Chris Shyi
7 years ago
committed by
GitHub
GitHub
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19 changed files with 809 additions and 424 deletions
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6.gitignore
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2musicvis/settings.py
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8recreate-db.txt
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2requirements.txt
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0sample-track-obj.py
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5spotifyvis/admin.py
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85spotifyvis/migrations/0001_initial.py
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78spotifyvis/models.py
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8spotifyvis/static/spotifyvis/css/dark_bg.css
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137spotifyvis/static/spotifyvis/scripts/genre_graph.js
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0spotifyvis/static/spotifyvis/scripts/user_data.js
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141spotifyvis/templates/spotifyvis/audio_features.html
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44spotifyvis/templates/spotifyvis/genre_graph.html
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13spotifyvis/templates/spotifyvis/index.html
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17spotifyvis/templates/spotifyvis/logged_in.html
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9spotifyvis/templates/spotifyvis/user_data.html
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20spotifyvis/urls.py
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465spotifyvis/utils.py
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167spotifyvis/views.py
@ -0,0 +1,8 @@ |
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# https://stackoverflow.com/a/34576062/8811872 |
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sudo su postgres |
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psql |
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drop database spotifyvis; |
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create database spotifyvis with owner django; |
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\q |
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exit |
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@ -1,3 +1,8 @@ |
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from django.contrib import admin |
from django.contrib import admin |
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from .models import Track, Artist, AudioFeatures, User |
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# Register your models here. |
# Register your models here. |
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admin.site.register(Track) |
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admin.site.register(Artist) |
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admin.site.register(AudioFeatures) |
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admin.site.register(User) |
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@ -1,85 +0,0 @@ |
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# Generated by Django 2.0.5 on 2018-06-03 23:01 |
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from django.db import migrations, models |
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import django.db.models.deletion |
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class Migration(migrations.Migration): |
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initial = True |
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dependencies = [ |
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] |
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operations = [ |
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migrations.CreateModel( |
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name='Artist', |
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fields=[ |
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('artist_id', models.CharField(max_length=30, primary_key=True, serialize=False)), |
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('name', models.CharField(max_length=50, unique=True)), |
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('genre', models.CharField(max_length=20)), |
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], |
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options={ |
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'verbose_name': 'Artist', |
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'verbose_name_plural': 'Artists', |
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}, |
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), |
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migrations.CreateModel( |
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name='Track', |
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fields=[ |
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('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), |
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('track_id', models.CharField(max_length=30)), |
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('year', models.PositiveSmallIntegerField()), |
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('popularity', models.DecimalField(decimal_places=2, max_digits=2)), |
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('runtime', models.PositiveSmallIntegerField()), |
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('name', models.CharField(max_length=75)), |
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], |
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options={ |
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'verbose_name': 'Track', |
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'verbose_name_plural': 'Tracks', |
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}, |
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), |
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migrations.CreateModel( |
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name='User', |
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fields=[ |
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('user_id', models.CharField(max_length=30, primary_key=True, serialize=False)), |
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('username', models.CharField(max_length=30)), |
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], |
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options={ |
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'verbose_name': 'User', |
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'verbose_name_plural': 'Users', |
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}, |
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), |
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migrations.CreateModel( |
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name='AudioFeatures', |
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fields=[ |
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('track', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, primary_key=True, serialize=False, to='spotifyvis.Track')), |
