fixed for basic plots, filter active

.github/agents/plot-creator.agent.md

@@ -0,0 +1,216 @@
+# Plot Creator Agent
+
+You are a specialized agent for creating data visualizations for the Voice Branding Qualtrics survey analysis project.
+
+## ⚠️ Critical Data Handling Rules
+
+1. **NEVER assume or load datasets without explicit user consent** - This is confidential data
+2. **NEVER guess file paths or dataset locations**
+3. **DO NOT assume data comes from a `Survey.get_*()` method** - Data may have been manually manipulated in a notebook
+4. **Use ONLY the data snippet provided by the user** for understanding structure and testing
+
+## 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)
+
+- Think critically about whether the requested plot is feasible with the available data.
+- Think critically about the best way to visualize the requested information, and if the requested chart type is appropriate. If not, consider alternatives and ask the user for confirmation before proceeding.
+
+### 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
+
+**Important:** Do NOT make assumptions about where this data came from. It may be:
+- Output from a `Survey.get_*()` method
+- Manually transformed in a notebook
+- A join of multiple data sources
+- Any other custom manipulation
+
+### 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, df: pl.LazyFrame | pl.DataFrame) -> tuple[pl.LazyFrame, dict | None]:
+    """Transform <input_description> to <output_description>.
+    
+    Original use-case: "<paste user's original question here>"
+    
+    This function <concise 1-2 sentence explanation of what it does>.
+    
+    Args:
+        df: Pre-fetched data as a Polars LazyFrame or DataFrame.
+    
+    Returns:
+        tuple: (LazyFrame with columns [...], Optional metadata dict)
+    """
+    # Implementation - transform the INPUT data only
+    # NEVER call self.get_*() methods here
+    return result, metadata
+```
+
+**Requirements:**
+- **NEVER retrieve data inside transform functions** - The function receives already-fetched data as input
+- Data retrieval (`get_*()` calls) stays in the notebook so analysts can see all steps
+- Method must return `(pl.LazyFrame, Optional[dict])` tuple
+- Docstring MUST contain the original question verbatim
+- Follow existing patterns class methods of the `QualtricsSurvey()` in `utils.py`
+
+**❌ BAD Example (do NOT do this):**
+```python
+def transform_character_trait_frequency(self, q: pl.LazyFrame):
+    # BAD: Fetching data inside transform function
+    char_df, _ = self.get_character_refine(q)  # ← WRONG!
+    # ... rest of transform
+```
+
+**✅ GOOD Example:**
+```python
+def transform_character_trait_frequency(self, char_df: pl.LazyFrame | pl.DataFrame):
+    # GOOD: Receives pre-fetched data as input
+    if isinstance(char_df, pl.LazyFrame):
+        char_df = char_df.collect()
+    # ... rest of transform
+```
+
+**In the notebook, the analyst writes:**
+```python
+char_data, _ = S.get_character_refine(data)  # Step visible to analyst
+trait_freq, _ = S.transform_character_trait_frequency(char_data)  # Transform step
+chart = S.plot_character_trait_frequency(trait_freq)
+```
+
+### Step 5: Create Temporary Test File
+Create `debug_plot_temp.py` for testing. **Prefer using the data snippet already provided by the user.**
+
+**Option A: Use provided data snippet (preferred)**
+If the user provided a `df.head()` or sample data output, create inline test data from it:
+
+```python
+"""Temporary test file for <plot_name>.
+Delete after testing.
+"""
+import polars as pl
+from theme import ColorPalette
+import altair as alt
+
+# ============================================================
+# TEST DATA (reconstructed from user's df.head() output)
+# ============================================================
+test_data = pl.DataFrame({
+    "Column1": ["value1", "value2", ...],
+    "Column2": [1, 2, ...],
+    # ... recreate structure from provided sample
+})
+# ============================================================
+
+# Test the plot function
+from plots import QualtricsPlotsMixin
+# ... test code
+```
+
+**Option B: Ask user (only if necessary)**
+Only ask the user for additional code if:
+- The provided sample is insufficient to test the plot logic
+- You need to understand complex data relationships not visible in the sample
+- The transformation requires understanding the full data pipeline
+
+If you must ask:
+> "The sample data you provided should work for basic testing. However, I need [specific reason]. Could you provide:
+> 1. [specific information needed]
+> 
+> If you'd prefer, I can proceed with a minimal test using the sample data you shared."
+
+### 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.transform_<name>(data)`
+**Plot function**: `S.plot_<name>(data, title="...")`
+
+**Usage example:**
+```python
+# Assuming you have your data already prepared as `plot_data`
+chart = S.plot_<name>(plot_data, title="Your Title Here")
+chart  # Display in Marimo
+```
+
+**Files modified:**
+- `utils.py` - Added `transform_<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 load confidential data without explicit user-provided code**
+2. **NEVER assume data source** - do not guess which `get_*()` method produced the data
+3. **NEVER modify Marimo notebooks** (`0X_*.py` files)
+4. **NEVER run Marimo notebooks for debugging**
+5. **ALL colors MUST come from `theme.py`** - use `ColorPalette.PRIMARY`, `ColorPalette.RANK_1`, etc.
+6. **If a new color is needed**, add it to `ColorPalette` in `theme.py` first
+7. **No changelog markdown files** - do not create new .md files documenting changes
+8. **Reading notebooks is OK** to understand function usage patterns
+9. **Getter methods return tuples**: `(LazyFrame, Optional[metadata])`
+10. **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

