Metadata-Version: 2.1
Name: quantulum3
Version: 0.3.0
Summary: Extract quantities from unstructured text.
Home-page: https://github.com/nielstron/quantulum3
Author: Marco Lagi, nielstron, sohrabtowfighi, grhawk and Rodrigo Castro
Author-email: n.muendler@web.de
License: MIT
Download-URL: https://github.com/marcolagi/quantulum3/tarball/0.1
Keywords: information extraction,quantities,units,measurements,nlp,natural language processing,text mining,text processing
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Development Status :: 3 - Alpha
Classifier: Natural Language :: English
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
Requires-Dist: inflect

quantulum3 [![Travis master build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=master "Travis master build state")](https://travis-ci.com/nielstron/quantulum3)  [![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=master)](https://coveralls.io/github/nielstron/quantulum3?branch=master) [![PyPI version](https://badge.fury.io/py/quantulum3.svg)](https://pypi.org/project/quantulum3/) ![ Supported python versions](https://img.shields.io/badge/python-3.6-red.svg)
==========

Python library for information extraction of quantities, measurements
and their units from unstructured text. It is Python 3 compatible fork of [recastrodiaz\'
fork](https://github.com/recastrodiaz/quantulum) of [grhawks\'
fork](https://github.com/grhawk/quantulum) of [the original by Marco
Lagi](https://github.com/marcolagi/quantulum).
The compatability with the newest version of sklearn is based on
the fork of [sohrabtowfighi](https://github.com/sohrabtowfighi/quantulum).

Installation
------------

First, install [`numpy`](https://pypi.org/project/numpy/), [`scipy`](https://www.scipy.org/install.html) and [`sklearn`](http://scikit-learn.org/stable/install.html).
Quantulum would still work without it, but it wouldn\'t be able to
disambiguate between units with the same name (e.g. *pound* as currency
or as unit of mass).

Then,

``` {.sourceCode .bash}
$ pip install quantulum3
```

If you want to train the classifier yourself, in addition to the packages above, you'll also need
the packages `stemming` and `wikipedia`. Use the method `train` in `quantulum3.classifier` to train the classifier.

You could also [download requirements_classifier.txt](https://raw.githubusercontent.com/nielstron/quantulum3/dev/requirements_classifier.txt)
and run `pip install requirements_classifier.txt`.

Contributing
------------

If you'd like to contribute follow these steps:
1. Clone a fork of this project into your workspace
2. `pip install pipenv`
3. Inside the project folder run `pipenv install --dev`
4. Make your changes
5. Run `scripts/format.sh` and `scripts/build.py` from the package root directory.
6. Test your changes with `coverage run --source=quantulum3 --omit="*test*" setup.py test` 
(Optional, will be done automatically after pushing)
7. Create a Pull Request when having commited and pushed your changes

`dev` build: 

[![Travis dev build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=dev "Travis dev build state")](https://travis-ci.com/nielstron/quantulum3)
 [![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=dev)](https://coveralls.io/github/nielstron/quantulum3?branch=dev)

Usage
-----

``` {.sourceCode .python}
>>> from quantulum3 import parser
>>> quants = parser.parse('I want 2 liters of wine')
>>> quants
[Quantity(2, 'litre')]
```

The *Quantity* class stores the surface of the original text it was
extracted from, as well as the (start, end) positions of the match:

``` {.sourceCode .python}
>>> quants[0].surface
u'2 liters'
>>> quants[0].span
(7, 15)
```

An inline parser that embeds the parsed quantities in the text is also
available (especially useful for debugging):

``` {.sourceCode .python}
>>> print parser.inline_parse('I want 2 liters of wine')
I want 2 liters {Quantity(2, "litre")} of wine
```

As the parser is also able to parse dimensionless numbers,
this library can also be used for simple number extraction.

``` {.sourceCode .python}
>>> print parser.parse('I want two')
[Quantity(2, 'dimensionless')]
```

Units and entities
------------------

All units (e.g. *litre*) and the entities they are associated to (e.g.
*volume*) are reconciled against WikiPedia:

``` {.sourceCode .python}
>>> quants[0].unit
Unit(name="litre", entity=Entity("volume"), uri=https://en.wikipedia.org/wiki/Litre)

>>> quants[0].unit.entity
Entity(name="volume", uri=https://en.wikipedia.org/wiki/Volume)
```

