# Stingray API¶

Library of Time Series Methods For Astronomical X-ray Data.

## Data Classes¶

These classes define basic functionality related to common data types and typical methods that apply to these data types, including basic read/write functionality. Currently implemented are stingray.Lightcurve and stingray.events.EventList.

### Lightcurve¶

class stingray.Lightcurve(time, counts, err=None, input_counts=True, gti=None, err_dist='poisson', mjdref=0, dt=None)[source]

Make a light curve object from an array of time stamps and an array of counts.

Parameters: time (iterable) – A list or array of time stamps for a light curve counts (iterable, optional, default None) – A list or array of the counts in each bin corresponding to the bins defined in time (note: use input_counts=False to input the count range, i.e. counts/second, otherwise use counts/bin). err (iterable, optional, default None) – A list or array of the uncertainties in each bin corresponding to the bins defined in time (note: use input_counts=False to input the count rage, i.e. counts/second, otherwise use counts/bin). If None, we assume the data is poisson distributed and calculate the error from the average of the lower and upper 1-sigma confidence intervals for the Poissonian distribution with mean equal to counts. input_counts (bool, optional, default True) – If True, the code assumes that the input data in counts is in units of counts/bin. If False, it assumes the data in counts is in counts/second. gti (2-d float array, default None) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] Good Time Intervals. They are not applied to the data by default. They will be used by other methods to have an indication of the “safe” time intervals to use during analysis. err_dist (str, optional, default None) – Statistical distribution used to calculate the uncertainties and other statistical values appropriately. Default makes no assumptions and keep errors equal to zero. mjdref (float) – MJD reference (useful in most high-energy mission data)
time

numpy.ndarray – The array of midpoints of time bins.

bin_lo

numpy.ndarray – The array of lower time stamp of time bins.

bin_hi

numpy.ndarray – The array of higher time stamp of time bins.

counts

numpy.ndarray – The counts per bin corresponding to the bins in time.

counts_err

numpy.ndarray – The uncertainties corresponding to counts

countrate

numpy.ndarray – The counts per second in each of the bins defined in time.

countrate_err

numpy.ndarray – The uncertainties corresponding to countrate

meanrate

float – The mean count rate of the light curve.

meancounts

float – The mean counts of the light curve.

n

int – The number of data points in the light curve.

dt

float – The time resolution of the light curve.

mjdref

float – MJD reference date (tstart / 86400 gives the date in MJD at the start of the observation)

tseg

float – The total duration of the light curve.

tstart

float – The start time of the light curve.

gti

2-d float array[[gti0_0, gti0_1], [gti1_0, gti1_1], ...] Good Time Intervals. They indicate the “safe” time intervals to be used during the analysis of the light curve.

err_dist

string – Statistic of the Lightcurve, it is used to calculate the uncertainties and other statistical values apropriately. It propagates to Spectrum classes.

Add the counts of two light curves element by element, assuming the light curves have the same time array.

This magic method adds two Lightcurve objects having the same time array such that the corresponding counts arrays get summed up.

GTIs are crossed, so that only common intervals are saved.

Examples

>>> time = [5, 10, 15]
>>> count1 = [300, 100, 400]
>>> count2 = [600, 1200, 800]
>>> gti1 = [[0, 20]]
>>> gti2 = [[0, 25]]
>>> lc1 = Lightcurve(time, count1, gti=gti1)
>>> lc2 = Lightcurve(time, count2, gti=gti2)
>>> lc = lc1 + lc2
>>> lc.counts
array([ 900, 1300, 1200])
__eq__(other_lc)[source]

Compares two Lightcurve objects.

Light curves are equal only if their counts as well as times at which those counts occur equal.

Example

>>> time = [1, 2, 3]
>>> count1 = [100, 200, 300]
>>> count2 = [100, 200, 300]
>>> lc1 = Lightcurve(time, count1)
>>> lc2 = Lightcurve(time, count2)
>>> lc1 == lc2
True
__getitem__(index)[source]

Return the corresponding count value at the index or a new Lightcurve object upon slicing.

This method adds functionality to retrieve the count value at a particular index. This also can be used for slicing and generating a new Lightcurve object. GTIs are recalculated based on the new light curve segment

If the slice object is of kind start:stop:step, GTIs are also sliced, and rewritten as zip(time - self.dt /2, time + self.dt / 2)

Parameters: index (int or slice instance) – Index value of the time array or a slice object.

Examples

>>> time = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> count = [11, 22, 33, 44, 55, 66, 77, 88, 99]
>>> lc = Lightcurve(time, count)
>>> lc[2]
33
>>> lc[:2].counts
array([11, 22])
__len__()[source]

Return the number of time bins of a light curve.

This method implements overrides the len function for a Lightcurve object and returns the length of the time array (which should be equal to the length of the counts and countrate arrays).

Examples

>>> time = [1, 2, 3]
>>> count = [100, 200, 300]
>>> lc = Lightcurve(time, count)
>>> len(lc)
3
__neg__()[source]

Implement the behavior of negation of the light curve objects.

The negation operator - is supposed to invert the sign of the count values of a light curve object.

Examples

>>> time = [1, 2, 3]
>>> count1 = [100, 200, 300]
>>> count2 = [200, 300, 400]
>>> lc1 = Lightcurve(time, count1)
>>> lc2 = Lightcurve(time, count2)
>>> lc_new = -lc1 + lc2
>>> lc_new.counts
array([100, 100, 100])
__sub__(other)[source]

Subtract the counts/flux of one light curve from the counts/flux of another light curve element by element, assuming the time arrays of the light curves match exactly.

This magic method takes two Lightcurve objects having the same time array and subtracts the counts of one Lightcurve with that of another, while also updating countrate, counts_err and countrate_err correctly.

GTIs are crossed, so that only common intervals are saved.

Examples

>>> time = [10, 20, 30]
>>> count1 = [600, 1200, 800]
>>> count2 = [300, 100, 400]
>>> gti1 = [[0, 35]]
>>> gti2 = [[0, 40]]
>>> lc1 = Lightcurve(time, count1, gti=gti1)
>>> lc2 = Lightcurve(time, count2, gti=gti2)
>>> lc = lc1 - lc2
>>> lc.counts
array([ 300, 1100,  400])
analyze_lc_chunks(chunk_length, func, fraction_step=1, **kwargs)[source]

Analyze segments of the light curve with any function.

Parameters: Other Parameters: chunk_length (float) – Length in seconds of the light curve segments func (function) – Function accepting a Lightcurve object as single argument, plus possible additional keyword arguments, and returning a number or a tuple - e.g., (result, error) where both result and error are numbers. fraction_step (float) – If the step is not a full chunk_length but less (e.g. a moving window), this indicates the ratio between step step and chunk_length (e.g. 0.5 means that the window shifts of half chunk_length) kwargs (keyword arguments) – These additional keyword arguments, if present, they will be passed to func start_times (array) – Lower time boundaries of all time segments. stop_times (array) – upper time boundaries of all segments. result (array of N elements) – The result of func for each segment of the light curve

Examples

>>> import numpy as np
>>> time = np.arange(0, 10, 0.1)
>>> counts = np.zeros_like(time) + 10
>>> lc = Lightcurve(time, counts)
>>> # Define a function that calculates the mean
>>> mean_func = lambda x: np.mean(x)
>>> # Calculate the mean in segments of 5 seconds
>>> start, stop, res = lc.analyze_lc_chunks(5, mean_func)
>>> len(res) == 2
True
>>> np.all(res == 10)
True
baseline(lam, p, niter=10, offset_correction=False)[source]

Calculate the baseline of the light curve, accounting for GTIs.

Parameters: Other Parameters: lam (float) – “smoothness” parameter. Larger values make the baseline stiffer Typically 1e2 < lam < 1e9 p (float) – “asymmetry” parameter. Smaller values make the baseline more “horizontal”. Typically 0.001 < p < 0.1, but not necessary. offset_correction (bool, default False) – by default, this method does not align to the running mean of the light curve, but it goes below the light curve. Setting align to True, an additional step is done to shift the baseline so that it is shifted to the middle of the light curve noise distribution. baseline – An array with the baseline of the light curve numpy.ndarray
change_mjdref(new_mjdref)[source]

Change the MJD reference time (MJDREF) of the light curve.

Times will be now referred to this new MJDREF

Parameters: new_mjdref (float) – New MJDREF new_lc – The new LC shifted by MJDREF lightcurve.Lightcurve object
estimate_chunk_length(min_total_counts=100, min_time_bins=100)[source]

Estimate a reasonable segment length for chunk-by-chunk analysis.

Choose a reasonable length for time segments, given a minimum number of total counts in the segment, and a minimum number of time bins in the segment.

The user specifies a condition on the total counts in each segment and the minimum number of time bins.

Other Parameters:

• min_total_counts (int) – Minimum number of counts for each chunk
• min_time_bins (int) – Minimum number of time bins
Returns:

chunk_length – The length of the light curve chunks that satisfies the conditions

Return type:

float

Examples

>>> import numpy as np
>>> time = np.arange(150)
>>> count = np.zeros_like(time) + 3
>>> lc = Lightcurve(time, count)
>>> lc.estimate_chunk_length(min_total_counts=10, min_time_bins=3)
4.0
>>> lc.estimate_chunk_length(min_total_counts=10, min_time_bins=5)
5.0
>>> count[2:4] = 1
>>> lc = Lightcurve(time, count)
>>> lc.estimate_chunk_length(min_total_counts=3, min_time_bins=1)
4.0
join(other)[source]

Join two lightcurves into a single object.

The new Lightcurve object will contain time stamps from both the objects. The counts and countrate attributes in the resulting object will contain the union of the non-overlapping parts of the two individual objects, or the average in case of overlapping time arrays of both Lightcurve objects.

Good Time Intervals are also joined.

Note : Ideally, the time array of both lightcurves should not overlap.

Parameters: other (Lightcurve object) – The other Lightcurve object which is supposed to be joined with. lc_new – The resulting Lightcurve object. Lightcurve object

Examples

>>> time1 = [5, 10, 15]
>>> count1 = [300, 100, 400]
>>> time2 = [20, 25, 30]
>>> count2 = [600, 1200, 800]
>>> lc1 = Lightcurve(time1, count1)
>>> lc2 = Lightcurve(time2, count2)
>>> lc = lc1.join(lc2)
>>> lc.time
array([ 5, 10, 15, 20, 25, 30])
>>> lc.counts
array([ 300,  100,  400,  600, 1200,  800])
static make_lightcurve(dt, tseg=None, tstart=None, gti=None, mjdref=0, use_hist=False)[source]

Make a light curve out of photon arrival times, with a given time resolution dt. Note that dt should be larger than the native time resolution of the instrument that has taken the data.

Parameters: toa (iterable) – list of photon arrival times dt (float) – time resolution of the light curve (the bin width) tseg (float, optional, default None) – The total duration of the light curve. If this is None, then the total duration of the light curve will be the interval between the arrival between the first and the last photon in toa. Note: If tseg is not divisible by dt (i.e. if tseg/dt is not an integer number), then the last fractional bin will be dropped! tstart (float, optional, default None) – The start time of the light curve. If this is None, the arrival time of the first photon will be used as the start time of the light curve. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] Good Time Intervals use_hist (bool) – Use np.histogram instead of np.bincounts. Might be advantageous for very short datasets. lc – A Lightcurve object with the binned light curve Lightcurve object
plot(witherrors=False, labels=None, axis=None, title=None, marker='-', save=False, filename=None)[source]

Plot the light curve using matplotlib.

Plot the light curve object on a graph self.time on x-axis and self.counts on y-axis with self.counts_err optionally as error bars.

Parameters: witherrors (boolean, default False) – Whether to plot the Lightcurve with errorbars or not labels (iterable, default None) – A list of tuple with xlabel and ylabel as strings. axis (list, tuple, string, default None) – Parameter to set axis properties of the matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for thematplotlib.pyplot.axis() method. title (str, default None) – The title of the plot. marker (str, default '-') – Line style and color of the plot. Line styles and colors are combined in a single format string, as in 'bo' for blue circles. See matplotlib.pyplot.plot for more options. save (boolean, optional, default False) – If True, save the figure with specified filename. filename (str) – File name of the image to save. Depends on the boolean save.

Read a Lightcurve object from file. Currently supported formats are

• pickle (not recommended for long-term storage)
• HDF5
• ASCII
Parameters: filename (str) – Path and file name for the file to be read. format_ (str) – Available options are ‘pickle’, ‘hdf5’, ‘ascii’ lc – If format\_ is ascii: astropy.table is returned. If format\_ is hdf5: dictionary with key-value pairs is returned. If format\_ is pickle: Lightcurve object is returned. astropy.table or dict or Lightcurve object
rebin(dt_new=None, f=None, method='sum')[source]

Rebin the light curve to a new time resolution. While the new resolution need not be an integer multiple of the previous time resolution, be aware that if it is not, the last bin will be cut off by the fraction left over by the integer division.

Parameters: Other Parameters: dt_new (float) – The new time resolution of the light curve. Must be larger than the time resolution of the old light curve! method ({sum | mean | average}, optional, default sum) – This keyword argument sets whether the counts in the new bins should be summed or averaged. f (float) – the rebin factor. If specified, it substitutes dt_new with f*self.dt lc_new – The Lightcurve object with the new, binned light curve. Lightcurve object
shift(time_shift)[source]

Shift the light curve and the GTIs in time.

Parameters: time_shift (float) – The time interval by which the light curve will be shifted (in the same units as the time array in Lightcurve new_lc – The new LC shifted by time_shift lightcurve.Lightcurve object
sort(reverse=False)[source]

Sort a Lightcurve object by time.

A Lightcurve can be sorted in either increasing or decreasing order using this method. The time array gets sorted and the counts array is changed accordingly.

Parameters: reverse (boolean, default False) – If True then the object is sorted in reverse order.

Example

>>> time = [2, 1, 3]
>>> count = [200, 100, 300]
>>> lc = Lightcurve(time, count)
>>> lc_new = lc.sort()
>>> lc_new.time
array([1, 2, 3])
>>> lc_new.counts
array([100, 200, 300])
Returns: lc_new – The Lightcurve object with sorted time and counts arrays. Lightcurve object
sort_counts(reverse=False)[source]

Sort a Lightcurve object in accordance with its counts array.

A Lightcurve can be sorted in either increasing or decreasing order using this method. The counts array gets sorted and the time array is changed accordingly.

Parameters: reverse (boolean, default False) – If True then the object is sorted in reverse order. lc_new – The Lightcurve object with sorted time and counts arrays. Lightcurve object

Examples

>>> time = [1, 2, 3]
>>> count = [200, 100, 300]
>>> lc = Lightcurve(time, count)
>>> lc_new = lc.sort_counts()
>>> lc_new.time
array([2, 1, 3])
>>> lc_new.counts
array([100, 200, 300])
split_by_gti()[source]

Split the current Lightcurve object into a list of Lightcurve objects, one for each continuous GTI segment as defined in the gti attribute.

Returns: list_of_lcs – A list of Lightcurve objects, one for each GTI segment list
truncate(start=0, stop=None, method='index')[source]

Truncate a Lightcurve object.

This method takes a start and a stop point (either as indices, or as times in the same unit as those in the time attribute, and truncates all bins before start and after stop, then returns a new Lightcurve object with the truncated light curve.

Parameters: start (int, default 0) – Index (or time stamp) of the starting point of the truncation. If no value is set for the start point, then all points from the first element in the time array are taken into account. stop (int, default None) – Index (or time stamp) of the ending point (exclusive) of the truncation. If no value of stop is set, then points including the last point in the counts array are taken in count. method ({index | time}, optional, default index) – Type of the start and stop values. If set to index then the values are treated as indices of the counts array, or if set to time, the values are treated as actual time values. lc_new – The Lightcurve object with truncated time and counts arrays. Lightcurve object

Examples

>>> time = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> count = [10, 20, 30, 40, 50, 60, 70, 80, 90]
>>> lc = Lightcurve(time, count)
>>> lc_new = lc.truncate(start=2, stop=8)
>>> lc_new.counts
array([30, 40, 50, 60, 70, 80])
>>> lc_new.time
array([3, 4, 5, 6, 7, 8])
>>> # Truncation can also be done by time values
>>> lc_new = lc.truncate(start=6, method='time')
>>> lc_new.time
array([6, 7, 8, 9])
>>> lc_new.counts
array([60, 70, 80, 90])
write(filename, format_='pickle', **kwargs)[source]

Write a Lightcurve object to file. Currently supported formats are

• pickle (not recommended for long-term storage)
• HDF5
• ASCII
Parameters: filename (str) – Path and file name for the output file. format_ (str) – Available options are ‘pickle’, ‘hdf5’, ‘ascii’

### EventList¶

class stingray.events.EventList(time=None, energy=None, ncounts=None, mjdref=0, dt=0, notes='', gti=None, pi=None)[source]

Basic class for event list data. Event lists generally correspond to individual events (e.g. photons) recorded by the detector, and their associated properties. For X-ray data where this type commonly occurs, events are time stamps of when a photon arrived in the detector, and (optionally) the photon energy associated with the event.

Parameters: Other Parameters: time (iterable) – A list or array of time stamps dt (float) – The time resolution of the events. Only relevant when using events to produce light curves with similar bin time. energy (iterable) – A list of array of photon energy values mjdref (float) – The MJD used as a reference for the time array. ncounts (int) – Number of desired data points in event list. gtis ([[gti0_0, gti0_1], [gti1_0, gti1_1], ...]) – Good Time Intervals pi (integer, numpy.ndarray) – PI channels notes (str) – Any useful annotations
time

numpy.ndarray – The array of event arrival times, in seconds from the reference MJD defined in mjdref

energy

numpy.ndarray – The array of photon energy values

ncounts

int – The number of data points in the event list

dt

float – The time resolution of the events. Only relevant when using events to produce light curves with similar bin time.

mjdref

float – The MJD used as a reference for the time array.

gtis

[[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time Intervals

pi

integer, numpy.ndarray – PI channels

static from_lc()[source]

Create an EventList from a stingray.Lightcurve object. Note that all events in a given time bin will have the same time stamp.

