Spaces:
Running
on
Zero
Running
on
Zero
| """ | |
| Thierry Bertin-Mahieux (2010) Columbia University | |
| [email protected] | |
| This code contains a set of getters functions to access the fields | |
| from an HDF5 song file (regular file with one song or | |
| aggregate / summary file with many songs) | |
| This is part of the Million Song Dataset project from | |
| LabROSA (Columbia University) and The Echo Nest. | |
| Copyright 2010, Thierry Bertin-Mahieux | |
| This program is free software: you can redistribute it and/or modify | |
| it under the terms of the GNU General Public License as published by | |
| the Free Software Foundation, either version 3 of the License, or | |
| (at your option) any later version. | |
| This program is distributed in the hope that it will be useful, | |
| but WITHOUT ANY WARRANTY; without even the implied warranty of | |
| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | |
| GNU General Public License for more details. | |
| You should have received a copy of the GNU General Public License | |
| along with this program. If not, see <http://www.gnu.org/licenses/>. | |
| """ | |
| import tables | |
| def open_h5_file_read(h5filename): | |
| """ | |
| Open an existing H5 in read mode. | |
| Same function as in hdf5_utils, here so we avoid one import | |
| """ | |
| return tables.open_file(h5filename, mode='r') | |
| def get_num_songs(h5): | |
| """ | |
| Return the number of songs contained in this h5 file, i.e. the number of rows | |
| for all basic informations like name, artist, ... | |
| """ | |
| return h5.root.metadata.songs.nrows | |
| def get_artist_familiarity(h5,songidx=0): | |
| """ | |
| Get artist familiarity from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_familiarity[songidx] | |
| def get_artist_hotttnesss(h5,songidx=0): | |
| """ | |
| Get artist hotttnesss from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_hotttnesss[songidx] | |
| def get_artist_id(h5,songidx=0): | |
| """ | |
| Get artist id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_id[songidx] | |
| def get_artist_mbid(h5,songidx=0): | |
| """ | |
| Get artist musibrainz id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_mbid[songidx] | |
| def get_artist_playmeid(h5,songidx=0): | |
| """ | |
| Get artist playme id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_playmeid[songidx] | |
| def get_artist_7digitalid(h5,songidx=0): | |
| """ | |
| Get artist 7digital id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_7digitalid[songidx] | |
| def get_artist_latitude(h5,songidx=0): | |
| """ | |
| Get artist latitude from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_latitude[songidx] | |
| def get_artist_longitude(h5,songidx=0): | |
| """ | |
| Get artist longitude from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_longitude[songidx] | |
| def get_artist_location(h5,songidx=0): | |
| """ | |
| Get artist location from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_location[songidx] | |
| def get_artist_name(h5,songidx=0): | |
| """ | |
| Get artist name from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.artist_name[songidx] | |
| def get_release(h5,songidx=0): | |
| """ | |
| Get release from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.release[songidx] | |
| def get_release_7digitalid(h5,songidx=0): | |
| """ | |
| Get release 7digital id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.release_7digitalid[songidx] | |
| def get_song_id(h5,songidx=0): | |
| """ | |
| Get song id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.song_id[songidx] | |
| def get_song_hotttnesss(h5,songidx=0): | |
| """ | |
| Get song hotttnesss from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.song_hotttnesss[songidx] | |
| def get_title(h5,songidx=0): | |
| """ | |
| Get title from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.title[songidx] | |
| def get_track_7digitalid(h5,songidx=0): | |
| """ | |
| Get track 7digital id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.metadata.songs.cols.track_7digitalid[songidx] | |
| def get_similar_artists(h5,songidx=0): | |
| """ | |
| Get similar artists array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.metadata.songs.nrows == songidx + 1: | |
| return h5.root.metadata.similar_artists[h5.root.metadata.songs.cols.idx_similar_artists[songidx]:] | |
| return h5.root.metadata.similar_artists[h5.root.metadata.songs.cols.idx_similar_artists[songidx]: | |
| h5.root.metadata.songs.cols.idx_similar_artists[songidx+1]] | |
| def get_artist_terms(h5,songidx=0): | |
| """ | |
| Get artist terms array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.metadata.songs.nrows == songidx + 1: | |
| return h5.root.metadata.artist_terms[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:] | |
| return h5.root.metadata.artist_terms[h5.root.metadata.songs.cols.idx_artist_terms[songidx]: | |
| h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]] | |
| def get_artist_terms_freq(h5,songidx=0): | |
| """ | |
| Get artist terms array frequencies. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.metadata.songs.nrows == songidx + 1: | |
