一、julia的多进程PMAP会自动返回计算的结果集，但是多线程则没有这个自动功能
#多进程的计算结果处理
results = pmap(fn,items) #执行多进程，计算结构储存在results变量里面
for result in results #计算结果逐个处理
......
end
二、多线程涉及原子操作，对于计算结果，如果直接定义一个list，用push!()来收集的话，你会发现收集到的结果集是混乱的，数量不足，这涉及到写的原子操作。
【1】如果每一次计算要输出大量数据，可以用IO操作，把数据存到本地，后续再读取进行处理
【2】如果每次计算要输出相同结构的数组（Array2），提前定义一个一维数组（result），数组的每一个值都是一个固定大小的数组（与Array2同形），多线程计算的时候，每个值经计算后，把计算结果数据写到result数组对应的序号位置里面。
【3】同理，当计算结果为dataframe时，我们提前定义一串dataframe组成的数组，每个线程计算完毕，回写计算结果到指定的地方，覆盖原来的dataframe。
【4】传值的时候，把输入包装成[index,value]的格式，处理完成后，直接回写计算结果到index位置的结果数组里面。
三、每一次的计算结果为一个array，用多线程进行多次计算，并收集计算结果，验证是否正确
using DataFrames
function my_fn(items)
return (idx = items[1],ary = [items[1],items[2],items[2] * 6])
end
function test()
a = rand(9000) #输入1
items = [[idx,item] for (idx,item) in zip(1:length(a),a)] #输入2
result = [Array{Any}(undef,3) for i in 1:length(a)] #用于结果的保存
items |> display
result |> display
Threads.@threads for x in items
#for x in items
res = my_fn(x)
idx = round(Int64,res.idx)
result[idx] = res.ary
end
result |> display
df = DataFrame()
df.a = a
df[!,:a1] .= 0.0
df[!,:a2] .= 0.0
df[!,:a3] .= 0.0
for (i,r) in zip(1:length(result),eachrow(df))
r.a1 = result[i][1]
r.a2 = result[i][2]
r.a3 = result[i][3]
end
df |> display
(df.a == df.a2) |> display
end
test()
[out]
9000-element Array{Array{Float64,1},1}:
[1.0, 0.070816232858125]
[2.0, 0.7853029984149891]
[3.0, 0.7210179200415938]
[4.0, 0.6981532249270992]
[5.0, 0.3480588007300216]
[6.0, 0.9018409131741212]
[7.0, 0.9751606776407826]
[8.0, 0.9197465832681542]
[9.0, 0.2259475070832464]
[10.0, 0.20112552323415334]
[11.0, 0.46591547485691565]
[12.0, 0.5345097038593318]
[13.0, 0.6768969926845438]
⋮
[8989.0, 0.07825019863405136]
[8990.0, 0.22229848459039636]
[8991.0, 0.7785638083457678]
[8992.0, 0.2849360457567198]
[8993.0, 0.6758274067512697]
[8994.0, 0.5370345216834027]
[8995.0, 0.3360797278108958]
[8996.0, 0.5362633226154412]
[8997.0, 0.8545612885488223]
[8998.0, 0.7897580081587134]
[8999.0, 0.6986728663571047]
[9000.0, 0.39289722732425036]
9000-element Array{Array{Any,1},1}:
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
⋮
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
[#undef, #undef, #undef]
9000-element Array{Array{Any,1},1}:
[1.0, 0.070816232858125, 0.42489739714874997]
[2.0, 0.7853029984149891, 4.711817990489934]
[3.0, 0.7210179200415938, 4.326107520249563]
[4.0, 0.6981532249270992, 4.188919349562595]
[5.0, 0.3480588007300216, 2.0883528043801296]
[6.0, 0.9018409131741212, 5.411045479044727]
[7.0, 0.9751606776407826, 5.850964065844696]
[8.0, 0.9197465832681542, 5.518479499608925]