Parameters: lc (stingray.Lightcurve object) – Light curve to use for creation of the event list. ev – The resulting list of photon arrival times generated from the light curve. EventList object
join(other)[source]

Join two EventList objects into one.

If both are empty, an empty EventList is returned.

GTIs are crossed if the event lists are over a common time interval, and appended otherwise.

pi and pha remain None if they are None in both. Otherwise, 0 is used as a default value for the EventList where they were None.

Parameters: other (EventList object) – The other EventList object which is supposed to be joined with. ev_new – The resulting EventList object. EventList object

Read an event list from a file on disk. The file must be either a Python pickle file (not recommended for long-term storage), an HDF5 file, an ASCII or a FITS file. The file can have the following attributes in the data or meta-data:

• time: the time stamps of the photon arrivals
• energy: the photon energy corresponding to each time stamp
• ncounts: the total number of photon counts recorded
• mjdref: a reference time in Modified Julian Date
• dt: the time resolution of the data
• notes: other possible meta-data
• gti: Good Time Intervals
• pi: some instruments record energies as “Pulse Invariant”, an integer number recorded from the Pulse Height Amplitude
Parameters: filename (str) – Name of the EventList object to be read. format (str) – Available options are pickle, hdf5, ascii and fits. ev – The EventList object reconstructed from file EventList object
simulate_energies(spectrum)[source]

Assign (simulate) energies to event list from a spectrum.

Parameters: spectrum (2-d array or list) – Energies versus corresponding fluxes. The 2-d array or list must have energies across the first dimension and fluxes across the second one.
simulate_times(lc, use_spline=False, bin_time=None)[source]

Randomly assign (simulate) photon arrival times to an EventList from a stingray.Lightcurve object, using the acceptance-rejection method.

Parameters: Other Parameters: lc (stingray.Lightcurve object) – use_spline (bool) – Approximate the light curve with a spline to avoid binning effects bin_time (float) – The bin time of the light curve, if it needs to be specified for improved precision times – Simulated photon arrival times array-like
to_lc(dt, tstart=None, tseg=None)[source]

Convert event list to a stingray.Lightcurve object.

Parameters: Other Parameters: dt (float) – Binning time of the light curve tstart (float) – Start time of the light curve tseg (float) – Total duration of light curve lc stingray.Lightcurve object
write(filename, format_='pickle')[source]

Write an EventList object to file. Possible file formats are pickle, hdf5, ascii or fits.

Parameters: filename (str) – Name and path of the file to save the event list to.. format (str) – The file format to store the data in. Available options are pickle, hdf5, ascii, fits

## Fourier Products¶

These classes implement commonly used Fourier analysis products, most importantly Crossspectrum and Powerspectrum, along with the variants for averaged cross/power spectra.

### Crossspectrum¶

class stingray.Crossspectrum(lc1=None, lc2=None, norm='none', gti=None)[source]

Make a cross spectrum from a (binned) light curve. You can also make an empty Crossspectrum object to populate with your own Fourier-transformed data (this can sometimes be useful when making binned power spectra).

Parameters: Other Parameters: lc1 (stingray.Lightcurve object, optional, default None) – The first light curve data for the channel/band of interest. lc2 (stingray.Lightcurve object, optional, default None) – The light curve data for the reference band. norm ({frac, abs, leahy, none}, default none) – The normalization of the (real part of the) cross spectrum. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care!
freq

numpy.ndarray – The array of mid-bin frequencies that the Fourier transform samples

power

numpy.ndarray – The array of cross spectra (complex numbers)

power_err

numpy.ndarray – The uncertainties of power. An approximation for each bin given by power_err= power/sqrt(m). Where m is the number of power averaged in each bin (by frequency binning, or averaging more than one spectra). Note that for a single realization (m=1) the error is equal to the power.

df

float – The frequency resolution

m

int – The number of averaged cross-spectra amplitudes in each bin.

n

int – The number of data points/time bins in one segment of the light curves.

nphots1

float – The total number of photons in light curve 1

nphots2

float – The total number of photons in light curve 2

coherence()[source]

Compute Coherence function of the cross spectrum.

Coherence is defined in Vaughan and Nowak, 1996 [vaughan-1996]. It is a Fourier frequency dependent measure of the linear correlation between time series measured simultaneously in two energy channels.

Returns: coh – Coherence function numpy.ndarray
rebin(df=None, f=None, method='mean')[source]

Rebin the cross spectrum to a new frequency resolution df.

Parameters: Other Parameters: df (float) – The new frequency resolution f (float) – the rebin factor. If specified, it substitutes df with f*self.df The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class AveragedPowerspectrum, too. bin_cs = Crossspectrum (or one of its subclasses) object
rebin_log(f=0.01)[source]

Logarithmic rebin of the periodogram. The new frequency depends on the previous frequency modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: f (float, optional, default 0.01) – parameter that steers the frequency resolution new_spec – The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class Crossspectrum (or one of its subclasses) object
time_lag()[source]

Calculate the fourier time lag of the cross spectrum. The time lag is calculate using the center of the frequency bins.

### Coherence¶

Convenience function to compute the coherence between two stingray.Lightcurve objects.

stingray.coherence(lc1, lc2)[source]

Estimate coherence function of two light curves. For details on the definition of the coherence, see [vaughan-1996].

Parameters: lc1 (stingray.Lightcurve object) – The first light curve data for the channel of interest. lc2 (stingray.Lightcurve object) – The light curve data for reference band coh – The array of coherence versus frequency np.ndarray

References

 [vaughan-1996] (1, 2) http://iopscience.iop.org/article/10.1086/310430/pdf

### Powerspectrum¶

class stingray.Powerspectrum(lc=None, norm='frac', gti=None)[source]

Make a Powerspectrum (also called periodogram) from a (binned) light curve. Periodograms can be normalized by either Leahy normalization, fractional rms normalizaation, absolute rms normalization, or not at all.

You can also make an empty Powerspectrum object to populate with your own fourier-transformed data (this can sometimes be useful when making binned power spectra).

Parameters: Other Parameters: lc (stingray.Lightcurve object, optional, default None) – The light curve data to be Fourier-transformed. norm ({leahy | frac | abs | none }, optional, default frac) – The normaliation of the power spectrum to be used. Options are leahy, frac, abs and none, default is frac. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care!
norm

{leahy | frac | abs | none } – the normalization of the power spectrun

freq

numpy.ndarray – The array of mid-bin frequencies that the Fourier transform samples

power

numpy.ndarray – The array of normalized squared absolute values of Fourier amplitudes

power_err

numpy.ndarray – The uncertainties of power. An approximation for each bin given by power_err= power/sqrt(m). Where m is the number of power averaged in each bin (by frequency binning, or averaging power spectrum). Note that for a single realization (m=1) the error is equal to the power.

df

float – The frequency resolution

m

int – The number of averaged powers in each bin

n

int – The number of data points in the light curve

nphots

float – The total number of photons in the light curve

_fourier_cross(lc1, lc2)

Fourier transform the two light curves, then compute the cross spectrum. Computed as CS = lc1 x lc2* (where lc2 is the one that gets complex-conjugated)

Parameters: lc1 (stingray.Lightcurve object) – One light curve to be Fourier transformed. Ths is the band of interest or channel of interest. lc2 (stingray.Lightcurve object) – Another light curve to be Fourier transformed. This is the reference band. fr – The squared absolute value of the Fourier amplitudes numpy.ndarray
_make_auxil_pds(lc1, lc2)

Helper method to create the power spectrum of both light curves independently.

Parameters: lc2 (lc1,) – Two light curves used for computing the cross spectrum.
_make_crossspectrum(lc1, lc2)

Auxiliary method computing the normalized cross spectrum from two light curves. This includes checking for the presence of and applying Good Time Intervals, computing the unnormalized Fourier cross-amplitude, and then renormalizing using the required normalization. Also computes an uncertainty estimate on the cross spectral powers.

Parameters: lc2 (lc1,) – Two light curves used for computing the cross spectrum.
_normalize_crossspectrum(unnorm_power, tseg)

Normalize the real part of the cross spectrum to Leahy, absolute rms^2, fractional rms^2 normalization, or not at all.

Parameters: unnorm_power (numpy.ndarray) – The unnormalized cross spectrum. tseg (int) – The length of the Fourier segment, in seconds. power – The normalized co-spectrum (real part of the cross spectrum). For ‘none’ normalization, imaginary part is returned as well. numpy.nd.array
_phase_lag()

Return the fourier phase lag of the cross spectrum.

_rms_error(powers)[source]

Compute the error on the fractional rms amplitude using error propagation. Note: this uses the actual measured powers, which is not strictly correct. We should be using the underlying power spectrum, but in the absence of an estimate of that, this will have to do.

$r = \sqrt{P}$
$\delta r = \frac{1}{2 * \sqrt{P}} \delta P$
Parameters: powers (iterable) – The list of powers used to compute the fractional rms amplitude. delta_rms – the error on the fractional rms amplitude float
classical_significances(threshold=1, trial_correction=False)[source]

Compute the classical significances for the powers in the power spectrum, assuming an underlying noise distribution that follows a chi-square distributions with 2M degrees of freedom, where M is the number of powers averaged in each bin.

Note that this function will only produce correct results when the following underlying assumptions are fulfilled:

1. The power spectrum is Leahy-normalized
2. There is no source of variability in the data other than the periodic signal to be determined with this method. This is important! If there are other sources of (aperiodic) variability in the data, this method will not produce correct results, but instead produce a large number of spurious false positive detections!
3. There are no significant instrumental effects changing the statistical distribution of the powers (e.g. pile-up or dead time)

By default, the method produces (index,p-values) for all powers in the power spectrum, where index is the numerical index of the power in question. If a threshold is set, then only powers with p-values below that threshold with their respective indices. If trial_correction is set to True, then the threshold will be corrected for the number of trials (frequencies) in the power spectrum before being used.

Parameters: threshold (float, optional, default 1) – The threshold to be used when reporting p-values of potentially significant powers. Must be between 0 and 1. Default is 1 (all p-values will be reported). trial_correction (bool, optional, default False) – A Boolean flag that sets whether the threshold will be corrected by the number of frequencies before being applied. This decreases the threshold (p-values need to be lower to count as significant). Default is False (report all powers) though for any application where threshold is set to something meaningful, this should also be applied! pvals – A list of (index, p-value) tuples for all powers that have p-values lower than the threshold specified in threshold. iterable
coherence()

Compute Coherence function of the cross spectrum.

Coherence is defined in Vaughan and Nowak, 1996 [vaughan-1996]. It is a Fourier frequency dependent measure of the linear correlation between time series measured simultaneously in two energy channels.

Returns: coh – Coherence function numpy.ndarray
compute_rms(min_freq, max_freq, white_noise_offset=0.0)[source]

Compute the fractional rms amplitude in the power spectrum between two frequencies.

Parameters: Other Parameters: min_freq (float) – The lower frequency bound for the calculation max_freq (float) – The upper frequency bound for the calculation white_noise_offset (float, default 0) – This is the white noise level, in Leahy normalization. In the ideal case, this is 2. Dead time and other instrumental effects can alter it. The user can fit the white noise level outside this function and it will get subtracted from powers here. rms – The fractional rms amplitude contained between min_freq and max_freq float
rebin(df=None, f=None, method='mean')[source]

Rebin the power spectrum.

Parameters: Other Parameters: df (float) – The new frequency resolution f (float) – the rebin factor. If specified, it substitutes df with f*self.df The newly binned power spectrum. bin_cs = Powerspectrum object
rebin_log(f=0.01)

Logarithmic rebin of the periodogram. The new frequency depends on the previous frequency modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: f (float, optional, default 0.01) – parameter that steers the frequency resolution new_spec – The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class Crossspectrum (or one of its subclasses) object
time_lag()

Calculate the fourier time lag of the cross spectrum. The time lag is calculate using the center of the frequency bins.

stingray.powerspectrum.classical_pvalue(power, nspec)[source]

Compute the probability of detecting the current power under the assumption that there is no periodic oscillation in the data.

This computes the single-trial p-value that the power was observed under the null hypothesis that there is no signal in the data.

Important: the underlying assumptions that make this calculation valid are:

1. the powers in the power spectrum follow a chi-square distribution
2. the power spectrum is normalized according to [Leahy 1983]_, such that the powers have a mean of 2 and a variance of 4
3. there is only white noise in the light curve. That is, there is no aperiodic variability that would change the overall shape of the power spectrum.

Also note that the p-value is for a single trial, i.e. the power currently being tested. If more than one power or more than one power spectrum are being tested, the resulting p-value must be corrected for the number of trials (Bonferroni correction).

Mathematical formulation in [Groth 1975]_. Original implementation in IDL by Anna L. Watts.

Parameters: power (float) – The squared Fourier amplitude of a spectrum to be evaluated nspec (int) – The number of spectra or frequency bins averaged in power. This matters because averaging spectra or frequency bins increases the signal-to-noise ratio, i.e. makes the statistical distributions of the noise narrower, such that a smaller power might be very significant in averaged spectra even though it would not be in a single power spectrum. pval – The classical p-value of the observed power being consistent with the null hypothesis of white noise float

References

### AveragedCrossspectrum¶

class stingray.AveragedCrossspectrum(lc1=None, lc2=None, segment_size=None, norm='none', gti=None)[source]

Make an averaged cross spectrum from a light curve by segmenting two light curves, Fourier-transforming each segment and then averaging the resulting cross spectra.

Parameters: Other Parameters: lc1 (stingray.Lightcurve object OR iterable of stingray.Lightcurve objects) – A light curve from which to compute the cross spectrum. In some cases, this would be the light curve of the wavelength/energy/frequency band of interest. lc2 (stingray.Lightcurve object OR iterable of stingray.Lightcurve objects) – A second light curve to use in the cross spectrum. In some cases, this would be the wavelength/energy/frequency reference band to compare the band of interest with. segment_size (float) – The size of each segment to average. Note that if the total duration of each Lightcurve object in lc1 or lc2 is not an integer multiple of the segment_size, then any fraction left-over at the end of the time series will be lost. Otherwise you introduce artifacts. norm ({frac, abs, leahy, none}, default none) – The normalization of the (real part of the) cross spectrum. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care!
freq

numpy.ndarray – The array of mid-bin frequencies that the Fourier transform samples

power

numpy.ndarray – The array of cross spectra

power_err

numpy.ndarray – The uncertainties of power. An approximation for each bin given by power_err= power/sqrt(m). Where m is the number of power averaged in each bin (by frequency binning, or averaging powerspectrum). Note that for a single realization (m=1) the error is equal to the power.

df

float – The frequency resolution

m

int – The number of averaged cross spectra

n

int – The number of time bins per segment of light curve

nphots1

float – The total number of photons in the first (interest) light curve

nphots2

float – The total number of photons in the second (reference) light curve

gti

2-d float array[[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. They are calculated by taking the common GTI between the two light curves

coherence()[source]

Averaged Coherence function.

Coherence is defined in Vaughan and Nowak, 1996 [vaughan-1996]__. It is a Fourier frequency dependent measure of the linear correlation between time series measured simultaneously in two energy channels.

Compute an averaged Coherence function of cross spectrum by computing coherence function of each segment and averaging them. The return type is a tuple with first element as the coherence function and the second element as the corresponding uncertainty associated with it.

Note : The uncertainty in coherence function is strictly valid for Gaussian statistics only.

Returns: (coh, uncertainty) – Tuple comprising the coherence function and uncertainty. tuple of np.ndarray
rebin(df=None, f=None, method='mean')

Rebin the cross spectrum to a new frequency resolution df.

Parameters: Other Parameters: df (float) – The new frequency resolution f (float) – the rebin factor. If specified, it substitutes df with f*self.df The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class AveragedPowerspectrum, too. bin_cs = Crossspectrum (or one of its subclasses) object
rebin_log(f=0.01)

Logarithmic rebin of the periodogram. The new frequency depends on the previous frequency modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: f (float, optional, default 0.01) – parameter that steers the frequency resolution new_spec – The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class Crossspectrum (or one of its subclasses) object
time_lag()[source]

Calculate time lag and uncertainty.

Equation from Bendat & Piersol, 2011 [bendat-2011]__.

Returns: lag (np.ndarray) – The time lag lag_err (np.ndarray) – The uncertainty in the time lag

### AveragedPowerspectrum¶

class stingray.AveragedPowerspectrum(lc=None, segment_size=None, norm='frac', gti=None)[source]

Make an averaged periodogram from a light curve by segmenting the light curve, Fourier-transforming each segment and then averaging the resulting periodograms.

Parameters: Other Parameters: lc (stingray.Lightcurveobject OR iterable of :class:stingray.Lightcurve objects) – The light curve data to be Fourier-transformed. segment_size (float) – The size of each segment to average. Note that if the total duration of each Lightcurve object in lc is not an integer multiple of the segment_size, then any fraction left-over at the end of the time series will be lost. norm ({leahy | frac | abs | none }, optional, default frac) – The normaliation of the periodogram to be used. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care!
norm

{leahy | frac | abs | none } – the normalization of the periodogram

freq

numpy.ndarray – The array of mid-bin frequencies that the Fourier transform samples

power

numpy.ndarray – The array of normalized squared absolute values of Fourier amplitudes

power_err

numpy.ndarray – The uncertainties of power. An approximation for each bin given by power_err= power/sqrt(m). Where m is the number of power averaged in each bin (by frequency binning, or averaging powerspectrum). Note that for a single realization (m=1) the error is equal to the power.

df

float – The frequency resolution

m

int – The number of averaged periodograms

n

int – The number of data points in the light curve

nphots

float – The total number of photons in the light curve

classical_significances(threshold=1, trial_correction=False)

Compute the classical significances for the powers in the power spectrum, assuming an underlying noise distribution that follows a chi-square distributions with 2M degrees of freedom, where M is the number of powers averaged in each bin.

