I've been doing a lot of React and TypeScript work lately. It had been a few years since I worked with them on a daily basis, and things are improving a lot. It's a breeze to work with some of these technologies to build web apps, and one of the newest additions that works well is Vite.
Is anyone else working with React these days? I will cover some of my learnings on YouTube and want to get a sense of interest. (Let me know on Discord.)
What's cool is that frameworks such as ONNX and TensorFlow have wide support to export and run models in the browser (web workers, WebGPU, WebAssembly) and you don't even need to build microservices for certain models. (Plus there's now support for Node.js to run in the browser as well!)
I just started a new daily file with my &ndaily Typinator text expansion.
This expansion archives my current daily file, an action I run whenever a daily file goes over seven thousand words.
It then creates a new file named —01_Daily_Part_94.md for Daily 94.
A few weeks ago, I paid for a Typinator 9 upgrade. The app is more modern, has light and dark modes, and promises long-term support. I'm glad they did that.
I'm a heavy user of Typinator and, someday, I'll create a list of all the things I use on a daily basis.
One of my most-used expansions—they've added usage stats (!)—is dtt, which would expand, today, to 230531.
No matter how small a piece of software is, it requires maintenance.
Except when it doesn't, which is true for certain programs without external dependencies and deprecated features.
The more code bases you rely on or develop, the more tiny efforts you'll have to put here and there to keep them running, especially if you want to keep the operating system up to date.
Hi Friends!
I'm hosting a live conversation today with special guests to celebrate my 100th YouTube live stream, Thursday, April 27, at 10:30 AM Pacific Time.
I've invited Adam Menges (ex-Lobe.ai), Joel Simon (Artbreeder), Jose Luis Garcia del Castillo (Harvard, ParametricCamp), and Kyle Steinfeld (University of California, Berkeley) to pick their brains on creative machine intelligence and how it's being used in academia and next-generation design tools.
The conversation will take place in Riverside at nono.ma/live/100.
With that link, you'll join as part of the audience and can participate in the chat. There's an option to "call in" and join the call, which we could use for questions or even to have everyone who wants to join at the end of the call.
Feel free to forward this invite to friends interested in AI & ML.
Thanks so much for being part of my journey.
Warmly,
Nono
Hi Friends—
Alex O'Connor is a researcher and machine learning manager.
I had the chance to pick his brain on the latest trends of generative AI — transformers, language and image models, fine-tuning, prompt engineering, tokenization, the latent space, adversarial attacks, and more.
Thanks to everyone who chatted with us during the YouTube premiere.
★ I'm excited to celebrate Live 100 with Special Guests tomorrow, April 27, at 1:30 PM ET with a conversation on creative machine intelligence with Adam Menges, Joel Simon, José Luis García del Castillo, and Kyle Steinfeld.
I'd love for you to join us live at nono.ma/live/100.
Warmly,
Nono
Recorded at The Palazzo, Las Vegas on December 2022.
00:00 · Introduction
00:40 · Machine learning
02:36 · Spam and scams
15:57 · Adversarial attacks
20:50 · Deep learning revolution
23:06 · Transformers
31:23 · Language models
37:09 · Zero-shot learning
42:16 · Prompt engineering
43:45 · Training costs and hardware
47:56 · Open contributions
51:26 · BERT and Stable Diffusion
54:42 · Tokenization
59:36 · Latent space
01:05:33 · Ethics
01:10:39 · Fine-tuning and pretrained models
01:18:43 · Textual inversion
01:22:46 · Dimensionality reduction
01:25:21 · Mission
01:27:34 · Advice for beginners
01:30:15 · Books and papers
01:34:17 · The lab notebook
01:44:57 · Thanks
Here are three ways to define a React component in TypeScript which, in the end, are three ways to define a function in TypeScript—React components are JavaScript functions.
const MyComponent = (text: string) => <>{text}</>
const MyComponent = (text: string) => {
return (
<>{text}</>
)
}
function MyComponent(text: string) {
return <>{text}</>
}
import * as React from 'react'
import * as Server from 'react-dom/server'
let Greet = () => <h1>Hello, Nono!</h1>
console.log(Server.renderToString(<div><Greet /></div>))
// <div><h1>Hello, Nono!</h1></div>
The tricky part is running this code.
