Merge d9d22b6 into 967b186

README.md

@@ -42,9 +42,16 @@
 * [License](#license)
 
 ## Description
-**PyDMD** is a Python package that uses **Dynamic Mode Decomposition** for a data-driven model simplification based on spatiotemporal coherent structures.
+**PyDMD** is a Python package that uses **Dynamic Mode Decomposition** for a data-driven model simplification based on spatiotemporal coherent structures. 
+
+Dynamic Mode Decomposition (DMD) is a model reduction algorithm developed by Schmid (see "Dynamic mode decomposition of numerical and experimental data"). Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See Kutz ("Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems") for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in Koopman ("Hamiltonian systems and transformation in Hilbert space"), along with examples in computational fluid dynamics.
+
+In the last years many variants arose, such as multiresolution DMD, compressed DMD, forward backward DMD, and higher order DMD among others, in order to deal with noisy data, big dataset, or spurius data for example. 
+
+In PyDMD we implemented the majority of the variants mentioned above with a user friendly interface. See the [**Examples**](#examples) section below and the [**Tutorials**](tutorials/README.md) to have an idea of the potential of this package.
+
+The research in the field is growing both in computational fluid dynamic and in structural mechanics, due to the equation-free nature of the model.
 
-See the [**Examples**](#examples) section below and the [**Tutorials**](tutorials/README.md) to have an idea of the potential of this package.
 
 
 ## Dependencies and installation

docs/source/index.rst

@@ -13,6 +13,14 @@ Description
 --------------------
 PyDMD is a Python package that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.
 
+Dynamic Mode Decomposition (DMD) is a model reduction algorithm developed by Schmid (see "Dynamic mode decomposition of numerical and experimental data"). Since then has emerged as a powerful tool for analyzing the dynamics of nonlinear systems. DMD relies only on the high-fidelity measurements, like experimental data and numerical simulations, so it is an equation-free algorithm. Its popularity is also due to the fact that it does not make any assumptions about the underlying system. See Kutz ("Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems") for a comprehensive overview of the algorithm and its connections to the Koopman-operator analysis, initiated in Koopman ("Hamiltonian systems and transformation in Hilbert space"), along with examples in computational fluid dynamics.
+
+In the last years many variants arose, such as multiresolution DMD, compressed DMD, forward backward DMD, and higher order DMD among others, in order to deal with noisy data, big dataset, or spurius data for example. 
+
+In the PyDMD package we implemented in Python the majority of the variants mentioned above with a user friendly interface. We also provide many tutorials that show all the characteristics of the software, ranging from the basic use case to the most sofisticated one allowed by the package. 
+
+The research in the field is growing both in computational fluid dynamic and in structural mechanics, due to the equation-free nature of the model.
+
 
 Installation
 --------------------

joss-paper/paper.bib

@@ -2,18 +2,22 @@
 %% http://bibdesk.sourceforge.net/
 
 
-%% Created for Marco Tezzele at 2018-01-04 17:00:05 +0100 
+%% Created for Marco Tezzele at 2018-01-29 10:21:30 +0100 
 
 
 %% Saved with string encoding Unicode (UTF-8) 
 
+
+
 @article{schmid2010dynamic,
 	Author = {Schmid, Peter J},
 	Date-Added = {2018-01-08 16:15:45 +0000},
-	Date-Modified = {2018-01-08 16:15:45 +0000},
+	Date-Modified = {2018-01-29 09:19:01 +0000},
+	Doi = {10.1017/S0022112010001217},
 	Journal = {Journal of fluid mechanics},
 	Pages = {5--28},
 	Publisher = {Cambridge University Press},
+	Read = {0},
 	Title = {Dynamic mode decomposition of numerical and experimental data},
 	Volume = {656},
 	Year = {2010}}
@@ -33,7 +37,8 @@ @article{koopman1931hamiltonian
 @book{kutz2016dynamic,
 	Author = {Kutz, J Nathan and Brunton, Steven L and Brunton, Bingni W and Proctor, Joshua L},
 	Date-Added = {2018-01-04 15:48:15 +0000},
-	Date-Modified = {2018-01-04 15:48:15 +0000},
+	Date-Modified = {2018-01-29 09:19:32 +0000},
+	Doi = {10.1137/1.9781611974508},
 	Publisher = {SIAM},
 	Title = {Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems},
 	Year = {2016}}

joss-paper/paper.md

@@ -39,7 +39,7 @@ As an exmaple, we show below few snapshots collected from a toy system with some
 
 Here we have the reconstruction of the dynamical system. You can observe the sensible reduction of the noise.
 
-![Reconstruction](../readme/dmd-example.gif)
+![Reconstruction](../readme/dmd-reconstruction.png)
 
 # Acknowledgements
 This work was partially supported by European Union Funding for Research and Innovation — Horizon 2020 Program — in the framework of European Research Council Executive Agency: H2020 ERC CoG 2015 AROMA-CFD project 681447 “Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics” P.I. Gianluigi Rozza.

tutorials/tutorial-2-adv-dmd.ipynb

@@ -26,7 +26,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -56,7 +58,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -101,7 +105,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -130,7 +136,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -157,7 +165,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -187,7 +197,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -209,13 +221,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now the cool trick: we combine the reconstructed dataset to create an animation that shows the evolution of our system."
+    "Now the cool trick: we combine the reconstructed dataset to create an animation that shows the evolution of our system. We underline that you need ffmpeg installed in order to see the video."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -4085,7 +4099,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -4123,21 +4139,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [Root]",
    "language": "python",
-   "name": "python3"
+   "name": "Python [Root]"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
   }
  },
  "nbformat": 4,