# 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
#     You should have received a copy of the GNU General Public License
#     along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
#===============================================================================



def extract_randomdist(description):
    " Extracts RandomDistribution objects from all variables"
    rk_rand = 0
    random_objects = []
    for variable in description['variables']:
        # Equation
        eq = variable['eq']
        # Dependencies
        dependencies = []
        # Search for all distributions
        for dist in available_distributions:
            matches = re.findall('(?P<pre>[^\w.])'+dist+'\(([^()]+)\)', eq)
            if matches == ' ':
                continue
            for l, v in matches:

                # Check the arguments
                arguments = v.split(',')

                # Check the number of provided arguments
                if len(arguments) < distributions_arguments[dist]:
                    Global._print(eq)
                    Global._error('The distribution ' + dist + ' requires ' + str(distributions_arguments[dist]) + 'parameters')

# 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

#     You should have received a copy of the GNU General Public License
#     along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
#===============================================================================



def extract_randomdist(description):
    " Extracts RandomDistribution objects from all variables"
    rk_rand = 0
    random_objects = []
    for variable in description['variables']:
        # Equation
        eq = variable['eq']
        # Dependencies
        dependencies = []
        # Search for all distributions
        for dist in available_distributions:
            matches = re.findall('(?P<pre>[^\w.])'+dist+'\(([^()]+)\)', eq)
            if matches == ' ':
                continue
            for l, v in matches:

                # Check the arguments
                arguments = v.split(',')

                # Check the number of provided arguments
                if len(arguments) < distributions_arguments[dist]:
                    Global._print(eq)
                    Global._error('The distribution ' + dist + ' requires ' + str(distributions_arguments[dist]) + 'parameters')

                elif len(arguments) > distributions_arguments[dist]: