oscaar Package¶
__init__ Module¶
IO Module¶
OSCAAR v2.0 Module for differential photometry
Developed by Brett Morris, 2011-2013
-
oscaar.IO.cd(a=None)[source]¶ Change to a different directory than the current one.
Parameters: a : string
Location of the directory to change to.
Notes
If a is empty, this function will change to the parent directory.
-
oscaar.IO.cp(a, b)[source]¶ Copy a file to another location.
Parameters: a : string
Path of the file to be copied.
b : string
Location where the file will be copied to.
-
oscaar.IO.load(inputPath)[source]¶ Load everything from a oscaar.dataBank() object in a python pickle using cPickle.
Parameters: inputPath : string
File location of an oscaar.dataBank() object to save into a pickle.
Returns: data : string
Path for the saved numpy-pickle.
-
oscaar.IO.parseRegionsFile(regsPath)[source]¶ Parse a regions file for a set of data.
Parameters: regsPath : string
Location of the regions file to be parsed.
Returns: init_x_list : array
An array containing the x-values of the parsed file.
init_y_list : array
An array containing the y-values of the parsed file.
-
oscaar.IO.plottingSettings(trackPlots, photPlots, statusBar=True)[source]¶ Description : Function for handling matplotlib figures across OSCAAR methods.
Parameters: trackPlots : bool
Used to turn the astrometry plots on and off.
photPlots : bool
Used to turn the aperture photometry plots on and off.
statusBar : bool, optional
Used to turn the status bar on and off.
Returns: [fig, subplotsDimensions, photSubplotsOffset] : [figure, int, int]
An array with 3 things. The first is the figure object from matplotlib that will be displayed while OSCAAR is running. The second is the integer value that designates the x and y dimensions of the subplots within the figure plot. The third is the the number correlating to the location of the aperture photometry plots, which depends on the values of trackPlots and photPlots.
statusBarFig : figure
A figure object from matplotlib showing the status bar for completion.
statusBarAx : figure.subplot
A subplot from a matplotlib figure object that represents what is drawn.
Notes
This list returned by plottingSettings() should be stored to a variable, and used as an argument in the phot() and trackSmooth() methods.
dataBank Module¶
oscaar v2.0 Module for differential photometry Developed by Brett Morris, 2011-2013 & minor modifications by Luuk Visser
-
class
oscaar.dataBank.dataBank(initParFilePath=None)[source]¶ Methods for easily storing and accessing information from the entire differential photometry process with OSCAAR.
Core Developer: Brett Morris
Methods
-
calcChiSq()[source]¶ Calculate the for the fluxes of each comparison star and the fluxes of the target star. This metric can be used to suggest which comparison stars have similar overall trends to the target star.
-
calcChiSq_multirad(apertureRadiusIndex)[source]¶ Calculate the for the fluxes of each comparison star and the fluxes of the target star. This metric can be used to suggest which comparison stars have similar overall trends to the target star.
-
calcMeanComparison(ccdGain=1)[source]¶ Take the regression-weighted mean of some of the comparison stars to produce one comparison star flux to compare to the target to produce a light curve.
The comparison stars used are those whose chi-squareds calculated by self.calcChiSq() are less than 2*sigma away from the other chi-squareds. This condition removes outliers.
-
calcMeanComparison_multirad(ccdGain=1)[source]¶ Take the regression-weighted mean of some of the comparison stars to produce one comparison star flux to compare to the target to produce a light curve.
The comparison stars used are those whose are less than away from the other :math:`$chi^2$`s. This condition removes outlier comparison stars, which can be caused by intrinsic variability, tracking inaccuracies, or other effects.
