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]