開始時刻から終了時刻までhh:mm形式の文字列を15分単位でリスト出力

指定の開始時刻から終了時刻まで、hh:mm形式の文字列を15分間隔でリスト出力するAppleScriptです。

ありもののサブルーチンを組み合わせて即席で作ったCocoa版のルーチンと、ChatGPT（GPT-5）に書かせた非Cocoa版のルーチンの2つがあり（ChatGPTにCocoa使用を指定せず）、速度を比較してみたら……

非Cocoa版のルーチンのほうが６倍ぐらい高速でした。データが小さすぎるとか、処理内容が小さすぎる場合にはCocoaの機能を利用しないほうが高速に処理できる場合があります。

Shane Stanleyの処理時間計測アプリ「ScriptGeek」が、かなりCocoa Scriptingの登場初期から存在しており、不思議に思ってきたものですが……処理内容の規模によってはCocoa Scriptingのほうが遅くなるケースがあるため、実際に書いて動かして計測してみないと、Cocoa Scriptingで高速化が達成できるかわからないところです。

このほか、AppleScriptのランタイム環境が何になるかによって実行速度は変わってきます。Xcode上で開発するCocoa Applicationなども実行特性が違うため、やはり動かして計測してみないと何がベストなのかはわかりません。

ただ、この程度の処理であれば、0.0028秒が0.4秒になったところで、何か実用上の差異が発生するわけでもありません。

追記：
追加で最速パターンを掲載しておきます。汎用性を求めて計算して出力していましたが、ほとんどの用途で毎回同じようなデータを出力することでしょう。なので、固定データを返すだけでいいだろうかと。高速版とくらべても65〜70倍ぐらい高速です（当たり前）。

AppleScript名：15分メッシュの時間文字列を生成.scptd

—
–　　Created by: Takaaki Naganoya
–　　Created on: 2025/08/13
—
–　　Copyright © 2025 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

set aRes to generateWorkUnitList() of me
–> {"5:15", "5:30", "5:45", "6:00", "6:15", "6:30", "6:45", "7:00", "7:15", "7:30", "7:45", "8:00", "8:15", "8:30", "8:45", "9:00", "9:15", "9:30", "9:45", "10:00", "10:15", "10:30", "10:45", "11:00", "11:15", "11:30", "11:45", "12:00", "12:15", "12:30", "12:45", "13:00", "13:15", "13:30", "13:45", "14:00", "14:15", "14:30", "14:45", "15:00", "15:15", "15:30", "15:45", "16:00", "16:15", "16:30", "16:45", "17:00", "17:15", "17:30", "17:45", "18:00", "18:15", "18:30", "18:45", "19:00", "19:15", "19:30", "19:45", "20:00", "20:15", "20:30", "20:45"}

on generateWorkUnitList()
　　set hList to {}
　　
　　repeat with h from 5 to 20 by 1
　　　　repeat with m from 0 to 59 by 15
　　　　　　if {h, m} is not equal to {5, 0} then
　　　　　　　　set mStr to numToZeroPaddingStr(m, 2, "0")
　　　　　　　　set the end of hList to (h as string) & ":" & mStr
　　　　　　end if
　　　　end repeat
　　end repeat
　　
　　return hList
end generateWorkUnitList

–整数の値に指定桁数ゼロパディングして文字列で返す
on numToZeroPaddingStr(aNum as integer, aDigit as integer, paddingChar as text)
　　set aNumForm to current application’s NSNumberFormatter’s alloc()’s init()
　　aNumForm’s setPaddingPosition:(current application’s NSNumberFormatterPadBeforePrefix)
　　aNumForm’s setPaddingCharacter:paddingChar
　　aNumForm’s setMinimumIntegerDigits:aDigit
　　
　　set bNum to current application’s NSNumber’s numberWithInt:aNum
　　set aStr to aNumForm’s stringFromNumber:bNum
　　
　　return aStr as text
end numToZeroPaddingStr

★Click Here to Open This Script　

AppleScript名：15分メッシュの時間文字列を生成（高速）.scpt

—
–　　Created by: Takaaki Naganoya
–　　Created on: 2025/08/13
—
–　　Copyright © 2025 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

set mList to retWorkingMeshStrList() of me
–> {"5:15", "5:30", "5:45", "6:00", "6:15", "6:30", "6:45", "7:00", "7:15", "7:30", "7:45", "8:00", "8:15", "8:30", "8:45", "9:00", "9:15", "9:30", "9:45", "10:00", "10:15", "10:30", "10:45", "11:00", "11:15", "11:30", "11:45", "12:00", "12:15", "12:30", "12:45", "13:00", "13:15", "13:30", "13:45", "14:00", "14:15", "14:30", "14:45", "15:00", "15:15", "15:30", "15:45", "16:00", "16:15", "16:30", "16:45", "17:00", "17:15", "17:30", "17:45", "18:00", "18:15", "18:30", "18:45", "19:00", "19:15", "19:30", "19:45", "20:00"}

