macOS Archives

Category Archives: macOS

Get A Day’s Schedule From Fantastical On The Command Line With Shortcuts

I use Fantastical as it’s a much cleaner and native interface than Google Calendar, which I’m stuck using.

I do like to use the command line more than GUIs and, while I have other things set up to work with Google Calendar from the CLI, I’ve always wanted to figure out how to pull data from Fantastical to it.

So, I figured out a shortcut + Bash script combo to do that, and posted it into the box below. The link to the shortcut is in the comments of the script.

#!/usr/bin/env bash

# Changelog:
#
# 2024-03-23: Script created for scheduling tasks on macOS.
#             Added error handling, usage information, and best practices.

# Usage:
#
# This script is intended to be used for getting the day's schedule from Fantastical
# It takes an optional date parameter in the format YYYY-MM-DD and uses the
# macOS 'shortcuts' command to run a scheduling query task. If no date is provided,
# or if the provided date is invalid, it defaults to today's date.
#
# Shortcut URL: https://www.icloud.com/shortcuts/7dc5cf4801394d05b9a71e5044fbf461

# Exit immediately if a command exits with a non-zero status.
set -o errexit
# Make sure the exit status of a pipeline is the status of the last command to exit with a non-zero status, or zero if no command exited with a non-zero status.
set -o pipefail

# Function to clean up temporary files before script exits
cleanup() {
    rm -f "${SAVED}" "${OUTPUT}"
}

# Trap to execute the cleanup function on script exit
trap cleanup EXIT

# Check if a date parameter is provided
if [ "$1" ]; then
    INPUT_DATE=$(date -j -f "%Y-%m-%d" "$1" "+%Y-%m-%d" 2>/dev/null) || {
        echo "Invalid date format. Please use YYYY-MM-DD. Defaulting to today's date." >&2
        INPUT_DATE=$(date "+%Y-%m-%d")
    }
else
    INPUT_DATE=$(date "+%Y-%m-%d")
fi

# Create temporary files for saving clipboard contents and output
SAVED=$(mktemp)
OUTPUT=$(mktemp)

# Save current clipboard contents
pbpaste >"${SAVED}"

# Copy the input date to the clipboard
echo "${INPUT_DATE}" | pbcopy

# Run the 'sched' shortcut
shortcuts run "sched"

# Save the output from the 'sched' shortcut
pbpaste >"${OUTPUT}"

# Restore the original clipboard contents
pbcopy <"${SAVED}"

# Display the output from the 'sched' shortcut
cat "${OUTPUT}"

Avoid libwebp Electron Woes On macOS With positron

2023-09-30 – 16:45
Posted in Cybersecurity, Go, Golang, Information Security, macOS
Comments (2)

If you’ve got 👀 on this blog (directly, or via syndication) you’d have to have been living under a rock to not know about the libwebp supply chain disaster. An unfortunate casualty of inept programming just happened to be any app in the Electron ecosystem that doesn’t undergo bleeding-edge updates.

Former cow-orker Tom Sellers (one of the best humans in cyber) did a great service to the macOS user community with tips on how to stay safe on macOS. His find + strings + grep combo was superbly helpful and I hope many macOS users did the command line dance to see how negligent their app providers were/are.

But, you still have to know what versions are OK and which ones are not to do that dance. And, having had yet-another immune system invasion (thankfully, not COVID, again) on top of still working through long COVID (#protip: you may be over the pandemic, but I guarantee it’s not done with you/us for a while) which re-sapped mobility energy, I put my sedentary time to less woesome use by hacking together a small, Golang macOS CLI to help ferret out bad Electron-based apps you may have installed.

I named it positron, since that’s kind of the opposite of Electron, and I was pretty creativity-challenged today.

It does virtually the same thing as Tom’s strings and grep does, just in a single, lightweight, universal, signed macOS binary.

When I ran it after the final build, all my Electron-based apps were 🔴. After deleting some, and updating others, this is my current status:

$ find /Applications -type f -name "*Electron Framework*" -exec ./positron "{}" \;
/Applications/Signal.app: Chrome/114.0.5735.289 Electron/25.8.4 🟢
/Applications/Keybase.app: Chrome/87.0.4280.141 Electron/11.5.0 🔴
/Applications/Raindrop.io.app: Chrome/102.0.5005.167 Electron/19.0.17 🔴
/Applications/1Password.app: Chrome/114.0.5735.289 Electron/25.8.1 🟢
/Applications/Replit.app: Chrome/116.0.5845.188 Electron/26.2.1 🟢
/Applications/lghub.app: Chrome/104.0.5112.65 Electron/20.0.0 🔴

It’s still on you to do the find (cooler folks run fd) since I’m not about to write a program that’ll rummage across your SSDs or disc drives, but it does all the MachO inspection internally, and then also does the SemVer comparison to let you know which apps still suck at keeping you safe.

FWIW, the Keybase folks did accept a PR for the libwebp thing, but darned if I will spend any time building it (I don’t run it anymore, anyway, so I should just delete it).

The aforementioned signed, universal, macOS binary is in the GitLab releases.

Stay safe out there!

Rust CLI For Apple’s WeatherKit REST API

Apple is in the final stages of shuttering the DarkSky service/API. They’ve replaced it with WeatherKit, which has both an xOS framework version as well as a REST API. To use either, you need to be a member of the Apple Developer Program (ADP) — $99.00/USD per-year — and calls to the service via either method are free up to 500K/month. After that, Apple has pricing tears.

As a result of the forced-ADP membership fee, I’m not sure how many folks are going to invest in building anything but freemium native or web apps. DarkSky had a generous free tier that only required an API key.

Since I had a {darksky} R package, I recently made a similar {weatherkit} package —https://rud.is/b/2022/07/07/introducing-weatherkit-the-eventual-replacement-r-package-for-darksky/ — complete with a CLI demo program.

Lots of R folks will disagree with the following, but R is a terrible language for CLI tools if you’re not already invested in the R ecosystem. CRAN makes it a pain to modify the user’s local system, and most R things have a ton of dependencies. So, while I generally code R-first, I do not code R-only, especially for CLI tools.

I like Rust more than Golang, and am also getting used to it over C/C++, so I threw together a Rust-based WeatherKit CLI tool shortly after the R one — https://github.com/hrbrmstr/weatherkit-rust. There’s documentation for how to cross all the t’s and dot all the i’s required to get authentication to work.

The GH releases have a signed macOS universal binary and I’m working on decomposing Starship’s wicked cool Rust release builder that uses the equally cool Google release-please to deal up binaries for virtually every platform.

I may make the Rust version a full WeatherKit API library, but I don’t know if I’m going to invest time into something that may just get shoved aside due to the hate I’m expecting to see pointed in Apple’s direction.

A zsh Helper Script For Updating macOS RStudio Daily Electron + Quarto CLI Installs

RStudio’s macOS Electron build is coming along quite nicely and is blazing fast on Apple Silicon.

I like to install the dailies, well, daily!; and, of late, RStudio and Quarto are joined at the hip. As a result, I regularly found myself having to manually update Quarto CLI right after updating RStudio, so I made a small zsh script that will use the new RStudio dailies JSON API to grab the latest daily, and the GitHub JSON API to grab the latest Quarto release, then install them both.

Caveats and limitations are in the repo.

RSwitch 2.1.2b Bug-fix Release

RStudio is continuing to refine their 👍🏽 new product version string scheme, with the most recent change using something like the following for daily builds:

RStudio-pro-2021.11.0-daily+23.pro1.dmg

The full URL for these Amazon S3-hosted builds is something like:

https://s3.amazonaws.com/rstudio-ide-build/desktop/macos/RStudio-pro-2021.11.0-daily%2B23.pro1.dmg

with the + getting URL encoded to %2B.

Unfortunately, AWS S3’s fairly unintelligent web server really wanted that + encoded to %2B for it to properly map to the retrieved-by-explicit-path-key bucket-backed object store. Since it was not, the download component of RSwitch v2.x failed with a somewhat inaccurate error message.

It was a minor fix that I finally had some spare cycles to make today.

You can download the RSwitch 2.1.2b universal binary here.

Hasty Bug-fix Release of RSwitch v2

Version 2 of RSwitch — the macOS menubar utility that enables fast and seamless switching between R versions on macOS and also provides notifications for new versions of RStudio Dailies has had a hasty update to deal with an error condition if/when the RStudio Dailies pages contain no links (they’re switching versioning schemes and I happened to catch this right at that time). The original RSwitch v1.x should be fine as long as the non-RStudio Pro dailies page isn’t busted.

Version 2.1.1b🔗 also adds a downloader for the arm64 version of the R-devel tarball:

The preferences also has a new button that lets you clear the cached previously seen latest daily download versions if you’re not getting notified (there could be a hiccup depending on when the last check was and the state of the daily download pages).

FIN

As noted, this is a hasty fix (right before heading out on holiday), so if there are issues it’ll be a bit before they get fixed.

New TabularData Available in Swift on macOS Monterery (et al)

WWDC 2021 is on this week and many new fun things are being introduced, including some data science-friendly additions to the frameworks that come with Xcode 13 and available on macOS 12+ (and its *OS cousins).

Specifically, Apple has made tabular data a first-class citizen with the new TabularData app service.

A future post will have some more expository, but here’s a sample of core operations including:

reading in tabular data from CSV or JSON
examining the structure
working with columns and/or rows
grouping and filtering operations
transforming and removing columns

I’ve tagged this with rstats as there are R equivalents included for each operation so R folks can translate any Swift code they see in the future.

import TabularData

// define some basic formatting options for data frame output
let dOpts = FormattingOptions(maximumLineWidth: 80, maximumCellWidth: 10, maximumRowCount: 20, includesColumnTypes: true)

// read in a CSV file
// R: xdf <- read.csv("mtcars.csv")
var xdf = try! DataFrame.init(contentsOfCSVFile: URL(fileURLWithPath: "mtcars.csv"))

// take a look at it
// R: print(xdf) # no more print() in further R equivalents; just assume interactive or wrap with print
print(xdf.description(options: dOpts))

┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0  │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 1  │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 2  │ 22.8     │ 4     │ 108.0    │ 93    │ 3.85     │ 2.32     │      ┆
│ 3  │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
│ 4  │ 18.7     │ 8     │ 360.0    │ 175   │ 3.15     │ 3.44     │      ┆
│ 5  │ 18.1     │ 6     │ 225.0    │ 105   │ 2.76     │ 3.46     │      ┆
│ 6  │ 14.3     │ 8     │ 360.0    │ 245   │ 3.21     │ 3.57     │      ┆
│ 7  │ 24.4     │ 4     │ 146.7    │ 62    │ 3.69     │ 3.19     │      ┆
│ 8  │ 22.8     │ 4     │ 140.8    │ 95    │ 3.92     │ 3.15     │      ┆
│ 9  │ 19.2     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 10 │ 17.8     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 11 │ 16.4     │ 8     │ 275.8    │ 180   │ 3.07     │ 4.07     │      ┆
│ 12 │ 17.3     │ 8     │ 275.8    │ 180   │ 3.07     │ 3.73     │      ┆
│ 13 │ 15.2     │ 8     │ 275.8    │ 180   │ 3.07     │ 3.78     │      ┆
│ 14 │ 10.4     │ 8     │ 472.0    │ 205   │ 2.93     │ 5.25     │      ┆
│ 15 │ 10.4     │ 8     │ 460.0    │ 215   │ 3.0      │ 5.424    │      ┆
│ 16 │ 14.7     │ 8     │ 440.0    │ 230   │ 3.23     │ 5.345    │      ┆
│ 17 │ 32.4     │ 4     │ 78.7     │ 66    │ 4.08     │ 2.2      │      ┆
│ 18 │ 30.4     │ 4     │ 75.7     │ 52    │ 4.93     │ 1.615    │      ┆
│ 19 │ 33.9     │ 4     │ 71.1     │ 65    │ 4.22     │ 1.835    │      ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more                                                               ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

// dimensions
// R: dim(xdf)
print(xdf.shape)

(rows: 32, columns: 11)

// head
// R: head(xdf)
print(xdf.prefix(5).description(options: dOpts))

┏━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃   ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 1 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 2 │ 22.8     │ 4     │ 108.0    │ 93    │ 3.85     │ 2.32     │      ┆
│ 3 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
│ 4 │ 18.7     │ 8     │ 360.0    │ 175   │ 3.15     │ 3.44     │      ┆
└───┴──────────┴───────┴──────────┴───────┴──────────┴──────────┴╌╌╌╌╌╌┘

// tail
// R: tail(xdf)
print(xdf.suffix(5).description(options: dOpts))

┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 27 │ 30.4     │ 4     │ 95.1     │ 113   │ 3.77     │ 1.513    │      ┆
│ 28 │ 15.8     │ 8     │ 351.0    │ 264   │ 4.22     │ 3.17     │      ┆
│ 29 │ 19.7     │ 6     │ 145.0    │ 175   │ 3.62     │ 2.77     │      ┆
│ 30 │ 15.0     │ 8     │ 301.0    │ 335   │ 3.54     │ 3.57     │      ┆
│ 31 │ 21.4     │ 4     │ 121.0    │ 109   │ 4.11     │ 2.78     │      ┆
└────┴──────────┴───────┴──────────┴───────┴──────────┴──────────┴╌╌╌╌╌╌┘

// column summaries
// summary(xdf)
print(xdf.summaryOfAllColumns().description(options: dOpts))

┏━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ count(mpg) ┃ uniqueCou… ┃ top(mpg) ┃ topFreque… ┃ count(cyl) ┃ 39   ┇
┃   ┃ <Int>      ┃ <Int>      ┃ <Double> ┃ <Int>      ┃ <Int>      ┃ more ┇
┡━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 32         │ 25         │ 21.4     │ 2          │ 32         │      ┆
└───┴────────────┴────────────┴──────────┴────────────┴────────────┴╌╌╌╌╌╌┘

// sort it
// R: library(tidyverse) # assume this going forward for R examples
// R: arrange(xdf, cyl)
xdf.sort(on: "cyl")

print(xdf.description(options: dOpts))

┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0  │ 22.8     │ 4     │ 108.0    │ 93    │ 3.85     │ 2.32     │      ┆
│ 1  │ 24.4     │ 4     │ 146.7    │ 62    │ 3.69     │ 3.19     │      ┆
│ 2  │ 22.8     │ 4     │ 140.8    │ 95    │ 3.92     │ 3.15     │      ┆
│ 3  │ 32.4     │ 4     │ 78.7     │ 66    │ 4.08     │ 2.2      │      ┆
│ 4  │ 30.4     │ 4     │ 75.7     │ 52    │ 4.93     │ 1.615    │      ┆
│ 5  │ 33.9     │ 4     │ 71.1     │ 65    │ 4.22     │ 1.835    │      ┆
│ 6  │ 21.5     │ 4     │ 120.1    │ 97    │ 3.7      │ 2.465    │      ┆
│ 7  │ 27.3     │ 4     │ 79.0     │ 66    │ 4.08     │ 1.935    │      ┆
│ 8  │ 26.0     │ 4     │ 120.3    │ 91    │ 4.43     │ 2.14     │      ┆
│ 9  │ 30.4     │ 4     │ 95.1     │ 113   │ 3.77     │ 1.513    │      ┆
│ 10 │ 21.4     │ 4     │ 121.0    │ 109   │ 4.11     │ 2.78     │      ┆
│ 11 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 12 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 13 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
│ 14 │ 18.1     │ 6     │ 225.0    │ 105   │ 2.76     │ 3.46     │      ┆
│ 15 │ 19.2     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 16 │ 17.8     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 17 │ 19.7     │ 6     │ 145.0    │ 175   │ 3.62     │ 2.77     │      ┆
│ 18 │ 18.7     │ 8     │ 360.0    │ 175   │ 3.15     │ 3.44     │      ┆
│ 19 │ 14.3     │ 8     │ 360.0    │ 245   │ 3.21     │ 3.57     │      ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more                                                               ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

// read in a JSON File
// R: xdf2 <- jsonlite::fromJSON("mtcars.json")
var xdf2 = try! DataFrame.init(contentsOfJSONFile: URL(fileURLWithPath: "mtcars.json"))

// bind the rows together
// R: xdf <- bind_rows(xdf, xdf2)
xdf.append(xdf2)

// get the new summary
// R: summary(xdf)
print(xdf.summaryOfAllColumns().description(options: dOpts))

┏━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ count(mpg) ┃ uniqueCou… ┃ top(mpg) ┃ topFreque… ┃ count(cyl) ┃ 39   ┇
┃   ┃ <Int>      ┃ <Int>      ┃ <Double> ┃ <Int>      ┃ <Int>      ┃ more ┇
┡━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 64         │ 25         │ 21.4     │ 4          │ 64         │      ┆
└───┴────────────┴────────────┴──────────┴────────────┴────────────┴╌╌╌╌╌╌┘

// basic filtering
// R: xdf.filter(cyl == 6)
print( xdf.filter(on: "cyl", Int.self) { (val) in val == 6 } )

┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 11 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 12 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 13 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
│ 14 │ 18.1     │ 6     │ 225.0    │ 105   │ 2.76     │ 3.46     │      ┆
│ 15 │ 19.2     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 16 │ 17.8     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 17 │ 19.7     │ 6     │ 145.0    │ 175   │ 3.62     │ 2.77     │      ┆
│ 32 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 33 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 35 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 4 more                                                                ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

// group by a column
// R: group_by(xdf, cyl)
print(xdf.grouped(by: "cyl"))

4
┏━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃   ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 22.8     │ 4     │ 108.0    │ 93    │ 3.85     │ 2.32     │      ┆
│ 1 │ 24.4     │ 4     │ 146.7    │ 62    │ 3.69     │ 3.19     │      ┆
│ 2 │ 22.8     │ 4     │ 140.8    │ 95    │ 3.92     │ 3.15     │      ┆
│ 3 │ 32.4     │ 4     │ 78.7     │ 66    │ 4.08     │ 2.2      │      ┆
│ 4 │ 30.4     │ 4     │ 75.7     │ 52    │ 4.93     │ 1.615    │      ┆
│ 5 │ 33.9     │ 4     │ 71.1     │ 65    │ 4.22     │ 1.835    │      ┆
│ 6 │ 21.5     │ 4     │ 120.1    │ 97    │ 3.7      │ 2.465    │      ┆
│ 7 │ 27.3     │ 4     │ 79.0     │ 66    │ 4.08     │ 1.935    │      ┆
│ 8 │ 26.0     │ 4     │ 120.3    │ 91    │ 4.43     │ 2.14     │      ┆
│ 9 │ 30.4     │ 4     │ 95.1     │ 113   │ 3.77     │ 1.513    │      ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 12 more                                                              ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

6
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 11 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 12 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 13 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
│ 14 │ 18.1     │ 6     │ 225.0    │ 105   │ 2.76     │ 3.46     │      ┆
│ 15 │ 19.2     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 16 │ 17.8     │ 6     │ 167.6    │ 123   │ 3.92     │ 3.44     │      ┆
│ 17 │ 19.7     │ 6     │ 145.0    │ 175   │ 3.62     │ 2.77     │      ┆
│ 32 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.62     │      ┆
│ 33 │ 21.0     │ 6     │ 160.0    │ 110   │ 3.9      │ 2.875    │      ┆
│ 35 │ 21.4     │ 6     │ 258.0    │ 110   │ 3.08     │ 3.215    │      ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 4 more                                                                ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

8
┏━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ mpg      ┃ cyl   ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ 5    ┇
┃    ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 18 │ 18.7     │ 8     │ 360.0    │ 175   │ 3.15     │ 3.44     │      ┆
│ 19 │ 14.3     │ 8     │ 360.0    │ 245   │ 3.21     │ 3.57     │      ┆
│ 20 │ 16.4     │ 8     │ 275.8    │ 180   │ 3.07     │ 4.07     │      ┆
│ 21 │ 17.3     │ 8     │ 275.8    │ 180   │ 3.07     │ 3.73     │      ┆
│ 22 │ 15.2     │ 8     │ 275.8    │ 180   │ 3.07     │ 3.78     │      ┆
│ 23 │ 10.4     │ 8     │ 472.0    │ 205   │ 2.93     │ 5.25     │      ┆
│ 24 │ 10.4     │ 8     │ 460.0    │ 215   │ 3.0      │ 5.424    │      ┆
│ 25 │ 14.7     │ 8     │ 440.0    │ 230   │ 3.23     │ 5.345    │      ┆
│ 26 │ 15.5     │ 8     │ 318.0    │ 150   │ 2.76     │ 3.52     │      ┆
│ 27 │ 15.2     │ 8     │ 304.0    │ 150   │ 3.15     │ 3.435    │      ┆
┢╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 18 more                                                               ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

// number of groups
// R: group_by(xdf, cyl) %>% group_keys() %>% nrow()
print(xdf.grouped(by: "cyl").count)

3

// group, manipulate (in this case, filter), and re-combine
// R: group_by(xdf) %>% filter(mpg < 20) %>% ungroup()
print(
  xdf.grouped(by: "cyl").mapGroups { (val) in
    val.filter(on: "mpg", Double.self) { (val) in val! < 20 }.base
  }.ungrouped()
)

┏━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ mpg      ┃ disp     ┃ hp    ┃ drat     ┃ wt       ┃ qsec     ┃ 5    ┇
┃   ┃ <Double> ┃ <Double> ┃ <Int> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 22.8     │ 108.0    │ 93    │ 3.85     │ 2.32     │ 18.61    │      ┆
│ 1 │ 24.4     │ 146.7    │ 62    │ 3.69     │ 3.19     │ 20.0     │      ┆
│ 2 │ 22.8     │ 140.8    │ 95    │ 3.92     │ 3.15     │ 22.9     │      ┆
│ 3 │ 32.4     │ 78.7     │ 66    │ 4.08     │ 2.2      │ 19.47    │      ┆
│ 4 │ 30.4     │ 75.7     │ 52    │ 4.93     │ 1.615    │ 18.52    │      ┆
│ 5 │ 33.9     │ 71.1     │ 65    │ 4.22     │ 1.835    │ 19.9     │      ┆
│ 6 │ 21.5     │ 120.1    │ 97    │ 3.7      │ 2.465    │ 20.01    │      ┆
│ 7 │ 27.3     │ 79.0     │ 66    │ 4.08     │ 1.935    │ 18.9     │      ┆
│ 8 │ 26.0     │ 120.3    │ 91    │ 4.43     │ 2.14     │ 16.7     │      ┆
│ 9 │ 30.4     │ 95.1     │ 113   │ 3.77     │ 1.513    │ 16.9     │      ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 182 more                                                                ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛

// look at one column
// R: xdf$cyl
print( xdf["cyl"] )

┏━━━━━━━┓
┃ cyl   ┃
┃ <Int> ┃
┡━━━━━━━┩
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
│ 4     │
┢╍╍╍╍╍╍╍┪
┇ 54 m… ┇
┗╍╍╍╍╍╍╍┛

// combine two columns and look at it
// R: mutate(xdf, cyl_mpg = sprintf("%s:%s", cyl, mpg) %>% select(-cyl, -mpg)
// R: unite(xdf, cyl_mpg, cyl, mpg, sep = ":") # alternate way
xdf.combineColumns("cyl", "mpg", into: "cyl_mpg") { (val1: Int?, val2: Double?) -> String in
  String(val1 ?? 0) + ":" + String(val2 ?? 0.0)
}

print(xdf["cyl_mpg"])

┏━━━━━━━━━━┓
┃ cyl_mpg  ┃
┃ <String> ┃
┡━━━━━━━━━━┩
│ 4:22.8   │
│ 4:24.4   │
│ 4:22.8   │
│ 4:32.4   │
│ 4:30.4   │
│ 4:33.9   │
│ 4:21.5   │
│ 4:27.3   │
│ 4:26.0   │
│ 4:30.4   │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 54 more  ┇
┗╍╍╍╍╍╍╍╍╍╍┛

// look at the colnames (^^ removes "cyl" and "mpg"
// R: colnames(xdf)
print(xdf.columns.map{ col in col.name })

["cyl_mpg", "disp", "hp", "drat", "wt", "qsec", "vs", "am", "gear", "carb"]

// turn an Int into a Double
// R: xdf$hp <- as.double(xdf$hp) # or use dplyr::mutate()
xdf.transformColumn("hp") { (val1: Int?) -> Double? in
  Double(val1 ?? 0)
}

print(xdf["hp"])

┏━━━━━━━━━━┓
┃ hp       ┃
┃ <Double> ┃
┡━━━━━━━━━━┩
│ 93.0     │
│ 62.0     │
│ 95.0     │
│ 66.0     │
│ 52.0     │
│ 65.0     │
│ 97.0     │
│ 66.0     │
│ 91.0     │
│ 113.0    │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 54 more  ┇
┗╍╍╍╍╍╍╍╍╍╍┛

// look at the coltypes
// R: sapply(mtcars, typeof)
print(xdf.columns.map{ col in col.wrappedElementType })

[Swift.String, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Double, Swift.Int, Swift.Int, Swift.Int, Swift.Int]

// distinct horsepower
// R: distinct(xdf, hp)
print(xdf["hp"].distinct())

┏━━━━━━━━━━┓
┃ hp       ┃
┃ <Double> ┃
┡━━━━━━━━━━┩
│ 93.0     │
│ 62.0     │
│ 95.0     │
│ 66.0     │
│ 52.0     │
│ 65.0     │
│ 97.0     │
│ 91.0     │
│ 113.0    │
│ 109.0    │
┢╍╍╍╍╍╍╍╍╍╍┪
┇ 12 more  ┇
┗╍╍╍╍╍╍╍╍╍╍┛

// row slices
// R: xdf[10,]
print(xdf.rows[10])

┏━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃    ┃ cyl_mpg  ┃ disp     ┃ hp       ┃ drat     ┃ wt       ┃ qsec     ┃ 4    ┇
┃    ┃ <String> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 10 │ 4:21.4   │ 121.0    │ 109.0    │ 4.11     │ 2.78     │ 18.6     │      ┆
└────┴──────────┴──────────┴──────────┴──────────┴──────────┴──────────┴╌╌╌╌╌╌┘

// R: xdf[3:10,]
print(xdf.rows[3...10])

Rows(base: 
┏━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳╍╍╍╍╍╍┓
┃   ┃ cyl_mpg  ┃ disp     ┃ hp       ┃ drat     ┃ wt       ┃ qsec     ┃ 4    ┇
┃   ┃ <String> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ <Double> ┃ more ┇
┡━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇╍╍╍╍╍╍┩
│ 0 │ 4:22.8   │ 108.0    │ 93.0     │ 3.85     │ 2.32     │ 18.61    │      ┆
│ 1 │ 4:24.4   │ 146.7    │ 62.0     │ 3.69     │ 3.19     │ 20.0     │      ┆
│ 2 │ 4:22.8   │ 140.8    │ 95.0     │ 3.92     │ 3.15     │ 22.9     │      ┆
│ 3 │ 4:32.4   │ 78.7     │ 66.0     │ 4.08     │ 2.2      │ 19.47    │      ┆
│ 4 │ 4:30.4   │ 75.7     │ 52.0     │ 4.93     │ 1.615    │ 18.52    │      ┆
│ 5 │ 4:33.9   │ 71.1     │ 65.0     │ 4.22     │ 1.835    │ 19.9     │      ┆
│ 6 │ 4:21.5   │ 120.1    │ 97.0     │ 3.7      │ 2.465    │ 20.01    │      ┆
│ 7 │ 4:27.3   │ 79.0     │ 66.0     │ 4.08     │ 1.935    │ 18.9     │      ┆
│ 8 │ 4:26.0   │ 120.3    │ 91.0     │ 4.43     │ 2.14     │ 16.7     │      ┆
│ 9 │ 4:30.4   │ 95.1     │ 113.0    │ 3.77     │ 1.513    │ 16.9     │      ┆
┢╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍╍╍╍╍┷╍╍╍╍╍╍┪
┇ 54 more                                                                    ┇
┗╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍╍┛
, subranges: _RangeSet(3..<11))

archinfo v0.4.0 Released

My archinfo utility is a small macOS command line application that displays process id, name, and architecture (arm64 or x86_64).

Version 0.4.0 adds the following capabilities:

Added --x86 (-x short option) to have archinfo only show x86_64 processes
Added --arm (-a short option) to have archinfo only show arm64 processes
Added --basename (-b short option) to havearchinfo` only show basenames of running processes vs the full path

The key driver for this update was to make it easier to only see the remaining, non-universal binary or ARM-only laggards.

Example runs with the new options:

$ archinfo --x86 --basename
  42801 x86_64 QtWebEngineProcess
  40408 x86_64 QtWebEngineProcess
  40378 x86_64 RStudio
  56426 x86_64 gpg-agent
   4244 x86_64 TwitchStudioStreamDeck
   4243 x86_64 QtWebEngineProcess
   4213 x86_64 QtWebEngineProcess
   4173 x86_64 kbfs
   4155 x86_64 keybase
   4140 x86_64 updater
   4065 x86_64 Keybase Helper (Renderer)
   4057 x86_64 Keybase Helper (Renderer)
   4056 x86_64 Keybase Helper
   4053 x86_64 Keybase Helper (GPU)
   4045 x86_64 krisp
   4043 x86_64 Keybase
   4035 x86_64 AdobeCRDaemon
   4033 x86_64 AdobeIPCBroker
   3993 x86_64 Elgato Control Center
   3971 x86_64 Stream Deck
   3959 x86_64 LogiVCCoreService

$ archinfo --x86 --basename --json
{"pid":42801,"arch":"x86_64","name":"QtWebEngineProcess"}
{"pid":40408,"arch":"x86_64","name":"QtWebEngineProcess"}
{"pid":40378,"arch":"x86_64","name":"RStudio"}
{"pid":56426,"arch":"x86_64","name":"gpg-agent"}
{"pid":4244,"arch":"x86_64","name":"TwitchStudioStreamDeck"}
{"pid":4243,"arch":"x86_64","name":"QtWebEngineProcess"}
{"pid":4213,"arch":"x86_64","name":"QtWebEngineProcess"}
{"pid":4173,"arch":"x86_64","name":"kbfs"}
{"pid":4155,"arch":"x86_64","name":"keybase"}
{"pid":4140,"arch":"x86_64","name":"updater"}
{"pid":4065,"arch":"x86_64","name":"Keybase Helper (Renderer)"}
{"pid":4057,"arch":"x86_64","name":"Keybase Helper (Renderer)"}
{"pid":4056,"arch":"x86_64","name":"Keybase Helper"}
{"pid":4053,"arch":"x86_64","name":"Keybase Helper (GPU)"}
{"pid":4045,"arch":"x86_64","name":"krisp"}
{"pid":4043,"arch":"x86_64","name":"Keybase"}
{"pid":4035,"arch":"x86_64","name":"AdobeCRDaemon"}
{"pid":4033,"arch":"x86_64","name":"AdobeIPCBroker"}
{"pid":3993,"arch":"x86_64","name":"Elgato Control Center"}
{"pid":3971,"arch":"x86_64","name":"Stream Deck"}
{"pid":3959,"arch":"x86_64","name":"LogiVCCoreService"}

You can find signed binaries and source code on the Releases page.