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        "author": "Marco Lagi, nielstron, sohrabtowfighi, grhawk and Rodrigo Castro",
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        "description": "quantulum3\n==========\n [![Travis master build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=master \"Travis master build state\")](https://travis-ci.com/nielstron/quantulum3)\n [![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=master)](https://coveralls.io/github/nielstron/quantulum3?branch=master)\n [![PyPI version](https://badge.fury.io/py/quantulum3.svg)](https://pypi.org/project/quantulum3/)\n ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/quantulum3.svg)\n [![PyPI - Status](https://img.shields.io/pypi/status/quantulum3.svg)](https://pypi.org/project/quantulum3/)\n \nPython library for information extraction of quantities, measurements\nand their units from unstructured text. It is able to disambiguate between similar\nlooking units based on their *k-nearest neighbours* in their [GloVe](https://nlp.stanford.edu/projects/glove/) vector representation\nand their [Wikipedia](https://en.wikipedia.org/) page.\n\nThis is the Python 3 compatible fork of [recastrodiaz\\'\nfork](https://github.com/recastrodiaz/quantulum) of [grhawks\\'\nfork](https://github.com/grhawk/quantulum) of [the original by Marco\nLagi](https://github.com/marcolagi/quantulum).\nThe compatibility with the newest version of sklearn is based on\nthe fork of [sohrabtowfighi](https://github.com/sohrabtowfighi/quantulum).\n\nInstallation\n------------\n\nFirst, install [`numpy`](https://pypi.org/project/numpy/), [`scipy`](https://www.scipy.org/install.html) and [`sklearn`](http://scikit-learn.org/stable/install.html).\nQuantulum would still work without those packages, but it wouldn\\'t be able to\ndisambiguate between units with the same name (e.g. *pound* as currency\nor as unit of mass).\n\nThen,\n\n```bash\n$ pip install quantulum3\n```\n\nUsage\n-----\n\n```pycon\n>>> from quantulum3 import parser\n>>> quants = parser.parse('I want 2 liters of wine')\n>>> quants\n[Quantity(2, 'litre')]\n```\n\nThe *Quantity* class stores the surface of the original text it was\nextracted from, as well as the (start, end) positions of the match:\n\n```pycon\n>>> quants[0].surface\nu'2 liters'\n>>> quants[0].span\n(7, 15)\n```\n\nThe *value* attribute provides the parsed numeric value and the *unit.name*\nattribute provides the name of the parsed unit:\n\n```pycon\n>>> quants[0].value\n2.0\n>>> quants[0].unit.name\n'litre'\n```\n\nAn inline parser that embeds the parsed quantities in the text is also\navailable (especially useful for debugging):\n\n```pycon\n>>> print parser.inline_parse('I want 2 liters of wine')\nI want 2 liters {Quantity(2, \"litre\")} of wine\n```\n\nAs the parser is also able to parse dimensionless numbers,\nthis library can also be used for simple number extraction.\n\n```pycon\n>>> print parser.parse('I want two')\n[Quantity(2, 'dimensionless')]\n```\n\nUnits and entities\n------------------\n\nAll units (e.g. *litre*) and the entities they are associated to (e.g.\n*volume*) are reconciled against WikiPedia:\n\n```pycon\n>>> quants[0].unit\nUnit(name=\"litre\", entity=Entity(\"volume\"), uri=https://en.wikipedia.org/wiki/Litre)\n\n>>> quants[0].unit.entity\nEntity(name=\"volume\", uri=https://en.wikipedia.org/wiki/Volume)\n```\n\nThis library includes more than 290 units and 75 entities. It also\nparses spelled-out numbers, ranges and uncertainties:\n\n```pycon\n>>> parser.parse('I want a gallon of beer')\n[Quantity(1, 'gallon')]\n\n>>> parser.parse('The LHC smashes proton beams at 12.8\u201313.0 TeV')\n[Quantity(12.8, \"teraelectronvolt\"), Quantity(13, \"teraelectronvolt\")]\n\n>>> quant = parser.parse('The LHC smashes proton beams at 12.9\u00b10.1 TeV')\n>>> quant[0].uncertainty\n0.1\n```\n\nNon-standard units usually don\\'t have a WikiPedia page. The parser will\nstill try to guess their underlying entity based on their\ndimensionality:\n\n```pycon\n>>> parser.parse('Sound travels at 0.34 km/s')[0].unit\nUnit(name=\"kilometre per second\", entity=Entity(\"speed\"), uri=None)\n```\n\nDisambiguation\n--------------\n\nIf the parser detects an ambiguity, a classifier based on the WikiPedia\npages of the ambiguous units or entities tries to guess the right one:\n\n```pycon\n>>> parser.parse('I spent 20 pounds on this!')