02_quant_analysis.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.19.2"
+__generated_with = "0.19.7"
 app = marimo.App(width="full")
 
 
@@ -16,8 +16,8 @@ def _():
 
     from speaking_styles import SPEAKING_STYLES
     return (
-        QualtricsSurvey,
         Path,
+        QualtricsSurvey,
         SPEAKING_STYLES,
         calculate_weighted_ranking_scores,
         check_progress,
@@ -49,7 +49,7 @@ def _(Path, file_browser, mo):
 
 
 @app.cell
-def _(QualtricsSurvey, QSF_FILE, RESULTS_FILE, mo):
+def _(QSF_FILE, QualtricsSurvey, RESULTS_FILE, mo):
     S = QualtricsSurvey(RESULTS_FILE, QSF_FILE)
     try:
         data_all = S.load_data()
@@ -383,6 +383,12 @@ def _(S, data, mo):
     return (vscales,)
 
 
+@app.cell
+def _(vscales):
+    print(vscales.collect().head())
+    return
+
+
 @app.cell
 def _(pl, vscales):
     # Count non-null values per row

03_quant_report.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.19.2"
+__generated_with = "0.19.7"
 app = marimo.App(width="full")
 
 with app.setup:
@@ -44,14 +44,14 @@ def _(QSF_FILE, RESULTS_FILE):
 
 
 @app.cell(hide_code=True)
-def _():
+def _(RESULTS_FILE, data_all):
-    mo.md(r"""
+    mo.md(rf"""
     ---
     # Load Data
 
-    **Dataset:** `{Path(RESULTS_FILE).name}`
+    **Dataset:** {Path(RESULTS_FILE).name}
 
-    **Responses**: `{data_all.collect().shape[0]}`
+    **Responses**: {data_all.collect().shape[0]}
     """)
     return
 
@@ -71,7 +71,6 @@ def _(S, data_all):
 
 
     mo.md(f"""
-    # Data Validation
 
     {check_progress(data_all)}
 
@@ -104,39 +103,20 @@ def _(data_all):
     return (data_validated,)
 
 
-@app.cell(hide_code=True)
+@app.cell
 def _():
 
 
     return
 
 
-@app.cell
-def _(data_validated):
-    data = data_validated
-
-    data.collect()
-    return (data,)
-
-
 @app.cell(hide_code=True)
 def _():
-    mo.md(r"""
+    # 
-    ---
-
-    # Introduction (Respondent Demographics)
-    """)
     return
 
 
 @app.cell
-def _(S, data):
-    demographics = S.get_demographics(data)[0].collect()
-    demographics
-    return (demographics,)
-
-
-@app.cell(hide_code=True)
 def _():
     mo.md(r"""
     ## Lucia confirmation missing 'Consumer' data
@@ -145,6 +125,13 @@ def _():
 
