#installation of python and environment setup

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CANT IT JUST WORK PLEASE PLEASE PLEASE

[oviraptoridae]

research & development is ongoing

since using jukebox for sampling material on albedo, i've been increasingly interested in ethically using ai as a tool to incorporate more into my own artwork. recently i've been experimenting with "commoncanvas", a stable diffusion model trained entirely on works in the creative commons. though i do not believe legality and ethics are equivalent, this provides me peace of mind that all of the training data was used consensually through the terms of the creative commons license. here's the paper on it for those who are curious! shoutout to @reachartwork for the inspiration & her informative posts about her process!

part 1: overview

i usually post finished works, so today i want to go more in depth & document the process of experimentation with a new medium. this is going to be a long and image-heavy post, most of it will be under the cut & i'll do my best to keep all the image descriptions concise.

for a point of reference, here is a digital collage i made a few weeks ago for the album i just released (shameless self promo), using photos from wikimedia commons and a render of a 3d model i made in blender:

and here are two images i made with the help of common canvas (though i did a lot of editing and post-processing, more on that process in a future post):

more about my process & findings under the cut, so this post doesn't get too long:

quick note for my setup: i am running this model locally on my own machine (rtx 3060, ubuntu 23.10), using the automatic1111 web ui. if you are on the same version of ubuntu as i am, note that you will probably have to build python 3.10.6 yourself (and be sure to use 'make altinstall' instead of 'make install' and change the line in the webui to use 'python3.10' instead of 'python3'. just mentioning this here because nobody else i could find had this exact problem and i had to figure it out myself)

part 2: initial exploration

all the images i'll be showing here are the raw outputs of the prompts given, with no retouching/regenerating/etc.

so: commoncanvas has 2 different types of models, the "C" and "NC" models, trained on their database of works under the CC Commercial and Non-Commercial licenses, respectively (i think the NC dataset also includes the commercial license works, but i may be wrong). the NC model is larger, but both have their unique strengths:

"a cat on the computer", "C" model

"a cat on the computer", "NC" model

they both take the same amount of time to generate (17 seconds for four 512x512 images on my 3060). if you're really looking for that early ai jank, go for the commercial model. one thing i really like about commoncanvas is that it's really good at reproducing the styles of photography i find most artistically compelling: photos taken by scientists and amateurs. (the following images will be described in the captions to avoid redundancy):

"grainy deep-sea rover photo of an octopus", "NC" model. note the motion blur on the marine snow, greenish lighting and harsh shadows here, like you see in photos taken by those rover submarines that scientists use to take photos of deep sea creatures (and less like ocean photography done for purely artistic reasons, which usually has better lighting and looks cleaner). the anatomy sucks, but the lighting and environment is perfect.

"beige computer on messy desk", "NC" model. the reflection of the flash on the screen, the reddish-brown wood, and the awkward angle and framing are all reminiscent of a photo taken by a forum user with a cheap digital camera in 2007.

so the noncommercial model is great for vernacular and scientific photography. what's the commercial model good for?

"blue dragon sitting on a stone by a river", "C" model. it's good for bad CGI dragons. whenever i request dragons of the commercial model, i either get things that look like photographs of toys/statues, or i get gamecube type CGI, and i love it.

here are two little green freaks i got while trying to refine a prompt to generate my fursona. (i never succeeded, and i forget the exact prompt i used). these look like spore creations and the background looks like a bryce render. i really don't know why there's so much bad cgi in the datasets and why the model loves going for cgi specifically for dragons, but it got me thinking...

"hollow tree in a magical forest, video game screenshot", "C" model

"knights in a dungeon, video game screenshot", "C" model

i love the dreamlike video game environments and strange CGI characters it produces-- it hits that specific era of video games that i grew up with super well.

part 3: use cases

if you've seen any of the visual art i've done to accompany my music projects, you know that i love making digital collages of surreal landscapes:

(this post is getting image heavy so i'll wrap up soon)

i'm interested in using this technology more, not as a replacement for my digital collage art, but along with it as just another tool in my toolbox. and of course...

