Merge pull request #48 from Hind-M/remove_broken_links

Remove references to the no more existing Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb file
GUDHI · Jul 9, 2021 · 66be627 · 66be627
2 parents 07a22e5 + 482d34e
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diff --git a/README.Rmd b/README.Rmd
@@ -119,8 +119,6 @@ In this [notebook](Tuto-GUDHI-optimization.ipynb), we will see how Gudhi and Ten
 
 [Confidence regions for persistence diagrams - data points](Tuto-GUDHI-ConfRegions-PersDiag-datapoints.ipynb)
 
-[Confidence regions with Bottleneck Bootstrap](Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb)
-
 [ATOL tutorial](https://github.com/martinroyer/atol/blob/master/demo/atol-demo.ipynb)
 
 [Perslay tutorial](https://github.com/MathieuCarriere/perslay/tree/master/tutorial)

diff --git a/README.md b/README.md
@@ -230,9 +230,6 @@ Diagram](Tuto-GUDHI-Expected-persistence-diagrams.ipynb)
 [Confidence regions for persistence diagrams - data
 points](Tuto-GUDHI-ConfRegions-PersDiag-datapoints.ipynb)
 
-[Confidence regions with Bottleneck
-Bootstrap](Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb)
-
 [ATOL
 tutorial](https://github.com/martinroyer/atol/blob/master/demo/atol-demo.ipynb)
 

diff --git a/Tuto-GUDHI-ConfRegions-PersDiag-datapoints.ipynb b/Tuto-GUDHI-ConfRegions-PersDiag-datapoints.ipynb
@@ -39,7 +39,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this tutorial, we introduce confidence regions for persistence diagrams built on a set of data points. We present the subsampling approach of [Fasy et al. 2014 AoS](https://projecteuclid.org/download/pdfview_1/euclid.aos/1413810729). An alternative method is the bottleneck bootstrap method introduced in [Chazal etal. 2018](http://www.jmlr.org/papers/v18/15-484.html) and presented in this [notebook](Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb). See [this notebook](https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-persistence-diagrams.ipynb) for an introduction to persistence diagrams with Gudhi."
+    "In this tutorial, we introduce confidence regions for persistence diagrams built on a set of data points. We present the subsampling approach of [Fasy et al. 2014 AoS](https://projecteuclid.org/download/pdfview_1/euclid.aos/1413810729). See [this notebook](https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-persistence-diagrams.ipynb) for an introduction to persistence diagrams with Gudhi."
    ]
   },
   {
@@ -277,7 +277,7 @@
    "source": [
     "Only the topological features above the red band are considered as significant. Here we select the main topological features by this way.\n",
     "\n",
-    "Generally speaking, the procedure is very conservative: the band is very large and only very few topological features are seen as significant. An alternative approach is the bottleneck bootstrap method, see this [notebook](Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb). "
+    "Generally speaking, the procedure is very conservative: the band is very large and only very few topological features are seen as significant."
    ]
   },
   {
@@ -405,7 +405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that only one topological feature of dimension 1 is seen as a significant. An alternative approach is the bottleneck bootstrap method, see this [notebook](Tuto-GUDHI-ConfRegions-PersDiag-BottleneckBootstrap.ipynb). "
+    "We see that only one topological feature of dimension 1 is seen as a significant."
    ]
   }
  ],

diff --git a/Tuto-GUDHI-alpha-complex-visualization.ipynb b/Tuto-GUDHI-alpha-complex-visualization.ipynb
@@ -312,7 +312,7 @@
     "\n",
     "You can visualize the simplicial complex thanks to [Mayavi](https://docs.enthought.com/mayavi/mayavi/).\n",
     "\n",
-    "One may be able to use [iPyVolume meshes](https://ipyvolume.readthedocs.io/en/latest/mesh.html) for instance.\n",
+    "One may be able to use [iPyVolume meshes](https://ipyvolume.readthedocs.io/en/latest/examples/mesh.html) for instance.\n",
     "But we are not going to make an exhaustive list of visualization tools in Python."
    ]
   }