 
 @app.cell
+def _(S, data_validated):
+    demographics = S.get_demographics(data_validated)[0].collect()
+    # demographics
+    return (demographics,)
+
+
+@app.cell(hide_code=True)
 def _(demographics):
     # Demographics where 'Consumer' is null
     demographics_no_consumer = demographics.filter(pl.col('Consumer').is_null())['_recordId'].to_list()
@@ -160,16 +147,82 @@ def _(data_all, demographics_no_consumer):
 
 
 @app.cell
-def _(data_all):
+def _():
+    mo.md(r"""
+    # Filter Data (Global corrections)
+    """)
+    return
+
+
+@app.cell
+def _(S):
+    filter_form = mo.md('''
+
+
+
+    {age}
+
+    {gender}
+
+    {ethnicity}
+
+    {income}
+
+    {consumer}
+    '''
+    ).batch(
+        age=mo.ui.multiselect(options=S.options_age, value=S.options_age, label="Select Age Group(s):"),
+        gender=mo.ui.multiselect(options=S.options_gender, value=S.options_gender, label="Select Gender(s):"),
+        ethnicity=mo.ui.multiselect(options=S.options_ethnicity, value=S.options_ethnicity, label="Select Ethnicities:"),
+        income=mo.ui.multiselect(options=S.options_income, value=S.options_income, label="Select Income Group(s):"),
+        consumer=mo.ui.multiselect(options=S.options_consumer, value=S.options_consumer, label="Select Consumer Groups:")
+    ).form()
+    mo.md(f'''
+    ---
+
+    # Data Filter
+
+    {filter_form}
+    ''')
+    return (filter_form,)
+
+
+@app.cell
+def _():
+    BEST_CHOSEN_CHARACTER = "the_coach"
+    return (BEST_CHOSEN_CHARACTER,)
+
+
+@app.cell
+def _(S, data_validated, filter_form):
+    mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
+    _d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
+
+    # Stop execution and prevent other cells from running if no data is selected
+    mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
+    data = _d
+
+    # data = data_validated
+    data.collect()
+    return (data,)
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell
+def _():
     # Check if all business owners are missing a 'Consumer type' in demographics
-    assert all([a is None for a in data_all.filter(pl.col('QID4') == 'Yes').collect()['Consumer'].unique()]) , "Not all business owners are missing 'Consumer type' in demographics."
+    # assert all([a is None for a in data_all.filter(pl.col('QID4') == 'Yes').collect()['Consumer'].unique()]) , "Not all business owners are missing 'Consumer type' in demographics."
     return
 
 
 @app.cell
 def _():
     mo.md(r"""
-    ## Demographic Distributions
+    # Demographic Distributions
     """)
     return
 
@@ -187,14 +240,13 @@ def _():
 
 
 @app.cell
-def _(S, demo_plot_cols, demographics):
+def _(S, data, demo_plot_cols):
     _content = """
-    ## Demographic Distributions
 
     """
     for c in demo_plot_cols:
         _fig = S.plot_demographic_distribution(
-            data=demographics,
+            data=S.get_demographics(data)[0],
             column=c,
             title=f"{c.replace('Bussiness', 'Business').replace('_', ' ')} Distribution of Survey Respondents"
         )
@@ -214,7 +266,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Best performing: Original vs Refined frankenstein
@@ -222,15 +274,15 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _(S, data):
     char_refine_rank = S.get_character_refine(data)[0]
     # print(char_rank.collect().head())
-    # print(char_refine_rank.collect().head())
+    print(char_refine_rank.collect().head())
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Character ranking points
@@ -266,6 +318,30 @@ def _(S, char_rank):
 
 
 @app.cell
+def _():
+    mo.md(r"""
+    ### Statistical Significance Character Ranking
+    """)
+    return
+
+
+@app.cell(disabled=True)
+def _(S, char_rank):
+    _pairwise_df, _meta = S.compute_ranking_significance(char_rank)
+
+    # print(_pairwise_df.columns)
+
+    mo.md(f"""
+
+
+    {mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))}
+
+    {mo.ui.altair_chart(S.plot_significance_summary(_pairwise_df, metadata=_meta))}
+    """)
+    return
+
+
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Character Ranking: times 1st place
@@ -306,9 +382,75 @@ def _():
     return
 
