def refine_attractor(initial_condition): t, V_j = self.traces.computeTraces(initial_condition, plotit=False) V_j = np.transpose(V_j) ti, trajectory = tl.phase_difference(V_j, V_trigger=type(self).V_trigger) try: new_attr = att.attractor(trajectory).classify() return new_attr except: return att.attractor( [[0., 0.]], initial_state=[0., 0.] ).classify()
def refine_attractor(initial_condition):
t, V_j = self.traces.computeTraces(initial_condition, plotit=False)
V_j = np.transpose(V_j)
ti, trajectory = tl.phase_difference(
V_j, V_trigger=type(self).V_trigger)
try:
new_attr = att.attractor(trajectory).classify()
return new_attr
except:
return att.attractor([[0., 0.]],
initial_state=[0., 0.]).classify()
def sweep_phase_space(self, jitter=False, widget=None, event=None):
self.dt, self.stride, self.N_output = self.system.dt, self.system.stride, self.system.N_output(self.traces.CYCLES)
self.echo('computing using '+torus_2D.PROCESSOR[self.USE_GPU]+' ...')
torus_2D.erase_traces(self)
self.erase_basins()
initial_phases = np.arange(0., 1., 1./float(self.GRID))
try: X = self.system.load_initial_conditions(initial_phases, jitter=jitter) # X[initial_condition, variable]
except: X = self.system.load_initial_conditions(initial_phases) # ... not all systems know about jittering.
t0 = time.time()
D = compute_phase_trace[self.USE_GPU](self, X)
self.echo('traces computed.')
# find attractors
attractors = [att.attractor( [[0., 0.]], initial_state=[0., 0.] ).classify()]
basin_hash = -np.ones((self.GRID, self.GRID), int)
for i in xrange(self.GRID):
for j in xrange(self.GRID):
trajectory = D[i*self.GRID+j]
initial_state = initial_phases[[j, i]]
try:
new_attractor = att.attractor( trajectory, initial_state=initial_state ).classify()
except:
basin_hash[i, j] = 0
continue
conditions = [attr_i.distance2attractor(new_attractor) > 0.1 for attr_i in attractors]
for (c, condition) in enumerate(conditions):
if not condition:
basin_hash[i, j] = c
if basin_hash[i, j] < 0:
attractors.append(new_attractor)
basin_hash[i, j] = len(attractors)-1
# refine attractor
def refine_attractor(initial_condition):
t, V_j = self.traces.computeTraces(initial_condition, plotit=False)
V_j = np.transpose(V_j)
ti, trajectory = tl.phase_difference(V_j, V_trigger=type(self).V_trigger)
try:
new_attr = att.attractor(trajectory).classify()
return new_attr
except:
return att.attractor( [[0., 0.]], initial_state=[0., 0.] ).classify()
initial_conditions = []
for i in xrange(1, len(attractors), 1):
attr = attractors[i]
initial_conditions.append( self.system.load_initial_condition(attr.initial_state[1], attr.initial_state[0]))
if len(initial_conditions):
self.traces.CYCLES *= 4
new_attractors = [att.attractor( [[0., 0.]], initial_state=[0., 0.] ).classify()]
new_attractors.extend( distribute.distribute(refine_attractor, 'initial_condition', initial_conditions) )
self.traces.CYCLES /= 4
attractors = [att.attractor( [[0., 0.]], initial_state=[0., 0.] ).classify()]
for (j, new_att_j) in enumerate(new_attractors):
conditions = [new_att_j.distance2attractor(attr_i) < 0.1 for attr_i in attractors]
if any(conditions):
which = conditions.index(True)
else:
which = len(attractors)
attractors.append(new_att_j)
for l in xrange(basin_hash.shape[0]):
for m in xrange(basin_hash.shape[1]):
if basin_hash[l, m] == j:
basin_hash[l, m] = which
basins = np.ones((self.GRID, self.GRID, 4), float)
for i in xrange(self.GRID):
for j in xrange(self.GRID):
basins[j, i, :3] = attractors[basin_hash[i, j]].color[:3]
# plot attractors
for attractor in attractors:
attractor.plot(self.ax_basins, 'o', mec='w', mew=1.0, ms=7.)
# plot basins
if self.basin_image == None:
self.basin_image = self.ax_basins.imshow(basins, interpolation='nearest', aspect='equal', extent=(0., 1., 0., 1.), origin='lower')
else:
self.basin_image.set_data(basins)
self.ax_basins.set_xlim(0., 1.)
self.ax_basins.set_ylim(0., 1.)