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('danceability', models.DecimalField(decimal_places=2, max_digits=2)), |
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('energy', models.DecimalField(decimal_places=2, max_digits=2)), |
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('loudness', models.DecimalField(decimal_places=2, max_digits=2)), |
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('speechiness', models.DecimalField(decimal_places=2, max_digits=2)), |
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('acousticness', models.DecimalField(decimal_places=2, max_digits=2)), |
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('instrumentalness', models.DecimalField(decimal_places=2, max_digits=2)), |
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('valence', models.DecimalField(decimal_places=2, max_digits=2)), |
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('tempo', models.DecimalField(decimal_places=2, max_digits=2)), |
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], |
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options={ |
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'verbose_name': 'AudioFeatures', |
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'verbose_name_plural': 'AudioFeatures', |
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}, |
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), |
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migrations.AddField( |
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model_name='track', |
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name='artist', |
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field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='spotifyvis.Artist'), |
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), |
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migrations.AddField( |
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model_name='track', |
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name='users', |
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field=models.ManyToManyField(to='spotifyvis.User'), |
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), |
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migrations.AlterUniqueTogether( |
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name='track', |
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unique_together={('track_id', 'artist')}, |
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), |
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] |
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@ -0,0 +1,8 @@ |
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body { |
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background-color: #1e1e1e; |
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} |
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h1,p { |
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color: grey; |
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} |
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@ -0,0 +1,137 @@ |
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function create_genre_graph(data) { |
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// convert strings to nums {{{ //
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data.forEach(function(d) { |
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d.num_songs = +d.num_songs; |
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console.log(d.genre, d.num_songs); |
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var artist_names = Object.keys(d.artists); |
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artist_names.forEach(function(e) { |
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d.artists[e] = +d.artists[e]; |
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console.log(e, d.artists[e]); |
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//console.log(e, d.artists[e], d.artists[e] + 1);
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}); |
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}); |
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// }}} convert strings to nums //
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// domains {{{ //
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data.sort(function(a, b) { |
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return b.num_songs - a.num_songs; |
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}); |
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x.domain(data.map(function(d) { |
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return d.genre; |
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})); |
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//y.domain([0, d3.max(data, function(d) { return d.num_songs; }) * 1.25]).nice();
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y.domain([0, d3.max(data, function(d) { |
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return d.num_songs; |
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})]).nice(); |
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// }}} domains //
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// setup bar colors {{{ //
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var max_artists = d3.max(data, function(d) { |
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return Object.keys(d.artists).length; |
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}); |
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var z = d3.scaleOrdinal().range(randomColor({ |
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count: max_artists, |
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luminosity: 'light', |
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})); |
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// }}} setup bar colors //
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for (var genre_dict of data) { |
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// process artist breakdown {{{ //
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var keys = Object.keys(genre_dict.artists); |
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var stack = d3.stack() |
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//.order(d3.stackOrderAscending)
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.order(d3.stackOrderDescending) |
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.keys(keys)([genre_dict.artists]) |
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//unpack the column
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.map((d, i) => { |
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return { |
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key: keys[i], |
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data: d[0] |
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} |
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}); |
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// }}} process artist breakdown //
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// add bars {{{ //
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g.append("g") |
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.selectAll("rect") |