 
+@app.cell
+def _(S, data):
+    char_df = S.get_character_refine(data)[0]
+    return (char_df,)
+
+
+@app.cell
+def _(S, char_df):
+    from theme import ColorPalette
+
+    # Assuming you already have char_df (your data from get_character_refine or similar)
+    characters = ['Bank Teller', 'Familiar Friend', 'The Coach', 'Personal Assistant']
+    character_colors = {
+        'Bank Teller': (ColorPalette.CHARACTER_BANK_TELLER, ColorPalette.CHARACTER_BANK_TELLER_HIGHLIGHT),
+        'Familiar Friend': (ColorPalette.CHARACTER_FAMILIAR_FRIEND, ColorPalette.CHARACTER_FAMILIAR_FRIEND_HIGHLIGHT),
+        'The Coach': (ColorPalette.CHARACTER_COACH, ColorPalette.CHARACTER_COACH_HIGHLIGHT),
+        'Personal Assistant': (ColorPalette.CHARACTER_PERSONAL_ASSISTANT, ColorPalette.CHARACTER_PERSONAL_ASSISTANT_HIGHLIGHT),
+    }
+
+    # Build consistent sort order (by total frequency across all characters)
+    all_trait_counts = {}
+    for char in characters:
+        freq_df, _ = S.transform_character_trait_frequency(char_df, char)
+        for row in freq_df.iter_rows(named=True):
+            all_trait_counts[row['trait']] = all_trait_counts.get(row['trait'], 0) + row['count']
+
+    consistent_sort_order = sorted(all_trait_counts.keys(), key=lambda x: -all_trait_counts[x])
+
+    _content = """"""
+    # Generate 4 plots (one per character)
+    for char in characters:
+        freq_df, _ = S.transform_character_trait_frequency(char_df, char)
+        main_color, highlight_color = character_colors[char]
+        chart = S.plot_single_character_trait_frequency(
+            data=freq_df,
+            character_name=char,
+            bar_color=main_color,
+            highlight_color=highlight_color,
+            trait_sort_order=consistent_sort_order,
+        )
+        _content += f"""
+        {mo.ui.altair_chart(chart)}
+
+
+    """
+
+    mo.md(_content)
+    return
+
+
+@app.cell(disabled=True)
+def _():
+    mo.md(r"""
+    ## Statistical significance best characters
+
+    zie chat
+    > voorbeeld: als de nr 1 en 2 niet significant verschillen maar wel van de nr 3 bijvoorbeeld is dat ook top. Beetje meedenkend over hoe ik het kan presenteren weetje wat ik bedoel?:)
+    >
+    """)
+    return
+
+
+@app.cell(disabled=True)
+def _():
+    return
+
+
 @app.cell
 def _():
-    # Join respondent 
     return
 
 
@@ -322,12 +464,208 @@ def _():
     return
 
 
+@app.cell
+def _():
+    COLOR_GENDER = True
+    return (COLOR_GENDER,)
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Top 8 Most Chosen out of 18
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    v_18_8_3 = S.get_18_8_3(data)[0]
+    return (v_18_8_3,)
+
+
+@app.cell
+def _(COLOR_GENDER, S, v_18_8_3):
+    S.plot_voice_selection_counts(v_18_8_3, title="Top 8 Voice Selection from 18 Voices", x_label='Voice', color_gender=COLOR_GENDER)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Top 3 most chosen out of 8
+    """)
+    return
+
+
+@app.cell
+def _(COLOR_GENDER, S, v_18_8_3):
+    S.plot_top3_selection_counts(v_18_8_3, title="Top 3 Voice Selection Counts from 8 Voices", x_label='Voice', color_gender=COLOR_GENDER)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Voice Ranking Weighted Score
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    top3_voices = S.get_top_3_voices(data)[0]
+    top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
+    return top3_voices, top3_voices_weighted
+
+
+@app.cell
+def _(COLOR_GENDER, S, top3_voices_weighted):
+    S.plot_weighted_ranking_score(top3_voices_weighted, title="Most Popular Voice - Weighted Popularity Score<br>(1st = 3pts, 2nd = 2pts, 3rd = 1pt)", color_gender=COLOR_GENDER)
+    return
+
+
 @app.cell(hide_code=True)
 def _():
     mo.md(r"""
-    ---
+    ## Which voice is ranked best in the ranking question for top 3?
 
