def get_average_measure(dirname, measure_func, t_steady=None):
"""
Calculate a measure of a model in an output directory, averaged over
all times when the model is at steady-state.
Parameters
----------
dirname: str
Output directory
measure_func: function
Function which takes a :class:`Model` instance as a single argument,
and returns the measure of interest, and its uncertainty.
t_steady: None or float
Time to consider the model to be at steady-state.
`None` means just consider the latest time.
Returns
-------
measure: numpy.ndarray
Measure.
measure_errs: numpy.ndarray
Measure uncertainty.
If no averaging is done, this is taken from the measure_func.
Otherwise, the standard error over all samples is used.
"""
if t_steady is None:
meas, meas_err = measure_func(get_recent_model(dirname))
return meas, meas_err
else:
ms = [filename_to_model(fname) for fname in get_filenames(dirname)]
ms_steady = [m for m in ms if m.t > t_steady]
meas_list = [measure_func(m) for m in ms_steady]
meases, meas_errs = zip(*meas_list)
return np.mean(meases), sem(meases)
def params(dirnames, param_func, t_steady=None):
"""Calculate a parameter of a set of model output directories,
for a measure function which returns an associated uncertainty.
Parameters
----------
dirnames: list[str]
Model output directory paths.
param_func: function
Function which takes a :class:`Model` instance as a single argument,
and returns the parameter of interest.
Returns
-------
params: numpy.ndarray
Parameters.
"""
return np.array([param_func(get_recent_model(d)) for d in dirnames])
def group_by_key(dirnames, key):
"""Group a set of output directories according to a model parameter.
Parameters
----------
dirnames: list[str]
Output directories
key: various
A field of a :class:`Model` instance.
Returns
-------
groups: dict[various: list[str]]
For each value of `key` that is found at least once in the models, a
list of the output directories where `key` is that value.
"""
groups = defaultdict(lambda: [])
for dirname in dirnames:
m = get_recent_model(dirname)
groups[m.__dict__[key]].append(dirname)
return dict(groups)
def get_equiv_chi_item(ud_0, dirnames):
key = get_equiv_chi_key(get_recent_model(dirnames[0]))
chi_equiv = get_equiv_chi(ud_0, dirnames)
return key, chi_equiv
plot_sets = [
PlotSet('blank_noclust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=0,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3', False),
PlotSet('blank_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=952,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3', True),
PlotSet('blank_multi_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=1.52e+03,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Walls_dim=2,L=2.5e+03,dx=40.3', False),
PlotSet('trap_noclust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=0,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6', False),
PlotSet('trap_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=686,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6', False),
PlotSet('trap_multi_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=1.6e+03,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=1,d=40.3,w=282,s=80.6', False),
PlotSet('traps_noclust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=0,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6', False),
PlotSet('traps_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=686,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6', False),
PlotSet('traps_multi_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=1.6e+03,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Traps_dim=2,L=2.5e+03,dx=40.3,n=5,d=40.3,w=282,s=80.6', False),
PlotSet('maze_noclust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Maze_dim=2,L=2.5e+03,dx=40.3,d=40,seed=1/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=0,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Maze_dim=2,L=2.5e+03,dx=40.3,d=40,seed=1', False),
PlotSet('maze_clust', '/Volumes/Backup/bannock_data/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=all,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Maze_dim=2,L=2.5e+03,dx=40.3,d=40,seed=1/Model2D_dim=2,seed=1,dt=0.1,L=2.5e+03,dx=40.3,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,D_rot=0.2,origin_flag=0,rho_0=0.001,chi=800,onesided_flag=1,force_mu=0,vicsek_R=0,walls=Maze_dim=2,L=2.5e+03,dx=40.3,d=40,seed=1', False),
]
for plot_set in plot_sets:
model = output_utils.get_recent_model(plot_set.dirname_path)
fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)
ejm_rcparams.prettify_axes(ax)
D_rho = cutils.get_D_rho(model.v_0, model.p_0, model.dim)
L_red = cutils.get_reduced_length(delta=model.c_sink, D_rho=D_rho,
x=model.L)
if plot_set.centre_flag:
# Centre particles in the axes.
r_com = get_r_com(model.r)
r = model.r - r_com
i_com = np.round(r_com / model.dx).astype(np.int)
def plot_vis(dirname):
dim = output_utils.get_recent_model(dirname).ships.dim
if dim == 1:
plot_1d(dirname)
elif dim == 2:
plot_2d(dirname)