... this isn't out of lack of skill to imagine or draw scifi/fantasy landscapes.

thank you for reading such a long post! i hope you got something out of this post; i think it's a good look into the "experimentation phase" of getting into a new medium. i'm not going into my post-processing / GIMP stuff in this post because it's already so long, but let me know if you want another post going into that!

good-faith discussion and questions are encouraged but i will disable comments if you don't behave yourselves. be kind to each other and keep it P.L.U.R.

[assignmentoc]

🚀 Mastering Data Analysis with NumPy: A Step-by-Step Mini Project

Data analysis becomes far more effective when the right tools are used to transform raw numerical data into meaningful insights. One of the most powerful tools for this purpose in Python is NumPy, a library designed for high-performance numerical computing and efficient array operations.

This mini project demonstrates how NumPy can be used to analyse sales data and generate business insights through structured calculations and statistical analysis.

🔹 Foundations of NumPy

NumPy, short for Numerical Python, provides support for large multidimensional arrays, matrices, and advanced mathematical functions.

Its core strength lies in N-dimensional array objects, which allow data to be stored in grid-like structures that make numerical computation faster and more efficient.

Another advantage of NumPy is its seamless integration with libraries such as Pandas, SciPy, and Matplotlib, enabling a complete data science workflow from analysis to visualization.

🔹 Project Setup and Data Loading

The project begins by setting up the environment using:pip install numpy import numpy as np

A sample dataset representing monthly sales across three regions was loaded into a NumPy array.

Example dataset:MonthRegion ARegion BRegion CJan200220250Feb210230260Mar215240270Apr225250280

This structure allows numerical operations to be performed quickly and efficiently.

🔹 Calculations and Data Analysis

Using NumPy functions, several calculations were performed:

• np.sum to calculate total sales per region • np.mean to compute average sales per month • np.std to measure sales variability (standard deviation) • np.argmax to identify the region with the highest growth

To improve interpretation, the dataset was also visualized using Matplotlib, which helped reveal trends across months.

🔹 Key Insights from the Analysis

🏆 Region C: Market Leader Region C recorded the highest total sales and demonstrated the most consistent performance.

📈 Region B: High Growth Potential Despite slightly lower total sales, Region B showed the highest percentage growth from January to April.

📊 Consistent Business Growth Average monthly sales increased steadily across all regions, indicating overall positive business expansion.

🔹 NumPy Pro Tips

✔ NumPy Arrays vs Python Lists NumPy arrays are faster and more memory efficient due to vectorized operations.

✔ Broadcasting NumPy can perform operations across arrays with different shapes without duplicating data.

✔ Machine Learning Foundation NumPy forms the backbone of many advanced libraries including TensorFlow and Scikit-learn.

💡 Final Thought

Even with a small dataset, NumPy enables powerful insights through efficient numerical computation. For anyone starting in data science, machine learning, or business analytics, mastering NumPy is an essential step toward building strong analytical skills.

[catbirdlinux]

Reticulum Meshchat on any Linux

A virtual Python Environment is the solid and stable way to install the Reticulum network stack on your Linux or BSD system. Tested on Ubuntu, Debian, Xebian, and Artix.

It may be tempting to just open a terminal and "sudo pip install" the packages, but don't do that! You could break your system or suffer a broken Reticulum setup at some time in the future. It is easy, pain-free, and fast to follow the tutorial and have it done in a way which won't break the next time you upgrade your system.

[etirabys]

I'd rather get a whole new computer science degree again than download a python module. My Mac replayed the "Janet, do you have a cactus" meme with me yesterday about whether certain python versions (and modules) were present or not

After pip failed, I tried directly downloading bs4 and running a setup script from the download directory, and found that I could now successfully instantiate a BeautifulSoup instance if I

- ran from the bs4 install directory - with the python2, never python3... - ...INTERPRETER, feeding in one line of code at a time

that's right, I could only get this basic ass web scraping module to work by feeding the interpreter of ancient, astronomically twilit python from my script.py line by line like it was a baby bird

As I went into a hysteria of rage on his bed (where I happened to be working), my boyfriend CJ sent me this XKCD (link), which was so real that – now leaning on his desks on my knuckles, staring at his monitor like a hunched and demented chimp-gargoyle, I started mildly hyperventilating