| return h5.root.metadata.artist_terms_freq[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:] | |
| return h5.root.metadata.artist_terms_freq[h5.root.metadata.songs.cols.idx_artist_terms[songidx]: | |
| h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]] | |
| def get_artist_terms_weight(h5,songidx=0): | |
| """ | |
| Get artist terms array frequencies. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.metadata.songs.nrows == songidx + 1: | |
| return h5.root.metadata.artist_terms_weight[h5.root.metadata.songs.cols.idx_artist_terms[songidx]:] | |
| return h5.root.metadata.artist_terms_weight[h5.root.metadata.songs.cols.idx_artist_terms[songidx]: | |
| h5.root.metadata.songs.cols.idx_artist_terms[songidx+1]] | |
| def get_analysis_sample_rate(h5,songidx=0): | |
| """ | |
| Get analysis sample rate from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.analysis_sample_rate[songidx] | |
| def get_audio_md5(h5,songidx=0): | |
| """ | |
| Get audio MD5 from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.audio_md5[songidx] | |
| def get_danceability(h5,songidx=0): | |
| """ | |
| Get danceability from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.danceability[songidx] | |
| def get_duration(h5,songidx=0): | |
| """ | |
| Get duration from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.duration[songidx] | |
| def get_end_of_fade_in(h5,songidx=0): | |
| """ | |
| Get end of fade in from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.end_of_fade_in[songidx] | |
| def get_energy(h5,songidx=0): | |
| """ | |
| Get energy from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.energy[songidx] | |
| def get_key(h5,songidx=0): | |
| """ | |
| Get key from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.key[songidx] | |
| def get_key_confidence(h5,songidx=0): | |
| """ | |
| Get key confidence from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.key_confidence[songidx] | |
| def get_loudness(h5,songidx=0): | |
| """ | |
| Get loudness from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.loudness[songidx] | |
| def get_mode(h5,songidx=0): | |
| """ | |
| Get mode from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.mode[songidx] | |
| def get_mode_confidence(h5,songidx=0): | |
| """ | |
| Get mode confidence from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.mode_confidence[songidx] | |
| def get_start_of_fade_out(h5,songidx=0): | |
| """ | |
| Get start of fade out from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.start_of_fade_out[songidx] | |
| def get_tempo(h5,songidx=0): | |
| """ | |
| Get tempo from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.tempo[songidx] | |
| def get_time_signature(h5,songidx=0): | |
| """ | |
| Get signature from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.time_signature[songidx] | |
| def get_time_signature_confidence(h5,songidx=0): | |
| """ | |
| Get signature confidence from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.time_signature_confidence[songidx] | |
| def get_track_id(h5,songidx=0): | |
| """ | |
| Get track id from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.analysis.songs.cols.track_id[songidx] | |
| def get_segments_start(h5,songidx=0): | |
| """ | |
| Get segments start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_start[h5.root.analysis.songs.cols.idx_segments_start[songidx]:] | |
| return h5.root.analysis.segments_start[h5.root.analysis.songs.cols.idx_segments_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_start[songidx+1]] | |
| def get_segments_confidence(h5,songidx=0): | |
| """ | |
| Get segments confidence array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_confidence[h5.root.analysis.songs.cols.idx_segments_confidence[songidx]:] | |
| return h5.root.analysis.segments_confidence[h5.root.analysis.songs.cols.idx_segments_confidence[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_confidence[songidx+1]] | |
| def get_segments_pitches(h5,songidx=0): | |
| """ | |
| Get segments pitches array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_pitches[h5.root.analysis.songs.cols.idx_segments_pitches[songidx]:,:] | |
| return h5.root.analysis.segments_pitches[h5.root.analysis.songs.cols.idx_segments_pitches[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_pitches[songidx+1],:] | |
| def get_segments_timbre(h5,songidx=0): | |
| """ | |
| Get segments timbre array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_timbre[h5.root.analysis.songs.cols.idx_segments_timbre[songidx]:,:] | |
| return h5.root.analysis.segments_timbre[h5.root.analysis.songs.cols.idx_segments_timbre[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_timbre[songidx+1],:] | |
| def get_segments_loudness_max(h5,songidx=0): | |
| """ | |
| Get segments loudness max array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_loudness_max[h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx]:] | |
| return h5.root.analysis.segments_loudness_max[h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_loudness_max[songidx+1]] | |