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.data(stack) |
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.enter().append("rect") |
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.attr("x", x(genre_dict.genre)) |
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.attr("y", function(d) { |
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return y(d.data[1]); |
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}) |
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.attr("height", d => y(d.data[0]) - y(d.data[1])) |
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.attr("width", x.bandwidth()) |
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.attr('fill', (d, i) => z(i)) |
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.append('title').text(d => d.key + ': ' + (d.data[1] - d.data[0])); |
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// }}} add bars //
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// x-axis {{{ //
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g.append("g") |
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.attr("class", "axis") |
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.attr("transform", "translate(0," + height + ")") |
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.call(d3.axisBottom(x)) |
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.selectAll(".tick text") |
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.call(wrap, x.bandwidth()); |
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// }}} x-axis //
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// y-axis {{{ //
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g.append("g") |
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.attr("class", "axis") |
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.call(d3.axisLeft(y).ticks(null, "s")) |
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.append("text") |
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.attr("x", 2) |
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.attr("y", y(y.ticks().pop()) + 0.5) |
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.attr("dy", "0.32em") |
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.attr("fill", "#000") |
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.attr("font-weight", "bold") |
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.attr("text-anchor", "start") |
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.text("Songs"); |
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// }}} y-axis //
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} |
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} |
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// wrap text {{{ //
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// https://gist.github.com/guypursey/f47d8cd11a8ff24854305505dbbd8c07#file-index-html
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function wrap(text, width) { |
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text.each(function() { |
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var text = d3.select(this), |
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words = text.text().split(/\s+/).reverse(), |
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word, |
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line = [], |
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lineNumber = 0, |
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lineHeight = 1.1, // ems
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y = text.attr("y"), |
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dy = parseFloat(text.attr("dy")), |
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tspan = text.text(null).append("tspan").attr("x", 0).attr("y", y).attr("dy", dy + "em") |
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while (word = words.pop()) { |
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line.push(word) |
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tspan.text(line.join(" ")) |
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if (tspan.node().getComputedTextLength() > width) { |
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line.pop() |
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tspan.text(line.join(" ")) |
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line = [word] |
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tspan = text.append("tspan").attr("x", 0).attr("y", y).attr("dy", `${++lineNumber * lineHeight + dy}em`).text(word) |
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} |
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} |
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}) |
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} |
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// }}} wrap text //
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@ -0,0 +1,141 @@ |
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{% load static %} |
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<!DOCTYPE html> |
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<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> |
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<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> |
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<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> |
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<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> |
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<head> |
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<meta charset="utf-8"> |
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<meta http-equiv="X-UA-Compatible" content="IE=edge"> |
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<title>User Spotify Data</title> |
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<meta name="description" content=""> |
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<meta name="viewport" content="width=device-width, initial-scale=1"> |
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<style> |
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.tick { |
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font-size: 15px; |
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} |
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</style> |
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</head> |
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<body> |
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<!--[if lt IE 7]> |
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<p class="browsehappy">You are using an <strong>outdated</strong> browser. Please <a href="#">upgrade your browser</a> to improve your experience.</p> |
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<![endif]--> |
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<p>Logged in as {{ user_id }}</p> |
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<script src="https://d3js.org/d3.v5.js"></script> |
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<script type="text/javascript"> |
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/** Queries the backend for audio feature data, draws the bar chart |
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* illustrating the frequencies of values, and appends the chart to |
