新手程序员在这里.我正在编写一个程序来分析点(单元格)的相对空间位置.程序从数组中获取边界和单元格类型,第1列中的x坐标,第2列中的y坐标和第3列中的单元格类型.然后,它会检查每个单元格的单元格类型以及与边界的适当距离.如果它通过,则计算它与阵列中每个其他单元格的距离,如果距离在指定的分析范围内,则将其添加到该距离的输出数组.
我的单元格标记程序是在wxpython中,所以我希望在python中开发这个程序并最终将它粘贴到GUI中.不幸的是,现在python在我的机器上运行核心循环需要大约20秒,而MATLAB可以执行~15循环/秒.由于我计划在大约30个案例中对几个探索性分析类型进行1000次循环(具有随机比较条件),这不是一个微不足道的差异.
我尝试运行一个分析器,数组调用是1/4的时间,几乎所有其余的都是未指定的循环时间.
这是主循环的python代码:
for basecell in range (0, cellnumber-1):
if firstcelltype == np.array((cellrecord[basecell,2])):
xloc=np.array((cellrecord[basecell,0]))
yloc=np.array((cellrecord[basecell,1]))
xedgedist=(xbound-xloc)
yedgedist=(ybound-yloc)
if xloc>excludedist and xedgedist>excludedist and yloc>excludedist and yedgedist>excludedist:
for comparecell in range (0, cellnumber-1):
if secondcelltype==np.array((cellrecord[comparecell,2])):
xcomploc=np.array((cellrecord[comparecell,0]))
ycomploc=np.array((cellrecord[comparecell,1]))
dist=math.sqrt((xcomploc-xloc)**2+(ycomploc-yloc)**2)
dist=round(dist)
if dist>=1 and dist<=analysisdist:
arraytarget=round(dist*analysisdist/intervalnumber)
addone=np.array((spatialraw[arraytarget-1]))
addone=addone+1
targetcell=arraytarget-1
np.put(spatialraw,[targetcell,targetcell],addone)
这是主循环的matlab代码:
for basecell = 1:cellnumber;
if firstcelltype==cellrecord(basecell,3);
xloc=cellrecord(basecell,1);
yloc=cellrecord(basecell,2);
xedgedist=(xbound-xloc);
yedgedist=(ybound-yloc);
if (xloc>excludedist) && (yloc>excludedist) && (xedgedist>excludedist) && (yedgedist>excludedist);
for comparecell = 1:cellnumber;
if secondcelltype==cellrecord(comparecell,3);
xcomploc=cellrecord(comparecell,1);
ycomploc=cellrecord(comparecell,2);
dist=sqrt((xcomploc-xloc)^2+(ycomploc-yloc)^2);
if (dist>=1) && (dist<=100.4999);
arraytarget=round(dist*analysisdist/intervalnumber); …