Howard Colin

I have been trying to get my head around the application of matrices in colour space transforms. As a learning exercise I thought it would be simpler to loose a dimension and plot the results on a 2D graph. The code is not mine, but I have been tinkering with it to get different results.

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

xvals = np.linspace(-4, 4, 9)
yvals = np.linspace(-3, 3, 7)
xygrid = np.column_stack([[x,y] for x in xvals for y in yvals])

a = np.column_stack([[0, 1], [1, 0]])
print(a)
uvgrid = np.dot(a, xygrid)

Matrix: (0 1, 1 0) is a permutation matrix in which each row contains one entry of 1 and 0’s elsewhere. The effect of which changes the order of each element in the dataset.

def colourizer(x, y):
    r = min(1, 1-y/3)
    g = min(1, 1+y/3)
    b = 1/4 + x/16
    return (r, g, b)

colours = list(map(colourizer, xygrid[0], xygrid[1]))

plt.figure(figsize=(4,4), facecolor="w")
plt.scatter(xygrid[0], xygrid[1], s=36, c=colours, edgecolor="none")
plt.grid(True)
plt.axis("equal")
plt.title=("Original grid in x-y space")

Here is our starting grid of equal spaced points

plt.figure(figsize=(4,4), facecolor="w")
plt.scatter(uvgrid[0], uvgrid[1], s=36, c=colours, edgecolor="none")
plt.grid(True)
plt.axis("equal")

We apply out transformation matrix to the grid and re-plot the result. As you can see the permutation transformation has taken place.

Application to colour

The above example is a simplified demonstration of how colour transformation matrices work. These are often 3x3 matrices and are applied to the 3D colour cube axis XYZ each representing RGB. When the vector is applied the resulting 3D cube is scaled to meet the bounds of the transform.

There is more to learn about this subject, I hope to be able to modify this code or use it as a starting point to develop my own version that visualizes 3D linear transformations. Another topic that I am also keen to study is interpolation and how it is applied in colour science, particularly it’s use in 3D look up tables.

I have overhauled my Obsidian setup for keeping track of my personal and work notes. I took a break away from this tool to play around with Craft, but I found that it didn’t offer the customisability that I wanted. I popped back to Obsidian for the first time in a while and discovered a wealth of new features and community plugins, some of which are insanely powerful.

My current workflow revolves around Things3, DevonThink and Obsidian. This is still a work in progress and it changes all the time. I think that there are probably more fancy automations that are available between DevonThink and Obsidian, but I am yet to find time to discover them.

My primary notes folder is stored in My Documents on my Mac, I use Syncthing to sync this to a central file server (more on this later) This is then pushed out to my Desktop Mac Mini. For iOS devices, it was a little tricky. I thought I could use the server to back up via rsync to the iCloud folder in iCloud Drive. This worked when it was run manually, though trying to schedule it via cron it kept failing with permissions errors. I got fed up and gave up, I will probably use obsidian sync for this.

Initial capture

For initial capture I am using things3 on iOS and OSX . I can create a new todo in my Notes project and jot down some rough ideas. This is even better now since the latest update allows the use of markdown.

Vault Layout

I have all new notes default to the inbox, where I can decide what I want to do with them after I have made them.

My default content template is powered with the Templater plugin from the community plugins, see the example below. Copy and paste if you would like to use it. Also let me know if you make any changes you think I might like.

---
tag: 🌱
date: <% tp.date.now("YYYY-MM-DD", 1) %>
modification date: <% tp.file.last_modified_date("dddd Do MMMM YYYY HH:mm:ss") %>
status: 🟥 🟧 🟩
date updated: '2021-08-15T14:01:28+01:00'

---

`ad-info
Summary:
`

## <% tp.file.title %>

- Source:
- Author:

## Notes

<!-- The main content of my thoughts really -->

### Links

<!-- Links to definition pages -->

For book notes I use the following template:

---
tags: 📗
date: <% tp.date.now("YYYY-MM-DD", 1) %>
status: 🟥 🟧 🟩
---
## <% tp.file.title %>
- Author: 
- ISBN:

`ad-info
Summary: 
`

## Key Ideas
<!-- The main content of my thoughts really -->

## Further Lines of Inquiry
<!-- What remains for you to consider? -->

## Quotes
<!-- Notable quotes with reference to their page or location -->

## TODO
- [ ] 

## Resources

—

I am also using the data view plugin to create an index view / overview of the status of the notes in my Notes folder. See the example below:

---
tag: 📚 
date: 2021-08-16
modification date: Sunday 15th August 2021 13:26:38
---

Reference [[🗂 Key]]

##  Not Yet Started

`dataview
list
from "🌿 Notes"
where status = "🟥"
sort file.name asc
`

## In Progress

`dataview
list
from "🌿 Notes"
where status = "🟧"
sort file.name asc
`


---
## Tasks
`dataview
task
from "🌿 Notes"
sort file.name asc
`

This allows me to see lists of the notes related to their status and corresponding todo’s. I like this as I can go directly to this note and see on what areas I need to work quickly and simply.