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* a designated parent element |
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* |
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* @param audioFeature: the name of the audio feature (string) |
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* @param intervalEndPoints: a sorted array of 5 real numbers defining the intervals (categories) of values, |
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* for example: |
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* [0, 0.25, 0.5, 0.75, 1.0] for instrumentalness would define ranges |
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* (0-0.25), (0.25-0.5), (0.5-0.75), (0.75-1.0) |
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* @param parentElem: the DOM element to append the graph to (a selector string) |
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* @return None |
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*/ |
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function drawAudioFeatGraph(audioFeature, intervalEndPoints, parentElem) { |
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let margin = {top: 20, right: 30, bottom: 30, left: 40}; |
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let width = 480 - margin.left - margin.right, |
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height = 270 - margin.top - margin.bottom; |
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let featureData = {}; |
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// Create the keys first in order |
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for (let index = 0; index < intervalEndPoints.length - 1; index++) { |
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let key = `${intervalEndPoints[index]} ~ ${intervalEndPoints[index + 1]}`; |
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featureData[key] = 0; |
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} |
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// define the vertical scaling function |
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let vScale = d3.scaleLinear().range([height, 0]); |
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d3.json(`/audio_features/${audioFeature}/{{ user_secret }}`) |
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.then(function(response) { |
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// categorize the data points |
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for (let dataPoint of response.data_points) { |
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dataPoint = parseFloat(dataPoint); |
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let index = intervalEndPoints.length - 2; |
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// find the index of the first element greater than dataPoint |
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while (dataPoint < intervalEndPoints[index]) { |
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index -= 1; |
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} |
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let key = `${intervalEndPoints[index]} ~ ${intervalEndPoints[index + 1]}`; |
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featureData[key] += 1; |
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} |
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let dataSet = Object.values(featureData); |
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let dataRanges = Object.keys(featureData); // Ranges of audio features, e.g. 0-0.25, 0.25-0.5, etc |
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let dataArr = []; |
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// turn the counts into an array of objects, e.g. {range: "0-0.25", counts: 5} |
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for (let i = 0; i < dataRanges.length; i++) { |
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dataArr.push({ |
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range: dataRanges[i], |
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counts: featureData[dataRanges[i]] |
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}); |
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} |
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vScale.domain([0, d3.max(dataSet)]).nice(); |
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let hScale = d3.scaleBand().domain(dataRanges).rangeRound([0, width]).padding(0.5); |
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let xAxis = d3.axisBottom().scale(hScale); |
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let yAxis = d3.axisLeft().scale(vScale); |
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let featureSVG = d3.select(parentElem) |
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.append('svg').attr('width', width + margin.left + margin.right) |
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.attr('height', height + margin.top + margin.bottom); |
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let featureGraph = featureSVG.append("g") |
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.attr("transform", `translate(${margin.left}, ${margin.top})`) |
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.attr("fill", "teal"); |
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featureGraph.selectAll(".bar") |
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.data(dataArr) |
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.enter().append('rect') |
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.attr('class', 'bar') |
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.attr('x', function(d) { return hScale(d.range); }) |
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.attr('y', function(d) { return vScale(d.counts); }) |
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.attr("height", function(d) { return height - vScale(d.counts); }) |
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.attr("width", hScale.bandwidth()); |
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// function(d) { return hScale(d.range); } |
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featureGraph.append('g') |
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.attr('class', 'axis') |
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.attr('transform', `translate(0, ${height})`) |
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.call(xAxis); |
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featureGraph.append('g') |
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.attr('class', 'axis') |
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.call(yAxis); |
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featureSVG.append("text") |
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.attr('x', (width / 2)) |
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.attr('y', (margin.top / 2)) |
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.attr('text-anchor', 'middle') |
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.style('font-size', '14px') |
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.text(`${capFeatureStr(audioFeature)}`); |
