def plot2():
fig5 = plt.figure()
x = map(lambda x: x + gauss(0,0.02)*(1-x*x), arange(-1,1,0.001))
y = map(lambda x: x + gauss(0,0.02)*(1-x*x), arange(-1,1,0.001))
z = map(lambda x: x + gauss(0,0.02)*(1-x*x), arange(-1,1,0.001))
line = array(zip(x,y,z))
lpc = LPCImpl(h = 0.05, mult = 2, it = 200, cross = False, scaled = False, convergence_at = 0.001)
lpc_curve = lpc.lpc(X=line)
ax = Axes3D(fig5)
ax.set_title('testNoisyLine2')
curve = lpc_curve[0]['save_xd']
ax.scatter(x,y,z, c = 'red')
ax.plot(curve[:,0],curve[:,1],curve[:,2])
saveToPdf(fig5, '/tmp/testNoisyLine2.pdf')
residuals_calc = LPCResiduals(line, tube_radius = 0.05, k = 10)
residual_diags = residuals_calc.getPathResidualDiags(lpc_curve[0])
fig6 = plt.figure()
#plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_num_NN'], drawstyle = 'step', linestyle = '--')
plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_mean_NN'])
plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_std_NN'])
saveToPdf(fig6, '/tmp/testNoisyLine2PathResiduals.pdf')
coverage_graph = residuals_calc.getCoverageGraph(lpc_curve[0], arange(0.001, .102, 0.005))
fig7 = plt.figure()
plt.plot(coverage_graph[0],coverage_graph[1])
saveToPdf(fig7, '/tmp/testNoisyLine2Coverage.pdf')
residual_graph = residuals_calc.getGlobalResiduals(lpc_curve[0])
fig8 = plt.figure()
plt.plot(residual_graph[0], residual_graph[1])
saveToPdf(fig8, '/tmp/testNoisyLine2Residuals.pdf')
fig9 = plt.figure()
plt.plot(range(len(lpc_curve[0]['lamb'])), lpc_curve[0]['lamb'])
saveToPdf(fig9, '/tmp/testNoisyLine2PathLength.pdf')
def helixHeteroscedasticDiags():
#Parameterise a helix (no noise)
fig5 = plt.figure()
t = arange(-1,1,0.0005)
x = map(lambda x: x + gauss(0,0.001 + 0.001*sin(2*pi*x)**2), (1 - t*t)*sin(4*pi*t))
y = map(lambda x: x + gauss(0,0.001 + 0.001*sin(2*pi*x)**2), (1 - t*t)*cos(4*pi*t))
z = map(lambda x: x + gauss(0,0.001 + 0.001*sin(2*pi*x)**2), t)
line = array(zip(x,y,z))
lpc = LPCImpl(h = 0.1, t0 = 0.1, mult = 1, it = 500, scaled = False, cross = False)
lpc_curve = lpc.lpc(X=line)
ax = Axes3D(fig5)
ax.set_title('helixHeteroscedastic')
curve = lpc_curve[0]['save_xd']
ax.scatter(x,y,z, c = 'red')
ax.plot(curve[:,0],curve[:,1],curve[:,2])
saveToPdf(fig5, '/tmp/helixHeteroscedastic.pdf')
residuals_calc = LPCResiduals(line, tube_radius = 0.2, k = 20)
residual_diags = residuals_calc.getPathResidualDiags(lpc_curve[0])
fig6 = plt.figure()
#plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_num_NN'], drawstyle = 'step', linestyle = '--')
plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_mean_NN'])
plt.plot(lpc_curve[0]['lamb'][1:], residual_diags['line_seg_std_NN'])
saveToPdf(fig6, '/tmp/helixHeteroscedasticPathResiduals.pdf')
coverage_graph = residuals_calc.getCoverageGraph(lpc_curve[0], arange(0.01, .052, 0.01))
fig7 = plt.figure()
plt.plot(coverage_graph[0],coverage_graph[1])
saveToPdf(fig7, '/tmp/helixHeteroscedasticCoverage.pdf')
residual_graph = residuals_calc.getGlobalResiduals(lpc_curve[0])
fig8 = plt.figure()
plt.plot(residual_graph[0], residual_graph[1])
saveToPdf(fig8, '/tmp/helixHeteroscedasticResiduals.pdf')
fig9 = plt.figure()
plt.plot(range(len(lpc_curve[0]['lamb'])), lpc_curve[0]['lamb'])
saveToPdf(fig9, '/tmp/helixHeteroscedasticPathLength.pdf')
def testResiduals(self): x = map(lambda x: x + gauss(0,0.005 + 0.3*x*x), arange(-1,1,0.005)) y = map(lambda x: x + gauss(0,0.005 + 0.3*x*x), arange(-1,1,0.005)) z = map(lambda x: x + gauss(0,0.005 + 0.3*x*x), arange(-1,1,0.005)) line = array(zip(x,y,z)) lpc = LPCImpl(h = 0.05, convergence_at = 0.0001, it = 100, mult = 2) lpc_curve = lpc.lpc(X=line) residuals_calc = LPCResiduals(line, tube_radius = 1) residuals_graph = residuals_calc.getGlobalResiduals(lpc_curve[0])
