def _plotResultsF(Snaps, mdb, outdir):
Xorg = Snaps['Xorg']
mn = np.nanmin(Xorg.flatten())
mx = np.nanmax(Xorg.flatten())
tl.plt.clf()
tl.plt.subplot(511)
tl.plt.plot(Xorg, ) # 'black')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Original')
tl.plt.xticks([])
tl.plt.subplot(512)
tl.plt.plot(Xorg, 'lightgrey')
tl.resetCol()
if (SMTH):
tl.plt.plot(tl.smoothWMAo(Snaps['Vf_full'], int(Snaps['pstep'])))
else:
tl.plt.plot(Snaps['Vf_full'])
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Forecast')
tl.plt.xticks([])
tl.plt.subplot(513)
tl.plt.plot(Snaps['Es_full'], 'lightgrey')
tl.plt.plot(Snaps['Ef_full'], 'yellowgreen')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylabel('RMSE (cast)')
tl.plt.xticks([])
emx = np.nanmax(Snaps['Es_full'].flatten())
tl.plt.ylim([0, emx])
tl.plt.subplot(514)
#tl.plt.plot(Snaps['Rf_full'])
tl.plt.imshow(_createImap(mdb, Snaps['Rf_full']),
cmap='gray',
interpolation='nearest',
aspect='auto')
tl.plt.xlim([0, len(Xorg)]) #; tl.plt.yticks([])
tl.plt.yticks(range(0, mdb.get_c()), range(1, mdb.get_c() + 1))
tl.plt.ylabel('R-ID (cast)')
tl.plt.xticks([])
tl.plt.subplot(515)
tl.plt.semilogy(Snaps['T_full'], '.', color='yellowgreen')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylabel('Speed')
#tl.savefig("%sout_Vf"%(outdir),'pdf')
tl.savefig("%sout_Vf" % (outdir), 'png')
tl.plt.close()
def _plotSS_t_G(idx, Snaps, outdir, SnapsHs):
tc = idx
tf_st = tc + Snaps['lstep']
tf_ed = tf_st + Snaps['pstep']
n = Snaps['n']
Xorg = Snaps['Xorg']
mn = np.nanmin(Xorg.flatten())
mx = np.nanmax(Xorg.flatten())
if (DBG): tl.msg([tc, tf_st, tf_ed])
tl.plt.clf()
tl.plt.subplot(511)
tl.plt.plot(Xorg[0:idx, :]) #, 'black')
tl.plt.plot(range(idx, n), Xorg[idx:n, :], 'lightgrey')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Original')
tl.plt.xticks([])
_plot_lines([tc], mn, mx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.subplot(512)
tl.plt.plot(Xorg[0:idx, :], 'lightgrey')
tl.resetCol()
tl.plt.plot(Snaps['Ve_full'][0:idx]) #, 'royalblue')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Estimation')
tl.plt.xticks([])
_plot_lines([tc], mn, mx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.subplot(513)
tl.plt.plot(Xorg[0:tf_ed, :], 'lightgrey')
tl.resetCol()
if (SMTH):
tl.plt.plot(
tl.smoothWMAo(Snaps['Vf_full'][0:tf_ed], int(Snaps['pstep'])))
else:
tl.plt.plot(Snaps['Vf_full'][0:tf_ed])
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Forecast')
tl.plt.xticks([])
_plot_lines([tc], mn, mx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.subplot(514)
tl.plt.plot(Snaps['Ef_full'][0:idx], 'yellowgreen')
tl.plt.plot(Snaps['Es_full'][0:idx], 'lightgrey')
tl.plt.xlim([0, len(Xorg)])
tl.plt.ylabel('RMSE')
tl.plt.xticks([])
emx = np.nanmax(Snaps['Es_full'].flatten())
_plot_lines([tc], 0, emx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], 0, emx, 0.5, 'r')
tl.plt.ylim([0, emx])
tl.plt.subplot(515)
tmx = -tl.INF
tmn = tl.INF
for i in range(0, MSCALE_H):
tl.plt.semilogy(SnapsHs[i]['T_full'][0:idx], '.', label='h=%d' % (i))
