pyradigms 0.0.5

Composing and decomposing linguistic paradigms.

pyradigms

Compose and decompose linguistic paradigms.

1. Installation
2. Usage
   1. Composing paradigms
   2. Decomposing paradigms

Installation

Use pip(3) install pyradigms or get the latest version from github with pip(3) install git+https://github.com/fmatter/pyradigms.git.

Usage

Basically, pyradigms is intended to convert between the two following formats:

Verb	Form	Tense	Person	Number	Mood
ma	ngamam	NFUT	1	SG	RLS
ma	nam	NFUT	2	SG	RLS
ma	mam	NFUT	3	SG	RLS
ma	thamam	NFUT	1+2	SG	RLS
…	…	…	…	…	…

ma	PST.IRR	PST.RLS	NFUT.IRR	NFUT.RLS
1SG	ngimi	me	ngama	ngamam
2SG	ni	ne	thama	nam
3SG	mi	me	kama	mam
1+2SG	thumi	thume	pama	thamam
…	…	…	…	…

In linguistic morphology, a paradigm is a collection of word forms belonging to the same lexeme, here for the Murrinhpatha verb root ma 'say, do'. The first table shows all word forms, with relevant grammatical categories like tense, person, etc. represented in their own column. The second table is what is conventionally called a paradigm, which shows person-number combinations on the left, and tense-mood combinations at the top, with cells only containing the word forms. pyradigms is primarily intended to create such paradigm tables from any list, allowing you to combine parameters in any way you like.

Composing paradigms

To create a paradigm, we need to specify at least x and y, lists of parameter(s) which contain the values to be used as column and row names, respectively. Optionally, a list z can be specified, which will be represented as several tables. We can also provide the following optional values:

• filters: a list of dicts. The dicts contain a parameter name as key, and a list as values. Only entries which have the specified value for the specified parameter will be used.
• ignore: a list of parameters which will be ignored completely.
• x_sort: a list of values according to which columns will be sorted.
• y_sort: a list of values according to which rows will be sorted.
• separators: a list of strings to be used as separators between two parameter values. The first one will be used, but multiple can be specified for decomposing paradigms, see below. Default value is ["."].
• content_string: The parameter which contain the values to be used as cell contents. Default value is "Form".
• person_values: List of strings which will be combined with other strings without using a separator. They will also be parsed accordingly. Default value is ["1", "2", "3", "1+3", "1+2"].

All these values are module-wide, so we specify them before composing or decomposing paradigms. There are three methods for constructing paradigms:

• compose_from_csv: takes a path to a csv file
• compose_paradigm: takes a pandas dataframe
• compose_from_text: takes a csv string

All three have the optional argument csv_output which can contain a path to a csv file. If present, the output will be written to that file.

Here is an example based on the Murrinhpatha verbs seen above, which can be found under examples/murrinhpatha_verb_entries.csv.

import pyradigms as pyd
pyd.x = ["Tense", "Mood"]
pyd.y = ["Person", "Number"]
pyd.z = ["Verb"]
pyd.y_sort = ["1SG", "2SG", "3SG", "1+2SG", "1PAUC", "2PAUC", "3PAUC", "1PL", "2PL", "3PL"]
paradigms = pyd.compose_from_csv("examples/murrinhpatha_verb_entries.csv")
print(paradigms["ma"])

This will:

1. use combinations of Tense as Mood as column names
2. use the combination of Person and Number as row names
3. create a table for each verb
4. sort the Person-Number combinations accordingly.

The resulting paradigm table:

ma	NFUT.RLS	PST.IRR	NFUT.IRR	PST.RLS
1SG	ngamam	ngimi	ngama	me
2SG	nam	ni	thama	ne
3SG	mam	mi	kama	me
1+2SG	thamam	thumi	pama	thume
1PAUC		ngumi	nguyema	ngume
2PAUC		numi	nuyema	nume
3PAUC		pumi	kuyema	pume
1PL	ngamam	ngumi	nguyema	ngume
2PL	namam	numi	nuyema	nume
3PL	pamam	pumi	kuyema	pume

If z is not an empty list, pyradigms will return a dict which has the z values as keys, containing pandas dataframes, therefore print(paradigms["ma"]). If z is an empty list, only a single dataframe will be returned.

