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        "author": "Jakob Mathiszig-Lee",
        "author_email": "jakob@mathisziglee.co.uk",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# PyTCI\n\nA python package for Target Controlled Infusions. \n\nSpawned from the NHS Hack Day project https://github.com/JMathiszig-Lee/Propofol, this splits out useful code into a package and updates it to python3\n\n[![Build Status](https://travis-ci.org/JMathiszig-Lee/PyTCI.svg?branch=master)](https://travis-ci.org/JMathiszig-Lee/PyTCI)\n[![Coverage Status](https://coveralls.io/repos/github/JMathiszig-Lee/PyTCI/badge.svg?branch=master&kill_cache=1)](https://coveralls.io/github/JMathiszig-Lee/PyTCI?branch=master)\n\n# Installation\nif using pip\n```python\npip install PyTCI\n```\nif using pipenv (you should, it's great)\n```python\npipenv install PyTCI\n```\n# Usage\nPyTCI currently supports the following:\n\n**Body Mass equations:**\n* BMI\n* Ideal body weight (Devine)\n* Adjusted body weight\n* James Equation\n* Boer\n* Hume(1966)\n* Hume(1971)\n* Janmahasation(2005)\n\nexample:\n```python\n>>> from PyTCI.weights import leanbodymass\n>>> leanbodymass.hume66(180, 60 'm')\n51.2\n```\n# Models:\n**Propofol**\n* Schnider\n* Marsh\n* Kataria\n* Paedfusor\n\n**Remifentanil**\n* Minto\n\n**Alfentanil**\n* Maitre\n\nexample:\n```python\n>>> from PyTCI.models import propofol\n>>> patient = propofol.Schnider(40, 70, 170, 'm')\n>>> patient.v2\n24\n```\n\nthe class methods ```give_drug``` and ```wait_time``` can he used to model propofol kinetics\n\nexample:\n```python\n>>> from PyTCI.models import propofol\n>>> patient = propofol.Marsh(90)\n>>> patient.give_drug(200)\n>>> patient.x1\n9.746588693957115\n>>> patient.wait_time(60)\n>>> patient.x1\n7.438318565317236\n```\n\n**Infusions**\n\nInfusions are currently only implemented for propofol\n\nThe two methods available are ```effect_bolus``` and ```plasma_infusion```\n\nEffect bolus returns the bolus (in mg) needed over 10 seconds to achieve the desired effect site concentration. It's input is the desired target in ug/ml and returns the bolus needed in mg\n```python\n>>> patient = propofol.Schnider(40, 70, 190, 'm')\n>>> patient.effect_bolus(6)\n95.1\n```\nthe function uses a simple search to find a dose that gets within 2% of the desired concentration \n\n\nPlasma_infusion takes desired plasma concentration(ug/ml), desired total time (seconds) and the time period for each segment (seconds) and returns a python list of the required infusions rates from every segment witin the total time specified in mg/sec\n```python\n>>> pt = propofol.Marsh(70)\n>>> pt.plasma_infusion(2, 60)\n[3.27269899102373, 0.1453355022895698, 0.14478000490919285, 0.14422948797801816, 0.1436839059972244, 0.143143213884116]\n>>> pt.plasma_infusion(2, 60, 30)\n[0.1420619352906052, 0.1417017659270992]\n\n```\n\n\n\nThe built in models inherit from a parent class.\nYou can define your own models and use the same functions to see how yours performs\n```python\nclass MyNewModel(Propofol):\n     def __init__(self, desired, arguments):\n        #my custom code to generate volumes and constants\n        self.v1 = a_constant * weight\n        self.v2 = a_constant * lean_body_mass\n        etc... etc...\n\n        #if you want to work with clearances rate constants must be generated\n        self.from_clearances(self)\n\n        #finally set up model \n        self.setup(self)\n\n```\n\n\n\n",
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