[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
   #####
   # Python
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.21
+    rev: v0.16.0
     hooks:
       # Run the linter
       - id: ruff-check
@@ -27,7 +27,7 @@ repos:
   #####
   # Quarto
   - repo: https://github.com/jolars/panache-pre-commit
-    rev: v2.61.0
+    rev: v3.0.1
     hooks:
       - id: panache-format
       - id: panache-lint

README.md

@@ -75,7 +75,7 @@ This source code in this repository is distributed in the hope that it will be u
 See the Apache Software License for more details.
 
 You should have received a copy of the Apache Software License along with this program.
-If not, see [http://www.apache.org/licenses/LICENSE-2.0.html](http://www.apache.org/licenses/LICENSE-2.0.html).
+If not, see <http://www.apache.org/licenses/LICENSE-2.0.html>.
 
 The source code forked from other open source projects will inherit its license.
 
@@ -92,7 +92,7 @@ Anyone is encouraged to contribute to the repository by [forking](https://help.g
 By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the [Apache Software License v2](http://www.apache.org/licenses/LICENSE-2.0.html) or later.
 
 All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived.
-Learn more at [http://www.cdc.gov/other/privacy.html](http://www.cdc.gov/other/privacy.html).
+Learn more at <http://www.cdc.gov/other/privacy.html>.
 
 ## Records Management Standard Notice
 

docs/index.md

@@ -13,7 +13,7 @@ It combines two distinct discrete convolutions which describe different processe
 - The **renewal equation** maps past infections to new infections using the generation interval distribution $w_\tau$.
 - The **observation equation** maps latent infections to expected observed events using the delay distribution $\pi_d$.
 
-#### Renewal equation
+### Renewal equation
 
 New infections arise from past infections through a generation interval distribution.
 
@@ -35,7 +35,7 @@ Here, $\tau$ indexes the generation interval.
 
 In PyRenew, the latent process is represented on a **per-capita scale** (infection proportion) and is multiplied by a population size downstream when connecting to count observations.
 
-#### Observation equation
+### Observation equation
 
 Infections are latent and are not directly observed; instead, the data consist of events that occur some time after infection, such as hospitalizations or emergency department visits.
 
@@ -55,7 +55,7 @@ $$
 
 Here, $d$ indexes lags in the infection-to-observation delay distribution.
 
-#### Stochastic observation model
+### Stochastic observation model
 
 The observation equation defines the expected number of observed events at time $t$, but the actual observed data are stochastic.
 

docs/tutorials/latent_infections.qmd

@@ -259,7 +259,7 @@ n_init = len(gen_int_pmf)
 n_samples = 200
 log_rt_time_0 = 0.5
 I0_val = 0.001
-rt_cap = 3.0
+rt_cap = 3.
 
 
 def sample_process(rt_process, label):

pyproject.toml

@@ -89,3 +89,4 @@ select = ["I", "E4", "E7", "E9", "F", "UP", "ANN"]
 
 [tool.ruff.lint.per-file-ignores]
 "test/**" = ["ANN"]
+"docs/tutorials/**" = ["ANN"]