Note that this function will only produce correct results when the following underlying assumptions are fulfilled:

1. The power spectrum is Leahy-normalized
2. There is no source of variability in the data other than the periodic signal to be determined with this method. This is important! If there are other sources of (aperiodic) variability in the data, this method will not produce correct results, but instead produce a large number of spurious false positive detections!
3. There are no significant instrumental effects changing the statistical distribution of the powers (e.g. pile-up or dead time)

By default, the method produces (index,p-values) for all powers in the power spectrum, where index is the numerical index of the power in question. If a threshold is set, then only powers with p-values below that threshold with their respective indices. If trial_correction is set to True, then the threshold will be corrected for the number of trials (frequencies) in the power spectrum before being used.

Parameters: threshold (float, optional, default 1) – The threshold to be used when reporting p-values of potentially significant powers. Must be between 0 and 1. Default is 1 (all p-values will be reported). trial_correction (bool, optional, default False) – A Boolean flag that sets whether the threshold will be corrected by the number of frequencies before being applied. This decreases the threshold (p-values need to be lower to count as significant). Default is False (report all powers) though for any application where threshold is set to something meaningful, this should also be applied! pvals – A list of (index, p-value) tuples for all powers that have p-values lower than the threshold specified in threshold. iterable
coherence()

Averaged Coherence function.

Coherence is defined in Vaughan and Nowak, 1996 [vaughan-1996]__. It is a Fourier frequency dependent measure of the linear correlation between time series measured simultaneously in two energy channels.

Compute an averaged Coherence function of cross spectrum by computing coherence function of each segment and averaging them. The return type is a tuple with first element as the coherence function and the second element as the corresponding uncertainty associated with it.

Note : The uncertainty in coherence function is strictly valid for Gaussian statistics only.

Returns: (coh, uncertainty) – Tuple comprising the coherence function and uncertainty. tuple of np.ndarray
compute_rms(min_freq, max_freq, white_noise_offset=0.0)

Compute the fractional rms amplitude in the power spectrum between two frequencies.

Parameters: Other Parameters: min_freq (float) – The lower frequency bound for the calculation max_freq (float) – The upper frequency bound for the calculation white_noise_offset (float, default 0) – This is the white noise level, in Leahy normalization. In the ideal case, this is 2. Dead time and other instrumental effects can alter it. The user can fit the white noise level outside this function and it will get subtracted from powers here. rms – The fractional rms amplitude contained between min_freq and max_freq float
rebin(df=None, f=None, method='mean')

Rebin the power spectrum.

Parameters: Other Parameters: df (float) – The new frequency resolution f (float) – the rebin factor. If specified, it substitutes df with f*self.df The newly binned power spectrum. bin_cs = Powerspectrum object
rebin_log(f=0.01)

Logarithmic rebin of the periodogram. The new frequency depends on the previous frequency modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: f (float, optional, default 0.01) – parameter that steers the frequency resolution new_spec – The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class Crossspectrum (or one of its subclasses) object
time_lag()

Calculate time lag and uncertainty.

Equation from Bendat & Piersol, 2011 [bendat-2011]__.

Returns: lag (np.ndarray) – The time lag lag_err (np.ndarray) – The uncertainty in the time lag

### Dynamical Powerspectrum¶

class stingray.DynamicalPowerspectrum(lc, segment_size, norm='frac', gti=None)[source]

Create a dynamical power spectrum, also often called a spectrogram.

This class will divide a Lightcurve object into segments of length segment_size, create a power spectrum for each segment and store all powers in a matrix as a function of both time (using the mid-point of each segment) and frequency.

This is often used to trace changes in period of a (quasi-)periodic signal over time.

Parameters: Other Parameters: lc (stingray.Lightcurve object) – The time series of which the Dynamical powerspectrum is to be calculated. segment_size (float, default 1) – Length of the segment of light curve, default value is 1 (in whatever units the time array in the Lightcurve object uses). norm ({leahy | frac | abs | none }, optional, default frac) – The normaliation of the periodogram to be used. gti (2-d float array) – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] – Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care!
segment_size

float – The size of each segment to average. Note that if the total duration of each Lightcurve object in lc is not an integer multiple of the segment_size, then any fraction left-over at the end of the time series will be lost.

dyn_ps

np.ndarray – The matrix of normalized squared absolute values of Fourier amplitudes. The axis are given by the freq and time attributes

norm

{leahy | frac | abs | none} – the normalization of the periodogram

freq

numpy.ndarray – The array of mid-bin frequencies that the Fourier transform samples

df

float – The frequency resolution

dt

float – The time resolution

classical_significances(threshold=1, trial_correction=False)

Compute the classical significances for the powers in the power spectrum, assuming an underlying noise distribution that follows a chi-square distributions with 2M degrees of freedom, where M is the number of powers averaged in each bin.

Note that this function will only produce correct results when the following underlying assumptions are fulfilled:

1. The power spectrum is Leahy-normalized
2. There is no source of variability in the data other than the periodic signal to be determined with this method. This is important! If there are other sources of (aperiodic) variability in the data, this method will not produce correct results, but instead produce a large number of spurious false positive detections!
3. There are no significant instrumental effects changing the statistical distribution of the powers (e.g. pile-up or dead time)

By default, the method produces (index,p-values) for all powers in the power spectrum, where index is the numerical index of the power in question. If a threshold is set, then only powers with p-values below that threshold with their respective indices. If trial_correction is set to True, then the threshold will be corrected for the number of trials (frequencies) in the power spectrum before being used.

Parameters: threshold (float, optional, default 1) – The threshold to be used when reporting p-values of potentially significant powers. Must be between 0 and 1. Default is 1 (all p-values will be reported). trial_correction (bool, optional, default False) – A Boolean flag that sets whether the threshold will be corrected by the number of frequencies before being applied. This decreases the threshold (p-values need to be lower to count as significant). Default is False (report all powers) though for any application where threshold is set to something meaningful, this should also be applied! pvals – A list of (index, p-value) tuples for all powers that have p-values lower than the threshold specified in threshold. iterable
coherence()

Averaged Coherence function.

Coherence is defined in Vaughan and Nowak, 1996 [vaughan-1996]__. It is a Fourier frequency dependent measure of the linear correlation between time series measured simultaneously in two energy channels.

Compute an averaged Coherence function of cross spectrum by computing coherence function of each segment and averaging them. The return type is a tuple with first element as the coherence function and the second element as the corresponding uncertainty associated with it.

Note : The uncertainty in coherence function is strictly valid for Gaussian statistics only.

Returns: (coh, uncertainty) – Tuple comprising the coherence function and uncertainty. tuple of np.ndarray
compute_rms(min_freq, max_freq, white_noise_offset=0.0)

Compute the fractional rms amplitude in the power spectrum between two frequencies.

Parameters: Other Parameters: min_freq (float) – The lower frequency bound for the calculation max_freq (float) – The upper frequency bound for the calculation white_noise_offset (float, default 0) – This is the white noise level, in Leahy normalization. In the ideal case, this is 2. Dead time and other instrumental effects can alter it. The user can fit the white noise level outside this function and it will get subtracted from powers here. rms – The fractional rms amplitude contained between min_freq and max_freq float
rebin(df=None, f=None, method='mean')

Rebin the power spectrum.

Parameters: Other Parameters: df (float) – The new frequency resolution f (float) – the rebin factor. If specified, it substitutes df with f*self.df The newly binned power spectrum. bin_cs = Powerspectrum object
rebin_frequency(df_new, method='sum')[source]

Rebin the Dynamic Power Spectrum to a new frequency resolution. Rebinning is an in-place operation, i.e. will replace the existing dyn_ps attribute.

While the new resolution need not be an integer multiple of the previous frequency resolution, be aware that if it is not, the last bin will be cut off by the fraction left over by the integer division.

Parameters: df_new (float) – The new frequency resolution of the Dynamical Power Spectrum. Must be larger than the frequency resolution of the old Dynamical Power Spectrum! method ({sum | mean | average}, optional, default sum) – This keyword argument sets whether the counts in the new bins should be summed or averaged.
rebin_log(f=0.01)

Logarithmic rebin of the periodogram. The new frequency depends on the previous frequency modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: f (float, optional, default 0.01) – parameter that steers the frequency resolution new_spec – The newly binned cross spectrum or power spectrum. Note: this object will be of the same type as the object that called this method. For example, if this method is called from AveragedPowerspectrum, it will return an object of class Crossspectrum (or one of its subclasses) object
rebin_time(dt_new, method='sum')[source]

Rebin the Dynamic Power Spectrum to a new time resolution. While the new resolution need not be an integer multiple of the previous time resolution, be aware that if it is not, the last bin will be cut off by the fraction left over by the integer division.

Parameters: dt_new (float) – The new time resolution of the Dynamical Power Spectrum. Must be larger than the time resolution of the old Dynamical Power Spectrum! method ({"sum" | "mean" | "average"}, optional, default "sum") – This keyword argument sets whether the counts in the new bins should be summed or averaged. time_new (numpy.ndarray) – Time axis with new rebinned time resolution. dynspec_new (numpy.ndarray) – New rebinned Dynamical Power Spectrum.
time_lag()

Calculate time lag and uncertainty.

Equation from Bendat & Piersol, 2011 [bendat-2011]__.

Returns: lag (np.ndarray) – The time lag lag_err (np.ndarray) – The uncertainty in the time lag
trace_maximum(min_freq=None, max_freq=None, sigmaclip=False)[source]

Return the indices of the maximum powers in each segment Powerspectrum between specified frequencies.

Parameters: min_freq (float, default None) – The lower frequency bound. max_freq (float, default None) – The upper frequency bound. max_positions – The array of indices of the maximum power in each segment having frequency between min_freq and max_freq. np.array

### CrossCorrelation¶

class stingray.CrossCorrelation(lc1=None, lc2=None, mode='same')[source]

Make a cross-correlation from a light curves.

You can also make an empty Crosscorrelation object to populate with your own cross-correlation data.

Parameters: lc1 (stingray.Lightcurve object, optional, default None) – The first light curve data for correlation calculations. lc2 (stingray.Lightcurve object, optional, default None) – The light curve data for the correlation calculations. mode ({full, valid, same}, optional, default same) – A string indicating the size of the correlation output. See the relevant scipy documentation [scipy-docs] for more details.
lc1

stingray.Lightcurve – The first light curve data for correlation calculations.

lc2

stingray.Lightcurve – The light curve data for the correlation calculations.

corr

numpy.ndarray – An array of correlation data calculated from two light curves

time_lags

numpy.ndarray – An array of all possible time lags against which each point in corr is calculated

dt

float – The time resolution of each light curve (used in time_lag calculations)

time_shift

float – Time lag that gives maximum value of correlation between two light curves. There will be maximum correlation between light curves if one of the light curve is shifted by time_shift.

n

int – Number of points in self.corr (length of cross-correlation data)

auto

bool – An internal flag to indicate whether this is a cross-correlation or an auto-correlation.

References

cal_timeshift(dt=1.0)[source]

Calculate the cross correlation against all possible time lags, both positive and negative.

Parameters: dt (float, optional, default 1.0) – Time resolution of the light curve, should be passed when object is populated with correlation data and no information about light curve can be extracted. Used to calculate time_lags. self.time_shift (float) – Value of the time lag that gives maximum value of correlation between two light curves. self.time_lags (numpy.ndarray) – An array of time_lags calculated from correlation data
plot(labels=None, axis=None, title=None, marker='-', save=False, filename=None, ax=None)[source]

Plot the Crosscorrelation as function using Matplotlib. Plot the Crosscorrelation object on a graph self.time_lags on x-axis and self.corr on y-axis

Parameters: labels (iterable, default None) – A list of tuple with xlabel and ylabel as strings. axis (list, tuple, string, default None) – Parameter to set axis properties of matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for matplotlib.pyplot.axis() function. title (str, default None) – The title of the plot. marker (str, default -) – Line style and color of the plot. Line styles and colors are combined in a single format string, as in 'bo' for blue circles. See matplotlib.pyplot.plot for more options. save (boolean, optional (default=False)) – If True, save the figure with specified filename. filename (str) – File name of the image to save. Depends on the boolean save. ax (matplotlib.Axes object) – An axes object to fill with the cross correlation plot.

### AutoCorrelation¶

class stingray.AutoCorrelation(lc=None, mode='same')[source]

Make an auto-correlation from a light curve. You can also make an empty Autocorrelation object to populate with your own auto-correlation data.

Parameters: lc (stingray.Lightcurve object, optional, default None) – The light curve data for correlation calculations. mode ({full, valid, same}, optional, default same) – A string indicating the size of the correlation output. See the relevant scipy documentation [scipy-docs] for more details.
lc1, lc2

stingray.Lightcurve – The light curve data for correlation calculations.

corr

numpy.ndarray – An array of correlation data calculated from lightcurve data

time_lags

numpy.ndarray – An array of all possible time lags against which each point in corr is calculated

dt

float – The time resolution of each lightcurve (used in time_lag calculations)

time_shift

float, zero – Max. Value of AutoCorrelation is always at zero lag.

n

int – Number of points in self.corr(Length of auto-correlation data)

cal_timeshift(dt=1.0)

Calculate the cross correlation against all possible time lags, both positive and negative.

Parameters: dt (float, optional, default 1.0) – Time resolution of the light curve, should be passed when object is populated with correlation data and no information about light curve can be extracted. Used to calculate time_lags. self.time_shift (float) – Value of the time lag that gives maximum value of correlation between two light curves. self.time_lags (numpy.ndarray) – An array of time_lags calculated from correlation data
plot(labels=None, axis=None, title=None, marker='-', save=False, filename=None, ax=None)

Plot the Crosscorrelation as function using Matplotlib. Plot the Crosscorrelation object on a graph self.time_lags on x-axis and self.corr on y-axis

Parameters: labels (iterable, default None) – A list of tuple with xlabel and ylabel as strings. axis (list, tuple, string, default None) – Parameter to set axis properties of matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for matplotlib.pyplot.axis() function. title (str, default None) – The title of the plot. marker (str, default -) – Line style and color of the plot. Line styles and colors are combined in a single format string, as in 'bo' for blue circles. See matplotlib.pyplot.plot for more options. save (boolean, optional (default=False)) – If True, save the figure with specified filename. filename (str) – File name of the image to save. Depends on the boolean save. ax (matplotlib.Axes object) – An axes object to fill with the cross correlation plot.

## Higher-Order Fourier and Spectral Timing Products¶

These classes implement higher-order Fourier analysis products (e.g. Bispectrum) and Spectral Timing related methods taking advantage of both temporal and spectral information in modern data sets.

### Bispectrum¶

class stingray.bispectrum.Bispectrum(lc, maxlag=None, window=None, scale='biased')[source]

Makes a Bispectrum object from a stingray.Lightcurve.

Bispectrum is a higher order time series analysis method and is calculated by indirect method as Fourier transform of triple auto-correlation function also called as 3rd order cumulant.

Parameters: lc (stingray.Lightcurve object) – The light curve data for bispectrum calculation. maxlag (int, optional, default None) – Maximum lag on both positive and negative sides of 3rd order cumulant (Similar to lags in correlation). if None, max lag is set to one-half of length of light curve. window ({uniform, parzen, hamming, hanning, triangular, welch, blackman, flat-top}, optional, default ‘uniform’) – Type of window function to apply to the data. scale ({biased, unbiased}, optional, default biased) – Flag to decide biased or unbiased normalization for 3rd order cumulant function.
lc

stingray.Lightcurve object – The light curve data to compute the Bispectrum.

fs

float – Sampling frequencies

n

int – Total Number of samples of light curve observations.

maxlag

int – Maximum lag on both positive and negative sides of 3rd order cumulant (similar to lags in correlation)

signal

numpy.ndarray – Row vector of light curve counts for matrix operations

scale

{biased, unbiased} – Flag to decide biased or unbiased normalization for 3rd order cumulant function.

lags

numpy.ndarray – An array of time lags for which 3rd order cumulant is calculated

freq

numpy.ndarray – An array of freq values for Bispectrum.

cum3

numpy.ndarray – A maxlag*2+1 x maxlag*2+1 matrix containing 3rd order cumulant data for different lags.

bispec

numpy.ndarray – A maxlag*2+1 x maxlag*2+1 matrix containing bispectrum data for different frequencies.

bispec_mag

numpy.ndarray – Magnitude of the bispectrum

bispec_phase

numpy.ndarray – Phase of the bispectrum

References

1) The biphase explained: understanding the asymmetries invcoupled Fourier components of astronomical timeseries by Thomas J. Maccarone Department of Physics, Box 41051, Science Building, Texas Tech University, Lubbock TX 79409-1051 School of Physics and Astronomy, University of Southampton, SO16 4ES

2) T. S. Rao, M. M. Gabr, An Introduction to Bispectral Analysis and Bilinear Time Series Models, Lecture Notes in Statistics, Volume 24, D. Brillinger, S. Fienberg, J. Gani, J. Hartigan, K. Krickeberg, Editors, Springer-Verlag, New York, NY, 1984.

3) Matlab version of bispectrum under following link. https://www.mathworks.com/matlabcentral/fileexchange/60-bisp3cum

Examples

>> from stingray.lightcurve import Lightcurve
>> from stingray.bispectrum import Bispectrum
>> lc = Lightcurve([1,2,3,4,5],[2,3,1,1,2])
>> bs = Bispectrum(lc,maxlag=1)
>> bs.lags
array([-1.,  0.,  1.])
>> bs.freq
array([-0.5,  0. ,  0.5])
>> bs.cum3
array([[-0.2976,  0.1024,  0.1408],
[ 0.1024,  0.144 , -0.2976],
[ 0.1408, -0.2976,  0.1024]])
>> bs.bispec_mag
array([[ 1.26336794,  0.0032    ,  0.0032    ],
[ 0.0032    ,  0.16      ,  0.0032    ],
[ 0.0032    ,  0.0032    ,  1.26336794]])
>> bs.bispec_phase
array([[ -9.65946229e-01,   2.25347190e-14,   3.46944695e-14],
[  0.00000000e+00,   3.14159265e+00,   0.00000000e+00],
[ -3.46944695e-14,  -2.25347190e-14,   9.65946229e-01]])
plot_cum3(axis=None, save=False, filename=None)[source]

Plot the 3rd order cumulant as function of time lags using matplotlib. Plot the cum3 attribute on a graph with the lags attribute on x-axis and y-axis and cum3 on z-axis

Parameters: axis (list, tuple, string, default None) – Parameter to set axis properties of matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for matplotlib.pyplot.axis() method. save (bool, optionalm, default False) – If True, save the figure with specified filename. filename (str) – File name and path of the image to save. Depends on the boolean save. plt – Reference to plot, call show() to display it matplotlib.pyplot object
plot_mag(axis=None, save=False, filename=None)[source]

Plot the magnitude of bispectrum as function of freq using matplotlib. Plot the bispec_mag attribute on a graph with freq attribute on the x-axis and y-axis and the bispec_mag attribute on the z-axis.

