Adding all README.md files into PDF documentation by converting them …

…to .rst rather than .html

README.md

@@ -1,4 +1,4 @@
-[![Build Status](https://travis-ci.com/QMCSoftware/QMCSoftware.svg?branch=master)](https://travis-ci.com/QMCSoftware/QMCSoftware)
+[![Build Status](https://travis-ci.com/QMCSoftware/QMCSoftware.png?branch=master)](https://travis-ci.com/QMCSoftware/QMCSoftware)
 
 # Quasi-Monte Carlo Community Software
 

docs/_sources/readme.rst.txt

@@ -1,23 +1,13 @@
 About Our QMC Software Community
-================================
+=================================
 
-.. raw:: html
-   :file: html_from_readme/QMCSoftware.html
+.. toctree::
+    :maxdepth: 1
 
-About Our Python QMC Software
-------------------------------
+    rts_from_readme/QMCSoftware.rst
 
-.. raw:: html
-   :file: html_from_readme/python_prototype.html
+    rts_from_readme/python_prototype.rst
 
-About QMCPy
-............
+    rts_from_readme/qmcpy.rst
 
-.. raw:: html
-   :file: html_from_readme/qmcpy.html
-
-About QMCPy Tests
-..................
-
-.. raw:: html
-   :file: html_from_readme/test.html
+    rts_from_readme/test.rst