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}); |
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} |
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/** |
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* Returns the audio feature name string with the first letter capitalized |
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* @param audioFeature: the name of the audio feature |
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* @returns the audio feature name string with the first letter capitalized |
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*/ |
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function capFeatureStr(audioFeature) { |
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return audioFeature.charAt(0).toUpperCase() + audioFeature.slice(1); |
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} |
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drawAudioFeatGraph("instrumentalness", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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drawAudioFeatGraph("valence", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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drawAudioFeatGraph("energy", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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drawAudioFeatGraph("tempo", [40, 80, 120, 160, 200], 'body'); |
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drawAudioFeatGraph("danceability", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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drawAudioFeatGraph("acousticness", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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drawAudioFeatGraph("loudness", [-60, -45, -30, -15, 0], 'body'); |
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drawAudioFeatGraph("speechiness", [0, 0.25, 0.5, 0.75, 1.0], 'body'); |
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</script> |
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</body> |
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</html> |
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@ -0,0 +1,44 @@ |
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<!-- header {{{ --> |
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<!DOC |
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<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> |
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<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> |
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<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> |
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<!--[if gt IE 8]><!--> |
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|
{% load static %} |
||||
|
<html class="no-js"> <!--<![endif]--> |
||||
|
<head> |
||||
|
<meta charset="utf-8"> |
||||
|
<meta http-equiv="X-UA-Compatible" content="IE=edge"> |
||||
|
<title>Test DB Page</title> |
||||
|
<meta name="description" content=""> |
||||
|
<meta name="viewport" content="width=device-width, initial-scale=1"> |
||||
|
<link rel="stylesheet" href="{% static 'spotifyvis/css/dark_bg.css' %}"> |
||||
|
</head> |
||||
|
|
||||
|
<!-- }}} header --> |
||||
|
|
||||
|
<body> |
||||
|
<script src="https://d3js.org/d3.v5.min.js"></script> |
||||
|
<script src="https://cdnjs.cloudflare.com/ajax/libs/randomcolor/0.5.2/randomColor.min.js"></script> |
||||
|
{% load static %} |
||||
|
<script src="{% static "spotifyvis/scripts/genre_graph.js" %}"></script> |
||||
|
|
||||
|
<svg width="1920" height="740"></svg> |
||||
|
<script> |
||||
|
var svg = d3.select("svg"), |
||||
|
margin = {top: 20, right: 20, bottom: 30, left: 40}, |
||||
|
width = +svg.attr("width") - margin.left - margin.right, |
||||
|
height = +svg.attr("height") - margin.top - margin.bottom, |
||||
|
g = svg.append("g").attr("transform", "translate(" + margin.left + "," + margin.top + ")"); |
||||
|
var x = d3.scaleBand() |
||||
|
.rangeRound([0, width]) |
||||
|
.paddingInner(0.05) |
||||
|
.align(0.1); |
||||
|
var y = d3.scaleLinear() |
||||
|
.rangeRound([height, 0]); |
||||
|
|
||||
|
d3.json("{% url "get_genre_data" user_secret %}").then(create_genre_graph); |
||||
|
</script> |
||||
|
</body> |
||||
|
</html> |
||||
@ -0,0 +1,17 @@ |
|||||
|
<!DOCTYPE html> |
||||
|
{% load static %} |
||||
|
<html lang="en"> |
||||
|
<head> |
||||
|
<meta charset="UTF-8"> |
||||
|
<title>Logged In</title> |
||||
|
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous"> |
||||
|
<link rel="stylesheet" href="{% static 'spotifyvis/css/dark_bg.css' %}"> |
||||
|
</head> |
||||
|
<body> |
||||
|
<h1>{{ user_id }}'s Graphs</h1> |
||||
|
<a class="btn btn-primary" href="/audio_features/{{ user_secret }}" |
||||
|
role="button">Audio Features</a> |
||||
|
<a class="btn btn-primary" href="{% url "display_genre_graph" user_secret %}" |
||||
|
role="button">Genres</a> |
||||
|
</body> |
||||
|
</html> |
||||
@ -1,9 +1,19 @@ |
|||||
from django.urls import path, include |
from django.urls import path, include |
||||
from . import views |
|
||||
|
from django.conf.urls import url |
||||
|
|
||||
|
from .views import * |
||||
|
|
||||
urlpatterns = [ |
urlpatterns = [ |
||||
path('', views.index, name='index'), |
|
||||
path('login', views.login, name='login'), |
|
||||
path('callback', views.callback, name='callback'), |
|
||||
path('user_data', views.user_data, name='user_data'), |
|
||||
|
path('', index, name='index'), |
||||
|
path('login', login, name='login'), |
||||
|
path('callback', callback, name='callback'), |
||||
|
path('user_data', user_data, name='user_data'), |
||||
|
path('admin_graphs', admin_graphs, name='admin_graphs'), |
||||
|
path('user_artists/<str:user_id>', get_artist_data, name='get_artist_data'), |
||||
|
path('api/user_genres/<str:user_secret>', get_genre_data, name='get_genre_data'), |
||||
|
path('graphs/genre/<str:client_secret>', display_genre_graph, |
||||
|
name='display_genre_graph'), |
||||
|
path('audio_features/<str:client_secret>', audio_features, name='audio_features'), |
||||
|
path('audio_features/<str:audio_feature>/<str:client_secret>', |
||||
|
get_audio_feature_data, name='get_audio_feature_data'), |
||||
] |
] |
||||
@ -1,300 +1,281 @@ |
|||||
|
# imports {{{ # |
||||
import requests |
import requests |
||||
import math |
import math |
||||
import pprint |
import pprint |
||||
from .models import Artist, User, Track, AudioFeatures |
|
||||
|
|
||||
|
from .models import * |
||||
|
from django.db.models import Count, Q, F |
||||
|
from django.http import JsonResponse |
||||
|
from django.core import serializers |
||||
|
import json |
||||
|
|
||||
|
# }}} imports # |
||||
|
|
||||
|
USER_TRACKS_LIMIT = 50 |
||||
|
ARTIST_LIMIT = 50 |
||||
|
FEATURES_LIMIT = 100 |
||||
|
# ARTIST_LIMIT = 25 |
||||
|
# FEATURES_LIMIT = 25 |
||||
|
|
||||
# parse_library {{{ # |
# parse_library {{{ # |
||||
|
|
||||
def parse_library(headers, tracks, library_stats, user): |
|
||||
"""Scans user's library for certain number of tracks to update library_stats with. |
|
||||
|
def parse_library(headers, tracks, user): |
||||
|
"""Scans user's library for certain number of tracks and store the information in a database |
||||
|
|
||||
:headers: For API call. |
