def plot_segts_fit(ts, seg_ts, seg_fit = None, imshow=False, shape_symbol_list=None):
plt.clf()
# iterate each segment and plot
for i in range(len(seg_ts)):
seg = seg_ts[i]
startpoint = seg[0]
endpoint = seg[1]
plt.plot(range(startpoint, endpoint), ts[startpoint:endpoint], linewidth=1, color='b')
if seg_fit is None:
plt.plot(range(startpoint, endpoint), fit_shape(ts[startpoint:endpoint])[0], linewidth=1, color='r')
else:
plt.plot(range(startpoint, endpoint), seg_fit[i], linewidth=1, color='r')
# plot vertical line to divide segments, just draw the line in the middle of two segments
for seg in seg_ts[:-1]:
# seg[1] is the exclusive(!!!) end point
seg_boundary = seg[1] - 0.5
plt.axvline(seg_boundary, linewidth=2, color='k')
titlestr = "#Segments={k}".format(k=str(len(seg_ts)))
# add the text of shape symbol as well
if shape_symbol_list:
ymin, ymax = plt.ylim()
ypos = ymin + (ymax-ymin) * 0.9
for i in range(len(seg_ts)):
seg = seg_ts[i]
# text_ypos = (seg[0] + seg[1])/2.005
text_ypos = seg[0] + 1
plt.text(text_ypos, ypos, shape_symbol_list[i], fontsize=14, color='green')
# display the word in the figure
titlestr = titlestr + ", symbolic representation = \"" + "".join(shape_symbol_list) + "\""
plt.title(titlestr, fontsize=15)
# TRICK: control if show the figure immediately
if imshow:
plt.show()
# for x1 in range(50, 2000, 100): original_list = np.arange(x0,x1,1) fit_x = original_list * original_list / 5.0 + 3*original_list print fit_x print fit_x print np.mean(fit_x) print np.std(fit_x, ddof=1) fit_xx = fit_x[::2] print fit_xx print np.mean(fit_xx) print np.std(fit_xx, ddof=1) fit_y, likelihood, shape_ind, shape_dir, std_y, mean_y, theta, alpha = fitting.fit_shape(fit_x) print fit_y, likelihood, shape_ind, shape_dir, alpha x_list = np.linspace(0,1,len(original_list)) print x_list mu = np.mean(np.power(x_list, 2)) std = np.std(np.power(x_list, 2), ddof=1) print mu, std # x_list = np.linspace(0,1,101) # print x_list # mu = np.mean(np.power(x_list, 2)) # std = np.std(np.power(x_list, 2), ddof=1) # print mu, std estimate_y = (((np.power((original_list - x0) / float(x1 - x0 - 1), 2) - mu) / std) * theta )* std_y + mean_y
