Metadata-Version: 2.1
Name: nhpp
Version: 0.0.1
Summary: Small package to enable creation of Non-homogeneous Poisson Processes.
Home-page: https://github.com/Kylexi/nhpp
Author: Matthew Campbell
Author-email: mmcampbell0@gmail.com
License: UNKNOWN
Description: # nhpp
        
        ## INSTALLATION: ```pip install nhpp```
        
        ## PURPOSE:
        This package is (currently) a standalone module for
        generating non-homogeneous Poisson processes (nhpp).
        Homogeneous Poisson processes are easily generated by specifying
        an arrival rate, lambda, then generating samples from 
        X ~ exp(1 / lambda). These samples indicate the inter-arrival
        times between events, or the delay between events.
        
        The above case is only true when lambda is a constant.
        Generalizing to the case of lambda(t), a time-dependent arrival
        rate, is much trickier to implement. Two main approaches exist
        to tackle this issue: (1) relate the INTEGRATED rate function
        LAMBDA(t) to a homogeneous Poisson process via an inversion function,
        or (2), use a "thinning" method which acts as an acceptance-rejection
        sampling routine.
        
        The method get_arrivals employs the former approach. The input
        allows the user to specify a piecewise linear approximation to their
        true arrival rate function. Returned is a list containing the arrival
        times governed by the arrival rate function.
        
        ## EXAMPLE USAGE
        
        ```
        # Specify the piecewise linear arrival rate via knots.
        # Below we specify arrival_rate = 1 at time = 0, arrival_rate = 2 at time = 5,
        # arrival_rate = 1 at time = 2.5 (linearity between time = 0 and time = 5), etc.
        >>> knots = {0: 1, 5: 2, 12: 0.3, 15: 0.3, 16: 0, 18: 0, 20: 2}
        
        >>> arrs = nhpp.get_arrivals()
        
        # Print out our arrival times.
        >>> for arr in arrs:
        		print(round(arr, 2))
        
        0
        0.08
        1.1
        1.14
        2.35
        2.41
        2.45
        2.91
        3.67
        4.41
        4.65
        4.7
        6.78
        7.13
        7.18
        8.12
        10.15
        18.33
        19.21
        19.53
        19.54
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
