# Path: stochpy/implementations/StochPyTools.py
# class Species():
# class StochPySSA_Shared(): 
#     def __init__(self):
#     def Parse(self,model_file,model_dir,IsTauleaping=False,IsNRM=False,IsDelayed = False,IsSMM = False,IsQuiet=False):
#     def SpeciesSelection(self):
#     def RateSelection(self):
#     def SetEvents(self):
#     def Propensities(self,IsTauleaping=False):
#     def BuildPropensityCodes(self, propensities = None): # 21-11-2013 
#     def HandleEvents(self,IsTauleapingStep=False):    
#     def AssignmentRules(self):
#     def rateFunc(self,rate_eval_code,r_vec):
#     def Initial_Conditions(self,IsTauleaping = False):              
#     def GenerateOutput(self,IsTauleaping = False,completion_delayed = False):


import sys,copy,time,os,random
from .StochPyTools import __species__,StochPySSA_Shared,np
            elif option == 5:
                try:
                    self.g_vector[i] = 3 + 1.5/(self.X_matrix[i]-1)
                except:
                    self.g_vector[i] = 3
            elif option == 6:
                try: 
                    self.g_vector[i] = 3 + 1.0/(self.X_matrix[i]-1) + 2.0/(self.X_matrix[i]-2)
                except:
                    self.g_vector[i] = 3            


    def GetMuVar(self):
        """ Calculate the estimates of mu and var for each species (i) """        
        for i,v_i in enumerate(self.parse.N_matrix):
            self.sim_mu[i] = np.dot(v_i,self.sim_a_mu)
            self.sim_var[i]= np.dot(v_i*v_i,self.sim_a_mu)            
            

    def GetTauPrime(self):
        """ Calculate tau' """
        part = np.divide(self.X_matrix.ravel(),self.g_vector)*self.sim_epsilon # eps*x[i]/g[i] for all i
        num = np.array([part,np.ones(len(part))])                  # eps*x[i]/g[i] for all i , 1 for all i
        numerator = num.max(axis=0)                                # max(eps*x[i]/g[i],1) for all i
        abs_mu = np.abs(self.sim_mu)                               # abs(mu) for all i
        bound1 = np.divide(numerator,abs_mu)                       # max(eps*x[i]/g[i],1) / abs(mu[i]) for all i
        numerator_square = np.square(numerator)    	
        bound2 = np.divide(numerator_square,self.sim_var)          # max(eps*x[i]/g[i],1)^2 / abs(mu[i]) for all i
        tau_primes = np.array([bound1,bound2])			
        try:

# Path: stochpy/implementations/StochPyTools.py
# class Species():
# class StochPySSA_Shared(): 
#     def __init__(self):
#     def Parse(self,model_file,model_dir,IsTauleaping=False,IsNRM=False,IsDelayed = False,IsSMM = False,IsQuiet=False):
#     def SpeciesSelection(self):
#     def RateSelection(self):
#     def SetEvents(self):
#     def Propensities(self,IsTauleaping=False):
#     def BuildPropensityCodes(self, propensities = None): # 21-11-2013 
#     def HandleEvents(self,IsTauleapingStep=False):    
#     def AssignmentRules(self):
#     def rateFunc(self,rate_eval_code,r_vec):
#     def Initial_Conditions(self,IsTauleaping = False):              
#     def GenerateOutput(self,IsTauleaping = False,completion_delayed = False):


# Path: stochpy/implementations/TauLeaping.py
import sys,copy,time,os,random
from .StochPyTools import __species__,StochPySSA_Shared,np

            elif option == 5:
                try:
                    self.g_vector[i] = 3 + 1.5/(self.X_matrix[i]-1)
                except:
                    self.g_vector[i] = 3
            elif option == 6:
                try: 
                    self.g_vector[i] = 3 + 1.0/(self.X_matrix[i]-1) + 2.0/(self.X_matrix[i]-2)
                except:
                    self.g_vector[i] = 3            


    def GetMuVar(self):
        """ Calculate the estimates of mu and var for each species (i) """        
        for i,v_i in enumerate(self.parse.N_matrix):
            self.sim_mu[i] = np.dot(v_i,self.sim_a_mu)
            self.sim_var[i]= np.dot(v_i*v_i,self.sim_a_mu)            
            

    def GetTauPrime(self):
        """ Calculate tau' """
        part = np.divide(self.X_matrix.ravel(),self.g_vector)*self.sim_epsilon # eps*x[i]/g[i] for all i
        num = np.array([part,np.ones(len(part))])                  # eps*x[i]/g[i] for all i , 1 for all i
        numerator = num.max(axis=0)                                # max(eps*x[i]/g[i],1) for all i
        abs_mu = np.abs(self.sim_mu)                               # abs(mu) for all i
        bound1 = np.divide(numerator,abs_mu)                       # max(eps*x[i]/g[i],1) / abs(mu[i]) for all i
        numerator_square = np.square(numerator)    	
        bound2 = np.divide(numerator_square,self.sim_var)          # max(eps*x[i]/g[i],1)^2 / abs(mu[i]) for all i
        tau_primes = np.array([bound1,bound2])			
        try:

            self.sim_tau_prime = np.min(tau_primes[~np.isnan(tau_primes)]) # min (bound1,bound2)