-    # Brand Character Results
+    (not best 3 out of 8 question)
+    """)
+    return
+
+
+@app.cell
+def _(COLOR_GENDER, S, top3_voices):
+    S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Top 3 Voice Rankings (1st, 2nd, 3rd)", color_gender=COLOR_GENDER)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ### Statistical significance for voice ranking
+    """)
+    return
+
+
+@app.cell
+def _():
+    # print(top3_voices.collect().head())
+    return
+
+
+@app.cell
+def _():
+
+    # _pairwise_df, _metadata = S.compute_ranking_significance(
+    #     top3_voices,alpha=0.05,correction="none")
+
+    # # View significant pairs
+    # # print(pairwise_df.filter(pl.col('significant') == True))
+
+    # # Create heatmap visualization
+    # _heatmap = S.plot_significance_heatmap(
+    #     _pairwise_df, 
+    #     metadata=_metadata,
+    #     title="Weighted Voice Ranking Significance<br>(Pairwise Comparisons)"
+    # )
+
+    # # Create summary bar chart
+    # _summary = S.plot_significance_summary(
+    #     _pairwise_df,
+    #     metadata=_metadata
+    # )
+
+    # mo.md(f"""
+    # {mo.ui.altair_chart(_heatmap)}
+
+    # {mo.ui.altair_chart(_summary)}
+    # """)
+    return
+
+
+@app.cell
+def _():
+    ## Voice Ranked 1st the most
+    return
+
+
+@app.cell
+def _(COLOR_GENDER, S, top3_voices):
+    S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice', color_gender=COLOR_GENDER)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Voice Scale 1-10
+    """)
+    return
+
+
+@app.cell
+def _(COLOR_GENDER, S, data):
+    # Get your voice scale data (from notebook)
+    voice_1_10, _ = S.get_voice_scale_1_10(data)
+    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)", color_gender=COLOR_GENDER)
+    return (voice_1_10,)
+
+
+@app.cell(disabled=True)
+def _():
+    mo.md(r"""
+    ### Statistical Significance (Scale 1-10)
+    """)
+    return
+
+
+@app.cell(disabled=True)
+def _(S, voice_1_10):
+    # Compute pairwise significance tests
+    pairwise_df, metadata = S.compute_pairwise_significance(
+        voice_1_10,
+        test_type="mannwhitney",  # or "ttest", "chi2", "auto"
+        alpha=0.05,
+        correction="bonferroni"   # or "holm", "none"
+    )
+
+    # View significant pairs
+    # print(pairwise_df.filter(pl.col('significant') == True))
+
+    # Create heatmap visualization
+    _heatmap = S.plot_significance_heatmap(
+        pairwise_df, 
+        metadata=metadata,
+        title="Voice Rating Significance<br>(Pairwise Comparisons)"
+    )
+
+    # Create summary bar chart
+    _summary = S.plot_significance_summary(
+        pairwise_df,
+        metadata=metadata
+    )
+
+    mo.md(f"""
+    {mo.ui.altair_chart(_heatmap)}
+
+    {mo.ui.altair_chart(_summary)}
+    """)
+    return
+
+
+@app.cell
+def _():
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ## Ranking points for Voice per Chosen Brand Character
+
+    **missing mapping**
     """)
     return
 
@@ -335,12 +673,261 @@ def _():
 @app.cell(hide_code=True)
 def _():
     mo.md(r"""
-    ---
+    ## Correlation Speaking Styles
-
-    # Spoken Voice Results
     """)
     return
 