You first need to build it, say, with esbuild, then execute it.
# Build with esbuild.
esbuild RenderToString.jsx --bundle --outfile=RenderToString.js
# Run with Node.js.
node RenderToString.js
# <div><h1>Hello, Nono!</h1></div>
Here's how to deploy your Vite app to your local network so you can access it from other devices connected to the same WiFi. Say, your iPhone or iPad.
npx vite --host {local-ip-address}
If you're on macOS, you can simply run the following.
npx vite --host $(ipconfig getifaddr en0)
A fresh Vite project will likely have a dev key in your package.json's scripts property mapping that Yarn or NPM command to Vite, e.g., "dev": "vite" so you can type yarn dev or npm run dev and have Vite run your application in development mode.
yarn dev
# VITE v4.2.1 ready in 165 ms
#
# ➜ Local: http://localhost:5173/
# ➜ Network: use --host to expose
# ➜ press h to show help
That's the same as running npx vite or ./node_modules/.bin/vite.
Before we can deploy to our IP address, we need to know what it is.
You can use ipconfing on Windows and ifconfig on macOS.
Henry Black shared a trick to get your Mac's local IP address with ifconfig.
ipconfig getifaddr en0
# 192.168.1.34
All you need to do is pass your IP address as Vite's --host argument.
npx vite --host $(ipconfig getifaddr en0)
# VITE v4.2.1 ready in 166 ms
#
# ➜ Local: http://192.168.1.34:5173/
# ➜ Network: http://192.168.1.34:5173/
# ➜ press h to show help
Now I can access my Vite app from other devices in the same network, which comes in handy if you want to test your app on other computers, phones, or tablets.
Remember, npx vite is interchangeable with yarn dev, npm run dev, or ./node_modules/.bin/vite`.
For more information, read Vite's Server Options.
If you found this useful, let me know at @nonoesp!
Here's how to connect and communicate with WebSocket servers from browser client applications using the WebSocket API and the WebSocket protocol.
// Create a WebSocket client in the browser.
const ws = new WebSocket("ws://localhost:1234");
// Log incoming messages to the console.
ws.onmessage = function (event) {
// This runs when receiving message.
console.log(event.data);
};
ws.onopen = () => {
// This runs when we connect.
// Submit a message to the server
ws.send(`Hello, WebSocket! Sent from a browser client.`);
};
Note that if you restart your Droplet you may have to restart services that are running in the background manually.
# Restart the Droplet now
shutdown -r now
In my case, if Nginx doesn't restart automatically after the restart, I need to run the following commands.
sudo fuser -k 80/tcp && sudo fuser -k 443/tcp
sudo service nginx restart
Here's a one-liner to turn any website into dark mode.
body, img { filter: invert(0.92) }
I apply this to selected sites using Stylebot, a Chrome extension that lets you apply custom CSS to specific websites.
In a nutshell, the CSS inverts the entire website and then inverts images again to render them normally.
You can adjust the invert filter's amount parameter, which in the example is set to 0.92.
0 would be no color inversion at all. 100 would be full-color inversion; whites turn black, and blacks turn white.
I often prefer to stay within 90–95% to reduce the contrast.
After two months of pause, we're preparing to release new Getting Simple podcast episodes.
Editing and publishing add friction and delays to my process, so I'm exploring code and ML workflows to post-process of episodes' audio and generate transcripts, summaries & notes.
I'm not there yet. But OpenAI's Whisper (free) and Descript (paid) already provide accurate transcriptions. Existing projects and companies use #GPT-like language models to extract episode keywords, topics, chapters & summaries.
We'll soon have automatic episode notes.
It's exciting. I think we're getting very, very close.