(for the record, having a virginal Python environment was one of the things I most enjoyed about getting a new Mac, it felt like my criminal record had been expunged)

[azaleakamellia]

Python: Geospatial Environment Setup (Part 2)

Hey again folks! I am here for the second part of Python environmental setup for a geospatial workspace. I published the first part of this post two weeks ago. So if you've not yet read that, I'll catch you up to speed with our checklist:

Install Python ☑

Install Miniconda ☑

Install the basic Python libraries ☑

Create a new environment for your workspace

Install geospatial Python libraries

🗃 Create a new environment for your workspace

Since we have actually manually set up our base environment quite thoroughly with all the basic libraries needed, to make our work easier, we can just clone the base environment and install all the additional essential libraries needed for geospatial analysis. This new environment will be called geopy. Feel free to use a name you identify most with.

Why don't we just create a new environment? Well, it means we have to start installing the Python libraries again from scratch. Although it is no trouble to do so, we want to avoid installing so many libraries all at once. As I mentioned in Part 1, there is always a risk where incomplete dependencies in one library will affect the installation of other libraries that you intend to install in one go. Since we already have a stable and usable base environment, we can proceed to use it as a sort of pre-made skeleton that we will build our geospatial workspace with.

1️⃣ At the Anaconda Command Prompt, type the following:

2️⃣ Press Enter and the environment will be clone for you. Once it is done, you can use the following command to check the availability of your environment 👇🏻

You should be able to see your geopy environment listed along with the base environment.

👩🏻‍💻 Install geospatial Python libraries

Here we will proceed with the installation of a few geospatial Python libraries that are essential to reading and exploring the vectors and rasters.

🔺 fiona: This library is the core that some of the more updated libraries depend on. It is a simple and straightforward library that reads and writes spatial data in the common Python IOs without relying on the infamous GDAL's OGR classes.

🔺 shapely: shapely library features the capability to manipulate and edit spatial vector data in the planar geometric plane. It is one of the core libraries that recent geospatial Python libraries rely on to enable the reading and editing of vector data.

🔺 pyproj: is the Python interface for the cartographic projections and coordinate system libraries. Another main library that enables the 'location' characteristics in your spatial data to be read.

🔺 rasterio: reads and writes raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.

🔺 geopandas: extends the pandas library to allow spatial operations on the geometric spatial data i.e shapefiles.

💀 As you might have noticed, we won't be doing any direct gdal library installation. It's mainly due to the fact that its installation is a process that seems to be accompanied by misery at every turn and involved workarounds that are pretty inconsistent for different individuals. Does it mean that we won't be using it for our Pythonic geospatial analysis? Heck no. But we will be taking advantage of the automatic dependency installation that comes with all the libraries above. The rasterio library depends on gdal and by installing it, we integrate the gdal library indirectly into our geospatial environment. I found that this method is the most fool-proof. Let's proceed to the installation of these libraries.

1️⃣ At the Anaconda Command Prompt, should you start from the beginning, ensure that your geopy environment is activated. If not, proceed to use the following command to activate geopy.

Once activated, we can install the libraries mentioned one after another. Nevertheless, you also have the option of installing them in one go directly using a single command 👇🏻

💀 geopandas is not included in this line-up NOT because we do not need it. It's another temperamental library that I prefer to isolate and install individually. If gdal is a rabid dog...then geopandas is a feral cat. You never know how-when-why it doesn't like you and forces a single 10-minute installation drag to hours.

3️⃣ Once you're done with installing the first line-up above, proceed with our feral cat below 👇🏻

4️⃣ Use the conda list command again to check if all the libraries have been installed successfully.

🎉Et voilá! Tahniah! You did it!🎉

🎯 The Jupyter Notebook

It should be the end of the road for the helluva task of creating the geospatial environment. But you're going to ask how to start using it anyway. To access this libraries and start analyzing, we can easily use the simple and straight-forward Jupyter Notebook. There are so many IDE choices out there but for data analysis, Jupyter Notebook suffices for me so far and if you are not familiar with Markdown, this tool will ease you into it slowly.