:headers: For API call. |
||||
:tracks: Number of tracks to get from user's library. |
:tracks: Number of tracks to get from user's library. |
||||
:library_stats: Dictionary containing the data mined from user's library |
|
||||
:user: a User object representing the user whose library we are parsing |
:user: a User object representing the user whose library we are parsing |
||||
|
|
||||
:returns: None |
:returns: None |
||||
|
|
||||
""" |
""" |
||||
# TODO: implement importing entire library with 0 as tracks param |
# TODO: implement importing entire library with 0 as tracks param |
||||
# number of tracks to get with each call |
|
||||
limit = 5 |
|
||||
# keeps track of point to get songs from |
# keeps track of point to get songs from |
||||
offset = 0 |
offset = 0 |
||||
payload = {'limit': str(limit)} |
|
||||
# use two separate variables to track, because the average popularity also requires num_samples |
|
||||
num_samples = 0 # number of actual track samples |
|
||||
feature_data_points = 0 # number of feature data analyses (some tracks do not have analyses available) |
|
||||
|
payload = {'limit': str(USER_TRACKS_LIMIT)} |
||||
|
artist_genre_queue = [] |
||||
|
features_queue = [] |
||||
|
|
||||
for _ in range(0, tracks, limit): |
|
||||
|
# iterate until hit requested num of tracks |
||||
|
for i in range(0, tracks, USER_TRACKS_LIMIT): |
||||
payload['offset'] = str(offset) |
payload['offset'] = str(offset) |
||||
saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', headers=headers, params=payload).json() |
|
||||
for track_dict in saved_tracks_response['items']: |
|
||||
num_samples += 1 |
|
||||
get_track_info(track_dict['track'], library_stats, num_samples) |
|
||||
# get_genre(headers, track_dict['track']['album']['id']) |
|
||||
audio_features_dict = get_audio_features(headers, track_dict['track']['id']) |
|
||||
if len(audio_features_dict) != 0: |
|
||||
# Track the number of audio analyses for calculating |
|
||||
# audio feature averages and standard deviations on the fly |
|
||||
feature_data_points += 1 |
|
||||
|
|
||||
for feature, feature_data in audio_features_dict.items(): |
|
||||
update_audio_feature_stats(feature, feature_data, feature_data_points, library_stats) |
|
||||
for artist_dict in track_dict['track']['artists']: |
|
||||
increase_artist_count(headers, artist_dict['name'], artist_dict['id'], library_stats) |
|
||||
# calculates num_songs with offset + songs retrieved |
|
||||
library_stats['num_songs'] = offset + len(saved_tracks_response['items']) |
|
||||
offset += limit |
|
||||
calculate_genres_from_artists(headers, library_stats) |
|
||||
pprint.pprint(library_stats) |
|
||||
|
|
||||
# }}} parse_library # |
|
||||
|
|
||||
def get_audio_features(headers, track_id): |
|
||||
"""Returns the audio features of a soundtrack |
|
||||
|
|
||||
Args: |
|
||||
headers: headers containing the API token |
|
||||
track_id: the id of the soundtrack, needed to query the Spotify API |
|
||||
|
|
||||
Returns: |
|
||||
A dictionary with the features as its keys, if audio feature data is missing for the track, |
|
||||
an empty dictionary is returned. |
|
||||
""" |
|
||||
|
|
||||
response = requests.get("https://api.spotify.com/v1/audio-features/{}".format(track_id), headers = headers).json() |
|
||||
if 'error' in response: |
|
||||
return {} |
|
||||
features_dict = {} |
|
||||
|
saved_tracks_response = requests.get('https://api.spotify.com/v1/me/tracks', |
||||
|
headers=headers, |
||||
|
params=payload).json() |
||||
|
|
||||
# Data that we don't need |
|
||||
useless_keys = [ |
|
||||
"key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", |
|
||||
] |
|
||||
for key, val in response.items(): |
|
||||
if key not in useless_keys: |
|
||||
features_dict[key] = val |
|
||||
|
|
||||
return features_dict |
|
||||
|
|
||||
|
|
||||
def update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size): |
|
||||
"""Calculates the standard deviation for a sample without storing all data points |
|
||||
|
|
||||
Args: |
|
||||
cur_mean: the current mean for N = (sample_size - 1) |
|
||||
cur_std_dev: the current standard deviation for N = (sample_size - 1) |
|
||||
new_data_point: a new data point |
|
||||
sample_size: sample size including the new data point |
|
||||
|
|
||||
Returns: |
|
||||
(new_mean, new_std_dev) |
|
||||
""" |
|
||||
# This is an implementation of Welford's method |
|
||||
# http://jonisalonen.com/2013/deriving-welfords-method-for-computing-variance/ |
|
||||
new_mean = ((sample_size - 1) * cur_mean + new_data_point) / sample_size |
|
||||
delta_variance = (new_data_point - new_mean) * (new_data_point - cur_mean) |
|
||||
new_std_dev = math.sqrt( |
|
||||
(math.pow(cur_std_dev, 2) * (sample_size - 2) + delta_variance) / ( |
|
||||
sample_size - 1 |
|
||||
)) |
|
||||
return new_mean, new_std_dev |
|
||||
|
for track_dict in saved_tracks_response['items']: |
||||
|
# add artists {{{ # |
||||
|
|
||||
|
# update artist info before track so that Track object can reference |
||||
|
# Artist object |
||||
|
track_artists = [] |
||||
|
for artist_dict in track_dict['track']['artists']: |
||||
|
artist_obj, artist_created = Artist.objects.get_or_create( |
||||
|
artist_id=artist_dict['id'], |
||||
|
name=artist_dict['name'],) |
||||
|
# only add/tally up artist genres if new |
||||
|
if artist_created: |
||||
|
artist_genre_queue.append(artist_obj) |
||||
|
if len(artist_genre_queue) == ARTIST_LIMIT: |
||||
|
add_artist_genres(headers, artist_genre_queue) |
||||
|
artist_genre_queue = [] |
||||
|
track_artists.append(artist_obj) |
||||
|
|
||||
|
# }}} add artists # |
||||
|
|
||||
|
# TODO: fix this, don't need any more |
||||
|
top_genre = "" |
||||
|
track_obj, track_created = save_track_obj(track_dict['track'], |
||||
|
track_artists, top_genre, user) |
||||
|
|
||||
|
# add audio features {{{ # |
||||
|
|
||||
|
# if a new track is not created, the associated audio feature does |
||||
|
# not need to be created again |
||||
|
if track_created: |
||||
|
features_queue.append(track_obj) |
||||
|
if len(features_queue) == FEATURES_LIMIT: |
||||
|
get_audio_features(headers, features_queue) |
||||
|
features_queue = [] |
||||
|
|
||||
|
# }}} add audio features # |
||||
|
|
||||
|
# temporary console logging |
||||
|
print("#{}-{}: {} - {}".format(offset + 1, |
||||
|
offset + USER_TRACKS_LIMIT, |
||||
|
track_obj.artists.first(), |
||||
|
track_obj.name)) |
||||
|
|
||||
def update_audio_feature_stats(feature, new_data_point, sample_size, library_stats): |
|
||||
"""Updates the audio feature statistics in library_stats |
|
||||
|
# calculates num_songs with offset + songs retrieved |
||||
|
offset += USER_TRACKS_LIMIT |
||||
|
|
||||
Args: |
|
||||
feature: the audio feature to be updated (string) |
|
||||
new_data_point: new data to update the stats with |
|
||||
sample_size: sample size including the new data point |
|
||||
library_stats Dictionary containing the data mined from user's Spotify library |
|
||||
|
# clean-up {{{ # |
||||
|
|
||||
|
# update remaining artists without genres and songs without features if |
||||
|
# there are any |
||||
|
if len(artist_genre_queue) > 0: |
||||
|
add_artist_genres(headers, artist_genre_queue) |
||||
|
if len(features_queue) > 0: |
||||
|
get_audio_features(headers, features_queue) |
