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How to use t_nl method in avocado
Best Python code snippet using avocado_python
trajectory_plot.py
Source:trajectory_plot.py 
1"""2Plot the state and input trajectory.3D. Malyuta -- ACL, University of Washington4B. Acikmese -- ACL, University of Washington5Copyright 2019 University of Washington. All rights reserved.6"""7import pickle8import numpy as np9import numpy.linalg as la10import matplotlib11import matplotlib.pyplot as plt12import progressbar13from csm import ApolloCSM14import tools15matplotlib.rc('font',**{'family':'serif','size':14})16matplotlib.rc('text', usetex=True)17matplotlib.rc('font',**{'family':'serif','size':14})18matplotlib.rc('text', usetex=True)19with open('data/tf_150_tc_2.pkl','rb') as f:20 data = pickle.load(f)21csm = ApolloCSM()22t_f = tools.get_values(data,'solver_input','t_f')[0]23q_f = tools.get_values(data,'solver_input','xf')[0]['q']24t_pulse_max = tools.get_values(data,'solver_input','t_pulse_max')[0]25r_a = tools.get_values(data,'solver_input','r_app')[0]26idx = 027t_sol = tools.get_values(data,'solver_output','optimizer','t')[idx]28p_sol = tools.get_values(data,'solver_output','optimizer','p')[idx]29v_sol = tools.get_values(data,'solver_output','optimizer','v')[idx]30q_sol = tools.get_values(data,'solver_output','optimizer','q')[idx]31omega_sol = tools.get_values(data,'solver_output','optimizer','w')[idx]32u_sol = tools.get_values(data,'solver_output','optimizer','u')[idx]33vc_sol = tools.get_values(data,'solver_output','history','vc')[idx][-1]34t_nl = tools.get_values(data,'solver_output','nl_prop','t')[idx]35p_nl = tools.get_values(data,'solver_output','nl_prop','p')[idx]36v_nl = tools.get_values(data,'solver_output','nl_prop','v')[idx]37q_nl = tools.get_values(data,'solver_output','nl_prop','q')[idx]38omega_nl = tools.get_values(data,'solver_output','nl_prop','w')[idx]39ang_err_sol = np.array([np.rad2deg(2.*np.arccos(max(-1,min(1,tools.qmult(40 tools.qconj(q_sol_k),q_f)[0])))) for q_sol_k in q_sol.T])41ang_err_nl = np.array([np.rad2deg(2.*np.arccos(max(-1,min(1,tools.qmult(42 tools.qconj(q_nl_k),q_f)[0])))) for q_nl_k in q_nl.T])43vc_sol_norm1 = np.array([la.norm(vc_sol[k],ord=1)44 for k in range(vc_sol.shape[0])])45rpy_sol = np.column_stack([tools.q2rpy(q_sol.T[k])46 for k in range(q_sol.shape[1])])47rpy_nl = np.column_stack([tools.q2rpy(q_nl.T[k])48 for k in range(q_nl.shape[1])])49time_app = np.array([t_nl[k] for k in range(p_nl.shape[1])50 if la.norm(p_nl[:,k]-p_nl[:,-1])<r_a])51# translation52fig = plt.figure(1)53plt.clf()54ax = fig.add_subplot(211)55ax.grid()56ax.plot(t_sol,p_sol[0],color='red',linestyle='none',marker='.',markersize=5)57ax.plot(t_sol,p_sol[1],color='green',linestyle='none',marker='.',markersize=5)58ax.plot(t_sol,p_sol[2],color='blue',linestyle='none',marker='.',markersize=5)59ax.plot(t_nl,p_nl[0],color='red')60ax.plot(t_nl,p_nl[1],color='green')61ax.plot(t_nl,p_nl[2],color='blue')62ax.set_xlabel('Time $t$ [s]')63ax.set_ylabel('Position [m]')64ax.autoscale(tight=True)65y_lim = ax.get_ylim()66x_lim = ax.get_xlim()67ax.fill_between(time_app,68 np.repeat(y_lim[0],time_app.size),69 np.repeat(y_lim[1],time_app.size),70 linewidth=0,color='black',alpha=0.2,zorder=1)71ax.set_xlim(x_lim)72ax.set_ylim(y_lim)73ax = fig.add_subplot(212)74ax.grid()75ax.plot(t_sol,v_sol[0],color='red',linestyle='none',marker='.',markersize=5)76ax.plot(t_sol,v_sol[1],color='green',linestyle='none',marker='.',markersize=5)77ax.plot(t_sol,v_sol[2],color='blue',linestyle='none',marker='.',markersize=5)78ax.plot(t_nl,v_nl[0],color='red')79ax.plot(t_nl,v_nl[1],color='green')80ax.plot(t_nl,v_nl[2],color='blue')81ax.set_xlabel('Time $t$ [s]')82ax.set_ylabel('Velocity [m/s]')83ax.autoscale(tight=True)84plt.tight_layout()85y_lim = ax.get_ylim()86x_lim = ax.get_xlim()87ax.fill_between(time_app,88 np.repeat(y_lim[0],time_app.size),89 np.repeat(y_lim[1],time_app.size),90 linewidth=0,color='black',alpha=0.2,zorder=1)91ax.set_xlim(x_lim)92ax.set_ylim(y_lim)93plt.show(block=False)94fig.savefig('./figures/translation.pdf',95 bbox_inches='tight',format='pdf',transparent=True)96# attitude97fig = plt.figure(2)98plt.clf()99ax = fig.add_subplot(211)100ax.grid()101ax.plot(t_sol,np.unwrap(rpy_sol[0],discont=359.)-360.,color='red',linestyle='none',marker='.',markersize=5)102ax.plot(t_sol,rpy_sol[1],color='green',linestyle='none',marker='.',markersize=5)103ax.plot(t_sol,rpy_sol[2],color='blue',linestyle='none',marker='.',markersize=5)104ax.plot(t_nl,np.unwrap(rpy_nl[0],discont=359.)-360.,color='red')105ax.plot(t_nl,rpy_nl[1],color='green')106ax.plot(t_nl,rpy_nl[2],color='blue')107ax.set_xlabel('Time $t$ [s]')108ax.set_ylabel('Roll, pitch, yaw [$^\circ$]')109ax.autoscale(tight=True)110y_lim = ax.get_ylim()111x_lim = ax.get_xlim()112ax.fill_between(time_app,113 np.repeat(y_lim[0],time_app.size),114 np.repeat(y_lim[1],time_app.size),115 linewidth=0,color='black',alpha=0.2,zorder=1)116ax.set_xlim(x_lim)117ax.set_ylim(y_lim)118ax = fig.add_subplot(212)119ax.grid()120ax.plot(t_sol,np.rad2deg(omega_sol[0]),color='red',linestyle='none',marker='.',markersize=5)121ax.plot(t_sol,np.rad2deg(omega_sol[1]),color='green',linestyle='none',marker='.',markersize=5)122ax.plot(t_sol,np.rad2deg(omega_sol[2]),color='blue',linestyle='none',marker='.',markersize=5)123ax.plot(t_nl,np.rad2deg(omega_nl[0]),color='red')124ax.plot(t_nl,np.rad2deg(omega_nl[1]),color='green')125ax.plot(t_nl,np.rad2deg(omega_nl[2]),color='blue')126ax.set_xlabel('Time $t$ [s]')127ax.set_ylabel('Angular velocity [$^\circ$/s]')128ax.autoscale(tight=True)129plt.tight_layout()130y_lim = ax.get_ylim()131x_lim = ax.get_xlim()132ax.fill_between(time_app,133 np.repeat(y_lim[0],time_app.size),134 np.repeat(y_lim[1],time_app.size),135 linewidth=0,color='black',alpha=0.2,zorder=1)136ax.set_xlim(x_lim)137ax.set_ylim(y_lim)138plt.show(block=False)139fig.savefig('./figures/attitude.pdf',140 bbox_inches='tight',format='pdf',transparent=True)141# RCS thrusts142fig = plt.figure(3,figsize=(9.6,9.86))143plt.clf()144for i in range(4): # quad number145 for j in range(4): # thruster of quad146 k = i*4+j147 ax = fig.add_subplot(4,4,k+1)148 ax.grid()149 ax.plot(t_sol[:-1],u_sol[k]*1e3,color='black',marker='.',150 linestyle='none',markersize=5)151 ax.axhline(y=csm.t_pulse_max*1e3,color='red',linestyle='--',linewidth=1)152 ax.axhline(y=csm.t_pulse_min*1e3,color='blue',linestyle='--',153 linewidth=1)154 ax.set_xlabel('Time $t$ [s]')155 ax.set_ylabel('Pulse width $\Delta t_k^{%d}$ [ms]'%(k+1))156 ax.set_title('Thruster $i=%d$'%(k+1))157 ax.autoscale(tight=True)158 yticks = (np.ceil(np.linspace(0,0.6,7)*1e3)).astype(int)159 xticks = (np.linspace(0,t_f,3)).astype(int)160 plt.yticks(yticks,yticks)161 plt.xticks(xticks,xticks)162 ax.set_ylim([0,t_pulse_max*1.1*1e3])163 if 'p_f' in csm.i2thruster[4*i+j]:164 y_lim = ax.get_ylim()165 x_lim = ax.get_xlim()166 ax.fill_between(time_app,167 np.repeat(y_lim[0],time_app.size),168 np.repeat(y_lim[1],time_app.size),169 linewidth=0,color='black',alpha=0.2,zorder=1)170 ax.set_xlim(x_lim)171 ax.set_ylim(y_lim)172 173plt.tight_layout()174plt.show(block=False)175fig.savefig('./figures/inputs.pdf',...
