Permalink
Cannot retrieve contributors at this time
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
550 lines (510 sloc)
28.1 KB
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
""" | |
Thierry Bertin-Mahieux (2010) Columbia University | |
tb2332@columbia.edu | |
This code contains a set of routines to create HDF5 files containing | |
features and metadata of a song. | |
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 os | |
import sys | |
import numpy as np | |
# code relies on pytables, see http://www.pytables.org | |
import tables | |
import hdf5_descriptors as DESC | |
from hdf5_getters import * | |
# musicbrainz related stuff | |
try: | |
from MBrainzDB import query as QUERYMB | |
except ImportError: | |
print 'need pg module and MBrainzDB folder of Python source code if you' | |
print 'want to use musicbrainz related functions, e.g. fill_hdf5_from_musicbrainz' | |
# description of the different arrays in the song file | |
ARRAY_DESC_SIMILAR_ARTISTS = 'array of similar artists Echo Nest id' | |
ARRAY_DESC_ARTIST_TERMS = 'array of terms (Echo Nest tags) for an artist' | |
ARRAY_DESC_ARTIST_TERMS_FREQ = 'array of term (Echo Nest tags) frequencies for an artist' | |
ARRAY_DESC_ARTIST_TERMS_WEIGHT = 'array of term (Echo Nest tags) weights for an artist' | |
ARRAY_DESC_SEGMENTS_START = 'array of start times of segments' | |
ARRAY_DESC_SEGMENTS_CONFIDENCE = 'array of confidence of segments' | |
ARRAY_DESC_SEGMENTS_PITCHES = 'array of pitches of segments (chromas)' | |
ARRAY_DESC_SEGMENTS_TIMBRE = 'array of timbre of segments (MFCC-like)' | |
ARRAY_DESC_SEGMENTS_LOUDNESS_MAX = 'array of max loudness of segments' | |
ARRAY_DESC_SEGMENTS_LOUDNESS_MAX_TIME = 'array of max loudness time of segments' | |
ARRAY_DESC_SEGMENTS_LOUDNESS_START = 'array of loudness of segments at start time' | |
ARRAY_DESC_SECTIONS_START = 'array of start times of sections' | |
ARRAY_DESC_SECTIONS_CONFIDENCE = 'array of confidence of sections' | |
ARRAY_DESC_BEATS_START = 'array of start times of beats' | |
ARRAY_DESC_BEATS_CONFIDENCE = 'array of confidence of sections' | |
ARRAY_DESC_BARS_START = 'array of start times of bars' | |
ARRAY_DESC_BARS_CONFIDENCE = 'array of confidence of bars' | |
ARRAY_DESC_TATUMS_START = 'array of start times of tatums' | |
ARRAY_DESC_TATUMS_CONFIDENCE = 'array of confidence of tatums' | |
ARRAY_DESC_ARTIST_MBTAGS = 'array of tags from MusicBrainz for an artist' | |
ARRAY_DESC_ARTIST_MBTAGS_COUNT = 'array of tag counts from MusicBrainz for an artist' | |
def fill_hdf5_from_artist(h5,artist): | |
""" | |
Fill an open hdf5 using all content in a artist object | |
from the Echo Nest python API | |
There could be overlap with fill_from_song and fill_from_track, | |
we assume the data is consistent! | |
""" | |
# get the metadata table, fill it | |
metadata = h5.root.metadata.songs | |
metadata.cols.artist_id[0] = artist.id | |
idsplitter = lambda x,y: x.split(':')[2] if x else y | |
metadata.cols.artist_mbid[0] = idsplitter(artist.get_foreign_id(idspace='musicbrainz'),'') | |
metadata.cols.artist_playmeid[0] = int(idsplitter(artist.get_foreign_id(idspace='playme'),-1)) | |
metadata.cols.artist_7digitalid[0] = int(idsplitter(artist.get_foreign_id(idspace='7digital'),-1)) | |
# fill the metadata arrays | |
group = h5.root.metadata | |
metadata.cols.idx_similar_artists[0] = 0 | |
group.similar_artists.append( np.array(map(lambda x : x.id,artist.get_similar(results=100)),dtype='string') ) | |
metadata.cols.idx_artist_terms[0] = 0 | |
group.artist_terms.append( np.array(map(lambda x : x.name,artist.get_terms()),dtype='string') ) | |
group.artist_terms_freq.append( np.array(map(lambda x : x.frequency,artist.get_terms()),dtype='float64') ) | |
