import scipydirect # model definitions from params import * # parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.0005 alpha = 0.005 feps = 1e-9 # script parameters aenvs = from_tau(np.arange(0.5, 8.0, 0.05)) pienvs = 0.7 nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination((aenvs, pienvs, maxfs, deltatols)) niter = int(niter) nburnin = int(nburnin) if parametercheck(datadir, sys.argv, paramscomb, nbatch): njob = int(sys.argv[1]) data = [] for i in progressbar(range(nbatch)): n = (njob-1) * nbatch + i aenv, pienv, maxf, deltatol = paramscomb[n]
# parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.005 alpha = 0.005 feps = 1e-9 # script parameters # hq scan #aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(20.0), 40, True)) #pienvs = np.linspace(0.0, 1.0, 41)[1:-1] # lq scan aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(20.0), 20, True)) pienvs = np.linspace(0.0, 1.0, 21)[1:-1] # tauenvcut #aenvs = from_tau([12, 0.8]) #pienvs = np.linspace(0.0, 1.0, 101)[1:-1] # pienvcut #aenvs = from_tau(np.arange(0.5, 8.0, 0.05)) #pienvs = 0.7 nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination( (lambdas, muss, cups, aenvs, pienvs, maxfs, deltatols)) niter = int(niter)
import scipydirect # model definitions from params import * # parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.0005 alpha = 0.005 feps = 1e-9 # script parameters aenvs = from_tau(np.arange(0.5, 8.0, 0.05)) pienvs = 0.7 nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination((aenvs, pienvs, maxfs, deltatols)) niter = int(niter) nburnin = int(nburnin) if parametercheck(datadir, sys.argv, paramscomb, nbatch): njob = int(sys.argv[1]) data = [] for i in progressbar(range(nbatch)): n = (njob - 1) * nbatch + i aenv, pienv, maxf, deltatol = paramscomb[n]
# parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.005 alpha = 0.005 feps = 1e-9 # script parameters # hq scan #aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(20.0), 40, True)) #pienvs = np.linspace(0.0, 1.0, 41)[1:-1] # lq scan aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(20.0), 20, True)) pienvs = np.linspace(0.0, 1.0, 21)[1:-1] # tauenvcut #aenvs = from_tau([12, 0.8]) #pienvs = np.linspace(0.0, 1.0, 101)[1:-1] # pienvcut #aenvs = from_tau(np.arange(0.5, 8.0, 0.05)) #pienvs = 0.7 nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination((lambdas, muss, cups, aenvs, pienvs, maxfs, deltatols)) niter = int(niter) nburnin = int(nburnin)
import scipydirect # model definitions from params import * # parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.0005 alpha = 0.005 feps = 1e-9 # script parameters aenvs = from_tau([12, 0.8]) pienvs = np.linspace(0.0, 1.0, 101)[1:-1] nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination((aenvs, pienvs, maxfs, deltatols)) niter = int(niter) nburnin = int(nburnin) if parametercheck(datadir, sys.argv, paramscomb, nbatch): njob = int(sys.argv[1]) data = [] for i in progressbar(range(nbatch)): n = (njob-1) * nbatch + i aenv, pienv, maxf, deltatol = paramscomb[n]
maxf=400, **commonoptkwargs) moptkwargs = dict(bounds=np.array([[0.0 + boundtol, 1.0], [0.0 + qboundtol, 1.0], [0.0 + boundtol, 1.0]]), maxf=5000, **commonoptkwargs) ioptkwargs = dict(bounds=np.array([[0.0 + boundtol, 1.0], [0.0 + qboundtol, 1.0]]), maxf=400, **commonoptkwargs) poptkwargs = dict(bounds=np.array([[0.0 + boundtol, 1.0 - boundtol]]), maxf=20, **commonoptkwargs) # script parameters aenvs = evolimmune.from_tau( np.logspace(np.log10(0.09), np.log10(20.0), num=20, endpoint=True)) nbatch = 1 datadir = 'data/' # define different boundaries to test and where to look for them # use some aenv cutoffs to save unnecessary computations paramscomb = expand_params([ ('ap', aenvs[[ 0, -1 ]]), # no change with aenv so it's enough to evaluate at extremities ('ac', aenvs), ('cm', filterarray(aenvs, 0.2, 1.0)), ('mi', filterarray(aenvs, 0.01, 1.0)), ('io', filterarray(aenvs, 0.01, 1.0)), ('pm', aenvs), ('pi', filterarray(aenvs, 0.0, 0.9)),
