def plot_vector_field(self, show=False, with_plot=True, with_return=False):
"""Plot the vector filed."""
utils.output('I am creating the vector field ...')
# Nullcline of the x variable
y_val = self.F_fx(self.resolutions[self.x_var])
y_val = np.asarray(y_val)
# visualization
if with_plot:
label = f"d{self.x_var}dt"
x_style = dict(color='lightcoral', alpha=.7, linewidth=4)
plt.plot(np.asarray(self.resolutions[self.x_var]),
y_val,
**x_style,
label=label)
plt.axhline(0)
plt.xlabel(self.x_var)
plt.ylabel(label)
plt.xlim(*utils.rescale(self.target_vars[self.x_var],
scale=(self.lim_scale - 1.) / 2))
plt.legend()
if show: plt.show()
# return
if with_return:
return y_val
def plot_vector_field(self, show=False):
"""Plot the vector filed.
Parameters
----------
show : bool
Whether show the figure.
Returns
-------
results : np.ndarray
The dx values.
"""
print('plot vector field ...')
# 1. Nullcline of the x variable
try:
y_val = self.get_f_dx()(self.resolutions[self.x_var])
except TypeError:
raise errors.ModelUseError(
'Missing variables. Please check and set missing '
'variables to "fixed_vars".')
# 2. visualization
label = f"d{self.x_var}dt"
x_style = dict(color='lightcoral', alpha=.7, linewidth=4)
plt.plot(self.resolutions[self.x_var], y_val, **x_style, label=label)
plt.axhline(0)
plt.xlabel(self.x_var)
plt.ylabel(label)
plt.xlim(*utils.rescale(self.target_vars[self.x_var],
scale=(self.options.lim_scale - 1.) / 2))
plt.legend()
if show:
plt.show()
return y_val
def plot_limit_cycle_by_sim(self,
initials,
duration,
tol=0.001,
show=False):
"""Plot trajectories according to the settings.
Parameters
----------
initials : list, tuple
The initial value setting of the targets. It can be a tuple/list of floats to specify
each value of dynamical variables (for example, ``(a, b)``). It can also be a
tuple/list of tuple to specify multiple initial values (for example,
``[(a1, b1), (a2, b2)]``).
duration : int, float, tuple, list
The running duration. Same with the ``duration`` in ``NeuGroup.run()``.
It can be a int/float (``t_end``) to specify the same running end time,
or it can be a tuple/list of int/float (``(t_start, t_end)``) to specify
the start and end simulation time. Or, it can be a list of tuple
(``[(t1_start, t1_end), (t2_start, t2_end)]``) to specify the specific
start and end simulation time for each initial value.
show : bool
Whether show or not.
"""
print('plot limit cycle ...')
# 1. format the initial values
if isinstance(initials, dict):
initials = [initials]
elif isinstance(initials, (list, tuple)):
if isinstance(initials[0], (int, float)):
initials = [{
self.dvar_names[i]: v
for i, v in enumerate(initials)
}]
elif isinstance(initials[0], dict):
initials = initials
elif isinstance(initials[0], (tuple, list)) and isinstance(
initials[0][0], (int, float)):
initials = [{
self.dvar_names[i]: v
for i, v in enumerate(init)
} for init in initials]
else:
raise ValueError
else:
raise ValueError
# 2. format the running duration
if isinstance(duration, (int, float)):
duration = [(0, duration) for _ in range(len(initials))]
elif isinstance(duration[0], (int, float)):
duration = [duration for _ in range(len(initials))]
else:
assert len(duration) == len(initials)
# 5. run the network
for init_i, initial in enumerate(initials):
traj_group = Trajectory(size=1,
integrals=self.model.integrals,
target_vars=initial,
fixed_vars=self.fixed_vars,
pars_update=self.pars_update,
scope=self.model.scopes)
# 5.2 run the model
traj_group.run(
duration=duration[init_i],
report=False,
)
x_data = traj_group.mon[self.x_var][:, 0]
y_data = traj_group.mon[self.y_var][:, 0]
max_index = utils.find_indexes_of_limit_cycle_max(x_data, tol=tol)
if max_index[0] != -1:
x_cycle = x_data[max_index[0]:max_index[1]]
y_cycle = y_data[max_index[0]:max_index[1]]
# 5.5 visualization
lines = plt.plot(x_cycle, y_cycle, label='limit cycle')
utils.add_arrow(lines[0])
else:
print(f'No limit cycle found for initial value {initial}')
# 6. visualization
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.options.lim_scale - 1.) / 2
plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
if show:
plt.show()
def plot_trajectory(self,
initials,
duration,
plot_duration=None,
axes='v-v',
show=False):
"""Plot trajectories according to the settings.