group.artist_terms_weight.append( np.array(map(lambda x : x.weight,artist.get_terms()),dtype='float64') ) | |
# done, flush | |
metadata.flush() | |
def fill_hdf5_from_song(h5,song): | |
""" | |
Fill an open hdf5 using all the content in a song object | |
from the Echo Nest python API. | |
Usually, fill_hdf5_from_track() will have been called first. | |
""" | |
# get the metadata table, fill it | |
metadata = h5.root.metadata.songs | |
metadata.cols.artist_familiarity[0] = song.get_artist_familiarity() | |
metadata.cols.artist_hotttnesss[0] = song.get_artist_hotttnesss() | |
metadata.cols.artist_id[0] = song.artist_id | |
metadata.cols.artist_latitude[0] = song.get_artist_location().latitude | |
metadata.cols.artist_location[0] = song.get_artist_location().location.encode('utf-8') if song.get_artist_location().location else '' | |
metadata.cols.artist_longitude[0] = song.get_artist_location().longitude | |
metadata.cols.artist_name[0] = song.artist_name.encode('utf-8') if song.artist_name else '' | |
metadata.cols.song_id[0] = song.id | |
metadata.cols.song_hotttnesss[0] = song.get_song_hotttnesss() | |
metadata.cols.title[0] = song.title.encode('utf-8') if song.title else '' | |
metadata.flush() | |
# get the analysis table | |
analysis = h5.root.analysis.songs | |
analysis.danceability = song.get_audio_summary().danceability | |
analysis.energy = song.get_audio_summary().energy | |
analysis.flush() | |
def fill_hdf5_from_track(h5,track): | |
""" | |
Fill an open hdf5 using all the content in a track object | |
from the Echo Nest python API | |
""" | |
# get the metadata table, fill it | |
metadata = h5.root.metadata.songs | |
#metadata.cols.analyzer_version[0] = track.analyzer_version | |
metadata.cols.artist_name[0] = getattr(track, 'artist', u'').encode('utf-8') | |
metadata.cols.release[0] = getattr(track, 'release', u'').encode('utf-8') | |
metadata.cols.title[0] = getattr(track, 'title', u'').encode('utf-8') | |
idsplitter_7digital = lambda x: int(x.split(':')[2]) if x and x.split(':')[0]=='7digital' else -1 | |
metadata.cols.release_7digitalid[0] = idsplitter_7digital(track.foreign_release_id) | |
metadata.cols.track_7digitalid[0] = idsplitter_7digital(track.foreign_id) | |
metadata.flush() | |
# get the analysis table, fill it | |
analysis = h5.root.analysis.songs | |
analysis.cols.analysis_sample_rate[0] = track.analysis_sample_rate | |
analysis.cols.audio_md5[0] = track.audio_md5 | |
analysis.cols.duration[0] = track.duration | |
analysis.cols.end_of_fade_in[0] = track.end_of_fade_in | |
analysis.cols.key[0] = track.key | |
analysis.cols.key_confidence[0] = track.key_confidence | |
analysis.cols.loudness[0] = track.loudness | |
analysis.cols.mode[0] = track.mode | |
analysis.cols.mode_confidence[0] = track.mode_confidence | |
analysis.cols.start_of_fade_out[0] = track.start_of_fade_out | |
analysis.cols.tempo[0] = track.tempo | |
analysis.cols.time_signature[0] = track.time_signature | |
analysis.cols.time_signature_confidence[0] = track.time_signature_confidence | |
analysis.cols.track_id[0] = track.id | |
analysis.flush() | |
group = h5.root.analysis | |
# analysis arrays (segments) | |
analysis.cols.idx_segments_start[0] = 0 | |
group.segments_start.append( np.array(map(lambda x : x['start'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_confidence[0] = 0 | |
group.segments_confidence.append( np.array(map(lambda x : x['confidence'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_pitches[0] = 0 | |
group.segments_pitches.append( np.array(map(lambda x : x['pitches'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_timbre[0] = 0 | |
group.segments_timbre.append( np.array(map(lambda x : x['timbre'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_loudness_max[0] = 0 | |
