# Path: ANNarchy/core/Random.py
# class RandomDistribution(object):
# class Uniform(RandomDistribution):
# class DiscreteUniform(RandomDistribution):
# class Normal(RandomDistribution):
# class LogNormal(RandomDistribution):
# class Exponential(RandomDistribution):
# class Gamma(RandomDistribution):
#     def get_values(self, shape):
#     def get_list_values(self, size):
#     def get_value(self):
#     def keywords(self):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, min, max):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, min, max):
#     def get_values(self, shape):
#     def latex(self):
#     def __init__(self, mu, sigma, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, mu, sigma, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, Lambda, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def __init__(self, alpha, beta=1.0, seed=-1, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):


import ANNarchy.core.Global as Global
import re
        import sympy
from ANNarchy.core.Random import available_distributions, distributions_arguments, distributions_equivalents
from ANNarchy.parser.Equation import Equation
from ANNarchy.parser.Function import FunctionParser
from ANNarchy.parser.StringManipulation import *
from ANNarchy.parser.ITE import *
        from inspect import getmembers
                Global._print(eq)
                Global._error('The distribution ' + dist + ' requires ' + str(distributions_arguments[dist]) + 'parameters')
            elif len(arguments) > distributions_arguments[dist]:
                Global._print(eq)
                Global._error('Too many parameters provided to the distribution ' + dist)
            # Process the arguments
            processed_arguments = ""
            for idx in range(len(arguments)):
                try:
                    arg = float(arguments[idx])
                except: # A global parameter
                    Global._print(eq)
                    Global._error('Random distributions for creating/pruning synapses must use foxed values.')

                processed_arguments += str(arg)
                if idx != len(arguments)-1: # not the last one
                    processed_arguments += ', '
            definition = distributions_equivalents[dist] + '(' + processed_arguments + ')'

            # Store its definition
            if rd:
                Global._print(eq)
                Global._error('Only one random distribution per equation is allowed.')


            rd = {'name': 'rand_' + str(0) ,
                    'origin': dist+'('+v+')',
                    'dist': dist,
                    'definition': definition,
                    'args' : processed_arguments,

# Path: ANNarchy/core/Random.py
# class RandomDistribution(object):
# class Uniform(RandomDistribution):
# class DiscreteUniform(RandomDistribution):
# class Normal(RandomDistribution):
# class LogNormal(RandomDistribution):
# class Exponential(RandomDistribution):
# class Gamma(RandomDistribution):
#     def get_values(self, shape):
#     def get_list_values(self, size):
#     def get_value(self):
#     def keywords(self):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, min, max):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, min, max):
#     def get_values(self, shape):
#     def latex(self):
#     def __init__(self, mu, sigma, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, mu, sigma, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def get_cpp_args(self):
#     def __init__(self, Lambda, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):
#     def __init__(self, alpha, beta=1.0, seed=-1, min=None, max=None):
#     def get_values(self, shape):
#     def latex(self):


# Path: ANNarchy/parser/Extraction.py
import ANNarchy.core.Global as Global
import re
        import sympy
from ANNarchy.core.Random import available_distributions, distributions_arguments, distributions_equivalents
from ANNarchy.parser.Equation import Equation
from ANNarchy.parser.Function import FunctionParser
from ANNarchy.parser.StringManipulation import *
from ANNarchy.parser.ITE import *
        from inspect import getmembers

                Global._print(eq)
                Global._error('The distribution ' + dist + ' requires ' + str(distributions_arguments[dist]) + 'parameters')
            elif len(arguments) > distributions_arguments[dist]:
                Global._print(eq)
                Global._error('Too many parameters provided to the distribution ' + dist)
            # Process the arguments
            processed_arguments = ""
            for idx in range(len(arguments)):
                try:
                    arg = float(arguments[idx])
                except: # A global parameter
                    Global._print(eq)
                    Global._error('Random distributions for creating/pruning synapses must use foxed values.')

                processed_arguments += str(arg)
                if idx != len(arguments)-1: # not the last one
                    processed_arguments += ', '
            definition = distributions_equivalents[dist] + '(' + processed_arguments + ')'

            # Store its definition
            if rd:
                Global._print(eq)
                Global._error('Only one random distribution per equation is allowed.')


            rd = {'name': 'rand_' + str(0) ,
                    'origin': dist+'('+v+')',
                    'dist': dist,
                    'definition': definition,
                    'args' : processed_arguments,

                    'template': distributions_equivalents[dist]}