I've also played with Spotify's pedalboard Python package to post-process audio without relying on a Digital Audio Workstation (DAW).
That's cool because I can create reusable scripts for specific recording conditions and forget about audio editing — say, compressing, limiting, applying noise gates, or normalization—things you'd otherwise do in Adobe Audition.
Let me know if you'd like to see these automations in the live stream and video tutorials or shared here on Twitter at @nonoesp.
The point of writing as a human is to express ourselves. To pour words on paper (or the screen) and reflect on who you are, to learn, to evolve, and to inspire others. You can influence and inspire your future self as well.
Yesterday, I woke up and started the day writing five hundred words before I did any work. This is a practice I follow and will continue to follow. It doesn't make sense to delegate this to a machine because the whole point is to pour things out of my mind. Maybe this can turn into a conversation with an AI in the long run. I talk, we discuss, and my virtual assistant takes notes and generates a document instead of typing at my desk with a keyboard.
Machine intelligence is here to stay, and we'll find it harder to be original as it improves. But we must remember that they work because of all the knowledge humans have created before, with our mistakes and biases. Only they'll get better if we continue to produce original content. That may be a mistaken assumption, but I believe it in some way. AI originality is probably down the road, and current systems can hallucinate. But I like to think we'll do better work together with them. We must wait until everything is stable to identify which parts won't be done by humans anymore. Maybe they will but at a scary-fast pace.
Writing is a medium for creative expression, as are drawing, singing, film, photography, and many, many other forms. Get a pen and write—express yourself. Type with your fingers or thumbs. Shoot a video. Take a photo. Doodle. Tell us a story.
I've installed vnstat on my M1 MacBook Pro with Homebrew to monitor my network usage over time.
# Install vnstat on macOS with Homebrew.
brew install vnstat
Make sure you start the vnstat service with brew for vnstat to monitor your network usage.
brew services start vnstat
vnstat will be running in the background, and you'll have to wait days for it to gather statistics and be able to show you, for instance, the average monthly usage.
› vnstat -m
# gif0: Not enough data available yet.
After a few minutes, you'll see stats on vnstat.
› vnstat -5
# en0 / 5 minute
#
# time rx | tx | total | avg. rate
# ------------------------+-------------+-------------+---------------
# 2023-03-19
# 12:45 839.44 MiB | 2.60 MiB | 842.04 MiB | 23.55 Mbit/s
# 12:50 226.26 MiB | 306.00 KiB | 226.56 MiB | 46.35 Mbit/s
# ------------------------+-------------+-------------+---------------
› vnstat -h
# en0 / hourly
#
# hour rx | tx | total | avg. rate
# ------------------------+-------------+-------------+---------------
# 2023-03-19
# 12:00 1.04 GiB | 2.90 MiB | 1.04 GiB | 28.10 Mbit/s
# ------------------------+-------------+-------------+---------------
› vnstat -m
# en0 / monthly
#
# month rx | tx | total | avg. rate
# ------------------------+-------------+-------------+---------------
# 2023-03 1.04 GiB | 2.90 MiB | 1.04 GiB | 28.10 Mbit/s
# ------------------------+-------------+-------------+---------------
# estimated 3.43 TiB | 9.56 GiB | 3.44 TiB |
Here are my highlights from Works Containing Material Generated by Artificial Intelligence.
One such recent development is the use of sophisticated artificial intelligence (“AI”) technologies capable of producing expressive material.[5] These technologies “train” on vast quantities of preexisting human-authored works and use inferences from that training to generate new content. Some systems operate in response to a user's textual instruction, called a “prompt.” [6] The resulting output may be textual, visual, or audio, and is determined by the AI based on its design and the material it has been trained on. These technologies, often described as “generative AI,” raise questions about whether the material they produce is protected by copyright, whether works consisting of both human-authored and AI-generated material may be registered, and what information should be provided to the Office by applicants seeking to register them.