group.segments_loudness_max.append( np.array(map(lambda x : x['loudness_max'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_loudness_max_time[0] = 0 | |
group.segments_loudness_max_time.append( np.array(map(lambda x : x['loudness_max_time'],track.segments),dtype='float64') ) | |
analysis.cols.idx_segments_loudness_start[0] = 0 | |
group.segments_loudness_start.append( np.array(map(lambda x : x['loudness_start'],track.segments),dtype='float64') ) | |
# analysis arrays (sections) | |
analysis.cols.idx_sections_start[0] = 0 | |
group.sections_start.append( np.array(map(lambda x : x['start'],track.sections),dtype='float64') ) | |
analysis.cols.idx_sections_confidence[0] = 0 | |
group.sections_confidence.append( np.array(map(lambda x : x['confidence'],track.sections),dtype='float64') ) | |
# analysis arrays (beats | |
analysis.cols.idx_beats_start[0] = 0 | |
group.beats_start.append( np.array(map(lambda x : x['start'],track.beats),dtype='float64') ) | |
analysis.cols.idx_beats_confidence[0] = 0 | |
group.beats_confidence.append( np.array(map(lambda x : x['confidence'],track.beats),dtype='float64') ) | |
# analysis arrays (bars) | |
analysis.cols.idx_bars_start[0] = 0 | |
group.bars_start.append( np.array(map(lambda x : x['start'],track.bars),dtype='float64') ) | |
analysis.cols.idx_bars_confidence[0] = 0 | |
group.bars_confidence.append( np.array(map(lambda x : x['confidence'],track.bars),dtype='float64') ) | |
# analysis arrays (tatums) | |
analysis.cols.idx_tatums_start[0] = 0 | |
group.tatums_start.append( np.array(map(lambda x : x['start'],track.tatums),dtype='float64') ) | |
analysis.cols.idx_tatums_confidence[0] = 0 | |
group.tatums_confidence.append( np.array(map(lambda x : x['confidence'],track.tatums),dtype='float64') ) | |
analysis.flush() | |
# DONE | |
def fill_hdf5_from_musicbrainz(h5,connect): | |
""" | |
Fill an open hdf5 using the musicbrainz server and data. | |
We assume this code is run after fill_hdf5_from_artist/song | |
because we need artist_mbid, artist_name, release and title | |
INPUT | |
h5 - open song file (append mode) | |
connect - open pg connection to musicbrainz_db | |
""" | |
# get info from h5 song file | |
ambid = h5.root.metadata.songs.cols.artist_mbid[0] | |
artist_name = h5.root.metadata.songs.cols.artist_name[0] | |
release = h5.root.metadata.songs.cols.release[0] | |
title = h5.root.metadata.songs.cols.title[0] | |
# get the musicbrainz table, fill it | |
musicbrainz = h5.root.musicbrainz.songs | |
musicbrainz.cols.year[0] = QUERYMB.find_year_safemode(connect,ambid,title,release,artist_name) | |
# fill the musicbrainz arrays | |
group = h5.root.musicbrainz | |
musicbrainz.cols.idx_artist_mbtags[0] = 0 | |
tags,tagcount = QUERYMB.get_artist_tags(connect, ambid, maxtags=20) | |
group.artist_mbtags.append( np.array(tags,dtype='string') ) | |
group.artist_mbtags_count.append( np.array(tagcount,dtype='float64') ) | |
# done, flush | |
musicbrainz.flush() | |
def fill_hdf5_aggregate_file(h5,h5_filenames,summaryfile=False): | |
""" | |
Fill an open hdf5 aggregate file using all the content from all the HDF5 files | |
listed as filenames. These HDF5 files are supposed to be filled already. | |
Usefull to create one big HDF5 file from many, thus improving IO speed. | |
For most of the info, we simply use one row per song. | |
For the arrays (e.g. segment_start) we need the indecies (e.g. idx_segment_start) | |
to know which part of the array belongs to one particular song. | |
If summaryfile=True, we skip arrays (indices all 0) | |
""" | |
# counter | |
counter = 0 | |
# iterate over filenames | |
for h5idx,h5filename in enumerate(h5_filenames): | |
# open h5 file | |
h5tocopy = open_h5_file_read(h5filename) | |
# get number of songs in new file | |
nSongs = get_num_songs(h5tocopy) | |
