Metadata-Version: 2.1
Name: squigglepy
Version: 0.3
Summary: Squiggle programming language for intuitive probabilistic estimation features in Python
Home-page: https://github.com/peterhurford/squigglepy
Author: Peter Hurford
Author-email: peter@peterhurford.com
License: UNKNOWN
Description: ## Squigglepy: Implementation of Squiggle in Python
        
        [Squiggle](https://www.squiggle-language.com/) is a "simple programming language for intuitive probabilistic estimation". It serves as its own standalone programming language with its own syntax, but it is implemented in JavaScript. I like the features of Squiggle and intend to use it frequently, but I also sometimes want to use similar functionalities in Python, especially alongside other Python statistical programming packages like Numpy, Pandas, and Matplotlib. The **squigglepy** package here implements many Squiggle-like functionalities in Python.
        
        
        ## Usage
        
        Here's the Squigglepy implementation of [the example from Squiggle Docs](https://www.squiggle-language.com/docs/Overview):
        
        ```Python
        import squigglepy as sq
        
        populationOfNewYork2022 = sq.to(8.1*million(), 8.4*million()) # This means that you're 90% confident the value is between 8.1 and 8.4 Million.
        
        def proportionOfPopulationWithPianos():
            percentage = sq.to(.2, 1)
            return sq.sample(percentage) * 0.01 # We assume there are almost no people with multiple pianos
        
        def pianoTunersPerPiano():
            pianosPerPianoTuner = sq.to(2*thousand(), 50*thousand())
            return 1 / sq.sample(pianosPerPianoTuner)
        
        def totalTunersIn2022():
            return (sq.sample(populationOfNewYork2022) *
                    proportionOfPopulationWithPianos() *
                    pianoTunersPerPiano())
        
        sq.get_percentiles(sq.sample(totalTunersIn2022, n=1000))
        ```
        
        And the version from the Squiggle doc that incorporates time:
        
        ```Python
        import squigglepy as sq
        K = sq.thousand(); M = sq.million()
        
        populationOfNewYork2022 = sq.to(8.1*M, 8.4*M)
        
        def proportionOfPopulationWithPianos():
            percentage = sq.to(.2, 1)
            return sq.sample(percentage) * 0.01
        
        def proportionOfPopulationWithPianos():
            percentage = sq.to(.2, 1)
            return sq.sample(percentage) * 0.01
        
        def pianoTunersPerPiano():
            pianosPerPianoTuner = sq.to(2*K, 50*K)
            return 1 / sq.sample(pianosPerPianoTuner)
        
        # Time in years after 2022
        def populationAtTime(t):
            averageYearlyPercentageChange = sq.to(-0.01, 0.05) # We're expecting NYC to continuously grow with an mean of roughly between -1% and +4% per year
            return sq.sample(populationOfNewYork2022) * ((sq.sample(averageYearlyPercentageChange) + 1) ** t)
        
        def totalTunersAtTime(t):
            return (populationAtTime(t) *
                    proportionOfPopulationWithPianos() *
                    pianoTunersPerPiano())
        
        sq.get_percentiles(sq.sample(lambda: totalTunersAtTime(2030-2022), n=1000))
        ```
        
        ## Additional Features
        
        Additional distributions:
        
        ```Python
        import squigglepy as sq
        
        # Normal distribution
        sq.sample(sq.norm(1, 3))  # 90% interval from 1 to 3
        
        # Distribution can be sampled with mean and sd too
        sq.sample(sq.norm(mean=2, sd=3))
        
        # Other distributions exist
        sq.sample(sq.lognorm(1, 10))
        sq.sample(sq.tdist(1, 10, t=5))
        
        # You can mix distributions together
        sq.sample(sq.mixture([sq.norm(1, 3),
                              sq.norm(4, 10),
                              sq.lognorm(1, 10)],  # Distributions to mix
                             [0.3, 0.3, 0.4]))     # These are the weights on each distribution
        
        # You can change the CI from 90% (default) to 80%
        sq.sample(sq.norm(1, 3, credibility=0.8))
        ```
        
        ## Installation
        
        `pip3 install squigglepy`
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
