Skip to main content

('A beets plugin for creating and exporting songs that match your running session.',)

Project description

Build Status Coverage Status PyPi PyPI pyversions MIT license

Going Running (Beets Plugin)

The beets-goingrunning is a beets plugin for obsessive-compulsive music geek runners. It lets you configure different training activities by filtering songs based on their tag attributes (bpm, length, mood, loudness, etc), generates a list of songs for that specific training and copies those songs to your player device.

Have you ever tried to beat your PR and have good old Bob singing about ganja in the background? It doesn’t really work. Or don't you know how those recovery session end up with the Crüe kickstarting your heart? You'll be up in your Zone 4 in no time.

The fact is that it is very difficult and time consuming to compile an appropriate playlist for a specific training session. This plugin tries to help runners with this by allowing them to use their own library.

Introduction

To use this plugin at its best and to benefit the most from your library, you will want to make sure that your songs have the most possible information on rhythm, moods, loudness, etc.

Without going into much detail the most fundamental information you will want to harvest is bpm. Normally, when you run a fast pace training you will keep your pace (the number of times your feet hit the ground in a minute) around 170-180. If you are listening to songs with the same rhythm it helps a lot. If your library has many songs without the bpm information (check with beet ls bpm:0) you will not be able to use those songs. So, you should consider updating them. There are many tools you can use:

  1. Use the built-in acousticbrainz plugin to fetch the bpm plus many other information about your songs. This is your starting point. It is as easy as beet cousticbrainz and it will do the rest. This tool is based on an on-line database so it will be able to fetch only what has been submitted by someone else. If you have many "uncommon" songs you will need to integrate it with other tools. (My library was still 30% uncovered after a full scan.)

  2. Use the bpmanalyser plugin. This will scan your songs and calculate the tempo (bpm) value for them. If you have a big collection it might take a while, but since this tool does not use an on-line database, you can potentially end up with 100% coverage. This plugin will only give you bpm info.

  3. Essentia extractors. The Acoustic Brainz (AB) project is based partly on these low and high level extractors. There is currently a highly under-development project xtractor plugin which aims to bring your library to 100% coverage. However, for the time being there are no distributable static extractors, so wou will have to compile your own extractors.

There are many other ways and tools we could list here but I think you got the point...

Installation

The plugin can be installed via:

$ pip install beets-goingrunning

Activate the plugin in your configuration file by adding goingrunning to the plugins section:

plugins:
    - goingrunning

Check if plugin is loaded with beet version. It should list 'goingrunning' amongst the loaded plugins.

Usage

Invoke the plugin as:

$ beet goingrunning training [options] [QUERY...]

or with the shorthand alias run:

$ beet run training [options] [QUERY...]

The following command line options are available:

--list [-l]: List all the configured trainings. With beet goingrunning --list you will be presented the list of the trainings you have configured in your configuration file.

--count [-c]: Count the number of songs available for a specific training. With beet goingrunning longrun --count you can see how many of your songs will fit the specifications for the longrun training.

--dry-run [-r]: Only display what would be done without actually making changes to the file system. The plugin will run without clearing the destination and without copying any files.

--quiet [-q]: Do not display any output from the command.

--version [-v]: Display the version number of the plugin. Useful when you need to report some issue and you have to state the version of the plugin you are using.

Configuration

All your configuration will need to be created under the key goingrunning. There are three concepts you need to know to configure the plugin: targets, trainings and flavours. They are explained in detail below.

Targets

Targets are named destinations on your file system to which you will be copying your songs. The targets key allows you to define multiple targets so that under a specific training session you will only need to refer to it with the target key.

The configuration of the target names MPD1 will look like this:

goingrunning:
    targets:
      MPD1:
        device_root: /media/MPD1/
        device_path: MUSIC/AUTO/
        clean_target: yes
        delete_from_device:
          - LIBRARY.DAT

The key device_root indicates where your operating system mounts the device. The key device_path indicates the folder inside the device to which your audio files will be copied. In the above example the final destination is /media/MPD1/MUSIC/AUTO/. It is assumed that the folder indicated in the device_path key exists. If it doesn't the plugin will exit with a warning.

The key clean_target, when set to yes, instructs the plugin to clean the device_path folder before copying the new songs to the device. This will remove all audio songs and playlists found in that folder.