MSP.py
Source:MSP.py
1import matplotlib.pyplot as plt2import torch3import torch.nn as nn4import torch.nn.functional as F5class MultiSimilarity(nn.Module):6 def __init__(self):7 super().__init__()8 def forward(self, s_g, t_g):9 # --------------------------------------------10 # Shape of s_g : ((s_nl,), (bs, s_ch, s_h, s_w))11 # Shape of t_g : ((t_nl,), (bs, t_ch, t_h, t_w))12 # --------------------------------------------13 s_nl = len(s_g)14 t_nl = len(t_g)15 s_g_mtx = torch.stack([self.get_sim_matrix(s_f) for s_f in s_g]) # (s_nl, bs, bs)16 t_g_mtx = torch.stack([self.get_sim_matrix(t_f) for t_f in t_g]) # (t_nl, bs, bs)17 s_g_mtx = torch.unsqueeze(s_g_mtx, dim=1).repeat(1, t_nl, 1, 1) # (s_nl, t_nl, bs, bs)18 t_g_mtx = torch.unsqueeze(t_g_mtx, dim=0).repeat(s_nl, 1, 1, 1) # (s_nl, t_nl, bs, bs)19 loss = (s_g_mtx - t_g_mtx).pow(2).view(s_nl, -1).mean(1).mean(0) # (1,)20 return loss21 def get_sim_matrix(self, f, is_at=True):22 # --------------------------------------------23 # Shape of f : (bs, ch, h, w)24 # --------------------------------------------25 if is_at:26 f = self.at(f)27 f = f.view(f.shape[0], -1) # (bs, ch * h * w)28 f = F.normalize(f, dim=1) # (bs, ch * h * w)29 mtx = torch.matmul(f, torch.t(f)) # (bs, bs)30 mtx = F.normalize(mtx, dim=1) # (bs, bs)31 return mtx32 def at(self, f, is_flat=True):33 # --------------------------------------------34 # Shape of f : (bs, ch, h, w)35 # --------------------------------------------36 out = f.pow(2).mean(1) # (bs, h, w)37 if is_flat:38 out =F.normalize(out.view(f.size(0), -1)) # (bs, h * w)39 return out40class MultiSimilarityPlotter(MultiSimilarity):41 def __init__(self):42 super().__init__()43 def plot(self, s_g, t_g, input, target, n_samp=10):44 # --------------------------------------------45 # Shape of s_g : ((s_nl,), (bs, s_ch, s_h, s_w))46 # Shape of t_g : ((t_nl,), (bs, t_ch, t_h, t_w))47 # Shape of input : (bs, ch, h, w)48 # Shape of target : (bs,)49 # --------------------------------------------50 s_g = [s_f.detach() for s_f in s_g]51 t_g = [t_f.detach() for t_f in t_g]52 input = input.permute(0, 2, 3, 1) # (bs, h, w, ch)53 s_attn_g = [self.at(s_f, is_flat=False) for s_f in s_g] # ((s_nl,), (bs, s_h, s_w))54 t_attn_g = [self.at(t_f, is_flat=False) for t_f in t_g] # ((t_nl,), (bs, t_h, t_w))55 s_sim_g = torch.stack([self.get_sim_matrix(s_f) for s_f in s_g]) # (s_nl, bs, bs)56 t_sim_g = torch.stack([self.get_sim_matrix(t_f) for t_f in t_g]) # (t_nl, bs, bs)57 s_pos_ind_g = torch.argsort(-s_sim_g, dim=2) # (s_nl, bs, bs)58 t_pos_ind_g = torch.argsort(-t_sim_g, dim=2) # (t_nl, bs, bs)59 s_neg_ind_g = torch.argsort(s_sim_g, dim=2) # (s_nl, bs, bs)60 t_neg_ind_g = torch.argsort(t_sim_g, dim=2) # (t_nl, bs, bs)61 pivot = 062 for i, (t_pos_ind_f, t_neg_ind_f, t_attn_f) in enumerate(zip(t_pos_ind_g, t_neg_ind_g, t_attn_g)):63 pos_ind = t_pos_ind_f[pivot, :n_samp] # (n_samp,)64 neg_ind = t_neg_ind_f[pivot, :n_samp] # (n_samp,)65 r_pos_imgs = input[pos_ind] # (n_samp, h, w, ch). Sampled raw images66 r_neg_imgs = input[neg_ind] # (n_samp, h, w, ch). Sampled raw images67 t_pos_attns = t_attn_f[pos_ind] # (n_samp, t_h, t_w)68 t_neg_attns = t_attn_f[neg_ind] # (n_samp, t_h, t_w)69 fig, axs = plt.subplots(4, n_samp) # 4 rows and n_samp columns70 for j in range(n_samp):71 axs[0, j].imshow(r_pos_imgs[j], interpolation="bicubic")72 axs[1, j].imshow(t_pos_attns[j], cmap="jet")73 axs[2, j].imshow(r_neg_imgs[j], interpolation="bicubic")74 axs[3, j].imshow(t_neg_attns[j], cmap="jet")...
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