DevonThink is a dumping ground for any reading material that needs to be processed. I use the clipping tool to grab pages of the web and then read them in DevonThink. I was using Evernote, but the recent change in pricing structure means that if you are on the lower tier you get constant annoying pop-up’s asking you to upgrade!!

Plugin’s I Am Using

Quick List of the plugins I am using:

• Dataview
• Admonition
• Advanced Tables
• Kindle Highlights
• Markdown Prettifier
• Metatable
• Natural Language Dates
• Paste URL into selection
• Templater

I have been playing around with logistic functions in Lattice to create contrast curves in 1D/3D LUT’s.

A logistic function is a common s-shaped curve. When applied to a log encoded image it simulates a contrast curve. The curve can be encoded into 1D or 3D LUT’s for application in video editing software and image processing.

When the above function is applied in Lattice the following result is achieved:

To learn more or play around yourself, find Lattice here:

https://videovillage.co/lattice/

I recently got hold of a couple of Yubikeys as an additional security measure. These are small devices that are plugged into computers or phones/tables and act as a physical 2FA method, some models can even be purchased with NFC capability.

So far the experience has been pretty good, most services I use have worked well with the security key and the process of using it is pretty simple. Plug in when prompted, touch the button and you’re good to go. The only account that I found tricky to work with was Microsoft 365 for business, for some reason I couldn’t find the security key settings in my account management panel.

Having this system set up for a while now, I wondered if it would be possible to set up a little security key server on my local network. The server could be a raspberry pi, and the key would be plugged into its USB port permanently. The USB port would then be shared over the local network with USB over IP. This way all computers connected over the local network can share the same security key. As opposed to having to unplug and move it to and from each computer I am working on at the time.

Think I will come back to this in the future to see if I can work something out, more to follow.

The past few years have probably been quite strange for everyone. As a freelancer I have found a great deal of my work completely dry up. It took a long time for any government support to be announced for the self-employed and staff employed on short-term contracts.

Whilst in lockdown, I spent time reading a great deal of books and studying things that interest me. I read books on mental models, productivity, learning. As well as books on computer science and photography.

Concepts are the Key

I have discovered that the best way to learn anything is to drill down to the core concepts of the topic.

When reading a text about something that you wish to learn, it is important that as a reader you are constantly asking questions about the text. As you hold these questions in your mind, it is also your responsibility as a reader to answer them. C. Van Doren & M. J. Adler Discuss how to be a demanding reader in their book How to Read a Book: The Classic Guide to Intelligent Reading.

Whilst asking these questions, we also need to make the book our own. By writing notes, underlining and highlighting on the page. Digital books are great for this, I love reading papers and chapters on my iPad using GoodNotes to highlight and scribble down ideas.

Connecting Concepts

Once we have isolated the concepts of the topic that we are wanting to study, we can the write our notes.

By writing your notes based around the concepts of the topic that you have read about. It frees you from the constraints of grouping your notes by book title or author.

The benefit from this, is that your notes are much more open and inclusive to other ideas from different writers. As you study a topic you will extract another authors concepts and different ideas will interconnect. As your notes grow you will start to see a bigger picture of the subject you are studying.

This does make note-taking and studying harder, but the rewards are greater. When writing our notes, we have to think deeply about how they fit with what we have already written. We are forced to look back over our writing and explore the connections.

By making the connections in our notes, we are creating connections in our mind. The repetition involved when doing this helps to cement the ideas into our memory, and we feel like we are truly understanding a topic. By connecting the unexpected we are also planting the seeds to grow new ideas, that could expand our levels of understanding even further.

A while ago; I explored the possibility of using neural networks to calculate colour transforms. My hypothesis was that a trained model would be able to apply a more accurate colour transform in a shorter amount of time compared to an equivalent 64x LUT.

I started tinkering around with Python and Tensorflow and had some promising results, but quickly found that I lacked the time and the computing power to train the models properly. As a result of this, it was tricky to get results good enough that would have made it worth exploring the project further.

Although using neural networks for colour transformations is probably impractical in professional applications. I am still keen to chip away at this as a personal research project when I have time.

Recently I came across a great tool for quickly sharing files across a network, croc. The tool works in the command line and allows two users to transfer data between two computers using a relay, data is encrypted using PAKE.

What is really useful is transfers can be resumed if they are interrupted.

I have been using this on a number of film production projects to quickly send pdfs and cdl’s with on-set colour decisions to our data management workstation which is operated off set. Generally the tool works well, and is much quicker than email or using a usb stick. However we have noticed that with larger transfers the utility does become less reliable, with transfers often freezing. We did get around this by using a self hosted docker image to host our own relay.

The croc utility is easy to install with the following command:

curl https://getcroc.schollz.com | bash

Which will download the correct version for your system. On macOS you can install the latest release with HomeBrew

brew install croc