[I]n 2018 the Office received an application for a visual work that the applicant described as “autonomously created by a computer algorithm running on a machine.” [7] The application was denied because, based on the applicant's representations in the application, the examiner found that the work contained no human authorship. After a series of administrative appeals, the Office's Review Board issued a final determination affirming that the work could not be registered because it was made “without any creative contribution from a human actor.”
In February 2023, the Office concluded that a graphic novel [9] comprised of human-authored text combined with images generated by the AI service Midjourney constituted a copyrightable work, but that the individual images themselves could not be protected by copyright.
In the Office's view, it is well-established that copyright can protect only material that is the product of human creativity. Most fundamentally, the term “author,” which is used in both the Constitution and the Copyright Act, excludes non-humans.
[I]n the current edition of the Compendium, the Office states that “to qualify as a work of 'authorship' a work must be created by a human being” and that it “will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author.”
Individuals who use AI technology in creating a work may claim copyright protection for their own contributions to that work.
Applicants should not list an AI technology or the company that provided it as an author or co-author simply because they used it when creating their work.
docker run -it -p HOST_PORT:CONTAINER_PORT your-image
When you run services inside of Docker in specific ports, those are internal ports on the virtual container environment. If you want to connect to those services from your machine, you need to expose ports to the outside world explicitly. In short, you need to map TCP ports in the container to ports on the Docker host, which may be your computer. Here's how to do it.
Let's imagine we have a Next.js app running inside our Docker container.
› docker run -it my-app-image
next dev
# ready - started server on 0.0.0.0:3000, url: http://localhost:3000
The site is exposed to port 3000 of the container, but we can't access it from our machine at http://localhost:3000.
Let's map the port.
› docker run -it -p 1234:3000 my-app-image
next dev
# ready - started server on 0.0.0.0:3000, url: http://localhost:3000
http://localhost:1234
1234, Docker forwards the communication to port 3000 of the container
You can upload Shorts to YouTube with the YouTube API as you would upload any other video. Simply ensure your video has an aspect ratio of 9:16 and is less than 60 seconds. YouTube will automatically set it as a Short.
Follow this guide to see how to upload videos to YouTube with the YouTube API.
Apple just unlocked new options to price apps: ten-cent steps between $0.10 to $10, fifty cents between $10 and $50, and so on and so forth.
Choose from 900 price points — nearly 10 times the number of price points previously available for paid apps and one-time in-app purchases. These options also offer more flexibility, increasing incrementally across price ranges (for example, every $0.10 up to $10, every $0.50 between $10 and $50, etc.).
Here's how to define simple async functions in TypeScript.
(async (/*arguments*/) => {/*function logic*/})(/*values*/);
// Define an asynchronous function.
const helloAsync = async() => { console.log("Hey, Async!"); }
// Call it asynchronously.
helloAsync();
(async(text: string) => { console.log(text); })("Hello, Async!")
(async(text: string) => { setTimeout(() => console.log(text), 2000); })("Hello, Async!")
// Say we have an async talk() function that logs text to the console.
const talk = async(text: string) => { console.log(text); }
// And a sleep() function that uses a Promise to wait for milliseconds.
const sleep = (ms: number) => {
return new Promise(resolve => setTimeout(resolve, ms));
}
// We can wrap calls to async functions in an async function.
// Then `await` to execute them synchronously.
(async () => {
await talk(`Hello!`);
await sleep(1000);
await talk(`What's up?`);
await sleep(2000);
await talk(`Bye now!`);
})();
Here's how to list the commits that happened between two tags.
git log --pretty=oneline 0.8.0...0.9.0
The two tags—in this case, 0.8.0 and 0.9.0—need to exist.
You can list existing tags in a repository as below.
git tag
You can list what packages are installed globally in your system with npm -g list—shorthand for npm --global list—whereas you'd list the packages installed in an NPM project with npm list.
Let's see an example of what the command might return.
npm -g list
# /opt/homebrew/lib
# ├── cross-env@7.0.3
# ├── http-server@14.1.1
# ├── node-gyp@9.3.1
# ├── npm@9.5.0
# ├── pm2@5.2.2
# ├── spoof@2.0.4
# ├── ts-node@10.9.1
# └── typescript@4.9.5
Here are some of the commands we used during the Creative Machine Learning Live 97.
