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import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
def ss_var_aniplot(theta, K, phi, R, tau, interval=500):
#Initialise a figure with five subplots
fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5,1)
fig.subplots_adjust(hspace=0.8)
nt, ns = K.shape[0], K.shape[1]
xs = np.linspace(0, ns, ns)
#Initialise line objects with independent axes
line1, = ax1.plot([], [], label='theta')
line2, = ax2.plot([], [], label='K')
line3, = ax3.plot([], [], label='phi')
line4, = ax4.plot([], [], label='R')
line5, = ax5.plot([], [], label='tau')
line = [line1, line2, line3, line4, line5]
ax1.set(ylim=(np.nanmin(theta),np.nanmax(theta)))
ax2.set(ylim=(np.nanmin(K),np.nanmax(K)))
ax3.set(ylim=(np.nanmin(phi),np.nanmax(phi)))
ax4.set(ylim=(np.nanmin(R[-1,100:700]),np.nanmax(R[-1,100:700])))
ax5.set(ylim=(np.nanmin(tau),np.nanmax(tau)))
for ax in [ax1, ax2, ax3, ax4, ax5]:
ax.legend(loc='upper right')
ax.set(xlim=(0,ns))
def animate(i):
line[0].set_data(xs, theta[i, :])
line[1].set_data(xs, K[i, :])
line[2].set_data(xs, phi[i, :])
line[3].set_data(xs, R[i, :])
line[4].set_data(xs, tau[i, :])
ax1.set_title('Time step: ' + str(i))
return line
anim = FuncAnimation(fig, animate, interval=interval, frames=nt-1, repeat=True, blit=False)
return anim
def anim_2Dplot(data, ymin=-1000, ymax=1000, interval=200):
#Initialise figure and set limits
fig, ax = plt.subplots(figsize=(4, 3))
ax.set(ylim=(ymin, ymax))
#For a single line
if data.ndim == 2:
#Extract the number of lines to plot, number of time iterations and the number of samples in each lines
nt, ns = data.shape[0], data.shape[1]
#Initialise 2Dline first iteration
xs = np.linspace(0, ns, ns)
line = ax.plot(xs, data[0, :], color='k')[0]
def animate(i):
line.set_ydata(data[i, :])
ax.set_title('Time step ' + str(i))
#For a set of lines
else:
#Extract the number of lines to plot, number of time iterations and the number of samples in each lines
n_channel, nt, ns = len(data), data.shape[1], data.shape[2]
#Initialise iterable 2Dline instances
xs = np.linspace(0, ns, ns)
lines = [ax.plot(xs, data[i, 0, :])[0] for i in range(n_channel)]
#Create animation function to cycle through the data
def animate(t):
ax.set_title('Time step ' + str(t))
for i, line in enumerate(lines):
line.set_ydata(data[i, t, :])
#Create the animation
anim = FuncAnimation(fig, animate, interval=interval, frames=nt-1, repeat=True, blit=True)
return anim
def xyzdif_aniplots(curve1, curve2, interval=500):
#Initialise a figure with five subplots
fig, (ax1, ax2, ax3) = plt.subplots(3,1)
fig.subplots_adjust(hspace=1.2)
nt, ns, ax = curve1.shape[0], curve1.shape[1], curve1.shape[2]
xs = np.linspace(0, ns, ns)
#Initialise line objects with independent axes
x_FS, = ax1.plot(xs, xs, label='Frenet-Serret')
x_MT, = ax1.plot(xs, xs, label='Transformation Matrix')
y_FS, = ax2.plot(xs, xs, label='Frenet-Serret')
y_MT, = ax2.plot(xs, xs, label='Transformation Matrix')
z_FS, = ax3.plot(xs, xs, label='Frenet-Serret')
z_MT, = ax3.plot(xs, xs, label='Transformation Matrix')
line = [x_FS, x_MT, y_FS, y_MT, z_FS, z_MT]
ax1.set(ylim=(0,210))
ax2.set(ylim=(-1,2))
ax3.set(ylim=(0,2.5))
ax1.legend(loc='center left', bbox_to_anchor=(-0.1, 1))
ax1.set_title('x-values')
ax2.set_title('y-values')
