Merge pull request #220 from materialsinnovation/develop

version 0.2.3

INSTALLATION.md

@@ -1,4 +1,5 @@
 # Installation
+The following steps outline the necessary requirements for a successful installation of PyMKS.
 
 Use pip,
 
@@ -54,16 +55,22 @@ See the [PyFFTW installation instructions](https://github.com/hgomersall/pyFFTW#
 
 ## Installation on Windows
 
-We recommend you download and install [Anaconda Python Distribution](http://continuum.io/downloads)
-for Python 2.7 and then download and install PyMKS using the [windows installer](https://www.dropbox.com/s/ja251y4f5ss2y44/PyMKS-x64-anaconda27.exe?dl=1).
+We recommend you download and install the [Anaconda Python Distribution](http://continuum.io/downloads)
+for Python 2.7 (x64) and then download and install PyMKS using the [windows installer](https://github.com/materialsinnovation/pymks/releases/download/version-0_2_1/PyMKS-x64-anaconda27.exe).
+
+## Installation on Mac OS X
+
+We recommend you download and install the [Anaconda Python Distibution](http://continuum.io/downloads)
+for Python 2.7 (x64). Once Anaconda has been installed, follow the above procedures to install SfePy.
+Finally, install PyMKS using `pip` as described above.
 
 ## Installation with Anaconda
 
 The [Anaconda Python Distributionn](https://store.continuum.io/cshop/anaconda/)
 contains all of the required packages outside of [SfePy][sfepy] and
 works on multiple platforms. [Download][conda] and
 [install](http://docs.continuum.io/anaconda/install.html) Anaconda, and
-use the Anaconda Command Prompt to install PyMKS using pip.
+use your terminal or shell to install PyMKS using pip.
 
 ## Requirements
 
@@ -82,4 +89,4 @@ list.
 [MKL]: https://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
 [pyfftw]: http://hgomersall.github.io/pyFFTW/
 [chris]: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
-[conda]: http://continuum.io/downloads
+[conda]: http://continuum.io/downloads

doc/API.rst

@@ -1,3 +1,5 @@
+:orphan:
+
 ===
 API
 ===

doc/EXAMPLES.rst

@@ -1,3 +1,5 @@
+:orphan:
+
 Examples
 ========
 

doc/MKSmodels.rst

@@ -2,7 +2,7 @@ MKS Models
 ==========
 
 MKSHomogenizationModel
---------------------
+----------------------
 .. autoclass:: pymks.mks_homogenization_model.MKSHomogenizationModel
    :members:
 

doc/_static/pymks.css

@@ -11,36 +11,36 @@ body {
 .navbar .nav > li > a, .navbar .nav > li > a:first-letter,
 .navbar .nav > li.current-menu-item > a,
 .navbar .nav > li.current-menu-ancestor > a {
-    font-size:      20px;
-    padding-top:    35px;
-    padding-bottom: 35px;
+    font-size:      18px;
+    padding-top:    20px;
+    padding-bottom: 20px;
     font-weight: bold;
 }
 
 /* Version Number */
 .navbar-default .navbar-text {
-    font-size:      20px;
-    padding-top:    16px;
-    padding-bottom: 16px;
+    font-size:      18px;
+    padding-top:    2px;
+    padding-bottom: 2px;
 }
 
 /* Logo and project title */
 .navbar-default .navbar-brand {
     color: #FFF;
     font-size: 25px;
-    padding-top:    14px;
-    padding-bottom: 14px;
+    padding-top:    3px;
+    padding-bottom: 3px;
 }
 
 /* Dropdown menu */
 .dropdown-menu > li > a {
-    font-size:      20px;
+    font-size:      18px;
 }
 
 /* Dropdown hover */
 /* Dropdown menu */
 .dropdown-menu > li > a:hover {
-    font-size:      20px;
+    font-size:      18px;
     background: #f46d43;
 }
 

doc/bases.rst

@@ -7,11 +7,11 @@ DiscreteIndicatorBasis
    :members:
 
 ContinuousIndicatorBasis
-----------------------
+------------------------
 .. autoclass:: pymks.bases.ContinuousIndicatorBasis
    :members:
 