Jupyter Notebook can be installed in your geopy environment as follows:

And proceed to use it by prompting it open via the command prompt

It ain't that bad, right? If you're still having problems with the steps, do check out the real-time video I created to demonstrate the installation. And feel free to share with us what sort of problems you have encountered and the workaround or solutions you implemented! It's almost never a straight line with this, trust me. As mentioned in the previous post, check out the quick demo below 👇🏻

🌏 Python: Geospatial Environment Setup 🎉

A deconstructed time-lapsed Python geospatial environment setup with `conda` from downloading necessary software to loading the Jupyter Note

See you guys again for another session on geospatial Python soon!

[tag-cat]

Programming has started!

We've been talking about what we want to do and what the finished site could be, but we're also actually doing it! So far, we have over 600 lines of code in 20 files, the basic setup of our development environment is done, the first tests run, and our data structure has been defined. We're currently setting up permission handling for our own API. The tagging team, in the meantime, has collected lots of data to start with so we can test our pages with realistic input.

Our programmers come from a variety of backgrounds, so deciding on a programming language, framework, and tools has not been easy. What is familiar ground for some sends others frantically googling. We're documenting our setup, installation, and structure so new members have an easier time getting started.

If you're a programmer interested in joining the team, here are the basics: We're using Flask, a lightweight python framework, the ORM SQLalchemy and pytest for the testing. Flask uses the templating engine Jinja2 to pass data to the HTML templates. Our local development server is set up with vagrant, and we use GitLab for version management, code review, and issue tracking.

Since the structure is set up and we’ve documented the basics, new team members should have an easy time getting started. If you have some time to spare or know someone who does: we’re hanging out on this Discord!

[michael-garza-author-blog]

Linux Simply

Nobody could make Linux simple.

It is as complex and layered as the biological world, if not more. Arch users can feel free to get their snickering out of the way now.

You can however, simplify the way you make Linux—a Linux distribution, that is.

And that’s exactly what the teams behind three leading Linux distributions, or distros for short, have done by implementing user-friendly environments that can be setup in minutes.

Perhaps you have heard of them while perusing CNET, TechRadar, or even Reddit.

Pop!_OS: maybe the most popular entry on this list. It is developed by System76, a Colorado based computer company, to run smoothly both on their own machines as well as virtually any 64-bit computer you pull from the shelves.

Solus OS: the open source underdog compiled from the ground up with no reliance on prior code.

Elementary OS: a long running and trusted Ubuntu derivative, which isn’t exactly free like most distros but has features to far outweigh its price tag.

Each Operating System (OS for short) has been designed to be accessible to the casual computer user, Linux novices and FOSS veterans alike.

Today we are going to take a look at why these three distributions are ideal for the beginner and for those who would like to speed date through the world of open source software.

We’ll begin with Pop!_OS

POP!_OS

Pop! Was System76’s brain baby to be used as their exit strategy from only offering stock Ubuntu on their machines. When Ubuntu’s parent company, Canonical, announced the system’s reversion from the Unity desktop environment to its predecessor GNOME, System76 went in pursuit of options.

And ended up at GNOME, themselves, ironically.

Not just any GNOME desktop environment, though. GNOME found here has been specially tweaked for the brand of System76.

The theme, design, and system options make it apparent that this OS is as brilliant with personality as it is with usability.

For instance, graphics driver support for AMD, Intel and NVIDIA chips right out of the box is a milestone for those interested in Linux gaming.

A slick and trimmed software center called Pop Shop makes it a joy to navigate through both open source packages and proprietary 3rd party apps. This comprehensive design is likely to shift focus from installation via terminal, though the function is still there for old-school code junkies.

The OS installer also allows easy disk encryption for assured privacy in an evolving digital world.

The standout feature of this distribution is its effort to minimize the learning curve. Every feature and application seems to be right in place. Within a few minutes you could fool your friends into thinking you’re a red-pill swallowing reality hacker whose DNA is coded in Python—or just that you’re a tech guru.

You can download Pop!_OS here: https://system76.com/pop

SOLUS OS

Solus OS was like an outlaw busting through the swinging doors of the Open Source saloon.