mn = np.nanmin(SnapsHs[i]['T_full'])
mx = np.nanmax(SnapsHs[i]['T_full'])
if (tmx < mx): tmx = mx
if (tmn > mn): tmn = mn
tl.plt.xlim([0, len(Xorg)])
_plot_lines([tc], tmn, tmx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], tmn, tmx, 0.5, 'r')
tl.plt.ylim([tmn, tmx])
tl.plt.ylabel('Speed')
if (outdir != ''):
tl.savefig("%sresult_G" % (outdir), 'pdf')
tl.savefig("%sresult_G" % (outdir), 'png')
def _plotSS_t_L(idx, Snaps, mdb):
#------------------------------------------------------#
tb_st = Snaps['Te'][idx, 0]
tb_ed = Snaps['Te'][idx, 1]
tp_st = Snaps['Te'][idx, 2]
tp_ed = Snaps['Te'][idx, 3]
tc_st = Snaps['Te'][idx, 4]
tc_ed = Snaps['Te'][idx, 5]
tf_st = Snaps['Te'][idx, 6]
tf_ed = Snaps['Te'][idx, 7]
#------------------------------------------------------#
if (DBG): tl.msg([tb_st, tb_ed, tp_st, tp_ed, tc_st, tc_ed, tf_st, tf_ed])
Xe_t = Snaps['Xe'][idx]
Ve_t = Snaps['Ve'][idx]
Re_t = Snaps['Re'][idx]
Xorg = Snaps['Xorg']
nduration = Snaps['n']
lstep = Snaps['lstep']
mn = np.nanmin(Xorg.flatten())
mx = np.nanmax(Xorg.flatten())
#-----------------------------------#
tl.plt.clf()
tl.plt.subplot(611)
tl.plt.plot(Xorg) #, 'black')
_plot_lines([tb_st, tb_ed, tp_st, tp_ed, tc_st, tc_ed], mn, mx, 0.5, 'b')
_plot_lines([tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.xlim([0, nduration])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Original')
tl.plt.xticks([])
tl.plt.subplot(612)
tl.plt.plot(Xorg, 'lightgrey', color=[0.8, 0.8, 0.8])
tl.resetCol()
if (SMTH):
tl.plt.plot(
range(0, tf_ed),
tl.smoothWMAo(Snaps['Vf_full'][0:tf_ed], int(Snaps['pstep'])))
else:
tl.plt.plot(range(0, tf_ed), Snaps['Vf_full'][0:tf_ed])
_plot_lines([tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.xlim([0, nduration])
tl.plt.ylim([mn, mx])
tl.plt.ylabel('Forecast')
tl.plt.xticks([])
tl.plt.subplot(613)
_plot_lines([tb_st, tb_ed, tp_st, tp_ed, tc_st, tc_ed], -1, 100, 0.5, 'b')
tl.plt.imshow(_createImap(mdb, Snaps['Re_full'][0:tc_ed]),
cmap='gray',
interpolation='nearest',
aspect='auto')
tl.plt.xlim([0, nduration]) #; tl.plt.yticks([])
tl.plt.yticks(range(0, mdb.get_c()), range(1, mdb.get_c() + 1))
tl.plt.xticks([])
tl.plt.ylabel('R-ID')
#tl.plt.subplot(12,1,6)
#_plot_lines([tf_st, tf_ed], -1, 100, 0.5, 'r')
#tl.plt.imshow(_createImap(mdb, Snaps['Rf_full'][0:tf_ed]), cmap='gray', interpolation='nearest', aspect='auto')
#tl.plt.xlim([0,nduration]) #; tl.plt.yticks([])
#tl.plt.yticks(range(0,mdb.get_c()), range(1,mdb.get_c()+1))
#-----------------------------------#
ss_st = int(max(0, tc_ed - lstep * 4))
ss_ed = int(min(tf_ed + lstep / 2, nduration))
#ss_st=int(max(0,tb_st-lstep*0)); ss_ed=int(min(tf_ed+lstep, nduration))
#tl.plt.subplot(223)
if (PLOT_R): tl.plt.subplot(234)
else: tl.plt.subplot(223)
#tl.plt.plot(range(ss_st,ss_ed), Snaps['Xorg'][ss_st:ss_ed]) #, 'black') #,:])
tl.plt.plot(range(ss_st, ss_ed), Snaps['Xorg'][ss_st:ss_ed], 'lightgrey')
tl.resetCol()
tl.plt.plot(range(ss_st, tc_ed),
Snaps['Xorg'][ss_st:tc_ed]) #, 'black') #,:])
_plot_lines([tb_st, tb_ed, tp_st, tp_ed, tc_st], mn, mx, 0.5, 'b')
