libigl_Martin/python/001_BasicTypes.py

from iglhelpers import *

############# Dense Matrix Types #############

# Create a numpy dense array
# 2 types are supported by the wrappers: float64 and int64
dense_matrix = np.array( [ (1,2,3), (4,5,6) , (7,8,9) ], dtype='float64')

# libigl wrappers uses Eigen as a matrix type, you can easily convert between numpy and Eigen using
# the helper function p2e. This operation duplicates the data.
dense_matrix_eigen = p2e(dense_matrix)

# The Eigen wrappers allows you to do operations directly on this matrix,
# without having to convert back to numpy
dense_matrix_eigen_2 = dense_matrix_eigen * dense_matrix_eigen

# You can also inspect the data without converting it ...
print("Eigen Matrix: \n", dense_matrix_eigen_2, "\n", sep='')

# and access single elements
print("Eigen Matrix(0,0): ", dense_matrix_eigen_2[0,0], "\n")

# To convert it back to a numpy array, use the helper function e2p
dense_matrix_2 = e2p(dense_matrix_eigen_2)
print("Numpy Array: \n", dense_matrix_2, "\n", sep='')

############# Sparse Matrix Types #############

# Sparse matrices are handled in a very similar way
# 2 types are supported by the wrappers: float64 and int64
sparse_matrix = sparse.rand(10, 10, 0.1)

# To convert to the eigen forma use p2e
sparse_matrix_eigen = p2e(sparse_matrix)

# They can directly be used plotted or used in computations
print("Sparse matrix Eigen: ", sparse_matrix_eigen, sep='')

# And converted back with e2p
sparse_matrix_2 = e2p(sparse_matrix_eigen)
print("Sparse matrix Numpy: ", sparse_matrix_2.todense(), sep='')