Not everyone was sure what to say when it arrived on the scene. It wasn’t derivative of Ubuntu or Debian or Redhat. It wasn’t taken from a slightly repackaged rib of Arch.

It was just Solus. And this was the concept they ran with.

It was the Solus team who introduced the Budgie desktop. Budgie has since been adopted by Ubuntu, Manjaro, and more.

Why? Because it’s so damn beautiful is why! The Budgie flavor contributes largely to Solus OS being on this list (no discredit to its other groundbreaking features)

Budgie has roots in the GNOME 3 Desktop as well, but wears its heritage more as an innovative badge of honor than a developmental burden.

Budgie is twice as simple to traverse and uses a fraction of the hardware resources of its relative.

It’s an office professional’s Linux distribution. Frills and endless tinkering are sidelined in favor of sensibility and ease of use. You want Spotify, Plex, or Skype? Don’t go hunt down the source code or even type t for terminal in the app search.

Solus OS makes licensed 3rd party software available to download straight from the software center. You can even find studio quality Audio mixers like Bitwig Studio.

The included office suite, Libreoffice, comes loaded with free and open source alternatives to Word, Excel, Publisher, and more.

You’ll have the ability to save in ‘open document’ formats as well as the proprietary DOC and DOCX. So you never have to worry about files not opening when they reach the other side.

And of course the Raven widget makes a calendar, notifications, and even system tweaking just a mouse hover away. Always there to be seen but never intrusive.

I’d like to think that Solus took the introduction of a new package system as an opportunity to create distance from hardline terminal use. Updates and software installation can be run solely from the software center. No more lines of dizzying code.

I’ll consider it a tipped hat to us m’normies.

You can download SOLUS OS with desktop environments including Budgie, GNOME, and MATE here: https://getsol.us/download/

ELEMENTARY OS

The oldest of the bunch despite its name.

Like many modern Linux distros, Elementary OS found its code in Ubuntu and expanded from there. It simply took a few left turns, rerouted and dropped off some baggage along the way.

It’s specifically marketed as a “fast, open, and privacy-respecting replacement for Windows and macOS.”1 No, really, it says so on their homepage.

It even resembles MacOS to a degree, if that’s any indication of the company’s target.

The lightweight interface was designated as a friendly tool for everyone. A system you could open up right from where you left off. The fiddling, the minutia of customization, the daily disaster-prevention management—all tossed aside.

It’s in no sense incapable of anything a Linux system is expected of. It just doesn’t make you think about those things.

You shouldn’t always need to adjust the ribbon or unjam the keys of a typewriter. Sometimes you just need ‘pen on paper.’

You could say that Elementary OS is the pen on paper of open source computing.

It’s still a Mont Blanc on a high quality moleskine. Simple, but satisfactory.

The stage of Elementary’s application is set by the Pantheon desktop, which you’ll find to be less customizable than say GNOME or Cinnamon. This could be said to act as “damage control” for the inexperienced Linux user.

It’s easier than you would think to break your environment in the excitement of mix-matching themes, icon packs, and installing widgets designed for other DE’s.

That headache is alleviated with Elementary, which adheres a straightforward take me as I am approach.

And when you take your first look at the home screen, I doubt you’ll find it a drawback.

For those who want to get some work done in peace and quiet, you’ll find:

Epiphany Web Browser

a file manager

Music and Video players

a Photo Viewer

a dedicated mail client

As well as a handful of other neat applications. Nothing too neat that it stops being essential though. Zero bloatware or spyware.

The key to Elementary is simple essentialism.

Writing that novel at a lakeside cabin? Tuning up this year’s budget? Or are you finally going to email that someone you met over Winter break?

In any case, it’s Elementary.

You can find and download Elementary here for a fee of your choice: https://elementary.io/

OVERVIEW

It would make sense, all features considered, that Pop!_OS would suit the likes of media professionals, avid gamers and developers.

Where Solus OS might be optimized for the business professional with a full schedule.

For casual home use, or emailing your family and friends your new favorite Youtube video, I’d recommend Elementary OS.

The question is, which one best suits you?

Stay tuned for more Linux and Tech news here, and thanks for reading!

Sources: 1) https://elementary.io/