Metadata-Version: 2.1
Name: PyTCI
Version: 0.0.1
Summary: A package for target controlled infusions
Home-page: https://github.com/JMathiszig-Lee/PyTCI
Author: Jakob Mathiszig-Lee
Author-email: jakob@mathisziglee.co.uk
License: UNKNOWN
Description: # PyTCI
        
        A python package for Target Controlled Infusions. 
        
        Spawned 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
        
        [![Build Status](https://travis-ci.org/JMathiszig-Lee/PyTCI.svg?branch=master)](https://travis-ci.org/JMathiszig-Lee/PyTCI)
        [![Coverage Status](https://coveralls.io/repos/github/JMathiszig-Lee/PyTCI/badge.svg?branch=master)](https://coveralls.io/github/JMathiszig-Lee/PyTCI?branch=master)
        
        # Installation
        if using pip
        ```python
        pip install PyTCI
        ```
        if using pipenv (you should, it's great)
        ```python
        pipenv install PyTCI
        ```
        # Usage
        PyTCI currently supports the following:
        
        **Body Mass equations:**
        * BMI
        * James Equation
        * Boer
        * Hume(1966)
        * Hume(1971)
        * Janmahasation(2005)
        
        example:
        ```python
        >>> from PyTCI.weights import leanbodymass
        >>> leanbodymass.hume66(180, 60 'm')
        51.2
        ```
        
        **Propofol models:**
        * Schnider
        * Marsh
        
        example:
        ```python
        >>> from PyTCI.models import propofol
        >>> patient = propofol.Schnider(40, 70, 170, 'm')
        >>> patient.v2
        24
        ```
        
        the class methods ```give_drug``` and ```wait_time``` can he used to model propofol kinetics
        
        example:
        ```python
        >>> from PyTCI.models import propofol
        >>> patient = propofol.Marsh(90)
        >>> patient.give_drug(200)
        >>> patient.x1
        7.9573934837092715
        >>> patient.wait_time(60)
        >>> patient.x1
        6.179147869674185
        ```
        
        The built in models inherit from a parent class.
        You can define your own models and use the same functions to see how yours performs
        ```python
        class MyNewModel(Propofol):
             def __init__(self, desired, arguments):
                # Initial concentration is zero in all components
                self.x1 = 0.0
                self.x2 = 0.0
                self.x3 = 0.0
                self.xeo = 0.0
        
                #my custom code to generate volumes and constants
        
        
                # divide by 60 as we will be working in seconds
                self.k10 /= 60
                self.k12 /= 60
                self.k13 /= 60
                self.k21 /= 60
                self.k31 /= 60
                self.keo /= 60
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
