self.stiffness.set_ydata(self.Y[1, :i])
return self.pendulum, self.spring1, self.spring2, self.inputIndicator1, self.inputIndicator1Arrow, self.inputIndicator2, self.inputIndicator2Arrow, self.input1, self.input2, self.angle, self.stiffness, self.timeStamp,
def start(self, interval):
self.anim = animation.FuncAnimation(self.fig,
self.animate,
frames=len(self.Time),
interval=interval,
blit=False)
if __name__ == '__main__':
Time, X, U, Y, plant1, plant2 = test_plant()
if len(sys.argv) - 1 != 0:
if '--savefigs' in sys.argv:
save_figures("visualizations/",
"v0",
params,
ReturnPath=False,
SaveAsPDF=True)
if '--animate' in sys.argv:
downsamplingFactor = 300
ani = animate_pendulum(
Time[::downsamplingFactor], X[:, ::downsamplingFactor],
U[:, ::downsamplingFactor], Y[:, ::downsamplingFactor],
plant2.desiredOutput[:, ::downsamplingFactor], **params)
ani.start(downsamplingFactor)
# ani.anim.save('test.mp4', writer='ffmpeg', fps=1000/downsamplingFactor)
plt.show()
def run_babbling_trial(self,
x1o,
plot=False,
saveFigures=False,
saveAsPDF=False,
returnData=False,
saveData=False,
saveParams=False):
is_number(x1o,
"Initial Joint Angle",
notes="Should be between 0 and 2 pi.")
if saveFigures == True:
assert plot == True, "No figures will be generated. Please select plot=True."
poorBabbling = True
count = 0
while poorBabbling:
## Generate babbling input
if self.babblingType == 'continuous':
self.generate_continuous_babbling_input()
else: #self.babblingType=='step'
self.generate_step_babbling_input()
## running the babbling data through the plant
X_o = self.plant.return_X_o(x1o, self.babblingSignals[0, :])
X, U, _ = self.plant.forward_simulation(
X_o, U=self.babblingSignals.T, addTitle="Motor Babbling"
) # need the transpose for the shapes to align (DAH)
tendonDeformation1 = np.array(
[-self.plant.rj, 0, self.plant.rm, 0, 0, 0]) @ X
tendonDeformation2 = np.array(
[self.plant.rj, 0, 0, 0, self.plant.rm, 0]) @ X
minDeformation = np.array([tendonDeformation1,
tendonDeformation2]).min()
if minDeformation < -0.02:
print(
"Poor Babbling! Large Negative Tendon Deformations. Try Again"
)
count += 1
assert count < 100, "count exceeded (negative tendon deformation)."
elif (abs(X[0, :].min() - self.plant.jointAngleBounds['LB']) <= 5 *
(np.pi / 180) and
abs(X[0, :].max() - self.plant.jointAngleBounds['UB']) <= 5 *
(np.pi / 180)):
poorBabbling = False
else:
print("Poor Babbling! Trying again...")
## plot (and save) figures
output = {}
if plot == True:
# self.plant.plot_states(X,Return=True)
self.plant.plot_states_and_inputs(X, U, returnFig=True)
self.plant.plot_output_power_spectrum_and_distribution(
X, returnFigs=True)
self.plant.plot_tendon_tension_deformation_curves(X)
self.plot_signals_power_spectrum_and_amplitude_distribution()
if saveFigures == True:
trialPath = save_figures("babbling_trials/",
babblingParams["Babbling Type"],
self.totalParams,
returnPath=True,
saveAsPDF=saveAsPDF,
saveAsMD=True)
if saveParams == True:
save_params_as_MAT(self.totalParams, path=trialPath)
if saveData == True:
self.save_data(X, filePath=trialPath)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
output["path"] = trialPath
return (output)
else:
output["path"] = trialPath
return (output)
else:
if saveParams == True:
save_params_as_MAT(self.totalParams)
if saveData == True:
self.save_data(X)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
return (output)
else:
if saveParams == True:
save_params_as_MAT(self.totalParams)
if saveData == True:
babblingTrial.save_data(X)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
return (output)
def run_babbling_trial(self,
x1o,
plot=False,
saveFigures=False,
saveAsPDF=False,
returnData=False,
saveData=False,
saveParams=False):
is_number(x1o,
"Initial Joint Angle",
notes="Should be between 0 and 2 pi.")
if saveFigures == True:
assert plot == True, "No figures will be generated. Please select plot=True."
