Update docs to show tutorials in the main page

LegrandNico · LegrandNico · commit 40fe04ba2999 · 2023-01-16T15:46:48.000+01:00
diff --git a/README.md b/README.md
@@ -40,7 +40,7 @@ metadpy first aims to be the Python equivalent of the [hMeta-d toolbox](https://
 
 For an extensive introduction to metadpy, you can navigate the following notebooks that are Python adaptations of the introduction to the [hMeta-d toolbox](https://github.com/metacoglab/HMeta-d) written in Matlab by Olivia Faul for the [Zurich Computational Psychiatry course](https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial).
 
-## Examples 
+## Examples
 
 | Notebook | Colab | nbViewer |
 | --- | ---| --- |
diff --git a/docs/source/examples/1-What metacognition looks like.ipynb b/docs/source/examples/1-What metacognition looks like.ipynb
@@ -5,7 +5,8 @@
    "metadata": {},
    "source": [
     "(tutorial_1)=\n",
-    "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  \n",
+    "# What metacognition looks like?\n",
+    "Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>  \n",
     "Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial"
    ]
   },
@@ -23,13 +24,6 @@
     "sns.set_context(\"talk\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# What metacognition looks like?"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/docs/source/examples/1-What metacognition looks like.md b/docs/source/examples/1-What metacognition looks like.md
@@ -13,7 +13,8 @@ kernelspec:
 ---
 
 (tutorial_1)=
-Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  
+# What metacognition looks like?
+Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>  
 Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial
 
 ```{code-cell} ipython3
@@ -25,10 +26,6 @@ from metadpy.utils import type2_SDT_simuation
 sns.set_context("talk")
 ```
 
-# What metacognition looks like?
-
-+++
-
 ## Simulating ratings
 
 +++
diff --git a/docs/source/examples/2-Fitting the model-MLE.ipynb b/docs/source/examples/2-Fitting the model-MLE.ipynb
@@ -4,7 +4,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(tutorial_2)=  \n",
+    "(tutorial_2)=\n",
+    "# Fitting the model\n",
     "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  \n",
     "Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial"
    ]
@@ -28,13 +29,6 @@
     "sns.set_context(\"talk\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Fitting the model"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/docs/source/examples/2-Fitting the model-MLE.md b/docs/source/examples/2-Fitting the model-MLE.md
@@ -12,7 +12,8 @@ kernelspec:
   name: python3
 ---
 
-(tutorial_2)=  
+(tutorial_2)=
+# Fitting the model
 Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  
 Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial
 
@@ -30,10 +31,6 @@ from metadpy.utils import (responseSimulation, trials2counts,
 sns.set_context("talk")
 ```
 
-# Fitting the model
-
-+++
-
 ## Calculating metacognition: the type-2 ROC curve
 
 +++
diff --git a/docs/source/examples/3-Comparison with the hmeta-d toolbox.ipynb b/docs/source/examples/3-Comparison with the hmeta-d toolbox.ipynb
@@ -7,6 +7,7 @@
    },
    "source": [
     "(tutorial_3)=\n",
+    "# Single subject\n",
     "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  \n",
     "Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial"
    ]
@@ -29,15 +30,6 @@
     "sns.set_context(\"talk\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rsAIxeFyWijX"
-   },
-   "source": [
-    "# Single subject"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/docs/source/examples/3-Comparison with the hmeta-d toolbox.md b/docs/source/examples/3-Comparison with the hmeta-d toolbox.md
@@ -15,6 +15,7 @@ kernelspec:
 +++ {"id": "a3o1EnPLWeAv"}
 
 (tutorial_3)=
+# Single subject
 Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>  
 Adapted from the tutorial proposed by the HMeta-d toolbox: https://github.com/metacoglab/HMeta-d/tree/master/CPC_metacog_tutorial
 
@@ -31,10 +32,6 @@ from metadpy.bayesian import hmetad
 sns.set_context("talk")
 ```
 
-+++ {"id": "rsAIxeFyWijX"}
-
-# Single subject
-
 ```{code-cell} ipython3
 :id: v8xhAXePWm2Y
 
diff --git a/docs/source/examples/Example 1 - Fitting MLE - Subject and group level.ipynb b/docs/source/examples/Example 1 - Fitting MLE - Subject and group level.ipynb
@@ -8,6 +8,7 @@
    },
    "source": [
     "(example_1)=\n",
+    "# Fitting at the subject level\n",
     "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>"
    ]
   },
@@ -42,16 +43,6 @@
     "We are going to see, however, that [metadpy](https://github.com/LegrandNico/metadpy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "unable-tattoo",
-   "metadata": {
-    "id": "unable-tattoo"
-   },
-   "source": [
-    "# Fitting at the subject level"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "current-valuation",
@@ -632,7 +623,7 @@
    "id": "379347c1",
    "metadata": {},
    "source": [
-    "# Watermark"
+    "## Watermark"
    ]
   },
   {
diff --git a/docs/source/examples/Example 1 - Fitting MLE - Subject and group level.md b/docs/source/examples/Example 1 - Fitting MLE - Subject and group level.md
@@ -15,6 +15,7 @@ kernelspec:
 +++ {"id": "copyrighted-border"}
 
 (example_1)=
+# Fitting at the subject level
 Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
 