 
+@app.cell
+def _(S, data, top3_voices):
+    ss_or, choice_map_or = S.get_ss_orange_red(data)
+    ss_gb, choice_map_gb = S.get_ss_green_blue(data)
+
+    # Combine the data
+    ss_all = ss_or.join(ss_gb, on='_recordId')
+    _d = ss_all.collect()
+
+    choice_map = {**choice_map_or, **choice_map_gb}
+    # print(_d.head())
+    # print(choice_map)
+    ss_long = utils.process_speaking_style_data(ss_all, choice_map)
+
+    df_style = utils.process_speaking_style_data(ss_all, choice_map)
+
+    vscales = S.get_voice_scale_1_10(data)[0]
+    df_scale_long = utils.process_voice_scale_data(vscales)
+
+    joined_scale = df_style.join(df_scale_long, on=["_recordId", "Voice"], how="inner")
+
+    df_ranking = utils.process_voice_ranking_data(top3_voices)
+    joined_ranking = df_style.join(df_ranking, on=['_recordId', 'Voice'], how='inner')
+    return joined_ranking, joined_scale
+
+
+@app.cell
+def _(joined_ranking):
+    joined_ranking.head()
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ### Colors vs Scale 1-10
+    """)
+    return
+
+
+@app.cell
+def _(S, joined_scale):
+    # Transform to get one row per color with average correlation
+    color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, SPEAKING_STYLES)
+    S.plot_speaking_style_color_correlation(
+        data=color_corr_scale,
+        title="Correlation: Speaking Style Colors and Voice Scale 1-10"
+    )
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ### Colors vs Ranking Points
+    """)
+    return
+
+
+@app.cell
+def _(S, joined_ranking):
+    color_corr_ranking, _ = utils.transform_speaking_style_color_correlation(
+        joined_ranking, 
+        SPEAKING_STYLES, 
+        target_column="Ranking_Points"
+    )
+    S.plot_speaking_style_color_correlation(
+        data=color_corr_ranking,
+        title="Correlation: Speaking Style Colors and Voice Ranking Points"
+    )
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ### Individual Traits vs Scale 1-10
+    """)
+    return
+
+
+@app.cell
+def _(S, joined_scale):
+    _content = """"""
+
+    for _style, _traits in SPEAKING_STYLES.items():
+        # print(f"Correlation plot for {style}...")
+        _fig = S.plot_speaking_style_correlation(
+            data=joined_scale,
+            style_color=_style,
+            style_traits=_traits,
+            title=f"Correlation: Speaking Style {_style} and Voice Scale 1-10",
+        )
+        _content += f"""
+    #### Speaking Style **{_style}**:
+
+    {mo.ui.altair_chart(_fig)}
+
+    """
+    mo.md(_content)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ### Individual Traits vs Ranking Points
+    """)
+    return
+
+
+@app.cell
+def _(S, joined_ranking):
+    _content = """"""
+
+    for _style, _traits in SPEAKING_STYLES.items():
+        # print(f"Correlation plot for {style}...")
+        _fig = S.plot_speaking_style_ranking_correlation(
+        data=joined_ranking,
+        style_color=_style,
+        style_traits=_traits,
+        title=f"Correlation: Speaking Style {_style} and Voice Ranking Points",
+    )
+        _content += f"""
+    #### Speaking Style **{_style}**:
+
+    {mo.ui.altair_chart(_fig)}
+
+    """
+    mo.md(_content)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ## Correlations when "Best Brand Character" is chosen
+
+    Select only the traits that fit with that character
+    """)
+    return
+
+
+@app.cell
+def _(BEST_CHOSEN_CHARACTER):
+    from reference import ORIGINAL_CHARACTER_TRAITS
+    chosen_bc_traits = ORIGINAL_CHARACTER_TRAITS[BEST_CHOSEN_CHARACTER]
+    return (chosen_bc_traits,)
+
+
+@app.cell
+def _(chosen_bc_traits):
+    STYLES_SUBSET = utils.filter_speaking_styles(SPEAKING_STYLES, chosen_bc_traits)
+    return (STYLES_SUBSET,)
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ### Individual Traits vs Ranking Points
+    """)
+    return
+
+
+@app.cell
+def _(BEST_CHOSEN_CHARACTER, S, STYLES_SUBSET, joined_ranking):
+    _content = ""
+    for _style, _traits in STYLES_SUBSET.items():
+        _fig = S.plot_speaking_style_ranking_correlation(
+            data=joined_ranking,
+            style_color=_style,
+            style_traits=_traits,
+            title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Ranking Points"""
+        )
+        _content += f"""
+    {mo.ui.altair_chart(_fig)}
+
+    """
+    mo.md(_content)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ### Individual Traits vs Scale 1-10
+    """)
+    return
+
+
+@app.cell
+def _(BEST_CHOSEN_CHARACTER, S, STYLES_SUBSET, joined_scale):
+    _content = """"""
+
+    for _style, _traits in STYLES_SUBSET.items():
+        # print(f"Correlation plot for {style}...")
+        _fig = S.plot_speaking_style_correlation(
+            data=joined_scale,
+            style_color=_style,
+            style_traits=_traits,
+            title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Scale 1-10""",
+        )
+        _content += f"""
+    {mo.ui.altair_chart(_fig)}
+
+    """
+    mo.md(_content)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ### Colors vs Scale 1-10 (Best Character)
+    """)
+    return
+
+
+@app.cell
+def _(BEST_CHOSEN_CHARACTER, S, STYLES_SUBSET, joined_scale):
+    # Transform to get one row per color with average correlation
+    _color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, STYLES_SUBSET)
+    S.plot_speaking_style_color_correlation(
+        data=_color_corr_scale,
+        title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Scale 1-10"""
+    )
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ### Colors vs Ranking Points (Best Character)
+    """)
+    return
+
+
+@app.cell
+def _(BEST_CHOSEN_CHARACTER, S, STYLES_SUBSET, joined_ranking):
+    _color_corr_ranking, _ = utils.transform_speaking_style_color_correlation(
+        joined_ranking, 
+        STYLES_SUBSET, 
+        target_column="Ranking_Points"
+    )
+    S.plot_speaking_style_color_correlation(
+        data=_color_corr_ranking,
+        title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Ranking Points"""
+    )
+    return
+
+
 if __name__ == "__main__":
     app.run()