 LegendreBasis
-----------------------
+-------------
 .. autoclass:: pymks.bases.LegendreBasis
    :members:

doc/conf.py

@@ -205,7 +205,7 @@
 # Grouping the document tree into LaTeX files. List of tuples
 # (source start file, target name, title, author, documentclass [howto/manual]).
 latex_documents = [
-    ('index', 'extremefill.tex', u'Extreme Fill Documentation',
+    ('index', 'pymks.tex', u'PyMKS Documentation',
      u'David Brough', 'manual'),
 ]
 
@@ -235,7 +235,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    ('index', 'extremefill', u'Extreme Fill Documentation',
+    ('index', 'pymks', u'PyMKS Documentation',
      [u'David Brough'], 1)
 ]
 
@@ -249,8 +249,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    ('index', 'extremefill', u'Extreme Fill Documentation',
-     u'David Brough', 'extremefill', 'Reproduce Extreme fill Paper.',
+    ('index', 'pymks', u'PyMKS Documentation',
+     u'David Brough', 'pymks', 'Materials Knowledge Systems in Python',
      'Miscellaneous'),
 ]
 

doc/datageneration.rst

@@ -10,23 +10,23 @@ make_elastic_FE_strain_delta
 .. autofunction:: pymks.datasets.make_elastic_FE_strain_delta
 
 make_elastic_FE_strain_random
-----------------------------
+-----------------------------
 .. autofunction:: pymks.datasets.make_elastic_FE_strain_random
 
 make_cahn_hilliard
-----------------------------
+------------------
 .. autofunction:: pymks.datasets.make_cahn_hilliard
 
 make_microstructure
-----------------------------
+-------------------
 .. autofunction:: pymks.datasets.make_microstructure
 
 make_checkerboard_microstructure
-----------------------------
+--------------------------------
 .. autofunction:: pymks.datasets.make_checkerboard_microstructure
 
 make_elastic_stress_random
-----------------------------
+--------------------------
 .. autofunction:: pymks.datasets.make_elastic_stress_random
 
 Simulations

doc/index.rst

@@ -11,6 +11,8 @@ Materials Knowledge Systems in Python
 
 .. include:: intro.rst
 
+.. include:: support.rst
+
 .. toctree::
    :hidden:
 

doc/intro.rst

@@ -17,6 +17,7 @@ section <THEORY.html>`__.
     
     import numpy as np
     import matplotlib.pyplot as plt
+
 Quantify Microstructures using 2-Point Statistics
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -30,6 +31,7 @@ Lets make two dual phase microstructures with different morphologies.
     X_2 = make_microstructure(n_samples=1, grain_size=(15, 95))
     
     X = np.concatenate((X_1, X_2))
+
 Throughout PyMKS ``X`` is used to represent microstructures. Now that we
 have made the two microstructures, lets take a look at them.
 
@@ -40,6 +42,7 @@ have made the two microstructures, lets take a look at them.
     draw_microstructures(X)
 
 
+
 .. image:: intro_files/intro_5_0.png
 
 
@@ -58,6 +61,7 @@ function.
     prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])
     X_ = prim_basis.discretize(X)
     X_corr = correlate(X_, periodic_axes=[0, 1])
+
 Let's take a look at the two autocorrelations and the cross-correlation
 for these two microstructures.
 
@@ -69,6 +73,7 @@ for these two microstructures.
     
     draw_correlations(X_corr[0])
 
+
 .. parsed-literal::
 
     (101, 101, 3)
@@ -83,6 +88,7 @@ for these two microstructures.
     draw_correlations(X_corr[1])
 
 
+
 .. image:: intro_files/intro_10_0.png
 
 
@@ -110,6 +116,7 @@ types of microstructures, totaling to 600 microstructures.
     
     X_train, y_train = make_elastic_stress_random(n_samples=n_samples, size=(51, 51),
                                                   grain_size=grain_size, seed=0)
+
 Once again, ``X_train`` is our microstructures. Throughout PyMKS ``y``
 is used as either the prpoerty or the field we would like to predict. In
 this case ``y_train`` is the effective stress values for ``X_train``.
@@ -121,6 +128,7 @@ microstructures.
     draw_microstructures(X_train[::200])
 
 
+
 .. image:: intro_files/intro_16_0.png
 
 
@@ -135,24 +143,28 @@ basis function. We will also specify which correlations we want.
     prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])
     homogenize_model = MKSHomogenizationModel(basis=prim_basis,
                                               correlations=[(0, 0), (1, 1), (0, 1)])
+
 Let's fit our model with the data we created.
 
