def t_measures(dirname, time_func, measure_func):
"""Calculate a measure over time for a single output directory,
and its uncertainty.
Parameters
----------
dirname: str
Path to a model output directory.
time_func: function
Function which takes a :class:`Model` instance as a single argument,
and returns its time.
measure_func: function
Function which takes a :class:`Model` instance as a single argument,
and returns the measure of interest, and its uncertainty.
Returns
-------
ts: np.ndarray
Times.
measures: np.ndarray
Measures.
measure_errs: np.ndarray
Measure uncertainties.
"""
ts, measures, measure_errs = [], [], []
for fname in get_filenames(dirname):
m = filename_to_model(fname)
ts.append(time_func(m))
meas, meas_err = measure_func(m)
measures.append(meas)
measure_errs.append(meas_err)
return np.array(ts), np.array(measures), np.array(measure_errs)
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 plot_2d(dirname):
fig = plt.figure()
ax_vis = fig.add_subplot(111)
fnames = output_utils.get_filenames(dirname)
m_0 = output_utils.filename_to_model(fnames[0])
L = m_0.ships.agents.positions.L
ax_vis.set_xlim(-L[0] / 2.0, L[0] / 2.0)
ax_vis.set_ylim(-L[1] / 2.0, L[1] / 2.0)
ax_vis.set_aspect('equal')
plt.subplots_adjust(left=0.25, bottom=0.25)
has_c_field = isinstance(m_0.ships.c_field, ahoy.fields.FoodField)
if has_c_field:
plot_c = VarPlot(m_0.ships.c_field.c, cmap=reds_cmap, axes=ax_vis)
plot_p = ax_vis.quiver(m_0.ships.agents.positions.r_w[:, 0],
m_0.ships.agents.positions.r_w[:, 1],
m_0.ships.agents.directions.u[:, 0],
m_0.ships.agents.directions.u[:, 1])
ax_slide = plt.axes([0.25, 0.1, 0.65, 0.03])
t_slider = Slider(ax_slide, 'Time', 0, len(fnames), valinit=0)
t_time = fig.text(0.1, 0.5, '')
def update(val):
fname_i = int(round(val))
if 0 <= fname_i < len(fnames):
m = output_utils.filename_to_model(fnames[fname_i])
if has_c_field:
plot_c.update(m.ships.c_field.c)
plot_p.set_offsets(m.ships.agents.positions.r_w)
plot_p.set_UVC(m.ships.agents.directions.u[:, 0],
m.ships.agents.directions.u[:, 1])
t_time.set_text('Time: {:g}'.format(m.ships.time.t))
fig.canvas.draw_idle()
t_slider.on_changed(update)
plt.show()
def plot_1d(dirname):
fig = plt.figure()
ax_vis = fig.add_subplot(211)
ax_d = fig.add_subplot(212)
fnames = output_utils.get_filenames(dirname)
m_0 = output_utils.filename_to_model(fnames[0])
L = m_0.ships.agents.positions.L
ax_vis.set_xlim(-L[0] / 2.0, L[0] / 2.0)
ax_d.set_xlim(-L[0] / 2.0, L[0] / 2.0)
dx = L / 100.0
plt.subplots_adjust(left=0.25, bottom=0.25)
plot_p = ax_vis.scatter(m_0.ships.agents.positions.r_w[:, 0],
np.zeros([m_0.ships.agents.n]))
d = m_0.ships.agents.positions.get_density_field(dx)
x = np.linspace(-L[0] / 2.0, L[0] / 2.0, d.shape[0])
plot_d = ax_d.bar(x, d, width=x[1] - x[0])
ax_slide = plt.axes([0.25, 0.1, 0.65, 0.03])
t_slider = Slider(ax_slide, 'Index', 0, len(fnames), valinit=0)
def update(val):
fname_i = int(round(val))
if 0 <= fname_i < len(fnames):