-
centroidInitialGuess(expNumber, star)[source]¶ Gets called for each exposure. If called on the first exposure, it will return the intial centroid guesses input by the DS9 regions file. If any other image and only one regions file has been submitted, it will return the previous centroid as the initial guess for subsequent exposures. If multiple regions files have been submitted, it will return the initial guesses in those regions files when the image path with index
expNumberis equivalent to the path stored for that regions file’s “Reference FITS image”.Parameters: expNumber : int
The index of the exposure currently being analyzed. The image gets called by its index from the list of image paths returned by getPaths().
star : str
The key from
allStarsDictthat corresponds to the star for which you’d like a centroid initial guess.Returns: est_x : float
Estimated centroid position of the star
staralong the x-axis of pixels for exposure indexexpNumberest_y : float
Estimated centroid position of the star
staralong the y-axis of pixels for exposure indexexpNumber
-
computeLightCurve(meanComparisonStar, meanComparisonStarError)[source]¶ Divide the target star flux by the mean comparison star to yield a light curve, save the light curve into the dataBank object.
INPUTS: meanComparisonStar - The fluxes of the (one) mean comparison star
RETURNS: self.lightCurve - The target star divided by the mean comparison star, i.e., the light curve.
-
computeLightCurve_multirad(meanComparisonStars, meanComparisonStarErrors)[source]¶ Divide the target star flux by the mean comparison star to yield a light curve, save the light curve into the dataBank object.
Parameters: meanComparisonStar : list
The fluxes of the (one) mean comparison star
Returns: self.lightCurves:
The fluxes of the target star divided by the fluxes of the mean comparison star, i.e., the light curve
self.lightCurveErrors:
The propagated errors on each relative flux in self.lightCurves
-
czechETDstring(apertureRadiusIndex)[source]¶ Returns a string containing the tab delimited light curve data for submission to the Czech Astronomical Society’s Exoplanet Transit Database, for submission here: http://var2.astro.cz/ETD/protocol.php
Parameters: apertureRadiusIndex : int
Index of the aperture radius from which to use for the light curve fluxes and errors.
-
getErrors(star)[source]¶ Return the errors for one star, where the star parameter is the key for the star of interest.
-
getErrors_multirad(star, apertureRadiusIndex)[source]¶ Return the errors for one star, where the star parameter is the key for the star of interest.
-
getFluxes(star)[source]¶ Return list of fluxes for the star with key
starParameters: star : str
Key for the star from the
allStarsDictdictionaryReturns: fluxes : list
List of fluxes for each aperture radius
-
getFluxes_multirad(star, apertureRadiusIndex)[source]¶ Return the fluxes for one star, where the star parameter is the key for the star of interest.
-
getPhotonNoise()[source]¶ Calculate photon noise using the lightCurve and the meanComparisonStar
RETURNS: self.photonNoise - The estimated photon noise limit
-
getScaledErrors(star)[source]¶ Return the scaled fluxes for one star, where the star parameter is the key for the star of interest.
-
getScaledErrors_multirad(star, apertureRadiusIndex)[source]¶ Return the scaled errors for star and one aperture, where the star parameter is the key for the star of interest.
-
getScaledFluxes(star)[source]¶ Return the scaled fluxes for one star, where the star parameter is the key for the star of interest.
-
getScaledFluxes_multirad(star, apertureRadiusIndex)[source]¶ Return the scaled fluxes for star and one aperture, where the star parameter is the key for the star of interest.
-
outOfTransit()[source]¶ Boolean array where True are the times in getTimes() that are before ingress or after egress.
Returns: List of bools
-
parseInit(initParFilePath=None)[source]¶ Parses init.par, a plain text file that contains all of the running parameters that control the differentialPhotometry.py script. init.par is written by the OSCAAR GUI or can be edited directly by the user.
Parameters: initParFilePath : str
Optional full path to the init.par file to use for the data
-
parseRawRegionsList(rawRegionsList)[source]¶ Split up the rawRegionsList, which should be in the format:
<first regions file>,<reference FITS file for the first regs file>;<second> regions file>, <reference FITS file for the first regs file>;....
into a list of regions files and a list of FITS reference files.
-
parseRegionsFile(regPath)[source]¶ Parses the regions files (.REG) created by DS9. These files are written in plain text, where each circuluar region’s centroid and radius are logged in the form “circle(x-centroid,`y-centroid`,`radius`)”. This method uses regular expressions to parse out the centroids.
Parameters: regPath : string
Path to the regions file to read
Returns: init_x_list : list
Initial estimates for the x-centroids
init_y_list : list
Initial estimates for the y-centroids
-
plot(pointsPerBin=10)[source]¶ Produce a plot of the light curve, show it. Over-plot 10-point median binning of the light curve.
Parameters: pointsPerBin : int, optional (default=10)
Integer number of points to accumulate per bin.
-
plotCentroidsTrace(pointsPerBin=10)[source]¶ Plot all centroid positions for a particular aperture radius, for each comparison star. The plot will be in (x,`y`) coordinates to visualize the physical image drift (this is not a plot as a function of time).
Parameters: pointsPerBin : int, optional (default=10)
Integer number of points to accumulate per bin.
apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius from which to produce the plot.
-
plotComparisonWeightings(apertureRadiusIndex=0)[source]¶ Plot histograms visualizing the relative weightings of the comparison stars used to produce the “mean comparison star”, from which the light curve is calculated.
Parameters: apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius from which to produce the plot.
-
plotLightCurve(pointsPerBin=10, apertureRadiusIndex=0)[source]¶ Produce a plot of the light curve, show it. Over-plot 10-point median binning of the light curve.
Parameters: pointsPerBin : int, optional (default=10)
Integer number of points to accumulate per bin.
apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius from which to produce the plot.
-
plotRawFluxes(apertureRadiusIndex=0, pointsPerBin=10)[source]¶ Plot all raw flux time series for a particular aperture radius, for each comparison star.
Parameters: pointsPerBin : int, optional (default=10)
Integer number of points to accumulate per bin.
apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius from which to produce the plot.
-
plotScaledFluxes(apertureRadiusIndex=0, pointsPerBin=10)[source]¶ Plot all scaled flux time series for a particular aperture radius, for each comparison star.
Parameters: pointsPerBin : int, optional (default=10)
Integer number of points to accumulate per bin.
apertureRadiusIndex : int, optional (default=0)
Index of the aperture radius list corresponding to the aperture radius from which to produce the plot.
-
scaleFluxes()[source]¶ When all fluxes have been collected, run this to re-scale the fluxes of each comparison star to the flux of the target star. Do the same transformation on the errors.
-
scaleFluxes_multirad()[source]¶ When all fluxes have been collected, run this to re-scale the fluxes of each comparison star to the flux of the target star. Do the same transformation on the errors.
-
setFlag(star, setting)[source]¶ Set flag for star with key <star> to <setting> where setting is a Boolean
-
storeCentroid(star, exposureNumber, xCentroid, yCentroid)[source]¶ Store the centroid data collected by trackSmooth
Parameters: star : string
Key for the star for which the centroid has been measured
exposureNumber : int
Index of exposure being considered
xCentroid : float
x-centroid of the star
yCentroid : float
y-centroid of the star
-
storeFlux(star, exposureNumber, rawFlux, rawError)[source]¶ - Store the flux and error data collected by phot
Parameters: star : string
Key for the star from the
allStarsDictdictionaryexposureNumber : int
Index of exposure being considered
rawFlux : float
flux measured, to be stored
rawError : float
flux uncertainty measured, to be stored
-
storeFluxes(star, exposureNumber, rawFluxes, rawErrors)[source]¶ Store the flux and error data collected by oscaar.phot()
Parameters: star : str
Key for the star from the allStarsDict dictionary
exposureNumber : int
Index of exposure being considered
rawFluxes : list of floats
flux measured, to be stored
rawErrors : list of floats
photon noise measured, to be stored
-
storeTime(expNumber)[source]¶ Store the time in JD from the FITS header. Parameters
- exposureNumber : string
- Index of exposure being considered
-
uncertaintyString()[source]¶ Returns: savestring : string
A string formatted for human-readable results from the MCMC process, with the best-fit parameters and the uncertainties
-
updateMCMC(bestp, allparams, acceptanceRate, dataBankPath, uncertainties)[source]¶ Assigns variables within the dataBank object for the results of an MCMC run.