[9.0, 0.2259475070832464, 1.3556850424994784]
[10.0, 0.20112552323415334, 1.20675313940492]
[11.0, 0.46591547485691565, 2.795492849141494]
[12.0, 0.5345097038593318, 3.207058223155991]
[13.0, 0.6768969926845438, 4.0613819561072635]
⋮
[8989.0, 0.07825019863405136, 0.46950119180430816]
[8990.0, 0.22229848459039636, 1.3337909075423782]
[8991.0, 0.7785638083457678, 4.671382850074607]
[8992.0, 0.2849360457567198, 1.709616274540319]
[8993.0, 0.6758274067512697, 4.054964440507618]
[8994.0, 0.5370345216834027, 3.2222071301004163]
[8995.0, 0.3360797278108958, 2.0164783668653747]
[8996.0, 0.5362633226154412, 3.217579935692647]
[8997.0, 0.8545612885488223, 5.127367731292933]
[8998.0, 0.7897580081587134, 4.73854804895228]
[8999.0, 0.6986728663571047, 4.1920371981426285]
[9000.0, 0.39289722732425036, 2.357383363945502]
9,000 rows × 4 columns
a a1 a2 a3
Float64 Float64 Float64 Float64
1 0.0708162 1.0 0.0708162 0.424897
2 0.785303 2.0 0.785303 4.71182
3 0.721018 3.0 0.721018 4.32611
4 0.698153 4.0 0.698153 4.18892
5 0.348059 5.0 0.348059 2.08835
6 0.901841 6.0 0.901841 5.41105
7 0.975161 7.0 0.975161 5.85096
8 0.919747 8.0 0.919747 5.51848
9 0.225948 9.0 0.225948 1.35569
10 0.201126 10.0 0.201126 1.20675
11 0.465915 11.0 0.465915 2.79549
12 0.53451 12.0 0.53451 3.20706
13 0.676897 13.0 0.676897 4.06138
14 0.547771 14.0 0.547771 3.28663
15 0.13939 15.0 0.13939 0.836339
16 0.665156 16.0 0.665156 3.99094
17 0.482515 17.0 0.482515 2.89509
18 0.619771 18.0 0.619771 3.71863
19 0.143206 19.0 0.143206 0.859238
20 0.814113 20.0 0.814113 4.88468
21 0.554832 21.0 0.554832 3.32899
22 0.837221 22.0 0.837221 5.02333
23 0.197084 23.0 0.197084 1.1825
24 0.204748 24.0 0.204748 1.22849
25 0.559978 25.0 0.559978 3.35987
26 0.09934 26.0 0.09934 0.59604
27 0.600058 27.0 0.600058 3.60035
28 0.16271 28.0 0.16271 0.976262
29 0.898825 29.0 0.898825 5.39295
30 0.266727 30.0 0.266727 1.60036
⋮ ⋮ ⋮ ⋮ ⋮
true
四、多线程，返回dataframe的收集，验证是否正确收集
using DataFrames
using Statistics
function my_fn(items)
df = DataFrame()
df[!,:id] = [items[1] for i in 1:10]
df[!,:sum] .= sum(df.id)
return (idx = items[1],df = df)
end
function test2()
a = rand(500) #输入1
items = [[idx,item] for (idx,item) in zip(1:length(a),a)] #输入2
result = [DataFrame() for i in 1:length(a)] #用于结果的保存
items |> display
result |> display
Threads.@threads for x in items
#for x in items
res = my_fn(x)
idx = round(Int64,res.idx)
result[idx] = res.df
end
result
end
df = test2()
[out]
500-element Array{Array{Float64,1},1}:
[1.0, 0.7141339538704334]
[2.0, 0.2751614187241258]
[3.0, 0.1276095685802059]
