| import numpy as np | |
| import mmap | |
| import os | |
| N = 5 | |
| dtype = [('a', np.float64), ('b', np.int32), ('c', np.float32), ('d', np.int16), ('e', 'S4')] | |
| names = [a[0] for a in dtype] | |
| columns = [np.random.normal(size=(N,)).astype(np.float64), | |
| np.random.normal(size=(N,)).astype(np.int32), |
| import os | |
| import numpy as np | |
| import argparse | |
| import simtk | |
| import simtk.unit as units | |
| import simtk.openmm as openmm | |
| import wcadimer |
| import numpy as np | |
| cimport numpy as np | |
| cimport cython | |
| @cython.boundscheck(False) | |
| @cython.wraparound(False) | |
| cpdef cython_lstsqr(x_ary, y_ary): | |
| """ Computes the least-squares solution to a linear matrix equation. """ | |
| cdef double x_avg, y_avg, var_x, cov_xy,\ | |
| slope, y_interc | |
| cdef double[:] x = x_ary # memory view |
| #============================================================================================= | |
| # MODULE DOCSTRING | |
| #============================================================================================= | |
| """ | |
| Custom integrators. | |
| DESCRIPTION | |
| import numpy as np | |
| cimport numpy as np | |
| cdef unsigned int fix |
I made a few optimizations to the cython code. The timings on my machine are:
from sequences import P53_HUMAN, P53_MOUSE
from alignment import align as py_align
from align_numpy import align as cy_align
from align_numpy2 import align as cy_align2
%timeit py_align(P53_HUMAN, P53_MOUSE)
1 loops, best of 3: 442 ms per loop
| <?xml version="1.0" encoding="UTF-8"?> | |
| <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/"> | |
| <channel rdf:about="http://scitation.aip.org"> | |
| <title>New content is available for The Journal of Chemical Physics</title> | |
| <link>http://scitation.aip.org</link> | |
| <description>New content is now available online. Please follow the links to view the content.</description> | |
| <items> | |
| <rdf:Seq> | |
| <rdf:li/> | |
| <rdf:li/> |
| In [1]: import numpy as np | |
| In [2]: from scipy.spatial.distance import cdist | |
| In [3]: from distlib import pairwise_cython_blas, pairwise_cython | |
| In [4]: a = np.random.random(size=(1000,3)) | |
| In [5]: %timeit cdist(a,a) | |
| 100 loops, best of 3: 11.3 ms per loop |
| from numba import jit, random, int_, float32, void, object_, double, uint32, ulong | |
| import math | |
| import numpy as np | |
| float32_2d = float32[:,::1] | |
| @jit | |
| class Force(object): | |
| @int_(float32[::1], float32[::1]) |