Parameters
----------
initials : list, tuple, dict
The initial value setting of the targets. It can be a tuple/list of floats to specify
each value of dynamical variables (for example, ``(a, b)``). It can also be a
tuple/list of tuple to specify multiple initial values (for example,
``[(a1, b1), (a2, b2)]``).
duration : int, float, tuple, list
The running duration. Same with the ``duration`` in ``NeuGroup.run()``.
It can be a int/float (``t_end``) to specify the same running end time,
or it can be a tuple/list of int/float (``(t_start, t_end)``) to specify
the start and end simulation time. Or, it can be a list of tuple
(``[(t1_start, t1_end), (t2_start, t2_end)]``) to specify the specific
start and end simulation time for each initial value.
plot_duration : tuple, list, optional
The duration to plot. It can be a tuple with ``(start, end)``. It can
also be a list of tuple ``[(start1, end1), (start2, end2)]`` to specify
the plot duration for each initial value running.
axes : str
The axes to plot. It can be:
- 'v-v'
Plot the trajectory in the 'x_var'-'y_var' axis.
- 't-v'
Plot the trajectory in the 'time'-'var' axis.
show : bool
Whether show or not.
"""
print('plot trajectory ...')
if axes not in ['v-v', 't-v']:
raise errors.ModelUseError(
f'Unknown axes "{axes}", only support "v-v" and "t-v".')
# 1. format the initial values
if isinstance(initials, dict):
initials = [initials]
elif isinstance(initials, (list, tuple)):
if isinstance(initials[0], (int, float)):
initials = [{
self.dvar_names[i]: v
for i, v in enumerate(initials)
}]
elif isinstance(initials[0], dict):
initials = initials
elif isinstance(initials[0], (tuple, list)) and isinstance(
initials[0][0], (int, float)):
initials = [{
self.dvar_names[i]: v
for i, v in enumerate(init)
} for init in initials]
else:
raise ValueError
else:
raise ValueError
# 2. format the running duration
if isinstance(duration, (int, float)):
duration = [(0, duration) for _ in range(len(initials))]
elif isinstance(duration[0], (int, float)):
duration = [duration for _ in range(len(initials))]
else:
assert len(duration) == len(initials)
# 3. format the plot duration
if plot_duration is None:
plot_duration = duration
if isinstance(plot_duration[0], (int, float)):
plot_duration = [plot_duration for _ in range(len(initials))]
else:
assert len(plot_duration) == len(initials)
# 5. run the network
for init_i, initial in enumerate(initials):
traj_group = Trajectory(size=1,
integrals=self.model.integrals,
target_vars=initial,
fixed_vars=self.fixed_vars,
pars_update=self.pars_update,
scope=self.model.scopes)
# 5.2 run the model
traj_group.run(
duration=duration[init_i],
report=False,
)
# 5.3 legend
legend = f'$traj_{init_i}$: '
for key in self.dvar_names:
legend += f'{key}={initial[key]}, '
legend = legend[:-2]
# 5.4 trajectory
start = int(plot_duration[init_i][0] / backend.get_dt())
end = int(plot_duration[init_i][1] / backend.get_dt())
# 5.5 visualization
if axes == 'v-v':
lines = plt.plot(traj_group.mon[self.x_var][start:end, 0],
traj_group.mon[self.y_var][start:end, 0],
label=legend)
utils.add_arrow(lines[0])
else:
plt.plot(traj_group.mon.ts[start:end],
traj_group.mon[self.x_var][start:end, 0],
label=legend + f', {self.x_var}')
plt.plot(traj_group.mon.ts[start:end],
traj_group.mon[self.y_var][start:end, 0],
label=legend + f', {self.y_var}')
# 6. visualization
if axes == 'v-v':
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.options.lim_scale - 1.) / 2
plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
else:
plt.legend(title='Initial values')
if show:
plt.show()
def plot_nullcline(self, numerical_setting=None, show=False):
"""Plot the nullcline.
