def test_profile():
ind_set = {'fr':[1,1],'m_cost':0,'energy':1,'rep_cost': 0 ,'lifespan':1,'fecund_genes':[0,.5,2,25],'max_energy':10}
tmp = L.Lattice(dims = [5,2],Kp = [.01,.02] )
n_patch = 10
groups = []
for i in range(n_patch):
z = []
for x in range(20):
indiv_dict = {'forage_rate':np.random.uniform(ind_set["fr"][0],ind_set["fr"][1]),'m_cost':ind_set["m_cost"],'energy':1,'rep_cost':ind_set["rep_cost"],'lifespan':ind_set["lifespan"],'groupID' : 1,'sex' : np.random.binomial(1,.5,1),'fecund_genes':np.random.uniform(ind_set["fecund_genes"][0],ind_set["fecund_genes"][1],(ind_set["fecund_genes"][2],ind_set["fecund_genes"][3])),"max_energy": ind_set["max_energy"]}
z.append(Ind.individual(**indiv_dict))
groups.append(G.group(z,i,ID=i))
tmp.groups = groups
n = 500
for x in range(n):
if x%1 == 0:
print x
tmp.mate(ind_set)
#tmp.disperse(.1,True,24)
tmp.colonize(10000,1,.01)
tmp.reproduce()
tmp.senesce(.05)
tmp.mutate(0.001)
#tmp.forage()
tmp.regenerate()
tmp.data_collect()
ih.write_ibmdata(tmp)
print "done"
and [2] is the number of chromosomes, usually 2, and position [3] is the length of each chromosome.
'''
ind_set = {'fr':[1,1],'m_cost':0,'energy':1,'rep_cost': 0 ,'lifespan':1,'fecund_genes':[.6,1,2,5],'max_energy':10}
tmp = L.Lattice(dims = [2,2],Kp = [.005,.01] )
groups = []
z = []
for x in range(20):
indiv_dict = {'forage_rate':np.random.uniform(ind_set["fr"][0],ind_set["fr"][1]),'m_cost':ind_set["m_cost"],'energy':1,'rep_cost':ind_set["rep_cost"],'lifespan':ind_set["lifespan"],'groupID' : 1,'sex' : np.random.binomial(1,.5,1),'fecund_genes':np.random.uniform(ind_set["fecund_genes"][0],ind_set["fecund_genes"][1],(ind_set["fecund_genes"][2],ind_set["fecund_genes"][3])),"max_energy": ind_set["max_energy"]}
z.append(Ind.individual(**indiv_dict))
for x in range(4):
groups.append(G.group([],x,ID=x))
groups[0] = G.group(z,0,ID=0)
tmp.groups = groups
n = 100
for x in range(n):
if x%1 == 0:
print x
def main():
'''
Description: Runs a full simulation of our spider model. Requires a command line input of a file with parameters.
Below is a full description of parameters. They must be named as described, separated by spaces.
ngens: The number of generations in the simulation
x_dim: The x dimensions of the lattice that we are simulating over.
y_dim: The y dimension of the lattice we are simulating over.
min_pop: The minimum population size expressed as c in 1/c = pop size, e.g. min_pop = 0.01 means a minimum population size of 100
max_pop: The maximum population size expressed as c in 1/c = pop size, e.g. min_pop = 0.01 means a minimum population size of 100
'''
#extract parameters
param_dict = read_args()
#Set global parameters
x_dim = int(param_dict['x_dim'])
y_dim = int(param_dict['y_dim'])
min_pop = param_dict['min_pop']
max_pop = param_dict['max_pop']
ngens = int(param_dict['ngens'])
min_col_size = int(param_dict['min_col_size'])
ind_set = {'fr':[1,1],'m_cost':0,'energy':1,'rep_cost': 0 ,'lifespan':1,'fecund_genes':[0,1,2,20],'max_energy':10}
tmp = L.Lattice(dims = [x_dim,y_dim],Kp = [max_pop,min_pop] )
n_patch = x_dim * y_dim
groups = []
for i in range(n_patch):
z = []
for x in range(20):
indiv_dict = {'forage_rate':np.random.uniform(ind_set["fr"][0],ind_set["fr"][1]),'m_cost':ind_set["m_cost"],'energy':1,'rep_cost':ind_set["rep_cost"],'lifespan':ind_set["lifespan"],'groupID' : 1,'sex' : np.random.binomial(1,.5,1),'fecund_genes':np.random.uniform(ind_set["fecund_genes"][0],ind_set["fecund_genes"][1],(ind_set["fecund_genes"][2],ind_set["fecund_genes"][3])),"max_energy": ind_set["max_energy"]}
z.append(Ind.individual(**indiv_dict))
groups.append(G.group(z,i,ID=i))
tmp.groups = groups
n = ngens
for x in range(n):
if x%1 == 0:
print x
tmp.mate(ind_set)
#tmp.disperse(.1,True,24)
tmp.colonize(min_col_size,.5,.001)
tmp.reproduce()
tmp.senesce(.05)
tmp.mutate(0.001)
#tmp.forage()
tmp.regenerate()
tmp.data_collect()
fName = "data/output_min_pop_"+str(min_pop)+"_min_col_size_"+str(min_col_size)+".csv"
ih.write_ibmdata(tmp,fName)
print "done"