Parameters: bestp : list
Best-fit parameters from the MCMC run. The list elements correspond to [<ratio of planetary radius to stellar radius>,<ratio of semi-major axis to stellar radius>,<inclination>,<mid-transit time>].
allparams : 2D matrix
This matrix represents the many “states”, “trails” or “links in the chain” that are accepted and saved throughout the Metropolis-Hastings process in the MCMC scripts. From allparams we can calculate the uncertainties on each best-fit parameter.
acceptanceRate : float
The final acceptance rate achieved by the chain; the ratio of the number of accepted states and the number of states attempted
dataBankPath : string
Path to the dataBank object pickle (aka “OSCAAR pkl”) to update
uncertainties : list of lists
uncertainties on each of the best-fit parameters in bestp
-
differentialPhotometry Module¶
fitting Module¶
-
oscaar.fitting.fitLinearTrend(xVector, yVector)[source]¶ Fit a line to the set {xVectorCropped,yVectorCropped}, then remove that linear trend from the full set {xVector,yVector}
-
oscaar.fitting.get_uncertainties(param, bestFitParameter)[source]¶ Find the uncertainties from a MCMC parameter chain.
Parameters: param : list
parameter chain from the completed MCMC algorithm
bestFitParam : float
the best-fit (chi-squared) minimizing value for the parameter chain
Returns: [plus,minus] : list of floats
the upper and lower 1-sigma uncertainties on the best fit parameter bestFitParameter
-
oscaar.fitting.mcmc(t, flux, sigma, initParams, func, Nsteps, beta, saveInterval, verbose=False, loadingbar=True)[source]¶ Markov Chain Monte Carlo routine for fitting. Takes a set of fluxes flux measured at times t with uncertainties sigma. Input fitting function func is fed initial parameters initParams and iterated through the chains a total of Nsteps times, randomly sampled from normal distributions with widths beta, and every saveInterval-th state in the chain is saved for later analysis.
Parameters: t : list
times
flux : list
fluxes
sigma : list
uncertainties in fluxes
initParams : list
initial parameter estimates, x_0 in Ford 2005
func : function
fitting function
Nsteps : int
number of iterations
beta : list
widths of normal distribution to randomly sample for each parameter
saveInterval : int
number of steps between “saving” the accepted parameter in the chain. Must satisfy
Nsteps % saveInterval ==0.Returns: bestp : list
parameters at minimum chi^2
x_0toN : array
trace of each parameter at each save step
acceptanceRate: float
the final acceptance rate of the chain
[R1] Eric Ford. “Quantifying the Uncertainty in the Orbits of Extrasolar Planets.” The Astronomical Journal, Volume 129, Issue 3, pp. 1706-1717. 2005.
-
oscaar.fitting.mcmc_iterate(t, flux, sigma, initParams, func, Nsteps, beta, saveInterval, verbose=False)[source]¶ MCMC routine specifically for optimizing the beta parameters with the optimizeBeta() function.
Parameters: t : list
time
- flux : list
fluxes
- sigma : list
uncertainties in fluxes
- initParams : list
initial parameter estimates, x_0 in Ford 2005
- func : function
fitting function
- Nsteps : int
number of steps to try in the chains
- beta : list
widths of normal distribution to randomly sample for each parameter
Returns: acceptanceRateArray : list
Acceptance rates for each beta_mu
[R2] Eric Ford. “Quantifying the Uncertainty in the Orbits of Extrasolar Planets.” The Astronomical Journal, Volume 129, Issue 3, pp. 1706-1717. 2005.