Parameters
----------
numerical_setting : dict, optional
Set the numerical method for solving nullclines.
For each function setting, it contains the following keywords:
coords
The coordination setting, it can be 'var1-var2' (which means
for each possible value 'var1' the optimizer method will search
the zero root of 'var2') or 'var2-var1' (which means iterate each
'var2' and get the optimization results of 'var1').
plot
It can be 'scatter' (default) or 'line'.
show : bool
Whether show the figure.
Returns
-------
values : dict
A dict with the format of ``{func1: (x_val, y_val), func2: (x_val, y_val)}``.
"""
print('plot nullcline ...')
if numerical_setting is None:
numerical_setting = dict()
x_setting = numerical_setting.get(self.x_eq_group.func_name, {})
y_setting = numerical_setting.get(self.y_eq_group.func_name, {})
x_coords = x_setting.get('coords', self.x_var + '-' + self.y_var)
y_coords = y_setting.get('coords', self.x_var + '-' + self.y_var)
x_plot_style = x_setting.get('plot', 'scatter')
y_plot_style = y_setting.get('plot', 'scatter')
xs = self.resolutions[self.x_var]
ys = self.resolutions[self.y_var]
# Nullcline of the y variable
y_style = dict(
color='cornflowerblue',
alpha=.7,
)
y_by_x = self.get_y_by_x_in_y_eq()
if y_by_x['status'] == 'sympy_success':
try:
y_values_in_y_eq = y_by_x['f'](xs)
except TypeError:
raise errors.ModelUseError(
'Missing variables. Please check and set missing '
'variables to "fixed_vars".')
x_values_in_y_eq = xs
plt.plot(xs,
y_values_in_y_eq,
**y_style,
label=f"{self.y_var} nullcline")
else:
x_by_y = self.get_x_by_y_in_y_eq()
if x_by_y['status'] == 'sympy_success':
try:
x_values_in_y_eq = x_by_y['f'](ys)
except TypeError:
raise errors.ModelUseError(
'Missing variables. Please check and set missing '
'variables to "fixed_vars".')
y_values_in_y_eq = ys
plt.plot(x_values_in_y_eq,
ys,
**y_style,
label=f"{self.y_var} nullcline")
else:
# optimization results
optimizer = self.get_f_optimize_y_nullcline(y_coords)
x_values_in_y_eq, y_values_in_y_eq = optimizer()
if x_plot_style == 'scatter':
plt.plot(x_values_in_y_eq,
y_values_in_y_eq,
'.',
**y_style,
label=f"{self.y_var} nullcline")
elif x_plot_style == 'line':
plt.plot(x_values_in_y_eq,
y_values_in_y_eq,
**y_style,
label=f"{self.y_var} nullcline")
else:
raise ValueError(f'Unknown plot style: {x_plot_style}')
# Nullcline of the x variable
x_style = dict(
color='lightcoral',
alpha=.7,
)
y_by_x = self.get_y_by_x_in_x_eq()
if y_by_x['status'] == 'sympy_success':
try:
y_values_in_x_eq = y_by_x['f'](xs)
except TypeError:
raise errors.ModelUseError(
'Missing variables. Please check and set missing '
'variables to "fixed_vars".')