A different example from the same data, where we filter only second person values, and then put person-number on the x, verbs on the y, and tense-mood on the z axis:

pyd.x = ["Person", "Number"]
pyd.y = ["Verb"]
pyd.z = ["Tense", "Mood"]
pyd.filters = {"Person": ["2"]}
pyd.x_sort=["2SG", "2PAUC", "2PL"]
paradigms = pyd.compose_from_csv("murrinhpatha_verb_entries.csv")
print(paradigms["NFUT.IRR"])

NFUT.IRR	2SG	2PAUC	2PL
rdi	thurdi	nudde	nuddi
ba	da	nuba	nuba
me	ne	nume	nume
li	tjili	nilli	nilli
bi	di	nubi	nubi
ni	thani	narne	narni
e	tje	ne	ne
ngi	thungi	nunge	nungi
…	…	…	…

Another example, with Latin noun forms:

pyd.x = ["Case"]
pyd.y = ["Noun"]
pyd.z = ["Number"]
pyd.y_sort = ["NOM", "GEN", "DAT", "ACC","ABL","VOC"]
paradigms = pyd.compose_from_csv("examples/latin_noun_entries_short.csv")
print(paradigms["SG"])
print(paradigms["PL"])

SG	GEN	NOM	ACC	ABL	DAT	VOC
uxor	uksoːris	uksor	uksoːrem	uksoːre	uksoːriː	uksor
aestus	ajstuːs	ajstus	ajstum	ajstuː	ajstuiː	ajstus
aqua	akwaj	akwa	akwam	akwaː	akwaj	akwa

PL	ACC	GEN	ABL	VOC	NOM	DAT
uxor	uksoːreːs	uksoːrum	uksoːribus	uksoːreːs	uksoːreːs	uksoːribus
aestus	ajstuːs	ajstuum	ajstibus	ajstuːs	ajstuːs	ajstibus
aqua	akwaːs	akwaːrum	akwiːs	akwaj	akwaj	akwiːs

Alternatively, we can generate tables for each case, and look at dative and ablative forms:

pyd.x = ["Noun"]
pyd.y = ["Number"]
pyd.z = ["Case"]
pyd.y_sort=["SG", "PL"]
paradigms = pyd.compose_from_csv("examples/latin_noun_entries_short.csv")
print(paradigms["DAT"])
print(paradigms["ABL"])

DAT	aqua	aestus	uxor
SG	akwaj	ajstuiː	uksoːriː
PL	akwiːs	ajstibus	uksoːribus

ABL	uxor	aestus	aqua
SG	uksoːre	ajstuː	akwaː
PL	uksoːribus	ajstibus	akwiːs

Of course, you can also use pyradigms for comparative tables containing multiple languages. For example, examples/cariban_swadesh_entries.csv contains some Swadesh entries for Cariban languages. The following code puts cognate sets (reconstructed forms) on the y-axis, languages on the x-axis, and filters for six languages (for vertical space limitations in this readme…).

pyd.x = ["Language"]
pyd.y = ["Cognateset"]
pyd.z = []
pyd.content_string = "Value"
lg_list = ["Werikyana", "Hixkaryána", "Waiwai", "Bakairi", "Arara", "Ikpeng"]
pyd.filters = {"Language": lg_list}
pyd.x_sort = lg_list
paradigms = pyd.compose_from_csv("examples/cariban_swadesh_entries.csv")
print(paradigms)

Cognateset	Werikyana	Hixkaryána	Waiwai	Bakairi	Arara	Ikpeng
*wewe	wewe	wewe	weewe
*punu	hunu	hun	ɸun	ũrũ	munu	mnu
*pitupə	hi	hut͡ʃhu	ɸit͡ʃho	tubɨ	iput	pitu
*jəje				e	jei	jaj
*jətɨpə	jot͡ʃpɨ	jot͡ʃhɨ	jot͡ʃho	ibɨrɨ	itpɨ	itpɨn

Alternatively, we can put cognate sets on the x-axis and languages on the y-axis:

pyd.z = []
pyd.y = ["Language"]
pyd.x = ["Cognateset"]
pyd.filters = {}
pyd.y_sort = ['Werikyana', 'Hixkaryána', 'Waiwai', 'Arara', 'Ikpeng', 'Bakairi', 'Tiriyó', 'Akuriyó', 'Karijona', 'Wayana', 'Apalaí', "Kari'ña", "Ye'kwana", 'Kapón', 'Akawaio', 'Ingarikó', 'Patamona', 'Pemón', 'Macushi', 'Panare', 'Tamanaku', 'Yawarana', 'Mapoyo', 'Kumaná', 'Upper Xingu Carib', 'Kuikuro', 'Yukpa', 'Japreria', 'Waimiri-Atroari']
paradigms = pyd.compose_from_csv("examples/cariban_swadesh_entries.csv")
print(paradigms)

