def plot(self,
ax=None,
color=np.random.rand(3),
show_domain=True,
res=(5, 5),
**kwargs):
try:
from tulip.graphics import newax, quiver
except:
logger.error('failed to import graphics')
warn('pyvectorized not found. No plotting.')
return
(x, res) = pc.grid_region(self.domain, res=res)
n = self.A.shape[0]
DA = self.A - np.eye(n)
v = DA.dot(x) + self.K
if ax is None:
ax, fig = newax()
if show_domain:
self.domain.plot(ax, color)
quiver(x, v, ax, **kwargs)
return ax
def plot(self, ax=None, show_domain=True):
if ax is None:
ax, fig = newax()
for subsystem in self.list_subsys:
subsystem.plot(ax, color=np.random.rand(3),
show_domain=show_domain)
return ax
def plot(self, ax=None, show_domain=True, **kwargs):
try:
from tulip.graphics import newax
except:
logger.error("failed to import graphics")
return
if ax is None:
ax, fig = newax()
for subsystem in self.list_subsys:
subsystem.plot(ax, color=np.random.rand(3), show_domain=show_domain, **kwargs)
return ax
def plot(self, ax=None, show_domain=True, **kwargs):
try:
from tulip.graphics import newax
except:
logger.error('failed to import graphics')
return
if ax is None:
ax, fig = newax()
for subsystem in self.list_subsys:
subsystem.plot(ax, color=np.random.rand(3),
show_domain=show_domain, **kwargs)
return ax
def plot_trajectory(ppp, x0, u_seq, ssys,
ax=None, color_seed=None):
"""Plots a PropPreservingPartition and the trajectory generated by x0
input sequence u_seq.
See Also
========
C{plot_partition}, plot
@type ppp: L{PropPreservingPartition}
@param x0: initial state
@param u_seq: matrix where each row contains an input
@param ssys: system dynamics
@param color_seed: see C{plot_partition}
@return: axis object
"""
try:
from tulip.graphics import newax
except:
logger.error('failed to import graphics.newax')
return
if ax is None:
ax, fig = newax()
plot_partition(plot_numbers=False, ax=ax, show=False)
A = ssys.A
B = ssys.B
if ssys.K is not None:
K = ssys.K
else:
K = np.zeros(x0.shape)
x = x0.flatten()
x_arr = x0
for i in range(u_seq.shape[0]):
x = (
np.dot(A, x).flatten() +
np.dot(B, u_seq[i, :] ).flatten() +
K.flatten())
x_arr = np.vstack([x_arr, x.flatten()])
ax.plot(x_arr[:,0], x_arr[:,1], 'o-')
return ax
def plot_trajectory(ppp, x0, u_seq, ssys,
ax=None, color_seed=None):
"""Plots a PropPreservingPartition and the trajectory generated by x0
input sequence u_seq.
See Also
========
C{plot_partition}, plot
@type ppp: L{PropPreservingPartition}
@param x0: initial state
@param u_seq: matrix where each row contains an input
@param ssys: system dynamics
@param color_seed: see C{plot_partition}
@return: axis object
"""
try:
from tulip.graphics import newax
except:
logger.error('failed to import graphics.newax')
return
if ax is None:
ax, fig = newax()
plot_partition(plot_numbers=False, ax=ax, show=False)
A = ssys.A
B = ssys.B
if ssys.K is not None:
K = ssys.K
else:
K = np.zeros(x0.shape)
x = x0.flatten()
x_arr = x0
for i in range(u_seq.shape[0]):
x = np.dot(A, x).flatten() +\
np.dot(B, u_seq[i, :] ).flatten() +\
K.flatten()
x_arr = np.vstack([x_arr, x.flatten()])
ax.plot(x_arr[:,0], x_arr[:,1], 'o-')
return ax
def plot(self, ax=None, color=np.random.rand(3), show_domain=True):
if quiver is None:
warn('pyvectorized not found. No plotting.')
