def run_chi_scan_1d():
extra_model_kwargs = {
'rho_0': 0.5,
'onesided_flag': True,
'origin_flag': True,
}
model_kwargs = dict(default_model_1d_kwargs, **extra_model_kwargs)
t_output_every = 1000.0
t_upto = 4e4
chis = np.linspace(0.0, 6.0, 28)
force_resume = True
parallel = True
model_kwarg_sets = []
origin_flags = [True, False]
onesided_flags = [True, False]
for origin_flag, onesided_flag, chi in product(origin_flags,
onesided_flags, chis):
model_kwargs_cur = model_kwargs.copy()
model_kwargs_cur['onesided_flag'] = onesided_flag
model_kwargs_cur['origin_flag'] = origin_flag
model_kwargs_cur['chi'] = chi
model_kwarg_sets.append(model_kwargs_cur)
run_kwarg_scan(model.Model1D, model_kwarg_sets,
t_output_every, t_upto, force_resume, parallel)
def run_chi_scan_2d_uniform():
extra_model_kwargs = {
'rho_0': 1e-3,
'walls': walls.Walls(L, dim=2, dx=dx),
}
model_kwargs = dict(default_model_2d_kwargs, **extra_model_kwargs)
t_output_every = 1000.0
t_upto = 8e4
chis = np.linspace(0.0, 800.0, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
origin_flags = [True, False]
onesided_flags = [True, False]
for origin_flag, onesided_flag, chi in product(origin_flags,
onesided_flags, chis):
model_kwargs_cur = model_kwargs.copy()
model_kwargs_cur['onesided_flag'] = onesided_flag
model_kwargs_cur['origin_flag'] = origin_flag
model_kwargs_cur['chi'] = chi
model_kwarg_sets.append(model_kwargs_cur)
run_kwarg_scan(model.Model2D, model_kwarg_sets,
t_output_every, t_upto, force_resume, parallel)
def run_trap_s_scan():
extra_model_kwargs = {
# Quarter dt to keep diffusion stable.
'dt': 0.025,
# Halve dx to let us do finer increments in `s`.
'dx': 20.0,
'origin_flag': True,
'rho_0': 1e-3,
'chi': None,
'onesided_flag': True,
}
model_kwargs = dict(default_model_2d_kwargs, **extra_model_kwargs)
t_output_every = 2000.0
t_upto = 16e4
chis = np.linspace(200.0, 600.0, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
for s in [20.0, 60.0, 100.0, 140.0, 180.0]:
walls_cur = walls.Traps(L, dx, n=1, d=trap_d, w=trap_w, s=trap_s)
for chi in chis:
model_kwargs_cur = model_kwargs.copy()
model_kwargs_cur['walls'] = walls_cur
model_kwargs_cur['chis'] = chi
model_kwarg_sets.append(model_kwargs_cur)
run_kwarg_scan(model.Model2D, model_kwarg_sets,
t_output_every, t_upto, force_resume, parallel)
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_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_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_chi_scan_traps():
extra_model_kwargs = {
'rho_0': 1e-3,
'onesided_flag': True,
'origin_flag': False,
'walls': walls.Traps(L, dx=dx, n=5, d=trap_d, w=trap_w, s=trap_s),
}
model_kwargs = dict(default_model_2d_kwargs, **extra_model_kwargs)
t_output_every = 1000.0
t_upto = 8e4
chis = np.linspace(0.0, 800.0, 22)
force_resume = True
parallel = True
model_kwarg_sets = []
for chi in chis:
model_kwargs_cur = model_kwargs.copy()
model_kwargs_cur['chi'] = chi
model_kwarg_sets.append(model_kwargs_cur)
run_kwarg_scan(model.Model2D, model_kwarg_sets,
t_output_every, t_upto, force_resume, parallel)
