[docs]

Author: LegrandNico

.github/workflows/docs.yml

@@ -25,7 +25,7 @@ jobs:
       - name: Build
         run: |
           pip install -r requirements-docs.txt
-          sphinx-build -b html docs docs/build/html
+          sphinx-build -b html docs/source docs/build/html
 
       - name: Deploy 🚀
         uses: JamesIves/github-pages-deploy-action@v4

.gitignore

@@ -6,7 +6,8 @@
 /dist
 /cardioception.egg-info
 /build
-docs/auto_examples
-docs/generated
-docs/api
-docs/build
+docs/source/auto_examples
+docs/source/generated
+docs/source/api
+docs/build
+docs/auto_examples

docs/source/api.rst

@@ -0,0 +1,8 @@
+API
+=====
+
+.. automodule:: cardioception.HRD
+    :members:
+
+.. automodule:: cardioception.HBC
+    :members:

docs/source/auto_examples/auto_examples_jupyter.zip

@@ -0,0 +1,38 @@
+:orphan:
+
+.. _general_examples:
+
+# Example gallery
+
+This section demonstrates some modelling approaches to analyze data from the Heart Rate Discrimination task.
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+
+.. raw:: html
+
+    </div>
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_

docs/source/auto_examples/auto_examples_python.zip

@@ -1,4 +1,4 @@
-## How to cite?
+# How to cite?
 
 If you are using the [cardioception](https://github.com/embodied-computation-group/Cardioception) package for your research, we ask you to cite the following paper in the final publication:
 

docs/source/auto_examples/index.rst

@@ -69,6 +69,8 @@
 # The master toctree document.
 master_doc = "index"
 
+nb_execution_timeout = 300
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -102,18 +104,14 @@
             icon="fas fa-box",
         ),
     ],
-    "logo_link": "https://embodied-computation-group.github.io/Carcioception/#",
     "logo": {
         "text": "Cardioception",
+        "link": "https://embodied-computation-group.github.io/Carcioception/#",
     },
 }
 
 html_sidebars = {"index": []}
 
-# Add any paths that contain custom static files (such as style sheets) here,
-# relative to this directory. They are copied after the builtin static files,
-# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ["_static"]
 html_css_files = [
     "https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css"
 ]

docs/cite.md renamed to docs/source/cite.md

@@ -22,8 +22,10 @@
       "outputs": [],
       "source": [
         "%%capture\n",
-        "! pip install arviz\n",
-        "! pip install pymc"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "    ! pip install arviz\n",
+        "    ! pip install pymc"
       ]
     },
     {
@@ -35,15 +37,16 @@
       },
       "outputs": [],
       "source": [
+        "import aesara.tensor as at\n",
         "import arviz as az\n",
         "import matplotlib.pyplot as plt\n",
         "import numpy as np\n",
         "import pandas as pd\n",
-        "import pymc as pm\n",
         "import seaborn as sns\n",
-        "import aesara.tensor as at\n",
         "from scipy.stats import norm\n",
         "\n",
+        "import pymc as pm\n",
+        "\n",
         "sns.set_context('talk')"
       ]
     },

docs/conf.py renamed to docs/source/conf.py

@@ -22,8 +22,10 @@
       "outputs": [],
       "source": [
         "%%capture\n",
-        "! pip install arviz\n",
-        "! pip install pymc"
+        "import sys\n",
+        "if 'google.colab' in sys.modules:\n",
+        "    ! pip install arviz\n",
+        "    ! pip install pymc"
       ]
     },
     {

docs/examples/1-psychophysics_subject_level.ipynb renamed to docs/source/examples/1-psychophysics_subject_level.ipynb

@@ -22,8 +22,10 @@
    "outputs": [],
    "source": [
     "%%capture\n",
-    "! pip install pymc\n",
-    "! pip install arviz"
+    "import sys\n",
+    "if 'google.colab' in sys.modules:\n",
+    "    ! pip install arviz\n",
+    "    ! pip install pymc"
    ]
   },
   {

docs/examples/2-psychophysics_group_level.ipynb renamed to docs/source/examples/2-psychophysics_group_level.ipynb