\n[Quantity(20, \"pound sterling\")]\n\n>>> parser.parse('It weighs no more than 20 pounds')\n[Quantity(20, \"pound-mass\")]\n```\n\nor:\n\n```pycon\n>>> text = 'The average density of the Earth is about 5.5x10-3 kg/cm\u00b3'\n>>> parser.parse(text)[0].unit.entity\nEntity(name=\"density\", uri=https://en.wikipedia.org/wiki/Density)\n\n>>> text = 'The amount of O\u2082 is 2.98e-4 kg per liter of atmosphere'\n>>> parser.parse(text)[0].unit.entity\nEntity(name=\"concentration\", uri=https://en.wikipedia.org/wiki/Concentration)\n```\n\nIn addition to that, the classifier is trained on the most similar words to\nall of the units surfaces, according to their distance in [GloVe](https://nlp.stanford.edu/projects/glove/)\nvector representation.\n\nTraining the classifier\n-----------------------\n\nIf you want to train the classifier yourself, in addition to the packages above, you'll also need\nthe packages `stemming` and `wikipedia`. \n\nYou could also [download requirements_classifier.txt](https://raw.githubusercontent.com/nielstron/quantulum3/dev/requirements_classifier.txt)\nand run \n```bash\n$ pip install -r requirements_classifier.txt\n```\nUse the script `scripts/train.py` or the method `train_classifier` in `quantulum3.classifier` to train the classifier.\n\nIf you want to create a new or different `similars.json`, install `pymagnitude`.\n\nFor the extraction of nearest neighbours from a vector word representation file, \nuse `scripts/extract_vere.py`. It automatically extracts the `k` nearest neighbours\nin vector space of the vector representation for each of the possible surfaces\nof the ambiguous units. The resulting neighbours are stored in `quantulum3/similars.json`\nand automatically included for training.\n\nThe file provided should be in `.magnitude` format as other formats are first\nconverted to a `.magnitude` file on-the-run. Check out\n[pre-formatted Magnitude formatted word-embeddings](https://github.com/plasticityai/magnitude#pre-converted-magnitude-formats-of-popular-embeddings-models)\nand [Magnitude](https://github.com/plasticityai/magnitude) for more information.\n\n\nManipulation\n------------\n\nWhile quantities cannot be manipulated within this library, there are\nmany great options out there:\n\n-   [pint](https://pint.readthedocs.org/en/latest/)\n-   [natu](http://kdavies4.github.io/natu/)\n-   [quantities](http://python-quantities.readthedocs.org/en/latest/)\n\nSpoken version\n--------------\n\nQuantulum classes include methods to convert them to a speakable unit.\n\n```pycon\n>>> parser.parse(\"Gimme 10e9 GW now!\")[0].to_spoken()\nten billion gigawatts\n>>> parser.inline_parse_and_expand(\"Gimme $1e10 now and also 1 TW and 0.5 J!\")\nGimme ten billion dollars now and also one terawatt and zero point five joules!\n```\n\nExtension\n---------\n\n#### Custom units\n\nIt is possible to add custom entities to be parsed by quantulum.\nSee below code for an example invocation.\n\n```pycon\n>>> from quantulum3.load import add_custom_unit, remove_custom_unit\n>>> add_custom_unit(name=\"schlurp\", surfaces=[\"slp\"], entity=\"dimensionless\")\n>>> parser.parse(\"This extremely sharp tool is precise up to 0.5 slp\")\n[Quantity(0.5, \"Unit(name=\"schlurp\", entity=Entity(\"dimensionless\"), uri=None)\")]\n```\n\nThe keyword arguments to the function `add_custom_unit` are directly translated\nto the properties of the unit to be created.\n\n#### Extending the set of default units\n\nSee *units.json* for the complete list of units and *entities.json* for\nthe complete list of entities. The criteria for adding units have been:\n\n-   the unit has (or is redirected to) a WikiPedia page\n-   the unit is in common use (e.g. not the [premetric Swedish units of\n    measurement](https://en.wikipedia.org/wiki/Swedish_units_of_measurement#Length)).