add myst notebook representation of examples (#283)

* add myst notebook representation of examples

* fix pre commit hook to work on notebooks onlyd

* fix incorrect mention to wiki

Author: OriolAbril

.jupytext.toml

@@ -0,0 +1,3 @@
+[formats]
+"examples/" = "ipynb"
+"myst_nbs/" = ".myst.md:myst"

.pre-commit-config.yaml

@@ -1,6 +1,12 @@
 repos:
+- repo: https://github.com/mwouts/jupytext
+  rev: v1.13.7
+  hooks:
+  - id: jupytext
+    files: ^examples/.+\.ipynb$
+    args: ["--sync"]
 - repo: https://github.com/psf/black
-  rev: 21.8b0
+  rev: 22.1.0
   hooks:
     - id: black-jupyter
 - repo: https://github.com/nbQA-dev/nbQA
@@ -48,7 +54,7 @@ repos:
       entry: '%load_ext watermark.*%watermark -n -u -v -iv -w'
       language: pygrep
       minimum_pre_commit_version: 2.8.0
-      name: Check notebooks have watermark (see Jupyter style guide from PyMC3 Wiki)
+      name: Check notebooks have watermark (see Jupyter style guide from PyMC docs)
       types: [jupyter]
     - id: add-tags
       entry: python scripts/add_tags.py
@@ -59,3 +65,9 @@ repos:
          - nbqa==1.1.1
          - beautifulsoup4==4.9.3
          - myst_parser==0.13.7
+- repo: https://github.com/mwouts/jupytext
+  rev: v1.13.7
+  hooks:
+  - id: jupytext
+    files: ^examples/.+\.ipynb$
+    args: ["--sync"]

examples/case_studies/BEST.ipynb

@@ -232,8 +232,8 @@
    "outputs": [],
    "source": [
     "with model:\n",
-    "    lambda_1 = group1_std ** -2\n",
-    "    lambda_2 = group2_std ** -2\n",
+    "    lambda_1 = group1_std**-2\n",
+    "    lambda_2 = group2_std**-2\n",
     "    group1 = pm.StudentT(\"drug\", nu=nu, mu=group1_mean, lam=lambda_1, observed=iq_drug)\n",
     "    group2 = pm.StudentT(\"placebo\", nu=nu, mu=group2_mean, lam=lambda_2, observed=iq_placebo)"
    ]
@@ -255,7 +255,7 @@
     "    diff_of_means = pm.Deterministic(\"difference of means\", group1_mean - group2_mean)\n",
     "    diff_of_stds = pm.Deterministic(\"difference of stds\", group1_std - group2_std)\n",
     "    effect_size = pm.Deterministic(\n",
-    "        \"effect size\", diff_of_means / np.sqrt((group1_std ** 2 + group2_std ** 2) / 2)\n",
+    "        \"effect size\", diff_of_means / np.sqrt((group1_std**2 + group2_std**2) / 2)\n",
     "    )"
    ]
   },

examples/case_studies/bayesian_ab_testing.ipynb

@@ -813,7 +813,7 @@
    "id": "c871fb6e",
    "metadata": {},
    "source": [
-    "### Generalising to multi-variant tests "
+    "### Generalising to multi-variant tests"
    ]
   },
   {

examples/case_studies/binning.ipynb

@@ -3267,7 +3267,7 @@
    "metadata": {},
    "source": [
     "## Authors\n",
-    "* Authored by [Eric Ma](https://github.com/ericmjl) and [Benjamin T. Vincent](https://github.com/drbenvincent) in September, 2021 ([pymc-examples#229](https://github.com/pymc-devs/pymc-examples/pull/229))\n"
+    "* Authored by [Eric Ma](https://github.com/ericmjl) and [Benjamin T. Vincent](https://github.com/drbenvincent) in September, 2021 ([pymc-examples#229](https://github.com/pymc-devs/pymc-examples/pull/229))"
    ]
   },
   {

examples/case_studies/blackbox_external_likelihood_numpy.ipynb

@@ -118,7 +118,7 @@
     "\n",
     "def my_loglike(theta, x, data, sigma):\n",
     "    model = my_model(theta, x)\n",
-    "    return -(0.5 / sigma ** 2) * np.sum((data - model) ** 2)"
+    "    return -(0.5 / sigma**2) * np.sum((data - model) ** 2)"
    ]
   },
   {