04_PPTX_Update_Images.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.19.2"
+__generated_with = "0.19.7"
 app = marimo.App(width="medium")
 
 with app.setup:
@@ -21,28 +21,19 @@ def _():
 
 @app.cell
 def _():
-    TAG_SOURCE = Path('data/reports/Perception-Research-Report.pptx')
+    TAG_SOURCE = Path('data/reports/Perception-Research-Report_2-2.pptx')
-    TAG_TARGET = Path('data/reports/Perception-Research-Report_tagged.pptx')
+    TAG_TARGET = Path('data/reports/Perception-Research-Report_2-2_tagged.pptx')
-    TAG_IMAGE_DIR = Path('figures/OneDrive_2026-01-28/')
+    TAG_IMAGE_DIR = Path('figures/2-2-26')
-    return TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET
+    return TAG_IMAGE_DIR, TAG_SOURCE
 
 
 @app.cell
-def _(TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET):
+def _(TAG_IMAGE_DIR, TAG_SOURCE):
-    utils.update_ppt_alt_text(ppt_path=TAG_SOURCE, image_source_dir=TAG_IMAGE_DIR, output_path=TAG_TARGET)
+    utils.update_ppt_alt_text(
-    return
+        ppt_path=TAG_SOURCE, 
-
+        image_source_dir=TAG_IMAGE_DIR, 
-
+        # output_path=TAG_TARGET
-@app.cell
+    )
-def _():
-    utils._calculate_file_sha1('figures/OneDrive_2026-01-28/All_Respondents/most_prominent_personality_traits.png')
-    return
-
-
-@app.cell
-def _():
-    utils._calculate_perceptual_hash('figures/Picture.png')
-
     return
 
 
@@ -56,26 +47,21 @@ def _():
 
 @app.cell
 def _():
-    REPLACE_SOURCE = Path('data/test_replace_source.pptx')
+    REPLACE_SOURCE = Path('data/reports/Perception-Research-Report_2-2.pptx')
-    REPLACE_TARGET = Path('data/test_replace_target.pptx')
+    REPLACE_TARGET = Path('data/reports/Perception-Research-Report_2-2_updated.pptx')
-    return REPLACE_SOURCE, REPLACE_TARGET
 