| def get_segments_loudness_max_time(h5,songidx=0): | |
| """ | |
| Get segments loudness max time array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_loudness_max_time[h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx]:] | |
| return h5.root.analysis.segments_loudness_max_time[h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_loudness_max_time[songidx+1]] | |
| def get_segments_loudness_start(h5,songidx=0): | |
| """ | |
| Get segments loudness start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.segments_loudness_start[h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx]:] | |
| return h5.root.analysis.segments_loudness_start[h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_segments_loudness_start[songidx+1]] | |
| def get_sections_start(h5,songidx=0): | |
| """ | |
| Get sections start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.sections_start[h5.root.analysis.songs.cols.idx_sections_start[songidx]:] | |
| return h5.root.analysis.sections_start[h5.root.analysis.songs.cols.idx_sections_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_sections_start[songidx+1]] | |
| def get_sections_confidence(h5,songidx=0): | |
| """ | |
| Get sections confidence array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.sections_confidence[h5.root.analysis.songs.cols.idx_sections_confidence[songidx]:] | |
| return h5.root.analysis.sections_confidence[h5.root.analysis.songs.cols.idx_sections_confidence[songidx]: | |
| h5.root.analysis.songs.cols.idx_sections_confidence[songidx+1]] | |
| def get_beats_start(h5,songidx=0): | |
| """ | |
| Get beats start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.beats_start[h5.root.analysis.songs.cols.idx_beats_start[songidx]:] | |
| return h5.root.analysis.beats_start[h5.root.analysis.songs.cols.idx_beats_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_beats_start[songidx+1]] | |
| def get_beats_confidence(h5,songidx=0): | |
| """ | |
| Get beats confidence array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.beats_confidence[h5.root.analysis.songs.cols.idx_beats_confidence[songidx]:] | |
| return h5.root.analysis.beats_confidence[h5.root.analysis.songs.cols.idx_beats_confidence[songidx]: | |
| h5.root.analysis.songs.cols.idx_beats_confidence[songidx+1]] | |
| def get_bars_start(h5,songidx=0): | |
| """ | |
| Get bars start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.bars_start[h5.root.analysis.songs.cols.idx_bars_start[songidx]:] | |
| return h5.root.analysis.bars_start[h5.root.analysis.songs.cols.idx_bars_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_bars_start[songidx+1]] | |
| def get_bars_confidence(h5,songidx=0): | |
| """ | |
| Get bars start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.bars_confidence[h5.root.analysis.songs.cols.idx_bars_confidence[songidx]:] | |
| return h5.root.analysis.bars_confidence[h5.root.analysis.songs.cols.idx_bars_confidence[songidx]: | |
| h5.root.analysis.songs.cols.idx_bars_confidence[songidx+1]] | |
| def get_tatums_start(h5,songidx=0): | |
| """ | |
| Get tatums start array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.tatums_start[h5.root.analysis.songs.cols.idx_tatums_start[songidx]:] | |
| return h5.root.analysis.tatums_start[h5.root.analysis.songs.cols.idx_tatums_start[songidx]: | |
| h5.root.analysis.songs.cols.idx_tatums_start[songidx+1]] | |
| def get_tatums_confidence(h5,songidx=0): | |
| """ | |
| Get tatums confidence array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.analysis.songs.nrows == songidx + 1: | |
| return h5.root.analysis.tatums_confidence[h5.root.analysis.songs.cols.idx_tatums_confidence[songidx]:] | |
| return h5.root.analysis.tatums_confidence[h5.root.analysis.songs.cols.idx_tatums_confidence[songidx]: | |
| h5.root.analysis.songs.cols.idx_tatums_confidence[songidx+1]] | |
| def get_artist_mbtags(h5,songidx=0): | |
| """ | |
| Get artist musicbrainz tag array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.musicbrainz.songs.nrows == songidx + 1: | |
| return h5.root.musicbrainz.artist_mbtags[h5.root.musicbrainz.songs.cols.idx_artist_mbtags[songidx]:] | |
| return h5.root.musicbrainz.artist_mbtags[h5.root.metadata.songs.cols.idx_artist_mbtags[songidx]: | |
| h5.root.metadata.songs.cols.idx_artist_mbtags[songidx+1]] | |
| def get_artist_mbtags_count(h5,songidx=0): | |
| """ | |
| Get artist musicbrainz tag count array. Takes care of the proper indexing if we are in aggregate | |
| file. By default, return the array for the first song in the h5 file. | |
| To get a regular numpy ndarray, cast the result to: numpy.array( ) | |
| """ | |
| if h5.root.musicbrainz.songs.nrows == songidx + 1: | |
| return h5.root.musicbrainz.artist_mbtags_count[h5.root.musicbrainz.songs.cols.idx_artist_mbtags[songidx]:] | |
| return h5.root.musicbrainz.artist_mbtags_count[h5.root.metadata.songs.cols.idx_artist_mbtags[songidx]: | |
| h5.root.metadata.songs.cols.idx_artist_mbtags[songidx+1]] | |
| def get_year(h5,songidx=0): | |
| """ | |
| Get release year from a HDF5 song file, by default the first song in it | |
| """ | |
| return h5.root.musicbrainz.songs.cols.year[songidx] | |