Added docstring for help

Author: lucasimi

docs/source/conf.py

@@ -46,6 +46,7 @@ def convert_py_to_ipynb():
 
 extensions = [
     "sphinx.ext.autodoc",
+    "sphinx.ext.doctest",
     "sphinx.ext.viewcode",
     "sphinx_rtd_theme",
     "nbsphinx",

src/tdamapper/__init__.py

@@ -0,0 +1,39 @@
+"""
+This library provides a Python implementation of the TDA Mapper algorithm,
+which is used for topological data analysis (TDA). The TDA Mapper algorithm
+is a method for extracting topological features from high-dimensional data
+by constructing a simplicial complex that captures the shape of the data.
+
+The `tdamapper` package includes the following main modules:
+- `core`: Contains the core implementation of the Mapper algorithm.
+- `cover`: Provides classes for creating _open covers_, which are collections of overlapping sets
+    that cover the data space.
+- `learn`: Includes classes compatible with scikit-learn's estimator API based on Mapper. These
+    classes can be used in scikit-learn pipelines.
+- `utils`: Provides utility functions for creating spatial indexes.
+- `plot`: Contains functions for visualizing the Mapper graph.
+
+To use the TDA Mapper algorithm, you can follow these steps:
+
+Examples
+--------
+>>> from sklearn.datasets import make_circles
+>>>
+>>> import numpy as np
+>>> from sklearn.decomposition import PCA
+>>> from sklearn.cluster import DBSCAN
+>>>
+>>> from tdamapper.learn import MapperAlgorithm
+>>> from tdamapper.cover import CubicalCover
+>>> from tdamapper.plot import MapperPlot
+>>>
+>>> X, labels = make_circles(n_samples=5000, noise=0.05, factor=0.3, random_state=42)
+>>> y = PCA(2, random_state=42).fit_transform(X)
+>>>
+>>> cover = CubicalCover(n_intervals=10, overlap_frac=0.3)
+>>> clust = DBSCAN()
+>>> graph = MapperAlgorithm(cover, clust).fit_transform(X, y)
+>>>
+>>> fig = MapperPlot(graph, dim=2, seed=42, iterations=60).plot_plotly(colors=labels)
+>>> fig.show(config={"scrollZoom": True})
+"""