# iterate over songs in one HDF5 (1 if regular file, more if aggregate file) | |
for songidx in xrange(nSongs): | |
# METADATA | |
row = h5.root.metadata.songs.row | |
row["artist_familiarity"] = get_artist_familiarity(h5tocopy,songidx) | |
row["artist_hotttnesss"] = get_artist_hotttnesss(h5tocopy,songidx) | |
row["artist_id"] = get_artist_id(h5tocopy,songidx) | |
row["artist_mbid"] = get_artist_mbid(h5tocopy,songidx) | |
row["artist_playmeid"] = get_artist_playmeid(h5tocopy,songidx) | |
row["artist_7digitalid"] = get_artist_7digitalid(h5tocopy,songidx) | |
row["artist_latitude"] = get_artist_latitude(h5tocopy,songidx) | |
row["artist_location"] = get_artist_location(h5tocopy,songidx) | |
row["artist_longitude"] = get_artist_longitude(h5tocopy,songidx) | |
row["artist_name"] = get_artist_name(h5tocopy,songidx) | |
row["release"] = get_release(h5tocopy,songidx) | |
row["release_7digitalid"] = get_release_7digitalid(h5tocopy,songidx) | |
row["song_id"] = get_song_id(h5tocopy,songidx) | |
row["song_hotttnesss"] = get_song_hotttnesss(h5tocopy,songidx) | |
row["title"] = get_title(h5tocopy,songidx) | |
row["track_7digitalid"] = get_track_7digitalid(h5tocopy,songidx) | |
# INDICES | |
if not summaryfile: | |
if counter == 0 : # we're first row | |
row["idx_similar_artists"] = 0 | |
row["idx_artist_terms"] = 0 | |
else: | |
row["idx_similar_artists"] = h5.root.metadata.similar_artists.shape[0] | |
row["idx_artist_terms"] = h5.root.metadata.artist_terms.shape[0] | |
row.append() | |
h5.root.metadata.songs.flush() | |
# ARRAYS | |
if not summaryfile: | |
h5.root.metadata.similar_artists.append( get_similar_artists(h5tocopy,songidx) ) | |
h5.root.metadata.artist_terms.append( get_artist_terms(h5tocopy,songidx) ) | |
h5.root.metadata.artist_terms_freq.append( get_artist_terms_freq(h5tocopy,songidx) ) | |
h5.root.metadata.artist_terms_weight.append( get_artist_terms_weight(h5tocopy,songidx) ) | |
# ANALYSIS | |
row = h5.root.analysis.songs.row | |
row["analysis_sample_rate"] = get_analysis_sample_rate(h5tocopy,songidx) | |
row["audio_md5"] = get_audio_md5(h5tocopy,songidx) | |
row["danceability"] = get_danceability(h5tocopy,songidx) | |
row["duration"] = get_duration(h5tocopy,songidx) | |
row["end_of_fade_in"] = get_end_of_fade_in(h5tocopy,songidx) | |
row["energy"] = get_energy(h5tocopy,songidx) | |
row["key"] = get_key(h5tocopy,songidx) | |
row["key_confidence"] = get_key_confidence(h5tocopy,songidx) | |
row["loudness"] = get_loudness(h5tocopy,songidx) | |
row["mode"] = get_mode(h5tocopy,songidx) | |
row["mode_confidence"] = get_mode_confidence(h5tocopy,songidx) | |
row["start_of_fade_out"] = get_start_of_fade_out(h5tocopy,songidx) | |
row["tempo"] = get_tempo(h5tocopy,songidx) | |
row["time_signature"] = get_time_signature(h5tocopy,songidx) | |
row["time_signature_confidence"] = get_time_signature_confidence(h5tocopy,songidx) | |
row["track_id"] = get_track_id(h5tocopy,songidx) | |
# INDICES | |
if not summaryfile: | |
if counter == 0 : # we're first row | |
row["idx_segments_start"] = 0 | |
row["idx_segments_confidence"] = 0 | |
row["idx_segments_pitches"] = 0 | |
row["idx_segments_timbre"] = 0 | |
row["idx_segments_loudness_max"] = 0 | |
row["idx_segments_loudness_max_time"] = 0 | |
row["idx_segments_loudness_start"] = 0 | |
row["idx_sections_start"] = 0 | |
row["idx_sections_confidence"] = 0 | |
row["idx_beats_start"] = 0 | |
row["idx_beats_confidence"] = 0 | |
row["idx_bars_start"] = 0 | |
row["idx_bars_confidence"] = 0 | |
row["idx_tatums_start"] = 0 | |
row["idx_tatums_confidence"] = 0 | |
else : # check the current shape of the arrays | |
row["idx_segments_start"] = h5.root.analysis.segments_start.shape[0] | |
row["idx_segments_confidence"] = h5.root.analysis.segments_confidence.shape[0] | |
row["idx_segments_pitches"] = h5.root.analysis.segments_pitches.shape[0] | |