Parameters: axis (list, tuple, string, default None) – Parameter to set axis properties of matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for matplotlib.pyplot.axis() method. save (bool, optional, default False) – If True, save the figure with specified filename and path. filename (str) – File name and path of the image to save. Depends on the bool save. plt – Reference to plot, call show() to display it matplotlib.pyplot object
plot_phase(axis=None, save=False, filename=None)[source]

Plot the phase of bispectrum as function of freq using matplotlib. Plot the bispec_phase attribute on a graph with phase attribute on the x-axis and y-axis and the bispec_phase attribute on the z-axis.

Parameters: axis (list, tuple, string, default None) – Parameter to set axis properties of matplotlib figure. For example it can be a list like [xmin, xmax, ymin, ymax] or any other acceptable argument for matplotlib.pyplot.axis() function. save (bool, optional, default False) – If True, save the figure with specified filename and path. filename (str) – File name and path of the image to save. Depends on the bool save. plt – Reference to plot, call show() to display it matplotlib.pyplot object

### Covariancespectrum¶

class stingray.Covariancespectrum(data, dt=None, band_interest=None, ref_band_interest=None, std=None)[source]

Compute a covariance spectrum for the data. The input data can be either in event data or pre-made light curves. Event data can either be in the form of a numpy.ndarray with (time stamp, energy) pairs or a stingray.events.EventList object. If light curves are formed ahead of time, then a list of stingray.Lightcurve objects should be passed to the object, ideally one light curve for each band of interest.

For the case where the data is input as a list of stingray.Lightcurve objects, the reference band(s) should either be

1. a single stingray.Lightcurve object,
2. a list of stingray.Lightcurve objects with the reference band for each band of interest pre-made, or
3. None, in which case reference bands will formed by combining all light curves except for the band of interest.

In the case of event data, band_interest and ref_band_interest can be (multiple) pairs of energies, and the light curves for the bands of interest and reference bands will be produced dynamically.

Parameters: data ({numpy.ndarray | stingray.events.EventList object | list of stingray.Lightcurve objects}) – data contains the time series data, either in the form of a 2-D array of (time stamp, energy) pairs for event data, or as a list of light curves. Note : The event list must be in sorted order with respect to the times of arrivals. dt (float) – The time resolution of the stingray.Lightcurve formed from the energy bin. Only used if data is an event list. band_interest ({None, iterable of tuples}) – If None, all possible energy values will be assumed to be of interest, and a covariance spectrum in the highest resolution will be produced. Note: if the input is a list of stingray.Lightcurve objects, then the user may supply their energy values here, for construction of a reference band. ref_band_interest ({None, tuple, stingray.Lightcurve, list of stingray.Lightcurve objects}) – Defines the reference band to be used for comparison with the bands of interest. If None, all bands except the band of interest will be used for each band of interest, respectively. Alternatively, a tuple can be given for event list data, which will extract the reference band (always excluding the band of interest), or one may put in a single stingray.Lightcurve object to be used (the same for each band of interest) or a list of stingray.Lightcurve objects, one for each band of interest. std (float or np.array or list of numbers) – The term std is used to calculate the excess variance of a band. If std is set to None, default Poisson case is taken and the std is calculated as mean(lc)**0.5. In the case of a single float as input, the same is used as the standard deviation which is also used as the std. And if the std is an iterable of numbers, their mean is used for the same purpose.
unnorm_covar

np.ndarray – An array of arrays with mid point band_interest and their covariance. It is the array-form of the dictionary energy_covar. The covariance values are unnormalized.

covar

np.ndarray – Normalized covariance spectrum.

covar_error

np.ndarray – Errors of the normalized covariance spectrum.

References

[1] Wilkinson, T. and Uttley, P. (2009), Accretion disc variability in the hard state of black hole X-ray binaries. Monthly Notices of the Royal Astronomical Society, 397: 666–676. doi: 10.1111/j.1365-2966.2009.15008.x

Examples

See the notebooks repository for detailed notebooks on the code.

### AveragedCovariancespectrum¶

class stingray.AveragedCovariancespectrum(data, segment_size, dt=None, band_interest=None, ref_band_interest=None, std=None)[source]

Compute a covariance spectrum for the data, defined in [covar spectrum]_ Equation 15.

Parameters: data ({numpy.ndarray | list of stingray.Lightcurve objects}) – data contains the time series data, either in the form of a 2-D array of (time stamp, energy) pairs for event data, or as a list of stingray.Lightcurve objects. Note : The event list must be in sorted order with respect to the times of arrivals. segment_size (float) – The length of each segment in the averaged covariance spectrum. The number of segments will be calculated automatically using the total length of the data set and the segment_size defined here. dt (float) – The time resolution of the stingray.Lightcurve formed from the energy bin. Only used if data is an event list. band_interest ({None, iterable of tuples}) – If None, all possible energy values will be assumed to be of interest, and a covariance spectrum in the highest resolution will be produced. Note: if the input is a list of stingray.Lightcurve objects, then the user may supply their energy values here, for construction of a reference band. ref_band_interest ({None, tuple, stingray.Lightcurve, list of stingray.Lightcurve objects}) – Defines the reference band to be used for comparison with the bands of interest. If None, all bands except the band of interest will be used for each band of interest, respectively. Alternatively, a tuple can be given for event list data, which will extract the reference band (always excluding the band of interest), or one may put in a single stingray.Lightcurve object to be used (the same for each band of interest) or a list of stingray.Lightcurve objects, one for each band of interest. std (float or np.array or list of numbers) – The term std is used to calculate the excess variance of a band. If std is set to None, default Poisson case is taken and the std is calculated as mean(lc)**0.5. In the case of a single float as input, the same is used as the standard deviation which is also used as the std. And if the std is an iterable of numbers, their mean is used for the same purpose.
unnorm_covar

np.ndarray – An array of arrays with mid point band_interest and their covariance. It is the array-form of the dictionary energy_covar. The covariance values are unnormalized.

covar

np.ndarray – Normalized covariance spectrum.

covar_error

np.ndarray – Errors of the normalized covariance spectrum.

References

### VarEnergySpectrum¶

Abstract base class for spectral timing products including both variability and spectral information.

class stingray.varenergyspectrum.VarEnergySpectrum(events, freq_interval, energy_spec, ref_band=None, bin_time=1, use_pi=False, segment_size=None, events2=None)[source]

Base class for variability-energy spectrum.

This class is only a base for the various variability spectra, and it’s not to be instantiated by itself.

Parameters: Other Parameters: events (stingray.events.EventList object) – event list freq_interval ([f0, f1], floats) – the frequency range over which calculating the variability quantity energy_spec (list or tuple (emin, emax, N, type)) – if a list is specified, this is interpreted as a list of bin edges; if a tuple is provided, this will encode the minimum and maximum energies, the number of intervals, and lin or log. ref_band ([emin, emax], floats; default None) – minimum and maximum energy of the reference band. If None, the full band is used. use_pi (bool, default False) – Use channel instead of energy events2 (stingray.events.EventList object) – event list for the second channel, if not the same. Useful if the reference band has to be taken from another detector.
events1

array-like – list of events used to produce the spectrum

events2

array-like – if the spectrum requires it, second list of events

freq_interval

array-like – interval of frequencies used to calculate the spectrum

energy_intervals

[[e00, e01], [e10, e11], ...] – energy intervals used for the spectrum

spectrum

array-like – the spectral values, corresponding to each energy interval

spectrum_error

array-like – the error bars corresponding to spectrum

### RmsEnergySpectrum¶

class stingray.varenergyspectrum.RmsEnergySpectrum(events, freq_interval, energy_spec, ref_band=None, bin_time=1, use_pi=False, segment_size=None, events2=None)[source]

Calculate the rms-Energy spectrum.

For each energy interval, calculate the power density spectrum in fractional r.m.s. normalization. If events2 is specified, the cospectrum is used instead of the PDS.

Parameters: Other Parameters: events (stingray.events.EventList object) – event list freq_interval ([f0, f1], list of float) – the frequency range over which calculating the variability quantity energy_spec (list or tuple (emin, emax, N, type)) – if a list is specified, this is interpreted as a list of bin edges; if a tuple is provided, this will encode the minimum and maximum energies, the number of intervals, and lin or log. ref_band ([emin, emax], float; default None) – minimum and maximum energy of the reference band. If None, the full band is used. use_pi (bool, default False) – Use channel instead of energy events2 (stingray.events.EventList object) – event list for the second channel, if not the same. Useful if the reference band has to be taken from another detector.
events1

array-like – list of events used to produce the spectrum

events2

array-like – if the spectrum requires it, second list of events

freq_interval

array-like – interval of frequencies used to calculate the spectrum

energy_intervals

[[e00, e01], [e10, e11], ...] – energy intervals used for the spectrum

spectrum

array-like – the spectral values, corresponding to each energy interval

spectrum_error

array-like – the errorbars corresponding to spectrum

### LagEnergySpectrum¶

class stingray.varenergyspectrum.LagEnergySpectrum(events, freq_interval, energy_spec, ref_band=None, bin_time=1, use_pi=False, segment_size=None, events2=None)[source]

Calculate the lag-energy spectrum.

For each energy interval, calculate the mean lag in the specified frequency range. If events2 is specified, the reference band is taken from the second event list.

Parameters: Other Parameters: events (stingray.events.EventList object) – event list freq_interval ([f0, f1], list of float) – the frequency range over which calculating the variability quantity energy_spec (list or tuple (emin, emax, N, type)) – if a list is specified, this is interpreted as a list of bin edges; if a tuple is provided, this will encode the minimum and maximum energies, the number of intervals, and lin or log. ref_band ([emin, emax], float; default None) – minimum and maximum energy of the reference band. If None, the full band is used. use_pi (bool, default False) – Use channel instead of energy events2 (stingray.events.EventList object) – event list for the second channel, if not the same. Useful if the reference band has to be taken from another detector.
events1

array-like – list of events used to produce the spectrum

events2

array-like – if the spectrum requires it, second list of events

freq_interval

array-like – interval of frequencies used to calculate the spectrum

energy_intervals

[[e00, e01], [e10, e11], ...] – energy intervals used for the spectrum

spectrum

array-like – the spectral values, corresponding to each energy interval

spectrum_error

array-like – the errorbars corresponding to spectrum

### ExcessVarianceSpectrum¶

class stingray.varenergyspectrum.ExcessVarianceSpectrum(events, freq_interval, energy_spec, bin_time=1, use_pi=False, segment_size=None, normalization='fvar')[source]

Calculate the Excess Variance spectrum.

For each energy interval, calculate the excess variance in the specified frequency range.

Parameters: Other Parameters: events (stingray.events.EventList object) – event list freq_interval ([f0, f1], list of float) – the frequency range over which calculating the variability quantity energy_spec (list or tuple (emin, emax, N, type)) – if a list is specified, this is interpreted as a list of bin edges; if a tuple is provided, this will encode the minimum and maximum energies, the number of intervals, and lin or log. ref_band ([emin, emax], floats; default None) – minimum and maximum energy of the reference band. If None, the full band is used. use_pi (bool, default False) – Use channel instead of energy
events1

array-like – list of events used to produce the spectrum

freq_interval

array-like – interval of frequencies used to calculate the spectrum

energy_intervals

[[e00, e01], [e10, e11], ...] – energy intervals used for the spectrum

spectrum

array-like – the spectral values, corresponding to each energy interval

spectrum_error

array-like – the errorbars corresponding to spectrum

## Utilities¶

Commonly used utility functionality, including Good Time Interval operations and input/output helper methods.

### GTI Functionality¶

Load Good Time Intervals (GTIs) from HDU EVENTS of file fits_file. File is expected to be in FITS format.

Parameters: fits_file (str) – File name and path for the fits file with the GTIs to be loaded gtistring (str) – If the name of the extension with the GTIs is not GTI, the alternative name can be set with this parameter gti_list – A list of GTI (start, stop) pairs extracted from the FITS file. list
stingray.gti.check_gtis(gti)[source]

Check if GTIs are well-behaved.

Check that:

1. the shape of the GTI array is correct;
2. no start > end
3. no overlaps.
Parameters: gti (list) – A list of GTI (start, stop) pairs extracted from the FITS file. TypeError – If GTIs are of the wrong shape ValueError – If GTIs have overlapping or displaced values
stingray.gti.create_gti_mask(time, gtis, safe_interval=0, min_length=0, return_new_gtis=False, dt=None, epsilon=0.001)[source]

Assumes that no overlaps are present between GTIs

Parameters: Other Parameters: time (numpy.ndarray) – An array of time stamps gtis ([[g0_0, g0_1], [g1_0, g1_1], ...], float array-like) – The list of GTIs safe_interval (float or [float, float]) – A safe interval to exclude at both ends (if single float) or the start and the end (if pair of values) of GTIs. min_length (float) – An optional minimum length for the GTIs to be applied. Only GTIs longer than min_length will be considered when creating the mask. return_new_gtis (bool) – If True, return the list of new GTIs (if min_length > 0) dt (float) – Time resolution of the data, i.e. the interval between time stamps epsilon (float) – fraction of dt that is tolerated at the borders of a GTI mask (bool array) – A mask labelling all time stamps that are included in the GTIs versus those that are not. new_gtis (Nx2 array) – An array of new GTIs created by this function
stingray.gti.create_gti_mask_complete(time, gtis, safe_interval=0, min_length=0, return_new_gtis=False, dt=None, epsilon=0.001)[source]

Create GTI mask, allowing for non-constant dt.

Assumes that no overlaps are present between GTIs

Parameters: Other Parameters: time (numpy.ndarray) – An array of time stamps gtis ([[g0_0, g0_1], [g1_0, g1_1], ...], float array-like) – The list of GTIs safe_interval (float or [float, float]) – A safe interval to exclude at both ends (if single float) or the start and the end (if pair of values) of GTIs. min_length (float) – An optional minimum length for the GTIs to be applied. Only GTIs longer than min_length will be considered when creating the mask. return_new_gtis (bool) – If True, return the list of new GTIs (if min_length > 0) dt (float) – Time resolution of the data, i.e. the interval between time stamps epsilon (float) – fraction of dt that is tolerated at the borders of a GTI mask (bool array) – A mask labelling all time stamps that are included in the GTIs versus those that are not. new_gtis (Nx2 array) – An array of new GTIs created by this function
stingray.gti.create_gti_from_condition(time, condition, safe_interval=0, dt=None)[source]

Create a GTI list from a time array and a boolean mask (condition).

Parameters: Returns: time (array-like) – Array containing time stamps condition (array-like) – An array of bools, of the same length of time. A possible condition can be, e.g., the result of lc > 0. gtis – The newly created GTIs [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] safe_interval (float or [float, float]) – A safe interval to exclude at both ends (if single float) or the start and the end (if pair of values) of GTIs. dt (float) – The width (in sec) of each bin of the time array. Can be irregular.
stingray.gti.cross_two_gtis(gti0, gti1)[source]

Extract the common intervals from two GTI lists EXACTLY.

Parameters: gti0 (iterable of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...]) – gti1 (iterable of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...]) – The two lists of GTIs to be crossed. gtis – The newly created GTIs [[gti0_0, gti0_1], [gti1_0, gti1_1], ...]

cross_gtis()
From multiple GTI lists, extract common intervals EXACTLY

Examples

>>> gti1 = np.array([[1, 2]])
>>> gti2 = np.array([[1, 2]])
>>> newgti = cross_gtis([gti1, gti2])
>>> np.all(newgti == [[1, 2]])
True
>>> gti1 = np.array([[1, 4]])
>>> gti2 = np.array([[1, 2], [2, 4]])
>>> newgti = cross_gtis([gti1, gti2])
>>> np.all(newgti == [[1, 4]])
True
stingray.gti.cross_gtis(gti_list)[source]

From multiple GTI lists, extract the common intervals EXACTLY.

Parameters: gti_list (array-like) – List of GTI arrays, each one in the usual format [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti0 – [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] The newly created GTIs 2-d float array

cross_two_gtis()
Extract the common intervals from two GTI lists EXACTLY
stingray.gti.get_btis(gtis, start_time=None, stop_time=None)[source]

From GTIs, obtain bad time intervals, i.e. the intervals not covered by the GTIs.

GTIs have to be well-behaved, in the sense that they have to pass check_gtis.

Parameters: gtis (iterable) – A list of GTIs start_time (float) – Optional start time of the overall observation (e.g. can be earlier than the first time stamp in gtis. stop_time (float) – Optional stop time of the overall observation (e.g. can be later than the last time stamp in gtis. btis – A list of bad time intervals numpy.ndarray
stingray.gti.gti_len(gti)[source]

Return the total good time from a list of GTIs.

Parameters: gti (iterable) – A list of Good Time Intervals gti_len – The sum of lengths of all GTIs float
stingray.gti.check_separate(gti0, gti1)[source]

Check if two GTIs do not overlap.

Parameters: gti0 (2-d float array) – List of GTIs of form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti1 (2-d float array) – List of GTIs of form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] separate – True if GTIs are mutually exclusive, False if not bool
stingray.gti.append_gtis(gti0, gti1)[source]

Union of two non-overlapping GTIs.

If the two GTIs “touch”, this is tolerated and the touching GTIs are joined in a single one.

Parameters: gti0 (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti1 (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti – The newly created GTI 2-d float array

Examples

>>> np.all(append_gtis([[0, 1]], [[2, 3]]) == [[0, 1], [2, 3]])
True
>>> np.all(append_gtis([[0, 1]], [[1, 3]]) == [[0, 3]])
True
stingray.gti.join_gtis(gti0, gti1)[source]

Union of two GTIs.

