Source code for photonpacket.hist1d

import numpy as np
from matplotlib import pyplot as plt
from .frameseries import frameseries


class hist1d:
    '''

    Paramters
    '''
    hist = np.array([])
    bins = np.array([])
    N = 0
    normed = False

    @staticmethod
    def fromcounts(N):
        '''

        Paramterers
        ---------
        N: np.array
            array of counts

        '''
        bins = np.arange(np.max(N)+2)
        h = np.histogram(N, bins=bins)
        return hist1d(h[0],h[1])

    @staticmethod
    def fromfs(fs,**kwargs):
        '''

        Parameters
        --------
        fs: frameseries
            frameseries object
        args:
            density - passed to numpy.histogram
            min - minimal bin value
            max - maximal bin value
            offset - offset from automatic maximal bin value
        '''
        dens = kwargs['density'] if 'density' in kwargs else False
        vmin = kwargs['min'] if 'min' in kwargs else 0
        offset = kwargs['offset'] if 'offset' in kwargs else 1
        vmax = kwargs['max'] if 'max' in kwargs else np.max(fs.N)+offset+1
        bins = np.arange(vmin,vmax+1)
        h = np.histogram(fs.N, bins=bins,density=dens)
        return hist1d(h[0],h[1])

    def __init__(self, hist, bins):
        '''

        '''
        self.hist = hist
        self.bins = bins
        self.N = np.sum(hist)

    def plot(self, log=False, normed=False):
        '''
        '''
        if normed:
            plt.bar(self.bins[:-1], self.hist/self.N, log=log)
        else:
            plt.bar(self.bins[:-1], self.hist, log=log)

    def mean(self):
        '''
        '''
        return np.average(self.bins[:-1], weights=self.hist, axis=0)

    def mean2(self):
        '''
        '''
        return np.average(self.bins[:-1], weights=self.hist, axis=0)**2

    def avgn2(self):
        '''
        '''
        return np.average(self.bins[:-1]**2, weights=self.hist, axis=0)

    def var(self):
        '''
        '''
        return self.avgn2() - self.mean2()

    def g2(self):
        '''
        '''
        return 1.0+(self.avgn2()-self.mean2()-self.mean())/self.mean2()

    def thmodes(self):
        '''
        '''
        return self.mean2()/(self.avgn2()-self.mean2()-self.mean())

    def subbinomial(self):
        '''
        '''
        pass

    def qmandel(self):
        '''
        '''
        pass