||||
|
|
||||
Returns: |
|
||||
None |
|
||||
""" |
|
||||
# first time the feature is considered |
|
||||
if sample_size < 2: |
|
||||
library_stats['audio_features'][feature] = { |
|
||||
"average": new_data_point, |
|
||||
"std_dev": 0, |
|
||||
} |
|
||||
else: |
|
||||
cur_mean = library_stats['audio_features'][feature]['average'] |
|
||||
cur_std_dev = library_stats['audio_features'][feature]['std_dev'] |
|
||||
new_mean, new_std_dev = update_std_dev(cur_mean, cur_std_dev, new_data_point, sample_size) |
|
||||
|
# }}} clean-up # |
||||
|
|
||||
library_stats['audio_features'][feature] = { |
|
||||
"average": new_mean, |
|
||||
"std_dev": new_std_dev |
|
||||
} |
|
||||
|
update_track_genres(user) |
||||
|
|
||||
|
# }}} parse_library # |
||||
|
|
||||
# increase_nested_key {{{ # |
|
||||
|
# update_track_genres {{{ # |
||||
|
|
||||
def increase_nested_key(top_key, nested_key, library_stats, amount=1): |
|
||||
"""Increases count for the value of library_stats[top_key][nested_key]. Checks if nested_key exists already and takes |
|
||||
appropriate action. |
|
||||
|
def update_track_genres(user): |
||||
|
"""Updates user's tracks with the most common genre associated with the |
||||
|
songs' artist(s). |
||||
|
|
||||
:top_key: First key of library_stats. |
|
||||
:nested_key: Key in top_key's dict for which we want to increase value of. |
|
||||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|
||||
|
:user: User object who's tracks are being updated. |
||||
|
|
||||
:returns: None |
:returns: None |
||||
|
|
||||
""" |
""" |
||||
if nested_key not in library_stats[top_key]: |
|
||||
library_stats[top_key][nested_key] = amount |
|
||||
else: |
|
||||
library_stats[top_key][nested_key] += amount |
|
||||
|
|
||||
# }}} increase_nested_key # |
|
||||
|
|
||||
# increase_artist_count {{{ # |
|
||||
|
|
||||
def increase_artist_count(headers, artist_name, artist_id, library_stats): |
|
||||
"""Increases count for artist in library_stats and stores the artist_id. |
|
||||
|
|
||||
:headers: For making the API call. |
|
||||
:artist_name: Artist to increase count for. |
|
||||
:artist_id: The Spotify ID for the artist. |
|
||||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|
||||
|
|
||||
:returns: None |
|
||||
|
user_tracks = Track.objects.filter(users__exact=user) |
||||
|
for track in user_tracks: |
||||
|
# just using this variable to save another call to db |
||||
|
track_artists = track.artists.all() |
||||
|
# set genres to first artist's genres then find intersection with others |
||||
|
shared_genres = track_artists.first().genres.all() |
||||
|
for artist in track_artists: |
||||
|
shared_genres = shared_genres.intersection(artist.genres.all()) |
||||
|
shared_genres = shared_genres.order_by('-num_songs') |
||||
|
|
||||
|
undefined_genre_obj = Genre.objects.get(name="undefined") |
||||
|
most_common_genre = shared_genres.first() if shared_genres.first() is \ |
||||
|
not undefined_genre_obj else shared_genres[1] |
||||
|
track.genre = most_common_genre if most_common_genre is not None \ |
||||
|
else undefined_genre_obj |
||||
|
track.save() |
||||
|
# print(track.name, track.genre) |
||||
|
|
||||
|
# }}} update_track_genres # |
||||
|
|
||||
|
# save_track_obj {{{ # |
||||
|
|
||||
|
def save_track_obj(track_dict, artists, top_genre, user): |
||||
|
"""Make an entry in the database for this track if it doesn't exist already. |
||||
|
|
||||
|
:track_dict: dictionary from the API call containing track information. |
||||
|
:artists: artists of the song, passed in as a list of Artist objects. |
||||
|
:top_genre: top genre associated with this track (see get_top_genre). |
||||
|
:user: User object for which this Track is to be associated with. |
||||
|
|
||||
|
:returns: (The created/retrieved Track object, created) |
||||
|
|
||||
""" |
""" |
||||
if artist_name not in library_stats['artists']: |
|
||||
library_stats['artists'][artist_name] = {} |
|
||||
library_stats['artists'][artist_name]['count'] = 1 |
|
||||
library_stats['artists'][artist_name]['id'] = artist_id |
|
||||
|
track_query = Track.objects.filter(track_id__exact=track_dict['id']) |
||||
|
if len(track_query) != 0: |
||||
|
return track_query[0], False |
||||
else: |
else: |
||||
library_stats['artists'][artist_name]['count'] += 1 |
|
||||
|
|
||||
# }}} increase_artist_count # |
|
||||
|
|
||||
def update_popularity_stats(new_data_point, library_stats, sample_size): |
|
||||
"""Updates the popularity statistics in library_stats |
|
||||
|
|
||||
Args: |
|
||||
new_data_point: new data to update the popularity stats with |
|
||||
library_stats: Dictionary containing data mined from user's Spotify library |
|
||||
sample_size: The sample size including the new data |
|
||||
|
new_track = Track.objects.create( |
||||
|
track_id=track_dict['id'], |
||||
|
year=track_dict['album']['release_date'].split('-')[0], |
||||
|
popularity=int(track_dict['popularity']), |
||||
|
runtime=int(float(track_dict['duration_ms']) / 1000), |
||||
|
name=track_dict['name'], |
||||
|
# genre=top_genre, |
||||
|
) |
||||
|
|
||||
|
# have to add artists and user after saving object since track needs to |
||||
|
# have ID before filling in m2m field |
||||
|
for artist in artists: |
||||
|
new_track.artists.add(artist) |
||||
|
new_track.users.add(user) |
||||
|
new_track.save() |
||||
|
return new_track, True |
||||
|
|
||||
|
# }}} save_track_obj # |
||||
|
|
||||
|
# get_audio_features {{{ # |
||||
|
|
||||
|
def get_audio_features(headers, track_objs): |
||||
|
"""Creates and saves a new AudioFeatures objects for the respective |
||||
|
track_objs. track_objs should contain the API limit for a single call |
||||
|
(FEATURES_LIMIT) for maximum efficiency. |
||||
|
|
||||
|
:headers: headers containing the API token |
||||
|
:track_objs: Track objects to associate with the new AudioFeatures object |
||||
|
|
||||
Returns: |
|
||||
None |
|
||||
|
:returns: None |
||||
""" |
""" |
||||
if sample_size < 2: |
|
||||
library_stats['popularity'] = { |
|
||||
"average": new_data_point, |
|
||||
"std_dev": 0, |
|
||||
} |
|
||||
else : |
|
||||
cur_mean_popularity = library_stats['popularity']['average'] |
|
||||
cur_popularity_stdev = library_stats['popularity']['std_dev'] |
|
||||
new_mean, new_std_dev = update_std_dev( |
|
||||
cur_mean_popularity, cur_popularity_stdev, new_data_point, sample_size) |
|
||||
library_stats['popularity'] = { |
|
||||
"average": new_mean, |
|
||||
"std_dev": new_std_dev, |
|
||||
} |
|
||||
|
|
||||
# get_track_info {{{ # |
|
||||
|
track_ids = str.join(",", [track_obj.track_id for track_obj in track_objs]) |
||||
|
params = {'ids': track_ids} |
||||
|
features_response = requests.get("https://api.spotify.com/v1/audio-features", |
||||
|
headers=headers,params=params).json()['audio_features'] |
||||
|
# pprint.pprint(features_response) |
||||
|
|
||||
|
useless_keys = [ "key", "mode", "type", "liveness", "id", "uri", "track_href", "analysis_url", "time_signature", ] |
||||
|
for i in range(len(track_objs)): |
||||
|
if features_response[i] is not None: |
||||
|
# Data that we don't need |
||||
|
cur_features_obj = AudioFeatures() |
||||
|
cur_features_obj.track = track_objs[i] |
||||
|
for key, val in features_response[i].items(): |
||||
|
if key not in useless_keys: |