on retWorkingMeshStrList()
　　set startSeconds to (5 * 3600) + (15 * 60) — 5:15 の相対秒
　　set endSeconds to (20 * 3600) — 20:00 の相対秒
　　set intervalSeconds to 15 * 60 — 15分間隔
　　
　　set timeList to {}
　　
　　repeat with t from startSeconds to endSeconds by intervalSeconds
　　　　set h to t div 3600
　　　　set m to (t mod 3600) div 60
　　　　— 2桁表示に整形
　　　　–set hStr to text -2 thru -1 of ("0" & h)
　　　　set hStr to h as string
　　　　set mStr to text -2 thru -1 of ("0" & m)
　　　　set end of timeList to (hStr & ":" & mStr)
　　end repeat
　　
　　return timeList
end retWorkingMeshStrList

★Click Here to Open This Script　

AppleScript名：15分メッシュの時間文字列を生成（最速）.scptd

—
–　　Created by: Takaaki Naganoya
–　　Created on: 2025/08/10
—
–　　Copyright © 2025 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4"
use framework "Foundation"
use scripting additions

set hList to generateWorkUnitList() of me

on generateWorkUnitList()
　　return {"5:15", "5:30", "5:45", "6:00", "6:15", "6:30", "6:45", "7:00", "7:15", "7:30", "7:45", "8:00", "8:15", "8:30", "8:45", "9:00", "9:15", "9:30", "9:45", "10:00", "10:15", "10:30", "10:45", "11:00", "11:15", "11:30", "11:45", "12:00", "12:15", "12:30", "12:45", "13:00", "13:15", "13:30", "13:45", "14:00", "14:15", "14:30", "14:45", "15:00", "15:15", "15:30", "15:45", "16:00", "16:15", "16:30", "16:45", "17:00", "17:15", "17:30", "17:45", "18:00", "18:15", "18:30", "18:45", "19:00", "19:15", "19:30", "19:45", "20:00"}
end generateWorkUnitList

★Click Here to Open This Script　

ChatGPTでchatに対する応答文を取得

Posted on 3月 6, 2023 by Takaaki Naganoya

OpenAIが提供しているREST APIを呼び出して、チャットに対する応答を生成するAppleScriptです。実行のためにはOpenAIのWebサイトにサインアップして、実行のためのAPI Keyを取得してください。

ChatGPTなどのサービスを提供しているOpenAIにサインアップして、各種サービスをAppleScriptから利用できます。Freeアカウントでは１分あたりに発行できるクエリー数の上限が低めに設定されていますが、実験を行う程度であれば十分なレベルでしょう。

https://platform.openai.com/docs/introduction

「chat」は、いわゆるChatGPTでよく知られている処理で、チャットに応答するものです。この呼び出し方に対して、さらにroleとして「system」「user」「assistant」などの役割を指定することで、チャットらしいやりとりを生成するようです（Chat completion）。

AppleScript名：Chat.scptd

—
–　　Created by: Takaaki Naganoya
–　　Created on: 2023/03/05
—
–　　Copyright © 2023 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

property NSString : a reference to current application’s NSString
property NSCountedSet : a reference to current application’s NSCountedSet
property NSJSONSerialization : a reference to current application’s NSJSONSerialization
property NSUTF8StringEncoding : a reference to current application’s NSUTF8StringEncoding

set myText to "こんにちは"

set barerKey to "xx-XXXXXXXxXxxxxXxxXXxXXXXxxxXXxxxxXXxxxXXxXXxxXXxx"

set aText to "curl https://api.openai.com/v1/chat/completions -H ’Content-Type: application/json’ -H ’Authorization: Bearer " & barerKey & "’ -d ’{＼"model＼": ＼"gpt-3.5-turbo＼",＼"messages＼": [{＼"role＼": ＼"user＼", ＼"content＼": ＼"" & myText & "＼"}]}’"
set sRes to do shell script aText

set jsonString to NSString’s stringWithString:sRes
set jsonData to jsonString’s dataUsingEncoding:(NSUTF8StringEncoding)
set aRes to NSJSONSerialization’s JSONObjectWithData:jsonData options:0 |error|:(missing value)
set chatRes to (aRes’s valueForKeyPath:"choices.message.content") as list
–>{"こんにちは！こんにちはと言うと、こんにちはと返してくださる方が多いですね。私はAIアシスタントなので、いつでもお話し相手になれます。何かお話を聞かせてください。"}
–> {"、こんにちは! 私はAIアシスタントです。何かお手伝いできることはありますか？"}
–> {"こんにちは！私はAIアシスタントです。何かお手伝いできますか？"}
–> {"、私はAIアシスタントです。何かお手伝いできることはありますか？"}
–> {"こんにちは！こんにちはは、日本語で「こんにちは」と書き、挨拶の一つです。相手と会話をする際に使われる一般的な挨拶の言葉で、おはようございます、こんにちは、こんばんはなどがあります。どうぞよろしくお願いします！"}

