Try out in mybinder:

3d plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.

Ipyvolume currently can

• Do (multi) volume rendering.
• Create scatter plots (up to ~1 million glyphs).
• Create quiver plots (like scatter, but with an arrow pointing in a particular direction).
• Render isosurfaces.
• Do lasso mouse selections.
• Render in the Jupyter notebook, or create a standalone html page (or snippet to embed in your page).
• Render in stereo, for virtual reality with Google Cardboard.
• Animate in d3 style, for instance if the x coordinates or color of a scatter plots changes.
• Animations / sequences, all scatter/quiver plot properties can be a list of arrays, which can represent time snapshots.
• Stylable (although still basic)
• Integrates with
  • ipywidgets for adding gui controls (sliders, button etc), see an example at the documentation homepage
  • bokeh by linking the selection
  • bqplot by linking the selection

Ipyvolume will probably, but not yet:

• Render labels in latex.
• Show a custom popup on hovering over a glyph.

Documentation is generated at readthedocs:

(see more at the documentation)

If you want to use Jupyter Lab, please use version 3.0.

Advice: Make sure you use conda or virtualenv. If you are not a root user and want to use the --user argument for pip, you expose the installation to all python environments, which is a bad practice, make sure you know what you are doing.

$ pip install ipyvolume

$ conda install -c conda-forge ipyvolume

If you are still using an old notebook version, ipyvolume and its dependend extension (widgetsnbextension) need to be enabled manually. If unsure, check which extensions are enabled:

$ jupyter nbextension list

If not enabled, enable them:

$ jupyter nbextension enable --py --sys-prefix ipyvolume
$ jupyter nbextension enable --py --sys-prefix widgetsnbextension

You have been warned, do this only if you know what you are doing, this might hunt you in the future, and now is a good time to consider learning virtualenv or conda.

$ pip install ipyvolume --user
$ jupyter nbextension enable --py --user ipyvolume
$ jupyter nbextension enable --py --user widgetsnbextension

$ git clone https://github.com/maartenbreddels/ipyvolume.git
$ cd ipyvolume
$ pip install -e . notebook jupyterlab
$ (cd js; npm run build)
$ jupyter nbextension install --py --overwrite --symlink --sys-prefix ipyvolume
$ jupyter nbextension enable --py --sys-prefix ipyvolume
# for jupyterlab (>=3.0), symlink share/jupyter/labextensions/bqplot-image-gl
$ jupyter labextension develop . --overwrite

Note: There is never a need to restart the notebook server, nbextensions are picked up after a page reload.

Start this command:

$ (cd js; npm run watch)

It will

• Watch for changes in the sourcecode and run the typescript compiler for transpilation of the src dir to the lib dir.
• Watch the lib dir, and webpack will build (among other things), ROOT/ipyvolume/static/index.js.

Refresh the page.

This repository is the portal for the course “Unemployment” taught by Pascal Michaillat at UC Santa Cruz. The course ID is ECON 182. The course portal contains the syllabus, provides a discussion forum, and hosts other course resources.

The course materials are available at https://pascalmichaillat.org/v/.

• Syllabus for Winter 2025
• Presentation schedule for Winter 2025
• Lecture handouts – The folder contains handouts distributed in lecture. The handouts are designed to help you develop your research ideas and collect questions about the lecture videos.
• Discussion forum – This collaborative discussion forum is designed to get you help quickly and efficiently. You can ask and answer questions, share updates, have open-ended conversations, and follow along course announcements.
• Reading material – The folder contains book chapters and articles that may be hard to find online.
• Lecture material – The folder contains discussions from lecture.
• Section material – The folder contains material from section.
• Presentations – The folder contains all the student presentations given during the quarter, and some presentation templates and examples.
• LaTeX code for presentation slides – Complete code to produce a presentation with LaTeX. Just upload the files to Overleaf and start writing your slides!
• LaTeX code for research paper – Complete code to produce a research paper with LaTeX. Just upload the files to Overleaf and start writing your paper!

This repository is licensed under the Creative Commons Attribution 4.0 International License.

TLDR; Text extraction, transcription, punctuation restoration, translation, summarization and text to speech

The goal of this project is to extend the functionalities of Fabric. I’m particularly interested in building pipelines using utilities like yt as a source and chaining them with the | operator in CI.

However, a major limitation exists: all operations are constrained by the LLM context. For extracting information from books, lengthy documents, or long video transcripts, content may get truncated.