First, create an Anaconda environment or install in your Python install with pip.
pip install imaginairy
Before running the commands below, I entered an interactive imaginAIry shell.
aimg
🤖🧠> # Commands here
# Upscale an image 4x with Real-ESRGAN.
upscale image.jpg
# Generate an image and animate the diffusion process.
imagine "a sunflower" --gif
# Generate an image and create a GIF comparing it with the original.
imagine "a sunflower" --compare-gif
# Schedule argument values.
edit input.jpg \
--prompt "a sunflower" \
--steps 21 \
--arg-schedule "prompt_strength[6:8:0.5]" \
--compilation-anim gif
Here's how to add NuGet packages from a local source to your Visual Studio project.
local-nugets).Tools > Options > NuGet Package Manager > Package Sources.Add button (the green cross) to create a new Package Source....) to browse and select the folder you previously created -- local-nugets in my case -- and then click on Update.local-nugets folder, and everything left is to install the package as follows.Project > Manage NuGet Packages > Browse.Install.
Here's how to randomize a list of strings in bash.
On macOS, you can use Terminal or iTerm2.
The shuf command shuffles a list that is "piped" to it.
An easy way to do that is to list a directory's contents with ls and then shuffle them.
ls ~/Desktop | shuf
The easiest way to shuffle a set of strings is to define an array in bash and shuffle it with shuf.
WORDS=('Milk' 'Bread' 'Eggs'); shuf -e ${WORDS[@]}
You can use pbcopy to copy the shuffled list to your clipboard.
WORDS=('Milk' 'Bread' 'Eggs' ); shuf -e ${WORDS[@]} | pbcopy
Another way to randomize a list of strings from bash is to create a text file, in this case named words.txt, with a string value per line.
Bread
Milk
Chicken
Turkey
Eggs
You can create this file manually or from the command-line with the following command.
echo "Bread\nMilk\nChicken\nTurkey\nEggs" > words.txt
Then, we cat the contents of words.txt and shuffle order of the lines with shuf.
cat words.txt | shuf
# Eggs
# Milk
# Chicken
# Turkey
# Bread
Again, you can save the result to the clipboard with pbcopy.
cat words.txt | shuf | pbcopy
If you found this useful, let me know!
Here's a Python class that can track and push metrics to AWS CloudWatch.
Metrics are reset to their initial values on creation and when metrics are uploaded to CloudWatch.
# metrics.py
'''
A metrics class ready to track and push metrics to AWS CloudWatch.
'''
from datetime import datetime
import os
import boto3
# CloudWatch metrics namespace.
METRICS_NAMESPACE = 'my_metrics_namespace'
# Duration to wait between metric uploads.
METRICS_UPLOAD_THRESHOLD_SECONDS = 50
class Metrics:
'''
Holds metrics, serializes them to CloudWatch format,
and ingests foreign metric values.
'''
def __init__(self):
self.reset()
def reset(self):
'''
Resets metric values and last upload time.
'''
self.last_upload_time = datetime.now()
# Your custom metrics and initial values
# Note that here we're using 'my_prefix' as
# a custom prefix in case you want this class
# to add a prefix namespace to all its metrics.
self.my_prefix_first_metric = 0
self.my_prefix_second_metric = 0
def to_data(self):
'''
Serializes metrics and their values.
'''
def to_cloudwatch_format(name, value):
return {'MetricName': name, 'Value': value}
result = []
for name, value in vars(self).items():
if name != 'last_upload_time':
result.append(to_cloudwatch_format(name, value))
return result
def ingest(self, metrics, prefix=''):
'''
Adds foreign metric values to this metrics object.