docs/_sources/rts_from_readme/QMCSoftware.rst.txt

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+.. contents::
+   :depth: 1
+..
+
+|Build Status|
+
+Quasi-Monte Carlo Community Software
+====================================
+
+.. raw:: html
+
+   <hr>
+
+Quasi-Monte Carlo (QMC) methods are used to approximate multivariate
+integrals. They have four main components: an integrand, a discrete
+distribution, summary output data, and stopping criterion. Information
+about the integrand is obtained as a sequence of values of the function
+sampled at the data-sites of the discrete distribution. The stopping
+criterion tells the algorithm when the user-specified error tolerance
+has been satisfied. We are developing a framework that allows
+collaborators in the QMC community to develop plug-and-play modules in
+an effort to produce more efficient and portable QMC software. Each of
+the above four components is an abstract class. Abstract classes specify
+the common properties and methods of all subclasses. The ways in which
+the four kinds of classes interact with each other are also specified.
+Subclasses then flesh out different integrands, sampling schemes, and
+stopping criteria. Besides providing developers a way to link their new
+ideas with those implemented by the rest of the QMC community, we also
+aim to provide practitioners with state-of-the-art QMC software for
+their applications.
+
+.. raw:: html
+
+   <hr>
+
+Citation
+--------
+
+If you find QMCPy helpful in your work, please support us by citing the
+following work:
+
+Fred J. Hickernell, Sou-Cheng T. Choi, and Aleksei Sorokin, “QMC
+Community Software.” Python software, 2019. Work in progress. Available
+from https://github.com/QMCSoftware/QMCSoftware
+
+.. raw:: html
+
+   <hr>
+
+Developers
+----------
+
+-  Sou-Cheng T. Choi
+-  Fred J. Hickernell
+-  Aleksei Sorokin
+
+.. raw:: html
+
+   <hr>
+
+Contributors
+------------
+
+-  Michael McCourt
+
+.. raw:: html
+
+   <hr>
+
+Acknowledgment
+--------------
+
+We thank Dirk Nuyens for fruitful discussions related to Magic Point
+Shop.
+
+.. raw:: html
+
+   <hr>
+
+References
+----------
+
+[1] F.Y. Kuo & D. Nuyens. “Application of quasi-Monte Carlo methods to
+elliptic PDEs with random diffusion coefficients - a survey of analysis
+and implementation”,Foundations of Computational Mathematics,
+16(6):1631-1696, 2016. (`springer
+link <https://link.springer.com/article/10.1007/s10208-016-9329-5>`__,
+`arxiv link <https://arxiv.org/abs/1606.06613>`__)
+
+[2] Fred J. Hickernell, Lan Jiang, Yuewei Liu, and Art B. Owen,
+“Guaranteed conservative fixed width confidence intervals via Monte
+Carlo sampling,” Monte Carlo and Quasi-Monte Carlo Methods 2012 (J.
+Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128,
+Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5
+
+[3] Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis
+Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan
+Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic
+Integration Library (Version 2.3) [MATLAB Software], 2019. Available
+from http://gailgithub.github.io/GAIL_Dev/
+
+[4] Sou-Cheng T. Choi, “MINRES-QLP Pack and Reliable Reproducible
+Research via Supportable Scientific Software,” Journal of Open Research
+Software, Volume 2, Number 1, e22, pp. 1-7, 2014.
+
+[5] Sou-Cheng T. Choi and Fred J. Hickernell, “IIT MATH-573 Reliable
+Mathematical Software” [Course Slides], Illinois Institute of
+Technology, Chicago, IL, 2013. Available from
+http://gailgithub.github.io/GAIL_Dev/
+
+[6] Daniel S. Katz, Sou-Cheng T. Choi, Hilmar Lapp, Ketan Maheshwari,
+Frank Loffler, Matthew Turk, Marcus D. Hanwell, Nancy Wilkins-Diehr,
+James Hetherington, James Howison, Shel Swenson, Gabrielle D. Allen,
+Anne C. Elster, Bruce Berriman, Colin Venters, “Summary of the First
+Workshop On Sustainable Software for Science: Practice and Experiences
+(WSSSPE1),” Journal of Open Research Software, Volume 2, Number 1, e6,
+pp. 1-21, 2014.
+
+[7] Fang, K.-T., & Wang, Y. (1994). Number-theoretic Methods in
+Statistics. London, UK: CHAPMAN & HALL
+
+[8] Lan Jiang, Guaranteed Adaptive Monte Carlo Methods for Estimating
+Means of Random Variables, PhD Thesis, Illinois Institute of Technology,
+2016.
+
+[9] Lluis Antoni Jimenez Rugama and Fred J. Hickernell, “Adaptive
+multidimensional integration based on rank-1 lattices,” Monte Carlo and
+Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools
+and D. Nuyens, eds.), Springer Proceedings in Mathematics and
+Statistics, vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1411.1966,
+pp. 407-422.
+
+[10] Kai-Tai Fang and Yuan Wang, Number-theoretic Methods in Statistics,
+Chapman & Hall, London, 1994.
+
+[11] Fred J. Hickernell and Lluis Antoni Jimenez Rugama, “Reliable
+adaptive cubature using digital sequences”, Monte Carlo and Quasi-Monte
+Carlo Methods: MCQMC, Leuven, Belgium, April 2014 (R. Cools and D.
+Nuyens, eds.), Springer Proceedings in Mathematics and Statistics,
+vol. 163, Springer-Verlag, Berlin, 2016, arXiv:1410.8615 [math.NA],
+pp. 367-383.
+
+.. |Build Status| image:: https://travis-ci.com/QMCSoftware/QMCSoftware.png?branch=master
+   :target: https://travis-ci.com/QMCSoftware/QMCSoftware

docs/_sources/rts_from_readme/python_prototype.rst.txt

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+.. contents::
+   :depth: 1
+..
+
+Python 3 Library of QMC Software
+================================
+
+.. raw:: html
+
+   <hr>
+
+QMCPy
+-----
+
+Package of main components
+
+-  Integrand classes
+-  True Measure classes
+-  Discrete Distribution classes
+-  Stopping Criterion classes
+-  Accumulate Data classes
+-  Third Party contributed classes
+-  integrate function
+
+.. raw:: html
+
+   <hr>
+
+workouts
+--------
+
+Example uses of QMCPy package
+
+.. raw:: html
+
+   <hr>
+
+test
+----
+
+Sets of long and short unittests
+
+.. raw:: html
+
+   <hr>
+
+outputs
+-------
+
+Logs and figures generated by workouts
+
+.. raw:: html
+
+   <hr>
+
+demos
+-----
+
+Example use of QMCPy as an independent package
+
+.. raw:: html
+
+   <hr>
+
+sphinx
+------
+
+Automated project documentation
+
+.. raw:: html
+
+   <hr>
+
+Installation
+------------
+
+::
+
+   pip install qmcpy
+
+A virtual environment is recommended for developers/contributors Ensure
+*…/python_prototypes/* is in your path Install dependencies with
+
+::
+
+   pip install requirements.txt