row["idx_segments_timbre"] = h5.root.analysis.segments_timbre.shape[0] | |
row["idx_segments_loudness_max"] = h5.root.analysis.segments_loudness_max.shape[0] | |
row["idx_segments_loudness_max_time"] = h5.root.analysis.segments_loudness_max_time.shape[0] | |
row["idx_segments_loudness_start"] = h5.root.analysis.segments_loudness_start.shape[0] | |
row["idx_sections_start"] = h5.root.analysis.sections_start.shape[0] | |
row["idx_sections_confidence"] = h5.root.analysis.sections_confidence.shape[0] | |
row["idx_beats_start"] = h5.root.analysis.beats_start.shape[0] | |
row["idx_beats_confidence"] = h5.root.analysis.beats_confidence.shape[0] | |
row["idx_bars_start"] = h5.root.analysis.bars_start.shape[0] | |
row["idx_bars_confidence"] = h5.root.analysis.bars_confidence.shape[0] | |
row["idx_tatums_start"] = h5.root.analysis.tatums_start.shape[0] | |
row["idx_tatums_confidence"] = h5.root.analysis.tatums_confidence.shape[0] | |
row.append() | |
h5.root.analysis.songs.flush() | |
# ARRAYS | |
if not summaryfile: | |
h5.root.analysis.segments_start.append( get_segments_start(h5tocopy,songidx) ) | |
h5.root.analysis.segments_confidence.append( get_segments_confidence(h5tocopy,songidx) ) | |
h5.root.analysis.segments_pitches.append( get_segments_pitches(h5tocopy,songidx) ) | |
h5.root.analysis.segments_timbre.append( get_segments_timbre(h5tocopy,songidx) ) | |
h5.root.analysis.segments_loudness_max.append( get_segments_loudness_max(h5tocopy,songidx) ) | |
h5.root.analysis.segments_loudness_max_time.append( get_segments_loudness_max_time(h5tocopy,songidx) ) | |
h5.root.analysis.segments_loudness_start.append( get_segments_loudness_start(h5tocopy,songidx) ) | |
h5.root.analysis.sections_start.append( get_sections_start(h5tocopy,songidx) ) | |
h5.root.analysis.sections_confidence.append( get_sections_confidence(h5tocopy,songidx) ) | |
h5.root.analysis.beats_start.append( get_beats_start(h5tocopy,songidx) ) | |
h5.root.analysis.beats_confidence.append( get_beats_confidence(h5tocopy,songidx) ) | |
h5.root.analysis.bars_start.append( get_bars_start(h5tocopy,songidx) ) | |
h5.root.analysis.bars_confidence.append( get_bars_confidence(h5tocopy,songidx) ) | |
h5.root.analysis.tatums_start.append( get_tatums_start(h5tocopy,songidx) ) | |
h5.root.analysis.tatums_confidence.append( get_tatums_confidence(h5tocopy,songidx) ) | |
# MUSICBRAINZ | |
row = h5.root.musicbrainz.songs.row | |
row["year"] = get_year(h5tocopy,songidx) | |
# INDICES | |
if not summaryfile: | |
if counter == 0 : # we're first row | |
row["idx_artist_mbtags"] = 0 | |
else: | |
row["idx_artist_mbtags"] = h5.root.musicbrainz.artist_mbtags.shape[0] | |
row.append() | |
h5.root.musicbrainz.songs.flush() | |
# ARRAYS | |
if not summaryfile: | |
h5.root.musicbrainz.artist_mbtags.append( get_artist_mbtags(h5tocopy,songidx) ) | |
h5.root.musicbrainz.artist_mbtags_count.append( get_artist_mbtags_count(h5tocopy,songidx) ) | |
# counter | |
counter += 1 | |
# close h5 file | |
h5tocopy.close() | |
def create_song_file(h5filename,title='H5 Song File',force=False,complevel=1): | |
""" | |
Create a new HDF5 file for a new song. | |
If force=False, refuse to overwrite an existing file | |
Raise a ValueError if it's the case. | |
Other optional param is the H5 file. | |
Setups the groups, each containing a table 'songs' with one row: | |
- metadata | |
- analysis | |
DETAIL | |
- we set the compression level to 1 by default, it uses the ZLIB library | |
to disable compression, set it to 0 | |
""" | |
# check if file exists | |
if not force: | |
if os.path.exists(h5filename): | |
raise ValueError('file exists, can not create HDF5 song file') | |
# create the H5 file | |
h5 = tables.openFile(h5filename, mode='w', title='H5 Song File') | |