return
(x, res) = pc.grid_region(self.domain)
n = self.A.shape[0]
DA = self.A - np.eye(n)
v = DA.dot(x)
if ax is None:
ax, fig = newax()
if show_domain:
self.domain.plot(ax, color)
quiver(x, v, ax)
return ax
def plot(self, ax=None, color=np.random.rand(3), show_domain=True, res=(5, 5), **kwargs):
try:
from tulip.graphics import newax, quiver
except:
logger.error("failed to import graphics")
warn("pyvectorized not found. No plotting.")
return
(x, res) = pc.grid_region(self.domain, res=res)
n = self.A.shape[0]
DA = self.A - np.eye(n)
v = DA.dot(x) + self.K
if ax is None:
ax, fig = newax()
if show_domain:
self.domain.plot(ax, color)
quiver(x, v, ax, **kwargs)
return ax
def plot_props(self, ax=None, text_color='yellow'):
"""Plot labeled regions of continuous propositions.
"""
try:
from tulip.graphics import newax
except:
logger.error('failed to import graphics')
return
if ax is None:
ax, fig = newax()
l, u = self.domain.bounding_box
ax.set_xlim(l[0, 0], u[0, 0])
ax.set_ylim(l[1, 0], u[1, 0])
for (prop, poly) in self.prop_regions.iteritems():
isect_poly = poly.intersect(self.domain)
isect_poly.plot(ax, color='none', hatch='/')
isect_poly.text(prop, ax, color=text_color)
return ax
def plot_props(self, ax=None, text_color='yellow'):
"""Plot labeled regions of continuous propositions.
"""
try:
from tulip.graphics import newax
except:
logger.error('failed to import graphics')
return
if ax is None:
ax, fig = newax()
l, u = self.domain.bounding_box
ax.set_xlim(l[0,0], u[0,0])
ax.set_ylim(l[1,0], u[1,0])
for (prop, poly) in self.prop_regions.items():
isect_poly = poly.intersect(self.domain)
isect_poly.plot(ax, color='none', hatch='/')
isect_poly.text(prop, ax, color=text_color)
return ax
def plot_partition(
ppp, trans=None, ppp2trans=None, only_adjacent=False,
ax=None, plot_numbers=True, color_seed=None, show=False
):
"""Plot partition with arrows from digraph.
For filtering edges based on label use L{plot_ts_on_partition}.
See Also
========
L{abstract.prop2partition.PropPreservingPartition}, L{plot_trajectory}
@type ppp: L{PropPreservingPartition}
@param trans: Transition matrix. If used,
then transitions in C{ppp} are shown with arrows.
Otherwise C{ppp.adj} is plotted.
To show C{ppp.adj}, pass: trans = True
@param plot_numbers: If True,
then annotate each Region center with its number.
@param show: If True, then show the plot.
Otherwise return axis object.
Axis object is good for creating custom plots.
@param ax: axes where to plot
@param color_seed: seed for reproducible random coloring
@param ppp2trans: order mapping ppp indices to trans states
@type ppp2trans: list of trans states
"""
if mpl is None:
warn('matplotlib not found')
return
# needs to be converted to adjacency matrix ?
if isinstance(trans, nx.MultiDiGraph):
if trans is not None and ppp2trans is None:
msg = 'trans is a networkx MultiDiGraph, '
msg += 'so ppp2trans required to define state order,\n'
msg += 'used when converting the graph to an adjacency matrix.'