\n\nIt\\'s easy to extend these two files to the units/entities of interest.\nHere is an example of an entry in *entities.json*:\n\n```json\n\"speed\": {\n    \"dimensions\": [{\"base\": \"length\", \"power\": 1}, {\"base\": \"time\", \"power\": -1}],\n    \"URI\": \"https://en.wikipedia.org/wiki/Speed\"\n}\n```\n\n-   The *name* of an entity is its key. Names are required to be unique.\n-   *URI* is the name of the wikipedia page of the entity. (i.e. `https://en.wikipedia.org/wiki/Speed` => `Speed`)\n-   *dimensions* is the dimensionality, a list of dictionaries each\n    having a *base* (the name of another entity) and a *power* (an\n    integer, can be negative).\n\nHere is an example of an entry in *units.json*:\n\n```json\n\"metre per second\": {\n    \"surfaces\": [\"metre per second\", \"meter per second\"],\n    \"entity\": \"speed\",\n    \"URI\": \"Metre_per_second\",\n    \"dimensions\": [{\"base\": \"metre\", \"power\": 1}, {\"base\": \"second\", \"power\": -1}],\n    \"symbols\": [\"mps\"]\n},\n\"year\": {\n    \"surfaces\": [ \"year\", \"annum\" ],\n    \"entity\": \"time\",\n    \"URI\": \"Year\",\n    \"dimensions\": [],\n    \"symbols\": [ \"a\", \"y\", \"yr\" ],\n    \"prefixes\": [ \"k\", \"M\", \"G\", \"T\", \"P\", \"E\" ]\n}\n```\n\n-   The *name* of a unit is its key. Names are required to be unique.\n-   *URI* follows the same scheme as in the *entities.json*\n-   *surfaces* is a list of strings that refer to that unit. The library\n    takes care of plurals, no need to specify them.\n-   *entity* is the name of an entity in *entities.json*\n-   *dimensions* follows the same schema as in *entities.json*, but the\n    *base* is the name of another unit, not of another entity.\n-   *symbols* is a list of possible symbols and abbreviations for that\n    unit.\n-   *prefixes* is an optional list. It can contain [Metric](https://en.wikipedia.org/wiki/Metric_prefix) and [Binary prefixes](https://en.wikipedia.org/wiki/Binary_prefix) and\n    automatically generates according units. If you want to\n    add specifics (like different surfaces) you need to create an entry for that\n    prefixes version on its own.\n\nAll fields are case sensitive.\n\nContributing\n------------\n`dev` build: \n\n[![Travis dev build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=dev \"Travis dev build state\")](https://travis-ci.com/nielstron/quantulum3)\n[![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=dev)](https://coveralls.io/github/nielstron/quantulum3?branch=dev)\n\nIf you'd like to contribute follow these steps:\n1. Clone a fork of this project into your workspace\n2. Run `pip install -e .` at the root of your development folder.\n3. `pip install pipenv` and `pipenv shell`\n4. Inside the project folder run `pipenv install --dev`\n5. Make your changes\n6. Run `scripts/format.sh` and `scripts/build.py` from the package root directory.\n7. Test your changes with `python3 setup.py test` \n(Optional, will be done automatically after pushing)\n8. Create a Pull Request when having commited and pushed your changes\n\nLanguage support\n----------------\n[![Travis dev build state](https://travis-ci.com/nielstron/quantulum3.svg?branch=language_support \"Travis dev build state\")](https://travis-ci.com/nielstron/quantulum3)\n[![Coverage Status](https://coveralls.io/repos/github/nielstron/quantulum3/badge.svg?branch=language_support)](https://coveralls.io/github/nielstron/quantulum3?branch=dev)\n\nThere is a branch for language support, namely `language_support`.\nFrom inspecting the `README` file in the `_lang` subdirectory and\nthe functions and values given in the new `_lang.en_US` submodule,\none should be able to create own language submodules.\nThe new language modules should automatically be invoked and be available,\nboth through the `lang=` keyword argument in the parser functions as well\nas in the automatic unittests.\n\nNo changes outside the own language submodule folder (i.e. `_lang.de_DE`) should\nbe necessary. If there are problems implementing a new language, don't hesitate to open an issue.\n\n\n\n",
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