34 port notebooks under modeldocs to quarto (#35)

* Porting to quarto and adding a makefile entry to render them

* Now it should be easier to deal with pre-commit under docs (only skipping cache)

* Forgot to remove the pyrenew_demo notebook

* Update model/Makefile

* Fixing typos

* Removes progress bar - adds doc/ figures - sets jupyter as kernel

* Update model/docs/getting-started.qmd

Co-authored-by: Dylan H. Morris <dylanhmorris@users.noreply.github.com>

---------

Co-authored-by: Dylan H. Morris <dylanhmorris@users.noreply.github.com>

.pre-commit-config.yaml

@@ -34,7 +34,7 @@ repos:
     hooks:
     -   id: detect-secrets
         args: ['--baseline', '.secrets.baseline',
-               '--exclude-files', 'model/docs/']
+               '--exclude-files', 'model/docs/*_cache']
         exclude: package.lock.json
 ####
 # Typos

model/Makefile

@@ -4,4 +4,12 @@ install:
 test:
 	poetry run pytest
 
-.PHONY: install test
+docs: docs/pyrenew_demo.md docs/getting-started.md
+
+docs/pyrenew_demo.md: docs/pyrenew_demo.qmd
+	quarto render docs/pyrenew_demo.qmd
+
+docs/getting-started.md: docs/getting-started.qmd
+	quarto render docs/getting-started.qmd
+
+.PHONY: install test docs