@@ -1,5 +1,3 @@
-.. _general_examples:
-
 # Example gallery
 
 This section demonstrates some modelling approaches to analyze data from the Heart Rate Discrimination task.

docs/examples/3-repeated_measures.ipynb renamed to docs/source/examples/3-repeated_measures.ipynb

@@ -4,7 +4,7 @@
 
 # Cardioception
 
-<img src="./images/logo.png" align="left" alt="cardioception" height="230" HSPACE=30>
+<img src="/images/logo.png" align="left" alt="cardioception" height="230" HSPACE=30>
 
 The Cardioception Python Package - Measuring Interoception with Psychopy - implements two measures of cardiac interoception (cardioception):
 1. The **Heartbeat counting task** developed by Rainer Schandry<sup>1,2</sup>. This task cardiac measures interoception by asking participants to count their heartbeats for a given period of time. An accuracy score is then derived by comparing the reported number of heartbeats and the true number of heartbeats.
@@ -59,16 +59,19 @@ More advanced subject and group-level Bayesian modeling approaches are described
 # Development
 This package was created and is maintained by [Nicolas Legrand](https://legrandnico.github.io/) and [Micah Allen](https://micahallen.org/) from the [ECG group](https://the-ecg.org/).
 
-<img src = "./images/LabLogo.png" height ="100"><img src = "./images/AU.png" height ="100">
+<img src = "/images/LabLogo.png" height ="100"><img src = "/images/AU.png" height ="100">
 
 # Credit
-Some icons used in the Figures or presented during the tasks were downloaded from **Flaticon** [www.flaticon.com](www.flaticon.com).
+Some icons used in the Figures or presented during the tasks were downloaded from [www.flaticon.com](www.flaticon.com).
 
-:::{toctree}
-:hidden:
 
-User guide <user_guide>
-API <api>
+```{toctree}
+---
+hidden:
+maxdepth: 3
+---
+User guide <user_guide.md>
+API <api.rst>
 Statistical analysis <examples/README.md>
-Cite <cite>
-:::
+Cite <cite.md>
+```

docs/examples/README.md renamed to docs/source/examples/README.md

@@ -1,5 +1,5 @@
 
-## Do you need help?
+# Do you need help?
 
 If you have questions regarding the tasks, want to report a bug or discuss data analysis, please ask on our public [![Gitter](https://badges.gitter.im/Cardioception/community.svg)](https://gitter.im/Cardioception/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge).
 
@@ -20,7 +20,7 @@ After extracting the content of the file, the package can be installed via the c
 
 <br clear="left"/>
 
-## Dependencies
+# Dependencies
 
 Cardioception has been tested with Python 3.7. We recommend to use the last install of Anaconda for Python 3.7 or latest (see https://www.anaconda.com/products/individual#download-section).
 
@@ -49,9 +49,7 @@ In addition, some function for HTML reports will require:
 The version provided here are the ones used when testing and runing cardioception locally, and are often the last ones. For several packages however, older version might also be compatibles. 
 
 Cardioception will automatically copy the images and sound files necessary to run the task correctly (~ 160 Mo). These files will be removed if you uninstall the package using `pip uninstall cardioception`.
-
-# Package modularity
-## Physiological recording
+# Physiological recording
 
 Both the Heartbeat counting task (HBC) and the heart rate discrimination task (HRD) require access to physiological recording device during the task to estimate the heart rate or count the number of heartbeats in a given time window. Cardioception natively supports:
 * The [Nonin 3012LP Xpod USB pulse oximeter](https://www.nonin.com/products/xpod/) together with [Nonin 8000SM 'soft-clip' fingertip sensors](https://www.nonin.com/products/8000s/)