theta = 3 # optimum of natural selection
gamma = 1.006e-07 # intensity of natural selection
r = 1 # growth rate
a = 2.346e-06 # intensity of competition
K = 10e12 # carrying capacity
nu=1.1e-05
Vmax = 1
scalar = 20000
#full tree and pruned tree directory
dir_path = './'
files = dir_path + 'treedata/'
td = DVTreeData(path=files, scalar=scalar)
# parameter settings
obs_param = DVParamLiang(gamma=gamma, a=a, K=K,h=1, nu=nu, r=1, theta=theta,V00=.1,V01=.1, Vmax=Vmax, inittrait=theta, initpop=500,
initpop_sigma = 10.0, break_on_mu=False)
population = 1000
obs_param_TVMlog10 = np.tile(obs_param, (population, 1))
simmodelTVM = dvcpp.DVSimTVMLog10(td, obs_param_TVMlog10)
valid_TVM = np.where(simmodelTVM['sim_time'] == td.sim_evo_time)[0].size
print(valid_TVM);
for rep in range(100):
simresult = DVSimTVMLog10(td,obs_param)
import numpy as np
import timeit as time
from dvtraitsim_shared import DVTreeData, DVParam
import dvtraitsim_cpp as dvcpp
from dvtraitsim_py import DVSim
import pycuda.driver as drv
from gpusim import gpusim
no_tree = 1
K = 10e8
nu = 1 / (100 * K)
gamma = 0.001
a = 0.1
dir_path = 'c:/Liang/Googlebox/Research/Project2'
files = dir_path + '/treesim_newexp/example%d/' % no_tree
td = DVTreeData(path=files, scalar=100000)
obs_param = DVParam(gamma=gamma,
a=a,
K=K,
nu=nu,
r=1,
theta=0,
Vmax=1,
inittrait=0,
initpop=500,
initpop_sigma=10.0,
break_on_mu=False)
start = drv.Event()
end = drv.Event()