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SimurateController.py
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SimurateController.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PhysicsSimulator.SinglePendulum.SinglePendulum import SinglePendulum
import os
import json
num = 6
path = "./toropogical_space_concentration" + str(num)
os.chdir(path)
with open('param.json', 'r') as json_file:
json_data = json.load(json_file)
print(json_data["MAX_x1"])
MASS = json_data["MASS"]
LENGTH = json_data["LENGTH"]
DRAG = json_data["DRAG"]
model = SinglePendulum(1. * np.pi, 0. * np.pi, mass=MASS, length=LENGTH, drag=DRAG)
u_set = np.array(json_data["u_set"])
# d = 0.1
# u_set = np.arange(-2, 2 + d, d)
toropogical_space_concentration = np.load("concentration.npy")
MAX_x1 = json_data["MAX_x1"]
MIN_x1 = json_data["MIN_x1"]
DELTA_x1 = json_data["DELTA_x1"]
MAX_x2 = json_data["MAX_x2"]
MIN_x2 = json_data["MIN_x2"]
DELTA_x2 = json_data["DELTA_x2"]
x1_set = np.arange(MIN_x1, MAX_x1 + DELTA_x1, DELTA_x1)
x2_set = np.arange(MIN_x2, MAX_x2 + DELTA_x2, DELTA_x2)
def show_plot(concentration, colbarLabel):
if type(colbarLabel) is not str:
TypeError("The required key {label!r} ""are not in kwargs".format(label=colbarLabel))
plt.figure(figsize=(7,5))
ex = [MIN_x1, MAX_x1, MIN_x2, MAX_x2]
plt.imshow(np.flip(concentration.T, 0),extent=ex,interpolation='nearest',cmap='Blues',aspect=(MAX_x1-MIN_x1)/(MAX_x2-MIN_x2),alpha=1)
plt.xlabel(r"$\theta$ [rad]")
plt.ylabel(r"$\dot \theta$ [rad/s]")
colbar = plt.colorbar()
colbar.set_label(colbarLabel)
plt.show()
def show_plot_in_range(concentration, max_value):
plt.figure(figsize=(7,5))
ex = [MIN_x1, MAX_x1, MIN_x2, MAX_x2]
plt.imshow(np.flip(np.where(concentration > max_value, max_value, concentration).T, 0),extent=ex,interpolation='nearest',cmap='Blues',aspect=(MAX_x1-MIN_x1)/(MAX_x2-MIN_x2),alpha=1)
plt.xlabel(r"$\theta$ [rad]")
plt.ylabel(r"$\dot \theta$ [rad/s]")
colbar = plt.colorbar()
colbar.set_label(r"$\rho$")
plt.show()
show_plot(toropogical_space_concentration, r"$\rho$")
max_value = 0.005
show_plot_in_range(toropogical_space_concentration, max_value)
x1_concentration_grad, x2_concentration_grad = np.gradient(toropogical_space_concentration, DELTA_x1, DELTA_x2)
def show_quiver(x, y):
fig, ax = plt.subplots()
x1_n = int(x1_set.size/40)
x2_n = int(x2_set.size/40)
fig_x1_set = x1_set[::x1_n]
fig_x2_set = x2_set[::x2_n]
fig_velocoty_x1_dot_set = x[::x1_n, ::x2_n].T
fig_velocoty_x2_dot_set = y[::x1_n, ::x2_n].T
q = ax.quiver(fig_x1_set, fig_x2_set, fig_velocoty_x1_dot_set, fig_velocoty_x2_dot_set)
ax.quiverkey(q, X=0.3, Y=1.1, U=10, label='Quiver key, length = 10', labelpos='E')
plt.show()
show_quiver(x1_concentration_grad, x2_concentration_grad)
size_grad = np.sqrt(x1_concentration_grad ** 2 + x2_concentration_grad ** 2)
x1_concentration_grad = x1_concentration_grad / size_grad
x2_concentration_grad = x2_concentration_grad / size_grad
x1_concentration_grad[np.where(x1_concentration_grad == 0)] = 0
x2_concentration_grad[np.where(x2_concentration_grad == 0)] = 0
show_quiver(x1_concentration_grad, x2_concentration_grad)
def value2list_num(x1, x2):
x1_num = int((x1 - MIN_x1)/DELTA_x1)
x2_num = int((x2 - MIN_x2)/DELTA_x2)
return (x1_num, x2_num)
def input(theta, theta_dot):
def norm_x(theta, theta_dot, u):
x = model.singlependulum_dynamics(theta, theta_dot, u)
return x/np.linalg.norm(x)
x_set = np.array([norm_x(theta, theta_dot, u) for u in u_set])
list_num = value2list_num(theta, theta_dot)
# print(theta, theta_dot)
desired_direction = np.array([x1_concentration_grad[list_num], x2_concentration_grad[list_num]])
direction_diff = [np.dot(x, desired_direction) for x in x_set]
u = u_set[np.where(direction_diff == max(direction_diff))]
if u.size == 1:
u = u[0]
else:
u = 0
print(u)
return u
input_set = np.array([[input(theta, theta_dot) for theta_dot in x2_set] for theta in x1_set])
print(input_set)
show_plot(input_set, r"$u[N*m]$")
# start simulation
singlePendulum = model
time = 0.
dt = 10**(-2)
max_step = 20 * 10**(2) + 1
df = pd.DataFrame(columns=['time', 'theta', 'theta_dot', 'input'])
# def add_data(df):
for s in range(0, max_step):
time = s * dt
singlePendulum.input = input_set[value2list_num(singlePendulum.state[0], singlePendulum.state[1])]
# singlePendulum.input = 4
tmp_data = tuple([time]) + singlePendulum.state + tuple([singlePendulum.input])
print(time)
tmp_se = pd.Series(tmp_data, index=df.columns)
df = df.append(tmp_se, ignore_index=True)
singlePendulum.step(dt)
# # df.to_csv("./data.csv", index=False)
fig = df.plot(x='time', y='theta', legend=False)
fig.set_xlabel(r"time [s]")
fig.set_ylabel(r"$\theta$ [rad]")
#df.plot(x='time', y='theta_dot')
fig = df.plot(x='time', y='input', legend=False)
fig.set_xlabel(r"time [s]")
fig.set_ylabel(r"$u$ [N*m]")
#df.plot(x='theta', y='theta_dot')
# show_plot_in_range(toropogical_space_concentration, max_value)
# show_plot(input_set)
# plt.show()
fig = plt.figure(figsize=(7,5))
ax = fig.subplots()
ex = [MIN_x1, MAX_x1, MIN_x2, MAX_x2]
ax.imshow(np.flip(input_set.T, 0),extent=ex,interpolation='nearest',cmap='Blues',aspect=(MAX_x1-MIN_x1)/(MAX_x2-MIN_x2),alpha=1)
# ax2 = ax.twinx()
ax.plot(df["theta"], df["theta_dot"], linewidth=1, color="crimson")
# ax2.set_ylim([MIN_x2, MAX_x2])
ax.set_xlabel(r"$\theta$ [rad]")
ax.set_ylabel(r"$\dot \theta$ [rad/s]")
plt.show()