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anda-0.0.8
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A package collecting various functions to work with ancient Mediterranean datasets (textual, spatial, etc.)
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | anda-0.0.8 |
| نام | anda |
| نسخه کتابخانه | 0.0.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Vojtech Kase |
| ایمیل نویسنده | vojtech.kase@gmail.com |
| آدرس صفحه اصلی | https://github.com/sdam-au/anda |
| آدرس اینترنتی | https://pypi.org/project/anda/ |
| مجوز | - |
# anda
[toc]
```bash
pip install anda
```
This is a Python package for collecting, manipulation and visualizing various ancient Mediterranean data. It focus on their temporal, textual and spatial aspects. It is structured into several gradually evolving submodules, namely `gr`, `imda`, `concs`, and `textnet`.
## anda.gr
```python
from anda import gr
```
This module is dedicated to preprocessing of ancient Greek textual data. It contains functions for lemmatization, posttagging and translation. It relies heavely on Morhesus Dictionary.
### Lemmatization
A minimal usage is to lemmatize individual word. You can either ask for only the first lemma (`return_first_lemma()`) or for all possibilities (`return_all_unique_lemmata()`. In most cases , the outcome is the same:
```python
gr.return_first_lemma("ἐπιστήμην")
> 'ἐπιστήμη'
gr.return_all_unique_lemmata("ἐπιστήμην")
> 'ἐπιστήμη'
```
Above these are functions `lemmatize_string()` and `gr.get_lemmatized_sentences()`. Both work with string of any length. The first returns a list of lemmata. The second returns a list of lemmatized sentences.
```python
string = "Πρότασις μὲν οὖν ἐστὶ λόγος καταφατικὸς ἢ ἀποφατικὸς τινὸς κατά τινος. Οὗτος δὲ ἢ καθόλου ἢ ἐν μέρει ἢ ἀδιόριστος. Λέγω δὲ καθόλου μὲν τὸ παντὶ ἢ μηδενὶ ὑπάρχειν, ἐν μέρει δὲ τὸ τινὶ ἢ μὴ τινὶ ἢ μὴ παντὶ ὑπάρχειν, ἀδιόριστον δὲ τὸ ὑπάρχειν ἢ μὴ ὑπάρχειν ἄνευ τοῦ καθόλου, ἢ κατὰ μέρος, οἷον τὸ τῶν ἐναντίων εἶναι τὴν αὐτὴν ἐπιστήμην ἢ τὸ τὴν ἡδονὴν μὴ εἶναι ἀγαθόν."
gr.lemmatize_string(string)
> ['πρότασις', 'λόγος', 'καταφατικός', 'ἀποφατικός', 'καθόλου', 'μέρος', 'ἀδιόριστος', 'λέγω', 'καθόλου', 'πᾶς', 'μηδείς', 'ὑπάρχω', 'μέρος', 'πᾶς', 'ὑπάρχω', 'ἀδιόριστον', 'ὑπάρχω', 'ὑπάρχω', 'ἄνευ', 'καθόλου', 'μέρος', 'οἷος', 'ἐναντίος', 'αὐτην', 'ἐπιστήμη', 'ἡδονην', 'ἀγαθός']
gr.get_lemmatized_sentences(string)
> [['πρότασις', 'λόγος', 'καταφατικός', 'ἀποφατικός'], ['καθόλου', 'μέρος', 'ἀδιόριστος'], ['λέγω', 'καθόλου', 'πᾶς', 'μηδείς', 'ὑπάρχω', 'μέρος', 'πᾶς', 'ὑπάρχω', 'ἀδιόριστον', 'ὑπάρχω', 'ὑπάρχω', 'ἄνευ', 'καθόλου', 'μέρος', 'οἷος', 'ἐναντίος', 'αὐτην', 'ἐπιστήμη', 'ἡδονην', 'ἀγαθός']]
```
All lemmatization functions can be further parametrized by several arguments
* `all_lemmata=False` :
* `filter_by_postag=["n","a","v"]`: returns only nouns ("n"), adjectives ("a") and verbs ("v")
* `involve_unknown=True`, if `False`, it returns only words found in the dictionary
Thus, you can run:
```python
lemmatized_sentences = gr.get_lemmatized_sentences(string, all_lemmata=False, filter_by_postag=["n","a","v"], involve_unknown=False)
print(lemmatized_sentences)
> [['λόγος'], ['μέρος'], ['πᾶς', 'μηδείς', 'ὑπάρχω', 'μέρος', 'πᾶς', 'ὑπάρχω', 'ὑπάρχω', 'ὑπάρχω', 'ἄνω/ἀνίημι', 'μέρος', 'οἷος', 'ἐναντίος', 'ἐπιστήμη', 'ἀγαθός']]
```
(1) `get_lemmatized_sentences(string, all_lemmata=False, filter_by_postag=None, involve_unknown=False)`: it receives a raw Greek text of any kind and extent as its input Such input is processed by a series of subsequent functions embedded within each other, which might be also used independently
(1) `get_sentences()` splits the string into sentences by common sentence separators.