★Click Here to Open This Script　

ChatGPTで質問に対する回答を生成（Compilations）

Posted on 2月 26, 2023 by Takaaki Naganoya

OpenAIが提供しているREST APIを呼び出して、質問に対する回答を生成するAppleScriptです。実行のためにはOpenAIのWebサイトにサインアップして、実行のためのAPI Keyを取得してください。

https://platform.openai.com/docs/introduction

「質問に対する回答を生成」は、いわゆるChatGPTでよく知られている処理で、対話的に質問文に回答するものです。回答内容が正しいかどうかはちょっとアレですが、自然言語処理もこのレベルまで来たのかと感心させられます。

対話っぽい動作（前回の問い合わせを踏まえた上で回答する）を考えて「user」パラメータを付けて呼び出していますが（ここも自分のユーザーアカウントに書き換えてください）、いまひとつWebブラウザ上で問い合わせを行ったときのような「つながり」を感じられないので、何かまだ指定する必要があるのかもしれません。

AppleScript名：Compilations（質問に対する回答を生成）.scptd

—
–　　Created by: Takaaki Naganoya
–　　Created on: 2023/02/24
—
–　　Copyright © 2023 Piyomaru Software, All Rights Reserved
—

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

set myText to "バンダイナムコのアーケードゲーム「戦場の絆1」の初代プロデューサーの名前は、小山順一朗さんです。"

set barerKey to "xx-XXXXXXXxXxxxxXxxXXxXXXXxxxXXxxxxXXxxxXXxXXxxXXxx"

set sRes to (do shell script "curl https://api.openai.com/v1/completions -H ’Content-Type: application/json’ -H ’Authorization: Bearer " & barerKey & "’ -d ’{＼"model＼": ＼"text-davinci-003＼", ＼"prompt＼": ＼"" & myText & "＼", ＼"max_tokens＼": 200, ＼"temperature＼": 0, ＼"user＼": ＼"maro_ml@piyocast.com＼"}’")

–>{|id|:"cmpl-6nMNGJzTFei6eMbn1rnMgMN9ML4qi", object:"text_completion", created:1.677222098E+9, model:"text-davinci-003", choices:{{|index|:0, finish_reason:"stop", logprobs:missing value, |text|:"小山順一朗さんは、バンダイナムコのゲームプロデューサーとして、「戦場の絆1」をはじめとした数々のアーケードゲームをプロデュースしてきました。また、「戦場の絆1」のプロデューサーとして、「戦場の絆2」「戦場の絆3」「戦場の絆4」などのシリーズをプロデュースしています。"}}, usage:{total_tokens:224, completion_tokens:163, prompt_tokens:61}}

★Click Here to Open This Script　

ChatGPTで文章の感情検出（Moderations）

Posted on 2月 26, 2023 by Takaaki Naganoya

OpenAIが提供しているREST APIを呼び出して、指定文章の感情検出を行うAppleScriptです。実行のためにはOpenAIのWebサイトにサインアップして、実行のためのAPI Keyを取得してください。

https://platform.openai.com/docs/introduction

「文章の感情検出」は、ユーザーサポートなどの現場においてユーザーからの投稿が質問などのものなのか、あるいは感情的な文章なのかといった「当たり」をつけるために用いられているといった印象があります。

ただ、「I want to kill him.」という例文に対して「hate」が検出されないなど、ややその評価内容には疑問の余地が残されているようです。

AppleScript名：Moderations（感情検出）.scptd

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

set aSentence to "I want to kill him."

set barerKey to "xx-XXXXXXXxXxxxxXxxXXxXXXXxxxXXxxxxXXxxxXXxXXxxXXxx"
set sRes to (do shell script "curl https://api.openai.com/v1/moderations -H ’Content-Type: application/json’ -H ’Authorization: Bearer " & barerKey & "’ -d ’{＼"input＼": ＼"" & aSentence & "＼" }’")

set mRes to aRes as {anything, record}
–> {|id|:"modr-6o8PPeHjWYIH5z5Qi79etbe8JaWY8", model:"text-moderation-004", results:{{flagged:true, category_scores:{sexual:3.26660915561661E-6, |sexual/minors|:2.58405265185502E-7, |hate/threatening|:2.28086973947939E-5, hate:0.007466733921, |self-harm|:1.23088886994083E-6, violence:0.794520378113, |violence/graphic|:4.90069034242424E-8}, categories:{sexual:false, |sexual/minors|:false, |hate/threatening|:false, hate:false, |self-harm|:false, violence:true, |violence/graphic|:false}}}}

