Metadata-Version: 2.1
Name: pyeee
Version: 0.4.1
Summary: A Python library providing parameter screening of computational models using the Morris method of Elementary Effects or its extension of Efficient Elementary Effects by Cuntz, Mai et al. (Water Res Research, 2015).
Home-page: https://github.com/mcuntz/pyeee
Author: Matthias Cuntz
Author-email: mc@macu.de
Maintainer: Matthias Cuntz
Maintainer-email: mc@macu.de
License: MIT
Description: # pyeee - Parameter screening of computational models
        <!-- pandoc -f gfm -o README.html -t html README.md -->
        
        A Python library for parameter screening of computational models
        using Morris' method of Elementary Effects or its extension of
        Efficient/Sequential Elementary Effects by Cuntz, Mai et al. (Water
        Res Research, 2015).
        
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        ## About pyeee
        
        *pyeee* is a Python library for performing parameter screening of
        computational models. It uses Morris' method of  Elementary Effects
        and also its extension of Efficient or Sequential Elementary Effects published of
        
        Cuntz, Mai *et al.* (2015)  
          Computationally inexpensive identification of noninformative model
        parameters by sequential screening  
          *Water Resources Research* 51, 6417-6441,
         doi:[10.1002/2015WR016907](http://doi.org/10.1002/2015WR016907).
        
        *pyeee* can be used with Python functions but wrappers are provided
        to use it with executables as well. Function evaluation can be
        distributed with Python's multiprocessing or via MPI.
        
        
        ## Documentation
        
        The complete documentation for *pyeee* is available from Read The Docs.
        
           http://pyeee.readthedocs.org/en/latest/
        
        
        ## Quick usage guide
        
        ### Simple Python function
        
        Consider the Ishigami-Homma function: $y = \sin(x_0) + a\,\sin(x_1)^2 + b\,x_2^4\sin(x_0)$.
        
        Taking $a = b = 1$ gives:
        
        ```python
        	import numpy as np
        	def ishigami1(x):
        	   return np.sin(x[0]) + np.sin(x[1])**2 + x[2]**4 * np.sin(x[0])
        ```
        
        The three paramters $x_0$, $x_1$, $x_2$ follow uniform distributions between $-\pi$ and $+\pi$.
        
        Morris' Elementary Effects can then be calculated like:
        
        ```python
        	npars = 3
        	# lower boundaries
        	lb = np.ones(npars) * (-np.pi)
        	# upper boundaries
        	ub = np.ones(npars) * np.pi
        
            # Elementary Effects
        	from pyeee import ee
            np.random.seed(seed=1023) # for reproducibility of examples
        	out = ee(ishigami1, lb, ub)
        ```
        
        which gives the Elementary Effects ($\mu*$):
        
        ```python
            # mu*
            print("{:.1f} {:.1f} {:.1f}".format(*out[:,0]))
            # gives: 212.4 0.6 102.8
        ```
        
        Sequential Elementary Effects distinguish between informative and
        uninformative parameters using several times Morris' Elementary Effects:
        
        ```python
        	# screen
        	from pyeee import eee
            np.random.seed(seed=1023) # for reproducibility of examples
        	out = eee(ishigami1, lb, ub)
        ```
        
        which returns a logical ndarray with True for the informative
        parameters and False for the uninformative parameters:
        
        ```python
            print(out)
            [ True False  True]
        ```
        
        ### Python function with extra parameters
        
        The function for pyeee must be of the form func(x). Use Python's
        partial from the functools module to pass other function parameters.
        
        For example pass the parameters $a$ and $b$ to the Ishigami-Homma function.
        
        ```python
        	from functools import partial
        
        	def ishigami(x, a, b):
        	   return np.sin(x[0]) + a * np.sin(x[1])**2 + b * x[2]**4 * np.sin(x[0])
        
        	def call_ishigami(ishi, a, b, x):
        	   return ishi(x, a, b)
        
        	# Partialise function with fixed parameters
        	a = 0.5
        	b = 2.0
        	func   = partial(call_ishigami, ishigami, a, b)
        	npars = 3
        
        	# lower boundaries
        	lb = np.ones(npars) * (-np.pi)
        	# upper boundaries
        	ub = np.ones(npars) * np.pi
        
            # Elementary Effects
        	out = ee(func, lb, ub)
        ```
        
        `partial` passes $a$ and $b$ to the
        function `call_ishigami` already during definition so that *pyeee*
        can then simply call it as `func(x)`, so that `x` is passed to
        `call_ishigami` as well.
        
        
        ### Function wrappers
        
        *pyeee* provides wrappers to use with partial.
        
        ```python
        	from pyeee import func_wrapper
        	args = [a, b]
        	kwargs = {}
        	func = partial(func_wrapper, ishigami, args, kwargs)
        
        	# screen
        	out = eee(func, lb, ub)
        ```
        
        There are wrappers to use with Python functions with or without
        masking parameters, as well as wrappers for external executables. See the
        documentation for details.
        
           http://pyeee.readthedocs.org/en/latest/
        
        
        ## Installation
        
        The easiest way to install is via `pip`::
        
            pip install pyeee
        
        See the [installation instructions](http://pyeee.readthedocs.io/en/latest/install.html) in the
        [documentation](http://pyeee.readthedocs.io) for more information.
        
        
        ## Requirements:
        
        - [NumPy](https://www.numpy.org)
        - [SciPy](https://www.scipy.org/scipylib)
        - [schwimmbad](https://github.com/adrn/schwimmbad)
        
        
        ## License
        
        *pyeee* is distributed under the MIT License.  
        See the [LICENSE](https://github.com/mcuntz/pyeee/LICENSE) file for details.
        
        Copyright (c) 2012-2019 Matthias Cuntz, Juliane Mai
        
        
        ## Contributing to pyeee
        
        Users are welcome to submit bug reports, feature requests, and code
        contributions to this project through GitHub.  
        More information is available in the
        [Contributing](http://pyeee.readthedocs.org/en/latest/contributing.html)
        guidelines.
        
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
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