If GTIs are mutually exclusive, it calls append_gtis. Otherwise we put the extremes of partially overlapping GTIs on an ideal line and look at the number of opened and closed intervals. When the number of closed and opened intervals is the same, the full GTI is complete and we close it.

In practice, we assign to each opening time of a GTI the value -1, and the value 1 to each closing time; when the cumulative sum is zero, the GTI has ended. The timestamp after each closed GTI is the start of a new one.

(cumsum)   -1   -2         -1   0   -1 -2           -1  -2  -1        0
GTI A      |-----:----------|   :    |--:------------|   |---:--------|
FINAL GTI  |-----:--------------|    |--:--------------------:--------|
GTI B            |--------------|       |--------------------|
Parameters: gti0 (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti1 (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] gti – The newly created GTI 2-d float array
stingray.gti.time_intervals_from_gtis(gtis, chunk_length, fraction_step=1, epsilon=1e-05)[source]

Compute start/stop times of equal time intervals, compatible with GTIs.

Used to start each FFT/PDS/cospectrum from the start of a GTI, and stop before the next gap in data (end of GTI).

Parameters: gtis (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] chunk_length (float) – Length of the time segments fraction_step (float) – If the step is not a full chunk_length but less (e.g. a moving window), this indicates the ratio between step step and chunk_length (e.g. 0.5 means that the window shifts of half chunk_length) spectrum_start_times (array-like) – List of starting times to use in the spectral calculations. spectrum_stop_times (array-like) – List of end times to use in the spectral calculations.
stingray.gti.bin_intervals_from_gtis(gtis, chunk_length, time, dt=None, fraction_step=1, epsilon=0.001)[source]

Compute start/stop times of equal time intervals, compatible with GTIs, and map them to the indices of an array of time stamps.

Used to start each FFT/PDS/cospectrum from the start of a GTI, and stop before the next gap in data (end of GTI). In this case, it is necessary to specify the time array containing the times of the light curve bins. Returns start and stop bins of the intervals to use for the PDS

Parameters: Other Parameters: gtis (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] chunk_length (float) – Length of each time segment time (array-like) – Array of time stamps dt (float, default median(diff(time))) – Time resolution of the light curve. epsilon (float, default 0.001) – The tolerance, in fraction of dt, for the comparisons at the borders fraction_step (float) – If the step is not a full chunk_length but less (e.g. a moving window), this indicates the ratio between step step and chunk_length (e.g. 0.5 means that the window shifts of half chunk_length) spectrum_start_bins (array-like) – List of starting bins in the original time array to use in spectral calculations. spectrum_stop_bins (array-like) – List of end bins to use in the spectral calculations.

Examples

>>> time = np.arange(0.5, 13.5)
>>> gtis = [[0, 5], [6, 8]]
>>> chunk_length = 2
>>> start_bins, stop_bins = bin_intervals_from_gtis(gtis,chunk_length,time)
>>> np.all(start_bins == [0, 2, 6])
True
>>> np.all(stop_bins == [2, 4, 8])
True
>>> np.all(time[start_bins[0]:stop_bins[0]] == [0.5, 1.5])
True
>>> np.all(time[start_bins[1]:stop_bins[1]] == [2.5, 3.5])
True
stingray.gti.gti_border_bins(gtis, time, dt=None, epsilon=0.001)[source]

Find the indices in a time array corresponding to the borders of GTIs.

GTIs shorter than the bin time are not returned.

Parameters: gtis (2-d float array) – List of GTIs of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] time (array-like) – Time stamps of light curve bins spectrum_start_bins (array-like) – List of starting bins of each GTI spectrum_stop_bins (array-like) – List of stop bins of each GTI. The elements corresponding to these bins should not be included.

Examples

>>> times = np.arange(0.5, 13.5)
>>> start_bins, stop_bins = gti_border_bins(
...    [[0, 5], [6, 8]], times)
>>> np.all(start_bins == [0, 6])
True
>>> np.all(stop_bins == [5, 8])
True
>>> np.all(times[start_bins[0]:stop_bins[0]] == [ 0.5, 1.5, 2.5, 3.5, 4.5])
True
>>> np.all(times[start_bins[1]:stop_bins[1]] == [6.5, 7.5])
True

### I/O Functionality¶

stingray.io.common_name(str1, str2, default='common')[source]

Strip two strings of the letters not in common.

Filenames must be of same length and only differ by a few letters.

Parameters: Other Parameters: str1 (str) – str2 (str) – default (str) – The string to return if common_str is empty common_str – A string containing the parts of the two names in common str
stingray.io.get_file_extension(fname)[source]

Get the extension from the file name.

In the case where the keyword is split in two, like

MJDREF = MJDREFI + MJDREFF

in some missions, this function returns the summed value. Otherwise, the content of the single keyword

Parameters: hdr (dict_like) – The FITS header structure, or a dictionary keyword (str) – The key to read in the header value – The value of the key, or None if something went wrong long double

Load event lists and GTIs from one or more files.

Loads event list from HDU EVENTS of file fits_file, with Good Time intervals. Optionally, returns additional columns of data from the same HDU of the events.

Parameters: fits_file (str) – The file name and absolute path to the event file. return_limits (bool, optional) – Return the TSTART and TSTOP keyword values additional_columns (list of str, optional) – A list of keys corresponding to the additional columns to extract from the event HDU (ex.: ['PI', 'X']) ev_list (array-like) – An array of time stamps of events gtis (list of the form [[gti0_0, gti0_1], [gti1_0, gti1_1], ...]) – Good Time Intervals additional_data (dict) – A dictionary, where each key is the one specified in additional_colums. The data are an array with the values of the specified column in the fits file. t_start, t_stop (float) – Start and stop times of the observation
stingray.io.mkdir_p(path)[source]

Safe mkdir function, found at [so-mkdir].

Parameters: path (str) – The absolute path to the directory to be created

Notes

Return a saved class instance.

Parameters: filename (str) – The name of the file to be retrieved. format (str) – The format used to store file. Supported formats are pickle, hdf5, ascii or fits. data – If format_ is pickle, an object is returned. If format_ is ascii, astropy.table object is returned. If format_ is hdf5 or ‘fits, a dictionary object is returned. {object | astropy.table | dict}

Read the header key key from HDU hdu of the file fits_file.

Parameters: Other Parameters: fits_file (str) – The file name and absolute path to the event file. key (str) – The keyword to be read hdu (int) – Index of the HDU extension from which the header key to be read. value – The value stored under key in fits_file object
stingray.io.ref_mjd(fits_file, hdu=1)[source]

Parameters: Other Parameters: fits_file (str) – The file name and absolute path to the event file. hdu (int) – Index of the HDU extension from which the header key to be read. mjdref – the reference MJD numpy.longdouble
stingray.io.savefig(filename, **kwargs)[source]

Save a figure plotted by matplotlib.

Note : This function is supposed to be used after the plot function. Otherwise it will save a blank image with no plot.

Parameters: filename (str) – The name of the image file. Extension must be specified in the file name. For example filename with .png extension will give a rasterized image while .pdf extension will give a vectorized output. kwargs (keyword arguments) – Keyword arguments to be passed to savefig function of matplotlib.pyplot. For example use bbox_inches=’tight’ to remove the undesirable whitepace around the image.
stingray.io.split_numbers(number)[source]

Split high precision number(s) into doubles. TODO: Consider the option of using a third number to specify shift.

Parameters: number (long double) – The input high precision number which is to be split number_I (double) – First part of high precision number number_F (double) – Second part of high precision number
stingray.io.write(input_, filename, format_='pickle', **kwargs)[source]

Pickle a class instance. For parameters depending on format_, see individual function definitions.

Parameters: object (a class instance) – The object to be stored filename (str) – The name of the file to be created format (str) – The format in which to store file. Formats supported are pickle, hdf5, ascii or fits

### Other Utility Functions¶

stingray.utils.simon(message, **kwargs)[source]

The Statistical Interpretation MONitor.

A warning system designed to always remind the user that Simon is watching him/her.

Parameters: message (string) – The message that is thrown kwargs (dict) – The rest of the arguments that are passed to warnings.warn
stingray.utils.rebin_data(x, y, dx_new, yerr=None, method='sum', dx=None)[source]

Rebin some data to an arbitrary new data resolution. Either sum the data points in the new bins or average them.

Parameters: Other Parameters: x (iterable) – The dependent variable with some resolution dx_old = x[1]-x[0] y (iterable) – The independent variable to be binned dx_new (float) – The new resolution of the dependent variable x yerr (iterable, optional) – The uncertainties of y, to be propagated during binning. method ({sum | average | mean}, optional, default sum) – The method to be used in binning. Either sum the samples y in each new bin of x, or take the arithmetic mean. dx (float) – The old resolution (otherwise, calculated from median diff) xbin (numpy.ndarray) – The midpoints of the new bins in x ybin (numpy.ndarray) – The binned quantity y ybin_err (numpy.ndarray) – The uncertainties of the binned values of y. step_size (float) – The size of the binning step
stingray.utils.rebin_data_log(x, y, f, y_err=None, dx=None)[source]

Logarithmic rebin of some data. Particularly useful for the power spectrum.

The new dependent variable depends on the previous dependent variable modified by a factor f:

$d\nu_j = d\nu_{j-1} (1+f)$
Parameters: Other Parameters: x (iterable) – The dependent variable with some resolution dx_old = x[1]-x[0] y (iterable) – The independent variable to be binned f (float) – The factor of increase of each bin wrt the previous one. yerr (iterable, optional) – The uncertainties of y to be propagated during binning. method ({sum | average | mean}, optional, default sum) – The method to be used in binning. Either sum the samples y in each new bin of x or take the arithmetic mean. dx (float, optional) – The binning step of the initial x xbin (numpy.ndarray) – The midpoints of the new bins in x ybin (numpy.ndarray) – The binned quantity y ybin_err (numpy.ndarray) – The uncertainties of the binned values of y step_size (float) – The size of the binning step
stingray.utils.look_for_array_in_array(array1, array2)[source]

Find a subset of values in an array.

Parameters: array1 (iterable) – An array with values to be searched array2 (iterable) – A second array which potentially contains a subset of values also contained in array1 array3 – An array with the subset of values contained in both array1 and array2 iterable
stingray.utils.is_string(s)[source]

Portable function to answer whether a variable is a string.

Parameters: s (object) – An object that is potentially a string isstring – A boolean decision on whether s is a string or not bool
stingray.utils.is_iterable(var)[source]

Test if a variable is an iterable.

Parameters: var (object) – The variable to be tested for iterably-ness is_iter – Returns True if var is an Iterable, False otherwise bool
stingray.utils.order_list_of_arrays(data, order)[source]

Sort an array according to the specified order.

Parameters: data (iterable) – data list or dict
stingray.utils.optimal_bin_time(fftlen, tbin)[source]

Vary slightly the bin time to have a power of two number of bins.

Given an FFT length and a proposed bin time, return a bin time slightly shorter than the original, that will produce a power-of-two number of FFT bins.

Parameters: fftlen (int) – Number of positive frequencies in a proposed Fourier spectrum tbin (float) – The proposed time resolution of a light curve res – A time resolution that will produce a Fourier spectrum with fftlen frequencies and a number of FFT bins that are a power of two float
stingray.utils.contiguous_regions(condition)[source]

Find contiguous True regions of the boolean array condition.

Return a 2D array where the first column is the start index of the region and the second column is the end index, found on [so-contiguous].

Parameters: condition (bool array) – idx – A list of integer couples, with the start and end of each True blocks in the original array [[i0_0, i0_1], [i1_0, i1_1], ...]

Notes

stingray.utils.is_int(obj)[source]

Test if object is an integer.

stingray.utils.get_random_state(random_state=None)[source]

Return a Mersenne Twister pseudo-random number generator.

Parameters: seed (integer or numpy.random.RandomState, optional, default None) – random_state mtrand.RandomState object
stingray.utils.baseline_als(x, y, lam=None, p=None, niter=10, return_baseline=False, offset_correction=False)[source]

Baseline Correction with Asymmetric Least Squares Smoothing.

Parameters: Other Parameters: x (array-like) – the sample time/number/position y (array-like) – the data series corresponding to x lam (float) – the lambda parameter of the ALS method. This control how much the baseline can adapt to local changes. A higher value corresponds to a stiffer baseline p (float) – the asymmetry parameter of the ALS method. This controls the overall slope tolerated for the baseline. A higher value correspond to a higher possible slope niter (int) – The number of iterations to perform return_baseline (bool) – return the baseline? offset_correction (bool) – also correct for an offset to align with the running mean of the scan y_subtracted (array-like, same size as y) – The initial time series, subtracted from the trend baseline (array-like, same size as y) – Fitted baseline. Only returned if return_baseline is True

Examples

>>> x = np.arange(0, 10, 0.01)
>>> y = np.zeros_like(x) + 10
>>> ysub = baseline_als(x, y)
>>> np.all(ysub < 0.001)
True
stingray.utils.excess_variance(lc, normalization='fvar')[source]

Calculate the excess variance.

Vaughan et al. 2003, MNRAS 345, 1271 give three measurements of source intrinsic variance: if a light curve has a total variance of $$S^2$$, and each point has an error bar $$\sigma_{err}$$, the excess variance is defined as

$\sigma_{XS} = S^2 - \overline{\sigma_{err}}^2;$

the normalized excess variance is the excess variance divided by the square of the mean intensity:

$\sigma_{NXS} = \dfrac{\sigma_{XS}}{\overline{x}^2};$

the fractional mean square variability amplitude, or $$F_{var}$$, is finally defined as

$F_{var} = \sqrt{\dfrac{\sigma_{XS}}{\overline{x}^2}}$
Parameters: lc (a Lightcurve object) – normalization (str) – if fvar, return the fractional mean square variability $$F_{var}$$. If none, return the unnormalized excess variance variance $$\sigma_{XS}$$. If norm_xs, return the normalized excess variance $$\sigma_{XS}$$ var_xs (float) var_xs_err (float)
stingray.utils.create_window(N, window_type='uniform')[source]

A method to create window functions commonly used in signal processing.

Windows supported are: Hamming, Hanning, uniform (rectangular window), triangular window, blackmann window among others.

Parameters: N (int) – Total number of data points in window. If negative, abs is taken. window_type ({uniform, parzen, hamming, hanning, triangular, welch, blackmann, flat-top}, optional, default uniform) – Type of window to create. window – Window function of length N. numpy.ndarray
stingray.utils.poisson_symmetrical_errors(counts)[source]

Optimized version of frequentist symmetrical errors.

Uses a lookup table in order to limit the calls to poisson_conf_interval

Parameters: counts (iterable) – An array of Poisson-distributed numbers err – An array of uncertainties associated with the Poisson counts in counts numpy.ndarray

Example

>>> from astropy.stats import poisson_conf_interval
>>> counts = np.random.randint(0, 1000, 100)
>>> # ---- Do it without the lookup table ----
>>> err_low, err_high = poisson_conf_interval(np.asarray(counts),
...                 interval='frequentist-confidence', sigma=1)
>>> err_low -= np.asarray(counts)
>>> err_high -= np.asarray(counts)
>>> err = (np.absolute(err_low) + np.absolute(err_high))/2.0
>>> # Do it with this function
>>> err_thisfun = poisson_symmetrical_errors(counts)
>>> # Test that results are always the same
>>> assert np.all(err_thisfun == err)

## Modeling¶

This subpackage defines classes and functions related to parametric modelling of various types of data sets. Currently, most functionality is focused on modelling Fourier products (especially power spectra and averaged power spectra), but rudimentary functionality exists for modelling e.g. light curves.

### Log-Likelihood Classes¶

These classes define basic log-likelihoods for modelling time series and power spectra. stingray.modeling.LogLikelihood is an abstract base class, i.e. a template for creating user-defined log-likelihoods and should not be instantiated itself. Based on this base class are several definitions for a stingray.modeling.GaussianLogLikelihood, appropriate for data with normally distributed uncertainties, a stingray.modeling.PoissonLogLikelihood appropriate for photon counting data, and a stingray.modeling.PSDLogLikelihood appropriate for (averaged) power spectra.

class stingray.modeling.LogLikelihood(x, y, model, **kwargs)[source]

Abstract Base Class defining the structure of a LogLikelihood object. This class cannot be called itself, since each statistical distribution has its own definition for the likelihood, which should occur in subclasses.

Parameters: x (iterable) – x-coordinate of the data. Could be multi-dimensional. y (iterable) – y-coordinate of the data. Could be multi-dimensional. model (an astropy.modeling.FittableModel instance) – Your model kwargs – keyword arguments specific to the individual sub-classes. For details, see the respective docstrings for each subclass
evaluate(parameters)[source]

This is where you define your log-likelihood. Do this, but do it in a subclass!

class stingray.modeling.GaussianLogLikelihood(x, y, yerr, model)[source]

Likelihood for data with Gaussian uncertainties. Astronomers also call this likelihood Chi-Squared, but be aware that this has nothing to do with the likelihood based on the Chi-square distribution, which is also defined as in of PSDLogLikelihood in this module!

Use this class here whenever your data has Gaussian uncertainties.

Parameters: x (iterable) – x-coordinate of the data y (iterable) – y-coordinte of the data yerr (iterable) – the uncertainty on the data, as standard deviation model (an astropy.modeling.FittableModel instance) – The model to use in the likelihood.
x

iterable – x-coordinate of the data

y

iterable – y-coordinte of the data

yerr

iterable – the uncertainty on the data, as standard deviation

model

an Astropy Model instance – The model to use in the likelihood.

npar

int – The number of free parameters in the model

evaluate(pars, neg=False)[source]

Evaluate the Gaussian log-likelihood for a given set of parameters.

Parameters: pars (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-likelihood. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-likelihood, i.e. -loglike, rather than loglike. This is useful e.g. for optimization routines, which generally minimize functions. loglike – The log(likelihood) value for the data and model. float
class stingray.modeling.PoissonLogLikelihood(x, y, model)[source]

Likelihood for data with uncertainties following a Poisson distribution. This is useful e.g. for (binned) photon count data.

