# Voice Branding Quantitative Analysis

## Running Marimo Notebooks

Running on Ct-105 for shared access:

```bash
uv run marimo run 02_quant_analysis.py --headless --port 8080
```

---

## Batch Report Generation

The quant report can be run with different filter combinations via CLI or automated batch processing.

### Single Filter Run (CLI)

Run the report script directly with JSON-encoded filter arguments:

```bash
# Single consumer segment
uv run python 03_quant_report.script.py --consumer '["Starter"]'

# Single age group
uv run python 03_quant_report.script.py --age '["18 to 21 years"]'

# Multiple filters combined
uv run python 03_quant_report.script.py --age '["18 to 21 years", "22 to 24 years"]' --gender '["Male"]'

# All respondents (no filters = defaults to all options selected)
uv run python 03_quant_report.script.py
```

Available filter arguments:
- `--age` — JSON list of age groups
- `--gender` — JSON list of genders  
- `--ethnicity` — JSON list of ethnicities
- `--income` — JSON list of income groups
- `--consumer` — JSON list of consumer segments

### Batch Runner (All Combinations)

Run all single-filter combinations automatically with progress tracking:

```bash
# Preview all combinations without running
uv run python run_filter_combinations.py --dry-run

# Run all combinations (shows progress bar)
uv run python run_filter_combinations.py

# Or use the registered CLI entry point
uv run quant-report-batch
uv run quant-report-batch --dry-run
```

This generates reports for:
- All Respondents (no filters)
- Each age group individually
- Each gender individually
- Each ethnicity individually
- Each income group individually
- Each consumer segment individually

Output figures are saved to `figures/<export_date>/<filter_slug>/`.

### Jupyter Notebook Debugging

The script auto-detects Jupyter/IPython environments. When running in VS Code's Jupyter extension, CLI args default to `None` (all options selected), so you can debug cell-by-cell normally.

---

## Adding Custom Filter Combinations

To add new filter combinations to the batch runner, edit `run_filter_combinations.py`:

### Checklist

1. **Open** `run_filter_combinations.py`

2. **Find** the `get_filter_combinations()` function

3. **Add** your combination to the list before the `return` statement:

```python
# Example: Add a specific age + consumer cross-filter
combinations.append({
    'name': 'Age-18to24_Consumer-Starter',  # Used for output folder naming
    'filters': {
        'age': ['18 to 21 years', '22 to 24 years'],
        'consumer': ['Starter']
    }
})
```

4. **Filter keys** must match CLI argument names (defined in `FILTER_CONFIG` in `03_quant_report.script.py`):
   - `age` — values from `survey.options_age`
   - `gender` — values from `survey.options_gender`
   - `ethnicity` — values from `survey.options_ethnicity`
   - `income` — values from `survey.options_income`
   - `consumer` — values from `survey.options_consumer`

5. **Check available values** by running:
```python
from utils import QualtricsSurvey
S = QualtricsSurvey('data/exports/2-2-26/...Labels.csv', 'data/exports/.../....qsf')
S.load_data()
print(S.options_age)
print(S.options_consumer)
# etc.
```

6. **Test** with dry-run first:
```bash
uv run python run_filter_combinations.py --dry-run
```

### Example: Adding Multiple Cross-Filters

```python
# In get_filter_combinations(), before return:

# Young professionals
combinations.append({
    'name': 'Young_Professionals',
    'filters': {
        'age': ['22 to 24 years', '25 to 34 years'],
        'consumer': ['Early Professional']
    }
})

# High income males
combinations.append({
    'name': 'High_Income_Male',
    'filters': {
        'income': ['$150,000 - $199,999', '$200,000 or more'],
        'gender': ['Male']
    }
})
```

### Notes

- **Empty filters dict** = all respondents (no filtering)
- **Omitted filter keys** = all options for that dimension selected
- **Output folder names** are auto-generated from active filters by `QualtricsSurvey.filter_data()`

---

## Adding a New Filter Dimension

To add an entirely new filter dimension (e.g., a new demographic question), edit **only** `FILTER_CONFIG` in `03_quant_report.script.py`:

### Checklist

1. **Ensure `QualtricsSurvey`** has the corresponding `options_*` attribute and `filter_data()` accepts the parameter

2. **Open** `03_quant_report.script.py`

3. **Find** `FILTER_CONFIG` near the top of the file:

```python
FILTER_CONFIG = {
    'age': 'options_age',
    'gender': 'options_gender',
    'ethnicity': 'options_ethnicity',
    'income': 'options_income',
    'consumer': 'options_consumer',
    # Add new filters here: 'newfilter': 'options_newfilter',
}
```

4. **Add** your new filter:

```python
FILTER_CONFIG = {
    'age': 'options_age',
    'gender': 'options_gender',
    'ethnicity': 'options_ethnicity',
    'income': 'options_income',
    'consumer': 'options_consumer',
    'region': 'options_region',  # ← New filter
}
```

This **automatically**:
- Adds `--region` CLI argument
- Includes it in Jupyter mode (defaults to all options)
- Passes it to `S.filter_data()`
- Writes it to the `.txt` filter description file

5. **Update** `run_filter_combinations.py` to generate combinations for the new filter (optional)