Skip to main content

NWB extensions for storing hierarchical behavioral data

Project description

ndx-hierarchical-behavioral-data Extension for NWB

PyPI version

schema schema

Installation

pip install ndx-hierarchical-behavioral-data

Usage

Use pre-made hierarchical transcription tables:

from ndx_hierarchical_behavioral_data.definitions.transcription import TIPhonemes, HBTSyllables, HBTWords, HBTSentences

# Phonemes level
phonemes = TIPhonemes()
phonemes.add_column('max_pitch', 'maximum pitch for this phoneme. NaN for unvoiced')
for i, p in enumerate('abcdefghijkl'):
    phonemes.add_interval(label=p, start_time=float(i), stop_time=float(i+1), max_pitch=i**2)

# Syllables level
syllables = HBTSyllables(lower_tier_table=phonemes)
syllables.add_interval(label='abc', next_tier=[0, 1, 2])
syllables.add_interval(label='def', next_tier=[3, 4, 5])
syllables.add_interval(label='ghi', next_tier=[6, 7, 8])
syllables.add_interval(label='jkl', next_tier=[9, 10, 11])

# Words level
words = HBTWords(lower_tier_table=syllables)
words.add_column('emphasis', 'boolean indicating whether this word was emphasized')
words.add_interval(label='A-F', next_tier=[0, 1], emphasis=False)
words.add_interval(label='G-L', next_tier=[2, 3], emphasis=True)

# Sentences level
sentences = HBTSentences(lower_tier_table=words)
sentences.add_interval(label='A-L', next_tier=[0, 1])

View individual tiers:

sentences.to_dataframe()
<html>
labelstart_timestop_timenext_tier
id
0A-L0.012.0label start_time stop_time \\id ...
</html>
words.to_dataframe()
<html>
label start_time stop_time next_tier emphasis
id
0 A-F 0.0 6.0 label start_time stop_time \\ id 0 abc 0.0 3.0 1 def 3.0 6.0 next_tier id 0 start_time stop_time label max_pitch id 0 0.0 1.0 a 0 1 1.0 2.0 b 1 2 2.0 3.0 c 4 1 start_time stop_time label max_pitch id 3 3.0 4.0 d 9 4 4.0 5.0 e 16 5 5.0 6.0 f 25 False
1 G-L 6.0 12.0 label start_time stop_time \\ id 2 ghi 6.0 9.0 3 jkl 9.0 12.0 next_tier id 2 start_time stop_time label max_pitch id 6 6.0 7.0 g 36 7 7.0 8.0 h 49 8 8.0 9.0 i 64 3 start_time stop_time label max_pitch id 9 9.0 10.0 j 81 10 10.0 11.0 k 100 11 11.0 12.0 l 121 True
</html>
syllables.to_dataframe()
<html>
labelstart_timestop_timenext_tier
id
0 abc 0.0 3.0 start_time stop_time label id 0 0.0 1.0 a 1 1.0 2.0 b 2 2.0 3.0 c
1 def 3.0 6.0 start_time stop_time label id 3 3.0 4.0 d 4 4.0 5.0 e 5 5.0 6.0 f
2 ghi 6.0 9.0 start_time stop_time label id 6 6.0 7.0 g 7 7.0 8.0 h 8 8.0 9.0 i
3 jkl 9.0 12.0 start_time stop_time label id 9 9.0 10.0 j 10 10.0 11.0 k 11 11.0 12.0 l
</html>
phonemes.to_dataframe()
<html>
start_time stop_time label max_pitch
id
0 0.0 1.0 a 0
1 1.0 2.0 b 1
2 2.0 3.0 c 4
3 3.0 4.0 d 9
4 4.0 5.0 e 16
5 5.0 6.0 f 25
6 6.0 7.0 g 36
7 7.0 8.0 h 49
8 8.0 9.0 i 64
9 9.0 10.0 j 81
10 10.0 11.0 k 100
11 11.0 12.0 l 121
</html>