This library includes more than 290 units and 75 entities. It also
parses spelled-out numbers, ranges and uncertainties:

``` {.sourceCode .python}
>>> parser.parse('I want a gallon of beer')
[Quantity(1, 'gallon')]

>>> parser.parse('The LHC smashes proton beams at 12.8–13.0 TeV')
[Quantity(12.8, "teraelectronvolt"), Quantity(13, "teraelectronvolt")]

>>> quant = parser.parse('The LHC smashes proton beams at 12.9±0.1 TeV')
>>> quant[0].uncertainty
0.1
```

Non-standard units usually don\'t have a WikiPedia page. The parser will
still try to guess their underlying entity based on their
dimensionality:

``` {.sourceCode .python}
>>> parser.parse('Sound travels at 0.34 km/s')[0].unit
Unit(name="kilometre per second", entity=Entity("speed"), uri=None)
```

Disambiguation
--------------

If the parser detects an ambiguity, a classifier based on the WikiPedia
pages of the ambiguous units or entities tries to guess the right one:

``` {.sourceCode .python}
>>> parser.parse('I spent 20 pounds on this!')
[Quantity(20, "pound sterling")]

>>> parser.parse('It weighs no more than 20 pounds')
[Quantity(20, "pound-mass")]
```

or:

``` {.sourceCode .python}
>>> text = 'The average density of the Earth is about 5.5x10-3 kg/cm³'
>>> parser.parse(text)[0].unit.entity
Entity(name="density", uri=https://en.wikipedia.org/wiki/Density)

>>> text = 'The amount of O₂ is 2.98e-4 kg per liter of atmosphere'
>>> parser.parse(text)[0].unit.entity
Entity(name="concentration", uri=https://en.wikipedia.org/wiki/Concentration)
```

Manipulation
------------

While quantities cannot be manipulated within this library, there are
many great options out there:

-   [pint](https://pint.readthedocs.org/en/latest/)
-   [natu](http://kdavies4.github.io/natu/)
-   [quantities](http://python-quantities.readthedocs.org/en/latest/)

Spoken version
--------------

Quantulum classes include methods to convert them to a speakable unit.

```python
>>> parser.parse("Gimme 10e9 GW now!")[0].to_spoken()
ten billion gigawatts
>>> parser.inline_parse_and_expand("Gimme $1e10 now and also 1 TW and 0.5 J!")
Gimme ten billion dollars now and also one terawatt and zero point five joules!
```

Extension
---------

See *units.json* for the complete list of units and *entities.json* for
the complete list of entities. The criteria for adding units have been:

-   the unit has (or is redirected to) a WikiPedia page
-   the unit is in common use (e.g. not the [premetric Swedish units of
    measurement](https://en.wikipedia.org/wiki/Swedish_units_of_measurement#Length)).

It\'s easy to extend these two files to the units/entities of interest.
Here is an example of an entry in *entities.json*:

``` {.sourceCode .python}
{
    "name": "speed",
    "dimensions": [{"base": "length", "power": 1}, {"base": "time", "power": -1}],
    "URI": "https://en.wikipedia.org/wiki/Speed"
}
```

-   *name* and *URI* are self explanatory.
-   *dimensions* is the dimensionality, a list of dictionaries each
    having a *base* (the name of another entity) and a *power* (an
    integer, can be negative).

Here is an example of an entry in *units.json*:

``` {.sourceCode .python}
{
    "name": "metre per second",
    "surfaces": ["metre per second", "meter per second"],
    "entity": "speed",
    "URI": "https://en.wikipedia.org/wiki/Metre_per_second",
    "dimensions": [{"base": "metre", "power": 1}, {"base": "second", "power": -1}],
    "symbols": ["mps"]
}
```

-   *name* and *URI* are self explanatory.
-   *surfaces* is a list of strings that refer to that unit. The library
    takes care of plurals, no need to specify them.
-   *entity* is the name of an entity in *entities.json*
-   *dimensions* follows the same schema as in *entities.json*, but the
    *base* is the name of another unit, not of another entity.
-   *symbols* is a list of possible symbols and abbreviations for that
    unit.

All fields are case sensitive.


