def bandura_lucky():
global g_7digital_cur
global g_7digital_nexts
global g_song_features
global g_nexts_features
print "bandura_lucky"
context = packContext( 0 );
if 1: #request.method == "POST":
#if request.method == "POST":
# updateParamsFromSite( request.form )
random_match = MX_traverse.pickRandomSong()
g_next_choice = 0
g_7digital_nexts = [[random_match]]
g_nexts_features = list()
print g_7digital_nexts[0][g_next_choice]
g_nexts_features.append( tuple(MX_traverse.get_song_by_7digital( g_7digital_nexts[0][g_next_choice] )) )
print "random choice: " + g_nexts_features[g_next_choice][0]
render_template("bandura.html", **context);
return redirect("/bandura")
def bandura_search():
global g_7digital_cur
global g_7digital_nexts
global g_song_features
global g_nexts_features
print "bandura_search"
if request.method == "POST":
print "'bandura search' called by POST"
#updateParamsFromSite( request.form )
context = packContext( 0 );
search_term = request.form["search_term"]
if search_term != '':
# seach the exact term in the DB
best_match = [MX_traverse.get_song_id_by_token(search_term)]
if best_match[0]=='':
# if the exact term wasn't found in the DB, perform a more flexible search using the EchoNest API
g_7digital_cur = ''
n_search_results = 2
while best_match[0]=='' and n_search_results<=1000:
n_search_results *= 5
z = py7D.request('track', 'search', q=search_term, pagesize=n_search_results)
if int(z['response']['searchResults']['totalItems'])>0:
search_results = z['response']['searchResults']['searchResult']
search_results2 = list()
print "possible matches: " + str(len(search_results))
for result in search_results:
record=dict()
record['track_7digitalid'] = result['track']['@id']
record['title'] = result['track']['title']
record['artist'] = result['track']['artist']['name']
print record['track_7digitalid'], record['title'], record['artist']
search_results2.append(record)
# intersect the EchoNest results with our DB
best_match = MX_traverse.findInSongsDB( [x['track_7digitalid'] for x in search_results2] )
if best_match[0]!='':
g_next_choice = 0
g_7digital_nexts = [[best_match[0]]]
g_nexts_features = list()
g_nexts_features.append( tuple(MX_traverse.get_song_by_7digital( g_7digital_nexts[0][g_next_choice] )) )
print "best match: " + g_nexts_features[g_next_choice][0]
else:
print "no match..."
else:
print "No search term..."
else:
context = dict()
"'bandura_search' called by GET"
render_template("bandura.html", **context);
return redirect("/bandura")
def bandura():
global p
global g_p
global g_7digital_cur
global g_7digital_nexts
global g_song_features
global g_nexts_features
global g_next_choice
print "bandura"
context = packContext( 0 );
if 1: #request.method == "POST":
if request.method == "POST":
print "'bandura' called by POST"
updateParamsFromSite( request.form )
context = packContext( 0 );
if g_7digital_nexts:
print "cur song id exists"
print g_7digital_nexts
g_7digital_cur = g_7digital_nexts[0][g_next_choice]
g_song_features = g_nexts_features[g_next_choice]
song_url = OAuth.get_song_url(g_7digital_cur);
g_7digital_nexts = MX_traverse.pickNextSongsWrapped( g_7digital_cur );
g_nexts_features = [MX_traverse.get_song_by_7digital( x ) for x in g_7digital_nexts[0]]
if g_p['save_playlist']:
MX_traverse.addSongToPlaylist( g_p['playlist_file'], g_song_features, song_url )
context['song_url'] = song_url+"#t="+str(g_p['play_interval'][0])+","+str(g_p['play_interval'][1])
context['id_7digital'] = g_7digital_cur
context['title'] = g_song_features[p['invkey']['title']]
context['artist'] = g_song_features[p['invkey']['artist_name']]
for i in range(len(g_nexts_features)):
context['n'+str(i)+'_id_7digital'] = g_nexts_features[i][p['invkey']['track_7digitalid']]
context['n'+str(i)+'_title'] = g_nexts_features[i][p['invkey']['title']]
context['n'+str(i)+'_artist'] = g_nexts_features[i][p['invkey']['artist_name']]
else:
print "cur song id does NOT exists"
else:
print "'bandura' called by GET"
print "rendering bandura"
return render_template("bandura.html", **context);
def setCurrentCalcNext( context = dict() ):
global p
global g_p
global g_7digital_cur
global g_7digital_nexts
global g_song_features
g_7digital_nexts = MX_traverse.pickNextSongsWrapped( g_7digital_cur );
g_song_features = MX_traverse.get_song_by_7digital( g_7digital_cur )
g_nexts_features = [MX_traverse.get_song_by_7digital( x ) for x in g_7digital_nexts[0]]
song_url = MX_traverse.get_song_url( g_7digital_cur )
if g_p['save_playlist']:
g_p['playlist_file'] = g_p['playlist_pref'] + time.strftime("%Y%m%d-%H%M%S") + '.csv'
MX_traverse.addSongToPlaylist( g_p['playlist_file'], g_song_features, song_url )
context['song_url'] = song_url+'#t='+str(g_p['play_interval'][0])+","+str(g_p['play_interval'][1])
context['id_7digital'] = g_7digital_cur
context['title'] = g_song_features[p['invkey']['title']]
context['artist'] = g_song_features[p['invkey']['artist_name']]
for i in range(len(g_nexts_features)):
context['n'+str(i)+'_id_7digital'] = g_nexts_features[i][p['invkey']['track_7digitalid']]
context['n'+str(i)+'_title'] = g_nexts_features[i][p['invkey']['title']]
context['n'+str(i)+'_artist'] = g_nexts_features[i][p['invkey']['artist_name']]
return context
def play_test():
if request.method == "POST":
# if request.form["user_play"] == "PlayThis":
print "this!"