ax3.set_title('z-values')
ax3.set_yticks((0,2.5))
for ax in [ax1, ax2, ax3]:
ax.set(xlim=(0,ns))
ax.set_xlabel('gauge number')
ax.set_ylabel('cm')
def animate(i):
line[0].set_ydata(curve1[i, :, 0])
line[1].set_ydata(curve2[i, :, 0])
line[2].set_ydata(curve1[i, :, 1])
line[3].set_ydata(curve2[i, :, 1])
line[4].set_ydata(curve1[i, :, 2])
line[5].set_ydata(curve2[i, :, 2])
ax1.set_title('Time step: ' + str(i))
return line
anim = FuncAnimation(fig, animate, interval=interval, frames=nt-1, repeat=True, blit=False)
return anim
################################# TRANSFORM TO 3D ANIMATED PLOTS ##############################
def plot_curve(P, P1=None, P2=None, TNB=None, name=None, name1=None, name2=None):
if TNB == 'TNB':
r_0, T, N, B = get_rTNB(P)
N_0 = N[0, :]
T_0 = T[0, :]
B_0 = B[0, :]
Path = pd.DataFrame({
"x": P[:, 0],
"y": P[:, 1],
"z": P[:, 2]})
Tpd = pd.DataFrame({
"x": [r_0[0], T_0[0]],
"y": [r_0[1], T_0[1]],
"z": [r_0[2], T_0[2]]})
Npd = pd.DataFrame({
"x": [r_0[0], N_0[0]],
"y": [r_0[1], N_0[1]],
"z": [r_0[2], N_0[2]]})
Bpd = pd.DataFrame({
"x": [r_0[0], B_0[0]],
"y": [r_0[1], B_0[1]],
"z": [r_0[2], B_0[2]]})
Path['Path'] = name
Tpd['Path'] = 'T'
Npd['Path'] = 'N'
Bpd['Path'] = 'B'
df = pd.concat([Tpd, Npd, Bpd, Path])
else:
P = np.abs(P)
Path = pd.DataFrame({
"x": P[:, 0],
"y": P[:, 1],
"z": P[:, 2]})
Path['Path'] = name
df = Path
if P1 is not None:
P1 = np.abs(P1)
Path1 = pd.DataFrame({
"x": P1[:, 0],
"y": P1[:, 1],
"z": P1[:, 2]})
Path1['Path'] = name1
df = pd.concat([df, Path1])
if P2 is not None:
P2 = np.abs(P2)
Path2 = pd.DataFrame({
"x": P2[:, 0],
"y": P2[:, 1],
"z": P2[:, 2]})
Path2['Path'] = name2
df = pd.concat([df, Path2])
fig = px.line_3d(df, x="x", y="y", z="z", color="Path")
#fig.update_layout(
# scene=dict(
# xaxis=dict(nticks=4, range=[0, 1], ),
# yaxis=dict(nticks=4, range=[0, 1], ),
# zaxis=dict(nticks=4, range=[0, 50], ), ),
# width=700,
# margin=dict(r=20, l=10, b=10, t=10))
fig.show()
return
def dif_xyz(P0, P1, P2, name):
n = int(P1.shape[0])
Coumpound_dif_1 = np.zeros((n))
Coumpound_dif_2 = np.zeros((n))
for i in np.arange(n - 1):
Coumpound_dif_1[i + 1] = Coumpound_dif_1[i] + np.abs(P0[i, 0] - P1[i, 0]) + np.abs(
P0[i, 1] - P1[i, 1]) + np.abs(P0[i, 2] - P1[i, 2])
Coumpound_dif_2[i + 1] = Coumpound_dif_2[i] + np.abs(P0[i, 0] - P2[i, 0]) + np.abs(
P0[i, 1] - P2[i, 1]) + np.abs(P0[i, 2] - P2[i, 2])
fig, axs = plt.subplots(2, 2)
fig.suptitle('Difference from true path')
axs[0, 0].plot(P0[:, 0] - P1[:-1, 0])
axs[0, 0].plot(P0[:, 0] - P2[:, 0])
axs[0, 0].set_title('x-axis')
# axs[0,0].set_ylabel('mm')
# axs[0,0].set_xlabel('measurement #')
axs[1, 0].plot(P0[:, 1] - P1[:-1, 1])
axs[1, 0].plot(P0[:, 0] - P2[:, 0])
axs[1, 0].set_title('y-axis')
# axs[1,0].set_ylabel('mm')
# axs[1,0].set_xlabel('measurement #')
# axs[1,0].set_ylim([-0.25,0.25])
axs[0, 1].plot(P0[:, 2] - P1[:-1, 2])
axs[0, 1].plot(P0[:, 2] - P2[:, 2])
axs[0, 1].set_title('z-axis')
# axs[0,1].set_ylabel('mm')
# axs[0,1].set_xlabel('measurement #')
# axs[0,1].set_ylim([-0.25,0.25])
axs[1, 1].plot(Coumpound_dif_1[:], label='Frenet_serret')
axs[1, 1].plot(Coumpound_dif_2[:], label='Transformation Matrix')
axs[1, 1].set_title('Compound')
# axs[1,1].set_ylabel('mm')
# axs[1,1].set_xlabel('measurement #')
fig.legend(loc=4)
fig.tight_layout()