[4.0, 0.7487211080799028]
[5.0, 0.04502993862700588]
[6.0, 0.5677883943419717]
[7.0, 0.5153946483331102]
[8.0, 0.27481996153220933]
[9.0, 0.6156652757688648]
[10.0, 0.20966088095430346]
[11.0, 0.09966629409893724]
[12.0, 0.21085954429893516]
[13.0, 0.9787876607926493]
⋮
[489.0, 0.4815375335767591]
[490.0, 0.39904159650432214]
[491.0, 0.01315641762397557]
[492.0, 0.3227240777954996]
[493.0, 0.5284580981245499]
[494.0, 0.911554332297341]
[495.0, 0.750858934405203]
[496.0, 0.5565927834214723]
[497.0, 0.19888221960592078]
[498.0, 0.565409358573782]
[499.0, 0.41314073181624766]
[500.0, 0.6128912616155733]
500-element Array{DataFrame,1}:
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
⋮
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
0×0 DataFrame
500-element Array{DataFrame,1}:
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 1.0 │ 10.0 │
│ 2 │ 1.0 │ 10.0 │
│ 3 │ 1.0 │ 10.0 │
│ 4 │ 1.0 │ 10.0 │
│ 5 │ 1.0 │ 10.0 │
│ 6 │ 1.0 │ 10.0 │
│ 7 │ 1.0 │ 10.0 │
│ 8 │ 1.0 │ 10.0 │
│ 9 │ 1.0 │ 10.0 │
│ 10 │ 1.0 │ 10.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 2.0 │ 20.0 │
│ 2 │ 2.0 │ 20.0 │
│ 3 │ 2.0 │ 20.0 │
│ 4 │ 2.0 │ 20.0 │
│ 5 │ 2.0 │ 20.0 │
│ 6 │ 2.0 │ 20.0 │
│ 7 │ 2.0 │ 20.0 │
│ 8 │ 2.0 │ 20.0 │
│ 9 │ 2.0 │ 20.0 │
│ 10 │ 2.0 │ 20.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 3.0 │ 30.0 │
│ 2 │ 3.0 │ 30.0 │
│ 3 │ 3.0 │ 30.0 │
│ 4 │ 3.0 │ 30.0 │
│ 5 │ 3.0 │ 30.0 │
│ 6 │ 3.0 │ 30.0 │
│ 7 │ 3.0 │ 30.0 │
│ 8 │ 3.0 │ 30.0 │
│ 9 │ 3.0 │ 30.0 │
│ 10 │ 3.0 │ 30.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 4.0 │ 40.0 │
│ 2 │ 4.0 │ 40.0 │
│ 3 │ 4.0 │ 40.0 │
│ 4 │ 4.0 │ 40.0 │
│ 5 │ 4.0 │ 40.0 │
│ 6 │ 4.0 │ 40.0 │
│ 7 │ 4.0 │ 40.0 │
│ 8 │ 4.0 │ 40.0 │
│ 9 │ 4.0 │ 40.0 │
│ 10 │ 4.0 │ 40.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 5.0 │ 50.0 │
│ 2 │ 5.0 │ 50.0 │
│ 3 │ 5.0 │ 50.0 │
│ 4 │ 5.0 │ 50.0 │
│ 5 │ 5.0 │ 50.0 │
│ 6 │ 5.0 │ 50.0 │
│ 7 │ 5.0 │ 50.0 │
│ 8 │ 5.0 │ 50.0 │
│ 9 │ 5.0 │ 50.0 │
│ 10 │ 5.0 │ 50.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 6.0 │ 60.0 │
│ 2 │ 6.0 │ 60.0 │
│ 3 │ 6.0 │ 60.0 │
│ 4 │ 6.0 │ 60.0 │
│ 5 │ 6.0 │ 60.0 │
│ 6 │ 6.0 │ 60.0 │
│ 7 │ 6.0 │ 60.0 │
│ 8 │ 6.0 │ 60.0 │
│ 9 │ 6.0 │ 60.0 │
│ 10 │ 6.0 │ 60.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 7.0 │ 70.0 │
│ 2 │ 7.0 │ 70.0 │
│ 3 │ 7.0 │ 70.0 │
│ 4 │ 7.0 │ 70.0 │
│ 5 │ 7.0 │ 70.0 │
│ 6 │ 7.0 │ 70.0 │
│ 7 │ 7.0 │ 70.0 │
│ 8 │ 7.0 │ 70.0 │
│ 9 │ 7.0 │ 70.0 │
│ 10 │ 7.0 │ 70.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 8.0 │ 80.0 │
│ 2 │ 8.0 │ 80.0 │
│ 3 │ 8.0 │ 80.0 │
│ 4 │ 8.0 │ 80.0 │
