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uses math to select wallpapers from Reddit

Project description

Redrum - Reddit Wallpaper Downloader and Ranker

.. image:: screenshot.png

Redrum is a Reddit wallpaper downloader which scores wallpapers and selects the best based on resolution, aspect ratio, and number of views. It remembers which wallpapers were selected previously so you never see the same image twice.

Install the systemd units to run the script every two hours.


1. Install through pip

.. code:: bash

pip3 install redrum

2. Edit ``~/.config/redrum.ini`` with your preferred options.

3. Install service for automatic wallpaper changing (optional)

.. code:: bash

# copy service files
cp -u systemd/* ~/.config/systemd/user/

# enable and start systemd timer
systemctl --user enable redrum.timer
systemctl --user start redrum.timer

# the service can be triggered manually as well
systemctl --user start redrum

note: If using a python3 virtualenv, change ``ExecStart`` in ``redrum.service`` to ``/path/to/venv/bin/redrum``


If redrum can't find a config file, it will create one in ``~/.config/redrum.ini`` automatically. You should update this file with your screen resolution and preferred subreddits, then run redrum again.

.. code:: bash

>>> redrum
No config found at /home/evan/.config/redrum.ini. Creating...
Update config with your preferred options and run redrum again.

>>> redrum
No previous score cache found at /home/evan/.cache/redrum_cache.json.
Indexing page #0 from subreddit winterporn
Indexing page #1 from subreddit winterporn
Indexing page #2 from subreddit winterporn
Selected (EarthPorn) with score 5.21729920261845e-05 out of 5971 images
The probability of selecting this image was 0.009851421028579594
Applying wallpaper

Scoring algorithm

Images are scored in three steps as follows:

1. Three input scores are calculated for each image in the cache

.. math::

pixel_score = [total image pixels] / [total screen pixels]

ratio_score = [image x-y ratio] / [screen x-y ratio]
[screen x-y ratio] / [image x-y ratio]
(whichever is less than 1)

views_score = [# views of this image] / [highest # views of all images]

2. Each input score is run through a sigmoid function, in this case, the `logistic function`_. This helps to provide stronger differentiation between a good and a bad image than could be afforded with a linear method. The logistic function is defined by its ``midpoint`` and the slope at the midpoint, ``k``.

.. image::
.. figure::
:width: 400px

Logistic function with ``midpoint=0``, ``k=1``

In `redrum` the logistic function has been normalized such that f(0) = 1.
.. math::

ratio_logistic_score = (1 + pow(math.e, -ratio_k * (1 - ratio_off)))/(1 + pow(math.e, -ratio_k * (ratio_score - ratio_cutoff)))
views_logistic_score = (1 + pow(math.e, -ratio_k * (1 - ratio_off)))/(1 + pow(math.e, -views_k * (views_score - views_cutoff)))
pixel_logistic_score = (1 + pow(math.e, -ratio_k * (1 - ratio_off)))/(1 + pow(math.e, -pixel_k * (pixel_score - pixel_cutoff)))

``pixel_score``, ``ratio_score``, and ``views_score`` each have their own ``midpoint`` and ``k``, which can be set in ``~/.config/redrum.ini``

.. _logistic function:

3. The three logistic scores are then multiplied to calculate an image's ``final_score``. This score is used to do a random weighted select of all images in the cache. An image with a ``final_score`` that is twice the score of another image is twice as likely to be selected during the random selection.

Tuning the scoring algorithm
You can adjust the scoring algorithm if you aren't satisfied with the images being selected. ``redrum_tune`` allows you to quickly adjust the ``midpoint`` and ``k`` of the logarithmic function and view its effects on the ``final_score``. You need to run ``pip install redrum[tune]`` to install the extra dependencies (matplotlib, numpy).

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