pyradigms 0.0.2

Creates paradigms from a table of entries with parameters.

pyradigms

Creates paradigms from a table of entries with parameters.

Getting started

pip install pyradigms

Usage

There are two separate methods; create_hash creates a three-dimensional dictionary from a .csv file with a list of forms with parameters, print_paradigms creates human-readable paradigms, also in .csv format, from such a dictionary. Both methods can be used on their own, but dictionaries produced by create_hash will automatically be used by print_paradigms. Example usage:

from pyradigms import Pyradigms
pd = Pyradigms("output.csv")
pd.create_hash("input.csv",x=["X"] ,y=["Y"] ,z=["Z"])
pd.print_paradigms()

print_paradigms

The print_paradigms method takes an argument tables, which is a three-dimensional dictionary, and prints them to the specified .csv output file. If no tables argument is passed, the dictionary created by create_hash will be used. Take the following dictionary of Bernese German verb forms as an example. It has three dimensions, the first being the meaning of the verb, the second being number, and the third being person.

bernese_verbs = {"to go": {
    "SG": {"1": "kɑː",
        "2": "kɛjʃ",
        "3": "kɛjtː"},
    "PL": {"1": "kœː",
        "2":"kœːtː",
        "3":"kœː"}
}, "to say": {
    "SG": {"1": "sækə",
        "2": "sɛjʃ",
        "3": "sɛjtː"},
    "PL": {"1": "sækə",
        "2":"sækətː",
        "3":"sækə"}
    }
}

With print_paradigms(bernese_verbs), a .csv file with the following content is produced:

to go	1	2	3
SG	kɑː	kɛjʃ	kɛjtː
PL	kœː	kœːtː	kœː

to say	1	2	3
SG	sækə	sɛjʃ	sɛjtː
PL	sækə	sækətː	sækə

In the .csv file, The first layer of the three-dimensional hash is represented in the z dimension, i.e. paradigm tables stacked vertically, the second layer is represented in the y axis of the individual tables, and the third layer is represented in the x axis.

create_hash

The create_hash method reads entries from a .csv file and produces a dictionary like the one above. The .csv file should have the following format, again illustrated with the Bernese German forms:

Verb	Number	Person	Form
to go	SG	1	kɑː
to go	SG	2	kɛjʃ
to go	SG	3	kɛjtː
to go	PL	1	kœː
to go	PL	2	kœːtː
to go	PL	3	kœː
to say	SG	1	sækə
to say	SG	2	sɛjʃ
to say	SG	3	sɛjtː
to say	PL	1	sækə
to say	PL	2	sækətː
to say	PL	3	sækə

In order to specify what parameter should be projected onto which dimension, the arguments x, y, and z must be passed. They each take a list of strings, the strings being parameters present in the .csv file. z represents the multiple paradigm tables listed vertically. y represents the rows of a single paradigm table. x represents the columns of a single paradigm table. The Form values are what is actually printed in the cells. Thus, with the following command, the example dictionary above is created from the example .csv structure above:

pd.create_hash(
    "bernese_verbs.csv",
    x = ["Verb"],
    y = ["Number"],
    z = ["Person"]
)

The resulting dictionary can then be printed to a paradigm table with pyradigms.print_paradigms().

When multiple strings are given for one dimension, the parameters are combined in the resulting paradigm. This is useful when there are more than three parameters one wants to represent. For example, the file examples/latin_verbs.csv has the columns Form Person Number Tense Verb Mood. It would make sense to combine person and number, as well as tense and mood. A separate paradigm should be produced for each verb. This is achieved with the following command:

pd.create_hash(
    "examples/latin_verbs.csv",
    x = ["Person", "Number"],
    y = ["Tense", "Mood"],
    z = ["Verb"]
)
pd.print_paradigms()

This results in the following paradigm list:

portaːre	1SG	2SG	3SG	1PL	2PL	3PL
PRS:IND	portoː	portaːs	portat	portaːmus	portaːtis	portant
PST.IPFV:IND	portaːbam	portaːbaːs	portaːbat	portaːbaːmus	portaːbaːtis	portaːbant
FUT:IND	portaːboː	portaːbis	portaːbit	portaːbimus	portaːbitis	portaːbunt
PRS:SUBJ	portem	porteːs	portet	porteːmus	porteːtis	portent
PST.IPFV:SUBJ	portaːrem	portaːreːs	portaːret	portaːreːmus	portaːreːtis	portaːrent

terːeːre	1SG	2SG	3SG	1PL	2PL	3PL
PRS:IND	terːeoː	terːeːs	terːet	terːeːmus	terːeːtis	terːent
PST.IPFV:IND	terːeːbam	terːeːbaːs	terːeːbat	terːeːbaːmus	terːeːbaːtis	terːeːbant
FUT:IND	terːeːboː	terːeːbis	terːeːbit	terːeːbimus	terːeːbitis	terːeːbunt
PRS:SUBJ	terːream	terːeaːs	terːeat	terːeaːmus	terːeaːtis	terːeant
PST.IPFV:SUBJ	terːeːrem	terːeːres	terːeret	terːeːreːmus	terːeːreːtis	terːeːrent

petere	1SG	2SG	3SG	1PL	2PL	3PL
PRS:IND	petoː	petis	petit	petimus	petitis	petunt
PST.IPFV:IND	peteːbam	peteːbas	peteːbat	peteːbaːmus	peteːbaːtis	peteːbant
FUT:IND	petam	peteːs	petet	peteːmus	peteːtis	petent
PRS:SUBJ	petam	petaːs	petat	petaːmus	petaːtis	petant
PST.IPFV:SUBJ	peteːbar	peteːbaːris; peteːbaːre	peteːbaːtur	peteːbaːmus	peteːbaːminiː	peteːbaːtur

It is also possible to specify a value for a given parameter, using the filtered_parameters argument, which takes a dictionary. Only forms with that value will then be represented in the resulting paradigm(s). For example, to only print indicative forms of the Latin verbs, the following command would be used:

pd.create_hash(
    "examples/latin_verbs.csv",
    x = ["Person", "Number"],
    y = ["Tense"],
    z = ["Verb"],
    filtered_parameters = {"Mood": "IND"}
)
pd.print_paradigms()