x_values_in_x_eq = xs
plt.plot(xs,
y_values_in_x_eq,
**x_style,
label=f"{self.x_var} nullcline")
else:
x_by_y = self.get_x_by_y_in_x_eq()
if x_by_y['status'] == 'sympy_success':
try:
x_values_in_x_eq = x_by_y['f'](ys)
except TypeError:
raise errors.ModelUseError(
'Missing variables. Please check and set missing '
'variables to "fixed_vars".')
y_values_in_x_eq = ys
plt.plot(x_values_in_x_eq,
ys,
**x_style,
label=f"{self.x_var} nullcline")
else:
# optimization results
optimizer = self.get_f_optimize_x_nullcline(x_coords)
x_values_in_x_eq, y_values_in_x_eq = optimizer()
# visualization
if y_plot_style == 'scatter':
plt.plot(x_values_in_x_eq,
y_values_in_x_eq,
'.',
**x_style,
label=f"{self.x_var} nullcline")
elif y_plot_style == 'line':
plt.plot(x_values_in_x_eq,
y_values_in_x_eq,
**x_style,
label=f"{self.x_var} nullcline")
else:
raise ValueError(f'Unknown plot style: {x_plot_style}')
# finally
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.options.lim_scale - 1.) / 2
plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
if show:
plt.show()
return {
self.x_eq_group.func_name: (x_values_in_x_eq, y_values_in_x_eq),
self.y_eq_group.func_name: (x_values_in_y_eq, y_values_in_y_eq)
}
def plot_bifurcation(self, with_plot=True, show=False, with_return=False,
tol_aux=1e-8, loss_screen=None):
utils.output('I am making bifurcation analysis ...')
xs = self.resolutions[self.x_var]
vps = bm.meshgrid(xs, *tuple(self.resolutions[p] for p in self.target_par_names))
vps = tuple(jnp.moveaxis(vp.value, 0, 1).flatten() for vp in vps)
candidates = vps[0]
pars = vps[1:]
fixed_points, _, pars = self._get_fixed_points(candidates, *pars,
tol_aux=tol_aux,
loss_screen=loss_screen,
num_seg=len(xs))
dfxdx = np.asarray(self.F_vmap_dfxdx(jnp.asarray(fixed_points), *pars))
pars = tuple(np.asarray(p) for p in pars)
if with_plot:
if len(self.target_pars) == 1:
container = {c: {'p': [], 'x': []} for c in stability.get_1d_stability_types()}
# fixed point
for p, x, dx in zip(pars[0], fixed_points, dfxdx):
fp_type = stability.stability_analysis(dx)
container[fp_type]['p'].append(p)
container[fp_type]['x'].append(x)
# visualization
plt.figure(self.x_var)
for fp_type, points in container.items():
if len(points['x']):
plot_style = stability.plot_scheme[fp_type]
plt.plot(points['p'], points['x'], '.', **plot_style, label=fp_type)
plt.xlabel(self.target_par_names[0])
plt.ylabel(self.x_var)
scale = (self.lim_scale - 1) / 2
plt.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.legend()
if show:
plt.show()
elif len(self.target_pars) == 2:
container = {c: {'p0': [], 'p1': [], 'x': []} for c in stability.get_1d_stability_types()}
# fixed point
for p0, p1, x, dx in zip(pars[0], pars[1], fixed_points, dfxdx):
fp_type = stability.stability_analysis(dx)
container[fp_type]['p0'].append(p0)
container[fp_type]['p1'].append(p1)
container[fp_type]['x'].append(x)
# visualization
fig = plt.figure(self.x_var)
ax = fig.add_subplot(projection='3d')
for fp_type, points in container.items():
if len(points['x']):
plot_style = stability.plot_scheme[fp_type]
xs = points['p0']
ys = points['p1']
zs = points['x']
ax.scatter(xs, ys, zs, **plot_style, label=fp_type)
ax.set_xlabel(self.target_par_names[0])
ax.set_ylabel(self.target_par_names[1])
ax.set_zlabel(self.x_var)
scale = (self.lim_scale - 1) / 2
ax.set_xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale))
ax.set_ylim(*utils.rescale(self.target_pars[self.target_par_names[1]], scale=scale))
ax.set_zlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
ax.grid(True)
ax.legend()
if show:
plt.show()
else:
raise errors.BrainPyError(f'Cannot visualize co-dimension {len(self.target_pars)} '
f'bifurcation.')