To address this, I started working on adding a summarization step before applying a fabric template, based on the document length. Additionally, I explored capabilities like transcripting, translating and listening to the pipeline result or saving it as an audio file for later consumption.

yt --transcript url | tp --cb | tts

tp --ebullets https://en.wikipedia.org/wiki/Text_processing

yt --transcript --lang en url | tp --cb --tr fr | tts

tp my_book.txt --eb | fabric --p extract_wisdom | tts --o my_book_wisdom.mp3

echo "Hello world!" | tp --tr zh | tts

tp doc_fr.txt --tr es > doc_es.txt

tp en.mp4 --tr fr

tp fr.mp3 --tr es | tts

tp es.mp3 --tr fr | tts --o fr.mp3 | tp fr.mp3 --tr en --o tr_en.txt

tp en.mp3 | fabric --p extract_ideas | tp --tr fr --o idées.txt

tp image.png

tp document.docx

tp receives from stdin or as first command line argument It accepts:

• Text.
• File path. Supported formats are: .aiff, .bmp, .cs, .csv, .doc, .docx, .eml, .epub, .flac, .gif, .htm, .html, .jpeg, .jpg, .json, .log, .md, .mkv, .mobi, .mp3, .mp4, .msg, .odt, .ogg, .pdf, .png, .pptx, .ps, .psv, .py, .rtf, .sql, .tff, .tif, .tiff, .tsv, .txt, .wav, .xls, .xlsx

tp accepts unformatted content, such as automatically generated YouTube transcripts. If the text lacks punctuation, it restores it before further processing, which is necessary for chunking and text-to-speech operations.

Converts audio and video files to text using Whisper.

The primary aim is to summarize books, large documents, or long video transcripts using an LLM with an 8K context size. Various summarization levels are available:

• Splits text into chunks.
• Summarizes all chunks as bullet points.
• Concatenates all bullet summaries.

The goal is to retain as much information as possible.

Executes as many extended bullet summary phases as needed to end up with a bullet summary smaller than an LLM context size.

A simple summarization that does not rely on bullet points.

Translates the output text to the desired language. Use two letters code such as en or fr.

usage: tp [-h] [--ebullets] [--cbullets] [--text] [--lang LANG] [--translate TRANSLATE] [--output_text_file_path OUTPUT_TEXT_FILE_PATH] [text_or_path]

tp (text processing) provides transcription, punctuation restoration, translation and summarization from stdin, text, url, or file path. Supported file formats are: .aiff, .bmp, .cs, .csv, .doc, .docx, .eml, .epub, .flac, .gif, .htm, .html, .jpeg, .jpg, .json, .log, .md, .mkv, .mobi, .mp3, .mp4, .msg, .odt, .ogg, .pdf, .png, .pptx, .ps, .psv, .py, .rtf, .sql, .tff, .tif, .tiff, .tsv, .txt, .wav, .xls, .xlsx

positional arguments:
  text_or_path          plain text; file path; file url

options:
  -h, --help            show this help message and exit
  --ebullets, --eb      Output an extended bullet summary
  --cbullets, --cb      Output a condensed bullet summary
  --text, --t           Output a textual summary
  --lang LANG, --l LANG
                        Forced processing language. Disables the automatic detection.
  --translate TRANSLATE, --tr TRANSLATE
                        Language to translate to
  --output_text_file_path OUTPUT_TEXT_FILE_PATH, --o OUTPUT_TEXT_FILE_PATH
                        output text file path

Listen to the pipeline result or save it as an audio file to listen later.

tts can also read text files, automatically detecting their language.

usage: tts.py [-h] [--output_file_path OUTPUT_FILE_PATH] [--lang LANG] [input_text_or_path]

tts (text to speech) reads text aloud or to mp3 file

positional arguments:
  input_text_or_path    Text to read or path of the text file to read.

options:
  -h, --help            show this help message and exit
  --output_file_path OUTPUT_FILE_PATH, --o OUTPUT_FILE_PATH
                        Output file path. If none, read aloud.
  --lang LANG, --l LANG
                        Forced language. Uses language detection if not provided.

