# Path: phievo/Networks/mutation.py
# T=1.0 #typical time scale
# C=1.0 #typical concentration
# L=1.0 #typical size for diffusion
#                 E_I=log(interval[1]/min_param) #range of possible energies
# def ligand_fct(random_generator):
# def build_lists(mutation_dict):
# def sample_dictionary_ranges(key,random_generator):
# def random_parameters(Types,random_generator):
# def rand_modify(self,random_generator):
#     def __init__(self,generator = random.Random()):
#     def compute_Cseed(self):
#     def random_Species(self, Type='Species'):
#     def random_Interaction(self,Interaction_Type):
#     def remove_Interaction(self,Type):
#     def random_remove_output(self):
#     def random_add_output(self):
#     def random_change_output(self):
#     def random_duplicate(self):
#     def mutate_Node(self,Type):
#     def build_mutations(self):
#         def update_dict(self,dictionary,key,name):
#     def compute_next_mutation(self):
#     def mutate_and_integrate(self,prmt,nnetwork,tgeneration,mutation=True):
# class Mutable_Network(classes_eds2.Network):


import random
from phievo.Networks import mutation

## Software parameters:
prmt['nseed'] = 5  # number of times entire evol procedure repeated, see main program.
prmt['firstseed'] = 0  #first seed
prmt['multipro_level']=1
prmt['pareto']=0
prmt['npareto_functions'] = 2
prmt['rshare']= 0.0
prmt['plot']=0
prmt['langevin_noise']=0


prmt['restart'] = {}
prmt['restart']['activated'] = False ## If false False, start the integration from scratch.
prmt['restart']['freq'] = 50  # save the complete population of networks every freq generations
## Customize restart point:
#prmt['restart']['seed'] =  0 # the directory of the population you want to restart from
#prmt['restart']['kgeneration'] = 50  # restart from After this generation number
#prmt['restart']['same_seed'] = True # Backup the random generator to the same seed

list_unremovable=['Input','Output']
list_types_output=['TF']

###############################
## DEFINE AN INITIAL NETWORK ##
###############################

def init_network():
   seed=int(random.random()*100000)
   g=random.Random(seed)

# Path: phievo/Networks/mutation.py
# T=1.0 #typical time scale
# C=1.0 #typical concentration
# L=1.0 #typical size for diffusion
#                 E_I=log(interval[1]/min_param) #range of possible energies
# def ligand_fct(random_generator):
# def build_lists(mutation_dict):
# def sample_dictionary_ranges(key,random_generator):
# def random_parameters(Types,random_generator):
# def rand_modify(self,random_generator):
#     def __init__(self,generator = random.Random()):
#     def compute_Cseed(self):
#     def random_Species(self, Type='Species'):
#     def random_Interaction(self,Interaction_Type):
#     def remove_Interaction(self,Type):
#     def random_remove_output(self):
#     def random_add_output(self):
#     def random_change_output(self):
#     def random_duplicate(self):
#     def mutate_Node(self,Type):
#     def build_mutations(self):
#         def update_dict(self,dictionary,key,name):
#     def compute_next_mutation(self):
#     def mutate_and_integrate(self,prmt,nnetwork,tgeneration,mutation=True):
# class Mutable_Network(classes_eds2.Network):


# Path: Examples/minimal_project/initialization.py
import random
from phievo.Networks import mutation


## Software parameters:
prmt['nseed'] = 5  # number of times entire evol procedure repeated, see main program.
prmt['firstseed'] = 0  #first seed
prmt['multipro_level']=1
prmt['pareto']=0
prmt['npareto_functions'] = 2
prmt['rshare']= 0.0
prmt['plot']=0
prmt['langevin_noise']=0


prmt['restart'] = {}
prmt['restart']['activated'] = False ## If false False, start the integration from scratch.
prmt['restart']['freq'] = 50  # save the complete population of networks every freq generations
## Customize restart point:
#prmt['restart']['seed'] =  0 # the directory of the population you want to restart from
#prmt['restart']['kgeneration'] = 50  # restart from After this generation number
#prmt['restart']['same_seed'] = True # Backup the random generator to the same seed

list_unremovable=['Input','Output']
list_types_output=['TF']

###############################
## DEFINE AN INITIAL NETWORK ##
###############################

def init_network():
   seed=int(random.random()*100000)
   g=random.Random(seed)

   net=mutation.Mutable_Network(g)