'''
input_metric_names = [attr for attr in dir(metrics)
if not callable(getattr(metrics, attr))
and not attr.startswith("__")]
# Iterate through foreign keys and add metric values.
for metric_name in input_metric_names:
# Get value of foreign metric.
input_metric_value = getattr(metrics, metric_name)
# Get metric key.
metric_key = f'{prefix}_{metric_name}'
# Get metric value.
metric_value = getattr(self, metric_key)
# Add foreign values to this metrics object.
setattr(
self,
metric_key,
input_metric_value + metric_value
)
def upload(self, force=False):
'''
Uploads metrics to CloudWatch when time since last
upload is above a duration or when forced.
'''
# Get time elapsed since last upload.
seconds_since_last_upload = \
(datetime.now() - self.last_upload_time).seconds
# Only upload if duration is greater than threshold,
# or when the force flag is set to True.
if seconds_since_last_upload > 50 or force:
# Upload metrics to CloudWatch.
cloudwatch = boto3.client(
'cloudwatch',
os.getenv('AWS_REGION')
)
cloudwatch.put_metric_data(
Namespace=METRICS_NAMESPACE,
MetricData=self.to_data()
)
# Reset metrics.
self.reset()
To use this class, we just have to instantiate a metrics object, track some metrics, and upload them.
# Create a metrics object.
metrics = Metrics()
# Add values to its metrics.
metrics.my_prefix_first_metric += 3
metrics.my_prefix_second_metric += 1
# Upload metrics to CloudWatch.
metrics.upload(force=True)
If you were processing metrics at a fast pace, you don't want to upload metrics every single time you increase their value, as otherwise CloudWatch will complain. In certain cases, AWS CloudWatch's limit is 5 transactions per second (TPS) per account or AWS Region. When this limit is reached, you'll receive a RateExceeded throttling error.
By calling metrics.upload(force=False) we only upload once every METRICS_UPLOAD_THRESHOLD_SECONDS. (In this example, at maximum every 50 seconds.)
import time
# Create a metrics object.
metrics = Metrics()
for i in range(0, 100, 1):
# Wait for illustration purposes,
# as if we were doing work.
time.sleep(1)
# Add values to its metrics.
metrics.my_prefix_first_metric += 3
metrics.my_prefix_second_metric += 1
# Only upload if more than the threshold
# duration has passed since we last uploaded.
metrics.upload()
# Force-upload metrics to CloudWatch once we're done.
metrics.upload(force=True)
Lastly, here's how to ingest foreign metrics with or without a prefix.
# We define a foreign metrics class.
class OtherMetrics:
def __init__(self):
self.reset()
def reset(self):
# Note that here we don't have 'my_prefix'.
self.first_metric = 0
self.second_metric = 0
# We instantiate both metric objects.
metrics = Metrics()
other_metrics = OtherMetrics()
# The foreign metrics track values.
other_metrics.first_metric += 15
other_metrics.second_metric += 3
# Then our main metrics class ingests those metrics.
metrics.ingest(other_metrics, prefix='my_prefix')
# Then our main metrics class has those values.
print(metrics.my_prefix_first_metric)
# Returns 15
print(metrics.my_prefix_second_metric)
# Returns 3
If you found this useful, let me know!
Take a look at other posts about code, Python, and Today I Learned(s).
Here's how to sort a Python dictionary by a key, a property name, of its items. Check this post if you're looking to sort a list of lists instead.
