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Creates paradigms from a table of entries with parameters.

Project description

pyradigms

Creates paradigms from a table of entries with parameters.

Getting started

pip install pyradigms

Usage

There are two separate methods; read_file 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. You can either first read a file and then print the desired paradigm, or you can print a paradigm directly from a three-dimensional hash.

read_file

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 column names present in the .csv file. z represents the multiple paradigm tables listed vertically or in multiple files in the output. y represents the rows of a single paradigm table. x represents the columns of a single paradigm table. These dimensions must all be lists of strings, if a list contains multiple strings, those parameters will be combined in the resulting paradigm. Printed in the cells of the paradigm are the values in the column Form; if your .csv file uses a different label, specify it with read_file(target_string=<label>)

Schematic usage:

import pyradigms
pd = pyradigms.Pyradigms()
pd.read_file(
    "input.csv",
    x = ["X1", "X2"],
    y = ["Y"],
    z = ["Z"],
)
pd.print_paradigms()

Here is an illustration from the included examples: The file examples/latin_verbs.csv has the columns Form Person Number Tense Verb Mood:

Form Person Number Tense Verb Mood
portoː 1 SG PRS portaːre IND
portaːs 2 SG PRS portaːre IND
portat 3 SG PRS portaːre IND
portaːmus 1 PL PRS portaːre IND
portaːtis 2 PL PRS portaːre IND
portant 3 PL PRS portaːre IND
terːeoː 1 SG PRS terːeːre IND

One could for example want to combine person and number, as well as tense and mood. A separate paradigm should be produced for each verb. This is achieved as follows:

import pyradigms
pd = pyradigms.Pyradigms()
pd.read_file(
    "latin_verbs.csv",
    x = ["Person", "Number"],
    y = ["Tense", "Mood"],
    z = ["Verb"],
)
pd.print_paradigms(name="latin_verb_paradigms")

This results in the following paradigm list (in "latin_verb_paradigms.csv"):

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

You can arrange and combine the parameters as you want. If you want to filter a certain parameter, you can add as many filtered_parameters as you want, and filter for a specific value. If a parameter appears on none of the three axes, and not in the filtered_parameters list, it will be ignored completely, and pyradigms will simply take the first form fulfilling all criteria! (for now)

Other options of read_file():

  • The option multiple_files=True distributes the output into multiple files, which represent the z axis.
  • The option display=True prints a pretty table in the command line output.
  • The options x_sort_order and y_sort_order take lists which will be used to sort the output along that axis.

An example: the following code combines person and mood on the x axis and uses a very idiosyncratic sort order for that axis. Number is on the y axis, verbs on the z axis; only present tense forms are taken into account. The output is pretty printed in the terminal; the z axis is distributed across three files.

pd.read_file(
    "latin_verbs.csv",
    x = ["Person", "Mood"],
    y = ["Number"],
    z = ["Verb"],
    filtered_parameters = {"Tense": "PRS"}
)
pd.print_paradigms(
	name="example_output",
	display=True,
	single_file=False,
	x_sort_order=["1IND", "2IND", "3IND", "3SUBJ", "2SUBJ", "1SUBJ"]
)
portaːre 1IND 2IND 3IND 3SUBJ 2SUBJ 1SUBJ
SG portoː portaːs portat portet porteːs portem
PL portaːmus portaːtis portant portent porteːtis porteːmus
terːeːre 1IND 2IND 3IND 3SUBJ 2SUBJ 1SUBJ
SG terːeoː terːeːs terːet terːeat terːeaːs terːream
PL terːeːmus terːeːtis terːent terːeant terːeaːtis terːeaːmus
petere 1IND 2IND 3IND 3SUBJ 2SUBJ 1SUBJ
SG petoː petis petit petat petaːs petam
PL petimus petitis petunt petant petaːtis petaːmus

print_paradigms

If you use pyradigms in your own application, you might have a three-dimensional dictionary already ready for it, rather than constructing it with pyradigms. In that case, you can pass the argument tables to print_paradigms(); the rest works identically. If no tables argument is passed, the dictionary created by read_file() 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.

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