Added Sphinx Gallery with three new python tutorials

.gitignore

@@ -149,4 +149,11 @@ dmypy.json
 # Files generated during package build and CMD check, unnecessary for the package
 atlasapprox.Rcheck/
 .Rhistory
-.RData
+.RData
+
+# sphinx gallery source and build files
+docs/source/_build
+docs/source/python/gallery
+
+# OS files
+**/.DS_Store

docs/gallery/python/quickstart.py → docs/gallery/python/0_quickstart.py

@@ -2,7 +2,8 @@
 Quickstart
 ==========
 
-This example shows a quick and easy example of how to use atlasapprox to get and plot the expression of some gees in a specific organ of a specific organism.
+This example shows a quick and easy example of how to use atlasapprox to get and plot the expression of some gees in a
+specific organ of a specific organism.
 """
 
 import matplotlib.pyplot as plt
@@ -19,3 +20,5 @@
 fig, ax = plt.subplots(figsize=(7, 4))
 sns.heatmap(expression, ax=ax)
 fig.tight_layout()
+
+# sphinx_gallery_thumbnail_path = '_static/quickstart.png'

docs/gallery/python/1_beginner_guide.py

@@ -0,0 +1,79 @@
+"""
+.. _beginner-guide:
+
+Beginner guide
+==============
+
+The `atlasapprox <https://atlasapprox.readthedocs.io/en/latest/index.html>`_ API provides access to approximated
+single-cell data across 30 species, including both animals and plants. You can explore data from species such as *Homo
+sapiens* (humans), *Mus musculus* (mice), *Arabidopsis thaliana* (thale cress), and *Zea mays* (corn). Follow this guide
+to get started with installation, basic usage, and example queries.
+"""
+
+# %%
+# To ensure consistent dependencies, setting up a virtual environment is recommended before installing the package.
+# Here's one way to do it:
+#
+# Create a virtual environment:
+#     ``python -m venv myenv``
+#
+# Activate your environment (use the appropriate command for your OS):
+#
+# For macOS/Linux users:
+#     ``source myenv/bin/activate``
+#
+# For Windows users:
+#     ``myenv\Scripts\activate``
+
+
+# %%
+# Installation
+# ^^^^^^^^^^^^
+# Use pip to install the *atlasapprox* Python package:
+#
+# ``pip install atlasapprox``
+
+# %%
+# Each time you work with the API, start by importing the *atlasapprox* Python package and instantiate the ``API``
+# project:
+
+import atlasapprox
+api = atlasapprox.API()
+
+# %%
+# Easy start: getting average gene expression
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+# The ``average`` function allows you to retrieve gene expression levels for selected genes in a specific organ of an
+# organism.
+#
+# The following example shows how to examine the average expression of five genes
+# (*COL13A1*, *COL14A1*, *TGFBI*, *PDGFRA*, *GZMA*) in the human lung:
+
+# Call API function with params
+avg_gene_expr_lung = api.average(
+    organism = "h_sapiens",
+    organ = "lung",
+    features = ["COL13A1", "COL14A1", "TGFBI", "PDGFRA", "GZMA"],
+    measurement_type = "gene_expression"
+)
+
+# Display the result
+avg_gene_expr_lung
+
+# %%
+# Understanding the output
+# ------------------------
+# This method returns a **pandas.DataFrame** where:
+#
+# - Each row represents a gene.
+# - Each column corresponds to a cell type.
+# - The values indicate the average gene expression, measured in counts per ten thousand (cptt).
+
+# %%
+# Conclusion
+# ^^^^^^^^^^
+# This tutorial provided a general beginner guide to using the *atlasapprox* Python package. For more detailed
+# information, refer to the official
+# `documentation <https://atlasapprox.readthedocs.io/en/latest/python/index.html>`_.
+
+# sphinx_gallery_thumbnail_path = '_static/beginner_guide.png'