if with_return:
return fixed_points, pars, dfxdx
def plot_bifurcation(self, with_plot=True, show=False, with_return=False,
tol_aux=1e-8, tol_unique=1e-2, tol_opt_candidate=None,
num_par_segments=1, num_fp_segment=1, nullcline_aux_filter=1.,
select_candidates='aux_rank', num_rank=100):
"""Make the bifurcation analysis.
Parameters
----------
with_plot: bool
Whether plot the bifurcation figure.
show: bool
Whether show the figure.
with_return: bool
Whether return the computed bifurcation results.
tol_aux: float
The loss tolerance of auxiliary function :math:`f_{aux}` to confirm the fixed
point. Default is 1e-7. Once :math:`f_{aux}(x_1) < \mathrm{tol\_aux}`,
:math:`x_1` will be a fixed point.
tol_unique: float
The tolerance of distance between candidate fixed points to confirm they are
the same. Default is 1e-2. If :math:`|x_1 - x_2| > \mathrm{tol\_unique}`,
then :math:`x_1` and :math:`x_2` are unique fixed points. Otherwise,
:math:`x_1` and :math:`x_2` will be treated as a same fixed point.
tol_opt_candidate: float, optional
The tolerance of auxiliary function :math:`f_{aux}` to select candidate
initial points for fixed point optimization.
num_par_segments: int, sequence of int
How to segment parameters.
num_fp_segment: int
How to segment fixed points.
nullcline_aux_filter: float
The
select_candidates: str
The method to select candidate fixed points. It can be:
- ``fx-nullcline``: use the points of fx-nullcline.
- ``fy-nullcline``: use the points of fy-nullcline.
- ``nullclines``: use the points in both of fx-nullcline and fy-nullcline.
- ``aux_rank``: use the minimal value of points for the auxiliary function.
num_rank: int
The number of candidates to be used to optimize the fixed points.
rank to use.
Returns
-------
results : tuple
Return a tuple of analyzed results:
- fixed points: a 2D matrix with the shape of (num_point, num_var)
- parameters: a 2D matrix with the shape of (num_point, num_par)
- jacobians: a 3D tensors with the shape of (num_point, 2, 2)
"""
utils.output('I am making bifurcation analysis ...')
if self._can_convert_to_one_eq():
if self.convert_type() == C.x_by_y:
X = self.resolutions[self.y_var].value
else:
X = self.resolutions[self.x_var].value
pars = tuple(self.resolutions[p].value for p in self.target_par_names)
mesh_values = jnp.meshgrid(*((X,) + pars))
mesh_values = tuple(jnp.moveaxis(v, 0, 1).flatten() for v in mesh_values)
candidates = mesh_values[0]
parameters = mesh_values[1:]
else:
if select_candidates == 'fx-nullcline':
fx_nullclines = self._get_fx_nullcline_points(num_segments=num_par_segments,
fp_aux_filter=nullcline_aux_filter)
candidates = fx_nullclines[0]
parameters = fx_nullclines[1:]
elif select_candidates == 'fy-nullcline':
fy_nullclines = self._get_fy_nullcline_points(num_segments=num_par_segments,
fp_aux_filter=nullcline_aux_filter)
candidates = fy_nullclines[0]
parameters = fy_nullclines[1:]
elif select_candidates == 'nullclines':
fx_nullclines = self._get_fx_nullcline_points(num_segments=num_par_segments,
fp_aux_filter=nullcline_aux_filter)
fy_nullclines = self._get_fy_nullcline_points(num_segments=num_par_segments,
fp_aux_filter=nullcline_aux_filter)
candidates = jnp.vstack([fx_nullclines[0], fy_nullclines[0]])
parameters = [jnp.concatenate([fx_nullclines[i], fy_nullclines[i]])
for i in range(1, len(fy_nullclines))]
elif select_candidates == 'aux_rank':
assert nullcline_aux_filter > 0.