# plot traces
N_ARROW = self.GRID/4 # Plot <X> arrow symbols in each direction.
for i in xrange(self.GRID):
for j in xrange(self.GRID):
d = D[i*self.GRID+j]
color = basins[j, i, :3]
plotArrow = self.PLOT_ARROWS and (i % N_ARROW == 0) and (j % N_ARROW == 0) # every n-th grid point for the top plot
try: tl.plot_phase_2D(d[:, 0], d[:, 1], axes=self.ax_traces, color=color, PI=0.5, arrows=plotArrow)
except: pass
for attractor in attractors:
attractor.plot(self.ax_traces, 'o', mec='w', mew=1.0, ms=7.)
self.fig.canvas.draw()
self.echo("used %.2g sec" % (time.time()-t0))
return basin_hash, attractors
def sweep_phase_space(self, widget=None, event=None):
self.dt, self.stride, self.N_output = self.system.dt, self.system.stride, self.system.N_output(
self.traces.CYCLES)
self.echo('computing using ' + torus_2D.PROCESSOR[self.USE_GPU] +
' ...')
torus_2D.erase_traces(self)
self.erase_basins()
initial_phases = np.arange(0., 1., 1. / float(self.GRID))
X = self.system.load_initial_conditions(
initial_phases) # X[initial_condition, variable]
t0 = time.time()
D = compute_phase_trace[self.USE_GPU](self, X)
self.echo('traces computed.')
# find attractors
attractors = [
att.attractor([[0., 0.]], initial_state=[0., 0.]).classify()
]
basin_hash = -np.ones((self.GRID, self.GRID), int)
for i in xrange(self.GRID):
for j in xrange(self.GRID):
trajectory = D[i * self.GRID + j]
initial_state = initial_phases[[j, i]]
try:
new_attractor = att.attractor(
trajectory, initial_state=initial_state).classify()
except:
basin_hash[i, j] = 0
continue
conditions = [
attr_i.distance2attractor(new_attractor) > 0.1
for attr_i in attractors
]
for (c, condition) in enumerate(conditions):
if not condition:
basin_hash[i, j] = c
if basin_hash[i, j] < 0:
attractors.append(new_attractor)
basin_hash[i, j] = len(attractors) - 1
# refine attractor
#"""
def refine_attractor(initial_condition):
t, V_j = self.traces.compute_traces(initial_condition,
plotit=False)
V_j = np.transpose(V_j)
ti, trajectory = tl.phase_difference(
V_j, V_trigger=type(self).V_trigger)
try:
new_attr = att.attractor(trajectory).classify()
return new_attr
except:
return att.attractor([[0., 0.]],
initial_state=[0., 0.]).classify()
initial_conditions = []
for i in xrange(1, len(attractors), 1):
attr = attractors[i]
initial_conditions.append(
self.system.load_initial_condition(attr.initial_state[1],
attr.initial_state[0]))
if len(initial_conditions):
self.traces.CYCLES *= 4
new_attractors = [
att.attractor([[0., 0.]], initial_state=[0., 0.]).classify()
]
new_attractors.extend(
fm.distribute(refine_attractor, 'initial_condition',
initial_conditions))
self.traces.CYCLES /= 4
#"""
attractors = [
att.attractor([[0., 0.]], initial_state=[0., 0.]).classify()
]
for (j, new_att_j) in enumerate(new_attractors):
conditions = [
new_att_j.distance2attractor(attr_i) < 0.1
for attr_i in attractors
]
if any(conditions):
which = conditions.index(True)
else:
which = len(attractors)
attractors.append(new_att_j)
for l in xrange(basin_hash.shape[0]):
for m in xrange(basin_hash.shape[1]):
if basin_hash[l, m] == j:
basin_hash[l, m] = which
basins = np.ones((self.GRID, self.GRID, 4), float)
for i in xrange(self.GRID):
for j in xrange(self.GRID):
basins[j, i, :3] = attractors[basin_hash[i, j]].color[:3]
# plot attractors
for attractor in attractors:
attractor.plot(self.ax_basins, 'o', mec='w', mew=1.0, ms=7.)
# plot basins
if self.basin_image == None:
self.basin_image = self.ax_basins.imshow(basins,
interpolation='nearest',
aspect='equal',
extent=(0., 1., 0., 1.),
origin='lower')
else:
self.basin_image.set_data(basins)
self.ax_basins.set_xlim(0., 1.)
self.ax_basins.set_ylim(0., 1.)
# plot traces
for i in xrange(self.GRID):
for j in xrange(self.GRID):
d = D[i * self.GRID + j]
color = basins[j, i, :3]
#"""
try:
tl.plot_phase_2D(d[:, 0],
d[:, 1],
axes=self.ax_traces,
c=color,
PI=0.5)
#last = d[-1, :]
#tl.plot_phase_2D(d[:, 0], d[:, 1], axes=self.ax_traces, c=tl.clmap(tl.PI2*last[1], tl.PI2*last[0]), PI=0.5)
#tl.plot_phase_2D(d[:, 0], d[:, 1], axes=self.ax_traces, c=tl.clmap_patterns(last[1], last[0]), PI=0.5)
except:
pass
#"""
for attractor in attractors:
attractor.plot(self.ax_traces, 'o', mec='w', mew=1.0, ms=7.)
self.fig.canvas.draw()
self.echo("used %.2g sec" % (time.time() - t0))
return basin_hash, attractors