-
class
oscaar.fitting.mcmcfit(dataBankPath, initParams, initBeta, Nsteps, saveInterval, idealAcceptanceRate, burnFraction)[source]¶ Methods
-
run(updatepkl=False, apertureRadiusIndex=0)[source]¶ Run the MCMC algorithms:
Parameters: updatepkl : bool, optional
update the OSCAAR save pkl file from which the data had been loaded with the MCMC best fit parameters, parameter chains, and acceptance rate.
apertureRadiusIndex : int, optional
Integer index of the aperture radius for which you’d like to compute the MCMC fit, from the aperture radius range list
-
-
oscaar.fitting.optimizeBeta(t, flux, sigma, initParams, func, beta, idealAcceptanceRate, plot=True)[source]¶ The beta input parameters for the MCMC function determine the acceptance rate of the Metropolis-Hastings algorithm. According to Ford 2005, the ideal acceptance rate is ~0.25 - ~0.44. This routine is designed to take an initial guess for each of the beta parameters and tweak them until they produce good acceptance rates for each parameter. This is achieved by randomly perturbing each initial parameter with the small perturbation by randomly sampling a normal distribution with a width given by the initial beta vector beta. optimizeBeta() then tries running an MCMC chain briefly to find the acceptance rate for that beta parameter. If the acceptance rates are two high, for example, then the beta is too low, and optimizeBeta() will increase beta. This process continues until the beta vector produces acceptance rates within 10% of the idealAcceptanceRate, which according to Ford (2005) should be between 0.25-0.44.
Parameters: t : list
time
flux : list
fluxes
sigma : list
uncertainties in fluxes
initParams : list
initial parameter estimates, x_0 in Ford 2005
func : function
fitting function
beta : list
widths of normal distribution to randomly sample for each parameter
idealAcceptanceRate : float
desired acceptance rate to be produced by the optimized beta
Returns: beta : list
the beta vector optimized so that running a MCMC chain should produce acceptance rates near idealAcceptanceRate (vector)
[R3] Eric Ford. “Quantifying the Uncertainty in the Orbits of Extrasolar Planets.” The Astronomical Journal, Volume 129, Issue 3, pp. 1706-1717. 2005.
-
oscaar.fitting.updatePKL(bestp, allparams, acceptanceRate, pklPath, uncertainties)[source]¶ Load an OSCAAR pkl, add the MCMC parameters to the file, save it again.
Parameters: bestp : list
best-fit values for each parameter
allparams : array
2D array where each saved state of the chains is stored along one dimension, for each fitting parameter (along the other)
acceptanceRate : float
the final acceptance rate acheived in the chain
pklPath : str
path to the pkl to overwrite.
mathMethods Module¶
oscaar v2.0 Module for differential photometry
Developed by Brett Morris, 2011-2013
-
oscaar.mathMethods.medianBin(time, flux, medianWidth)[source]¶ Produce median binning of a flux vector
Parameters: time : list or numpy.ndarray
List of times in time series
flux : list or numpy.ndarray
List of fluxes, one for each time in time vector
medianWidth : int
Width of each bin in units of data points
Returns: [binnedTime, binnedFlux, binnedStd] : [list, list, list] or [numpy.ndarray, numpy.ndarray, numpy.ndarray]
The times, fluxes and uncertainties on each binned point, where binnedTime is the time for each bin, binnedFlux is the median flux in each bin, and binnedStd is the standard deviation of the points within each bin
-
oscaar.mathMethods.regressionScale(comparisonFlux, targetFlux, time, ingress, egress, returncoeffs=False)[source]¶ Use a least-squares regression to stretch and offset a comparison star fluxes to scale them to the relative intensity of the target star. Only do this regression considering the out-of-transit portions of the light curve.
Parameters: comparisonFlux : numpy.ndarray
Flux of a comparison star
targetFlux : numpy.ndarray
Flux of the target star
time : numpy.ndarray
List of times for each flux measurement in JD
ingress : float
Time of ingress (JD, assuming time list is in JD)
egress : float
Time of egress (JD, assuming time list is in JD)
Returns: scaledVector : numpy.ndarray
Rescaled version of the comparisonFlux vector using the above described process
oscaarGUI Module¶
other Module¶
-
oscaar.other.gd2jd(*date)[source]¶ gd2jd.py converts a UT Gregorian date to Julian date.