 .. code:: python
 
     homogenize_model.fit(X_train, y_train, periodic_axes=[0, 1])
+
 Now let's make some new data to see how good our model is.
 
 .. code:: python
 
     n_samples = [10, 10, 10]
     X_test, y_test = make_elastic_stress_random(n_samples=n_samples, size=(51, 51),
                                                 grain_size=grain_size, seed=100)
+
 We will try and predict the effective stress of our ``X_test``
 microstructures.
 
 .. code:: python
 
     y_pred = homogenize_model.predict(X_test, periodic_axes=[0, 1])
+
 The ``MKSHomogenizationModel`` generates low dimensional representations
 of microstructures and regression methods to predict effective
 properties. Take a look at the low dimensional representations.
@@ -161,8 +173,9 @@ properties. Take a look at the low dimensional representations.
 
     from pymks.tools import draw_components
     
-    draw_components(homogenize_model.fit_data, homogenize_model.predict_data, 
-                    label_1='Training Data', label_2='Testing Data')
+    draw_components([homogenize_model.reduced_fit_data, homogenize_model.reduced_predict_data], 
+                    ['Training Data', 'Testing Data'])
+
 
 
 .. image:: intro_files/intro_26_0.png
@@ -182,6 +195,7 @@ Now let's look at a goodness of fit plot for our
     draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Testing Data'])
 
 
+
 .. image:: intro_files/intro_28_0.png
 
 
@@ -204,6 +218,7 @@ First we need some data, so let's make some.
     from pymks.datasets import make_elastic_FE_strain_delta
     
     X_delta, y_delta = make_elastic_FE_strain_delta()
+
 Once again, ``X_delta`` is our microstructures and ``y_delta`` is our
 local strain fields. We need to discretize the microstructure again so
 we will also use the same basis function.
@@ -214,11 +229,13 @@ we will also use the same basis function.
     
     prim_basis = PrimitiveBasis(n_states=2)
     localize_model = MKSLocalizationModel(basis=prim_basis)
+
 Let's use the data to fit our ``MKSLocalizationModel``.
 
 .. code:: python
 
     localize_model.fit(X_delta, y_delta)
+
 Now that we have fit our model, we will create a random microstructure
 and compute its local strain field using finite element analysis. We
 will then try and reproduce the same strain field with our model.
@@ -228,6 +245,7 @@ will then try and reproduce the same strain field with our model.
     from pymks.datasets import make_elastic_FE_strain_random
     
     X_test, y_test = make_elastic_FE_strain_random()
+
 Let's look at the microstructure and its local strain field.
 
 .. code:: python
@@ -237,6 +255,7 @@ Let's look at the microstructure and its local strain field.
     draw_microstructure_strain(X_test[0], y_test[0])
 
 
+
 .. image:: intro_files/intro_40_0.png
 
 
@@ -252,6 +271,7 @@ and compare the predicted and computed local strain field.
     draw_strains_compare(y_test[0], y_pred[0])
 
 
+
 .. image:: intro_files/intro_42_0.png
 
 

doc/support.rst

@@ -0,0 +1,15 @@
+Support
+=======
+
+Financial support for the development of PyMKS the MKS framework was
+provided by the
+`Georgia Tech Institute of Materials`_, the
+`National Institute of Standards and Technology`_, the
+`Air Force Office of Scientific Research`_, the `Office of Naval Research`_, and the
+`National Science Foundation`_.
+
+.. _`Georgia Tech Institute of Materials`: http://materials.gatech.edu/
+.. _`National Institute of Standards and Technology`: http://www.nist.gov/mml/msed/index.cfm
+.. _`Air Force Office of Scientific Research`: http://www.wpafb.af.mil/AFRL/afosr/
+.. _`Office of Naval Research`: http://www.onr.navy.mil/
+.. _`National Science Foundation`: http://www.nsf.gov/