Parameters: x (iterable) – x-coordinate of the data y (iterable) – y-coordinte of the data model (an astropy.modeling.FittableModel instance) – The model to use in the likelihood.
x

iterable – x-coordinate of the data

y

iterable – y-coordinte of the data

yerr

iterable – the uncertainty on the data, as standard deviation

model

an astropy.modeling.FittableModel instance – The model to use in the likelihood.

npar

int – The number of free parameters in the model

evaluate(pars, neg=False)[source]

Evaluate the log-likelihood for a given set of parameters.

Parameters: pars (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-likelihood. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-likelihood, i.e. -loglike, rather than loglike. This is useful e.g. for optimization routines, which generally minimize functions. loglike – The log(likelihood) value for the data and model. float
class stingray.modeling.PSDLogLikelihood(freq, power, model, m=1)[source]

A likelihood based on the Chi-square distribution, appropriate for modelling (averaged) power spectra. Note that this is not the same as the statistic astronomers commonly call Chi-Square, which is a fit statistic derived from the Gaussian log-likelihood, defined elsewhere in this module.

Parameters: freq (iterable) – Array with frequencies power (iterable) – Array with (averaged/singular) powers corresponding to the frequencies in freq model (an astropy.modeling.FittableModel instance) – The model to use in the likelihood. m (int) – 1/2 of the degrees of freedom
x

iterable – x-coordinate of the data

y

iterable – y-coordinte of the data

yerr

iterable – the uncertainty on the data, as standard deviation

model

an astropy.modeling.FittableModel instance – The model to use in the likelihood.

npar

int – The number of free parameters in the model

evaluate(pars, neg=False)[source]

Evaluate the log-likelihood for a given set of parameters.

Parameters: pars (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-likelihood. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-likelihood, i.e. -loglike, rather than loglike. This is useful e.g. for optimization routines, which generally minimize functions. loglike – The log(likelihood) value for the data and model. float
class stingray.modeling.LaplaceLogLikelihood(x, y, yerr, model)[source]

A Laplace likelihood for the cospectrum.

Parameters: x (iterable) – Array with independent variable y (iterable) – Array with dependent variable model (an astropy.modeling.FittableModel instance) – The model to use in the likelihood. yerr (iterable) – Array with the uncertainties on y, in standard deviation
x

iterable – x-coordinate of the data

y

iterable – y-coordinte of the data

yerr

iterable – the uncertainty on the data, as standard deviation

model

an astropy.modeling.FittableModel instance – The model to use in the likelihood.

npar

int – The number of free parameters in the model

evaluate(pars, neg=False)[source]

Evaluate the log-likelihood for a given set of parameters.

Parameters: pars (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-likelihood. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-likelihood, i.e. -loglike, rather than loglike. This is useful e.g. for optimization routines, which generally minimize functions. loglike – The log(likelihood) value for the data and model. float

### Posterior Classes¶

These classes define basic posteriors for parametric modelling of time series and power spectra, based on the log-likelihood classes defined in Log-Likelihood Classes. stingray.modeling.Posterior is an abstract base class laying out a basic template for defining posteriors. As with the log-likelihood classes above, several posterior classes are defined for a variety of data types.

Note that priors are not pre-defined in these classes, since they are problem dependent and should be set by the user. The convenience function stingray.modeling.set_logprior() can be useful to help set priors for these posterior classes.

class stingray.modeling.Posterior(x, y, model, **kwargs)[source]

Define a Posterior object.

The Posterior describes the Bayesian probability distribution of a set of parameters $$\theta$$ given some observed data $$D$$ and some prior assumptions $$I$$.

It is defined as

$p(\theta | D, I) = p(D | \theta, I) p(\theta | I)/p(D| I)$

where $$p(D | \theta, I)$$ describes the likelihood, i.e. the sampling distribution of the data and the (parametric) model, and $$p(\theta | I)$$ describes the prior distribution, i.e. our information about the parameters $$\theta$$ before we gathered the data. The marginal likelihood $$p(D| I)$$ describes the probability of observing the data given the model assumptions, integrated over the space of all parameters.

Parameters: x (iterable) – The abscissa or independent variable of the data. This could in principle be a multi-dimensional array. y (iterable) – The ordinate or dependent variable of the data. model (astropy.modeling.models instance) – The parametric model supposed to represent the data. For details see the astropy.modeling documentation kwargs – keyword arguments related to the subclasses of Posterior. For details, see the documentation of the individual subclasses

References

• Sivia, D. S., and J. Skilling. “Data Analysis: A Bayesian Tutorial. 2006.”
• Gelman, Andrew, et al. Bayesian data analysis. Vol. 2. Boca Raton, FL, USA: Chapman & Hall/CRC, 2014.
• von Toussaint, Udo. “Bayesian inference in physics.” Reviews of Modern Physics 83.3 (2011): 943.
• Hogg, David W. “Probability Calculus for inference”. arxiv: 1205.4446
logposterior(t0, neg=False)[source]

Definition of the log-posterior. Requires methods loglikelihood and logprior to both be defined.

Note that loglikelihood is set in the subclass of Posterior appropriate for your problem at hand, as is logprior.

Parameters: t0 (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-posterior. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-posterior, i.e. -lpost, rather than lpost. This is useful e.g. for optimization routines, which generally minimize functions. lpost – The value of the log-posterior for the given parameters t0 float
class stingray.modeling.GaussianPosterior(x, y, yerr, model, priors=None)[source]

A general class for two-dimensional data following a Gaussian sampling distribution.

Parameters: x (numpy.ndarray) – independent variable y (numpy.ndarray) – dependent variable yerr (numpy.ndarray) – measurement uncertainties for y model (instance of any subclass of astropy.modeling.FittableModel) – The model for the power spectrum. priors (dict of form {"parameter name": function}, optional) – A dictionary with the definitions for the prior probabilities. For each parameter in model, there must be a prior defined with a key of the exact same name as stored in model.param_names. The item for each key is a function definition defining the prior (e.g. a lambda function or a scipy.stats.distribution.pdf. If priors = None, then no prior is set. This means priors need to be added by hand using the set_logprior() function defined in this module. Note that it is impossible to call a Posterior object itself or the self.logposterior method without defining a prior.
logposterior(t0, neg=False)

Definition of the log-posterior. Requires methods loglikelihood and logprior to both be defined.

Note that loglikelihood is set in the subclass of Posterior appropriate for your problem at hand, as is logprior.

Parameters: t0 (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-posterior. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-posterior, i.e. -lpost, rather than lpost. This is useful e.g. for optimization routines, which generally minimize functions. lpost – The value of the log-posterior for the given parameters t0 float
class stingray.modeling.PoissonPosterior(x, y, model, priors=None)[source]

Posterior for Poisson light curve data. Primary intended use is for modelling X-ray light curves, but alternative uses are conceivable.

Parameters: x (numpy.ndarray) – The independent variable (e.g. time stamps of a light curve) y (numpy.ndarray) – The dependent variable (e.g. counts per bin of a light curve) model (instance of any subclass of astropy.modeling.FittableModel) – The model for the power spectrum. priors (dict of form {"parameter name": function}, optional) – A dictionary with the definitions for the prior probabilities. For each parameter in model, there must be a prior defined with a key of the exact same name as stored in model.param_names. The item for each key is a function definition defining the prior (e.g. a lambda function or a scipy.stats.distribution.pdf. If priors = None, then no prior is set. This means priors need to be added by hand using the set_logprior() function defined in this module. Note that it is impossible to call a Posterior object itself or the self.logposterior method without defining a prior.
x

numpy.ndarray – The independent variable (list of frequencies) stored in ps.freq

y

numpy.ndarray – The dependent variable (list of powers) stored in ps.power

model

instance of any subclass of astropy.modeling.FittableModel – The model for the power spectrum.

logposterior(t0, neg=False)

Definition of the log-posterior. Requires methods loglikelihood and logprior to both be defined.

Note that loglikelihood is set in the subclass of Posterior appropriate for your problem at hand, as is logprior.

Parameters: t0 (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-posterior. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-posterior, i.e. -lpost, rather than lpost. This is useful e.g. for optimization routines, which generally minimize functions. lpost – The value of the log-posterior for the given parameters t0 float
class stingray.modeling.PSDPosterior(freq, power, model, priors=None, m=1)[source]

Posterior distribution for power spectra. Uses an exponential distribution for the errors in the likelihood, or a $$\chi^2$$ distribution with $$2M$$ degrees of freedom, where $$M$$ is the number of frequency bins or power spectra averaged in each bin.

Parameters: ps ({stingray.Powerspectrum | stingray.AveragedPowerspectrum} instance) – the stingray.Powerspectrum object containing the data model (instance of any subclass of astropy.modeling.FittableModel) – The model for the power spectrum. priors (dict of form {"parameter name": function}, optional) – A dictionary with the definitions for the prior probabilities. For each parameter in model, there must be a prior defined with a key of the exact same name as stored in model.param_names. The item for each key is a function definition defining the prior (e.g. a lambda function or a scipy.stats.distribution.pdf. If priors = None, then no prior is set. This means priors need to be added by hand using the set_logprior() function defined in this module. Note that it is impossible to call a Posterior object itself or the self.logposterior method without defining a prior. m (int, default 1) – The number of averaged periodograms or frequency bins in ps. Useful for binned/averaged periodograms, since the value of m will change the likelihood function!
ps

{stingray.Powerspectrum | stingray.AveragedPowerspectrum} instance – the stingray.Powerspectrum object containing the data

x

numpy.ndarray – The independent variable (list of frequencies) stored in ps.freq

y

numpy.ndarray – The dependent variable (list of powers) stored in ps.power

model

instance of any subclass of astropy.modeling.FittableModel – The model for the power spectrum.

logposterior(t0, neg=False)

Definition of the log-posterior. Requires methods loglikelihood and logprior to both be defined.

Note that loglikelihood is set in the subclass of Posterior appropriate for your problem at hand, as is logprior.

Parameters: t0 (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-posterior. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-posterior, i.e. -lpost, rather than lpost. This is useful e.g. for optimization routines, which generally minimize functions. lpost – The value of the log-posterior for the given parameters t0 float
class stingray.modeling.LaplacePosterior(x, y, yerr, model, priors=None)[source]

A general class for two-dimensional data following a Gaussian sampling distribution.

Parameters: x (numpy.ndarray) – independent variable y (numpy.ndarray) – dependent variable yerr (numpy.ndarray) – measurement uncertainties for y, in standard deviation model (instance of any subclass of astropy.modeling.FittableModel) – The model for the power spectrum. priors (dict of form {"parameter name": function}, optional) – A dictionary with the definitions for the prior probabilities. For each parameter in model, there must be a prior defined with a key of the exact same name as stored in model.param_names. The item for each key is a function definition defining the prior (e.g. a lambda function or a scipy.stats.distribution.pdf. If priors = None, then no prior is set. This means priors need to be added by hand using the set_logprior() function defined in this module. Note that it is impossible to call a Posterior object itself or the self.logposterior method without defining a prior.
logposterior(t0, neg=False)

Definition of the log-posterior. Requires methods loglikelihood and logprior to both be defined.

Note that loglikelihood is set in the subclass of Posterior appropriate for your problem at hand, as is logprior.

Parameters: t0 (numpy.ndarray) – An array of parameters at which to evaluate the model and subsequently the log-posterior. Note that the length of this array must match the free parameters in model, i.e. npar neg (bool, optional, default False) – If True, return the negative log-posterior, i.e. -lpost, rather than lpost. This is useful e.g. for optimization routines, which generally minimize functions. lpost – The value of the log-posterior for the given parameters t0 float

### Parameter Estimation Classes¶

These classes implement functionality related to parameter estimation. They define basic fit and sample methods using scipy.optimize and emcee, respectively, for optimization and Markov Chain Monte Carlo sampling. stingray.modeling.PSDParEst implements some more advanced functionality for modelling power spectra, including both frequentist and Bayesian searches for (quasi-)periodic signals.

class stingray.modeling.ParameterEstimation(fitmethod='BFGS', max_post=True)[source]

Parameter estimation of two-dimensional data, either via optimization or MCMC. Note: optimization with bounds is not supported. If something like this is required, define (uniform) priors in the ParametricModel instances to be used below.

Parameters: fitmethod (string, optional, default L-BFGS-B) – Any of the strings allowed in scipy.optimize.minimize in the method keyword. Sets the fit method to be used. max_post (bool, optional, default True) – If True, then compute the Maximum-A-Posteriori estimate. If False, compute a Maximum Likelihood estimate.
calibrate_lrt(lpost1, t1, lpost2, t2, sample=None, neg=True, max_post=False, nsim=1000, niter=200, nwalkers=500, burnin=200, namestr='test', seed=None)[source]

Calibrate the outcome of a Likelihood Ratio Test via MCMC.

In order to compare models via likelihood ratio test, one generally aims to compute a p-value for the null hypothesis (generally the simpler model). There are two special cases where the theoretical distribution used to compute that p-value analytically given the observed likelihood ratio (a chi-square distribution) is not applicable:

• the models are not nested (i.e. Model 1 is not a special, simpler case of Model 2),
• the parameter values fixed in Model 2 to retrieve Model 1 are at the edges of parameter space (e.g. if one must set, say, an amplitude to zero in order to remove a component in the more complex model, and negative amplitudes are excluded a priori)

In these cases, the observed likelihood ratio must be calibrated via simulations of the simpler model (Model 1), using MCMC to take into account the uncertainty in the parameters. This function does exactly that: it computes the likelihood ratio for the observed data, and produces simulations to calibrate the likelihood ratio and compute a p-value for observing the data under the assumption that Model 1 istrue.

If max_post=True, the code will use MCMC to sample the posterior of the parameters and simulate fake data from there.

If max_post=False, the code will use the covariance matrix derived from the fit to simulate data sets for comparison.

Parameters: lpost1 (object of a subclass of Posterior) – The Posterior object for model 1 t1 (iterable) – The starting parameters for model 1 lpost2 (object of a subclass of Posterior) – The Posterior object for model 2 t2 (iterable) – The starting parameters for model 2 neg (bool, optional, default True) – Boolean flag to decide whether to use the negative log-likelihood or log-posterior max_post (bool, optional, default False) – If True, set the internal state to do the optimization with the log-likelihood rather than the log-posterior. pvalue – p-value ‘n stuff float [0,1]
compute_lrt(lpost1, t1, lpost2, t2, neg=True, max_post=False)[source]

This function computes the Likelihood Ratio Test between two nested models.

Parameters: lpost1 (object of a subclass of Posterior) – The Posterior object for model 1 t1 (iterable) – The starting parameters for model 1 lpost2 (object of a subclass of Posterior) – The Posterior object for model 2 t2 (iterable) – The starting parameters for model 2 neg (bool, optional, default True) – Boolean flag to decide whether to use the negative log-likelihood or log-posterior max_post (bool, optional, default False) – If True, set the internal state to do the optimization with the log-likelihood rather than the log-posterior. lrt (float) – The likelihood ratio for model 2 and model 1 res1 (OptimizationResults object) – Contains the result of fitting lpost1 res2 (OptimizationResults object) – Contains the results of fitting lpost2
fit(lpost, t0, neg=True, scipy_optimize_options=None)[source]

Do either a Maximum-A-Posteriori (MAP) or Maximum Likelihood (ML) fit to the data.

MAP fits include priors, ML fits do not.

Parameters: lpost (Posterior (or subclass) instance) – and instance of class Posterior or one of its subclasses that defines the function to be minimized (either in loglikelihood or logposterior) t0 ({list | numpy.ndarray}) – List/array with set of initial parameters neg (bool, optional, default True) – Boolean to be passed to lpost, setting whether to use the negative posterior or the negative log-likelihood. Useful for optimization routines, which are generally defined as minimization routines. scipy_optimize_options (dict, optional, default None) – A dictionary with options for scipy.optimize.minimize, directly passed on as keyword arguments. res – An object containing useful summaries of the fitting procedure. For details, see documentation of class:OptimizationResults. OptimizationResults object
sample(lpost, t0, cov=None, nwalkers=500, niter=100, burnin=100, threads=1, print_results=True, plot=False, namestr='test')[source]

Sample the Posterior distribution defined in lpost using MCMC. Here we use the emcee package, but other implementations could in principle be used.

Parameters: lpost (instance of a Posterior subclass) – and instance of class Posterior or one of its subclasses that defines the function to be minimized (either in loglikelihood or logposterior) t0 (iterable) – list or array containing the starting parameters. Its length must match lpost.model.npar. nwalkers (int, optional, default 500) – The number of walkers (chains) to use during the MCMC procedure. The more walkers are used, the slower the estimation will be, but the better the final distribution is likely to be. niter (int, optional, default 100) – The number of iterations to run the MCMC chains for. The larger this number, the longer the estimation will take, but the higher the chance that the walkers have actually converged on the true posterior distribution. burnin (int, optional, default 100) – The number of iterations to run the walkers before convergence is assumed to have occurred. This part of the chain will be discarded before sampling from what is then assumed to be the posterior distribution desired. threads (int, optional, default 1) – The number of threads for parallelization. Default is 1, i.e. no parallelization print_results (bool, optional, default True) – Boolean flag setting whether the results of the MCMC run should be printed to standard output. Default: True plot (bool, optional, default False) – Boolean flag setting whether summary plots of the MCMC chains should be produced. Default: False namestr (str, optional, default test) – Optional string for output file names for the plotting. res – An object of class SamplingResults summarizing the results of the MCMC run. class:SamplingResults object
simulate_lrts(s_all, lpost1, t1, lpost2, t2, max_post=True, seed=None)[source]

Simulate likelihood ratios. For details, see definitions in the subclasses that implement this task.

class stingray.modeling.PSDParEst(ps, fitmethod='BFGS', max_post=True)[source]

Parameter estimation for parametric modelling of power spectra.

This class contains functionality that allows parameter estimation and related tasks that involve fitting a parametric model to an (averaged) power spectrum.

Parameters: ps (class:stingray.Powerspectrum or class:stingray.AveragedPowerspectrum object) – The power spectrum to be modelled fitmethod (str, optional, default BFGS) – A string allowed by scipy.optimize.minimize as a valid fitting method max_post (bool, optional, default True) – If True, do a Maximum-A-Posteriori (MAP) fit, i.e. fit with priors, otherwise do a Maximum Likelihood fit instead
calibrate_highest_outlier(lpost, t0, sample=None, max_post=False, nsim=1000, niter=200, nwalkers=500, burnin=200, namestr='test', seed=None)[source]

Calibrate the highest outlier in a data set using MCMC-simulated power spectra.