│ 5 │ 8.0 │ 80.0 │
│ 6 │ 8.0 │ 80.0 │
│ 7 │ 8.0 │ 80.0 │
│ 8 │ 8.0 │ 80.0 │
│ 9 │ 8.0 │ 80.0 │
│ 10 │ 8.0 │ 80.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 9.0 │ 90.0 │
│ 2 │ 9.0 │ 90.0 │
│ 3 │ 9.0 │ 90.0 │
│ 4 │ 9.0 │ 90.0 │
│ 5 │ 9.0 │ 90.0 │
│ 6 │ 9.0 │ 90.0 │
│ 7 │ 9.0 │ 90.0 │
│ 8 │ 9.0 │ 90.0 │
│ 9 │ 9.0 │ 90.0 │
│ 10 │ 9.0 │ 90.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 10.0 │ 100.0 │
│ 2 │ 10.0 │ 100.0 │
│ 3 │ 10.0 │ 100.0 │
│ 4 │ 10.0 │ 100.0 │
│ 5 │ 10.0 │ 100.0 │
│ 6 │ 10.0 │ 100.0 │
│ 7 │ 10.0 │ 100.0 │
│ 8 │ 10.0 │ 100.0 │
│ 9 │ 10.0 │ 100.0 │
│ 10 │ 10.0 │ 100.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 11.0 │ 110.0 │
│ 2 │ 11.0 │ 110.0 │
│ 3 │ 11.0 │ 110.0 │
│ 4 │ 11.0 │ 110.0 │
│ 5 │ 11.0 │ 110.0 │
│ 6 │ 11.0 │ 110.0 │
│ 7 │ 11.0 │ 110.0 │
│ 8 │ 11.0 │ 110.0 │
│ 9 │ 11.0 │ 110.0 │
│ 10 │ 11.0 │ 110.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 12.0 │ 120.0 │
│ 2 │ 12.0 │ 120.0 │
│ 3 │ 12.0 │ 120.0 │
│ 4 │ 12.0 │ 120.0 │
│ 5 │ 12.0 │ 120.0 │
│ 6 │ 12.0 │ 120.0 │
│ 7 │ 12.0 │ 120.0 │
│ 8 │ 12.0 │ 120.0 │
│ 9 │ 12.0 │ 120.0 │
│ 10 │ 12.0 │ 120.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 13.0 │ 130.0 │
│ 2 │ 13.0 │ 130.0 │
│ 3 │ 13.0 │ 130.0 │
│ 4 │ 13.0 │ 130.0 │
│ 5 │ 13.0 │ 130.0 │
│ 6 │ 13.0 │ 130.0 │
│ 7 │ 13.0 │ 130.0 │
│ 8 │ 13.0 │ 130.0 │
│ 9 │ 13.0 │ 130.0 │
│ 10 │ 13.0 │ 130.0 │
⋮
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 489.0 │ 4890.0 │
│ 2 │ 489.0 │ 4890.0 │
│ 3 │ 489.0 │ 4890.0 │
│ 4 │ 489.0 │ 4890.0 │
│ 5 │ 489.0 │ 4890.0 │
│ 6 │ 489.0 │ 4890.0 │
│ 7 │ 489.0 │ 4890.0 │
│ 8 │ 489.0 │ 4890.0 │
│ 9 │ 489.0 │ 4890.0 │
│ 10 │ 489.0 │ 4890.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 490.0 │ 4900.0 │
│ 2 │ 490.0 │ 4900.0 │
│ 3 │ 490.0 │ 4900.0 │
│ 4 │ 490.0 │ 4900.0 │
│ 5 │ 490.0 │ 4900.0 │
│ 6 │ 490.0 │ 4900.0 │
│ 7 │ 490.0 │ 4900.0 │
│ 8 │ 490.0 │ 4900.0 │
│ 9 │ 490.0 │ 4900.0 │
│ 10 │ 490.0 │ 4900.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 491.0 │ 4910.0 │
│ 2 │ 491.0 │ 4910.0 │
│ 3 │ 491.0 │ 4910.0 │
│ 4 │ 491.0 │ 4910.0 │
│ 5 │ 491.0 │ 4910.0 │
│ 6 │ 491.0 │ 4910.0 │
│ 7 │ 491.0 │ 4910.0 │
│ 8 │ 491.0 │ 4910.0 │
│ 9 │ 491.0 │ 4910.0 │
│ 10 │ 491.0 │ 4910.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 492.0 │ 4920.0 │
│ 2 │ 492.0 │ 4920.0 │
│ 3 │ 492.0 │ 4920.0 │
│ 4 │ 492.0 │ 4920.0 │
│ 5 │ 492.0 │ 4920.0 │
│ 6 │ 492.0 │ 4920.0 │
│ 7 │ 492.0 │ 4920.0 │
│ 8 │ 492.0 │ 4920.0 │
│ 9 │ 492.0 │ 4920.0 │
│ 10 │ 492.0 │ 4920.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 493.0 │ 4930.0 │
