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dynamics_plotting.py
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dynamics_plotting.py
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d import Axes3D
import pylab
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class plot_dynamics(nn.Module):
def __init__(self, f, V, reset_model = False):
super().__init__()
self.f = f
self.V = V
self.reset_model = reset_model
def get_trajectory(self, x0, steps):
x = x0
X = torch.empty([steps,x.shape[-1]])
X[0,:] = x.squeeze()
for i in range(steps-1):
with torch.no_grad():
x = self.f(x)
X[i+1,:] = x
return X.detach().numpy()
def plot_trajectory(self, x0, kwargs, sample_paths = 1, show_ls = True, steps = 600, xy_plane = True, ax = plt):
if show_ls:
x = np.arange(-20.0, 20.0, 0.25)
y = np.arange(-20.0, 20.0, 0.25)
X, Y = np.meshgrid(x, y)
Z = np.ndarray(X.shape)
for i in range(0, len(x)):
for j in range(0, len(x)):
z = torch.tensor([[X[i][j],Y[i][j]]], dtype = torch.float)
Z[i][j] = (self.V(z))
# Create contour lines or level curves using matpltlib.pyplot module
contours = ax.contour(X, Y, Z, linewidths = 1)
# Display z values on contour lines
ax.clabel(contours, inline=1, fontsize=10, fmt = '%1.2f')
if xy_plane:
for i in range(sample_paths):
if self.reset_model:
self.f.reset()
X_val = self.get_trajectory(x0, steps)
if i > 0:
kwargs["label"] = None
ax.plot(X_val[:,0],X_val[:,1], **kwargs)
if i==0:
ax.plot(X_val[-1,0], X_val[-1,1], color = "tab:blue", marker = '*', markersize = 10)
else:
for i in range(sample_paths):
X_val = self.get_trajectory(x0, steps = steps)
if i > 0:
kwargs["label"] = None
ax.plot(X_val, **kwargs)
return X_val
def surface_plot(self, x0, plot_dynamics = True):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-1.0, 1.0, 0.01)
X, Y = np.meshgrid(x, y)
Z = np.ndarray(X.shape)
for i in range(0, len(x)):
for j in range(0, len(x)):
z = torch.tensor([[X[i][j],Y[i][j]]], dtype = torch.float)
Z[i][j] = (self.V(z))
ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='none', alpha=.5)
if plot_dynamics:
X_val = self.get_trajectory(self.f, x0)
X_val = torch.tensor(X_val, dtype = torch.float).view((-1,1,2))
with torch.no_grad():
V_vals = (self.V(X_val)).squeeze()
ax.plot3D(X_val[:,:,0],X_val[:,:,1],V_vals, 'r')
ax.scatter(X_val[:,:,0],X_val[:,:,1],V_vals, color = 'r',)
ax.grid(False)
ax.w_yaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
ax.zaxis.set_ticklabels([])
ax.xaxis.set_ticks([])
ax.yaxis.set_ticks([])
ax.zaxis.set_ticks([])
plt.show()
class plot_dynamics_3D(nn.Module):
def __init__(self, f, V, show_mu = False, is_stochastic = False):
super().__init__()
self.f = f
self.V = V
self.show_mu = show_mu
self.is_stochastic = is_stochastic
def get_trajectory(self, x0, steps):
if self.show_mu:
mu = x0
X = torch.empty([steps,mu.shape[-1]])
X[0,:] = mu.squeeze()
else:
x = x0
X = torch.empty([steps, x.shape[-1]])
X[0,:] = x.squeeze()
for i in range(steps-1):
if self.show_mu:
pi, normal = self.f(mu)
mu = torch.sum(pi.probs.view(-1,1)*normal.loc,1).view(-1,1,x0.shape[-1])
X[i+1,:] = mu
else:
if self.is_stochastic:
x = self.f.sample(x)
else:
x = self.f(x)
X[i+1,:] = x
return X.detach().numpy()
def plot_trajectory(self, x0, kwargs, sample_paths = 1, steps = 200):
for i in range(sample_paths):
X_val = self.get_trajectory(x0, steps)
if i > 0:
kwargs["label"] = None
plt.plot(X_val[:, 0], X_val[:, 1], X_val[:, 2], **kwargs)
return X_val