In short, the procedure does a MAP fit to the data, computes the statistic

$\max{(T_R = 2(\mathrm{data}/\mathrm{model}))}$

and then does an MCMC run using the data and the model, or generates parameter samples from the likelihood distribution using the derived covariance in a Maximum Likelihood fit. From the (posterior) samples, it generates fake power spectra. Each fake spectrum is fit in the same way as the data, and the highest data/model outlier extracted as for the data. The observed value of $$T_R$$ can then be directly compared to the simulated distribution of $$T_R$$ values in order to derive a p-value of the null hypothesis that the observed $$T_R$$ is compatible with being generated by noise.

Parameters: lpost (stingray.modeling.PSDPosterior object) – An instance of class stingray.modeling.PSDPosterior that defines the function to be minimized (either in loglikelihood or logposterior) t0 ({list | numpy.ndarray}) – List/array with set of initial parameters sample (SamplingResults instance, optional, default None) – If a sampler has already been run, the SamplingResults instance can be fed into this method here, otherwise this method will run a sampler automatically max_post (bool, optional, default False) – If True, do MAP fits on the power spectrum to find the highest data/model outlier Otherwise, do a Maximum Likelihood fit. If True, the simulated power spectra will be generated from an MCMC run, otherwise the method will employ the approximated covariance matrix for the parameters derived from the likelihood surface to generate samples from that likelihood function. nsim (int, optional, default 1000) – Number of fake power spectra to simulate from the posterior sample. Note that this number sets the resolution of the resulting p-value. For nsim=1000, the highest resolution that can be achieved is $$10^{-3}$$. niter (int, optional, default 200) – If sample is None, this variable will be used to set the number of steps in the MCMC procedure after burn-in. nwalkers (int, optional, default 500) – If sample is None, this variable will be used to set the number of MCMC chains run in parallel in the sampler. burnin (int, optional, default 200) – If sample is None, this variable will be used to set the number of burn-in steps to be discarded in the initial phase of the MCMC run namestr (str, optional, default test) – A string to be used for storing MCMC output and plots to disk seed (int, optional, default None) – An optional number to seed the random number generator with, for reproducibility of the results obtained with this method. pval – The p-value that the highest data/model outlier is produced by random noise, calibrated using simulated power spectra from an MCMC run. float

References

For more details on the procedure employed here, see

calibrate_lrt(lpost1, t1, lpost2, t2, sample=None, neg=True, max_post=False, nsim=1000, niter=200, nwalkers=500, burnin=200, namestr='test', seed=None)

Calibrate the outcome of a Likelihood Ratio Test via MCMC.

In order to compare models via likelihood ratio test, one generally aims to compute a p-value for the null hypothesis (generally the simpler model). There are two special cases where the theoretical distribution used to compute that p-value analytically given the observed likelihood ratio (a chi-square distribution) is not applicable:

• the models are not nested (i.e. Model 1 is not a special, simpler case of Model 2),
• the parameter values fixed in Model 2 to retrieve Model 1 are at the edges of parameter space (e.g. if one must set, say, an amplitude to zero in order to remove a component in the more complex model, and negative amplitudes are excluded a priori)

In these cases, the observed likelihood ratio must be calibrated via simulations of the simpler model (Model 1), using MCMC to take into account the uncertainty in the parameters. This function does exactly that: it computes the likelihood ratio for the observed data, and produces simulations to calibrate the likelihood ratio and compute a p-value for observing the data under the assumption that Model 1 istrue.

If max_post=True, the code will use MCMC to sample the posterior of the parameters and simulate fake data from there.

If max_post=False, the code will use the covariance matrix derived from the fit to simulate data sets for comparison.

Parameters: lpost1 (object of a subclass of Posterior) – The Posterior object for model 1 t1 (iterable) – The starting parameters for model 1 lpost2 (object of a subclass of Posterior) – The Posterior object for model 2 t2 (iterable) – The starting parameters for model 2 neg (bool, optional, default True) – Boolean flag to decide whether to use the negative log-likelihood or log-posterior max_post (bool, optional, default False) – If True, set the internal state to do the optimization with the log-likelihood rather than the log-posterior. pvalue – p-value ‘n stuff float [0,1]
compute_lrt(lpost1, t1, lpost2, t2, neg=True, max_post=False)

This function computes the Likelihood Ratio Test between two nested models.

Parameters: lpost1 (object of a subclass of Posterior) – The Posterior object for model 1 t1 (iterable) – The starting parameters for model 1 lpost2 (object of a subclass of Posterior) – The Posterior object for model 2 t2 (iterable) – The starting parameters for model 2 neg (bool, optional, default True) – Boolean flag to decide whether to use the negative log-likelihood or log-posterior max_post (bool, optional, default False) – If True, set the internal state to do the optimization with the log-likelihood rather than the log-posterior. lrt (float) – The likelihood ratio for model 2 and model 1 res1 (OptimizationResults object) – Contains the result of fitting lpost1 res2 (OptimizationResults object) – Contains the results of fitting lpost2
fit(lpost, t0, neg=True, scipy_optimize_options=None)[source]

Do either a Maximum-A-Posteriori (MAP) or Maximum Likelihood (ML) fit to the power spectrum.

MAP fits include priors, ML fits do not.

Parameters: lpost (stingray.modeling.PSDPosterior object) – An instance of class stingray.modeling.PSDPosterior that defines the function to be minimized (either in loglikelihood or logposterior) t0 ({list | numpy.ndarray}) – List/array with set of initial parameters neg (bool, optional, default True) – Boolean to be passed to lpost, setting whether to use the negative posterior or the negative log-likelihood. scipy_optimize_options (dict, optional, default None) – A dictionary with options for scipy.optimize.minimize, directly passed on as keyword arguments. res – An object containing useful summaries of the fitting procedure. For details, see documentation of OptimizationResults. OptimizationResults object
plotfits(res1, res2=None, save_plot=False, namestr='test', log=False)[source]

Plotting method that allows to plot either one or two best-fit models with the data.

Plots a power spectrum with the best-fit model, as well as the data/model residuals for each model.

Parameters: res1 (OptimizationResults object) – Output of a successful fitting procedure res2 (OptimizationResults object, optional, default None) – Optional output of a second successful fitting procedure, e.g. with a competing model save_plot (bool, optional, default False) – If True, the resulting figure will be saved to a file namestr (str, optional, default test) – If save_plot is True, this string defines the path and file name for the output plot log (bool, optional, default False) – If True, plot the axes logarithmically.
sample(lpost, t0, cov=None, nwalkers=500, niter=100, burnin=100, threads=1, print_results=True, plot=False, namestr='test')[source]

Sample the posterior distribution defined in lpost using MCMC. Here we use the emcee package, but other implementations could in principle be used.

Parameters: lpost (instance of a Posterior subclass) – and instance of class Posterior or one of its subclasses that defines the function to be minimized (either in loglikelihood or logposterior) t0 (iterable) – list or array containing the starting parameters. Its length must match lpost.model.npar. nwalkers (int, optional, default 500) – The number of walkers (chains) to use during the MCMC procedure. The more walkers are used, the slower the estimation will be, but the better the final distribution is likely to be. niter (int, optional, default 100) – The number of iterations to run the MCMC chains for. The larger this number, the longer the estimation will take, but the higher the chance that the walkers have actually converged on the true posterior distribution. burnin (int, optional, default 100) – The number of iterations to run the walkers before convergence is assumed to have occurred. This part of the chain will be discarded before sampling from what is then assumed to be the posterior distribution desired. threads (int, optional, default 1) – The number of threads for parallelization. Default is 1, i.e. no parallelization print_results (bool, optional, default True) – Boolean flag setting whether the results of the MCMC run should be printed to standard output plot (bool, optional, default False) – Boolean flag setting whether summary plots of the MCMC chains should be produced namestr (str, optional, default test) – Optional string for output file names for the plotting. res – An object containing useful summaries of the sampling procedure. For details see documentation of SamplingResults. SamplingResults object
simulate_highest_outlier(s_all, lpost, t0, max_post=True, seed=None)[source]

Simulate $$n$$ power spectra from a model and then find the highest data/model outlier in each.

The data/model outlier is defined as

$\max{(T_R = 2(\mathrm{data}/\mathrm{model}))} .$
Parameters: s_all (numpy.ndarray) – A list of parameter values derived either from an approximation of the likelihood surface, or from an MCMC run. Has dimensions (n, ndim), where n is the number of simulated power spectra to generate, and ndim the number of model parameters. lpost (instance of a Posterior subclass) – an instance of class Posterior or one of its subclasses that defines the function to be minimized (either in loglikelihood or logposterior) t0 (iterable) – list or array containing the starting parameters. Its length must match lpost.model.npar. max_post (bool, optional, default False) – If True, do MAP fits on the power spectrum to find the highest data/model outlier Otherwise, do a Maximum Likelihood fit. If True, the simulated power spectra will be generated from an MCMC run, otherwise the method will employ the approximated covariance matrix for the parameters derived from the likelihood surface to generate samples from that likelihood function. seed (int, optional, default None) – An optional number to seed the random number generator with, for reproducibility of the results obtained with this method. max_y_all – An array of maximum outliers for each simulated power spectrum numpy.ndarray
simulate_lrts(s_all, lpost1, t1, lpost2, t2, seed=None)[source]

Simulate likelihood ratios for two given models based on MCMC samples for the simpler model (i.e. the null hypothesis).

Parameters: s_all (numpy.ndarray of shape (nsamples, lpost1.npar)) – An array with MCMC samples derived from the null hypothesis model in lpost1. Its second dimension must match the number of free parameters in lpost1.model. lpost1 (LogLikelihood or Posterior subclass object) – Object containing the null hypothesis model t1 (iterable of length lpost1.npar) – A starting guess for fitting the model in lpost1 lpost2 (LogLikelihood or Posterior subclass object) – Object containing the alternative hypothesis model t2 (iterable of length lpost2.npar) – A starting guess for fitting the model in lpost2 max_post (bool, optional, default True) – If True, then lpost1 and lpost2 should be Posterior subclass objects; if False, then lpost1 and lpost2 should be LogLikelihood subclass objects seed (int, optional default None) – A seed to initialize the numpy.random.RandomState object to be passed on to _generate_data. Useful for producing exactly reproducible results lrt_sim – An array with the simulated likelihood ratios for the simulated data numpy.ndarray

### Auxiliary Classes¶

These are helper classes instantiated by stingray.modeling.ParameterEstimation and its subclasses to organize the results of model fitting and sampling in a more meaningful, easily accessible way.

class stingray.modeling.OptimizationResults(lpost, res, neg=True)[source]

Helper class that will contain the results of the regression. Less fiddly than a dictionary.

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression res (instance of scipy.OptimizeResult) – The object containing the results from a optimization run
neg

bool, optional, default True – A flag that sets whether the log-likelihood or negative log-likelihood is being used

result

float – The result of the optimization, i.e. the function value at the minimum that the optimizer found

p_opt

iterable – The list of parameters at the minimum found by the optimizer

model

astropy.models.Model instance – The parametric model fit to the data

cov

numpy.ndarray – The covariance matrix for the parameters, has shape (len(p_opt), len(p_opt))

err

numpy.ndarray – The standard deviation of the parameters, derived from the diagonal of cov. Has the same shape as p_opt

mfit

numpy.ndarray – The values of the model for all x

deviance

float – The deviance, calculated as -2*log(likelihood)

aic

float – The Akaike Information Criterion, derived from the log(likelihood) and often used in model comparison between non-nested models; For more details, see [aic]

bic

float – The Bayesian Information Criterion, derived from the log(likelihood) and often used in model comparison between non-nested models; For more details, see [bic]

merit

float – sum of squared differences between data and model, normalized by the model values

dof

int – The number of degrees of freedom in the problem, defined as the number of data points - the number of parameters

sexp

int2*(number of parameters)*(number of data points)

ssd

floatsqrt(2*(sexp)), expected sum of data-model residuals

sobs

float – sum of data-model residuals

References

_compute_covariance(lpost, res)[source]

Compute the covariance of the parameters using inverse of the Hessian, i.e. the second-order derivative of the log-likelihood. Also calculates an estimate of the standard deviation in the parameters, using the square root of the diagonal of the covariance matrix.

The Hessian is either estimated directly by the chosen method of fitting, or approximated using the statsmodel approx_hess function.

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression res (instance of scipy’s OptimizeResult class) – The object containing the results from a optimization run
_compute_criteria(lpost)[source]

Compute various information criteria useful for model comparison in non-nested models.

Currently implemented are the Akaike Information Criterion [aic] and the Bayesian Information Criterion [bic].

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression
_compute_model(lpost)[source]

Compute the values of the best-fit model for all x.

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression
_compute_statistics(lpost)[source]

Compute some useful fit statistics, like the degrees of freedom and the figure of merit.

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression
print_summary(lpost, log=None)[source]

Print a useful summary of the fitting procedure to screen or a log file.

Parameters: lpost (instance of Posterior or one of its subclasses) – The object containing the function that is being optimized in the regression log (logging handler, optional, default None) – A handler used for logging the output properly
class stingray.modeling.SamplingResults(sampler, ci_min=5, ci_max=95)[source]

Helper class that will contain the results of the sampling in a handy format.

Less fiddly than a dictionary.

Parameters: sampler (emcee.EnsembleSampler object) – The object containing the sampler that’s done all the work. ci_min (float out of [0,100]) – The lower bound percentile for printing credible intervals on the parameters ci_max (float out of [0,100]) – The upper bound percentile for printing credible intervals on the parameters
samples

numpy.ndarray – An array of samples from the MCMC run, including all chains flattened into one long (nwalkers*niter, ndim) array

nwalkers

int – The number of chains used in the MCMC procedure

niter

int – The number of MCMC iterations in each chain

ndim

int – The dimensionality of the problem, i.e. the number of parameters in the model

acceptance

float – The mean acceptance ratio, calculated over all chains

L

float – The product of acceptance ratio and number of samples

acor

float – The autocorrelation length for the chains; should be shorter than the chains themselves for independent sampling

rhat

float – weighted average of between-sequence variance and within-sequence variance; Gelman-Rubin convergence statistic [gelman-rubin]

mean

numpy.ndarray – An array of size ndim, with the posterior means of the parameters derived from the MCMC chains

std

numpy.ndarray – An array of size ndim with the posterior standard deviations of the parameters derived from the MCMC chains

ci

numpy.ndarray – An array of shape (ndim, 2) containing the lower and upper bounds of the credible interval (the Bayesian equivalent of the confidence interval) for each parameter using the bounds set by ci_min and ci_max

References

 [gelman-rubin] (1, 2, 3) https://projecteuclid.org/euclid.ss/1177011136
_check_convergence(sampler)[source]

Compute common statistics for convergence of the MCMC chains. While you can never be completely sure that your chains converged, these present reasonable heuristics to give an indication whether convergence is very far off or reasonably close.

Currently implemented are the autocorrelation time [autocorr] and the Gelman-Rubin convergence criterion [gelman-rubin].

Parameters: sampler (an emcee.EnsembleSampler object) –

References

_compute_rhat(sampler)[source]

Compute Gelman-Rubin convergence criterion [gelman-rubin].

Parameters: sampler (an emcee.EnsembleSampler object) –
_infer(ci_min=5, ci_max=95)[source]

Infer the Posterior means, standard deviations and credible intervals (i.e. the Bayesian equivalent to confidence intervals) from the Posterior samples for each parameter.

Parameters: ci_min (float) – Lower bound to the credible interval, given as percentage between 0 and 100 ci_max (float) – Upper bound to the credible interval, given as percentage between 0 and 100
plot_results(nsamples=1000, fig=None, save_plot=False, filename='test.pdf')[source]

Plot some results in a triangle plot. If installed, will use [corner] for the plotting, if not, uses its own code to make a triangle plot.

By default, this method returns a matplotlib.Figure object, but if save_plot=True the plot can be saved to file automatically,

Parameters: nsamples (int, default 1000) – The maximum number of samples used for plotting. fig (matplotlib.Figure instance, default None) – If created externally, you can pass a Figure instance to this method. If none is passed, the method will create one internally. save_plot (bool, default False) – If True save the plot to file with a file name specified by the keyword filename. If False just return the Figure object filename (str) – Name of the output file with the figure

References

print_results(log=None)[source]

Print results of the MCMC run on screen or to a log-file.

Parameters: log (a logging.getLogger() object) – Object to handle logging output

### Convenience Functions¶

These functions are designed to help the user perform common tasks related to modelling and parameter estimation. In particular, the function stingray.modeling.set_logprior() is designed to help users set priors in their stingray.modeling.Posterior subclass objects.

stingray.modeling.set_logprior(lpost, priors)[source]

This function constructs the logprior method required to successfully use a Posterior object.

All instances of class Posterior and its subclasses require to implement a logprior methods. However, priors are strongly problem-dependent and therefore usually user-defined.

This function allows for setting the logprior method on any instance of class Posterior efficiently by allowing the user to pass a dictionary of priors and an instance of class Posterior.