GROQ_API_KEY=gsk_
LITE_LLM_URI='http://localhost:4000/'
SMALL_CONTEXT_MODEL_NAME="groq/llama3-8b-8192"
SMALL_CONTEXT_MAX_TOKENS=8192

• Make script executable chmod +x tts.py

• Create symlink : Link the script to a directory that’s in your PATH sudo ln -s tts.py /usr/local/bin/tts

Small countdown timer and stopwatch module.

npm:

$ npm install tiny-timer

Yarn:

$ yarn add tiny-timer

const Timer = require('tiny-timer')

const timer = new Timer()

timer.on('tick', (ms) => console.log('tick', ms))
timer.on('done', () => console.log('done!'))
timer.on('statusChanged', (status) => console.log('status:', status))

timer.start(5000) // run for 5 seconds

Optionally set the refresh interval in ms, or stopwatch mode instead of countdown.

Starts timer running for a duration specified in ms. Optionally override the default refresh interval in ms.

Stops timer.

Pauses timer.

Resumes timer.

Event emitted every interval with the current time in ms.

Event emitted when the timer reaches the duration set by calling timer.start().

Event emitted when the timer status changes.

Gets the current time in ms.

Gets the total duration the timer is running for in ms.

Gets the current status of the timer as a string: running, paused or stopped.

1. Open Terminal:
   Access the terminal on your Ubuntu server. You can use SSH if you are connecting to a remote server.

2. Install Git (If Not Already Installed):
   Make sure Git is installed on your system. If it is not, you can install it with the following commands:
   sudo apt update
   sudo apt install git -y

3. Clone the GitHub Repository:
   Use the git clone command to download the repository containing the script. Replace REPOSITORY_URL with the appropriate GitHub repository URL. For example:
   git clone https://github.com/jamaludin1991/Install-DeepSeek-R1-with-Ollama-on-Ubuntu-Server—Automated-Bash-Script.git
   After running this command, a new folder with the repository name will be created in the current directory.

4. Navigate to the Repository Directory:
   Change into the directory of the cloned repository:
   cd repo-name

5. Make the Script Executable:
   Find the script file you want to run (e.g., install_deepseek_r1.sh) and make it executable:
   chmod +x install_deepseek_r1.sh

6. Run the Script with Sudo:
   Execute the script using sudo to provide administrative privileges:
   sudo ./install_deepseek_r1.sh

7. Wait for the Process to Complete:
   The script will start running. Wait until all steps are completed. You will see output in the terminal indicating the progress of the installation.

8. Verify the Installation:
   After the script finishes, you can verify that the DeepSeek-R1 model has been successfully downloaded by running:
   ollama list

9. Running the Model:
   To run the DeepSeek-R1 model, use the following command:
   ollama run deepseek-r1

Additional Notes:
Ensure you have a stable internet connection during the installation process, as the script will download Ollama and the DeepSeek-R1 model.
If you encounter errors during installation, check the error messages in the terminal for more information about what might have gone wrong.
If the DeepSeek-R1 model is not available, make sure to check the correct model name using the command ollama list.

https://colab.research.google.com/drive/1r7XeSxH5v–EfhCDpjZmJ_mIWz9IfoyM#scrollTo=1p-u99S5ZxEf

This repository contains implementations of several Hidden Markov Models (HMM) designed to analyze trading data with various levels of indicator integration and correction methods. The models achieve different performance accuracies, with some versions reaching up to 97% accuracy based on backtesting metrics.

1. Clone the repository:

   git clone https://github.com/rainerigius/hidden-markov-model.git

2. Install dependencies: To ensure proper functionality, install the required packages:

   pip install -r requirements.txt

• hmmlearn: For training and evaluating Hidden Markov Models.
• numpy, pandas: For data manipulation and numerical operations.
• joblib: For saving and loading model files.
• scikit-learn: For data preprocessing, scaling, and other utility functions.
• matplotlib, seaborn: For data visualization.

The repository includes several Python scripts, each implementing a different HMM with varying configurations of indicators and metrics:

• hmm_87%30_ind+_correction.py: Implements an HMM model with 87% accuracy, utilizing 30 indicators and a correction method.
• hmm_87%_with_30_indicators.py: HMM model achieving 87% accuracy with 30 indicators.
• hmm_88%.py: An HMM model with a slightly higher accuracy of 88%.
• hmm_97%_updated_metrics.py: A refined version of the 97% accuracy model, with updated metrics and performance improvements.
• hmm_97%.py: A previous version of the 97% accuracy model.
• hmm_d_97%.py: Another version of the 97% accuracy model, potentially using different datasets or indicators.

• oldtemplate.py: The original template for implementing HMM models, which provides a foundational structure for building more advanced models.

