threeML.utils.binner module
- class threeML.utils.binner.Rebinner(vector_to_rebin_on, min_value_per_bin, mask=None)[source]
Bases:
object
A class to rebin vectors keeping a minimum value per bin. It supports array with a mask, so that elements excluded through the mask will not be considered for the rebinning
- property grouping
- property n_bins
Returns the number of bins defined.
- Returns
- class threeML.utils.binner.TemporalBinner(list_of_intervals=())[source]
Bases:
threeML.utils.time_interval.TimeIntervalSet
An extension of the TimeInterval set that includes binning capabilities
- classmethod bin_by_bayesian_blocks(arrival_times, p0, bkg_integral_distribution=None)[source]
Divide a series of events characterized by their arrival time in blocks of perceptibly constant count rate. If the background integral distribution is given, divide the series in blocks where the difference with respect to the background is perceptibly constant.
- Parameters
arrival_times – An iterable (list, numpy.array…) containing the arrival time of the events. NOTE: the input array MUST be time-ordered, and without duplicated entries. To ensure this, you may execute the following code: tt_array = numpy.asarray(self._arrival_times) tt_array = numpy.unique(tt_array) tt_array.sort() before running the algorithm.
p0 – The probability of finding a variations (i.e., creating a new block) when there is none. In other words, the probability of a Type I error, i.e., rejecting the null-hypothesis when is true. All found variations will have a post-trial significance larger than p0.
- :param bkg_integral_distributionthe integral distribution for the
background counts. It must be a function of the form f(x), which must return the integral number of counts expected from the background component between time 0 and x.
- classmethod bin_by_constant(arrival_times, dt)[source]
Create bins with a constant dt
- Parameters
dt – temporal spacing of the bins
- Returns
None
- classmethod bin_by_custom(starts, stops)[source]
Simplicity function to make custom bins. This form keeps introduction of custom bins uniform for other binning methods
- Parameters
start – start times of the bins
stop – stop times of the bins
- Returns
- classmethod bin_by_significance(arrival_times, background_getter, background_error_getter=None, sigma_level=10, min_counts=1, tstart=None, tstop=None)[source]
Bin the data to a given significance level for a given background method and sigma method. If a background error function is given then it is assumed that the error distribution is gaussian. Otherwise, the error distribution is assumed to be Poisson.
- Parameters
background_getter – function of a start and stop time that returns background counts
background_error_getter – function of a start and stop time that returns background count errors
sigma_level – the sigma level of the intervals
min_counts – the minimum counts per bin
- Returns