NWB extensions for storing hierarchical behavioral data

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ndx-hierarchical-behavioral-data Extension for NWB

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()

	label	start_time	stop_time	next_tier
id
0	A-L	0.0	12.0	label start_time stop_time \\id ...

words.to_dataframe()

	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

syllables.to_dataframe()

	label	start_time	stop_time	next_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

phonemes.to_dataframe()

	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

Hierarchical dataframe:

sentences.to_hierarchical_dataframe()

												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

Hierachical columns, flattened rows:

from hdmf.common.hierarchicaltable import flatten_column_index

flatten_column_index(sentences.to_hierarchical_dataframe(), 1)

													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

Denormalized dataframe:

from hdmf.common.hierarchicaltable import to_hierarchical_dataframe

to_hierarchical_dataframe(sentences).reset_index()

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

Denormalized dataframe with flattened columns:

from hdmf.common.hierarchicaltable import flatten_column_index

flatten_column_index(sentences.to_hierarchical_dataframe(), 1).reset_index()

	sentences_label	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	A-L	12.0	0	A-F	0.0	6.0	False	0	abc	0.0	3.0	0	0.0	1.0	a	0
1	A-L	12.0	0	A-F	0.0	6.0	False	0	abc	0.0	3.0	1	1.0	2.0	b	1
2	A-L	12.0	0	A-F	0.0	6.0	False	0	abc	0.0	3.0	2	2.0	3.0	c	4
3	A-L	12.0	0	A-F	0.0	6.0	False	1	def	3.0	6.0	3	3.0	4.0	d	9
4	A-L	12.0	0	A-F	0.0	6.0	False	1	def	3.0	6.0	4	4.0	5.0	e	16
5	A-L	12.0	0	A-F	0.0	6.0	False	1	def	3.0	6.0	5	5.0	6.0	f	25
6	A-L	12.0	1	G-L	6.0	12.0	True	2	ghi	6.0	9.0	6	6.0	7.0	g	36
7	A-L	12.0	1	G-L	6.0	12.0	True	2	ghi	6.0	9.0	7	7.0	8.0	h	49
8	A-L	12.0	1	G-L	6.0	12.0	True	2	ghi	6.0	9.0	8	8.0	9.0	i	64
9	A-L	12.0	1	G-L	6.0	12.0	True	3	jkl	9.0	12.0	9	9.0	10.0	j	81
10	A-L	12.0	1	G-L	6.0	12.0	True	3	jkl	9.0	12.0	10	10.0	11.0	k	100
11	A-L	12.0	1	G-L	6.0	12.0	True	3	jkl	9.0	12.0	11	11.0	12.0	l	121

This extension was created using ndx-template.

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This version

0.1.2

Nov 22, 2022

0.1.1

Jul 10, 2020

0.1.0

Jul 9, 2020

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ndx-hierarchical-behavioral-data 0.1.2

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ndx-hierarchical-behavioral-data Extension for NWB

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