feps = 1e-9 boundtol = 0.005 qboundtol = deltatol xtol = 0.025 xtolbound = 0.01 alpha = 0.005 disp = True # kwargs for the optimization algorithms commonoptkwargs = dict(deltatol=deltatol, deltainit=deltainit, feps=feps, errorcontrol=True, paired=True, alpha=alpha, disp=disp) coptkwargs = dict(bounds=np.array([[0.0+qboundtol, 1.0], [0.0+boundtol, 1.0]]), maxf=400, **commonoptkwargs) moptkwargs = dict(bounds=np.array([[0.0+boundtol, 1.0], [0.0+qboundtol, 1.0], [0.0+boundtol, 1.0]]), maxf=5000, **commonoptkwargs) ioptkwargs = dict(bounds=np.array([[0.0+boundtol, 1.0], [0.0+qboundtol, 1.0]]), maxf=400, **commonoptkwargs) poptkwargs = dict(bounds=np.array([[0.0+boundtol, 1.0-boundtol]]), maxf=20, **commonoptkwargs) # script parameters aenvs = evolimmune.from_tau(np.logspace(np.log10(0.09), np.log10(20.0), num=20, endpoint=True)) nbatch = 1 datadir = 'data/' # define different boundaries to test and where to look for them # use some aenv cutoffs to save unnecessary computations paramscomb = expand_params([('ap', aenvs[[0, -1]]), # no change with aenv so it's enough to evaluate at extremities ('ac', aenvs), ('cm', filterarray(aenvs, 0.2, 1.0)), ('mi', filterarray(aenvs, 0.01, 1.0)), ('io', filterarray(aenvs, 0.01, 1.0)), ('pm', aenvs), ('pi', filterarray(aenvs, 0.0, 0.9)), ('po', aenvs[[0, -1]]), ('pc', aenvs), ])
import numpy as np from evolimmune import (from_tau, mus_from_str, cup_from_str, agentbasedsim_evol, zstogrowthrate) import cevolimmune from misc import * # general model parameters lambdas = 3.0 muss = '1.0-2.0*epsilon/(1.0+epsilon), 1.0+0.8*epsilon' cups = '0.1*pup+pup**2' # finite population model parameters Ls = 1 ninds = [50, 100, 1000] aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(11.0), 40, True)) pienvs = [0.3, 0.5, 0.7] # numerical parameters ngens = [100000] # parameter evolution parameters mutrates = lambda gen: 1e-2 * np.exp(-gen / 1e4) mutsizes = lambda gen: 0.25 * np.exp(-gen / 1e4) # script parameters nbatch = 1 nruns = 50 datadir = 'data' paramscomb = params_combination(
import numpy as np from evolimmune import (from_tau, mus_from_str, cup_from_str, agentbasedsim_evol, zstogrowthrate) import cevolimmune from misc import * # general model parameters lambdas = 3.0 muss = '1.0-2.0*epsilon/(1.0+epsilon), 1.0+0.8*epsilon' cups = '0.1*pup+pup**2' # finite population model parameters Ls = 1 ninds = [50, 100, 1000] aenvs = from_tau(np.logspace(np.log10(0.09), np.log10(11.0), 40, True)) pienvs = [0.3, 0.5, 0.7] # numerical parameters ngens = [100000] # parameter evolution parameters mutrates = lambda gen: 1e-2 * np.exp(-gen/1e4) mutsizes = lambda gen: 0.25 * np.exp(-gen/1e4) # script parameters nbatch = 1 nruns = 50 datadir = 'data' paramscomb = params_combination((Ls, lambdas, muss, cups, aenvs, pienvs, ninds, mutrates, mutsizes))
import scipydirect # model definitions from params import * # parameters of numerical algorithms niter = 1e6 nburnin = 1e4 # first stage numerical parameters maxfs = 10000 # second stage numerical parameters deltainit = 0.02 deltatols = 0.0005 alpha = 0.005 feps = 1e-9 # script parameters aenvs = from_tau([12, 0.8]) pienvs = np.linspace(0.0, 1.0, 101)[1:-1] nbatch = 1 disp = True datadir = 'data/' paramscomb = params_combination((aenvs, pienvs, maxfs, deltatols)) niter = int(niter) nburnin = int(nburnin) if parametercheck(datadir, sys.argv, paramscomb, nbatch): njob = int(sys.argv[1]) data = [] for i in progressbar(range(nbatch)): n = (njob - 1) * nbatch + i aenv, pienv, maxf, deltatol = paramscomb[n]