Language	*punu	*pitupə	*jəje	*jətɨpə	*wewe
Werikyana	hunu	hi		jot͡ʃpɨ	wewe
Hixkaryána	hun	hut͡ʃhu		jot͡ʃhɨ	wewe
Waiwai	ɸun	ɸit͡ʃho		jot͡ʃho	weewe
Arara	munu	iput	jei	itpɨ
Ikpeng	mnu	pitu	jaj	itpɨn
Bakairi	ũrũ	tubɨ	e	ibɨrɨ
Tiriyó	pun	pihpə		jetɨpə	wewe
Akuriyó	puunu	pihpə		jeʔpə	wewe
Karijona	bunu	hitihə		ijetihɨ	wewe
Wayana	punu	pitpə		jetpə	wewe
Apalaí	pu	piʔpo		zeʔpo	wewe
Kari'ña	pun	piʔpo		jeʔpo	wewe
Ye'kwana	hunu	hiʔhə	ree	jeeʔhə
Akawaio	pun	piʔpə	jɨi	əʔpɨ
Ingarikó	pun	piʔpɨ	jɨi	əʔpɨ
Pemón	pun	piʔpə	jəi	jeʔpə
Macushi	pun	piʔpɨ	jei	jeʔpɨ
Panare	-pu	pihpə	ije	jəhpə
Tamanaku	punu	pitpe	jeje	jetpe
Yawarana	puunu	pihpə	jəəje	jəspə
Mapoyo	punu	piʔpə	jəhe	jəʔpə
Kuikuro	huŋu	hiɟo	i	ipɨɣɨ
Yukpa	pu			jopo	we
Waimiri-Atroari	pɨnɨ	biʃi		jɨhɨ	wiwe

Decomposing paradigms

This was added as a secondary functionality and is somewhat experimental. The basic idea is that it allows you to decompose a paradigm which is already in the traditional linguistic format, into a list of parametrized rows. This can be useful if you already have a nicely formatted paradigm somewhere, but need it in an explicit list format -- for example, to recompose it in a different layout.

The file examples/icelandic_pronoun_paradigm.csv contains the personal pronouns of Icelandic:

	1	2	3M	3F	3N
NOM.SG	ég	þú	hann	hún	það
ACC.SG	mig	þig	hann	hana	það
DAT.SG	mér	þér	honum	henni	því
GEN.SG	mín	þín	hans	hennar	þess
NOM.PL	við	þið	þeir	þær	þau
ACC.PL	okkur	ykkur	þá	þær	þau
DAT.PL	okkur	ykkur	þeim	þeim	þeim
GEN.PL	okkar	ykkar	þeirra	þeirra	þeirra

x is a combination of person and gender, although first and second person have no gender distinction. y is a combination of case and number. If we define these parameters accordingly, we get the following:

pyd.x = ["Person", "Gender"]
pyd.y = ["Case", "Number"]
pyd.z = []
entries = pyd.decompose_from_csv("examples/icelandic_pronoun_paradigm.csv")
print(entries)

Person	Gender	Case	Number	Value
1		NOM	SG	ég
1		ACC	SG	mig
1		DAT	SG	mér
1		GEN	SG	mín
1		NOM	PL	við
1		ACC	PL	okkur
1		DAT	PL	okkur
1		GEN	PL	okkar
2		NOM	SG	þú
2		ACC	SG	þig
2		DAT	SG	þér
2		GEN	SG	þín
2		NOM	PL	þið
2		ACC	PL	ykkur
2		DAT	PL	ykkur
2		GEN	PL	ykkar
3	M	NOM	SG	hann
3	M	ACC	SG	hann
3	M	DAT	SG	honum
3	M	GEN	SG	hans
3	M	NOM	PL	þeir
3	M	ACC	PL	þá
3	M	DAT	PL	þeim
3	M	GEN	PL	þeirra
3	F	NOM	SG	hún
3	F	ACC	SG	hana
3	F	DAT	SG	henni
3	F	GEN	SG	hennar
3	F	NOM	PL	þær
3	F	ACC	PL	þær
3	F	DAT	PL	þeim
3	F	GEN	PL	þeirra
3	N	NOM	SG	það
3	N	ACC	SG	það
3	N	DAT	SG	því
3	N	GEN	SG	þess
3	N	NOM	PL	þau
3	N	ACC	PL	þau
3	N	DAT	PL	þeim
3	N	GEN	PL	þeirra

