🚚 reorganize structure of docs (#81)

* 🚚 reorganize structure

* 🐛 fix markdown export for new docs structure

* 🐛 update folder name

Author: enryH

.github/workflows/updt_markdowns.yml

@@ -34,7 +34,8 @@ jobs:
         working-directory: docs
         run: |
           sphinx-apidoc --force --implicit-namespaces --module-first -o reference ../src/acore
-          rm -r api_examples
+          rm -r api_examples_module
+          rm -r examples_single_analysis
           rm -r example_data
           echo "# Test" > index.md
           sphinx-build --keep-going -b markdown ./ ./_build_markdown

docs/api_examples_module/0_inputs.ipynb

@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ca88adc0",
+   "metadata": {},
+   "source": [
+    "# Input Formats\n",
+    "\n",
+    "All omics data is tabular. The `rows` are the samples, and the `columns` are the features. The `rows` and `columns` can be annotated with metadata. The data is stored in a `dataframe` object, which is a 2D labeled data structure with columns of potentially different types.\n",
+    "\n",
+    "Features:\n",
+    "\n",
+    "- `df.index.name` - name of the index (row labels)\n",
+    "- `df.columns.name` - name of the columns (column labels)\n",
+    "\n",
+    "> MultiIndex objects are not supported as most libarires do not support them. metadata is stored in a separate dataframe.\n",
+    "\n",
+    "\n",
+    "The approach is to keep it simple, just use a `dataframe` object, which can be easily serialized to a human readable format, e.g. a `csv` file. The file-format can be anything and the validity of the data is check when data is used, mainly using pandera or a few custom functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d56e23a",
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/api_examples_module/0_inputs.py

@@ -0,0 +1,26 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.19.3
+# ---
+
+# %% [markdown]
+# # Input Formats
+#
+# All omics data is tabular. The `rows` are the samples, and the `columns` are the features. The `rows` and `columns` can be annotated with metadata. The data is stored in a `dataframe` object, which is a 2D labeled data structure with columns of potentially different types.
+#
+# Features:
+#
+# - `df.index.name` - name of the index (row labels)
+# - `df.columns.name` - name of the columns (column labels)
+#
+# > MultiIndex objects are not supported as most libarires do not support them. metadata is stored in a separate dataframe.
+#
+#
+# The approach is to keep it simple, just use a `dataframe` object, which can be easily serialized to a human readable format, e.g. a `csv` file. The file-format can be anything and the validity of the data is check when data is used, mainly using pandera or a few custom functions.
+
+# %%

docs/api_examples_module/index.md

@@ -0,0 +1,9 @@
+# Overview
+
+Examples for specific modules, each highlighting the
+
+1. desired input for that module (loaded from file)
+2. the available functions and associated output(s) of that module (saved to file)
+
+For in-depth explanations of the functionality, links to the single analysis examples
+are provided within the module examples.