我有以下函数使用生成器循环遍历大型坐标数组。由于性能非常重要,我尝试将其转换为cython。
cython实现中是否还有其他可以提高性能的变化?也许就像使用cpython数组声明数组一样?或者
geometry_converter.pyx:
def esriJson_to_CV(geometry, geometry_type):
def compress_geometry(coords):
cdef int previous_x, previous_y, current_x, current_y
iterator = iter(coords)
previous_x, previous_y = iterator.next()
yield previous_x
yield previous_y
for current_x, current_y in iterator:
yield previous_x - current_x
yield previous_y - current_y
previous_x, previous_y = current_x, current_y
if geometry_type == "POINT":
converted_geometry = [int(geometry["x"]), int(geometry["y"])]
elif geometry_type == "POLYLINE":
converted_geometry = [list(compress_geometry(path)) for path in geometry["paths"]]
elif geometry_type == "POLYGON":
converted_geometry = [list(compress_geometry(ring)) for ring in geometry["rings"]]
else:
raise Exception("geometry_converter.esriJSON_to_CV - {} geometry type not supported".format(geometry_type))
return converted_geometry
基准测试:
import time
from functools import wraps
import numpy as np
import geometry_converter as gc
def timethis(func):
'''
Decorator that reports the execution time.
'''
@wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
end = time.time()
print(func.__name__, end-start)
return result
return wrapper
def prepare_data(featCount, size):
"""create numpy array with coords and fields"""
input = []
for i in xrange(0, featCount):
polygon = {"rings" : []}
ys = np.random.uniform(0.0,89.0,size).tolist()
xs = np.random.uniform(-179.0,179.0,size).tolist()
polygon["rings"].append(zip(xs,ys))
input.append(polygon)
return input
@timethis
def process_data(data):
output = [gc.esriJson_to_CV(x, "POLYGON") for x in data]
return output
data = prepare_data(1000, 1000000)
out = process_data(data)
print(out[0][0][0:10])
答案 0 :(得分:1)
Cython并不神奇。如果没有使用它的静态类型,Cython性能提升大多数时候都没有真正意义。
要获得可观的性能提升,您必须使用cython类型声明。
例如,而不是:
x = int()
你会这样做:
cdef int x
您可以在cython documentation中详细了解如何使用它们。