Pamphile Roy tupui

## halton.py
"""Halton low discrepancy sequence.

This snippet implements the Halton sequence following the generalization of
a sequence of *Van der Corput* in n-dimensions.

---------------------------

MIT License

Copyright (c) 2017 Pamphile Tupui ROY

## discerpancy.py
"""Discrepancy of a sample.

Compute the centered discrepancy on a given sample.
It is a measure of the uniformity of the points in the parameter space.
The lower the value is, the better the coverage of the parameter space is.

---------------------------
MIT License
Copyright (c) 2017 Pamphile Tupui ROY
Permission is hereby granted, free of charge, to any person obtaining a copy

## crypto_wallet_status.py
"""Get info about Cryptocurrency wallets.

Gather information about the market and you own wallet. Using transactions
file, it also compare your investment with the available capital.

Transactions are to be stored in JSON files formated as:

{
    "2018_01_15": {
        "rate": 1.505,

## quantile_dotplot.py
"""Quantile dotplot.

Based on R code from https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md

Reference:

Matthew Kay, Tara Kola, Jessica Hullman, Sean Munson. When (ish) is My Bus?
User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems.
CHI 2016. DOI: 10.1145/2858036.2858558

## pod.py
"""Proper Orthogonal Decomposition.

Demonstrate how to use it.

---------------------------

MIT License

Copyright (c) 2018 Pamphile Tupui ROY

## sobol_saltelli.py
"""Sobol' indices.

Compute Sobol' variance based sensitivity indices.
Use formulations from Saltelli2010.

Reference:

Saltelli et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index,
Computer Physics Communications, 2010. DOI: 10.1016/j.cpc.2009.09.018

## minimum_spanning_tree.py
"""Minimum Spanning Tree.

MST is used here as a discrepancy criterion.
Comparing two different designs: the higher the mean, the better the design is
in terms of space filling.

---------------------------

MIT License

## cosi.py
"""Cosine transformation sensitivity indices.

Using a Discrete Cosine Transformation (DCT), it is possible to compute
first order sensitivity indices.

Reference:

Plischke E., How to compute variance-based sensitivity indicators with your
spreadsheet software, Environmental Modelling & Software,
2012. DOI: 10.1016/j.envsoft.2012.03.004

## sobol_visually.py
r"""Visual explanation of Sobol' indices.

Sobol' indices are metrics to express sensitivity of the output from
perturbations comming from input parameters. First order indices write

.. math:: S_{x_i} = \frac{\mathbb{V}_i(Y)}{\mathbb{V}[Y]} =
          \frac{\mathbb{\mathbb{V}}[\mathbb{E}(Y|x_i)]}{\mathbb{V}[Y]}

The following is using the Ishigami function

## moment_independent_visually.py
r"""Visual explanation of Moment independent sensitivity analysis.

Moment-based method are based on the whole PDF to mitigate these
issues (Borgonovo2007). Based on the unconditional PDF, a conditional PDF per
parameter is computed. The more the conditional PDF deviates from the
unconditional PDF, the more the parameter has an impact on the quantity of
interest. The same procedure can be done using the Empirical Cumulative
Density Function (ECDF), respectively with the unconditional ECDF.
This visually shows this procedure. Bins of samples (red circles) are used to
compute a conditional PDF of the output. This PDF is compared to the
	"""Halton low discrepancy sequence.

	This snippet implements the Halton sequence following the generalization of
	a sequence of Van der Corput in n-dimensions.

	---------------------------

	MIT License

	Copyright (c) 2017 Pamphile Tupui ROY
	"""Discrepancy of a sample.

	Compute the centered discrepancy on a given sample.
	It is a measure of the uniformity of the points in the parameter space.
	The lower the value is, the better the coverage of the parameter space is.

	---------------------------
	MIT License
	Copyright (c) 2017 Pamphile Tupui ROY
	Permission is hereby granted, free of charge, to any person obtaining a copy
	"""Get info about Cryptocurrency wallets.

	Gather information about the market and you own wallet. Using transactions
	file, it also compare your investment with the available capital.

	Transactions are to be stored in JSON files formated as:

	{
	"2018_01_15": {
	"rate": 1.505,
	"""Quantile dotplot.

	Based on R code from https://github.com/mjskay/when-ish-is-my-bus/blob/master/quantile-dotplots.md

	Reference:

	Matthew Kay, Tara Kola, Jessica Hullman, Sean Munson. When (ish) is My Bus?
	User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems.
	CHI 2016. DOI: 10.1145/2858036.2858558
	"""Proper Orthogonal Decomposition.

	Demonstrate how to use it.

	---------------------------

	MIT License

	Copyright (c) 2018 Pamphile Tupui ROY
	"""Sobol' indices.

	Compute Sobol' variance based sensitivity indices.
	Use formulations from Saltelli2010.

	Reference:

	Saltelli et al. Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index,
	Computer Physics Communications, 2010. DOI: 10.1016/j.cpc.2009.09.018
	"""Minimum Spanning Tree.

	MST is used here as a discrepancy criterion.
	Comparing two different designs: the higher the mean, the better the design is
	in terms of space filling.

	---------------------------

	MIT License
	"""Cosine transformation sensitivity indices.

	Using a Discrete Cosine Transformation (DCT), it is possible to compute
	first order sensitivity indices.

	Reference:

	Plischke E., How to compute variance-based sensitivity indicators with your
	spreadsheet software, Environmental Modelling & Software,
	2012. DOI: 10.1016/j.envsoft.2012.03.004
	r"""Visual explanation of Sobol' indices.

	Sobol' indices are metrics to express sensitivity of the output from
	perturbations comming from input parameters. First order indices write

	.. math:: S_{x_i} = \frac{\mathbb{V}_i(Y)}{\mathbb{V}[Y]} =
	\frac{\mathbb{\mathbb{V}}[\mathbb{E}(Y\|x_i)]}{\mathbb{V}[Y]}

	The following is using the Ishigami function
	r"""Visual explanation of Moment independent sensitivity analysis.

	Moment-based method are based on the whole PDF to mitigate these
	issues (Borgonovo2007). Based on the unconditional PDF, a conditional PDF per
	parameter is computed. The more the conditional PDF deviates from the
	unconditional PDF, the more the parameter has an impact on the quantity of
	interest. The same procedure can be done using the Empirical Cumulative
	Density Function (ECDF), respectively with the unconditional ECDF.
	This visually shows this procedure. Bins of samples (red circles) are used to
	compute a conditional PDF of the output. This PDF is compared to the