blissify/mpd/client.py
Phyks (Lucas Verney) 345383e3a5 Bump Bliss version
Now using 3 tempo values, updated Bliss version.

_Note_: You should rebuild your database after upgrading to this commit.
Next major breaking changes will be handled by dedicated update scripts
to avoid having to rebuild the entire database.
2016-07-24 15:31:18 +02:00

281 lines
12 KiB
Python
Executable File

#!/usr/bin/env python3
"""
This is a client for MPD to generate a random playlist starting from the last
song of the current playlist and iterating using values computed using Bliss.
MPD connection settings are taken from environment variables, following MPD_HOST
and MPD_PORT scheme described in `mpc` man.
You can pass an integer argument to the script to change the length of the
generated playlist (default is to add 20 songs).
"""
import logging
import math
import os
import random
import sqlite3
import socket
import sys
import mpd
class PersistentMPDClient(mpd.MPDClient):
"""
From
https://github.com/schamp/PersistentMPDClient/blob/master/PersistentMPDClient.py
"""
def __init__(self, socket=None, host=None, port=None):
super().__init__()
self.socket = socket
self.host = host
self.port = port
self.do_connect()
# get list of available commands from client
self.command_list = self.commands()
# commands not to intercept
self.command_blacklist = ['ping']
# wrap all valid MPDClient functions
# in a ping-connection-retry wrapper
for cmd in self.command_list:
if cmd not in self.command_blacklist:
if hasattr(super(PersistentMPDClient, self), cmd):
super_fun = super(PersistentMPDClient, self).__getattribute__(cmd)
new_fun = self.try_cmd(super_fun)
setattr(self, cmd, new_fun)
# create a wrapper for a function (such as an MPDClient
# member function) that will verify a connection (and
# reconnect if necessary) before executing that function.
# functions wrapped in this way should always succeed
# (if the server is up)
# we ping first because we don't want to retry the same
# function if there's a failure, we want to use the noop
# to check connectivity
def try_cmd(self, cmd_fun):
def fun(*pargs, **kwargs):
try:
self.ping()
except (mpd.ConnectionError, OSError):
self.do_connect()
return cmd_fun(*pargs, **kwargs)
return fun
# needs a name that does not collide with parent connect() function
def do_connect(self):
try:
try:
self.disconnect()
# if it's a TCP connection, we'll get a socket error
# if we try to disconnect when the connection is lost
except mpd.ConnectionError:
pass
# if it's a socket connection, we'll get a BrokenPipeError
# if we try to disconnect when the connection is lost
# but we have to retry the disconnect, because we'll get
# an "Already connected" error if we don't.
# the second one should succeed.
except BrokenPipeError:
try:
self.disconnect()
except:
print("Second disconnect failed, yikes.")
if self.socket:
self.connect(self.socket, None)
else:
self.connect(self.host, self.port)
except socket.error:
print("Connection refused.")
logging.basicConfig(level=logging.INFO)
_QUEUE_LENGTH = 20
_DISTANCE_THRESHOLD = 4.0
_SIMILARITY_THRESHOLD = 0.95
if "XDG_DATA_HOME" in os.environ:
_BLISSIFY_DATA_HOME = os.path.expandvars("$XDG_DATA_HOME/blissify")
else:
_BLISSIFY_DATA_HOME = os.path.expanduser("~/.local/share/blissify")
def main(queue_length):
# Get MPD connection settings
try:
mpd_host = os.environ["MPD_HOST"]
mpd_password, mpd_host = mpd_host.split("@")
except KeyError:
mpd_host = "localhost"
mpd_password = None
try:
mpd_port = os.environ["MPD_PORT"]
except KeyError:
mpd_port = 6600
# Connect to MPD²
client = PersistentMPDClient(host=mpd_host, port=mpd_port)
if mpd_password is not None:
client.password(mpd_password)
# Connect to db
db_path = os.path.join(_BLISSIFY_DATA_HOME, "db.sqlite3")
logging.debug("Using DB path: %s." % (db_path,))
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
conn.execute('pragma foreign_keys=ON')
cur = conn.cursor()
# Ensure random is not enabled
status = client.status()
if int(status["random"]) != 0:
logging.warning("Random mode is enabled. Are you sure you want it?")
# Take the last song from current playlist and iterate from it
playlist = client.playlist()
if len(playlist) > 0:
current_song = playlist[-1].replace("file:", "").strip()
# If current playlist is empty
else:
# Add a random song to start with
all_songs = [x["file"] for x in client.listall() if "file" in x]
current_song = random.choice(all_songs)
client.add(current_song)
logging.info("Currently played song is %s." % (current_song,))
# Get current song coordinates
cur.execute("SELECT id, tempo1, tempo2, tempo3, amplitude, frequency, attack, filename FROM songs WHERE filename=?", (current_song,))
current_song_coords = cur.fetchone()
if current_song_coords is None:
logging.warning("Current song %s is not in db. You should update the db." %
(current_song,))
client.close()
client.disconnect()
sys.exit(1)
for i in range(queue_length):
# Get cached distances from db
cur.execute(