│ 2 │ 493.0 │ 4930.0 │
│ 3 │ 493.0 │ 4930.0 │
│ 4 │ 493.0 │ 4930.0 │
│ 5 │ 493.0 │ 4930.0 │
│ 6 │ 493.0 │ 4930.0 │
│ 7 │ 493.0 │ 4930.0 │
│ 8 │ 493.0 │ 4930.0 │
│ 9 │ 493.0 │ 4930.0 │
│ 10 │ 493.0 │ 4930.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 494.0 │ 4940.0 │
│ 2 │ 494.0 │ 4940.0 │
│ 3 │ 494.0 │ 4940.0 │
│ 4 │ 494.0 │ 4940.0 │
│ 5 │ 494.0 │ 4940.0 │
│ 6 │ 494.0 │ 4940.0 │
│ 7 │ 494.0 │ 4940.0 │
│ 8 │ 494.0 │ 4940.0 │
│ 9 │ 494.0 │ 4940.0 │
│ 10 │ 494.0 │ 4940.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 495.0 │ 4950.0 │
│ 2 │ 495.0 │ 4950.0 │
│ 3 │ 495.0 │ 4950.0 │
│ 4 │ 495.0 │ 4950.0 │
│ 5 │ 495.0 │ 4950.0 │
│ 6 │ 495.0 │ 4950.0 │
│ 7 │ 495.0 │ 4950.0 │
│ 8 │ 495.0 │ 4950.0 │
│ 9 │ 495.0 │ 4950.0 │
│ 10 │ 495.0 │ 4950.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 496.0 │ 4960.0 │
│ 2 │ 496.0 │ 4960.0 │
│ 3 │ 496.0 │ 4960.0 │
│ 4 │ 496.0 │ 4960.0 │
│ 5 │ 496.0 │ 4960.0 │
│ 6 │ 496.0 │ 4960.0 │
│ 7 │ 496.0 │ 4960.0 │
│ 8 │ 496.0 │ 4960.0 │
│ 9 │ 496.0 │ 4960.0 │
│ 10 │ 496.0 │ 4960.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 497.0 │ 4970.0 │
│ 2 │ 497.0 │ 4970.0 │
│ 3 │ 497.0 │ 4970.0 │
│ 4 │ 497.0 │ 4970.0 │
│ 5 │ 497.0 │ 4970.0 │
│ 6 │ 497.0 │ 4970.0 │
│ 7 │ 497.0 │ 4970.0 │
│ 8 │ 497.0 │ 4970.0 │
│ 9 │ 497.0 │ 4970.0 │
│ 10 │ 497.0 │ 4970.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 498.0 │ 4980.0 │
│ 2 │ 498.0 │ 4980.0 │
│ 3 │ 498.0 │ 4980.0 │
│ 4 │ 498.0 │ 4980.0 │
│ 5 │ 498.0 │ 4980.0 │
│ 6 │ 498.0 │ 4980.0 │
│ 7 │ 498.0 │ 4980.0 │
│ 8 │ 498.0 │ 4980.0 │
│ 9 │ 498.0 │ 4980.0 │
│ 10 │ 498.0 │ 4980.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 499.0 │ 4990.0 │
│ 2 │ 499.0 │ 4990.0 │
│ 3 │ 499.0 │ 4990.0 │
│ 4 │ 499.0 │ 4990.0 │
│ 5 │ 499.0 │ 4990.0 │
│ 6 │ 499.0 │ 4990.0 │
│ 7 │ 499.0 │ 4990.0 │
│ 8 │ 499.0 │ 4990.0 │
│ 9 │ 499.0 │ 4990.0 │
│ 10 │ 499.0 │ 4990.0 │
10×2 DataFrame
│ Row │ id │ sum │
│ │ Float64 │ Float64 │
├─────┼─────────┼─────────┤
│ 1 │ 500.0 │ 5000.0 │
│ 2 │ 500.0 │ 5000.0 │
│ 3 │ 500.0 │ 5000.0 │
│ 4 │ 500.0 │ 5000.0 │
│ 5 │ 500.0 │ 5000.0 │
│ 6 │ 500.0 │ 5000.0 │
│ 7 │ 500.0 │ 5000.0 │
│ 8 │ 500.0 │ 5000.0 │
│ 9 │ 500.0 │ 5000.0 │
│ 10 │ 500.0 │ 5000.0 │
验证收集到的结果
for (idx,item) in zip(1:length(df),df)
println("====")
begin idx - item[1,:id] end |> println
end
println("输出0.0则为计算正确，序号对得上")
[out]
====
0.0
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====
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====
0.0
====
0.0
输出0.0则为计算正确，序号对得上
【遇到的问题】
在线程中调用pycall引入的numpy,talib等包的函数时，直接死机，不知道什么原因，talib和numpy应该用到多线程，不知道具体什么原因，以后查资料看吧。