• liquidity.py: Script that calculates liquidity metrics for trading data.
• oos_test.py: Out-of-sample testing script for evaluating model performance on unseen data.
• state_transition_diagram: Visualization of the state transitions for the HMM models.

The repository includes a few CSV files that contain sample data:

• btc.csv, BTC_1H.csv, csv/BTC_2H.csv, etc.: Bitcoin price data at various timeframes.
• data/bitcoin_state_changes.csv: Data capturing state transitions for Bitcoin, likely used in the HMM training process.

Pre-trained models are saved in the models directory with joblib:

• model_hmm_85%_30ind_updated.joblib: A pre-trained HMM model with 85% accuracy using 30 indicators.
• model_hmm_88%.joblib: A pre-trained HMM model with 88% accuracy.
• model_hmm_98%.joblib: A highly accurate pre-trained HMM model with 98% accuracy.

Each model script can be executed directly or used as part of a larger analysis pipeline. For example, to run the hmm_97%_updated_metrics.py model, execute:

python hmm_97%_updated_metrics.py

Results are printed to the console or saved in designated output files for review.

Hidden Markov Models (HMMs) are statistical models that assume the system being modeled is a Markov process with hidden states. In this project, the HMMs are trained on historical trading data, aiming to predict price movements based on various indicators. Each HMM script uses different sets of indicators and configurations to optimize performance. Accuracy percentages indicate the effectiveness of each model based on backtesting metrics.

Val request = new HttpGet(“https://api.twitter.com/1.1/followers/ids.json?cursor=-1&screen_name=” + name)

First the user is given a Choice to enter a screen name of his choice. Once the screen name has been inputted the follower’s id

Once screen name RevistaCOFEPRIS is entered the follower id’s count are displayed as shown below

Phase-2 Document: https://github.com/cmoulika009/Principles-of-Big-Data-Management/blob/master/PB%20Phase-2-%20Team%2011/PB%20Phase-2%20Team-11.pdf

Final Project Document: https://github.com/cmoulika009/Principles-of-Big-Data-Management/blob/master/PB%20Phase-3-%20Team-11/PB%20Phase-3%20Team-11.pdf

Tweet Location: https://www.dropbox.com/s/04zebrisw6jm6n0/Disease_Tweets.json?dl=0

Youtube Video: https://youtu.be/dRO-2chnycM

I am no longer activly maintaing this theme, I will try to check out pull requests if possible

A responsive hugo theme with awesome font’s, charts and light-box galleries, the theme is based on Uno for ghost.
A example site is available at hugouno.fredrikloch.me

A Swedish translation is available in the branch feature/swedish

To take full advantage of the features in this theme you can add variables to your site config file, the following is the example config from the example site:

languageCode = "en-us"
contentdir = "content"
publishdir = "public"
builddrafts = false
baseurl = "http://fredrikloch.me/"
canonifyurls = true
title = "Fredrik Loch"
author = "Fredrik Loch"
copyright = "This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License."


[indexes]
   category = "categories"
   tag = "tags"
[Params]
  AuthorName = "Fredrik"
  github = "Senjindarashiva"
  bitbucket = "floch"
  flickr = "senjin"
  twitter = "senjindarshiva"
  email = "mail@fredrikloch.me"
  description = ""
  cv = "/pages/cv"
  legalnotice = "/pages/legal-notice"
  muut = "fredrikloch"
  linkedin = "fredrikloch"
  cover = "/images/background-cover.jpg"
  logo = "/img/logo-1.jpg"

If you prefer to use discourse replace the “muut” line with the following(remember the trailing slash)

  discourse = "http://discourse.yoursite.com/"

If you prefer to use disqus replace the “muut” line with the following

  disqus = "disqusUsername"

To create charts I use Chart.js which can be configured through basic js files. To add a chart to a post use the following short-code:

{{% chart id="basicChart" width=860 height=400 js="../../js/chartData.js" %}}

Where the javascript file specified contains the data for the chart, a basic example could look like this:

$(function(){
  var chartData = {
      labels: ["Jekyll", "Hugo", "Wintersmith"],
      datasets: [
          {
              label: "Mean build time",
              fillColor: "#E1EDD7",
              strokeColor: "#E1EDD7",
              highlightFill: "#C1D8AB",
              highlightStroke: "#C1D8AB",
              data: [784, 100, 5255]
          }
      ]
  };

  var ctx = $('#basicChart').get(0).getContext("2d");
  var myBarChart = new Chart(ctx).Bar(chartData, {
      scaleBeginAtZero : true,
      responsive: true,
      maintainAspectRatio: false,
    }
  );
  })

To add a gallery to the site we use basic html together with lightGallery to create a responsive light-box gallery.