Usage: gd2jd.py (2009, 02, 25, 01, 59, 59)
To get the current Julian date: import time gd2jd(time.gmtime())
Hours, minutesutes and/or seconds can be omitted – if so, they are assumed to be zero.
Year and month are converted to type INT, but all others can be type FLOAT (standard practice would suggest only the final element of the date should be float)
-
oscaar.other.jd2gd(jd, returnString=False)[source]¶ Task to convert a list of julian dates to gregorian dates description at http://mathforum.org/library/drmath/view/51907.html Original algorithm in Jean Meeus, “Astronomical Formulae for Calculators”
2009-02-15 13:36 IJC: Converted to importable, callable function
Note from author: This script is buggy and reports Julian dates which are off by a day or two, depending on how far back you go. For example, 11 March 1609 converted to JD will be off by two days. 20th and 21st century seem to be fine, though.
Note from Brett Morris: This conversion routine matches up to the “Numerical Recipes” in C version from 2010-2100 CE, so I think we’ll be ok for oscaar’s purposes.
photometry Module¶
oscaar v2.0 Module for differential photometry Developed by Brett Morris, 2011-2013
-
oscaar.photometry.multirad(image, xCentroid, yCentroid, apertureRadii, plottingThings, annulusOuterRadiusFactor=2.8, annulusInnerRadiusFactor=1.4, ccdGain=1, plots=False)[source]¶ Method for aperture photometry.
Parameters: image : numpy.ndarray
FITS image opened with PyFITS
xCentroid : float
Stellar centroid along the x-axis (determined by trackSmooth or equivalent)
yCentroid : float
Stellar centroid along the y-axis (determined by trackSmooth or equivalent)
apertureRadii : list
List of aperture radii (floats) to feed to phot().
annulusInnerRadiusFactor : float
Measure the background for sky background subtraction fron an annulus from a factor of annulusInnerRadiusFactor bigger than the apertureRadius to one a factor annulusOuterRadiusFactor bigger.
annulusOuterRadiusFactor : float
Measure the background for sky background subtraction fron an annulus a factor of annulusInnerRadiusFactor bigger than the apertureRadius to one a factor annulusOuterRadiusFactor bigger.
ccdGain : float
Gain of your detector, used to calculate the photon noise
plots : bool
If `plots`=True, display plots showing the aperture radius and annulus radii overplotted on the image of the star
Returns: rawFlux : float
The background-subtracted flux measured within the aperture
rawError : float
The photon noise (limiting statistical) Poisson uncertainty on the measurement of rawFlux
errorFlag : bool
Boolean corresponding to whether or not any error occured when running oscaar.phot(). If an error occured, the flag is True; otherwise False.
Core developer: Brett Morris (NASA-GSFC)
-
oscaar.photometry.phot(image, xCentroid, yCentroid, apertureRadius, plottingThings, annulusOuterRadiusFactor=2.8, annulusInnerRadiusFactor=1.4, ccdGain=1, plots=False)[source]¶ Method for aperture photometry.
Parameters: image : numpy.ndarray
FITS image opened with PyFITS
xCentroid : float
Stellar centroid along the x-axis (determined by trackSmooth or equivalent)
yCentroid : float
Stellar centroid along the y-axis (determined by trackSmooth or equivalent)
apertureRadius : float
Radius in pixels from centroid to use for source aperture
annulusInnerRadiusFactor : float
Measure the background for sky background subtraction fron an annulus from a factor of annulusInnerRadiusFactor bigger than the apertureRadius to one a factor annulusOuterRadiusFactor bigger.
annulusOuterRadiusFactor : float
Measure the background for sky background subtraction fron an annulus a factor of annulusInnerRadiusFactor bigger than the apertureRadius to one a factor annulusOuterRadiusFactor bigger.
ccdGain : float
Gain of your detector, used to calculate the photon noise
plots : bool
If `plots`=True, display plots showing the aperture radius and annulus radii overplotted on the image of the star
Returns: rawFlux : float
The background-subtracted flux measured within the aperture
rawError : float
The photon noise (limiting statistical) Poisson uncertainty on the measurement of rawFlux
errorFlag : bool
Boolean corresponding to whether or not any error occured when running oscaar.phot(). If an error occured, the flag is True; otherwise False.