candidates, parameters = self._get_fp_candidates_by_aux_rank(num_segments=num_par_segments,
num_rank=num_rank)
else:
raise ValueError
candidates, _, parameters = self._get_fixed_points(candidates,
*parameters,
tol_aux=tol_aux,
tol_unique=tol_unique,
tol_opt_candidate=tol_opt_candidate,
num_segment=num_fp_segment)
candidates = np.asarray(candidates)
parameters = np.stack(tuple(np.asarray(p) for p in parameters)).T
utils.output('I am trying to filter out duplicate fixed points ...')
final_fps = []
final_pars = []
for par in np.unique(parameters, axis=0):
ids = np.where(np.all(parameters == par, axis=1))[0]
fps, ids2 = utils.keep_unique(candidates[ids], tolerance=tol_unique)
final_fps.append(fps)
final_pars.append(parameters[ids[ids2]])
final_fps = np.vstack(final_fps) # with the shape of (num_point, num_var)
final_pars = np.vstack(final_pars) # with the shape of (num_point, num_par)
jacobians = np.asarray(self.F_vmap_jacobian(jnp.asarray(final_fps), *final_pars.T))
utils.output(f'{C.prefix}Found {len(final_fps)} fixed points.')
# remember the fixed points for later limit cycle plotting
self._fixed_points = (final_fps, final_pars)
if with_plot:
# bifurcation analysis of co-dimension 1
if len(self.target_pars) == 1:
container = {c: {'p': [], self.x_var: [], self.y_var: []}
for c in stability.get_2d_stability_types()}
# fixed point
for p, xy, J in zip(final_pars, final_fps, jacobians):
fp_type = stability.stability_analysis(J)
container[fp_type]['p'].append(p[0])
container[fp_type][self.x_var].append(xy[0])
container[fp_type][self.y_var].append(xy[1])
# visualization
for var in self.target_var_names:
plt.figure(var)
for fp_type, points in container.items():
if len(points['p']):
plot_style = stability.plot_scheme[fp_type]
plt.plot(points['p'], points[var], '.', **plot_style, label=fp_type)
plt.xlabel(self.target_par_names[0])
plt.ylabel(var)
scale = (self.lim_scale - 1) / 2
plt.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[var], scale=scale))
plt.legend()
if show:
plt.show()
# bifurcation analysis of co-dimension 2
elif len(self.target_pars) == 2:
container = {c: {'p0': [], 'p1': [], self.x_var: [], self.y_var: []}
for c in stability.get_2d_stability_types()}
# fixed point
for p, xy, J in zip(final_pars, final_fps, jacobians):
fp_type = stability.stability_analysis(J)
container[fp_type]['p0'].append(p[0])
container[fp_type]['p1'].append(p[1])
container[fp_type][self.x_var].append(xy[0])
container[fp_type][self.y_var].append(xy[1])
# visualization
for var in self.target_var_names:
fig = plt.figure(var)
ax = fig.add_subplot(projection='3d')
for fp_type, points in container.items():
if len(points['p0']):
plot_style = stability.plot_scheme[fp_type]
xs = points['p0']
ys = points['p1']
zs = points[var]
ax.scatter(xs, ys, zs, **plot_style, label=fp_type)
ax.set_xlabel(self.target_par_names[0])
ax.set_ylabel(self.target_par_names[1])
ax.set_zlabel(var)
scale = (self.lim_scale - 1) / 2
ax.set_xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale))
ax.set_ylim(*utils.rescale(self.target_pars[self.target_par_names[1]], scale=scale))
ax.set_zlim(*utils.rescale(self.target_vars[var], scale=scale))
ax.grid(True)
ax.legend()
if show:
plt.show()
else:
raise ValueError('Unknown length of parameters.')
if with_return:
return final_fps, final_pars, jacobians
def plot_limit_cycle_by_sim(self,
initials,
duration,
tol=0.01,
show=False,
dt=None):
"""Plot trajectories according to the settings.
