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porous_ahoy_run.py
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porous_ahoy_run.py
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from __future__ import print_function, division
from itertools import product
import numpy as np
from agaro import run_utils
from ahoy.model import Model
default_model_kwargs = {
'seed': 1,
'dim': 2,
'dt': 0.01,
'n': 5000,
'v_0': 20.0,
'dt_mem': 0.1,
't_mem': 5.0,
'pore_R': 20.0,
'pore_turner': 'align',
'L': np.array([300.0, 300.0]),
'Dr_0': 1.0,
'p_0': 1.0,
}
# Better to do this with named tuples
combo_to_chi = {
('Dr_0', False, False): 0.38747846573137146,
('Dr_0', False, True): 0.91610356364005729,
('Dr_0', True, False): 0.55171912452935623,
('Dr_0', True, True): 0.95031324074045198,
('p_0', False, False): 0.38527303561393739,
('p_0', False, True): 0.85519056757936063,
('p_0', True, False): 0.54788034865582758,
('p_0', True, True): 0.88731933438050015
}
def run_spatial():
extra_model_kwargs = {
'spatial_flag': True,
'periodic_flag': True,
'pore_flag': True,
'pore_pf': 0.0707,
'tumble_flag': True,
'tumble_chemo_flag': True,
'temporal_chemo_flag': True,
'onesided_flag': True,
'chi': 0.5,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
model = Model(**model_kwargs)
t_output_every = 50.0
t_upto = 100.0
output_dir = None
force_resume = None
run_utils.run_model(t_output_every, output_dir, m=model,
force_resume=force_resume, t_upto=t_upto)
def run_Dr_scan_uniform():
extra_model_kwargs = {
'spatial_flag': True,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
t_output_every = 50.0
t_upto = 1000.0
noise_0s = np.logspace(-3, 2, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
dims = [1, 2]
noise_vars = ['Dr_0', 'p_0']
for noise_var, dim, noise_0 in product(noise_vars, dims, noise_0s):
model_kwargs_cur = model_kwargs.copy()
if noise_var == 'Dr_0':
if dim == 1:
continue
model_kwargs_cur['rotation_flag'] = True
model_kwargs_cur['tumble_flag'] = False
model_kwargs_cur['Dr_0'] = noise_0
else:
model_kwargs_cur['rotation_flag'] = False
model_kwargs_cur['tumble_flag'] = True
model_kwargs_cur['p_0'] = noise_0
model_kwargs_cur['dim'] = dim
model_kwarg_sets.append(model_kwargs_cur)
run_utils.run_kwarg_scan(Model, model_kwarg_sets,
t_output_every, t_upto,
force_resume=force_resume, parallel=parallel)
def run_chi_scan():
extra_model_kwargs = {
'spatial_flag': True,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
t_output_every = 50.0
t_upto = 300.0
chis = np.linspace(0.0, 0.99, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
dims = [1, 2]
noise_vars = ['Dr_0', 'p_0']
onesided_flags = [True, False]
temporal_chemo_flags = [True, False]
combos = product(noise_vars, dims, onesided_flags, temporal_chemo_flags,
chis)
for noise_var, dim, onesided_flag, temporal_chemo_flag, chi in combos:
model_kwargs_cur = model_kwargs.copy()
if noise_var == 'Dr_0':
if dim == 1:
continue
model_kwargs_cur['rotation_flag'] = True
model_kwargs_cur['rotation_chemo_flag'] = True
model_kwargs_cur['tumble_flag'] = False
model_kwargs_cur['tumble_chemo_flag'] = False
else:
model_kwargs_cur['rotation_flag'] = False
model_kwargs_cur['rotation_chemo_flag'] = False
model_kwargs_cur['tumble_flag'] = True
model_kwargs_cur['tumble_chemo_flag'] = True
model_kwargs_cur['dim'] = dim
model_kwargs_cur['onesided_flag'] = onesided_flag
model_kwargs_cur['temporal_chemo_flag'] = temporal_chemo_flag
model_kwargs_cur['chi'] = chi
model_kwarg_sets.append(model_kwargs_cur)
run_utils.run_kwarg_scan(Model, model_kwarg_sets,
t_output_every, t_upto,
force_resume=force_resume, parallel=parallel)
def run_pf_scan():
extra_model_kwargs = {
'spatial_flag': True,
'periodic_flag': True,
'pore_flag': True,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
t_output_every = 50.0
t_upto = 1000.0
