我尝试在<a>
之前和class1
关闭<lable>
标记之后在</label>
中插入</a>
标记。
我的代码
<div class="class1>
<label class="class2">test</label>
</div>
我想在javascript受影响后,代码输出看起来像这个
<div class="class1>
<a>
<label class="class2">test</label>
</a>
</div>
请帮助我得到这个结果。
答案 0 :(得分:0)
我在php中的程序得到了这个结果我
<div class="class1>
<label class="class2">test</label>
</div>
我想在我的php项目结果中使用此类标记时,java脚本获取更改代码到我解释的上层信息 请给我简单的解决方法
答案 1 :(得分:0)
https://jsfiddle.net/nk50eLpv/
from multiprocessing import Pool
import numpy as np
# Global variables are OK, as long as their contents are not modified, although
# these might just as well be moved into the worker function or an initializer
nx = 20
ny = 30
myList1 = [0]*100
myList2 = [1]*25
value1 = np.zeros(nx)
value2 = np.zeros(ny)
def calc_meanvals_for(pair):
"""Process a reasonably sized chunk of the problem"""
i, j = pair
f = calc(value1[i], value2[j])
results = []
for k, data1 in enumerate(myList1):
for p, data2 in enumerate(myList2):
meanval = np.sum(f[:]/data1)*data2
results.append((i,j,k,p,meanval))
return results
# This module will be imported by every worker - that's how they will be able
# to find the global variables and the calc function - so make sure to check
# if this the main program, because without that, every worker will start more
# workers, each of which will start even more, and so on, in an endless loop
if __name__ == '__main__':
# Create a pool of worker processes, each able to use a CPU core
pool = Pool()
# Prepare the arguments, one per function invocation (tuples to fake multiple)
arg_pairs = [(i,j) for i in range(nx) for j in range(ny)]
# Now comes the parallel step: given a function and a list of arguments,
# have a worker invoke that function with one argument until all arguments
# have been used, collecting the return values in a list
return_values = pool.map(calc_meanvals_for, arg_pairs)
# Since the function also returns a list, there's now a list of lists - consider
# itertools.chain.from_iterable to flatten them - to be processed further
store = np.zeros(nx, ny, len(myList1), len(myList2))
for results in return_values:
for i, j, k, p, meanval in results:
store[i,j,k,p] = meanval