📝 clarify model script selection in generated outputs

CHANGELOG.md

@@ -5,6 +5,7 @@ All notable changes to this project will be documented in this file.
 ## [Unreleased]
 
 ### Added
+- Clarified model-selection documentation so README and user-facing guides explain that `--model` selects both the epidemiological model and the generated script (`si.py`, `sir.py`, or `seir.py`)
 - Comprehensive documentation overhaul
   - Updated pyproject.toml with proper package description
   - Completely rewrote README.md with installation instructions, prerequisites, troubleshooting, advanced usage, and comprehensive examples

README.md

@@ -42,7 +42,7 @@ Once installed, you can run `laser-init` from the command line:
 laser-init NGA 2 2000 2025
 ```
 
-This will create a directory `NGA/2000/` with all necessary data files and a ready-to-run SEIR model script.
+This will create a directory `NGA/2000/` with all necessary data files and a ready-to-run model script. By default this is `seir.py`; use `--model SI` or `--model SIR` to generate `si.py` or `sir.py` instead.
 
 ## Basic Usage
 
@@ -115,11 +115,13 @@ total 12440
 
 - **`config.yaml`**: Configuration file referencing data files and model parameters
 
-- **`seir.py`**: Ready-to-run SEIR model script
+- **Model script**: One of `si.py`, `sir.py`, or `seir.py`, depending on `--model`
+  - Default output: `seir.py`
   - Run with: `python3 ./seir.py` or `python3 ./seir.py --config config.yaml`
+  - If you generated a different model, run `python3 ./si.py` or `python3 ./sir.py` instead
   - Requires `laser.generic` package
 
-- **`plot.py`**: Supporting script, called from `seir.py`, for generating simulation result plots
+- **`plot.py`**: Supporting script, called from the generated model script, for generating simulation result plots
 
 - **Demographic data files**:
   - `age_dist.csv`: Population age distribution for the start year
@@ -147,6 +149,8 @@ laser-init NGA 2 2000 2025 --model SIR   # Susceptible-Infectious-Recovered
 laser-init NGA 2 2000 2025 --model SEIR  # Susceptible-Exposed-Infectious-Recovered
 ```
 
+This option also determines the generated script name: `si.py`, `sir.py`, or `seir.py`.
+
 - **SI**: Simple model with susceptible and infectious states
 - **SIR**: Adds recovery (immunity) to the model
 - **SEIR**: Includes an exposed (latent) period before infectiousness
@@ -257,6 +261,8 @@ cd NGA/2000
 python3 ./seir.py
 ```
 
+If you generated a different model with `--model`, run the matching script instead: `python3 ./si.py` or `python3 ./sir.py`.
+
 The model will:
 1. Load configuration and data from `config.yaml`
 2. Initialize the population across administrative units
@@ -353,7 +359,7 @@ done
 
 The generated model scripts are fully editable Python files. Common modifications:
 
-1. **Change initial seed locations**: Edit the seeding logic in `seir.py`
+1. **Change initial seed locations**: Edit the seeding logic in the generated model script (`si.py`, `sir.py`, or `seir.py`)
 2. **Modify transmission parameters**: Adjust `r0`, `beta`, or duration parameters
 3. **Add interventions**: Insert intervention logic into the model components (see [LASER documentation](https://laser.idmod.org/laser-generic))
 4. **Customize plots**: Modify `plot.py` or import it and call specific plot functions

docs/configuration.md

@@ -110,7 +110,7 @@ simulation:
 
 ### Running with Custom Config
 
-The generated model scripts accept a `--config` option:
+The generated model scripts (`si.py`, `sir.py`, or `seir.py`) accept a `--config` option:
 
 ```shell
 # Use default config.yaml in same directory
@@ -343,7 +343,7 @@ NGA/
 └── 2000/
     ├── NGA_admin2.gpkg          # Geospatial data
     ├── config.yaml              # Run configuration
-    ├── seir.py                  # Model script
+    ├── seir.py                  # Model script by default; may be si.py or sir.py with --model
     ├── plot.py                  # Plotting script
     ├── age_dist.csv             # Demographics
     ├── cxr.csv                  # Birth/death rates
@@ -464,6 +464,8 @@ python3 ./seir.py
 python3 ./seir.py --data-dir /full/path/to/NGA/2000
 ```
 
+If you generated an SI or SIR model, use `python3 ./si.py` or `python3 ./sir.py` for those commands.
+
 ## Next Steps
 
 - [User Guide](userguide.md) - Comprehensive workflows and tutorials

docs/index.md

@@ -40,7 +40,7 @@ This creates a complete modeling environment in `NGA/2000/` with:
 
 - GeoPackage with administrative boundaries and population
 - Demographic data files (birth rates, death rates, age distribution)
-- Ready-to-run SEIR model script
+- Ready-to-run model script (`seir.py` by default, or `si.py` / `sir.py` with `--model`)
 - Validation plots and PDF report
 - Configuration file for easy customization
 

docs/models.md

@@ -378,18 +378,24 @@ cd KEN/2010
 python3 ./seir.py
 ```
 