examples/case_studies/conditional-autoregressive-model.ipynb

@@ -1503,7 +1503,7 @@
    "source": [
     "`theano.scan` is much faster than using a python for loop, but it is still quite slow. One approach for improving it is to use linear algebra. That is, we should try to find a way to use matrix multiplication instead of looping (if you have experience in using MATLAB, it is the same philosophy). In our case, we can totally do that.  \n",
     "\n",
-    "For a similar problem, you can also have a look of [my port of Lee and Wagenmakers' book](https://github.com/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3). For example, in Chapter 19, the Stan code use [a for loop to generate the likelihood function](https://github.com/stan-dev/example-models/blob/master/Bayesian_Cognitive_Modeling/CaseStudies/NumberConcepts/NumberConcept_1_Stan.R#L28-L59), and I [generate the matrix outside and use matrix multiplication etc](http://nbviewer.jupyter.org/github/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3/blob/master/CaseStudies/NumberConceptDevelopment.ipynb#19.1-Knower-level-model-for-Give-N) to archive the same purpose.  "
+    "For a similar problem, you can also have a look of [my port of Lee and Wagenmakers' book](https://github.com/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3). For example, in Chapter 19, the Stan code use [a for loop to generate the likelihood function](https://github.com/stan-dev/example-models/blob/master/Bayesian_Cognitive_Modeling/CaseStudies/NumberConcepts/NumberConcept_1_Stan.R#L28-L59), and I [generate the matrix outside and use matrix multiplication etc](http://nbviewer.jupyter.org/github/junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3/blob/master/CaseStudies/NumberConceptDevelopment.ipynb#19.1-Knower-level-model-for-Give-N) to archive the same purpose."
    ]
   },
   {
@@ -3286,7 +3286,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As you can see above, the sparse representation returns the same estimates, while being much faster than any other implementation. "
+    "As you can see above, the sparse representation returns the same estimates, while being much faster than any other implementation."
    ]
   },
   {

examples/case_studies/factor_analysis.ipynb

@@ -608,7 +608,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "* This notebook was written by [chartl](https://github.com/chartl) on May 6, 2019 and updated by [Christopher Krapu](https://github.com/ckrapu) on April 4, 2021. "
+    "* This notebook was written by [chartl](https://github.com/chartl) on May 6, 2019 and updated by [Christopher Krapu](https://github.com/ckrapu) on April 4, 2021."
    ]
   },
   {

examples/case_studies/hierarchical_partial_pooling.ipynb

@@ -43,7 +43,7 @@
     "\n",
     "The idea of hierarchical partial pooling is to model the global performance, and use that estimate to parameterize a population of players that accounts for differences among the players' performances. This tradeoff between global and individual performance will be automatically tuned by the model. Also, uncertainty due to different number of at bats for each player (*i.e.* informatino) will be automatically accounted for, by shrinking those estimates closer to the global mean.\n",
     "\n",
-    "For far more in-depth discussion please refer to Stan [tutorial](http://mc-stan.org/documentation/case-studies/pool-binary-trials.html) {cite:p}`carpenter2016hierarchical` on the subject. The model and parameter values were taken from that example.\n"
+    "For far more in-depth discussion please refer to Stan [tutorial](http://mc-stan.org/documentation/case-studies/pool-binary-trials.html) {cite:p}`carpenter2016hierarchical` on the subject. The model and parameter values were taken from that example."
    ]
   },
   {
@@ -588,7 +588,7 @@
     "\n",
     ":::{bibliography}\n",
     ":filter: docname in docnames\n",
-    ":::\n"
+    ":::"
    ]
   }
  ],

examples/case_studies/item_response_nba.ipynb

@@ -812,7 +812,7 @@
     "- y: the difference between the raw mean probability (from the data) and the posterior mean probability for each disadvantaged and committing player\n",
     "- x: as a function of the number of observations per disadvantaged and committing player.\n",
     "\n",
-    "These plots show, as expected, that the hierarchical structure of our model tends to estimate posteriors towards the global mean for players with a low amount of observations. "
+    "These plots show, as expected, that the hierarchical structure of our model tends to estimate posteriors towards the global mean for players with a low amount of observations."
    ]
   },
   {
@@ -1015,7 +1015,7 @@
    "source": [
     "### Discovering extra hierarchical structure\n",
     "\n",
-    "A natural question to ask is whether players skilled as disadvantaged players (i.e. players with high `θ`) are also likely to be skilled as committing players  (i.e. with high `b`), and the other way around. So, the next two plots show the `θ` (resp. `b`) score for the top players with respect to `b` ( resp.`θ`). "
+    "A natural question to ask is whether players skilled as disadvantaged players (i.e. players with high `θ`) are also likely to be skilled as committing players  (i.e. with high `b`), and the other way around. So, the next two plots show the `θ` (resp. `b`) score for the top players with respect to `b` ( resp.`θ`)."
    ]
   },
   {
@@ -1098,7 +1098,7 @@
    "metadata": {},
    "source": [
     "These plots suggest that scoring high in `θ` does not correlate with high or low scores in `b`. Moreover, with a little knowledge of NBA basketball, one can visually note that a higher score in `b` is expected from players playing center or forward rather than guards or point guards. \n",
-    "Given the last observation, we decide to plot a histogram for the occurence of different positions for top disadvantaged (`θ`) and committing (`b`) players. Interestingly, we see below that the largest share of best disadvantaged players are guards, meanwhile, the largest share of best committing players are centers (and at the same time a very small share of guards). "
+    "Given the last observation, we decide to plot a histogram for the occurence of different positions for top disadvantaged (`θ`) and committing (`b`) players. Interestingly, we see below that the largest share of best disadvantaged players are guards, meanwhile, the largest share of best committing players are centers (and at the same time a very small share of guards)."
    ]
   },
   {