model/docs/.gitignore

@@ -0,0 +1 @@
+!*png

model/docs/getting-started.md

@@ -0,0 +1,207 @@
+# Getting started with pyrenew
+
+
+This document illustrates two features of `pyrenew`: (a) the set of
+included `RandomVariable`es, and (b) model composition.
+
+## Hospitalizations model
+
+`pyrenew` has five main components:
+
+- Utility and math functions,
+- The `processes` sub-module,
+- The `observations` sub-module,
+- The `latent` sub-module, and
+- The `models` sub-module
+
+All three of `process`, `observation`, and `latent` contain classes that
+inherit from the meta class `RandomVariable`. The classes under `model`
+inherit from the meta class `Model`. The following diagram illustrates
+the composition the model `pyrenew.models.HospitalizationsModel`:
+
+``` mermaid
+flowchart TB
+
+    subgraph randprocmod["Processes module"]
+        direction TB
+        simprw["SimpleRandomWalkProcess"]
+        rtrw["RtRandomWalkProcess"]
+    end
+
+    subgraph latentmod["Latent module"]
+        direction TB
+        hosp_latent["Hospitalizations"]
+        inf_latent["Infections"]
+    end
+
+    subgraph obsmod["Observations module"]
+        direction TB
+        pois["PoissonObservation"]
+        nb["NegativeBinomialObservation"]
+    end
+
+    subgraph models["Models module"]
+        direction TB
+        basic["RtInfectionsRenewalModel"]
+        hosp["HospitalizationsModel"]
+    end
+
+    rp(("RandomVariable")) --> |Inherited by| randprocmod
+    rp -->|Inherited by| latentmod
+    rp -->|Inherited by| obsmod
+
+
+    model(("Model")) -->|Inherited by| models
+
+    simprw -->|Composes| rtrw
+    rtrw -->|Composes| basic
+    inf_latent -->|Composes| basic
+    basic -->|Composes| hosp
+
+
+    obsmod -->|Composes|models
+    hosp_latent -->|Composes| hosp
+
+    %% Metaclasses
+    classDef Metaclass color:black,fill:white
+    class rp,model Metaclass
+
+    %% Random process
+    classDef Randproc fill:purple,color:white
+    class rtrw,simprw Randproc
+
+    %% Models
+    classDef Models fill:teal,color:white
+    class basic,hosp Models
+```
+
+We start by loading the needed components to build a basic renewal
+model:
+
+``` python
+import jax.numpy as jnp
+import numpy as np
+import numpyro as npro
+from pyrenew.process import RtRandomWalkProcess
+from pyrenew.latent import Infections
+from pyrenew.observation import PoissonObservation
+from pyrenew.model import RtInfectionsRenewalModel
+```
+
+    /home/xrd4/.cache/pypoetry/virtualenvs/pyrenew-B3vwhbMF-py3.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
+      from .autonotebook import tqdm as notebook_tqdm
+    An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
+
+In the basic renewal model we can define three components: Rt, latent
+infections, and observed infections.
+
+``` python
+latent_infections = Infections(
+    gen_int=jnp.array([0.25, 0.25, 0.25, 0.25]),
+    )
+
+observed_infections = PoissonObservation(
+    rate_varname='latent',
+    counts_varname='observed_infections',
+    )
+
+rt_proc = RtRandomWalkProcess()
+```
+
+With observation process for the latent infections, we can build the
+basic renewal model, and generate a sample calling the `sample()`
+method:
+
+``` python
+model1 = RtInfectionsRenewalModel(
+    Rt_process=rt_proc,
+    latent_infections=latent_infections,
+    observed_infections=observed_infections,
+    )
+
+np.random.seed(223)
+with npro.handlers.seed(rng_seed=np.random.randint(1, 60)):
+    sim_data = model1.sample(constants=dict(n_timepoints=30))
+
+sim_data
+```
+
+    InfectModelSample(Rt=Array([1.2022278, 1.2111099, 1.2325984, 1.2104921, 1.2023039, 1.1970979,
+           1.2384264, 1.2423582, 1.245498 , 1.241344 , 1.2081108, 1.1938375,
+           1.271196 , 1.3189521, 1.3054799, 1.3165426, 1.291952 , 1.3026639,
+           1.2619467, 1.2852622, 1.3121517, 1.2888998, 1.2641873, 1.2580931,
+           1.2545817, 1.3092988, 1.2488269, 1.2397509, 1.2071848, 1.2334517,
+           1.21868  ], dtype=float32), latent=Array([ 3.7023427,  4.850682 ,  6.4314823,  8.26245  ,  6.9874763,
+            7.940377 ,  9.171101 , 10.051114 , 10.633459 , 11.729475 ,
+           12.559867 , 13.422887 , 15.364211 , 17.50132  , 19.206314 ,
+           21.556652 , 23.78112  , 26.719398 , 28.792412 , 32.40454  ,
+           36.641006 , 40.135487 , 43.60607  , 48.055103 , 52.829704 ,
+           60.43277  , 63.97854  , 69.82776  , 74.564415 , 82.88904  ,
+           88.73811  ], dtype=float32), observed=Array([ 4,  3,  6,  5,  7,  7, 10, 11,  6,  9,  7, 13, 16, 19, 20, 27, 23,
+           31, 28, 30, 43, 42, 55, 57, 44, 52, 64, 52, 77, 85, 94],      dtype=int32))
+
+The `sample()` method of the `RtInfectionsRenewalModel` returns a list
+composed of the `Rt` and `infections` sequences.
+
+``` python
+import matplotlib.pyplot as plt
+
+fig, axs = plt.subplots(1, 2)
+
+# Rt plot
+axs[0].plot(range(0, 31), sim_data[0])
+axs[0].set_ylabel('Rt')
+
+# Infections plot
+axs[1].plot(range(0, 31), sim_data[1])
+axs[1].set_ylabel('Infections')
+
+fig.suptitle('Basic renewal model')
+fig.supxlabel('Time')
+plt.tight_layout()
+plt.show()
+```
+
+<img
+src="getting-started_files/figure-commonmark/basic-fig-output-1.png"
+id="basic-fig" />
+
+Let’s see how the estimation would go
+
+``` python
+import jax
+
+model_data = {'n_timepoints': len(sim_data[1])-1}
+
+model1.run(
+    num_warmup=2000,
+    num_samples=1000,
+    random_variables=dict(observed_infections=sim_data.observed),
+    constants=model_data,
+    rng_key=jax.random.PRNGKey(54),
+    mcmc_args=dict(progress_bar=False),
+    )
+```
+
+Now, let’s investigate the output
+
+``` python
+import polars as pl
+samps = model1.spread_draws([('Rt', 'time')])
+
+fig, ax = plt.subplots(figsize=[4, 5])
+
+ax.plot(sim_data[0])
+samp_ids = np.random.randint(size=25, low=0, high=999)
+for samp_id in samp_ids:
+    sub_samps = samps.filter(pl.col("draw") == samp_id).sort(pl.col('time'))
+    ax.plot(sub_samps.select("time").to_numpy(),
+            sub_samps.select("Rt").to_numpy(), color="darkblue", alpha=0.1)
+ax.set_ylim([0.4, 1/.4])
+ax.set_yticks([0.5, 1, 2])
+ax.set_yscale("log")
+```
+
+<img
+src="getting-started_files/figure-commonmark/output-rt-output-1.png"
+id="output-rt" />