Update beets to 1.4.7

Also updates:
- colorama-0.4.1
- jellyfish-0.6.1
- munkres-1.0.12
- musicbrainzngs-0.6
- mutagen-1.41.1
- pyyaml-3.13
- six-1.12.0
- unidecode-1.0.23

libs/beetsplug/absubmit.py

@@ -0,0 +1,167 @@
+# -*- coding: utf-8 -*-
+# This file is part of beets.
+# Copyright 2016, Pieter Mulder.
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files (the
+# "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+#
+# The above copyright notice and this permission notice shall be
+# included in all copies or substantial portions of the Software.
+
+"""Calculate acoustic information and submit to AcousticBrainz.
+"""
+
+from __future__ import division, absolute_import, print_function
+
+import errno
+import hashlib
+import json
+import os
+import subprocess
+import tempfile
+
+from distutils.spawn import find_executable
+import requests
+
+from beets import plugins
+from beets import util
+from beets import ui
+
+
+class ABSubmitError(Exception):
+    """Raised when failing to analyse file with extractor."""
+
+
+def call(args):
+    """Execute the command and return its output.
+
+    Raise a AnalysisABSubmitError on failure.
+    """
+    try:
+        return util.command_output(args)
+    except subprocess.CalledProcessError as e:
+        raise ABSubmitError(
+            u'{0} exited with status {1}'.format(args[0], e.returncode)
+        )
+
+
+class AcousticBrainzSubmitPlugin(plugins.BeetsPlugin):
+
+    def __init__(self):
+        super(AcousticBrainzSubmitPlugin, self).__init__()
+
+        self.config.add({'extractor': u''})
+
+        self.extractor = self.config['extractor'].as_str()
+        if self.extractor:
+            self.extractor = util.normpath(self.extractor)
+            # Expicit path to extractor
+            if not os.path.isfile(self.extractor):
+                raise ui.UserError(
+                    u'Extractor command does not exist: {0}.'.
+                    format(self.extractor)
+                )
+        else:
+            # Implicit path to extractor, search for it in path
+            self.extractor = 'streaming_extractor_music'
+            try:
+                call([self.extractor])
+            except OSError:
+                raise ui.UserError(
+                    u'No extractor command found: please install the '
+                    u'extractor binary from http://acousticbrainz.org/download'
+                )
+            except ABSubmitError:
+                # Extractor found, will exit with an error if not called with
+                # the correct amount of arguments.
+                pass
+
+            # Get the executable location on the system, which we need
+            # to calculate the SHA-1 hash.
+            self.extractor = find_executable(self.extractor)
+
+        # Calculate extractor hash.
+        self.extractor_sha = hashlib.sha1()
+        with open(self.extractor, 'rb') as extractor:
+            self.extractor_sha.update(extractor.read())
+        self.extractor_sha = self.extractor_sha.hexdigest()
+
+    base_url = 'https://acousticbrainz.org/api/v1/{mbid}/low-level'
+
+    def commands(self):
+        cmd = ui.Subcommand(
+            'absubmit',
+            help=u'calculate and submit AcousticBrainz analysis'
+        )
+        cmd.func = self.command
+        return [cmd]
+
+    def command(self, lib, opts, args):
+        # Get items from arguments
+        items = lib.items(ui.decargs(args))
+        for item in items:
+            analysis = self._get_analysis(item)
+            if analysis:
+                self._submit_data(item, analysis)
+
+    def _get_analysis(self, item):
+        mbid = item['mb_trackid']
+        # If file has no mbid skip it.
+        if not mbid:
+            self._log.info(u'Not analysing {}, missing '
+                           u'musicbrainz track id.', item)
+            return None
+
+        # Temporary file to save extractor output to, extractor only works
+        # if an output file is given. Here we use a temporary file to copy
+        # the data into a python object and then remove the file from the
+        # system.
+        tmp_file, filename = tempfile.mkstemp(suffix='.json')
+        try:
+            # Close the file, so the extractor can overwrite it.
+            os.close(tmp_file)
+            try:
+                call([self.extractor, util.syspath(item.path), filename])
+            except ABSubmitError as e:
+                self._log.warning(
+                    u'Failed to analyse {item} for AcousticBrainz: {error}',
+                    item=item, error=e
+                )
+                return None
+            with open(filename, 'rb') as tmp_file:
+                analysis = json.load(tmp_file)
+            # Add the hash to the output.
+            analysis['metadata']['version']['essentia_build_sha'] = \
+                self.extractor_sha
+            return analysis
+        finally:
+            try:
+                os.remove(filename)
+            except OSError as e:
+                # ENOENT means file does not exist, just ignore this error.
+                if e.errno != errno.ENOENT:
+                    raise
+
+    def _submit_data(self, item, data):
+        mbid = item['mb_trackid']
+        headers = {'Content-Type': 'application/json'}
+        response = requests.post(self.base_url.format(mbid=mbid),
+                                 json=data, headers=headers)
+        # Test that request was successful and raise an error on failure.
+        if response.status_code != 200:
+            try:
+                message = response.json()['message']
+            except (ValueError, KeyError) as e:
+                message = u'unable to get error message: {}'.format(e)
+            self._log.error(
+                u'Failed to submit AcousticBrainz analysis of {item}: '
+                u'{message}).', item=item, message=message
+            )
+        else:
+            self._log.debug(u'Successfully submitted AcousticBrainz analysis '
+                            u'for {}.', item)