File size: 21,702 Bytes
df9cc8d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 |
"""
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] |