API Documentation


Fitting

sntd.fitting.fit_data(curves=None, snType='Ia', bands=None, models=None, params=None, bounds={}, ignore=None, constants={}, method='parallel', t0_guess=None, effect_names=[], effect_frames=[], batch_init=None, cut_time=None, dust=None, microlensing=None, fitOrder=None, color_bands=None, min_points_per_band=3, identify_micro=False, fit_prior=None, par_or_batch='parallel', batch_partition=None, nbatch_jobs=None, batch_python_path=None, n_per_node=1, wait_for_batch=False, band_order=None, set_from_simMeta=None, guess_amplitude=True, trial_fit=True, clip_data=False, kernel='RBF', refImage='image_1', nMicroSamples=100, color_curve=None, warning_supress=True, verbose=True, **kwargs)[source]

The main high-level fitting function.

Parameters:
  • curves (curveDict) – The curveDict object containing the multiple images to fit.
  • snType (str) – The supernova classification
  • bands (list of Bandpass or str, or Bandpass or str) – The band(s) to be fit
  • models (list of Model or str, or Model or str) – The model(s) to be used for fitting to the data
  • params (list of str) – The parameters to be fit for the models inside of the parameter models
  • bounds (dict) – A dictionary with parameters in params as keys and a tuple of bounds as values
  • ignore (list of str) – List of parameters to ignore
  • constants (dict) – Dictionary with parameters as keys and the constant value you want to set them to as values
  • method (str or list) – Needs to be ‘parallel’, ‘series’, or ‘color’, or a list containting one or more of these
  • t0_guess (dict) – Dictionary with image names (i.e. ‘image_1’,’image_2’) as keys and a guess for time of peak as values
  • effect_names (list of str) – List of effect names if model contains a PropagationEffect.
  • effect_frames (list of str) – List of the frames (e.g. obs or rest) that correspond to the effects in effect_names
  • batch_init (str) – A string to be pasted into the batch python file (e.g. extra imports or filters added to sncosmo.)
  • cut_time (list) – The start and end (rest frame) phase that you want to fit in, default accept all phases.
  • dust (sncosmo.PropagationEffect) – An sncosmo dust propagation effect to include in the model
  • microlensing (str) – If None microlensing is ignored, otherwise should be str (e.g. achromatic, chromatic)
  • fitOrder (list) – The order you want to fit the images if using parallel method (default chooses by npoints/SNR)
  • color_bands (list) – If using multiple methods (in batch mode), the subset of bands to use for color fitting.
  • min_points_per_band (int) – Only accept bands to fit with this number of points fitting other criterion (e.g. minsnr)
  • identify_micro (bool) – If True, function is run to attempt to identify bands where microlensing is least problematic.
  • fit_prior (curveDict or bool) – if implementing parallel method alongside others and fit_prior is True, will use output of parallel as prior for series/color. If SNTD curveDict object, used as prior for series or color.
  • par_or_batch (str) – if providing a list of SNe, par means multiprocessing and batch means sbatch. Must supply other batch parameters if batch is chosen, so parallel is default.
  • batch_partition (str) – The name of the partition for sbatch command
  • nbatch_jobs (int) – number of jobs (10 jobs for 100 light curves is 10 light curves per job)
  • batch_python_path (str) – path to python you want to use for batch mode (if different from current)
  • n_per_node (int) – Number of SNe to fit per node (in series) in batch mode.
  • wait_for_batch (bool) – if false, submits job in the background. If true, waits for job to finish (shows progress bar) and returns output.
  • band_order (list) – If you want colors to be fit in a specific order (e.g. B-V instead of V-B depending on band order)
  • set_from_simMeta (dict) – Dictionary where keys are model parameters and values are the corresponding key in the curveDict.images.simMeta dictionary (e.g. {‘z’:’sim_redshift’} if you want to set the model redshift based on a simulated redshift in simMeta called ‘sim_redshfit’)
  • guess_amplitude (bool) – If True, the amplitude parameter for the model is estimated, as well as its bounds
  • trial_fit (bool) – If true, a simple minuit fit is performed to locate the parameter space for nestle fits, otherwise the full parameter range in bounds is used.
  • clip_data (bool) – If true, criterion like minsnr and cut_time actually will remove data from the light curve, as opposed to simply not fitting those data points.
  • kernel (str) – The kernel to use for microlensing GPR
  • refImage (str) – The name of the image you want to be the reference image (i.e. image_1,image_2, etc.)
  • nMicroSamples (int) – The number of pulls from the GPR posterior you want to use for microlensing uncertainty estimation
  • color_curve (astropy.Table) – A color curve to define the relationship between bands for parameterized light curve model.
  • warning_supress (bool) – Turns on or off warnings
  • verbose (bool) – Turns on/off the verbosity flag
Returns:

fitted_curveDict – The same curveDict that was passed to fit_data, but with new fits and time delay measurements included. List if list was provided.
Return type:

curveDict or list

Examples

>>> fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F125W'],
        params=['x0','x1','t0','c'],constants={'z':1.33},bounds={'t0':(-15,15),'x1':(-2,2),'c':(0,1)},
        method='parallel',microlensing=None)

Simulation

sntd.simulation.createMultiplyImagedSN(sourcename, snType, redshift, z_lens=None, telescopename='telescope', objectName='object', time_delays=[10.0, 50.0], magnifications=[2.0, 1.0], numImages=2, cadence=5, epochs=30, clip_time=[-30, 150], bands=['F105W', 'F160W'], start_time=None, gain=200.0, skynoiseRange=(1, 1.1), timeArr=None, zpsys='ab', zp=None, microlensing_type=None, microlensing_params=[], ml_loc=[None, None], dust_model='CCM89Dust', av_host=0.3, av_lens=None, fix_luminosity=False, minsnr=0.0, scatter=True, snrFunc=None)[source]

Generate a multiply-imaged SN light curve set, with user-specified time delays and magnifications.

Parameters:
  • sourcename (Source or str) – The model for the spectral evolution of the source. If a string is given, it is used to retrieve a Source from the registry.
  • snType (str) – The classification of the supernova
  • redshift (float) – Redshift of the source
  • z_lens (float) – Redshift of the lens
  • telescopename (str) – The name of the telescope used for observations
  • objectName (str) – The name of the simulated supernova
  • time_delays (list of float) – The relative time delays for the multiple images of the supernova. Must be same length as numImages
  • magnifications (list of float) – The relative magnifications for the multiple images of hte supernova. Must be same length as numImages
  • numImages (int) – The number of images to simulate
  • cadence (float) – The cadence of the simulated observations (if timeArr is not defined)
  • epochs (int) – The number of simulated observations (if timeArr is not defined)
  • clip_time (list) – Rest frame phase start and end time, will clip output table to these values.
  • bands (list of Bandpass or str) – The bandpass(es) used for simulated observations
  • start_time (float) – Start time for the leading image. If None, start will be the first value in the time array, and the peak will be this ± the relative time delay for the leading image.
  • gain (float) – Gain of the telescope “obtaining” the simulated observations (if snrFunc not defined)
  • skynoiseRange (list of float) – The left and right bounds of sky noise used to define observational noise (if snrFunc not defined)
  • timeArr (list of float) – A list of times that define the simulated observation epochs
  • zpsys (str or MagSystem) – The zero-point system used to define the photometry
  • zp (float or list of float) – The zero-point used to define the photometry, list if simulating multiple bandpasses. Then this list must be the same length as bands
  • microlensing_type (str) – If microlensing is to be included, defines whether it is “AchromaticSplineMicrolensing” or “AchromaticMicrolensing”
  • microlensing_params (array or list of int) – If using AchromaticSplineMicrolensing, then this params list must give three values for [nanchor, sigmadm, nspl]. If using AchromaticMicrolensing, then this must be a microcaustic defined by a 2D numpy array
  • ml_loc (list) – List containing tuple locations of SN on microlensing map (random if Nones)
  • dust_model (str) – The dust model to be used for simulations, see sncosmo documentation for options
  • av_host (float) – The A<sub>V</sub> parameter for the simulated dust effect in the source plane
  • av_lens (float) – The A<sub>V</sub> parameter for the simulated dust effect in the lens plane
  • fix_luminosity (bool) – Set the luminosity of every SN to be the peak of the distribution
  • minsnr (float) – A minimum SNR threshold for observations when defining uncertainty
  • scatter (bool) – Boolean that decides whether Gaussian scatter is applied to simulated observations
  • snrFunc (interp1d or dict) – An interpolation function that defines the signal to noise ratio (SNR) as a function of magnitude in the AB system. Used to define the observations instead of telescope parameters like gain and skynoise. This can be a dictionary, so that it’s different for each filter with filters as keys and interpolation functions as values.
Returns:

MISN – A curveDict object containing each of the multiply-imaged SN light curves and the simulation parameters.
Return type:

curveDict

Examples

>>> myMISN = sntd.createMultiplyImagedSN('salt2', 'Ia', 1.33,z_lens=.53, bands=['F110W'],
    zp=[26.8], cadence=5., epochs=35.,skynoiseRange=(.001,.005),gain=70. , time_delays=[10., 78.],
    magnifications=[7,3.5], objectName='My Type Ia SN', telescopename='HST',minsnr=5.0)

I/O

class sntd.curve_io.curve(zpsys='AB')[source]

SNTD class that describes each image of a MISN

bands = None

str band names used

Type:@type
fits = None

newDict Contains fit information from fit_data

Type:@type
meta = None

dict The metadata for the curveDict object, intialized with an empty “info” key value pair. It’s populated by added _metachar__ characters into the header of your data file.

Type:@type
simMeta = None

dict A dictionary containing simulation metadata if this is a simulated curve object

Type:@type
table = None

astropy.table.Table A table containing the data, used for SNCosmo functions, etc.

Type:@type
zpsys = None

str The zero-point system for this curve object

Type:@type
class sntd.curve_io.curveDict(telescopename='Unknown', object='Unknown')[source]

The main object for SNTD. This organizes a MISN, containing the multiple light curves in self.images, all the fits, etc.

add_curve(myCurve, key=None)[source]

Adds a curve object to the existing curveDict (i.e. adds an image to a MISN)

Parameters:
  • myCurve (sntd.curve) – The curve to add to self.
  • key (str) – The key you want to save this as, default is ‘image_1,image_2,etc.’
Returns:

self
Return type:

sntd.curve_io.curveDict
bands = None

list The list of bands contained inside this curveDict

Type:@type
color_table(band1, band2, time_delays=None, referenceImage='image_1', ignore_images=[], static=False, model=None, minsnr=0.0)[source]

Takes the multiple images in self.images and combines the data into a single color curve using defined time delays and magnifications or best (quick) guesses.

Parameters:
  • band1 (str) – The first band for color curve
  • band2 (str) – The second band for color curve
  • time_delays (dict) – Dictionary with image names as keys and relative time delays as values (e.g. {‘image_1’:0,’image_2’:20}). Guessed if None.
  • referenceImage (str) – The image you want to be the reference (e.g. image_1, image_2, etc.)
  • ignore_images (list) – List of images you do not want to include in the color curve.
  • static (bool) – Make the color curve, don’t shift the data
  • model (Model) – If you want to use an sncosmo Model (and the guess_t0_amplitude method) to guess time delays
  • minsnr (float) – Cut data that don’t meet this threshold before making the color curve.
Returns:

self
Return type:

sntd.curve_io.curveDict
combine_curves(time_delays=None, magnifications=None, referenceImage='image_1', static=False, model=None, minsnr=0)[source]

Takes the multiple images in self.images and combines the data into a single light curve using defined time delays and magnifications or best (quick) guesses.

Parameters:
  • time_delays (dict) – Dictionary with image names as keys and relative time delays as values (e.g. {‘image_1’:0,’image_2’:20}). Guessed if None.
  • magnifications (dict) – Dictionary with image names as keys and relative magnifications as values (e.g. {‘image_1’:0,’image_2’:20}). Guessed if None.
  • referenceImage (str) – The image you want to be the reference (e.g. image_1, image_2, etc.)
  • ignore_images (list) – List of images you do not want to include in the color curve.
  • static (bool) – Make the color curve, don’t shift the data
  • model (Model) – If you want to use an sncosmo Model (and the guess_t0_amplitude method) to guess time delays
  • minsnr (float) – Cut data that don’t meet this threshold before making the color curve.
Returns:

self
Return type:

sntd.curve_io.curveDict
meta = None

dict The metadata for the curveDict object, intialized with an empty “info” key value pair. It’s populated by added _metachar__ characters into the header of your data file.