Parameters
----------
initials : list, tuple
The initial value setting of the targets.
- It can be a tuple/list of floats to specify each value of dynamical variables
(for example, ``(a, b)``).
- It can also be a tuple/list of tuple to specify multiple initial values (for
example, ``[(a1, b1), (a2, b2)]``).
duration : int, float, tuple, list
The running duration. Same with the ``duration`` in ``NeuGroup.run()``.
- It can be a int/float (``t_end``) to specify the same running end time,
- Or it can be a tuple/list of int/float (``(t_start, t_end)``) to specify
the start and end simulation time.
- Or, it can be a list of tuple (``[(t1_start, t1_end), (t2_start, t2_end)]``)
to specify the specific start and end simulation time for each initial value.
show : bool
Whether show or not.
"""
utils.output('I am plotting the limit cycle ...')
# 1. format the initial values
initials = utils.check_initials(initials, self.target_var_names)
# 2. format the running duration
assert isinstance(duration, (int, float))
dt = math.get_dt() if dt is None else dt
traject_model = utils.TrajectModel(initial_vars=initials,
integrals={
self.x_var: self.F_int_x,
self.y_var: self.F_int_y
},
dt=dt)
mon_res = traject_model.run(duration=duration)
# 5. run the network
for init_i, initial in enumerate(zip(*list(initials.values()))):
# 5.2 run the model
x_data = mon_res[self.x_var][:, init_i]
y_data = mon_res[self.y_var][:, init_i]
max_index = utils.find_indexes_of_limit_cycle_max(x_data, tol=tol)
if max_index[0] != -1:
x_cycle = x_data[max_index[0]:max_index[1]]
y_cycle = y_data[max_index[0]:max_index[1]]
# 5.5 visualization
lines = plt.plot(x_cycle, y_cycle, label='limit cycle')
utils.add_arrow(lines[0])
else:
utils.output(
f'No limit cycle found for initial value {initial}')
# 6. visualization
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.lim_scale - 1.) / 2
plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
if show:
plt.show()
def plot_trajectory(self,
initials,
duration,
plot_durations=None,
axes='v-v',
dt=None,
show=False,
with_plot=True,
with_return=False,
**kwargs):
"""Plot trajectories according to the settings.
Parameters
----------
initials : list, tuple, dict
The initial value setting of the targets. It can be a tuple/list of floats to specify
each value of dynamical variables (for example, ``(a, b)``). It can also be a
tuple/list of tuple to specify multiple initial values (for example,
``[(a1, b1), (a2, b2)]``).
duration : int, float, tuple, list
The running duration. Same with the ``duration`` in ``NeuGroup.run()``.
- It can be a int/float (``t_end``) to specify the same running end time,
- Or it can be a tuple/list of int/float (``(t_start, t_end)``) to specify
the start and end simulation time.
- Or, it can be a list of tuple (``[(t1_start, t1_end), (t2_start, t2_end)]``)
to specify the specific start and end simulation time for each initial value.
plot_durations : tuple, list, optional
The duration to plot. It can be a tuple with ``(start, end)``. It can
also be a list of tuple ``[(start1, end1), (start2, end2)]`` to specify
the plot duration for each initial value running.
axes : str
The axes to plot. It can be:
- 'v-v': Plot the trajectory in the 'x_var'-'y_var' axis.
- 't-v': Plot the trajectory in the 'time'-'var' axis.
show : bool
Whether show or not.
