Merge pull request #61 from pythonhealthdatascience/dev

Dev

Author: amyheather

CITATION.cff

@@ -3,7 +3,7 @@
 
 cff-version: 1.2.0
 title: >-
-  Python DES RAP Template
+  Simple M/M/s queuing model: Python DES RAP
 message: >-
   If you use this software, please cite it using the
   metadata from this file.
@@ -22,8 +22,8 @@ authors:
 repository-code: >-
   https://github.com/pythonhealthdatascience/pydesrap_mms
 abstract: >-
-  A template for creating discrete-event simulation (DES) models in Python
-  within a reproducible analytical pipeline (RAP).
+  Reproducible analytical pipeline (RAP) for python discrete-event simulation
+  (DES) implementing a simple M/M/s queueing model.
 license: MIT
 version: '1.2.0'
 date-released: '2025-03-26'

README.md

@@ -81,6 +81,12 @@ experiment.run_reps()
 bash run_notebooks.sh
 ```
 
+To run one notebook from the command line (with the same settings - clearing the meta data etc):
+
+```{.bash}
+bash run_notebooks.sh notebooks/notebook_name.ipynb
+```
+
 **Run tests:**
 
 ```{.r}
@@ -221,9 +227,9 @@ Run times from our analyses (on Intel Core i7-12700H, 32GB RAM, Ubuntu 24.04.1):
 
 If you use this repository, please cite either the GitHub repository or Zenodo:
 
-> Heather, A. Monks, T. (2025). Python DES RAP Template. GitHub. https://github.com/pythonhealthdatascience/pydesrap_mms.
+> Heather, A. Monks, T. (2025). Simple M/M/s queuing model: Python DES RAP. GitHub. https://github.com/pythonhealthdatascience/pydesrap_mms.
 >
-> Heather, A. Monks, T. (2025). Python DES RAP Template. Zenodo. https://doi.org/10.5281/zenodo.14622466
+> Heather, A. Monks, T. (2025). Simple M/M/s queuing model: Python DES RAP. Zenodo. https://doi.org/10.5281/zenodo.14622466
 
 **Contributors:**
 

notebooks/choosing_replications.ipynb

@@ -3,21 +3,26 @@
 # Get the conda environment's jupyter path
 CONDA_JUPYTER=$(dirname "$(which python)")/jupyter
 
-# Loop through all notebooks in the specified directory
-for nb in notebooks/*.ipynb; do
+run_notebook() {
+    local nb="$1"
     echo "🏃 Running notebook: $nb"
-    
-    # Execute and update the notebook in-place
-    # With some processing to remove metadata created by nbconvert
     if "${CONDA_JUPYTER}" nbconvert --to notebook --inplace --execute \
         --ClearMetadataPreprocessor.enabled=True \
         --ClearMetadataPreprocessor.clear_notebook_metadata=False \
         --ClearMetadataPreprocessor.preserve_cell_metadata_mask="kernelspec" \
-        "$nb"; then
+        "$nb"
+    then
         echo "✅ Successfully processed: $nb"
     else
         echo "❌ Error processing: $nb"
     fi
-    
     echo "-------------------------"
-done
+}
+
+if [[ -n "$1" ]]; then
+    run_notebook "$1"
+else
+    for nb in notebooks/*.ipynb; do
+        run_notebook "$nb"
+    done
+fi

outputs/choose_reps_mean_q_time_nurse.png

@@ -26,7 +26,7 @@ def confidence_interval_method(
     metrics,
     param=Param(),
     alpha=0.05,
-    desired_precision=0.05,
+    desired_precision=0.1,
     min_rep=3,
     verbose=False
 ):

run_notebooks.sh

@@ -17,7 +17,7 @@ def confidence_interval_method_simple(
     replications,
     metrics,
     param=Param(),
-    desired_precision=0.05,
+    desired_precision=0.1,
     min_rep=3,
     verbose=False
 ):

simulation/confidence_interval_method.py

@@ -54,7 +54,7 @@ class ReplicationsAlgorithm:
     def __init__(
         self,
         alpha=0.05,
-        half_width_precision=0.05,
+        half_width_precision=0.1,
         initial_replications=3,
         look_ahead=5,
         replication_budget=1000