As a last example, examples/mapudungun_verb_paradigms.csv contains three verb paradigms from Mapudungun, separated by a double line break:

kon-	1SG	1DU	1PL	2SG	2DU	2PL	3SG	3DU	3PL
IND	konün	koniyu	koniyiñ	konimi	konimu	konimün	koni	koningu	koningün
SBJV	konli	konliyu	konliyiñ	konülmi	konülmu	konülmün	konle	konle engu	konle engün
IMP	konchi			konnge	konmu	konmün	konpe	konpe engu	konpe engün

pi-	1SG	1DU	1PL	2SG	2DU	2PL	3SG	3DU	3PL
IND	pin	piyu	piiñ	pimi	pimu	pimün	pi	pingu	pingün
SBJV	pili	piliyu	piliyiñ	pilmi	pilmu	pilmün	pile	pile engu	pile engün
IMP	pichi			pinge	pimu	pimün	pipe	pipe engu	pipe engün

tripa-	1SG	1DU	1PL	2SG	2DU	2PL	3SG	3DU	3PL
IND	tripan	tripayu	tripaiñ	tripaymi	tripaymu	tripaymün	tripay	tripayngu	tripayngün
SBJV	tripali	tripaliyu	tripaliyiñ	tripalmi	tripalmu	tripalmün	tripale	tripale engu	tripale engü
IMP	tripachi			tripange	tripamu	tripamün	tripape	tripape engu	tripape engün

We can extract the entries as follows:

pyd.x = ["Person", "Number"]
pyd.y = ["Mood"]
pyd.z = ["Verb"]
print(pyd.decompose_from_csv("examples/mapudungun_verb_paradigms.csv"))

Person	Number	Verb	Mood	Form
1	SG	kon-	IND	konün
1	SG	kon-	SBJV	konli
1	SG	kon-	IMP	konchi
1	DU	kon-	IND	koniyu
1	DU	kon-	SBJV	konliyu
1	PL	kon-	IND	koniyiñ
1	PL	kon-	SBJV	konliyiñ
2	SG	kon-	IND	konimi
2	SG	kon-	SBJV	konülmi
2	SG	kon-	IMP	konnge
2	DU	kon-	IND	konimu
2	DU	kon-	SBJV	konülmu
2	DU	kon-	IMP	konmu
2	PL	kon-	IND	konimün
2	PL	kon-	SBJV	konülmün
2	PL	kon-	IMP	konmün
3	SG	kon-	IND	koni
3	SG	kon-	SBJV	konle
3	SG	kon-	IMP	konpe
3	DU	kon-	IND	koningu
3	DU	kon-	SBJV	konle engu
3	DU	kon-	IMP	konpe engu
3	PL	kon-	IND	koningün
3	PL	kon-	SBJV	konle engün
3	PL	kon-	IMP	konpe engün
1	SG	pi-	IND	pin
1	SG	pi-	SBJV	pili
1	SG	pi-	IMP	pichi
1	DU	pi-	IND	piyu
1	DU	pi-	SBJV	piliyu
1	PL	pi-	IND	piiñ
1	PL	pi-	SBJV	piliyiñ
2	SG	pi-	IND	pimi
2	SG	pi-	SBJV	pilmi
2	SG	pi-	IMP	pinge
2	DU	pi-	IND	pimu
2	DU	pi-	SBJV	pilmu
2	DU	pi-	IMP	pimu
2	PL	pi-	IND	pimün
2	PL	pi-	SBJV	pilmün
2	PL	pi-	IMP	pimün
3	SG	pi-	IND	pi
3	SG	pi-	SBJV	pile
3	SG	pi-	IMP	pipe
3	DU	pi-	IND	pingu
3	DU	pi-	SBJV	pile engu
3	DU	pi-	IMP	pipe engu
3	PL	pi-	IND	pingün
3	PL	pi-	SBJV	pile engün
3	PL	pi-	IMP	pipe engün
1	SG	tripa-	IND	tripan
1	SG	tripa-	SBJV	tripali
1	SG	tripa-	IMP	tripachi
1	DU	tripa-	IND	tripayu
1	DU	tripa-	SBJV	tripaliyu
1	PL	tripa-	IND	tripaiñ
1	PL	tripa-	SBJV	tripaliyiñ
2	SG	tripa-	IND	tripaymi
2	SG	tripa-	SBJV	tripalmi
2	SG	tripa-	IMP	tripange
2	DU	tripa-	IND	tripaymu
2	DU	tripa-	SBJV	tripalmu
2	DU	tripa-	IMP	tripamu
2	PL	tripa-	IND	tripaymün
2	PL	tripa-	SBJV	tripalmün
2	PL	tripa-	IMP	tripamün
3	SG	tripa-	IND	tripay
3	SG	tripa-	SBJV	tripale
3	SG	tripa-	IMP	tripape
3	DU	tripa-	IND	tripayngu
3	DU	tripa-	SBJV	tripale engu
3	DU	tripa-	IMP	tripape engu
3	PL	tripa-	IND	tripayngün
3	PL	tripa-	SBJV	tripale engü
3	PL	tripa-	IMP	tripape engün