★Click Here to Open This Script　

ChatGPTで文章のベクトル化（Embedding）

Posted on 2月 26, 2023 by Takaaki Naganoya

OpenAIが提供しているREST APIを呼び出して、指定文章のベクトル化を行うAppleScriptです。実行のためにはOpenAIのWebサイトにサインアップして、実行のためのAPI Keyを取得してください。

https://platform.openai.com/docs/introduction

「文章のベクトル化」は、大量の候補文を用意しておいてベクトル化し、新たにユーザーが与えた文章との「類似度」を計算するためのものです。

AppleScript名：Embedding（文章のベクトル化）.scptd

use AppleScript version "2.4" — Yosemite (10.10) or later
use framework "Foundation"
use scripting additions

set myText to "今日はいい天気です。"

set barerKey to "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
set sRes to (do shell script "curl https://api.openai.com/v1/embeddings -H ’Content-Type: application/json’ -H ’Authorization: Bearer " & barerKey & "’ -d ’{＼"input＼": ＼"" & myText & "＼", ＼"model＼":＼"text-embedding-ada-002＼"}’")

set jsonString to NSString’s stringWithString:sRes
set jsonData to jsonString’s dataUsingEncoding:(NSUTF8StringEncoding)
set aRes to NSJSONSerialization’s JSONObjectWithData:jsonData options:0 |error|:(missing value)
set modelRes to aRes as {anything, record}
–> {object:"list", |data|:{{|index|:0, object:"embedding", embedding:{0.0024673557, 0.0037366515, -0.005702117, -0.025023261, 0.010659494, -0.0024349757, 0.0038791234, -0.031266123, 0.005248797, -0.023909388, -0.0052941293, 0.008710219, -0.0028316306, -0.019039437, -0.013780926, -0.009519719, 0.01660446, -0.020762052, 0.019881317, -0.025748573, 0.037897546, 0.025981707, -0.006569901, -0.01387159, -0.023209982, 0.0051743235, 0.014182438, -0.018974677, 0.031032987, -0.026655212, 0.029919116, -0.00975933, -0.007019983, 0.022381052, 0.010186747, -0.032120954, -0.023054557, 0.01536107, -0.0049476633, 0.005919063, 0.01542583, -0.019363238, 0.015335166, -0.0029741025, -0.02567086, 0.008626031, -0.008295755, 0.0018116607, -0.004814905, 0.028546205, 0.011533754, -0.006395049, -0.040876508, -0.038855996, -5.7595916E-4, 0.006106867, -0.014078822, -1.681736E-4, -0.005365365, -0.01260877, -0.0025499247, -0.013949302, -0.009280107, 0.01925962, 0.007266071, -0.01451919, -0.020917477, -0.0051937513, -0.018197557, 0.009701047, 0.008813835, -0.0028105837, -4.6546242E-4, -0.035669804, 0.008813835, 0.0023507876, -0.013003807, -0.010562355, -0.0016028098, 0.008904499, 0.015995719, -0.03269084, -0.0109638665, -0.006909891, 0.0061554373, 0.0031861917, -0.014117678, 0.019440949, -1.770781E-4, -0.017057782, 0.011592038, 0.0014376718, 0.023171125, -0.0041090213, -0.006637899, 0.0035877035, 0.01633247, 0.031136604, 0.011158146, -0.026784733, -0.0041122595, -0.0016950928, 2.7664658E-4, 0.0033416154, -0.051367626, -0.0082892785, 0.027173292, -0.008082047, 0.02120242, -0.038234297, -0.015179742, -0.006909891, 4.5251043E-4, -0.039918058, -0.024518132, 0.008962783, 0.004562341, 0.0014311958, -0.012168402, -3.0922896E-4, 0.018624973, 0.010070179, -0.0012701054, -0.004737193, 0.006450095, 0.01150785, -0.02890886, -0.016228853, -0.011650322, 8.2771364E-4, 0.01426015, 0.005112801, 0.0041187354, 0.007466827, 0.0037139854, 0.012984378, -0.025632005, 0.0197777, -0.010756634, 0.018093942, 0.006236387, 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★Click Here to Open This Script　

AppleScriptの穴

Useful & Practical AppleScript archive. Click '★Click Here to Open This Script' Link to download each AppleScript

タグ: ChatGPT

ChatGPTでchatに対する応答文を取得

ChatGPTで質問に対する回答を生成（Compilations）

ChatGPTで文章の感情検出（Moderations）

ChatGPTで文章のベクトル化（Embedding）