two-sample KS test from ks_2samp

Parameters:

attrs1 : list of attributes

List of conditions in first sample

attrs2 : list of attributes

List of conditions in second sample

Returns:

pvalue : float

p value.

Examples

>>> import numpy as np
>>> import batman
>>> from salter import LightCurve
>>> # Create example transiting planet properties
>>> params = batman.TransitParams()
>>> params.t0 = 0.5
>>> params.rp = 0.1
>>> params.per = 1
>>> params.duration = 0.3
>>> params.inc = 90
>>> params.w = 90
>>> params.ecc = 0
>>> params.a = 10
>>> params.limb_dark = 'quadratic'
>>> params.u = [0.2, 0.1]
>>> # Create example transit light curves:
>>> transits = [LightCurve(times=i + np.linspace(0, 1, 500),
>>>                        fluxes=np.random.randn(500),
>>>                        params=params) for i in range(10)]
>>> r = Residuals(transits, params)
>>> # How significant is the difference between the distributions of the fluxes in and out-of-transit?
>>> r.ks('out_of_transit', 'in_transit')
0.91710727901331124
>>> # How significant is the difference between the distributions of the in-transit fluxes before and after midtransit?
>>> r.ks(['in_transit', 'before_midtransit'], ['in_transit', 'after_midtransit'])
0.39171715554793468

plot()

Generate a quick plot of the transit residuals.

Returns:

fig : Figure

Figure object

ax : Axes

axis object

ttest(attrs1, attrs2)

Independent two-sample T test from ttest_ind.

Parameters:

attrs1 : list of attributes

List of conditions in first sample

attrs2 : list of attributes

List of conditions in second sample

Returns:

pvalue : float

p value.

Examples

>>> import numpy as np
>>> import batman
>>> from salter import LightCurve
>>> # Create example transiting planet properties
>>> params = batman.TransitParams()
>>> params.t0 = 0.5
>>> params.rp = 0.1
>>> params.per = 1
>>> params.duration = 0.3
>>> params.inc = 90
>>> params.w = 90
>>> params.ecc = 0
>>> params.a = 10
>>> params.limb_dark = 'quadratic'
>>> params.u = [0.2, 0.1]
>>> # Create example transit light curves:
>>> transits = [LightCurve(times=i + np.linspace(0, 1, 500),
>>>                        fluxes=np.random.randn(500),
>>>                        params=params) for i in range(10)]
>>> # Create residuals object
>>> r = Residuals(transits, params)
>>> # How significant is the difference between the means of the fluxes in and out-of-transit?
>>> r.ttest('out_of_transit', 'in_transit')
0.310504218041
>>> # How significant is the difference between the means of the in-transit fluxes before and after midtransit?
>>> r.ttest(['in_transit', 'before_midtransit'], ['in_transit', 'after_midtransit'])
0.823997471194