Some devices might have library files or other data files which need to be deleted in order for the device to reindex the new songs. These files can be added to the delete_from_device key. The files listed here are relative to the device_root directive.

Trainings

Trainings are the central concept behind the plugin. When you are "going running" you will already have in mind the type of training you will be doing. This configuration section allows you to preconfigure filters that will allow you to launch a beet run 10K command whilst you are tying your shoelaces and be out of the house as quick as possible. In fact, the trainings section is there for you to be able to preconfigure these trainings.

The configuration of a hypothetical 10K training might look like this:

goingrunning:
  trainings:
    10K: 
      query:
        bpm: 160..180
        mood_aggressive: 0.6..
        ^genre: Reggae
      ordering:
        bpm: 100
        average_loudness: 50
      use_flavours: []
      duration: 60
      target: MPD1

query

The keys under the query section are exactly the same ones that you use when you are using beets for any other operation. Whatever is described in the beets query documentation applies here with two restriction: you must query specific fields in the form of field: value and (for now) regular expressions are not supported.

ordering

Your songs are ordered based on a scoring system. What you indicate under the ordering section is the fields by which the songs will be ordered and the weight each one of them will have on the final score. The weight can go from -100 to 100. Negative numbers indicate a reverse ordering. (...probably need more explanation?...)

use_flavours

You will find that many of the query specification that you come up with will be repeated across different trainings. To reduce repetition and at the same time to be able to combine many different recipes you can use flavours. Similarly to targets, instead of defining the queries directly on your training you can define queries in a separate section called flavours (see below) and then use the use_flavours key to indicate which flavours to use. The order in which flavours are indicated is important: the first one has the highest priority meaning that it will overwrite any keys that might be found in subsequent flavours.

duration

The duration is expressed in minutes and serves the purpose of defining the total length of the training so that the plugin can select the exact number of songs.

target

This key indicates to which target (defined in the targets section) your songs will be copied to.

the fallback training

You might also define a special fallback training:

goingrunning:
  trainings:
    fallback: 
      target: my_other_player

Any key not defined in a specific training will be looked up from the fallback training. So, if in the 10K example you were to remove the target key, it would be looked up from the fallback training and your songs would be copied to the my_other_device target.

Flavours

The flavours section serves the purpose of defining named queries. If you have 5 different high intensity trainings different in length but sharing queries about bpm, mood and loudness, you can create a single definition here, called flavour, and reuse that flavour in your different trainings with the use_flavours key.

Note: Because flavours are only used to group query elements, the query key should not be used here (like it is in trainings).

goingrunning:
  flavours:
    overthetop:
      bpm: 170..
      mood_aggressive: 0.8..
      average_loudness: 50..
    rocker:
      genre: Rock
    metallic:
      genre: Metal
  sunshine:
      genre: Reggae
    60s:
      year: 1960..1969
    chillout:
      bpm: 1..120
      mood_happy: 0.5..0.99

This way, from the above flavours you might add use_flavours: [overthetop, rock, 60s] to one training and use_flavours: [overthetop, metallic] to another so they will share the same overthetop intensity definition whilst having different genre preferences. Similarly, your recovery session might use use_flavours: [chillout, sunshine].

Using a separate configuration file

In my experience the configuration section can grow quite long depending on your needs, so I find it useful to keep my goingrunning specific configuration in a separate file and from the main configuration file include it like this:

include: 
    - plg_goingrunning.yaml

Examples

Show all the configured trainings:

$ beet goingrunning --list

Check what would be done for the 10K training:

$ beet goingrunning 10K --dry-run

Let's go! Copy your songs to your device based on the 10K training and using the plugin shorthand:

$ beet run 10K

Do the same as above but today you feel Ska:

$ beet run 10K genre:ska

Issues

If something is not working as expected please use the Issue tracker. If the documentation is not clear please use the Issue tracker. If you have a feature request please use the Issue tracker. In any other situation please use the Issue tracker.

Roadmap

Please check the ROADMAP file. If there is a feature you would like to see but which is not planned, create a feature request in the Issue tracker.

Final Remarks

Enjoy!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for beets-goingrunning, version 1.2.0
Filename, size File type Python version Upload date Hashes
Filename, size beets_goingrunning-1.2.0-py3-none-any.whl (20.3 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size beets-goingrunning-1.2.0.tar.gz (17.9 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page