_plot_lines([tc_ed, tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.xlim(ss_st, ss_ed)
tl.plt.ylim([mn, mx])
tl.plt.title('Org tc:%d, Vc [%d:%d]' % (tc_ed, tc_st, tc_ed))
if (PLOT_R): tl.plt.subplot(235)
else: tl.plt.subplot(224)
tl.plt.plot(range(ss_st, ss_ed), Snaps['Xorg'][ss_st:ss_ed], 'lightgrey')
tl.resetCol()
tl.plt.plot(range(tb_st, tf_ed), Ve_t) #, 'tomato')
_plot_lines([tb_st, tb_ed, tp_st, tp_ed, tc_st], mn, mx, 0.5, 'b')
_plot_lines([tc_ed, tf_st, tf_ed], mn, mx, 0.5, 'r')
tl.plt.xlim(ss_st, ss_ed)
tl.plt.ylim([mn, mx])
tl.plt.title('Cast Vf [%d:%d]' % (tf_st, tf_ed))
if (PLOT_R):
tl.plt.subplot(236)
tl.plt.plot(range(tb_st, tf_ed), Re_t)
tl.plt.xlim(ss_st, ss_ed)
def _plot(nlds, disp=False, nf=-1, fn=''):
if (nf == -1): nf = int(nlds.n)
(Sta, Obs) = nlds.gen(nf)
(StaL, ObsL) = nlds.gen_lin(nf)
err_o = tl.RMSE(nlds.data, 0 * nlds.data)
err = tl.RMSE(Obs[0:len(nlds.data), :], nlds.data)
errL = tl.RMSE(ObsL[0:len(nlds.data), :], nlds.data)
data = nlds.data
mn = np.nanmin(data.flatten())
mx = np.nanmax(data.flatten())
mnx = abs(mx - mn)
mx += 0.1 * mnx
mn -= 0.1 * mnx
#fig=tl.figure(10)
tl.plt.clf()
#
tl.plt.subplot(221)
tl.resetCol()
tl.plt.plot(data, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.ylim([mn, mx])
tl.plt.title("Original data")
#
tl.plt.subplot(222)
tl.resetCol()
tl.plt.plot(data, '--', lw=1)
tl.resetCol()
tl.plt.plot(Obs, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.ylim([mn, mx])
tl.plt.title("Original vs. Estimation: err %.2f (%.2f)" %
(err, err / err_o))
#
tl.plt.subplot(223)
tl.plt.plot(Sta, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.title("s(t) --- Sta (hidden) k:%d" % (nlds.k))
#
tl.plt.subplot(224)
for i in range(0, len(Obs[0])):
tl.plt.fill_between(range(0, len(Obs)),
Obs[:, i] + 2 * err,
Obs[:, i] - 2 * err,
facecolor=[0.8, 0.8, 0.8],
alpha=1.0,
linewidth=0.0)
tl.plt.plot(Obs, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.ylim([mn, mx])
tl.plt.title("v(t) --- Obs (est)")
#
tl.plt.tight_layout(pad=1.0, w_pad=1.0, h_pad=1.0)
if (False):
tl.plt.subplot(322)
tl.resetCol()
tl.plt.plot(data, '--', lw=1)
tl.resetCol()
tl.plt.plot(ObsL, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.ylim([mn, mx])
tl.plt.title("Original vs. LDS: errL %.2f (%.2f)" %
(errL, errL / err_o))
#
tl.plt.subplot(324)
tl.plt.plot(StaL, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.title("s(t) --- Sta (LDS) k:%d" % (nlds.k))
#
tl.plt.subplot(326)
for i in range(0, len(Obs[0])):
tl.plt.fill_between(range(0, len(ObsL)),
ObsL[:, i] + 2 * errL,
ObsL[:, i] - 2 * errL,
facecolor=[0.8, 0.8, 0.8],
alpha=1.0,
linewidth=0.0)
tl.plt.plot(ObsL, '-', lw=1)
tl.plt.xlim([0, nf])
tl.plt.ylim([mn, mx])
tl.plt.title("v(t) --- Obs (LDS)")
#
if (fn != ''): tl.savefig(fn + '_plot', 'pdf')
tl.plt.draw()
tl.plt.show(block=disp)
if (disp != True): tl.plt.close()
# save data (optional)
if (FULLSAVE):
tl.save_txt(data, fn + '_seq_data.txt')
tl.save_txt(Sta, fn + '_seq_Sta.txt')
tl.save_txt(Obs, fn + '_seq_Obs.txt')
tl.save_txt(ObsL, fn + '_seq_ObsL.txt')