Hierarchical dataframe:

sentences.to_hierarchical_dataframe()
<html>
source_table phonemes
label id start_time stop_time label max_pitch
sentences_id sentences_label sentences_start_time sentences_stop_time words_id words_label words_start_time words_stop_time words_emphasis syllables_id syllables_label syllables_start_time syllables_stop_time
0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 0 0.0 1.0 a 0
3.0 1 1.0 2.0 b 1
3.0 2 2.0 3.0 c 4
1 def 3.0 6.0 3 3.0 4.0 d 9
6.0 4 4.0 5.0 e 16
6.0 5 5.0 6.0 f 25
1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 6 6.0 7.0 g 36
9.0 7 7.0 8.0 h 49
9.0 8 8.0 9.0 i 64
3 jkl 9.0 12.0 9 9.0 10.0 j 81
12.0 10 10.0 11.0 k 100
12.0 11 11.0 12.0 l 121
</html>

Hierachical columns, flattened rows:

sentences.to_hierarchical_dataframe(flat_column_index=True)
<html>
id start_time stop_time label max_pitch
sentences_id sentences_label sentences_start_time sentences_stop_time words_id words_label words_start_time words_stop_time words_emphasis syllables_id syllables_label syllables_start_time syllables_stop_time
0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 0 0.0 1.0 a 0
3.0 1 1.0 2.0 b 1
3.0 2 2.0 3.0 c 4
1 def 3.0 6.0 3 3.0 4.0 d 9
6.0 4 4.0 5.0 e 16
6.0 5 5.0 6.0 f 25
1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 6 6.0 7.0 g 36
9.0 7 7.0 8.0 h 49
9.0 8 8.0 9.0 i 64
3 jkl 9.0 12.0 9 9.0 10.0 j 81
12.0 10 10.0 11.0 k 100
12.0 11 11.0 12.0 l 121
</html>

Denormalized dataframe:

sentences.to_denormalized_dataframe()
<html>
source_table sentences words syllables phonemes
label id label start_time stop_time id label start_time stop_time emphasis id label start_time stop_time id start_time stop_time label max_pitch
0 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 0 0.0 1.0 a 0
1 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 1 1.0 2.0 b 1
2 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 2 2.0 3.0 c 4
3 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 3 3.0 4.0 d 9
4 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 4 4.0 5.0 e 16
5 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 5 5.0 6.0 f 25
6 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 6 6.0 7.0 g 36
7 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 7 7.0 8.0 h 49
8 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 8 8.0 9.0 i 64
9 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 9 9.0 10.0 j 81
10 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 10 10.0 11.0 k 100
11 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 11 11.0 12.0 l 121
</html>

Denormalized dataframe with flattened columns:

sentences.to_denormalized_dataframe(flat_column_index=True)
<html>
sentences_id sentences_label sentences_start_time sentences_stop_time words_id words_label words_start_time words_stop_time words_emphasis syllables_id syllables_label syllables_start_time syllables_stop_time id start_time stop_time label max_pitch
0 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 0 0.0 1.0 a 0
1 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 1 1.0 2.0 b 1
2 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 0 abc 0.0 3.0 2 2.0 3.0 c 4
3 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 3 3.0 4.0 d 9
4 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 4 4.0 5.0 e 16
5 0 A-L 0.0 12.0 0 A-F 0.0 6.0 False 1 def 3.0 6.0 5 5.0 6.0 f 25
6 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 6 6.0 7.0 g 36
7 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 7 7.0 8.0 h 49
8 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 2 ghi 6.0 9.0 8 8.0 9.0 i 64
9 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 9 9.0 10.0 j 81
10 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 10 10.0 11.0 k 100
11 0 A-L 0.0 12.0 1 G-L 6.0 12.0 True 3 jkl 9.0 12.0 11 11.0 12.0 l 121
</html>

This extension was created using ndx-template.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for ndx-hierarchical-behavioral-data, version 0.1.1
Filename, size File type Python version Upload date Hashes
Filename, size ndx_hierarchical_behavioral_data-0.1.1-py2.py3-none-any.whl (14.6 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size ndx-hierarchical-behavioral-data-0.1.1.tar.gz (22.0 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page