Parameters: lpost (Posterior object) – An instance of class Posterior or any of its subclasses priors (dict) – A dictionary containing the prior definitions. Keys are parameter names as defined by the astropy.models.FittableModel instance supplied to the model parameter in Posterior. Items are functions that take a parameter as input and return the log-prior probability of that parameter. logprior – The function definition for the prior function

Examples

Make a light curve and power spectrum

>>> photon_arrivals = np.sort(np.random.uniform(0,1000, size=10000))
>>> lc = Lightcurve.make_lightcurve(photon_arrivals, dt=1.0)
>>> ps = Powerspectrum(lc, norm="frac")

Define the model

>>> pl = models.PowerLaw1D()
>>> pl.x_0.fixed = True

Instantiate the posterior:

>>> lpost = PSDPosterior(ps.freq, ps.power, pl, m=ps.m)

Define the priors:

>>> p_alpha = lambda alpha: ((-1. <= alpha) & (alpha <= 5.))
>>> p_amplitude = lambda amplitude: ((-10 <= np.log(amplitude)) &
...                                 ((np.log(amplitude) <= 10.0)))
>>> priors = {"alpha":p_alpha, "amplitude":p_amplitude}

Set the logprior method in the lpost object:

>>> lpost.logprior = set_logprior(lpost, priors)
stingray.modeling.scripts.fit_powerspectrum(ps, model, starting_pars, max_post=False, priors=None, fitmethod='L-BFGS-B')[source]

Fit a number of Lorentzians to a power spectrum, possibly including white noise. Each Lorentzian has three parameters (amplitude, centroid position, full-width at half maximum), plus one extra parameter if the white noise level should be fit as well. Priors for each parameter can be included in case max_post = True, in which case the function will attempt a Maximum-A-Posteriori fit. Priors must be specified as a dictionary with one entry for each parameter. The parameter names are (amplitude_i, x_0_i, fwhm_i) for each i out of a total of N Lorentzians. The white noise level has a parameter amplitude_(N+1). For example, a model with two Lorentzians and a white noise level would have parameters: [amplitude_0, x_0_0, fwhm_0, amplitude_1, x_0_1, fwhm_1, amplitude_2].

Parameters: ps (Powerspectrum) – A Powerspectrum object with the data to be fit model (astropy.modeling.models class instance) – The parametric model supposed to represent the data. For details see the astropy.modeling documentation starting_pars (iterable) – The list of starting guesses for the optimizer. See explanation above for ordering of parameters in this list. fit_whitenoise (bool, optional, default True) – If True, the code will attempt to fit a white noise level along with the Lorentzians. Be sure to include a starting parameter for the optimizer in starting_pars! max_post (bool, optional, default False) – If True, perform a Maximum-A-Posteriori fit of the data rather than a Maximum Likelihood fit. Note that this requires priors to be specified, otherwise this will cause an exception! priors ({dict | None}, optional, default None) – Dictionary with priors for the MAP fit. This should be of the form {“parameter name”: probability distribution, …} fitmethod (string, optional, default "L-BFGS-B") – Specifies an optimization algorithm to use. Supply any valid option for scipy.optimize.minimize. parest (PSDParEst object) – A PSDParEst object for further analysis res (OptimizationResults object) – The OptimizationResults object storing useful results and quantities relating to the fit

Examples

We start by making an example power spectrum with three Lorentzians >>> m = 1 >>> nfreq = 100000 >>> freq = np.linspace(1, 1000, nfreq)

>>> np.random.seed(100)  # set the seed for the random number generator
>>> noise = np.random.exponential(size=nfreq)
>>> model = models.PowerLaw1D() + models.Const1D()
>>> model.x_0_0.fixed = True
>>> alpha_0 = 2.0
>>> amplitude_0 = 100.0
>>> amplitude_1 = 2.0
>>> model.alpha_0 = alpha_0
>>> model.amplitude_0 = amplitude_0
>>> model.amplitude_1 = amplitude_1
>>> p = model(freq)
>>> power = noise * p
>>> ps = Powerspectrum()
>>> ps.freq = freq
>>> ps.power = power
>>> ps.m = m
>>> ps.df = freq[1] - freq[0]
>>> ps.norm = "leahy"

Now we have to guess starting parameters. For each Lorentzian, we have amplitude, centroid position and fwhm, and this pattern repeats for each Lorentzian in the fit. The white noise level is the last parameter. >>> t0 = [80, 1.5, 2.5]

Let’s also make a model to test: >>> model_to_test = models.PowerLaw1D() + models.Const1D() >>> model_to_test.amplitude_1.fixed = True

We’re ready for doing the fit: >>> parest, res = fit_powerspectrum(ps, model_to_test, t0)

res contains a whole array of useful information about the fit, for example the parameters at the optimum: >>> p_opt = res.p_opt

stingray.modeling.scripts.fit_lorentzians(ps, nlor, starting_pars, fit_whitenoise=True, max_post=False, priors=None, fitmethod='L-BFGS-B')[source]

Fit a number of Lorentzians to a power spectrum, possibly including white noise. Each Lorentzian has three parameters (amplitude, centroid position, full-width at half maximum), plus one extra parameter if the white noise level should be fit as well. Priors for each parameter can be included in case max_post = True, in which case the function will attempt a Maximum-A-Posteriori fit. Priors must be specified as a dictionary with one entry for each parameter. The parameter names are (amplitude_i, x_0_i, fwhm_i) for each i out of a total of N Lorentzians. The white noise level has a parameter amplitude_(N+1). For example, a model with two Lorentzians and a white noise level would have parameters: [amplitude_0, x_0_0, fwhm_0, amplitude_1, x_0_1, fwhm_1, amplitude_2].

Parameters: ps (Powerspectrum) – A Powerspectrum object with the data to be fit nlor (int) – The number of Lorentzians to fit starting_pars (iterable) – The list of starting guesses for the optimizer. See explanation above for ordering of parameters in this list. fit_whitenoise (bool, optional, default True) – If True, the code will attempt to fit a white noise level along with the Lorentzians. Be sure to include a starting parameter for the optimizer in starting_pars! max_post (bool, optional, default False) – If True, perform a Maximum-A-Posteriori fit of the data rather than a Maximum Likelihood fit. Note that this requires priors to be specified, otherwise this will cause an exception! priors ({dict | None}, optional, default None) – Dictionary with priors for the MAP fit. This should be of the form {“parameter name”: probability distribution, …} fitmethod (string, optional, default "L-BFGS-B") – Specifies an optimization algorithm to use. Supply any valid option for scipy.optimize.minimize. parest (PSDParEst object) – A PSDParEst object for further analysis res (OptimizationResults object) – The OptimizationResults object storing useful results and quantities relating to the fit

Examples

We start by making an example power spectrum with three Lorentzians >>> np.random.seed(400) >>> nlor = 3

>>> x_0_0 = 0.5
>>> x_0_1 = 2.0
>>> x_0_2 = 7.5
>>> amplitude_0 = 150.0
>>> amplitude_1 = 50.0
>>> amplitude_2 = 15.0
>>> fwhm_0 = 0.1
>>> fwhm_1 = 1.0
>>> fwhm_2 = 0.5

We will also include a white noise level: >>> whitenoise = 2.0

>>> model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \
...         models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \
...         models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \
...         models.Const1D(whitenoise)
>>> freq = np.linspace(0.01, 10.0, 10.0/0.01)
>>> p = model(freq)
>>> noise = np.random.exponential(size=len(freq))
>>> power = p*noise
>>> ps = Powerspectrum()
>>> ps.freq = freq
>>> ps.power = power
>>> ps.df = ps.freq[1] - ps.freq[0]
>>> ps.m = 1

Now we have to guess starting parameters. For each Lorentzian, we have amplitude, centroid position and fwhm, and this pattern repeats for each Lorentzian in the fit. The white noise level is the last parameter. >>> t0 = [150, 0.4, 0.2, 50, 2.3, 0.6, 20, 8.0, 0.4, 2.1]

We’re ready for doing the fit: >>> parest, res = fit_lorentzians(ps, nlor, t0)

res contains a whole array of useful information about the fit, for example the parameters at the optimum: >>> p_opt = res.p_opt

## Pulsar¶

This submodule broadly defines functionality related to (X-ray) pulsar data analysis, especially periodicity searches.

stingray.pulse.fftfit(prof, template=None, quick=False, sigma=None, use_bootstrap=False, **fftfit_kwargs)[source]

Align a template to a pulse profile.

Parameters: Other Parameters: prof (array) – The pulse profile template (array, default None) – The template of the pulse used to perform the TOA calculation. If None, a simple sinusoid is used sigma (array) – error on profile bins (currently has no effect) use_bootstrap (bool) – Calculate errors using a bootstrap method, with fftfit_error **fftfit_kwargs (additional arguments for fftfit_error) mean_amp, std_amp (floats) – Mean and standard deviation of the amplitude mean_phase, std_phase (floats) – Mean and standard deviation of the phase
stingray.pulse.fftfit_error(template, sigma=None, **fftfit_kwargs)[source]

Calculate the error on the fit parameters from FFTFIT.

Parameters: Other Parameters: phase (array) – The phases corresponding to each bin of the profile prof (array) – The pulse profile template (array) – The template of the pulse used to perform the TOA calculation p0 (list) – The initial parameters for the fit nstep (int, optional, default 100) – Number of steps for the bootstrap method mean_amp, std_amp (floats) – Mean and standard deviation of the amplitude mean_phase, std_phase (floats) – Mean and standard deviation of the phase nstep (int, optional, default 100) – Number of steps for the bootstrap method sigma (array, default None) – error on profile bins. If None, the square root of the mean profile is used.
stingray.pulse.fftfit_fun(profile, template, amplitude, phase)[source]

Function to be minimized for the FFTFIT method.

From Taylor (1992).

Parameters: profile (array) – The pulse profile template (array) – A pulse shape template, of the same length as profile. phase (amplitude,) – The amplitude and phase of the template w.r.t the real profile. fftfit_chisq – The chi square-like statistics of FFTFIT float
stingray.pulse.fold_detection_level(nbin, epsilon=0.01, ntrial=1)[source]

Return the detection level for a folded profile.

See Leahy et al. (1983).

Parameters: Other Parameters: nbin (int) – The number of bins in the profile epsilon (float, default 0.01) – The fractional probability that the signal has been produced by noise ntrial (int) – The number of trials executed to find this profile detlev – The epoch folding statistics corresponding to a probability epsilon * 100 % that the signal has been produced by noise float
stingray.pulse.fold_events(times, *frequency_derivatives, **opts)[source]

Epoch folding with exposure correction.

Parameters: Other Parameters: times (array of floats) – fdot, fddot... (f,) – The frequency and any number of derivatives. nbin (int, optional, default 16) – The number of bins in the pulse profile weights (float or array of floats, optional) – The weights of the data. It can either be specified as a single value for all points, or an array with the same length as time gtis ([[gti0_0, gti0_1], [gti1_0, gti1_1], …], optional) – Good time intervals ref_time (float, optional, default 0) – Reference time for the timing solution expocorr (bool, default False) – Correct each bin for exposure (use when the period of the pulsar is comparable to that of GTIs) phase_bins (array of floats) The phases corresponding to the pulse profile profile (array of floats) The pulse profile profile_err (array of floats) The uncertainties on the pulse profile
stingray.pulse.fold_profile_probability(stat, nbin, ntrial=1)[source]

Calculate the probability of a certain folded profile, due to noise.

Parameters: Other Parameters: stat (float) – The epoch folding statistics nbin (int) – The number of bins in the profile ntrial (int) – The number of trials executed to find this profile p – The probability that the profile has been produced by noise float
stingray.pulse.get_TOA(prof, period, tstart, template=None, additional_phase=0, quick=False, debug=False, use_bootstrap=False, **fftfit_kwargs)[source]

Calculate the Time-Of-Arrival of a pulse.

Parameters: Other Parameters: prof (array) – The pulse profile template (array, default None) – The template of the pulse used to perform the TOA calculation, if any. Otherwise use the default of fftfit tstart (float) – The time at the start of the pulse profile nstep (int, optional, default 100) – Number of steps for the bootstrap method toa, toastd – Mean and standard deviation of the TOA floats
stingray.pulse.phase_exposure(start_time, stop_time, period, nbin=16, gtis=None)[source]

Calculate the exposure on each phase of a pulse profile.

Parameters: Other Parameters: stop_time (start_time,) – Starting and stopping time (or phase if period==1) period (float) – The pulse period (if 1, equivalent to phases) nbin (int, optional, default 16) – The number of bins in the profile gtis ([[gti00, gti01], [gti10, gti11], …], optional, default None) – Good Time Intervals expo – The normalized exposure of each bin in the pulse profile (1 is the highest exposure, 0 the lowest) array of floats
stingray.pulse.pulse_phase(times, *frequency_derivatives, **opts)[source]

Calculate pulse phase from the frequency and its derivatives.

Parameters: Other Parameters: times (array of floats) – The times at which the phase is calculated *frequency_derivatives (floats) – List of derivatives in increasing order, starting from zero. ph0 (float) – The starting phase to_1 (bool, default True) – Only return the fractional part of the phase, normalized from 0 to 1 phases – The absolute pulse phase array of floats
stingray.pulse.stat(profile, err=None)[source]

Calculate the epoch folding statistics ‘a la Leahy et al. (1983).

Parameters: Other Parameters: profile (array) – The pulse profile err (float or array) – The uncertainties on the pulse profile stat – The epoch folding statistics float
stingray.pulse.z2_n_detection_level(n=2, epsilon=0.01, ntrial=1)[source]

Return the detection level for the Z^2_n statistics.

See Buccheri et al. (1983), Bendat and Piersol (1971).

Parameters: Other Parameters: n (int, default 2) – The n in $Z^2_n$ epsilon (float, default 0.01) – The fractional probability that the signal has been produced by noise ntrial (int) – The number of trials executed to find this profile detlev – The epoch folding statistics corresponding to a probability epsilon * 100 % that the signal has been produced by noise float
stingray.pulse.z2_n_probability(z2, n=2, ntrial=1)[source]

Calculate the probability of a certain folded profile, due to noise.

Parameters: Other Parameters: z2 (float) – A Z^2_n statistics value n (int, default 2) – The n in $Z^2_n$ ntrial (int) – The number of trials executed to find this profile p – The probability that the Z^2_n value has been produced by noise float
stingray.pulse.z_n(phase, n=2, norm=1)[source]

Z^2_n statistics, a` la Buccheri+03, A&A, 128, 245, eq. 2.

Parameters: Other Parameters: phase (array of floats) – The phases of the events n (int, default 2) – The n in $Z^2_n$. norm (float or array of floats) – A normalization factor that gets multiplied as a weight. z2_n – The Z^2_n statistics of the events. float

## Simulator¶

This submodule defines extensive functionality related to simulating spectral-timing data sets, including transfer and window functions, simulating light curves from power spectra for a range of stochastic processes.

class stingray.simulator.simulator.Simulator(dt=1, N=1024, mean=0, rms=1, red_noise=1, random_state=None)[source]

Methods to simulate and visualize light curves.

TODO: Improve documentation

Parameters: dt (int, default 1) – time resolution of simulated light curve N (int, default 1024) – bins count of simulated light curve mean (float, default 0) – mean value of the simulated light curve rms (float, default 1) – fractional rms of the simulated light curve, actual rms is calculated by mean*rms red_noise (int, default 1) – multiple of real length of light curve, by which to simulate, to avoid red noise leakage random_state (int, default None) – seed value for random processes
count_channels()[source]

delete_channel(channel)[source]

Delete an energy channel.

delete_channels(channels)[source]

Delete multiple energy channels.

get_all_channels()[source]

Get lightcurves belonging to all channels.

get_channel(channel)[source]

Get lightcurve belonging to the energy channel.

get_channels(channels)[source]

Get multiple light curves belonging to the energy channels.

powerspectrum(lc, seg_size=None)[source]

Make a powerspectrum of the simulated light curve.

Parameters: lc (lightcurve.Lightcurve object OR) – iterable of lightcurve.Lightcurve objects The light curve data to be Fourier-transformed. power – The array of normalized squared absolute values of Fourier amplitudes numpy.ndarray

Imports Simulator object.

Parameters: filename (str) – Name of the Simulator object to be read. format (str) – Available option is ‘pickle.’ object Simulator object
relativistic_ir(t1=3, t2=4, t3=10, p1=1, p2=1.4, rise=0.6, decay=0.1)[source]

Construct a realistic impulse response considering the relativistic effects.

Parameters: t1 (int) – primary peak time t2 (int) – secondary peak time t3 (int) – end time p1 (float) – value of primary peak p2 (float) – value of secondary peak rise (float) – slope of rising exponential from primary peak to secondary peak decay (float) – slope of decaying exponential from secondary peak to end time h – Constructed impulse response numpy.ndarray
simple_ir(start=0, width=1000, intensity=1)[source]

Construct a simple impulse response using start time, width and scaling intensity. To create a delta impulse response, set width to 1.

Parameters: start (int) – start time of impulse response width (int) – width of impulse response intensity (float) – scaling parameter to set the intensity of delayed emission corresponding to direct emission. h – Constructed impulse response numpy.ndarray
simulate(*args)[source]

Simulate light curve generation using power spectrum or impulse response.

Examples

• x = simulate(beta):

For generating a light curve using power law spectrum.

Parameters:
• beta : float Defines the shape of spectrum
• x = simulate(s):

For generating a light curve from user-provided spectrum.

Parameters:
• s : array-like power spectrum
• x = simulate(model):

For generating a light curve from pre-defined model

Parameters:
• model : astropy.modeling.Model the pre-defined model
• x = simulate(‘model’, params):

For generating a light curve from pre-defined model

Parameters:
• model : string the pre-defined model
• params : list iterable or dict the parameters for the pre-defined model
• x = simulate(s, h):

For generating a light curve using impulse response.

Parameters:
• s : array-like Underlying variability signal
• h : array-like Impulse response
• x = simulate(s, h, ‘same’):

For generating a light curve of same length as input signal, using impulse response.

Parameters:
• s : array-like Underlying variability signal
• h : array-like Impulse response
• mode : str mode can be ‘same’, ‘filtered, or ‘full’. ‘same’ indicates that the length of output light curve is same as that of input signal. ‘filtered’ means that length of output light curve is len(s) - lag_delay ‘full’ indicates that the length of output light curve is len(s) + len(h) -1
Parameters: args – See examples below. lightCurve LightCurve object
simulate_channel(channel, *args)[source]

Simulate a lightcurve and add it to corresponding energy channel.

Parameters: channel (str) – range of energy channel (e.g., 3.5-4.5) *args – see description of simulate() for details lightCurve LightCurve object
write(filename, format_='pickle')[source]

Exports Simulator object.

Parameters: filename (str) – Name of the Simulator object to be created. format (str) – Available options are ‘pickle’ and ‘hdf5’.

## Exceptions¶

Some basic Stingray-related errors and exceptions.

class stingray.exceptions.StingrayError[source]