"SELECT id, filename, distance, similarity, tempo1, tempo2, tempo3, amplitude, frequency, attack FROM (SELECT s2.id AS id, s2.filename AS filename, s2.tempo1 AS tempo1, s2.tempo2 AS tempo2, s2.tempo3 AS tempo3, s2.amplitude AS amplitude, s2.frequency AS frequency, s2.attack AS attack, distances.distance AS distance, distances.similarity AS similarity FROM distances INNER JOIN songs AS s1 ON s1.id=distances.song1 INNER JOIN songs AS s2 on s2.id=distances.song2 WHERE s1.filename=? UNION SELECT s1.id as id, s1.filename AS filename, s1.tempo1 AS tempo1, s1.tempo2 AS tempo2, s1.tempo3 AS tempo3, s1.amplitude AS amplitude, s1.frequency AS frequency, s1.attack AS attack, distances.distance as distance, distances.similarity AS similarity FROM distances INNER JOIN songs AS s1 ON s1.id=distances.song1 INNER JOIN songs AS s2 on s2.id=distances.song2 WHERE s2.filename=?) ORDER BY distance ASC",
(current_song_coords["filename"], current_song_coords["filename"]))
cached_distances = [row
for row in cur.fetchall()
if ("file: %s" % (row["filename"],)) not in client.playlist()]
cached_distances_songs = [i["filename"] for i in cached_distances]
# If distance to closest song is ok, just add the song
if len(cached_distances) > 0:
if(cached_distances[0]["distance"] < _DISTANCE_THRESHOLD and
cached_distances[0]["similarity"] > _SIMILARITY_THRESHOLD):
# Push it on the queue
client.add(cached_distances[0]["filename"])
# Continue using latest pushed song as current song
logging.info("Using cached distance. Found %s. Distance is (%f, %f)." %
(cached_distances[0]["filename"],
cached_distances[0]["distance"],
cached_distances[0]["similarity"]))
current_song_coords = cached_distances[0]
continue
# Get all other songs coordinates
closest_song = None
cur.execute("SELECT id, tempo1, tempo2, tempo3, amplitude, frequency, attack, filename FROM songs")
for tmp_song_data in cur.fetchall():
if(tmp_song_data["filename"] == current_song_coords["filename"] or
tmp_song_data["filename"] in cached_distances_songs or
("file: %s" % (tmp_song_data["filename"],)) in client.playlist()):
# Skip current song and already processed songs
logging.debug("Skipping %s." % (tmp_song_data["filename"]))
continue
# Compute distance
distance = math.sqrt(
(current_song_coords["tempo1"] - tmp_song_data["tempo1"])**2 +
(current_song_coords["tempo2"] - tmp_song_data["tempo2"])**2 +
(current_song_coords["tempo3"] - tmp_song_data["tempo3"])**2 +
(current_song_coords["amplitude"] - tmp_song_data["amplitude"])**2 +
(current_song_coords["frequency"] - tmp_song_data["frequency"])**2 +
(current_song_coords["attack"] - tmp_song_data["attack"])**2
)
similarity = (
(current_song_coords["tempo1"] * tmp_song_data["tempo1"] +
current_song_coords["tempo2"] * tmp_song_data["tempo2"] +
current_song_coords["tempo3"] * tmp_song_data["tempo3"] +
current_song_coords["amplitude"] * tmp_song_data["amplitude"] +
current_song_coords["frequency"] * tmp_song_data["frequency"] +
current_song_coords["attack"] * tmp_song_data["attack"]) /
(
math.sqrt(
current_song_coords["tempo1"]**2 +
current_song_coords["tempo2"]**2 +
current_song_coords["tempo3"]**2 +
current_song_coords["amplitude"]**2 +
current_song_coords["frequency"]**2 +
current_song_coords["attack"]**2) *
math.sqrt(
tmp_song_data["tempo1"]**2 +
tmp_song_data["tempo2"]**2 +
tmp_song_data["tempo3"]**2 +
tmp_song_data["amplitude"]**2 +
tmp_song_data["frequency"]**2 +
tmp_song_data["attack"]**2)
)
)
logging.debug("Distance between %s and %s is (%f, %f)." %
(current_song_coords["filename"],
tmp_song_data["filename"], distance, similarity))
# Store distance in db cache
try:
logging.debug("Storing distance in database.")
conn.execute(
"INSERT INTO distances(song1, song2, distance, similarity) VALUES(?, ?, ?, ?)",
(current_song_coords["id"], tmp_song_data["id"], distance,
similarity))
conn.commit()
except sqlite3.IntegrityError:
logging.warning("Unable to insert distance in database.")
conn.rollback()
# Update the closest song
# TODO: Find a better heuristic?
if closest_song is None or distance < closest_song[1]:
closest_song = (tmp_song_data, distance, similarity)
# If distance is ok, break from the loop
if(distance < _DISTANCE_THRESHOLD and
similarity > _SIMILARITY_THRESHOLD):
break
# If a close enough song is found
if(distance < _DISTANCE_THRESHOLD and
similarity > _SIMILARITY_THRESHOLD):
# Push it on the queue
client.add(tmp_song_data["filename"])
# Continue using latest pushed song as current song
logging.info("Found a close song: %s. Distance is (%f, %f)." %
(tmp_song_data["filename"], distance, similarity))
current_song_coords = tmp_song_data
continue
# If no song found, take the closest one
else:
logging.info("No close enough song found. Using %s. Distance is (%f, %f)." %
(closest_song[0]["filename"], closest_song[1],
closest_song[2]))
current_song_coords = closest_song[0]
client.add(closest_song[0]["filename"])
continue
conn.close()
client.close()
client.disconnect()
if __name__ == "__main__":
queue_length = _QUEUE_LENGTH
if len(sys.argv) > 1:
try:
queue_length = int(sys.argv[1])
except ValueError:
sys.exit("Usage: %s [PLAYLIST_LENGTH]" % (sys.argv[0],))
main(queue_length)