||||
|
setattr(cur_features_obj, key, val) |
||||
|
cur_features_obj.save() |
||||
|
|
||||
def get_track_info(track_dict, library_stats, sample_size): |
|
||||
"""Get all the info from the track_dict directly returned by the API call in parse_library. |
|
||||
|
# }}} get_audio_features # |
||||
|
|
||||
:track_dict: Dict returned from the API call containing the track info. |
|
||||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|
||||
:sample_size: The sample size so far including this track |
|
||||
|
def process_artist_genre(genre_name, artist_obj): |
||||
|
"""Increase count for correspoding Genre object to genre_name and add that |
||||
|
Genre to artist_obj. |
||||
|
|
||||
|
:genre_name: Name of genre. |
||||
|
:artist_obj: Artist object to add Genre object to. |
||||
:returns: None |
:returns: None |
||||
|
|
||||
""" |
""" |
||||
# popularity |
|
||||
update_popularity_stats(track_dict['popularity'], library_stats, sample_size) |
|
||||
|
|
||||
# year |
|
||||
year_released = track_dict['album']['release_date'].split('-')[0] |
|
||||
increase_nested_key('year_released', year_released, library_stats) |
|
||||
|
genre_obj, created = Genre.objects.get_or_create(name=genre_name, |
||||
|
defaults={'num_songs':1}) |
||||
|
if not created: |
||||
|
genre_obj.num_songs = F('num_songs') + 1 |
||||
|
genre_obj.save() |
||||
|
artist_obj.genres.add(genre_obj) |
||||
|
artist_obj.save() |
||||
|
|
||||
# artist |
|
||||
# artist_names = [artist['name'] for artist in track_dict['artists']] |
|
||||
# for artist_name in artist_names: |
|
||||
# increase_nested_key('artists', artist_name) |
|
||||
|
# add_artist_genres {{{ # |
||||
|
|
||||
# runtime |
|
||||
library_stats['total_runtime'] += float(track_dict['duration_ms']) / (1000 * 60) |
|
||||
|
|
||||
# }}} get_track_info # |
|
||||
|
|
||||
# calculate_genres_from_artists {{{ # |
|
||||
|
|
||||
def calculate_genres_from_artists(headers, library_stats): |
|
||||
"""Tallies up genre counts based on artists in library_stats. |
|
||||
|
def add_artist_genres(headers, artist_objs): |
||||
|
"""Adds genres to artist_objs and increases the count the respective Genre |
||||
|
object. artist_objs should contain the API limit for a single call |
||||
|
(ARTIST_LIMIT) for maximum efficiency. |
||||
|
|
||||
:headers: For making the API call. |
:headers: For making the API call. |
||||
:library_stats: Dictionary containing the data mined from user's Spotify library |
|
||||
|
:artist_objs: List of Artist objects for which to add/tally up genres for. |
||||
|
|
||||
:returns: None |
:returns: None |
||||
|
|
||||
""" |
""" |
||||
for artist_entry in library_stats['artists'].values(): |
|
||||
artist_response = requests.get('https://api.spotify.com/v1/artists/' + artist_entry['id'], headers=headers).json() |
|
||||
# increase each genre count by artist count |
|
||||
for genre in artist_response['genres']: |
|
||||
increase_nested_key('genres', genre, library_stats, artist_entry['count']) |
|
||||
|
artist_ids = str.join(",", [artist_obj.artist_id for artist_obj in artist_objs]) |
||||
|
params = {'ids': artist_ids} |
||||
|
artists_response = requests.get('https://api.spotify.com/v1/artists/', |
||||
|
headers=headers, params=params).json()['artists'] |
||||
|
# pprint.pprint(artists_response) |
||||
|
for i in range(len(artist_objs)): |
||||
|
if len(artists_response[i]['genres']) == 0: |
||||
|
process_artist_genre("undefined", artist_objs[i]) |
||||
|
else: |
||||
|
for genre in artists_response[i]['genres']: |
||||
|
process_artist_genre(genre, artist_objs[i]) |
||||
|
|
||||
|
# }}} add_artist_genres # |
||||
|
|
||||
# }}} calculate_genres_from_artists # |
|
||||
|
# get_artists_in_genre {{{ # |
||||
|
|
||||
def process_library_stats(library_stats): |
|
||||
"""Processes library_stats into format more suitable for D3 consumption |
|
||||
|
def get_artists_in_genre(user, genre, max_songs): |
||||
|
"""Return count of artists in genre. |
||||
|
|
||||
Args: |
|
||||
library_stats: Dictionary containing the data mined from user's Spotify library |
|
||||
|
:user: User object to return data for. |
||||
|
:genre: genre to count artists for. |
||||
|
:max_songs: max total songs to include to prevent overflow due to having |
||||
|
multiple artists on each track. |
||||
|
|
||||
Returns: |
|
||||
A new dictionary that contains the data in library_stats, in a format more suitable for D3 consumption |
|
||||
|
:returns: dict of artists in the genre along with the number of songs they |
||||
|
have. |
||||
""" |
""" |
||||
processed_library_stats = {} |
|
||||
for key in library_stats: |
|
||||
if key == 'artists' or key == 'genres' or key == 'year_released': |
|
||||
for inner_key in library_stats[key]: |
|
||||
if key not in processed_library_stats: |
|
||||
processed_library_stats[key] = [] |
|
||||
processed_item_key = '' # identifier key for each dict in the list |
|
||||
count = 0 |
|
||||
if 'artist' in key: |
|
||||
processed_item_key = 'name' |
|
||||
count = library_stats[key][inner_key]['count'] |
|
||||
elif 'genre' in key: |
|
||||
processed_item_key = 'genre' |
|
||||
count = library_stats[key][inner_key] |
|
||||
else: |
|
||||
processed_item_key = 'year' |
|
||||
count = library_stats[key][inner_key] |
|
||||
|
|
||||
processed_library_stats[key].append({ |
|
||||
processed_item_key: inner_key, |
|
||||
"count": count |
|
||||
}) |
|
||||
elif key == 'audio_features': |
|
||||
for audio_feature in library_stats[key]: |
|
||||
if 'audio_features' not in processed_library_stats: |
|
||||
processed_library_stats['audio_features'] = [] |
|
||||
processed_library_stats['audio_features'].append({ |
|
||||
'feature': audio_feature, |
|
||||
'average': library_stats[key][audio_feature]['average'], |
|
||||
'std_dev': library_stats[key][audio_feature]['std_dev'] |
|
||||
}) |
|
||||
# TODO: Not sure about final form for 'popularity' |
|
||||
# elif key == 'popularity': |
|
||||
# processed_library_stats[key] = [] |
|
||||
# processed_library_stats[key].append({ |
|
||||
|
|
||||
# }) |
|
||||
elif key == 'num_songs' or key == 'total_runtime' or key == 'popularity': |
|
||||
processed_library_stats[key] = library_stats[key] |
|
||||
|
|
||||
return processed_library_stats |
|
||||
|
genre_obj = Genre.objects.get(name=genre) |
||||
|
artist_counts = (Artist.objects.filter(track__users=user) |
||||
|
.filter(genres=genre_obj) |
||||
|
.annotate(num_songs=Count('track', distinct=True)) |
||||
|
.order_by('-num_songs') |
||||
|
) |
||||
|
processed_artist_counts = {} |
||||
|
songs_added = 0 |
||||
|
for artist in artist_counts: |
||||
|
# hacky way to not have total count overflow due to there being multiple |
||||
|
# artists on a track |
||||
|
if songs_added + artist.num_songs <= max_songs: |
||||
|
processed_artist_counts[artist.name] = artist.num_songs |
||||
|
songs_added += artist.num_songs |
||||
|
# processed_artist_counts = [{'name': artist.name, 'num_songs': artist.num_songs} for artist in artist_counts] |
||||
|
# processed_artist_counts = {artist.name: artist.num_songs for artist in artist_counts} |
||||
|
# pprint.pprint(processed_artist_counts) |
||||
|
return processed_artist_counts |
||||
|
|
||||
|
# }}} get_artists_in_genre # |
||||
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