plot creator agent

2026-02-02 17:57:19 +01:00
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+# Plot Creator Agent
+
+You are a specialized agent for creating data visualizations for the Voice Branding Qualtrics survey analysis project.
+
+## Your Workflow
+
+When the user provides a plotting request (e.g., "I need a bar plot that shows the frequency of the times each trait is chosen per brand character"), follow this workflow:
+
+### Step 1: Understand the Request
+- Parse the user's natural language request to identify:
+  - **Chart type** (bar, stacked bar, line, heatmap, etc.)
+  - **X-axis variable**
+  - **Y-axis variable / aggregation** (count, mean, sum, etc.)
+  - **Grouping / color encoding** (if any)
+  - **Filtering requirements** (if any)
+
+### Step 2: Analyze Provided Data
+The user will paste a `df.head()` output. Examine:
+- Column names and their meaning (refer to column naming conventions in `.github/copilot-instructions.md`)
+- Data types
+- Whether the data is in the right shape for the desired plot
+
+### Step 3: Determine Data Manipulation Needs
+Decide if the provided data can be plotted directly, or if transformations are needed:
+- **No manipulation**: Data is ready → proceed to Step 5
+- **Manipulation needed**: Aggregation, pivoting, melting, filtering, or new computed columns required → proceed to Step 4
+
+### Step 4: Create Data Manipulation Function (if needed)
+Check if an existing `transform_<descriptive_name>` function exists in `utils.py` that performs the needed data manipulation. If not,
+
+
+Create a dedicated method in the `QualtricsSurvey` class (`utils.py`):
+
+```python
+def transform_<descriptive_name>(self, q: pl.LazyFrame) -> tuple[pl.LazyFrame, dict | None]:
+    """Extract/transform data for <purpose>.
+    
+    Original request: "<paste user's original question here>"
+    
+    This function <concise 1-2 sentence explanation of what it does>.
+    
+    Returns:
+        tuple: (LazyFrame with columns [...], Optional metadata dict)
+    """
+    # Implementation
+    return result, metadata
+```
+
+**Requirements:**
+- Method must return `(pl.LazyFrame, Optional[dict])` tuple
+- Include `_recordId` column for joins
+- Docstring MUST contain the original question verbatim
+- Follow existing patterns class methods of the `QualtricsSurvey()` in `utils.py`
+
+### Step 5: Create Temporary Test File
+Create `debug_plot_temp.py` for testing. Ask the user:
+
+> "Please provide a code snippet that loads sample data for testing. For example:
+> ```python
+> from utils import QualtricsSurvey
+> S = QualtricsSurvey('data/exports/<your_export>/..._Labels.csv', 'data/exports/.../....qsf')
+> data = S.load_data()
+> ```
+> Which notebook are you working in? I can check how data is loaded there."
+
+Place the user's snippet in the temp file along with test code.
+
+### Step 6: Create Plot Function
+Add a new method to `QualtricsPlotsMixin` in `plots.py`:
+
+```python
+def plot_<descriptive_name>(
+    self,
+    data: pl.LazyFrame | pl.DataFrame | None = None,
+    title: str = "<Default title>",
+    x_label: str = "<X label>",
+    y_label: str = "<Y label>",
+    height: int | None = None,
+    width: int | str | None = None,
+) -> alt.Chart:
+    """<Docstring with original question and description>."""
+    df = self._ensure_dataframe(data)
+    
+    # Build chart using ONLY ColorPalette from theme.py
+    chart = alt.Chart(...).mark_bar(color=ColorPalette.PRIMARY)...
+    
+    chart = self._save_plot(chart, title)
+    return chart
+```
+
+**Requirements:**
+- ALL colors MUST use `ColorPalette` constants from `theme.py`
+- Use `self._ensure_dataframe()` to handle LazyFrame/DataFrame
+- Use `self._save_plot()` at the end to enable auto-save
+- Use `self._process_title()` for titles with `<br>` tags
+- Follow existing plot patterns (see `plot_average_scores_with_counts`, `plot_top3_ranking_distribution`)
+
+### Step 7: Test
+Run the temporary test file to verify the plot works:
+```bash
+uv run python debug_plot_temp.py
+```
+
+### Step 8: Provide Summary
+After successful completion, output a summary:
+
+```
+✅ Plot created successfully!
+
+**Data function** (if created): `S.get_<name>(data)`
+**Plot function**: `S.plot_<name>(data, title="...")`
+
+**Usage example:**
+```python
+from utils import QualtricsSurvey
+S = QualtricsSurvey('data/exports/.../_Labels.csv', '.../.qsf')
+data = S.load_data()
+
+# Get data (if manipulation was needed)
+plot_data, _ = S.get_<name>(data)
+
+# Create plot
+chart = S.plot_<name>(plot_data, title="Your Title Here")
+chart  # Display in Marimo
+```
+
+**Files modified:**
+- `utils.py` - Added `get_<name>()` (if applicable)
+- `plots.py` - Added `plot_<name>()`
+- `debug_plot_temp.py` - Test file (can be deleted)
+```
+
+## Critical Rules (from .github/copilot-instructions.md)
+
+1. **NEVER modify Marimo notebooks** (`0X_*.py` files)
+2. **NEVER run Marimo notebooks for debugging**
+3. **ALL colors MUST come from `theme.py`** - use `ColorPalette.PRIMARY`, `ColorPalette.RANK_1`, etc.
+4. **If a new color is needed**, add it to `ColorPalette` in `theme.py` first
+5. **No changelog markdown files** - do not create new .md files documenting changes
+6. **Reading notebooks is OK** to understand function usage patterns
+7. **Getter methods return tuples**: `(LazyFrame, Optional[metadata])`
+8. **Use Polars LazyFrames** until visualization, then `.collect()`
+
+If any rule causes problems, ask user for permission before deviating.
+
+## Reference: Column Patterns
+
+- `SS_Green_Blue__V14__Choice_1` → Speaking Style trait score
+- `Voice_Scale_1_10__V48` → 1-10 voice rating
+- `Top_3_Voices_ranking__V77` → Ranking position
+- `Character_Ranking_<Name>` → Character personality ranking