+If you generated a different model with `--model`, run `python3 ./si.py` or `python3 ./sir.py` instead.
+
 ### With Custom Config
 
 ```shell
 python3 ./seir.py --config custom_config.yaml
 ```
 
+Use the matching script name here if you generated `si.py` or `sir.py`.
+
 ### With Custom Data Directory
 
 ```shell
 python3 ./seir.py --data-dir /path/to/data
 ```
 
+Use the matching script name here if you generated `si.py` or `sir.py`.
+
 ### Output
 
 Models generate plots in the output directory:

docs/quickstart.md

@@ -46,7 +46,7 @@ You should see:
 ```text
 NGA_admin2.gpkg          # GeoPackage with boundaries and population
 config.yaml              # Model configuration
-seir.py                  # Ready-to-run SEIR model
+seir.py                  # Ready-to-run model script (default; use --model for si.py or sir.py)
 plot.py                  # Plotting utilities
 age_dist.csv             # Age distribution data
 cxr.csv                  # Birth and death rates
@@ -67,6 +67,8 @@ Execute the generated model:
 python3 ./seir.py
 ```
 
+If you generated a different model with `--model`, run `python3 ./si.py` or `python3 ./sir.py` instead.
+
 The model will:
 
 1. Load configuration and data
@@ -124,7 +126,7 @@ simulation:
     gravity_c: 2
 ```
 
-### Model Script (seir.py)
+### Model Script (`si.py`, `sir.py`, or `seir.py`)
 
 The generated Python script is fully editable. Common customizations:
 
@@ -237,7 +239,7 @@ If processing fails with memory errors:
 
 ### Model Fails to Run
 
-If `python3 ./seir.py` fails:
+If the generated model script fails:
 
 - Ensure you're in the environment where `laser-init` is installed
 - Check that `laser.generic` is installed: `pip list | grep laser`

docs/troubleshooting.md

@@ -360,10 +360,12 @@ Common issues and solutions when using `laser-init`.
 
 3. **Run with verbose output**:
    ```shell
-   python seir.py --help  # See available options
+   python seir.py --help  # See available options for the default model script
    python seir.py --config config.yaml  # Explicit config
    ```
 
+   If you generated a different model, replace `seir.py` with `si.py` or `sir.py`.
+
 4. **Check Python version**:
    ```shell
    python --version  # Should be 3.10+

docs/userguide.md

@@ -89,6 +89,8 @@ cd KEN/2010
 python3 ./seir.py
 ```
 
+If you selected a different model with `--model`, run the matching script instead: `python3 ./si.py` or `python3 ./sir.py`.
+
 The simulation will run and generate output plots showing disease spread across Kenya's districts.
 
 ## Understanding the Workflow
@@ -157,7 +159,7 @@ KEN/
 └── 2010/
     ├── KEN_admin2.gpkg          # Geospatial data (main input)
     ├── config.yaml              # Model configuration
-    ├── seir.py                  # SEIR model script
+    ├── seir.py                  # Model script by default; may be si.py or sir.py with --model
     ├── plot.py                  # Plotting utilities
     ├── age_dist.csv             # Age distribution
     ├── cxr.csv                  # Crude birth/death rates
@@ -310,6 +312,8 @@ python3 ./seir.py
 # Results are saved as PNG files in the current directory
 ```
 
+Use `python3 ./si.py` or `python3 ./sir.py` here if you generated one of those model variants.
+
 ### Workflow 2: Comparing Data Sources
 
 Test different shapefile sources to find the best coverage:
@@ -397,6 +401,8 @@ done
 echo "Parameter sweep complete!"
 ```
 
+If you are sweeping an SI or SIR run, substitute `si.py` or `sir.py` in the copy and execution commands.
+
 ### Workflow 6: Integration with Custom Analysis
 
 Use `laser-init` data in custom scripts:
@@ -438,7 +444,7 @@ print("Custom analysis saved to custom_analysis.png")
 
 ### Understanding Model Outputs
 
-After running the generated model script (e.g., `seir.py`), you'll find several output plots:
+After running the generated model script (for example, `seir.py`), you'll find several output plots:
 
 1. **Stacked Compartments**: Shows S, E, I, R populations over time
    - Look for epidemic peak timing
@@ -512,13 +518,15 @@ Then rerun:
 python3 ./seir.py --config config.yaml
 ```
 
+If you generated `si.py` or `sir.py`, rerun that script instead.
+
 ### Editing Model Scripts
 
-The generated Python3 ./scripts are fully editable. Common modifications:
+The generated model scripts are fully editable. Common modifications:
 
 #### Change Initial Seeding
 
-In `seir.py`, find the seeding logic:
+In the generated model script, find the seeding logic:
 
 ```python
 # Default: Seed largest population center
@@ -547,7 +555,7 @@ scenario.at[seed_idx, "I"] = 100
 #### Modify Transmission Parameters
 
 ```python
-# Location in seir.py
+# Location in the generated model script
 params = PropertySet(config["simulation"])
 
 # Add custom parameters