distribution_from_fitter

"""
If it will be helpful, please like this post
"""
#https://stats.stackexchange.com/questions/302774/does-uniform-distribution-belong-to-location-and-scale-familya
def get_upd_distr(dict_: dict):
    full_dict = {}
    for key, values in dict_.items():
        if key not in full_dict:
            full_dict[key] = {}
        if key == "norm":
            if 'mean' not in full_dict[key]:
                full_dict[key]['mean'] = values[0]
            if 'sd' not in full_dict[key]:
                full_dict[key]['sd'] = values[1]

        if key == "cauchy":
            #https://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm
            if 't' not in full_dict[key]:
                full_dict[key]['t'] = values[0]
            if 's' not in full_dict[key]:
                full_dict[key]['s'] = values[1]

        if key == 'expon':
            if 'Xmin' not in full_dict[key]:
                full_dict[key]['Xmin'] = values[0]
            if '1/lambda' not in full_dict[key]:
                full_dict[key]['1/lambda'] = values[1]

        if key == "chi2":
            #https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm
            #https://web.ma.utexas.edu/users/mks/M358KInstr/tDistandDF.pdf

            if 'df' not in full_dict[key]:
                full_dict[key]['df'] = values[0]
            if 'mean' not in full_dict[key]:
                full_dict[key]['mean'] = values[1]
            if 'sd' not in full_dict[key]:
                full_dict[key]['sd'] = values[2]


        if key == "lognorm":
            #https://statisticsbyjim.com/probability/lognormal-distribution/
            #https://stackoverflow.com/questions/8747761/scipy-lognormal-distribution-parameters
            if 'sd' not in full_dict[key]:
                full_dict[key]['sd'] = values[0]
            if 'mean' not in full_dict[key]:
                full_dict[key]['mean'] = values[1]
            if 'alpha' not in full_dict[key]:
                full_dict[key]['alpha'] = values[2]

        if key == 'uniform':
            #https://statisticsbyjim.com/probability/gamma-distribution/
            if 'a' not in full_dict[key]:
                full_dict[key]['a'] = values[0]
            if '|b-a|' not in full_dict[key]:
                full_dict[key]['|b-a|'] = values[1]

        if key == 'rayleigh':
            #https://anylogic.help/advanced/functions/rayleigh.html
            #https://stats.stackexchange.com/questions/224416/parameter-estimation-of-a-rayleigh-random-variable-with-an-offset --useful
            if 'mean' not in full_dict[key]:
                full_dict[key]['mean'] = values[0]
            if 'sd' not in full_dict[key]:
                full_dict[key]['sd'] = values[1]

        if key == 'powerlaw':   #TODO understand what is it
            if 'alpha' not in full_dict[key]:
                full_dict[key]['alpha'] = values[0]

            if 'Xmin' not in full_dict[key]:
                full_dict[key]['Xmin'] = values[1]
            if 'D' not in full_dict[key]:
                full_dict[key]['D'] = values[2]

        if key == 'exponpow':   #TODO understand what is it
            if 'b' not in full_dict[key]:
                full_dict[key]['b'] = values[0]
            if 'loc' not in full_dict[key]:
                full_dict[key]['loc'] = values[1]
            if 'scale' not in full_dict[key]:
                full_dict[key]['scale'] = values[2]

        if key == 'gamma':
            #https://stackoverflow.com/questions/2896179/fitting-a-gamma-distribution-with-python-scipy
            #https://statisticsbyjim.com/probability/gamma-distribution/
            #https://en.wikipedia.org/wiki/Gamma_distribution
            #https://pythonguides.com/python-scipy-stats-fit/
            if 'alpha' not in full_dict[key]:
                full_dict[key]['alpha'] = values[0]
            if 'loc' not in full_dict[key]:
                full_dict[key]['loc'] = values[1]
            if 'beta' not in full_dict[key]:
                full_dict[key]['beta'] = values[2]

        if key == 'beta':
            # https://stats.stackexchange.com/questions/68983/beta-distribution-fitting-in-scipy

            if 'alpha' not in full_dict[key]:
                full_dict[key]['alpha'] = values[0]
            if 'beta' not in full_dict[key]:
                full_dict[key]['beta'] = values[1]
            if 'lower limit' not in full_dict[key]:
                full_dict[key]['lower limit'] = values[2]
            if 'upper limit - lower limit' not in full_dict[key]:
                full_dict[key]['upper limit - lower limit'] = values[3]
        if key == 'burr':
            """
            In terms of mathematical statistics, the loc and scale parameters for the Burr distribution correspond to the shift and scale parameters.

            The loc parameter represents a shift (or offset) of the distribution along the axis of values. If loc is a, then the distribution will be shifted by a units to the right (positive value) or to the left (negative value).
            The scale parameter represents the scale that affects the spread of the data. If scale is equal to b, then the distribution will be scaled along the axis of values.
            So you can rewrite the values of loc and scale in terms of mathematical statistics as follows:

            Shift (loc):μ=loc

            Scale (scale):σ=scale

            Here
            μ is the mean (shift), and σ is the standard deviation (scale).
            """
            if 'c' not in full_dict[key]:
                full_dict[key]['c'] = values[0]
            if 'd' not in full_dict[key]:
                full_dict[key]['d'] = values[1]
            if 'mean' not in full_dict[key]:
                full_dict[key]['mean'] = values[2]
            if 'sd' not in full_dict[key]:
                full_dict[key]['sd'] = values[3]

    return full_dict