Metadata-Version: 2.1
Name: pyeee
Version: 2.1
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
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Topic :: Scientific/Engineering :: Hydrology
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Utilities
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: schwimmbad

# 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).

The package uses several functions of the JAMS Python package
https://github.com/mcuntz/jams_python  
The JAMS package and *hesseflux* are synchronised irregularly.

*pyeee* can be used with Python functions but also with external programs, using
for example [partialwrap](http://partialwrap.readthedocs.org/en/latest).
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, 10)
```

which gives the Elementary Effects ($\mu*$):

```python
    # mu*
    print("{:.1f} {:.1f} {:.1f}".format(*out[:,0]))
    # gives: 173.1 0.6 61.7
```

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, ntfirst=10)
```

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
    import numpy as np
    from pyeee import ee
    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_func_ab(func, a, b, x):
       return func(x, a, b)

    # Partialise function with fixed parameters
    a = 0.5
    b = 2.0
    func  = partial(call_func_ab, ishigami, a, b)
    npars = 3

    # lower boundaries
    lb = np.ones(npars) * (-np.pi)
    # upper boundaries
    ub = np.ones(npars) * np.pi

    # Elementary Effects
    np.random.seed(seed=1023) # for reproducibility of examples
    out = ee(func, lb, ub, 10)
```

Figuratively speaking, `partial` passes `a` and `b` to the function
`call_func_ab` already during definition so that *pyeee* can then simply call it
as `func(x)`, so that `x` is passed to `call_func_ab` as well.


### Function wrappers

We recommend to use the package
[partialwrap](http://partialwrap.readthedocs.org/en/latest), which provides
wrappers to use with partial.

```python
    from pyeee.utils 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 programs.

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) 2013-2020 Matthias Cuntz, Juliane Mai

The project structure is based on a [template](https://github.com/MuellerSeb/template) provided by [Sebastian Müller](https://github.com/MuellerSeb).

## 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.


