from scipy import sparse
from numpy.linalg import norm

vector1 = sparse.csr_matrix([ 0 for i in xrange(4000000) ], dtype = float64)

#just to test I set a few points to a value higher than 0

vector1[ (0, 10) ] = 5
vector1[ (0, 1500) ] = 80
vector1[ (0, 2000000) ] = 6

n = norm(t1)

ValueError: dimension mismatch

norm(asarray(vector1.todense()))

(vector1.data ** 2).sum()

# sparseLib.pyx
#cython: boundscheck=False
from cython.parallel cimport prange
from cython.view cimport array as cvarray

import numpy as np

from libc.math cimport sqrt

cpdef double sparseNorm2(double [:] data) nogil:
  cdef long i
  cdef double value = 0.0
  for i in xrange(data.shape[0]):
    value += data[i]*data[i]
  return sqrt(value)