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+:orphan:
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+.. To get rid of WARNING: document isn't included in any toctree
+
+================
+Datasets manual
+================
+
+Datasets generators
+===================
+
+We provide the generation of different customizable datasets to use as inputs for Gudhi complexes and data structures.
+
+Points generators
+------------------
+
+The module **points** enables the generation of random points on a sphere, random points on a torus and as a grid.
+
+Points on sphere
+^^^^^^^^^^^^^^^^
+
+The function **sphere** enables the generation of random i.i.d. points uniformly on a (d-1)-sphere in :math:`R^d`.
+The user should provide the number of points to be generated on the sphere :code:`n_samples` and the ambient dimension :code:`ambient_dim`.
+The :code:`radius` of sphere is optional and is equal to **1** by default.
+Only random points generation is currently available.
+
+The generated points are given as an array of shape :math:`(n\_samples, ambient\_dim)`.
+
+Example
+"""""""
+
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+   from gudhi import AlphaComplex
+
+   # Generate 50 points on a sphere in R^2
+   gen_points = points.sphere(n_samples = 50, ambient_dim = 2, radius = 1, sample = "random")
+
+   # Create an alpha complex from the generated points
+   alpha_complex = AlphaComplex(points = gen_points)
+   
+.. autofunction:: gudhi.datasets.generators.points.sphere
+
+Points on a flat torus
+^^^^^^^^^^^^^^^^^^^^^^
+
+You can also generate points on a torus.
+
+Two functions are available and give the same output: the first one depends on **CGAL** and the second does not and consists of full python code.
+
+On another hand, two sample types are provided: you can either generate i.i.d. points on a d-torus in :math:`R^{2d}` *randomly* or on a *grid*.
+
+First function: **ctorus**
+"""""""""""""""""""""""""""
+
+The user should provide the number of points to be generated on the torus :code:`n_samples`, and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`.
+The :code:`sample` argument is optional and is set to **'random'** by default.
+In this case, the returned generated points would be an array of shape :math:`(n\_samples, 2*dim)`.
+Otherwise, if set to **'grid'**, the points are generated on a grid and would be given as an array of shape:
+
+.. math::
+
+   ( ⌊n\_samples^{1 \over {dim}}⌋^{dim}, 2*dim )
+
+**Note 1:** The output array first shape is rounded down to the closest perfect :math:`dim^{th}` power.
+
+**Note 2:** This version is recommended when the user wishes to use **'grid'** as sample type, or **'random'** with a relatively small number of samples (~ less than 150).
+
+Example
+"""""""
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+
+   # Generate 50 points randomly on a torus in R^6
+   gen_points = points.ctorus(n_samples = 50, dim = 3)
+   
+   # Generate 27 points on a torus as a grid in R^6
+   gen_points = points.ctorus(n_samples = 50, dim = 3, sample = 'grid')
+
+.. autofunction:: gudhi.datasets.generators.points.ctorus
+
+Second function: **torus**
+"""""""""""""""""""""""""""
+
+The user should provide the number of points to be generated on the torus :code:`n_samples` and the dimension :code:`dim` of the torus on which points would be generated in :math:`R^{2dim}`.
+The :code:`sample` argument is optional and is set to **'random'** by default.
+The other allowed value of sample type is **'grid'**.
+
+**Note:** This version is recommended when the user wishes to use **'random'** as sample type with a great number of samples and a low dimension.
+
+Example
+"""""""
+.. code-block:: python
+
+   from gudhi.datasets.generators import points
+
+   # Generate 50 points randomly on a torus in R^6
+   gen_points = points.torus(n_samples = 50, dim = 3)
+   
+   # Generate 27 points on a torus as a grid in R^6
+   gen_points = points.torus(n_samples = 50, dim = 3, sample = 'grid')
+
+
+.. autofunction:: gudhi.datasets.generators.points.torus
+
+
+Fetching datasets
+=================
+
+We provide some ready-to-use datasets that are not available by default when getting GUDHI, and need to be fetched explicitly.
+
+By **default**, the fetched datasets directory is set to a folder named **'gudhi_data'** in the **user home folder**.
+Alternatively, it can be set using the **'GUDHI_DATA'** environment variable.
+
+.. autofunction:: gudhi.datasets.remote.fetch_bunny
+
+.. figure:: ./img/bunny.png
+     :figclass: align-center
+
+     3D Stanford bunny with 35947 vertices.
+
+
+.. autofunction:: gudhi.datasets.remote.fetch_spiral_2d
+
+.. figure:: ./img/spiral_2d.png
+     :figclass: align-center
+
+     2D spiral with 114562 vertices.
+
+.. autofunction:: gudhi.datasets.remote.clear_data_home