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antimon2/MyTimeByHolyTraits.jl.ipynb

Last active March 16, 2024 00:22
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MyTimes.py.ipynb
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MyTimeByHolyTraits.jl.ipynb
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MyTimes.py.ipynb
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MyTimeWithInvoke.jl.ipynb
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.525Z",
"end_time": "2024-03-16T09:16:58.236000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "versioninfo()",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "Julia Version 1.10.2\nCommit bd47eca2c8a (2024-03-01 10:14 UTC)\nBuild Info:\n Official https://julialang.org/ release\nPlatform Info:\n OS: Linux (x86_64-linux-gnu)\n CPU: 12 × Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz\n WORD_SIZE: 64\n LIBM: libopenlibm\n LLVM: libLLVM-15.0.7 (ORCJIT, skylake)\nThreads: 1 default, 0 interactive, 1 GC (on 12 virtual cores)\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## `AbstractTime`"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.526Z",
"end_time": "2024-03-16T09:16:58.582000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "abstract type AbstractTime end\n\nfunction gethour end\nfunction getminute end\nfunction getsecond end",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 2,
"data": {
"text/plain": "getsecond (generic function with 0 methods)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.526Z",
"end_time": "2024-03-16T09:16:58.583000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function Base.show(io::IO, time::AbstractTime)\n print(io,\n string(gethour(time), pad=2),\n ':',\n string(getminute(time), pad=2),\n ':',\n string(getsecond(time), pad=2))\nend",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## `MyTime`"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.527Z",
"end_time": "2024-03-16T09:16:58.586000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "struct MyTime <: AbstractTime\n hour::Int\n minute::Int\n second::Int\nend\n\ngethour(time::MyTime) = time.hour\ngetminute(time::MyTime) = time.minute\ngetsecond(time::MyTime) = time.second",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 4,
"data": {
"text/plain": "getsecond (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.528Z",
"end_time": "2024-03-16T09:16:58.718000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "mytime1 = MyTime(14, 28, 57)",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
"text/plain": "14:28:57"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.529Z",
"end_time": "2024-03-16T09:16:58.763000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "println(mytime1)",
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": "14:28:57\n",
"name": "stdout"
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.529Z",
"end_time": "2024-03-16T09:16:59.105000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "string(mytime1)",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 7,
"data": {
"text/plain": "\"14:28:57\""
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## `MyTime2`"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.530Z",
"end_time": "2024-03-16T09:16:59.115000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "struct MyTime2 <: AbstractTime\n seconds::Int\nend\n\ngethour(time::MyTime2) = time.seconds ÷ 3600\ngetminute(time::MyTime2) = time.seconds ÷ 60 % 60\ngetsecond(time::MyTime2) = time.seconds % 60\ngetseconds(time::MyTime2) = time.seconds",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": "getseconds (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.531Z",
"end_time": "2024-03-16T09:16:59.128000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function Base.show(io::IO, time::MyTime2)\n @invoke show(io, time::AbstractTime) # `invoke(show, Tuple{IO, AbstractTime}, io, time)` と同じ\n print(io, \" (\", getseconds(time), \"sec(s).)\")\nend",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.531Z",
"end_time": "2024-03-16T09:16:59.255000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "mytime2 = MyTime2(10000)",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": "02:46:40 (10000sec(s).)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.532Z",
"end_time": "2024-03-16T09:16:59.275000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "string(mytime2)",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": "\"02:46:40 (10000sec(s).)\""
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Point"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "+ `@invoke fn(arg::AnSupertype)` のように書くと、`fn(arg)` を呼ぶときに引数の型が `AnSupertype` で定義されたメソッドを選択・実行（＝invoke）できる！\n+ ※このように書けるのは Julia v1.9 以降\n + Julia v1.7/1.8 は `Base.@invoke ～` と書く必要あり\n + Julia v1.6 以前は `invoke(fn, Tuple{AnSupertype}, arg)` のように書く必要あり"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## `MyTimeWithMS`"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.532Z",
"end_time": "2024-03-16T09:16:59.285000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "struct MyTimeWithMS <: AbstractTime\n hms::MyTime\n ms::Int\n \n MyTimeWithMS(h, m, s, ms) = new(MyTime(h, m, s), ms)\nend\n\ngethour(time::MyTimeWithMS) = gethour(time.hms)\ngetminute(time::MyTimeWithMS) = getminute(time.hms)\ngetsecond(time::MyTimeWithMS) = getsecond(time.hms)\ngetmillisecond(time::MyTimeWithMS) = time.ms",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": "getmillisecond (generic function with 1 method)"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.533Z",
"end_time": "2024-03-16T09:16:59.286000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "function Base.show(io::IO, time::MyTimeWithMS)\n @invoke show(io, time::AbstractTime) # `invoke(show, Tuple{IO, AbstractTime}, io, time)` と同じ\n print(io, '.', string(getmillisecond(time), pad=3))\nend",
"execution_count": 13,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.534Z",
"end_time": "2024-03-16T09:16:59.422000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "mytime3 = MyTimeWithMS(12, 34, 56, 789)",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": "12:34:56.789"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.535Z",
"end_time": "2024-03-16T09:16:59.442000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "string(mytime3)",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": "\"12:34:56.789\""
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Point"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "+ Julia では（構造の）**継承はできない**（`MyTime` の派生型として `MyTimeWithMS` を定義できない）ので、似たようなことをしたい場合はこのように（ほぼ必然的に）**委譲** を利用することになる。\n+ 説明のために `@invoke` を用いた実装を示したが、以下のように書いても良い： \n ```julia\n function Base.show(io::IO, time::MyTimeWithMS)\n print(io, time.hms, '.', string(getmillisecond(time), pad=3))\n end\n ```"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## ベンチマーク"
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.535Z",
"end_time": "2024-03-16T09:16:59.626000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "using BenchmarkTools",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.536Z",
"end_time": "2024-03-16T09:16:59.705000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "using Random",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.536Z",
"end_time": "2024-03-16T09:17:03.632000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "Random.seed!(1234)\n@benchmark string(MyTime(h, m, s)) setup=(h=rand(0:23);m=rand(0:59);s=rand(0:59))",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": "BenchmarkTools.Trial: 10000 samples with 365 evaluations.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m254.304 ns\u001b[22m\u001b[39m … \u001b[35m 7.042 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 94.36%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m260.433 ns \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m277.859 ns\u001b[22m\u001b[39m ± \u001b[32m211.581 ns\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m4.17% ± 5.27%\n\n \u001b[39m▃\u001b[39m█\u001b[34m▇\u001b[39m\u001b[39m▅\u001b[39m▃\u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\n \u001b[39m█\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▃\u001b[32m▄\u001b[39m\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[39m▆\u001b[39m▄\u001b[39m▅\u001b[39m▃\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▂\u001b[39m▃\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m▇\u001b[39m▇\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m▇\u001b[39m▇\u001b[39m▆\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▂\u001b[39m▄\u001b[39m▂\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▅\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m \u001b[39m█\n 254 ns\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 439 ns \u001b[0m\u001b[1m<\u001b[22m\n\n Memory estimate\u001b[90m: \u001b[39m\u001b[33m424 bytes\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m9\u001b[39m."
