def compareReconstructedWorms(cl, eigenworms, avTrue, thetaTrue, pc3, avP, tP,
tPnew):
pcsNew, meanAngle, lengths, refPoint = dh.calculateEigenwormsFromCL(
cl, eigenworms)
pc3New, pc2New, pc1New = pcsNew
cl2 = dh.calculateCLfromEW(pcsNew, eigenworms, meanAngle, lengths,
refPoint)
cl = cl[:, :-1, :]
cl = ndimage.gaussian_filter1d(cl, 5, 1)
# transform eigenworms exactly the same way. Otherwise we get some artefacts from nans
r = (pcsNew[2]**2 + pcsNew[1]**2)
r = np.repeat(np.median(r), len(r))
#lengths = 5
#=============================================================================#
# here we reconstruct from the true angular velocity to check the math. This is smoothed, so we need to compare with this version
#=============================================================================#
xt, yt, zt = dh.recrWorm(avTrue, pc3, thetaTrue, r=r, show=False)
pcsR = np.vstack([zt, yt, xt])
clApprox = dh.calculateCLfromEW(pcsR, eigenworms, meanAngle, lengths,
refPoint)
#=============================================================================#
# reconstruct worm from predicted angular velocity.
#=============================================================================#
xP, yP, zP = dh.recrWorm(avP, tP, thetaTrue, r=r)
pcsP = np.vstack([zP, yP, xP])
clPred = dh.calculateCLfromEW(pcsP, eigenworms, meanAngle, lengths,
refPoint)
#=============================================================================#
# reconstruct worm from non-linear corrected turns
#=============================================================================#
xP, yP, zP = dh.recrWorm(avP, tPnew, thetaTrue, r=r)
pcsP = np.vstack([zP, yP, xP])
cl3 = dh.calculateCLfromEW(pcsP, eigenworms, meanAngle, lengths, refPoint)
#=============================================================================#
# reconstruct worm from non-linear corrected turns
#=============================================================================#
# center around midpoint
originalCMS = np.tile(np.mean(cl2, axis=1)[:, np.newaxis, :], (1, 99, 1))
clApprox -= originalCMS
cl -= originalCMS
cl2 -= originalCMS
clPred -= originalCMS
cl3 -= originalCMS
return cl, cl2, clPred, cl3
cl = moving['CL']
#=============================================================================#
# for debugging recreate an existing, approximated shape from 3 eigenworms
#=============================================================================#
pcsNew, meanAngle, lengths, refPoint = dh.calculateEigenwormsFromCL(
cl, eigenworms)
pc3New, pc2New, pc1New = pcsNew
cl2 = dh.calculateCLfromEW(pcsNew, eigenworms, meanAngle, lengths, refPoint)
# transform eigenworms exactly the same way. Otherwise we get some artefacts from nans
r = (pcsNew[2]**2 + pcsNew[1]**2)
r = np.repeat(np.mean((pcsNew[2]**2 + pcsNew[1]**2)), len(pcsNew[0]))
#=============================================================================#
# here we reconstruct from the true angular velocity to check the math. This is smoothed, so we need to compare with this version
#=============================================================================#
xt, yt, zt = dh.recrWorm(avTrue, pc3, thetaTrue, r=r, show=False)
pcsR = np.vstack([zt, yt, xt])
clApprox = dh.calculateCLfromEW(pcsR, eigenworms, meanAngle, lengths, refPoint)
#=============================================================================#
# load predicted worm
#=============================================================================#
avP = movingAnalysis['ElasticNet']['AngleVelocity']['output'][:len(pcs[0])]
tP = movingAnalysis['ElasticNet']['Eigenworm3']['output'][:len(pcs[0])]
print 'R2'
print movingAnalysis['ElasticNet']['AngleVelocity']['score'], movingAnalysis[
'ElasticNet']['AngleVelocity']['scorepredicted']
print movingAnalysis['ElasticNet']['Eigenworm3']['score'], movingAnalysis[
'ElasticNet']['Eigenworm3']['scorepredicted']
################################################