Type:@type
object = None

str Object of interest

Type:@type
plot_fit(method='parallel', par_image=None)[source]

Makes a corner plot based on one of the fitting methods

Parameters:method (str) – parallel, series, or color
Returns:figure object
Return type:figure
plot_object(bands='all', savefig=False, plot3D=False, filename='mySN', orientation='horizontal', method='separate', showModel=False, showFit=False, showMicro=False, **kwargs)[source]
Plot the multiply-imaged SN light curves and show/save to a file.
Each subplot shows a single-band light curve, for all images of the SN.
Parameters:
  • bands (str or list of str) – ‘all’ = plot all bands; or provide a list of bands to plot
  • savefig (bool) – boolean to save or not save plot
  • plot3D (bool) – boolean to plot in 3D with plotly
  • filename (str) – if savefig is True, this is the output filename
  • orientation (str) – ‘horizontal’ = all subplots are in a single row ‘vertical’ = all subplots are in a single column
  • method (str) – Plots the result of separate, series, or color curve method
  • showModel (bool) – If true, the underlying model before microlensing is plotted as well
  • showFit (bool) – If true and it exists, the best fit model from self.images[‘image’].fits.model is plotted
  • showMicro (bool) – If true and it exists, the simulated microlensing is plotted as well
Returns:

figure
Return type:

~matplotlib.pyplot.figure
table = None

Table The astropy table containing all of the data in your data file

Type:@type
telescopename = None

str Name of the telescope that the data were gathered from

Type:@type
sntd.curve_io.read_data(filename, **kwargs)[source]
Used to read a light curve or curve object in pickle format.
Either way, it’ll come out as a curve object.
Parameters:filename (str) – Name of the file to be read (ascii or pickle)
Returns:curve
Return type:curve or curveDict
sntd.curve_io.write_data(curves, filename=None, protocol=-1)[source]
Used to write a curveDict object to a pickle
to be read later
Parameters:
  • curves (~sntd.curveDict) –
  • filename (str) – Name of output file
  • protocol (int) – Pickling protocol
Returns:
Return type:

None
sntd.curve_io.table_factory(tables, telescopename='Unknown', object_name=None)[source]

This function will create a new curve object using an astropy table or tables.

Parameters:
  • tables (~astropy.table.Table or list) – Astropy table with all of your data from your data file, or a list of such tables.
  • telescopename (str) – Name of telescope for labeling purposes inside curve object
  • object_name (str) – Name of object for labeling purposes inside curve object (e.g. SN2006jf, etc.)
Returns:

curve
Return type:

curve

Microlensing

sntd.ml.realizeMicro(arand=0.25, debug=0, kappas=0.75, kappac=0.15, gamma=0.76, eps=0.6, nray=300, minmass=10, maxmass=10, power=-2.35, pixmax=5, pixminx=0, pixminy=0, pixdif=10, fracpixd=0.3, iwrite=0, verbose=False)[source]

Creates a microcaustic realization based on Wambsganss 1990 microlens code. All parameters are optional as they have defaults, see Wambsganss documentation for details on parameters.

sntd.ml.microcaustic_field_to_curve(field, time, zl, zs, velocity=<Quantity 10000. km / s>, M=<Quantity 1.98840987e+30 kg>, loc='Random', plot=False)[source]

Convolves an expanding photosphere (achromatic disc) with a microcaustic to generate a magnification curve.

Parameters:
  • field (numpy.ndarray) – An opened fits file of a microcaustic, can be generated by realizeMicro
  • time (numpy.array) – Time array you want for microlensing magnification curve, explosion time is 0
  • zl (float) – redshift of the lens
  • zs (float) – redshift of the source
  • velocity (float* astropy.units.Unit) – The average velocity of the expanding photosphere
  • M (float* Unit) – The mass of the deflector
  • loc (str or tuple) – Random is defualt for location of the supernova, or pixel (x,y) coordiante can be specified
  • plot (bool) – If true, plots the expanding photosphere on the microcaustic
Returns:

  • time (numpy.array) – The time array for the magnification curve
  • dmag (numpy.array) – The magnification curve.
class sntd.ml.AchromaticMicrolensing(time, dmag, magformat='multiply', **kwargs)[source]

An achromatic microlensing object, defined filter to filter.

propagate(phase, wave, flux)[source]

Propagate the magnification onto the model’s flux output.

class sntd.ml.ChromaticFilterMicrolensing(times, dmags, bands, magformat='multiply', **kwargs)[source]

A chromatic microlensing object, defined filter to filter.

propagate(phase, wave, flux)[source]

Propagate the magnification onto the model’s flux output.