# set filter level | |
h5.filters = tables.Filters(complevel=complevel,complib='zlib') | |
# setup the groups and tables | |
# group metadata | |
group = h5.createGroup("/",'metadata','metadata about the song') | |
table = h5.createTable(group,'songs',DESC.SongMetaData,'table of metadata for one song') | |
r = table.row | |
r.append() # filled with default values 0 or '' (depending on type) | |
table.flush() | |
# group analysis | |
group = h5.createGroup("/",'analysis','Echo Nest analysis of the song') | |
table = h5.createTable(group,'songs',DESC.SongAnalysis,'table of Echo Nest analysis for one song') | |
r = table.row | |
r.append() # filled with default values 0 or '' (depending on type) | |
table.flush() | |
# group musicbrainz | |
group = h5.createGroup("/",'musicbrainz','data about the song coming from MusicBrainz') | |
table = h5.createTable(group,'songs',DESC.SongMusicBrainz,'table of data coming from MusicBrainz') | |
r = table.row | |
r.append() # filled with default values 0 or '' (depending on type) | |
table.flush() | |
# create arrays | |
create_all_arrays(h5,expectedrows=3) | |
# close it, done | |
h5.close() | |
def create_aggregate_file(h5filename,title='H5 Aggregate File',force=False,expectedrows=1000,complevel=1, | |
summaryfile=False): | |
""" | |
Create a new HDF5 file for all songs. | |
It will contains everything that are in regular song files. | |
Tables created empty. | |
If force=False, refuse to overwrite an existing file | |
Raise a ValueError if it's the case. | |
If summaryfile=True, creates a sumary file, i.e. no arrays | |
Other optional param is the H5 file. | |
DETAILS | |
- if you create a very large file, try to approximate correctly | |
the number of data points (songs), it speeds things up with arrays (by | |
setting the chunking correctly). | |
- we set the compression level to 1 by default, it uses the ZLIB library | |
to disable compression, set it to 0 | |
Setups the groups, each containing a table 'songs' with one row: | |
- metadata | |
- analysis | |
""" | |
# check if file exists | |
if not force: | |
if os.path.exists(h5filename): | |
raise ValueError('file exists, can not create HDF5 song file') | |
# summary file? change title | |
if summaryfile: | |
title = 'H5 Summary File' | |
# create the H5 file | |
h5 = tables.openFile(h5filename, mode='w', title='H5 Song File') | |
# set filter level | |
h5.filters = tables.Filters(complevel=complevel,complib='zlib') | |
# setup the groups and tables | |
# group metadata | |
group = h5.createGroup("/",'metadata','metadata about the song') | |
table = h5.createTable(group,'songs',DESC.SongMetaData,'table of metadata for one song', | |
expectedrows=expectedrows) | |
# group analysis | |
group = h5.createGroup("/",'analysis','Echo Nest analysis of the song') | |
table = h5.createTable(group,'songs',DESC.SongAnalysis,'table of Echo Nest analysis for one song', | |
expectedrows=expectedrows) | |
# group musicbrainz | |
group = h5.createGroup("/",'musicbrainz','data about the song coming from MusicBrainz') | |
table = h5.createTable(group,'songs',DESC.SongMusicBrainz,'table of data coming from MusicBrainz', | |
expectedrows=expectedrows) | |
# create arrays | |
if not summaryfile: | |
create_all_arrays(h5,expectedrows=expectedrows) | |
# close it, done | |
h5.close() | |
def create_all_arrays(h5,expectedrows=1000): | |
""" | |
Utility functions used by both create_song_file and create_aggregate_files, | |
creates all the EArrays (empty). | |
INPUT | |
h5 - hdf5 file, open with write or append permissions | |
metadata and analysis groups already exist! | |
""" | |
# group metadata arrays | |
group = h5.root.metadata | |
h5.createEArray(where=group,name='similar_artists',atom=tables.StringAtom(20,shape=()),shape=(0,),title=ARRAY_DESC_SIMILAR_ARTISTS) | |
