94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
from fmframework.net.network import Network as FmNet
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from spotframework.net.network import Network as SpotNet
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from spotframework.net.user import NetworkUser
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from spotframework.model.uri import Uri
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from google.cloud import bigquery
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from csv import DictWriter
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import datetime
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import os
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from log import logger
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import analysis.cache
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spotnet = SpotNet(NetworkUser(client_id=os.environ['SPOT_CLIENT'],
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client_secret=os.environ['SPOT_SECRET'],
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refresh_token=os.environ['SPOT_REFRESH']).refresh_access_token())
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fmnet = FmNet(username='sarsoo', api_key=os.environ['FM_CLIENT'])
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cache = analysis.cache.load_cache_from_storage()
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client = bigquery.Client()
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# Perform a query.
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QUERY = (
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'SELECT '
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' DISTINCT uri, track, album, artist '
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'FROM `sarsooxyz.scrobbles.*` '
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'WHERE '
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' uri IS NOT NULL '
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'ORDER BY artist '
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)
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logger.info('querying uris')
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query_job = client.query(QUERY)
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rows = query_job.result()
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features = []
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for_pulling = []
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logger.info('polling cache')
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for row in rows:
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cache_entry = cache.get_track(row.track, row.artist)
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if cache_entry is not None:
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if cache_entry.get('features') is None:
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features.append(cache_entry)
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continue
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for_pulling.append(row)
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logger.info('pulling tracks')
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tracks = spotnet.get_tracks(uri_strings=[i.uri for i in for_pulling])
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if tracks is not None:
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logger.info('populating features')
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tracks = spotnet.populate_track_audio_features(tracks)
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features += [i.audio_features.to_dict() for i in tracks if i.audio_features is not None]
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logger.info('caching pulled')
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for cacheable in for_pulling:
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track = next((i for i in tracks if str(i.uri) == cacheable.uri), None)
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if track is not None and track.audio_features is not None:
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cache.set_track(name=cacheable.track, artist=cacheable.artist, audio_features=track.audio_features.to_dict())
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logger.info('dumping')
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date = str(datetime.date.today())
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with open(f'{date}_features.csv', 'w', newline='') as fileobj:
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headers = ['acousticness',
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'analysis_url',
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'danceability',
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'duration_ms',
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'energy',
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'uri',
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'instrumentalness',
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'key',
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'key_code',
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'liveness',
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'loudness',
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'mode',
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'speechiness',
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'tempo',
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'time_signature',
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'track_href',
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'valence']
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writer = DictWriter(fileobj, fieldnames=headers, dialect='excel-tab')
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writer.writeheader()
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for feature in features:
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writer.writerow(feature)
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analysis.cache.write_cache_to_storage(cache)
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