# echonest_id = request.form["user_play"]
# id_7digital = get_7digital_id( echonext_id )
# song_url = OAuth.get_song_url(id_7digital)
# print song_url
# return render_template("play_specific.html", api_data=song_url)
# else:
print "next! " + request.form["user_play"];
# echonest_id = "SOBOAQC12A8C13E3E9"#request.form["user_play"]
# echonest_id_next = MX_traverse.pickNextSongWrapped( echonest_id ) #echonext_id
# print echonest_id, echonest_id_next
id_7digital = request.form["user_play"]#MX_traverse.get_7digital_id( echonest_id_next )
song_url = OAuth.get_song_url(id_7digital);
# print song_url
id_7digital_next = MX_traverse.pickNextSongWrapped( id_7digital );#echonext_id
request.form["user_play"] = id_7digital_next
return render_template("play_specific.html", api_data=song_url)
else:
print "moshe"
return render_template("play_test.html")
def constructGraph( conn, p, song_features_table_name, artist_similarities_table_name, graph_table_name, debug_print=False ):
cur = conn.cursor()
MX_common.resetTable( conn, graph_table_name, ['targeto text', 'similaro text, dist real'] )
# read songs_list
q = "SELECT \
song_id \
FROM "\
+song_features_table_name +";"
cur.execute(q)
songs_list = cur.fetchall()
node_stats = dict()
node_stats['neighbors_cur_artist'] = list()
node_stats['neighbors_topo'] = list()
node_stats['neighbors_metric'] = list()
node_stats['mean_dist'] = list()
node_stats['similar_artists'] = list()
edge_stats = dict()
edge_stats['dist'] = list()
edge_stats['vdist'] = list()
# for each song (the focal song), find its neighbors
for focal_song_id in songs_list[::1]:
focal_song_id = focal_song_id[0]
if debug_print:
print "focal_song: " + focal_song_id
# fetch all focal song's features
q = "SELECT \
* \
FROM "\
+song_features_table_name+" "\
"WHERE \
song_id='" + focal_song_id + "';"
cur.execute(q)
focal_song_f = cur.fetchall()
# find all songs that are TOPOLOGICALLY close (based on artist similarity):
# find all song of current artist
q = "SELECT "\
+"song_id "\
+"FROM "\
+song_features_table_name+" "\
+"WHERE "\
+"artist_id='" + focal_song_f[0][p['invkey']['artist_id']] + "';"
cur.execute(q)
song_ids_cur_artist = cur.fetchall()
node_stats['neighbors_cur_artist'].append(len(song_ids_cur_artist))
if debug_print:
for song_id in song_ids_cur_artist:
print "same artist songs: " + song_id[0]
# find all artists similar to current artist
q = "SELECT "\
+"similaro "\
+"FROM "\
+artist_similarities_table_name+" "\
+"WHERE "\
+"targeto='" + focal_song_f[0][p['invkey']['artist_id']] + "';"
cur.execute(q)
artist_ids_topo = cur.fetchall()
node_stats['similar_artists'].append(len(artist_ids_topo))
song_ids_topo = list()
# add all song IDs of the similar artists
for artist_id in artist_ids_topo:
# fetch all songs of this artist
q = "SELECT "\
+"song_id "\
+"FROM "\
+song_features_table_name+" "\
+"WHERE "\
+"artist_id='" + artist_id[0] + "';"
cur.execute(q)
cur_song_ids = cur.fetchall()
# if len(cur_song_ids)>0:
# cur_song_ids = cur_song_ids[0]
for song_id in cur_song_ids:
song_ids_topo.append(song_id)
# stage 2 - goo deeper into the tree of artist_similarities. This requires holding a fifo of nodes / some sort of recursion
node_stats['neighbors_topo'].append(len(song_ids_topo))
if debug_print:
for song_id in song_ids_topo:
print "similar artists songs: " + song_id[0]
# find all songs that are METRICALLY close (based on song features)
song_ids_metric = list()