## Generate babbling input
if self.babblingType == 'continuous':
self.generate_continuous_babbling_input()
else: #self.babblingType=='step'
self.generate_step_babbling_input()
## running the babbling data through the plant
X_o = self.plant.return_X_o(x1o, self.babblingSignals[0, :])
X, U, _ = self.plant.forward_simulation(
X_o, U=self.babblingSignals.T, addTitle="Motor Babbling"
) # need the transpose for the shapes to align (DAH)
## plot (and save) figures
output = {}
if plot == True:
self.plant.plot_states(X)
self.plant.plot_joint_angle_power_spectrum_and_distribution(X)
self.plant.plot_tendon_tension_deformation_curves(X)
self.plot_signals_power_spectrum_and_amplitude_distribution()
if saveFigures == True:
trialPath = save_figures("babbling_trials/",
"propAmp",
self.totalParams,
returnPath=True,
saveAsPDF=saveAsPDF)
if saveParams == True:
save_params_as_MAT(self.totalParams, path=trialPath)
if saveData == True:
self.save_data(X, path=trialPath)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
output["path"] = trialPath
return (output)
else:
output["path"] = trialPath
return (output)
else:
if saveParams == True:
save_params_as_MAT(self.totalParams)
if saveData == True:
self.save_data(X)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
return (output)
else:
if saveParams == True:
save_params_as_MAT(self.totalParams)
if saveData == True:
self.save_data(X)
if returnData == True:
output["time"] = self.plant.time
output["X"] = X
output["U"] = U
return (output)
for el in np.linspace(0,
np.round(delta_lT_max / 0.01) * 0.01,
int(np.round(delta_lT_max / 0.01)) + 1)
])
cax2.set_yticklabels([
'{:0.2f}'.format(el)
for el in np.linspace(0,
np.round(delta_lT_max / 0.01) * 0.01,
int(np.round(delta_lT_max / 0.01)) + 1)
])
if i == 0:
Path = save_figures("./Figures/Iso_Error/",
"Iso_" + str(100 * fT_array[i]) + "_MVC", {
"Initial Force": initial_force,
"Slack to Optimal Ratio": l_Ts_over_l_To
},
figs=[fig2],
SaveAsPDF=True,
ReturnPath=True)
else:
save_figures("./Figures/Iso_Error/",
"Iso_" + str(int(100 * fT_array[i])) + "_MVC", {},
SubFolder=Path[-18:],
figs=[fig2],
SaveAsPDF=True)
plt.close(fig2)
statusbar.update(i)
save_figures("./Figures/Iso_Error/",
"Iso_MVC_all_levels", {},
SubFolder=Path[-18:],
f"PCC = {pearsonr(lm2o,MAE2)[0]:0.3f}",
transform=ax2.transAxes,
horizontalalignment='center',
verticalalignment='center',
color="k",
fontsize=14,
bbox=dict(boxstyle='round,pad=0.5', edgecolor='k', facecolor='w'))
ax1.set_ylim([0, 2])
ax2.set_ylim([0, 2])
# plt.show()
folderPath = save_figures(
"output_figures/integrator_backstepping_sinusoidal_activations_fixed_tensions/",
"1DOF_2DOA", {
"Initial Muscle Lengths": InitialMuscleLengths,
"Initial Tendon Tensions": InitialTensions
},
returnPath=True,
saveAsPDF=True,
saveAsMD=True)
# save_figures("output_figures/integrator_backstepping_sinusoidal_activations_fixed_muscle_lengths/","1DOF_2DOA_v1.0",SaveAsPDF=True)
plt.close('all')
FormatedSaveData = {
"States": TotalX,
"Input": TotalU,
"Error": Error,
"Initial Tensions": InitialTensionsFromSuccessfulTrials
}
pickle.dump(FormatedSaveData, open(folderPath / "output.pkl", "wb"))
else:
print("All Trials Unsuccessful...")
0, 1 / min(stuff['Slack_Opt_Tendon_Ratio']),
1 / min(stuff['Slack_Opt_Tendon_Ratio'])
], [1, 1, 0], 'k--')
ax.set_xlabel(r"$\hat{l}^T$", fontsize=14)
ax.set_ylabel(r"$\hat{f}^T$", fontsize=14)
cax, _ = matplotlib.colorbar.make_axes(ax)
cbar = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap)
cax.set_ylabel(r"$\dfrac{l_{T,o}}{l_{T,s}}$", fontsize=14)
cbar.set_ticks([(
int(100 *
(el - np.floor(1 /
(max(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01) /
(np.ceil(1 / (min(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01 -
np.floor(1 / (max(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01)) /
100) for el in np.arange(
np.floor(1 / (max(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01,
np.ceil(1 / (min(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01 +
1e-4, 0.01)])
cax.set_yticklabels([
'{:0.2f}'.format(el) for el in np.arange(
np.floor(1 / (max(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01,
np.ceil(1 / (min(stuff['Slack_Opt_Tendon_Ratio'])) / 0.01) * 0.01 +
1e-4, 0.01)
])
save_figures("./Figures/feasible_cTkT/",
"fT_lT_plot_from_cT_kT_range", {},
figs=[fig2],
SaveAsPDF=True)
plt.close(fig2)