Fixed all the binder links.

dynamicslab · Feb 25, 2023 · d57737f · d57737f
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diff --git a/examples/10_PDEFIND_examples.ipynb b/examples/10_PDEFIND_examples.ipynb
@@ -10,7 +10,7 @@
     "This notebook provides a simple overview of the PDE functionality of PySINDy, following the examples in the PDE-FIND paper (Rudy, Samuel H., Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz. \"Data-driven discovery of partial differential equations.\" Science Advances 3, no. 4 (2017): e1602614.). Jupyter notebook written by Alan Kaptanoglu.\n",
     "\n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/10_PDEFIND_examples.ipynb)"
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/10_PDEFIND_examples.ipynb)"
    ]
   },
   {

diff --git a/examples/11_SSR_FROLS_examples.ipynb b/examples/11_SSR_FROLS_examples.ipynb
@@ -11,7 +11,7 @@
     "Jupyter notebook written by Alan Kaptanoglu and Jared Callaham.\n",
     "\n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/11_SSR_FROLS_examples.ipynb)"
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/11_SSR_FROLS_examples.ipynb)"
    ]
   },
   {

diff --git a/examples/12_weakform_SINDy_examples.ipynb b/examples/12_weakform_SINDy_examples.ipynb
@@ -12,7 +12,7 @@
     "As of release 1.7, the weak formulation is vectorized and much faster to compute. The \"num_pts_per_domain\" variable is now deprecated. It can still be passed to the WeakPDELibrary to avoid breaking old code, but it will raise a DeprecationWarning and do nothing to the library.\n",
     "\n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/12_weakform_SINDy_examples.ipynb)"
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/12_weakform_SINDy_examples.ipynb)"
    ]
   },
   {
@@ -1476,7 +1476,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1490,7 +1490,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/examples/13_ensembling.ipynb b/examples/13_ensembling.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Ensembling Feature Overview\n",
     "Ensembling is a fancy name for sub-sampling the data and generating $n_\\text{models}$ from regressing onto each of these sub-samples. In practice this helps to robustify the regressions against outliers and other issues. We highly recommend checking out the following paper for understanding the usefulness of these methods against noisy data: \n",
-    "#### Fasel, Urban, et al. \"Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.\" arXiv preprint arXiv:2111.10992 (2021). https://arxiv.org/abs/2111.10992\n",
+    "#### Fasel, U., Kutz, J. N., Brunton, B. W., & Brunton, S. L. (2022). Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. Proceedings of the Royal Society A, 478(2260), 20210904. https://arxiv.org/abs/2111.10992\n",
     "\n",
     "This notebook provides an overview of the basic and advanced functionality of using ensemble methods in PySINDy. Ensembling robustifies the SINDy method. Written by Alan Kaptanoglu and Urban Fasel. \n",
     "\n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/13_ensembling.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/13_ensembling.ipynb)\n",
     "\n",
     "There are many variants of ensembling/subsampling strategies and post-processing methods. We will show the following useful variants below: <br> <br>\n",
     "Ensembling: <br>\n",

diff --git a/examples/14_cavity_flow.ipynb b/examples/14_cavity_flow.ipynb
@@ -25,7 +25,7 @@
     "This Jupyter notebook example is written by Jared Callaham and demonstrates the use of SINDy to learn a model for the quasiperiodic dynamics in a shear-driven cavity at Re=7500, following [Callaham, Brunton, and Loiseau (2021)](https://arxiv.org/abs/2106.02409).  It will focus on working through a relatively involved fluid dynamics example, rather than comparing optimizers, libraries, etc.  This example also includes some other more advanced SINDy \"tricks\" like using dynamic mode decomposition to rotate the generalized coordinates and co-opting some of the SINDy infrastructure to perform nonlinear dimensionality reduction.\n",
     "\n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/14_cavity_flow.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/14_cavity_flow.ipynb)\n",
     "\n",
     "As in the cylinder wake example (see notebook 3), the reduced-order model appproximates the 2D time-varying velocity field $\\mathbf{u}(\\mathbf{x}, t)$ with the POD expansion\n",
     "$$\n",

diff --git a/examples/15_pysindy_lectures.ipynb b/examples/15_pysindy_lectures.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "This notebook is a summary of the PySINDy YouTube lectures found [here](https://www.youtube.com/playlist?list=PLN90bHJU-JLoOfEk0KyBs2qLTV7OkMZ25). \n",
     "An interactive version of this notebook is available on binder\n",
-    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7?filepath=examples/15_pysindy_lectures.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/15_pysindy_lectures.ipynb)\n",
     "\n",
     "These examples show how to use the SINDy method in practice, addressing the following practical questions:\n",
     "1. How does one choose the thresholding parameter $\\lambda$?\n",
@@ -2229,7 +2229,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -2243,7 +2243,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/examples/16_noise_robustness/16_benchmark_paper.ipynb b/examples/16_noise_robustness/16_benchmark_paper.ipynb
@@ -13,7 +13,10 @@
     "\n",
     "In addition, we investigate how Pareto-optimal models generated from sparse system identification methods depend on the dynamical properties of the equations, finding to leading order that the performance of these methods is independent of the dynamical properties of these equations, including the amount of chaos, scale separation, degree of nonlinearity, and, surprisingly, the syntactic complexity.\n",
     " \n",
-    "We will use the dysts database, containingn over 100 chaotic systems. We will investigate a subset of the systems that are polynomially nonlinear, with highest polynomial degree <= 4. All of the following 70 systems are bounded and exhibit strange attractors."
+    "We will use the dysts database, containingn over 100 chaotic systems. We will investigate a subset of the systems that are polynomially nonlinear, with highest polynomial degree <= 4. All of the following 70 systems are bounded and exhibit strange attractors.\n",
+    "\n",
+    "An interactive version of this notebook is available on binder\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/16_noise_robustness/16_benchmark_paper.ipynb)"
    ]
   },
   {

diff --git a/examples/16_noise_robustness/optimizer_comparison.ipynb b/examples/16_noise_robustness/optimizer_comparison.ipynb
@@ -7,7 +7,10 @@
     "## Full optimizer comparison\n",
     "Notebook written by Lanyue Zhang and Alan Kaptanoglu\n",
     "\n",
-    "This post-processing file takes the results from Pareto-optimal scans of the dysts database with different optimizers and varying amounts of noise, as in the example Jupyter notebook 16_benchmark_paper.ipynb. The primary goal of this notebook is to fully reproduce the results shown in our new system identification benchmark paper."
+    "This post-processing file takes the results from Pareto-optimal scans of the dysts database with different optimizers and varying amounts of noise, as in the example Jupyter notebook 16_benchmark_paper.ipynb. The primary goal of this notebook is to fully reproduce the results shown in our new system identification benchmark paper.\n",
+    "\n",
+    "An interactive version of this notebook is available on binder\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dynamicslab/pysindy/v1.7.3?filepath=examples/16_noise_robustness/optimizer_comparison.ipynb)"
    ]
   },
   {