# A list of people
people = [
{'name': 'Nono', 'age': 32, 'location': 'Spain'},
{'name': 'Alice', 'age': 20, 'location': 'Wonderland'},
{'name': 'Phillipe', 'age': 100, 'location': 'France'},
{'name': 'Jack', 'age': 45, 'location': 'Caribbean'},
]
# Sort people by age, ascending
people_sorted_by_age_asc = sorted(people, key=lambda x: x['age'])
print(people_sorted_by_age_asc)
# [
# {'name': 'Alice', 'age': 20, 'location': 'Wonderland'},
# {'name': 'Nono', 'age': 32, 'location': 'Spain'},
# {'name': 'Jack', 'age': 45, 'location': 'Caribbean'},
# {'name': 'Phillipe', 'age': 100, 'location': 'France'}
# ]
# Sort people by age, descending
people_sorted_by_age_desc = sorted(people, key=lambda x: -x['age'])
print(people_sorted_by_age_desc)
# [
# {'name': 'Phillipe', 'age': 100, 'location': 'France'},
# {'name': 'Jack', 'age': 45, 'location': 'Caribbean'},
# {'name': 'Nono', 'age': 32, 'location': 'Spain'},
# {'name': 'Alice', 'age': 20, 'location': 'Wonderland'}
# ]
# Sort people by name, ascending
people_sorted_by_name_desc = sorted(people, key=lambda x: x['name'])
print(people_sorted_by_name_desc)
# [
# {'name': 'Alice', 'age': 20, 'location': 'Wonderland'},
# {'name': 'Jack', 'age': 45, 'location': 'Caribbean'},
# {'name': 'Nono', 'age': 32, 'location': 'Spain'},
# {'name': 'Phillipe', 'age': 100, 'location': 'France'}
# ]
You can measure the time elapsed during the execution of Python commands by keeping a reference to the start time and then subtracting the current time at any point on your program from that start time to obtain the duration between two points in time.
from datetime import datetime
import time
# Define the start time.
start = datetime.now()
# Run some code..
time.sleep(2)
# Get the time delta since the start.
elapsed = datetime.now() - start
# datetime.timedelta(seconds=2, microseconds=005088)
# 0:00:02.005088
# Get the seconds since the start.
elapsed_seconds = elapsed.seconds
# 2
Let's create two helper functions to get the current time (i.e. now) and the elapsed time at any moment.
# Returns current time
# (and, if provided, prints the event's name)
def now(eventName = ''):
if eventName:
print(f'Started {eventName}..')
return datetime.now()
# Store current time as `start`
start = now()
# Returns time elapsed since `beginning`
# (and, optionally, prints the duration in seconds)
def elapsed(beginning = start, log = False):
duration = datetime.now() - beginning;
if log:
print(f'{duration.seconds}s')
return duration
With those utility functions defined, we can measure the duration of different events.
# Define time to wait
wait_seconds = 2
# Measure duration (while waiting for 2 seconds)
beginning = now(f'{wait_seconds}-second wait.')
# Wait.
time.sleep(wait_seconds)
# Get time delta.
elapsed_time = elapsed(beginning, True)
# Prints 0:00:02.004004
# Get seconds.
elapsed_seconds = elapsed_time.seconds
# Prints 2
# Get microseconds.
elapsed_microseconds = elapsed_time.microseconds
# Prints 4004
If you found this useful, you might want to join my mailing lists; or take a look at other posts about code, Python, React, and TypeScript.
Here's how to sort a Python list by a key of its items. Check this post if you're looking to sort a list of dictionaries instead.
# A list of people
# name, age, location
people = [
['Nono', 32, 'Spain'],
['Alice', 20, 'Wonderland'],
['Phillipe', 100, 'France'],
['Jack', 45, 'Caribbean'],
]
# Sort people by age, ascending
people_sorted_by_age_asc = sorted(people, key=lambda x: x[1])
# [
# ['Alice', 20, 'Wonderland'],
# ['Nono', 32, 'Spain'],
# ['Jack', 45, 'Caribbean'],
# ['Phillipe', 100, 'France']
# ]
# Sort people by age, descending
people_sorted_by_age_desc = sorted(people, key=lambda x: -x[1])
# [
# ['Phillipe', 100, 'France'],
# ['Jack', 45, 'Caribbean'],
# ['Nono', 32, 'Spain'],
# ['Alice', 20, 'Wonderland']
# ]
# Sort people by name, ascending
people_sorted_by_name_desc = sorted(people, key=lambda x: x[0])
# [
# ['Alice', 20, 'Wonderland'],
# ['Jack', 45, 'Caribbean'],
# ['Nono', 32, 'Spain'],
# ['Phillipe', 100, 'France']
# ]