(2) `lemmatize_string(sentence)` first calls `tokenize_string()`, which makes a basic cleaning and stopwords filtering for the sentence, and returns a list of words. Subsequently, each word from the tokenized sentence is sent either to `return_first_lemma()` or to `return_all_unique_lemmata()`, on the basis of the value of the parameter `all_lemmata=` (set to `False` by default).
(4) `return_all_unique_lemmata()`goes to the `morpheus_dict` values and returns all unique lemmata.
(5) Parameter `filter_by_postag=` (default `None`) enables to sub-select chosen word types from the tokens, on the basis of first character in the tag "p" . Thus, to choose only nouns, adjectives, and verbs, you can set `filter_by_postag=["n", "a", "v"].` PREFERENCE: If verb, noun, and adjective variants are available, only then noun and adjective form is returned. If both noun and adjective is available, only noun is returned.
### Translation
Next to the lemmatization, there is also a series of functions for translations, like `return_all_unique_translations(word, filter_by_postag=None, involve_unknown=False)`, useful for any wordform, and `lemma_translator(word)`, where we already have a lemma.
```python
gr.return_all_unique_translations("ὑπάρχειν", filter_by_postag=None, involve_unknown=False)
> 'to begin, make a beginning'
gr.lemma_translator("λόγος")
> 'the word'
```
### Morphological analysis
You can also do a morphological analysis of a string
```python
gr.morphological_analysis(string)[1:4]
> [{'i': '564347',
'f': 'μέν',
'b': 'μεν',
'l': 'μέν',
'e': 'μεν',
'p': 'g--------',
'd': '20753',
's': 'on the one hand, on the other hand',
'a': None},
{'i': '642363',
'f': 'οὖν',
'b': 'ουν',
'l': 'οὖν',
'e': 'ουν',
'p': 'g--------',
'd': '23870',
's': 'really, at all events',
'a': None},
{'i': '264221',
'f': 'ἐστί',
'b': 'εστι',
'l': 'εἰμί',
'e': 'ειμι',
'p': 'v3spia---',
'd': '9722',
's': 'I have',
'a': None}]
```
## imda
This module will serve for importing various ancient Mediterranean resources. Most of them will be imported directly from open third-party online resources. However, some of them have been preprocessed as part of the SDAM project.
The ideal is that it will work like this:
```
imda.list_datasets()
>>> ['roman_provinces_117', 'EDH', 'roman_cities_hanson', 'orbis_network']
```
And:
```python
rp = imda.import_dataset("roman_provinces_117", "gdf")
type(rp)
>>>geopandas.geodataframe
```
## concs
This module contains functions for working
## textnet
This module contains functions for generating, analyzing and visualizing word co-occurrence networks. It has been designed especially for working with textual data in ancient Greek.
## Versions history
* 0.0.8 - bugs removed
* 0.0.7 - `filter_by_postag` with preference of nouns and adjectives by default
* 0.0.6 - greek dictionaries included within the package
* 0.0.5 - experimenting with data inclusion
* 0.0.4 - docs
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.4 | Python |
نحوه نصب
نصب پکیج whl anda-0.0.8:
pip install anda-0.0.8.whl
نصب پکیج tar.gz anda-0.0.8:
pip install anda-0.0.8.tar.gz