Core developer: Brett Morris (NASA-GSFC)
systematics Module¶
-
oscaar.systematics.meanDarkFrame(darksPath)[source]¶ Returns the mean dark frame calculated from each dark frame in darksPath. If there is only one file present in darksPath, use the dimensions of that image to produce a dummy dark frame.
Parameters: darksPath : list of strings
Paths to the dark frames
Returns: The mean of the dark frames in darksPath
-
oscaar.systematics.standardFlatMaker(flatImagesPath, flatDarkImagesPath, masterFlatSavePath, plots=False)[source]¶ Make a master flat by taking a mean of a group of flat fields
Parameters: flatImagesPath : string
Path to the flat field exposures
flatDarkImagesPath : string
Path to the flat field darks
masterFlatSavePath : string
Where to save the master flat that is created
plots : bool
Plot the master flat on completion when plots=True
-
oscaar.systematics.twilightFlatMaker(flatImagesPath, flatDarkImagesPath, masterFlatSavePath, plots=False)[source]¶ Make a master flat using a series of images taken at twilight by fitting the individual pixel intensities over time using least-squares and use the intercept as the normalizing factor in the master flat.
Parameters: flatImagesPath : string
Path to the flat field exposures
flatDarkImagesPath : string
Path to the flat field darks
masterFlatSavePath : string
Where to save the master flat that is created
plots : bool
Plot the master flat on completion when plots=True
timeConversions Module¶
-
oscaar.timeConversions.dateobs2jd(ut)[source]¶ Convert times from Universal Time (UT) to Julian Date (JD), splitting the date and time at the “T”.
Parameters: ut : string
Time in Universial Time (UT), in the format: “<YYYY:MM:DD>T<HH:MM:SS>”
Returns: jd : float
Julian Date (JD)
-
oscaar.timeConversions.findKeyword(fitsFile)[source]¶ Parameters: fitsfile : string
Path to a FITS file
Returns: (useKeyword, allKeys, conversionFunction) : tuple
where - useKeyword is the FITS header keyword that should be used to find the time of the exposure, - allKeys is the list of all header keywords in the first exposure - conversionFunction is a function that will convert the time value stored in the keyword denoted by useKeyword to Julian Date
transitModel Module¶
transitModel.py defines the function occultquad(), which loads the C library containing the function of the same name so that analytical transit light curves can be produced in python by passing pythonic arguments to the C code.
-
oscaar.transitModel.ellipe(k)[source]¶ Computes polynomial approximation for the complete elliptic integral of the second kind (Hasting’s approximation)
-
oscaar.transitModel.ellipk(k)[source]¶ Computes polynomial approximation for the complete elliptic integral of the first kind (Hasting’s approximation):
-
oscaar.transitModel.ellippi(n, k)[source]¶ Computes the complete elliptical integral of the third kind using the algorithm of Bulirsch (1965)
-
oscaar.transitModel.occultquad(t, modelParams)[source]¶ - Calculates the analytical transit light curve for a planet occulting a star, according to the formalism of Mandel & Agol (2002) [R4].
Parameters: t : list or numpy.ndarray
List of the times sampled in Julian Date
- modelParams : list
List of the planetary system parameters, in the following order: - : Ratio of the radius of the planet to the radius of the star - : Ratio of the semi-major axis to the radius of the star - : Orbital period - : Limb-darkening coefficient, linear - : Limb-darkening coefficient, quadratic - : Eccentricity - longPericenter: Longitude of pericenter - : Mid-transit time (JD)
Returns: F : numpy.ndarray
Relative fluxes at each time of the time vector t
[R4] Mandel & Agol. “Analytic Light Curvesfrom glob import glob
for Planetary Transit Searches”.
The Astrophysical Journal, Volume 580, Issue 2, pp. L171-L175. 2002.