"""
utils.output('I am plotting the trajectory ...')
if axes not in ['v-v', 't-v']:
raise errors.AnalyzerError(
f'Unknown axes "{axes}", only support "v-v" and "t-v".')
# check the initial values
initials = utils.check_initials(initials, self.target_var_names)
# 2. format the running duration
assert isinstance(duration, (int, float))
# 3. format the plot duration
plot_durations = utils.check_plot_durations(plot_durations, duration,
initials)
# 5. run the network
dt = math.get_dt() if dt is None else dt
traject_model = utils.TrajectModel(initial_vars=initials,
integrals={
self.x_var: self.F_int_x,
self.y_var: self.F_int_y
},
dt=dt)
mon_res = traject_model.run(duration=duration)
if with_plot:
# plots
for i, initial in enumerate(zip(*list(initials.values()))):
# legend
legend = f'$traj_{i}$: '
for j, key in enumerate(self.target_var_names):
legend += f'{key}={round(float(initial[j]), 4)}, '
legend = legend[:-2]
# visualization
start = int(plot_durations[i][0] / dt)
end = int(plot_durations[i][1] / dt)
if axes == 'v-v':
lines = plt.plot(mon_res[self.x_var][start:end, i],
mon_res[self.y_var][start:end, i],
label=legend,
**kwargs)
utils.add_arrow(lines[0])
else:
plt.plot(mon_res.ts[start:end],
mon_res[self.x_var][start:end, i],
label=legend + f', {self.x_var}',
**kwargs)
plt.plot(mon_res.ts[start:end],
mon_res[self.y_var][start:end, i],
label=legend + f', {self.y_var}',
**kwargs)
# visualization of others
if axes == 'v-v':
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.lim_scale - 1.) / 2
plt.xlim(
*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(
*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
else:
plt.legend(title='Initial values')
if show:
plt.show()
if with_return:
return mon_res
def plot_nullcline(self,
with_plot=True,
with_return=False,
y_style=None,
x_style=None,
show=False,
coords=None,
tol_nullcline=1e-7):
"""Plot the nullcline."""
utils.output('I am computing fx-nullcline ...')
if coords is None:
coords = dict()
x_coord = coords.get(self.x_var, None)
y_coord = coords.get(self.y_var, None)
# Nullcline of the x variable
xy_values_in_fx, = self._get_fx_nullcline_points(coords=x_coord,
tol=tol_nullcline)
x_values_in_fx = np.asarray(xy_values_in_fx[:, 0])
y_values_in_fx = np.asarray(xy_values_in_fx[:, 1])
if with_plot:
if x_style is None:
x_style = dict(
color='cornflowerblue',
alpha=.7,
)
fmt = x_style.pop('fmt', '.')
plt.plot(x_values_in_fx,
y_values_in_fx,
fmt,
**x_style,
label=f"{self.x_var} nullcline")
# Nullcline of the y variable
utils.output('I am computing fy-nullcline ...')
xy_values_in_fy, = self._get_fy_nullcline_points(coords=y_coord,
tol=tol_nullcline)
x_values_in_fy = np.asarray(xy_values_in_fy[:, 0])
y_values_in_fy = np.asarray(xy_values_in_fy[:, 1])
if with_plot:
if y_style is None:
y_style = dict(
color='lightcoral',
alpha=.7,
)
fmt = y_style.pop('fmt', '.')
plt.plot(x_values_in_fy,
y_values_in_fy,
fmt,
**y_style,
label=f"{self.y_var} nullcline")
if with_plot:
plt.xlabel(self.x_var)
plt.ylabel(self.y_var)
scale = (self.lim_scale - 1.) / 2
plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale))
plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale))
plt.legend()
if show:
plt.show()
if with_return:
return {
self.x_var: (x_values_in_fx, y_values_in_fx),
self.y_var: (x_values_in_fy, y_values_in_fy)
}
