mass = mass[mass > 0.0]
# print the error and show the figure
print '\nGlobal error = %.10e' % np.abs(np.sum(fields['m']) - 1.0)
hlp.scatterEllipses(mass,
cm[:, 1:],
cov=sxx[:, 1:, 1:],
nstd=1.5,
ran=(box[0][0], box[1][0], box[0][1], box[1][1]),
title='Example 5: Summing over voxels')
# In[ ]:
reload(psi)
psi.write(fields, 'test.out')
# print "read"
# fields = psi.readFields('test.out')
# In[ ]:
fields = psi.read('test.out')
# flatten arrays, divide by mass, and filter out voxels with zero mass
# mass = fields['m'].reshape((-1,1))
# cm = fields['x'].reshape((-1,3))/mass
# cm = cm[np.all(mass>0.0, axis=1)]
# sxx = fields['xx'].reshape((-1,3,3))/mass.reshape((-1,1,1))
# sxx = sxx[np.all(mass>0.0, axis=1)]
# mass = mass[mass>0.0]
cm = fields['x'].reshape((-1,3))/mass
cm = cm[np.all(mass>0.0, axis=1)]
sxx = fields['xx'].reshape((-1,3,3))/mass.reshape((-1,1,1))
sxx = sxx[np.all(mass>0.0, axis=1)]
mass = mass[mass>0.0]
# print the error and show the figure
print '\nGlobal error = %.10e' % np.abs(np.sum(fields['m'])-1.0)
hlp.scatterEllipses(mass, cm[:,1:], cov=sxx[:,1:,1:], nstd = 1.5, ran=(box[0][0], box[1][0], box[0][1], box[1][1]), title='Example 5: Summing over voxels')
# In[ ]:
reload(psi)
psi.write(fields, 'test.out')
# print "read"
# fields = psi.readFields('test.out')
# In[ ]:
fields = psi.read('test.out')
# flatten arrays, divide by mass, and filter out voxels with zero mass
# mass = fields['m'].reshape((-1,1))
# cm = fields['x'].reshape((-1,3))/mass
# cm = cm[np.all(mass>0.0, axis=1)]