for key in p['relevant_features']:
key_range = ( focal_song_f[key] - p['unit_vec'][key]*p['construct_neighborhood_metric_diameter'] , focal_song_f[key] + )
q = "SELECT "\
+"song_id "\
+"FROM "\
+song_features_table_name+" "\
+"WHERE "\
+key+ ">="+ str(key_range[0]) + " AND "+ key+ "<="+ str(key_range[1])+";"
cur.execute(q)
cur_song_ids = cur.fetchall()
for id in cur_song_ids[0]:
song_ids_metric.append(id)
node_stats['neighbors_metric'].append(len(song_ids_metric))
# combine all potential neighbors song IDs
song_ids_combined = list(set(song_ids_cur_artist) | set(song_ids_topo) | set(song_ids_metric))
# for each potential neighbor, measure its distance from the focal point
potential_neighbors = list()
for neighbor_song_id in song_ids_combined:
# print "song id: "+neighbor_song_id
# fetch features for each potential neighbor
q = "SELECT \
*\
FROM "\
+song_features_table_name+" "\
+"WHERE "\
+"song_id='" + neighbor_song_id[0] + "';"
cur.execute(q)
neighbor_song_f = cur.fetchall()
# calculate distance to current song
sdist = MX_traverse.calcDistance(focal_song_f[0], neighbor_song_f[0], p['distance_type_construct'])
# filter songs that are too distant
if sdist[0] < p['maximal_overall_distance']:
potential_neighbors.append((neighbor_song_id[0],sdist[0], sdist[1]))
# sort potential neighbors by distance
# potential_neighbors = sorted(potential_neighbors, key=lambda x: x[1])
if len(potential_neighbors)>0:
node_stats['mean_dist'].append( np.mean([x[1] for x in potential_neighbors]) )
else:
node_stats['mean_dist'].append(-1)
# add potential neighbors and truncate, if there are too many, based on distance
for neighborhood_type in neighboorhoodtypes:
for neighbor in potential_neighbors[neighborhood_type][0:min(max_MXG_neighbors[neighborhood_type], len(potential_neighbors[neighborhood_type]))]:
# print "n0 " + neighbor[0]
# print "n1 " + str(neighbor[1])
q = "INSERT INTO "\
+graph_table_name+" \
(targeto, similaro, dist) \
VALUES \
('"+ focal_song_id +"', '"+ neighbor[0] +"', '"+ str(neighbor[1]) +"');"
cur.execute(q)
q = "INSERT INTO "\
+graph_table_name+" \
(targeto, similaro, dist) \
VALUES \
('"+ neighbor[0] +"', '"+ focal_song_id +"', '"+ str(neighbor[1]) +"');"
cur.execute(q)
edge_stats['dist'].append( neighbor[1] )
edge_stats['vdist'].append( neighbor[2] )
# MXG_target.append( song_id[0] )
# MXG_source.append( potential_neighbors[0] )
return node_stats, edge_stats
# return render_template("play_specific.html", api_data=song_url)
# else:
print "next! " + request.form["user_play"];
# echonest_id = "SOBOAQC12A8C13E3E9"#request.form["user_play"]
# echonest_id_next = MX_traverse.pickNextSongWrapped( echonest_id ) #echonext_id
# print echonest_id, echonest_id_next
id_7digital = request.form["user_play"]#MX_traverse.get_7digital_id( echonest_id_next )
song_url = OAuth.get_song_url(id_7digital);
# print song_url
id_7digital_next = MX_traverse.pickNextSongWrapped( id_7digital );#echonext_id
request.form["user_play"] = id_7digital_next
return render_template("play_specific.html", api_data=song_url)
else:
print "moshe"
return render_template("play_test.html")
@app.errorhandler(404)
def page_not_found(error):
return "Sorry, this page was not found.", 404
# always the last function !!!!!!
if __name__ == "__main__":
print "init"
MX_traverse.init_globals()
app.run(host="0.0.0.0", port=int("4070"))