 ```{code-cell} ipython3
@@ -36,10 +37,6 @@ In this notebook, we are going to estimate meta-*d'* using Maximum Likelihood Es
 
 We are going to see, however, that [metadpy](https://github.com/LegrandNico/metadpy) greatly simplifies the preprocessing of raw data, letting the user fit the model for many participants/groups/conditions from the results data frame in a single command call. Another advantage here is that the python code supporting the model fitting is optimized using [Numba](http://numba.pydata.org/), which greatly improves its performance.
 
-+++ {"id": "unable-tattoo"}
-
-# Fitting at the subject level
-
 +++ {"id": "current-valuation"}
 
 ## From response-signal arrays
@@ -207,7 +204,7 @@ plt.tight_layout()
 sns.despine()
 ```
 
-# Watermark
+## Watermark
 
 ```{code-cell} ipython3
 %load_ext watermark
diff --git a/docs/source/examples/Example 2 - Fitting Bayesian - Subject level (pymc).ipynb b/docs/source/examples/Example 2 - Fitting Bayesian - Subject level (pymc).ipynb
@@ -7,7 +7,8 @@
     "id": "regulated-swiss"
    },
    "source": [
-    "(example_2)=  \n",
+    "(example_2)=\n",
+    "# Fitting data at the subject level\n",
     "Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>"
    ]
   },
@@ -25,16 +26,6 @@
     "from metadpy.bayesian import hmetad"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "fewer-sister",
-   "metadata": {
-    "id": "fewer-sister"
-   },
-   "source": [
-    "# Fitting data at the subject level"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "operating-aerospace",
@@ -381,7 +372,7 @@
    "id": "f1714be8",
    "metadata": {},
    "source": [
-    "# Watermark"
+    "## Watermark"
    ]
   },
   {
diff --git a/docs/source/examples/Example 2 - Fitting Bayesian - Subject level (pymc).md b/docs/source/examples/Example 2 - Fitting Bayesian - Subject level (pymc).md
@@ -14,7 +14,8 @@ kernelspec:
 
 +++ {"id": "regulated-swiss"}
 
-(example_2)=  
+(example_2)=
+# Fitting data at the subject level
 Author: Nicolas Legrand <nicolas.legrand@cfin.au.dk>
 
 ```{code-cell} ipython3
@@ -25,10 +26,6 @@ import numpy as np
 from metadpy.bayesian import hmetad
 ```
 
-+++ {"id": "fewer-sister"}
-
-# Fitting data at the subject level
-
 +++ {"id": "operating-aerospace"}
 
 ## From response-signal arrays
@@ -78,7 +75,7 @@ outputId: 174b4bec-f2a1-4f33-e7e4-fbc91924f1b3
 az.summary(traces, var_names=["c1", "d1", "meta_d", "cS1", "cS2"])
 ```
 
-# Watermark
+## Watermark
 
 ```{code-cell} ipython3
 %load_ext watermark
diff --git a/docs/source/tutorials.md b/docs/source/tutorials.md
@@ -14,13 +14,13 @@ examples/*
 
 | Notebook | Colab |
 | --- | ---|
-| {ref}`tutorial_1` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/1-What%20%metacognition%20%looks%20%like.ipynb)
-| {ref}`tutorial_2` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/2-Fitting%20%the%20%model-MLE.ipynb)
-| {ref}`tutorial_3` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/3-Comparison%20%with%20%the%20%hmeta-d%20%toolbox.ipynb)
+| {ref}`tutorial_1` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/1-What%20metacognition%20looks%20like.ipynb)
+| {ref}`tutorial_2` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/2-Fitting%20the%20model-MLE.ipynb)
+| {ref}`tutorial_3` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/3-Comparison%20with%20the%20hmeta-d%20toolbox.ipynb)
 
 ## Examples
 
 | Notebook | Colab |
 | --- | ---|
-| {ref}`example_1` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/Example%20%1%20%-%20%Fitting%20%MLE%20%-%20%Subject%20%and%20%group%20%level.ipynb)
-| {ref}`example_2` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/Example%20%2%20%-%20%Fitting%20%Bayesian%20%-%20%Subject%20%levelExample%20%1%20%-%20%Fitting%20%MLE%20%-%20%Subject%20%and%20%group%20%level(pymc).ipynb)
+| {ref}`example_1` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/Example%201%20-%20Fitting%20MLE%20-%20Subject%20and%20group%20level.ipynb)
+| {ref}`example_2` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/embodied-computation-group/metadpy/blob/master/docs/source/examples/Example%202%20-%20Fitting%20Bayesian%20-%20Subject%20level%20(pymc).ipynb)