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.537Z",
"end_time": "2024-03-16T09:17:05.274000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "Random.seed!(1234)\n@benchmark string(MyTime2(secs)) setup=(secs=rand(0:60*60*24-1))",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": "BenchmarkTools.Trial: 10000 samples with 199 evaluations.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m428.714 ns\u001b[22m\u001b[39m … \u001b[35m 10.469 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 94.56%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m448.271 ns \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m475.761 ns\u001b[22m\u001b[39m ± \u001b[32m472.965 ns\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m4.92% ± 4.73%\n\n \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[34m█\u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n \u001b[39m▁\u001b[39m▂\u001b[39m▇\u001b[39m▅\u001b[39m█\u001b[34m█\u001b[39m\u001b[39m▇\u001b[39m▂\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[32m▁\u001b[39m\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m \u001b[39m▁\n 429 ns\u001b[90m Histogram: frequency by time\u001b[39m 658 ns \u001b[0m\u001b[1m<\u001b[22m\n\n Memory estimate\u001b[90m: \u001b[39m\u001b[33m576 bytes\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m12\u001b[39m."
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2024-03-16T00:16:55.538Z",
"end_time": "2024-03-16T09:17:06.791000+09:00"
},
"trusted": true
},
"cell_type": "code",
"source": "Random.seed!(1234)\n@benchmark string(MyTimeWithMS(h, m, s, ms)) setup=(h=rand(0:23);m=rand(0:59);s=rand(0:59);ms=rand(0:999))",
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 20,
"data": {
"text/plain": "BenchmarkTools.Trial: 10000 samples with 210 evaluations.\n Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m): \u001b[39m\u001b[36m\u001b[1m359.471 ns\u001b[22m\u001b[39m … \u001b[35m 10.526 μs\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m0.00% … 95.62%\n Time \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m): \u001b[39m\u001b[34m\u001b[1m367.214 ns \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m): \u001b[39m0.00%\n Time \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m): \u001b[39m\u001b[32m\u001b[1m400.515 ns\u001b[22m\u001b[39m ± \u001b[32m484.719 ns\u001b[39m \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m): \u001b[39m6.22% ± 4.95%\n\n \u001b[39m▆\u001b[34m█\u001b[39m\u001b[39m▆\u001b[39m▁\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m▁\n \u001b[39m█\u001b[34m█\u001b[39m\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▄\u001b[39m▄\u001b[32m▃\u001b[39m\u001b[39m▄\u001b[39m▄\u001b[39m▄\u001b[39m▅\u001b[39m▇\u001b[39m▇\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▅\u001b[39m▆\u001b[39m▇\u001b[39m▆\u001b[39m▆\u001b[39m▇\u001b[39m▆\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▃\u001b[39m▆\u001b[39m▆\u001b[39m▆\u001b[39m▅\u001b[39m▆\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m▅\u001b[39m▅\u001b[39m▄\u001b[39m▅\u001b[39m \u001b[39m█\n 359 ns\u001b[90m \u001b[39m\u001b[90mHistogram: \u001b[39m\u001b[90m\u001b[1mlog(\u001b[22m\u001b[39m\u001b[90mfrequency\u001b[39m\u001b[90m\u001b[1m)\u001b[22m\u001b[39m\u001b[90m by time\u001b[39m 678 ns \u001b[0m\u001b[1m<\u001b[22m\n\n Memory estimate\u001b[90m: \u001b[39m\u001b[33m608 bytes\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m13\u001b[39m."
},
"metadata": {}
}
]
}
],
"metadata": {
"kernelspec": {
"name": "julia-1.10",
"display_name": "Julia 1.10.2",
"language": "julia"
},
"language_info": {
"file_extension": ".jl",
"name": "julia",
"mimetype": "application/julia",
"version": "1.10.2"
},
"gist": {
"id": "",
"data": {
"description": "MyTimeWithInvoke.ipynb",
"public": true
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Raw
Project.toml
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
[compat]
julia = "1.6"
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