<ul style="list-style: none;" id="lightGallery">
    <li data-src="https://github.com/RMBLRX/pathToImg.jpg">
        <img src="pathToThumb.jpg"></img>
    </li>
    <li data-src="https://github.com/RMBLRX/pathToImg.jpg">
        <img src="pathToThumb.jpg"></img>
    </li>
</ul>

<script src=../../js/lightGallery.min.js></script>
<script>
    $("#lightGallery").lightGallery();
</script>

Cover page
The landing page for Hugo-Uno is a full screen ‘cover’ featuring your avatar, blog title, mini-bio and cover image.

Built with SASS, using BEM
If you know HTML and CSS making modifications to the theme should be super simple.

Responsive
Hugo-Uno looks great on all devices, even those weird phablets that nobody buys.

Moot comments
Moot integration allows users to comment on your posts.

Font-awesome icons
For more information on available icons: font-awesome

No-JS fallback
While JS is widely used, some themes and websites don’t provide fallback for when no JS is available (I’m looking at you Squarespace). If for some weird reason a visitor has JS disabled your blog will still be usable.

Creative Commons Attribution 4.0 International

In order to develop or make changes to the theme you will need to have the sass compiler and bourbon both installed.

To check installation run the following commands from a terminal and you should see the > cli output but your version numbers may vary.

** SASS **

sass -v
> Sass 3.3.4 (Maptastic Maple)

If for some reason SASS isn’t installed then either follow the instructions from the Sass install page or run bundle install in the project root.

** Bourbon **

bourbon help
> Bourbon 3.1.8

If Bourbon isn’t installed follow the installation instructions on the Bourbon website or run bundle install in the project root.

Once installation is verified we will need to go mount the bourbon mixins into the scss folder.

From the project root run bourbon install with the correct path

bourbon install --path static/scss
> bourbon files installed to static/scss/bourbon/

Now that we have the bourbon mixins inside of the scss src folder we can now use the sass cli command to watch the scss files for changes and recompile them.

sass --watch static/scss:static/css
>>>> Sass is watching for changes. Press Ctrl-C to stop.

To minify the css files use the following command in the static folder

curl -X POST -s --data-urlencode 'input@static/css/uno.css' http://cssminifier.com/raw > static/css/uno.min.css

I have two reasons:

1. to refuse the unauthorized logins, and
2. log the attempts

There is a logon trigger which checks the

• Oracle user
• OS user
• IP address of the client
• Program / Application

If the login allowed then goes on, but if it did not, then logs the data and raise an error.

For DBA roled users the login is allowed all the time despite the trigger is invalid nor raises an error.

There is a table to controll the logins:

  ORACLE_USER             VARCHAR2 (   400 )
  OS_USER                 VARCHAR2 (   400 )
  IP_ADDRESS              VARCHAR2 (   400 )
  PROGRAM                 VARCHAR2 (   400 )
  ENABLED                 CHAR     (     1 )     Y or N

This table contains the valid user/client/program combinations.
The column values will use with LIKE, so it can be pattern.
i.e. “%” means “every” user/IP address/program e.t.c.
But ‘%’,’%’,’%’,’%’,’Y’ means anybody from anywhere, and this overwrites any other rules!
The refused logon data will be logged into LCL_LOG table.
There is an ENABLED column in the LCL_TABLE too, so you can disabled the logins anytime to set this value to “N”.

The whole solution is not too complicated, so see the install script file for more details!

QRealTime Plugin allows you to:

• Create new survey form directly from GIS layers in QGIS
• Synchronise data from ODK Aggregate, KoboToobox, and ODK Central servers
• Import data from server

• QGIS installed

Installation steps:

1. Open Plugin Manager and search for QRealTime plugin and install it.
2. And restart QGIS so that changes in environment take effect.

From the main menu choose Plugins –> QRealTime –> QRealTime Setting
Here you have three tabs one for Aggregate, KoboToolBox, and Central Choose one of the tabs and Enter url (required).
For Kobo server url can be:
https://kobo.humanitarianresponse.info/ or https://kf.kobotoolbox.org/ for humanitarian and researcher account respectively
Other fields are optional.
You can create a free account in KoboToolbox here
You can set up ODK Central here