beets-goingrunning 1.1.0

A beets plugin for creating and exporting songs matching your running session.

Going Running (beets plugin)

A beets plugin for insane obsessive-compulsive music geeks.

The beets-goingrunning plugin is for obsessive-compulsive music geek runners. It lets you configure different training activities by filtering songs based on their speed(bpm) and duration (or any other queries) and attempts to generate a list of songs for that training.

Introduction

To use this plugin at its best and to benefit the most from your library, you will need to make sure that you have bpm information on all of your songs. Since this plugin uses the bpm information to select songs, the songs with bpm=0 will be ignored (check with beet ls bpm:0). If you have many you should update them. There are two ways:

1. Use the built-in acousticbrainz plugin to fetch the bpm information for your songs. It does a lot for well know songs but 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 you can potentially end up with 100% coverage.

The following explains how to use the beets-goingrunning plugin. If something is not clear please use the Issue tracker. Also, if there is a feature not present, please check in the roadmap if it is planned. If not, create a feature request in the Issue tracker.

Installation

The plugin can be installed via:

$ pip install beets-goingrunning

Activate the plugin in your configuration file:

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...]

The following switches are available:

--list [-l]: List all the configured trainings with their attributes. With this switch you do not enter the name of the training, just beet goingrunning --list

--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 there are in your library that fit your specs.

--dry-run [-r]: Only display what would be done without actually making changes to the file system.

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

Configuration

Your default configuration is:

goingrunning:
    query:
      bpm: 90..150
      length: 90..240
    ordering:
      year+: 100
      bpm+: 100
    duration: 60
    targets: []
    target: none
    clean_target: no

There are two concepts you need to know to configure the plugin: targets and trainings:

• 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 whilst the target key allows you to specify the name of your default player to which the plugin will always copy your songs (if not otherwise specified in the configuration of a specific training).

goingrunning:
    # [...]
    targets:
        my_player_1:
            device_root: /mnt/player_1
            device_path: 
        my_other_player:
            device_root: /media/player_2
            device_path: Songs
    target: my_player_1
    # [...]

• Trainings are stored named queries into your library. They have two main attributes (query and ordering) by which the plugin will decide which songs to chose and in what order to put them. The duration attribute (expressed in minutes) is used for limiting the number of songs selected. The keys under query and ordering are the same as you would use them on the command line. A training can optionally declare the target and other attributes to override those present at root level (directly under the goingrunning key).

A common configuration section will look something like this:

goingrunning:
    # [...]
    target: my_player_1
    targets:
        my_player_1:
            device_root: /mnt/player_1
            device_path: 
        my_other_player:
            device_root: /media/player_2
            device_path: Songs
            clean_target: yes
            delete_from_device:
                - STDBDATA.DAT
                - STDBSTR.DAT
    trainings:
        longrun: 
            query:
              bpm: 90..150
              length: 90..240
            duration: 90
        10K: 
            query:
              bpm: 150..180
              length: 120..240
            duration: 90
            target: my_other_player
    # [...]

Once you have configured your targets and created your trainings, connect your device to your pc and launch:

$ beet goingrunning longrun

and the songs matching that training will be copied to it.

The path where the songs will be copied is given by the device_root + device_path. This means that for my_player_1 the songs will be copied to the /mnt/player_1/ folder whilst for the my_player_2 target they will be copied to the /media/player_2/Songs/ folder. The clean_target attribute will instruct the plugin to clean these folders before copying the new songs. The option delete_from_device allows you to list additional files that need to be removed. The files listed here are relative to the device_root directive.

For now, within a training the selection of the songs is completely random and no ordering is applied. One of the future plans is to allow you to be more in control of the song selection and song ordering. You can of course use the usual query syntax to fine tune your selection (see examples below) but the ordering will still be casual.

All the configuration options are looked up in the entire configuration tree. This means that whilst the songs for the the 10K training will be copied to the my_other_player target, the longrun training (which does not declare this attribute), will use that on the root level: my_player_1. This holds for all attributes.

The clean_target attribute, when set to yes will ensure that all songs are removed from the target before copying the new songs.

Examples:

Show all the configured trainings:

$ beet goingrunning --list

Check what the longrun training would do:

$ beet goingrunning longrun --dry-run

Now do it! Copy your songs to your target based on the longrun training:

$ beet goingrunning longrun

Do the same as above but today you feel reggae:

$ beet goingrunning longrun genre:Reggae

Final Remarks:

• give feedback
• contribute
• enjoy!