h5.createEArray(group,'artist_terms',tables.StringAtom(256,shape=()),(0,),ARRAY_DESC_ARTIST_TERMS, | |
expectedrows=expectedrows*40) | |
h5.createEArray(group,'artist_terms_freq',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_ARTIST_TERMS_FREQ, | |
expectedrows=expectedrows*40) | |
h5.createEArray(group,'artist_terms_weight',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_ARTIST_TERMS_WEIGHT, | |
expectedrows=expectedrows*40) | |
# group analysis arrays | |
group = h5.root.analysis | |
h5.createEArray(where=group,name='segments_start',atom=tables.Float64Atom(shape=()),shape=(0,),title=ARRAY_DESC_SEGMENTS_START) | |
h5.createEArray(group,'segments_confidence',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SEGMENTS_CONFIDENCE, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'segments_pitches',tables.Float64Atom(shape=()),(0,12),ARRAY_DESC_SEGMENTS_PITCHES, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'segments_timbre',tables.Float64Atom(shape=()),(0,12),ARRAY_DESC_SEGMENTS_TIMBRE, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'segments_loudness_max',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SEGMENTS_LOUDNESS_MAX, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'segments_loudness_max_time',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SEGMENTS_LOUDNESS_MAX_TIME, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'segments_loudness_start',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SEGMENTS_LOUDNESS_START, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'sections_start',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SECTIONS_START, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'sections_confidence',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_SECTIONS_CONFIDENCE, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'beats_start',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_BEATS_START, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'beats_confidence',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_BEATS_CONFIDENCE, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'bars_start',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_BARS_START, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'bars_confidence',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_BARS_CONFIDENCE, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'tatums_start',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_TATUMS_START, | |
expectedrows=expectedrows*300) | |
h5.createEArray(group,'tatums_confidence',tables.Float64Atom(shape=()),(0,),ARRAY_DESC_TATUMS_CONFIDENCE, | |
expectedrows=expectedrows*300) | |
# group musicbrainz arrays | |
group = h5.root.musicbrainz | |
h5.createEArray(where=group,name='artist_mbtags',atom=tables.StringAtom(256,shape=()),shape=(0,),title=ARRAY_DESC_ARTIST_MBTAGS, | |
expectedrows=expectedrows*5) | |
h5.createEArray(group,'artist_mbtags_count',tables.IntAtom(shape=()),(0,),ARRAY_DESC_ARTIST_MBTAGS_COUNT, | |
expectedrows=expectedrows*5) | |
def open_h5_file_read(h5filename): | |
""" | |
Open an existing H5 in read mode. | |
""" | |
return tables.openFile(h5filename, mode='r') | |
def open_h5_file_append(h5filename): | |
""" | |
Open an existing H5 in append mode. | |
""" | |
return tables.openFile(h5filename, mode='a') | |
################################################ MAIN ##################################### | |
def die_with_usage(): | |
""" HELP MENU """ | |
print 'hdf5_utils.py' | |
print 'by T. Bertin-Mahieux (2010) Columbia University' | |
print '' | |
print 'should be used as a library, contains functions to create' | |
print 'HDF5 files for the Million Song Dataset project' | |
sys.exit(0) | |
if __name__ == '__main__': | |
# help menu | |
die_with_usage() | |