docs/_sources/rts_from_readme/qmcpy.rst.txt

@@ -0,0 +1,103 @@
+.. contents::
+   :depth: 1
+..
+
+QMCPy
+=====
+
+.. raw:: html
+
+   <hr>
+
+Integrand
+---------
+
+The function to integrate *Abstract class with concrete implementations*
+
+-  Linear: :math:`\:\: y_i = \sum_{j=0}^{d-1}(x_{ij})`
+-  Keister: :math:`\:\: y_i = \pi^{d/2} * \cos(||x_i||_2)`
+-  Asian Call
+
+   -  :math:`S_i(t_j)=S(0)e^{(r-\frac{\sigma^2}{2})t_j+\sigma\mathcal{B}(t_j)}`
+   -  discounted call payoff
+      :math:`= max(\frac{1}{d}\sum_{j=0}^{d-1} S(jT/d)-K)\;,\: 0)`
+   -  discounted put payoff
+      :math:`= max(K-\frac{1}{d}\sum_{j=0}^{d-1} S(jT/d))\;,\: 0)`
+
+.. raw:: html
+
+   <hr>
+
+True Measure
+------------
+
+General measure used to define the integrand *Abstract class with
+concrete implementations*
+
+-  Uniform: :math:`\:\: \mathcal{U}(a,b)`
+-  Gaussian: :math:`\:\: \mathcal{N}(\mu,\sigma^2)`
+-  Brownian Motion:
+   :math:`\:\: \mathcal{B}(t_j)=B(t_{j-1})+Z_j\sqrt{t_j-t_{j-1}} \;` for
+   :math:`\;Z_j \sim \mathcal{N}(0,1)`
+
+.. raw:: html
+
+   <hr>
+
+Discrete Distribution
+---------------------
+
+Sampling nodes iid or lds (low-discrepancy sequence) *Abstract class
+with concrete implementations*
+
+-  IID Standard Uniform:
+   :math:`\:\: x_j \overset{iid}{\sim} \mathcal{U}(0,1)`
+-  IID Standard Gaussian:
+   :math:`\:\: x_j \overset{iid}{\sim} \mathcal{N}(0,1)`
+-  Lattice (base 2):
+   :math:`\:\: x_j \overset{lds}{\sim} \mathcal{U}(0,1)`
+-  Sobol (base 2): :math:`\:\: x_j \overset{lds}{\sim} \mathcal{U}(0,1)`
+
+.. raw:: html
+
+   <hr>
+
+Stopping Criterion
+------------------
+
+The stopping criterion to determine sufficient approximation *Abstract
+class with concrete implementations* Central Limit Theorem (CLT)
+:math:`\; \hat{\mu}_n = \overline{Y}_n \approx\mathcal{N}(\mu,\frac{\sigma^2}{n})`\ 
+:math:`\; \mathbb{P}[\hat{\mu}_n-\frac{\mathcal{Z}_{\alpha/2}\hat{\sigma}_n}{\sqrt{n}} \leq \mu \leq \hat{\mu}_n+\frac{\mathcal{Z}_{\alpha/2}\hat{\sigma}_n}{\sqrt{n}}] \approx 1-\alpha`
+
+-  CLT for :math:`x_i\sim` iid
+-  CLT Repeated for :math:`\{x_{r,i}\}_{r=1}^R \sim` lds
+
+.. raw:: html
+
+   <hr>
+
+Accumulate Data Class
+---------------------
+
+Stores data values of corresponding stopping criterion procedure
+*Abstract class with concrete implementations*
+
+-  Mean Variance Data (Controlled by CLT)
+-  Mean Variance Repeated Data (Controlled by CLT Repeated)
+
+.. raw:: html
+
+   <hr>
+
+Integrate Method
+----------------
+
+Repeatedly samples the integrand at nodes generated by the discrete
+distribution and transformed to mimic the integrand’s true measure until
+the Stopping Criterion is met *Function with arguments:*
+
+-  Integrand object
+-  True Measure object
+-  Discrete Distribution object
+-  Stopping Criterion object