pore_pfs = np.linspace(0.0, 0.8, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
pore_turners = ['stall', 'bounce_back', 'reflect', 'align']
noise_vars = ['Dr_0', 'p_0']
for noise_var, pore_turner, pore_pf in product(noise_vars, pore_turners,
pore_pfs):
model_kwargs_cur = model_kwargs.copy()
if noise_var == 'Dr_0':
model_kwargs_cur['rotation_flag'] = True
model_kwargs_cur['tumble_flag'] = False
else:
model_kwargs_cur['rotation_flag'] = False
model_kwargs_cur['tumble_flag'] = True
model_kwargs_cur['pore_turner'] = pore_turner
model_kwargs_cur['pore_pf'] = pore_pf
model_kwarg_sets.append(model_kwargs_cur)
run_utils.run_kwarg_scan(Model, model_kwarg_sets,
t_output_every, t_upto,
force_resume=force_resume, parallel=parallel)
def run_Dr_scan_porous():
extra_model_kwargs = {
'spatial_flag': True,
'periodic_flag': True,
'pore_flag': True,
'pore_pf': 0.5,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
t_output_every = 50.0
t_upto = 1000.0
noise_0s = np.logspace(-3, 2, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
pore_turners = ['stall', 'bounce_back', 'reflect', 'align']
noise_vars = ['Dr_0', 'p_0']
for noise_var, pore_turner, noise_0 in product(noise_vars, pore_turners,
noise_0s):
model_kwargs_cur = model_kwargs.copy()
if noise_var == 'Dr_0':
model_kwargs_cur['rotation_flag'] = True
model_kwargs_cur['tumble_flag'] = False
model_kwargs_cur['Dr_0'] = noise_0
else:
model_kwargs_cur['rotation_flag'] = False
model_kwargs_cur['tumble_flag'] = True
model_kwargs_cur['p_0'] = noise_0
model_kwargs_cur['pore_turner'] = pore_turner
model_kwarg_sets.append(model_kwargs_cur)
run_utils.run_kwarg_scan(Model, model_kwarg_sets,
t_output_every, t_upto,
force_resume=force_resume, parallel=parallel)
def run_pf_scan_drift():
extra_model_kwargs = {
'spatial_flag': True,
'periodic_flag': True,
'pore_flag': True,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
t_output_every = 50.0
t_upto = 1000.0
pore_pfs = np.linspace(0.0, 0.8, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
noise_vars = ['Dr_0', 'p_0']
onesided_flags = [True, False]
temporal_chemo_flags = [True, False]
combos = product(noise_vars, onesided_flags, temporal_chemo_flags,
pore_pfs)
for noise_var, onesided_flag, temporal_chemo_flag, pore_pf in combos:
model_kwargs_cur = model_kwargs.copy()
if noise_var == 'Dr_0':
model_kwargs_cur['rotation_flag'] = True
model_kwargs_cur['rotation_chemo_flag'] = True
model_kwargs_cur['tumble_flag'] = False
model_kwargs_cur['tumble_chemo_flag'] = False
else:
model_kwargs_cur['rotation_flag'] = False
model_kwargs_cur['rotation_chemo_flag'] = False
model_kwargs_cur['tumble_flag'] = True
model_kwargs_cur['tumble_chemo_flag'] = True
model_kwargs_cur['onesided_flag'] = onesided_flag
model_kwargs_cur['temporal_chemo_flag'] = temporal_chemo_flag
key = noise_var, onesided_flag, temporal_chemo_flag
model_kwargs_cur['chi'] = combo_to_chi[key]
model_kwargs_cur['pore_pf'] = pore_pf
model_kwarg_sets.append(model_kwargs_cur)
run_utils.run_kwarg_scan(Model, model_kwarg_sets,
t_output_every, t_upto,
force_resume=force_resume, parallel=parallel)
def run_field():
rho_0 = 0.002
c_delta_0 = 0.1
c_delta = c_delta_0 / rho_0
extra_model_kwargs = {
'rho_0': rho_0,
'spatial_flag': True,
'periodic_flag': True,
'pore_flag': True,
'origin_flags': np.array([True, False]),
'L': np.array([1000.0, 300.0]),
'pore_pf': 0.1,
'c_field_flag': True,
'c_dx': 50.0,
'c_D': 10.0,
'c_delta': c_delta,
'c_0': 1.0,
'tumble_flag': True,
'tumble_chemo_flag': True,
'chi': 100.0,
'temporal_chemo_flag': False,
}
model_kwargs = dict(default_model_kwargs, **extra_model_kwargs)
model = Model(**model_kwargs)
t_output_every = 1.0
t_upto = 50.0
output_dir = None
force_resume = None
run_utils.run_model(t_output_every, output_dir, m=model,
force_resume=force_resume, t_upto=t_upto)