Metadata-Version: 2.1
Name: preposterous
Version: 0.0.2
Summary: A simple library for estimating the impact of an intervention, with humility
Home-page: https://github.com/matthewwritter/preposterous
Author: Matthew Ritter
Maintainer: Matthew Ritter
License: MIT
Description: Pre/Posterous
        =============
        
        Estimate the impact of an intervention, with simplicity and humility
        
        **Pre/Posterous is under active development** and the current release
        can be considered a 'proof of concept'. The largest restriction is that
        it only imports data from the `Reporter
        app <http://www.reporter-app.com/>`__, which is my current default
        recommendation for anyone who is trying to track something for
        Quantified Self.
        
        Installation
        ------------
        
        You can install with ``pip install preposterous``
        
        If you want to install from source, then clone this repository and run
        ``pip install -e .`` from the project root.
        
        Testing
        -------
        
        Tests can be seen in the ``tests/`` directory and run with ``pytest``
        
        Use cases
        ---------
        
        Quantified Self data
        ~~~~~~~~~~~~~~~~~~~~
        
        The primary use case is for quantified self, where you have periodic
        measurements of the target metric (weight, categorical sleep quality,
        ect) and potential interventions (medications, diet shifts, ect). This
        library can organize these into 'natural experiments' that point the way
        towards a causal relationship
        
        *Warning* Python is pretty great, but nothing can replace a well powered
        `Double Blind Randomized Controlled
        Trial <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196997/>`__ for
        establishing causality. That said, many (most?) situations do not lend
        themselves to RCTs, and yet we're still forced to make decisions. That's
        where tools like this, used with an appreciation for non-binary modes of
        belief, can be helpful.
        
        Example
        -------
        
        ::
        
            import preposterous.preposterous as ppl
            pdf = ppl.PrePostDF()
            pdf.add_outcome(filename='data/sample_reporter_output.csv')
            pdf.add_intervention(filename='data/sample_reporter_output.csv')
        
            # Sanity check the data
            pdf.basic_info()
        
            # Basic statistical test of difference between periods pre and post intervention
            pdf.fisher_test(intervention='Exercise')
        
            # Bayesian comparison of relative impact of multiple interventions
            # (note that the sample data only contains one)
            # Output is written to an image file named 'relative_effectiveness_YYYYMMDD.png'
            pdf.outcomes(
                positive_outcomes=['Totally fine'],
                negative_outcomes=['Noticeable', 'Distracting'],
                window=3
            )
            pdf.calculate_relative_effectiveness()
            _ = pdf.plot_relative_effectiveness()
        
        
Keywords: quantified self,quantifiedself,statistics,bayesian statistics,time series
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Natural Language :: English
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Provides-Extra: testing
