Functions and classes

Here you can find the documentation on the functions and classes of the hep_spt package.

Functions

adbin_hist(arr[, nbins, range, weights, …])	Create an adaptive binned histogram in N dimensions.
adbin_hist1d(arr[, nbins, range, weights, …])	Create an adaptive binned histogram in one dimension.
adbin_hist1d_edges(arr[, nbins, range, …])	Create adaptive binned edges to make a histogram from the given data.
adbin_hist2d_rectangles(bins, smp[, range, …])	Create a list of rectangles from a list of bins.
available_styles()	Get a list with the names of the available styles.
bit_length(arg)	Get the length of the binary representation of the given value(s).
calc_poisson_fu(m[, cl])	Return the lower and upper frequentist uncertainties for a poisson distribution with mean “m”.
calc_poisson_llu(m[, cl])	Calculate poisson uncertainties based on the logarithm of likelihood.
cfe(edges)	Calculate the centers of a set of bins given their edges.
clopper_pearson_int(k, N[, cl])	Return the frequentist Clopper-Pearson interval of having “k” events in “N”.
clopper_pearson_unc(k, N[, cl])	Return the frequentist Clopper-Pearson uncertainties of having “k” events in “N”.
cls_hypo(func, *args, **kwargs)	Create an instance of CLsHypo given a probability density(mass) function, as an object of type scipy.stats.rv_discrete or scipy.stats.rv_continuous.
cls_ts(alt, null)	Create an instance of CLsTS from the alternative an null hypotheses.
corr_hist2d(matrix, titles[, frmt, vmin, …])	Plot a correlation matrix in the given axes.
errorbar_hist(arr[, bins, range, weights, …])	Calculate the values needed to create an error bar histogram.
gauss_unc(s[, cl])	Calculate the gaussian uncertainty for a given confidence level.
gcd(a, b, *args)	Calculate the greatest common divisor of a set of numbers.
ibinary_repr(arg)	Get the binary representation of the given value(s).
is_power_2(arg)	Determine whether the input number(s) is a power of 2 or not.
ks_2samp(a, b[, wa, wb])	Compute the Kolmogorov-Smirnov statistic on 2 samples.
lcm(a, b, *args)	Calculate the least common multiple of a set of numbers.
modified_format(kwargs)	Modify the matplotlib format in this context.
next_power_2(arg)	Calculate the next number(s) greater than that(those) given and being a power(s) of 2.
opt_fig_div(naxes)	Get the optimal figure division for a given number of axes, where all the axes have the same dimensions.
path_to_styles()	Retrieve the path to the directory containing the styles.
poisson_fu(m)	Return the poisson frequentist uncertainty at one standard deviation of confidence level.
poisson_llu(m)	Return the poisson uncertainty at one standard deviation of confidence level.
profile(x, y[, bins, range, weights, std_type])	Calculate the profile from a 2D data sample.
pull(vals, err, ref[, ref_err])	Get the pull with the associated errors for a given set of values and a reference.
residual(vals, err, ref[, ref_err])	Calculate the residual with its errors, for a set of values with respect to a reference.
rv_random_sample(func[, size])	Create a random sample from the given rv_frozen object.
samples_cycler(smps, *args, **kwargs)	Generate a cycler.Cycler object were the labels are defined by “smps”, and the other parameters are left to the user.
set_style(*args)	Set the style for matplotlib to one within this project.
stat_values(arr[, axis, weights])	Calculate mean and variance and standard deviations of the sample and the mean from the given array.
sw2_unc(arr[, bins, range, weights])	Calculate the errors using the sum of squares of weights.
text_in_rectangles(recs, txt[, cax])	Write text inside matplotlib.patches.Rectangle instances.
wald_int(k, N[, cl])	Calculate the symmetric Wald interval of having “k” elements in “N”.
wald_unc(k, N[, cl])	Calculate the symmetric Wald uncertainty of having “k” elements in “N”.
wald_weighted_int(k, N[, cl])	Calculate the symmetric Wald interval for a weighted sample, where “k” is the array of weights in the survival sample and “N” in the main sample.
wald_weighted_unc(k, N[, cl])	Calculate the symmetric Wald uncertainty for a weighted sample, where “k” is the array of weights in the survival sample and “N” in the main sample.
weights_by_edges(values, edges, weights)	Assign a weight to the values in an input array using a set of edges.
wilson_int(k, N[, cl])	Calculate the Wilson interval of having “k” elements in “N”.
wilson_unc(k, N[, cl])	Calculate the Wilson uncertainties of having “k” elements in “N”.

Classes

AdBin(arr[, range, weights])	Represent a n-dimensional adaptive bin.
CLsHypo(pf)	Represent an hypothesis to be used in the CLs method.
CLsHypo_continuous(pdf)	Represent an hypothesis which works on a continuous domain.
CLsHypo_discrete(pmf)	Represent an hypothesis which works on a discrete domain.
CLsTS(alt, null)	Base class to represent the test-statistics function to work with the CLs method.
CLsTS_continuous(alt, null)	Base class to represent the test-statistics function to work with the CLs method using hypothesis working on a continuous domain.
CLsTS_discrete(alt, null)	Base class to represent the test-statistics function to work with the CLs method using hypothesis working on a discrete domain.
FlatDistTransform(points[, values, kind])	Build the class from a given set of values following a certain distribution (the use of weights is allowed), or x and y values of a PDF.

Class Inheritance Diagram