This python package based on numpy that can be used for memory efficient interval-constant vector operating.
It based on an assumption that data in vector has interval-constant type. Having this assumption we can more efficiently utilize RAM memory.
For example:
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0] is interval-constant type.
[0, 7, 1, 2, 1, 4, 7, 7, 5, 9] is not interval-constant type.
For example this code allows to use 5 time less memory than simple vector of length 10000
from .sparce_vector import SparseVector
import numpy as np
sp_vec = SparseVector(np.arange(10000) // 10,
dtype=np.int64)
# returns numpy array
sp_vec[3:6]
#returns whole array as numpy array
sp_vec[:]On the real data this package can achieve much better results. For example on this real data compression exceeded 1.4 * 10^4.
This format makes available to use much more bioinformatics data.
- Nazar Beknazarov