raise Exception(msg)
trans = nx.to_numpy_matrix(trans, nodelist=ppp2trans)
trans = np.array(trans)
l,u = ppp.domain.bounding_box
arr_size = (u[0,0]-l[0,0])/50.0
# new figure ?
if ax is None:
ax, fig = newax()
# no trans given: use partition's
if trans is True and ppp.adj is not None:
ax.set_title('Adjacency from Partition')
trans = ppp.adj
elif trans is None:
trans = 'none'
else:
ax.set_title('Adjacency from given Transitions')
ax.set_xlim(l[0,0],u[0,0])
ax.set_ylim(l[1,0],u[1,0])
# repeatable coloring ?
if color_seed is not None:
prng = np.random.RandomState(color_seed)
else:
prng = np.random.RandomState()
# plot polytope patches
for i, reg in enumerate(ppp.regions):
# select random color,
# same color for all polytopes in each region
col = prng.rand(3)
# single polytope or region ?
reg.plot(color=col, ax=ax)
if plot_numbers:
reg.text(str(i), ax, color='red')
# not show trans ?
if trans is 'none':
if show:
mpl.pyplot.show()
return ax
# plot transition arrows between patches
rows, cols = np.nonzero(trans)
for i, j in zip(rows, cols):
# mask non-adjacent cell transitions ?
if only_adjacent:
if ppp.adj[i, j] == 0:
continue
plot_transition_arrow(ppp.regions[i], ppp.regions[j], ax, arr_size)
if show:
mpl.pyplot.show()
return ax
def plot_partition(
ppp, trans=None, ppp2trans=None, only_adjacent=False,
ax=None, plot_numbers=True, color_seed=None
):
"""Plot partition with arrows from digraph.
For filtering edges based on label use L{plot_ts_on_partition}.
See Also
========
L{abstract.prop2partition.PropPreservingPartition}, L{plot_trajectory}
@type ppp: L{PropPreservingPartition}
@param trans: Transition matrix. If used,
then transitions in C{ppp} are shown with arrows.
Otherwise C{ppp.adj} is plotted.
To show C{ppp.adj}, pass: trans = True
@param plot_numbers: If True,
then annotate each Region center with its number.
@param ax: axes where to plot
@param color_seed: seed for reproducible random coloring
@param ppp2trans: order mapping ppp indices to trans states
@type ppp2trans: list of trans states
"""
try:
import matplotlib as mpl
from tulip.graphics import newax
except:
logger.error('failed to import matplotlib')
return
# needs to be converted to adjacency matrix ?
if isinstance(trans, nx.MultiDiGraph):
if trans is not None and ppp2trans is None:
msg = 'trans is a networkx MultiDiGraph, '
msg += 'so ppp2trans required to define state order,\n'
msg += 'used when converting the graph to an adjacency matrix.'
raise Exception(msg)
trans = nx.to_numpy_matrix(trans, nodelist=ppp2trans)
trans = np.array(trans)
l,u = ppp.domain.bounding_box
arr_size = (u[0,0]-l[0,0])/50.0
# new figure ?
if ax is None:
ax, fig = newax()
# no trans given: use partition's
if trans is True and ppp.adj is not None:
ax.set_title('Adjacency from Partition')
trans = ppp.adj
elif trans is None:
trans = 'none'
else:
ax.set_title('Adjacency from given Transitions')
ax.set_xlim(l[0,0],u[0,0])
ax.set_ylim(l[1,0],u[1,0])
# repeatable coloring ?
if color_seed is not None:
prng = np.random.RandomState(color_seed)
else:
prng = np.random.RandomState()
# plot polytope patches
for i, reg in enumerate(ppp.regions):
# select random color,
# same color for all polytopes in each region
col = prng.rand(3)
# single polytope or region ?
reg.plot(color=col, ax=ax)
if plot_numbers:
reg.text(str(i), ax, color='red')
# not show trans ?
if trans is 'none':
mpl.pyplot.show()
return ax
# plot transition arrows between patches
rows, cols = np.nonzero(trans)
for i, j in zip(rows, cols):
# mask non-adjacent cell transitions ?
if only_adjacent:
if ppp.adj[i, j] == 0:
continue
plot_transition_arrow(ppp.regions[i], ppp.regions[j], ax, arr_size)
mpl.pyplot.show()
return ax