m = output_utils.filename_to_model(fnames[fname_i])
plot_p.set_offsets(np.array([m.ships.agents.positions.r_w[:, 0],
np.zeros([m.ships.agents.n])]).T)
ds = m.ships.agents.positions.get_density_field(dx)
for rect, d in zip(plot_d, ds):
rect.set_height(d)
ax_d.set_ylim(0.0, 1.05 * ds.max())
fig.canvas.draw_idle()
t_slider.on_changed(update)
plt.show()
def plot_linear_density(dirname):
fig = plt.figure()
ax_d = fig.add_subplot(211)
ax_c = fig.add_subplot(212)
fnames = output_utils.get_filenames(dirname)
m_0 = output_utils.filename_to_model(fnames[0])
L = m_0.ships.agents.positions.L
dx = L[0] / 100.0
plt.subplots_adjust(left=0.25, bottom=0.25)
ds, xbs = utils.get_linear_density(m_0, dx)
plot_d = ax_d.bar(xbs[:-1], ds, width=xbs[1] - xbs[0])
c_field = m_0.ships.c_field.c
plot_c = ax_c.scatter(c_field.mesh.cellCenters[0, :], c_field.value)
ax_slide = plt.axes([0.25, 0.1, 0.65, 0.03])
t_slider = Slider(ax_slide, 'Index', 0, len(fnames), valinit=0)
ax_d.set_xlim(-L[0] / 2.0, L[0] / 2.0)
ax_c.set_xlim(-L[0] / 2.0, L[0] / 2.0)
ax_c.set_ylim(0.0, m_0.ships.c_field.c_0)
def update(val):
fname_i = int(round(val))
if 0 <= fname_i < len(fnames):
m = output_utils.filename_to_model(fnames[fname_i])
ds, xbs = utils.get_linear_density(m, dx)
for rect, d in zip(plot_d, ds):
rect.set_height(d)
c_field = m.ships.c_field.c
plot_c.set_offsets(np.array([c_field.mesh.cellCenters[0, :],
c_field.value]).T)
fig.canvas.draw_idle()
t_slider.on_changed(update)
plt.show()
PlotSet = namedtuple('PlotSet', ['dirname_path', 'label', 'color'])
cs = iter(ejm_rcparams.set2)
plot_sets = [
PlotSet('/Volumes/Backup/bannock_data/Model1D_dim=1,seed=1,dt=0.1,L=2.5e+03,dx=40,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,origin_flag=0,rho_0=0.5,chi=all,onesided_flag=1,vicsek_R=0/Model1D_dim=1,seed=1,dt=0.1,L=2.5e+03,dx=40,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,origin_flag=0,rho_0=0.5,chi=0,onesided_flag=1,vicsek_R=0',
r'$\chi = 0$', next(cs)),
PlotSet('/Volumes/Backup/bannock_data/Model1D_dim=1,seed=1,dt=0.1,L=2.5e+03,dx=40,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,origin_flag=0,rho_0=0.5,chi=all,onesided_flag=1,vicsek_R=0/Model1D_dim=1,seed=1,dt=0.1,L=2.5e+03,dx=40,c_D=1e+03,c_sink=0.01,c_source=1,v_0=20,p_0=1,origin_flag=0,rho_0=0.5,chi=6,onesided_flag=1,vicsek_R=0',
r'$\chi = 6$', next(cs)),
]
t_steady = 20000.0
bins = 400
for plot_set in plot_sets:
fnames = output_utils.get_filenames(plot_set.dirname_path)
ms = [output_utils.filename_to_model(fname) for fname in fnames]
ms_steady = [m for m in ms if m.t > t_steady]
m_0 = ms_steady[-1]
rhos = [get_rho(m, bins) for m in ms_steady]
rho = np.mean(rhos, axis=0)
rho_err = sem(rhos, axis=0)
rho_red = cutils.get_reduced_rho(m_0.rho_0, rho)
rho_red_err = cutils.get_reduced_rho(m_0.rho_0, rho_err)
x = np.linspace(-m_0.L / 2.0, m_0.L / 2.0, rho.shape[0])
D_rho = cutils.get_D_rho(m_0.v_0, m_0.p_0, m_0.dim)
x_red = cutils.get_reduced_length(m_0.c_sink, D_rho, x)