-
+    NEW_IMAGES_DIR = Path('figures/2-2-26')
-app._unparsable_cell(
+    return NEW_IMAGES_DIR, REPLACE_SOURCE
-    r"""
-    IMAGE_FILE = Path('figures/OneDrive_2026-01-28/Cons-Early_Professional/cold_distant_approachable_familiar_warm.png'
-    """,
-    name="_"
-)
 
 
 @app.cell
-def _(IMAGE_FILE, REPLACE_SOURCE, REPLACE_TARGET):
+def _(NEW_IMAGES_DIR, REPLACE_SOURCE):
-    utils.pptx_replace_named_image(
+    # get all files in the image source directory and subdirectories
-        presentation_path=REPLACE_SOURCE,
+    results = utils.pptx_replace_images_from_directory(
-        target_tag=utils.image_alt_text_generator(IMAGE_FILE),
+        REPLACE_SOURCE,            # Source presentation path,
-        new_image_path=IMAGE_FILE,
+        NEW_IMAGES_DIR,          # Source directory with new images
-        save_path=REPLACE_TARGET)
+        # REPLACE_TARGET  # Output path (optional, defaults to overwrite)
+    )
     return
 
 

docs/statistical-significance-guide.md

@@ -0,0 +1,428 @@
+# Statistical Significance Testing Guide
+
+A beginner-friendly reference for choosing the right statistical test and correction method for your Voice Branding analysis.
+
+---
+
+## Table of Contents
+1. [Quick Decision Flowchart](#quick-decision-flowchart)
+2. [Understanding Your Data Types](#understanding-your-data-types)
+3. [Available Tests](#available-tests)
+4. [Multiple Comparison Corrections](#multiple-comparison-corrections)
+5. [Interpreting Results](#interpreting-results)
+6. [Code Examples](#code-examples)
+
+---
+
+## Quick Decision Flowchart
+
+```
+What kind of data do you have?
+│
+├─► Continuous scores (1-10 ratings, averages)
+│   │
+│   └─► Use: compute_pairwise_significance()
+│       │
+│       ├─► Data normally distributed? → test_type="ttest"
+│       └─► Not sure / skewed data?   → test_type="mannwhitney" (safer choice)
+│
+└─► Ranking data (1st, 2nd, 3rd place votes)
+    │
+    └─► Use: compute_ranking_significance()
+        (automatically uses proportion z-test)
+```
+
+---
+
+## Understanding Your Data Types
+
+### Continuous Data
+**What it looks like:** Numbers on a scale with many possible values.
+
+| Example | Data Source |
+|---------|-------------|
+| Voice ratings 1-10 | `get_voice_scale_1_10()` |
+| Speaking style scores | `get_ss_green_blue()` |
+| Any averaged scores | Custom aggregations |
+
+```
+shape: (5, 3)
+┌───────────┬─────────────────┬─────────────────┐
+│ _recordId │ Voice_Scale__V14│ Voice_Scale__V04│
+│ str       │ f64             │ f64             │
+├───────────┼─────────────────┼─────────────────┤
+│ R_001     │ 7.5             │ 6.0             │
+│ R_002     │ 8.0             │ 7.5             │
+│ R_003     │ 6.5             │ 8.0             │
+```
+
+### Ranking Data
+**What it looks like:** Discrete ranks (1, 2, 3) or null if not ranked.
+
+| Example | Data Source |
+|---------|-------------|
+| Top 3 voice rankings | `get_top_3_voices()` |
+| Character rankings | `get_character_ranking()` |
+
+```
+shape: (5, 3)
+┌───────────┬──────────────────┬──────────────────┐
+│ _recordId │ Top_3__V14       │ Top_3__V04       │
+│ str       │ i64              │ i64              │
+├───────────┼──────────────────┼──────────────────┤
+│ R_001     │ 1                │ null             │  ← V14 was ranked 1st
+│ R_002     │ 2                │ 1                │  ← V04 was ranked 1st
+│ R_003     │ null             │ 3                │  ← V04 was ranked 3rd
+```
+
+### ⚠️ Aggregated Data (Cannot Test!)
+**What it looks like:** Already summarized/totaled data.
+
+```
+shape: (3, 2)
+┌───────────┬────────────────┐
+│ Character │ Weighted Score │  ← ALREADY AGGREGATED
+│ str       │ i64            │     Lost individual variance
+├───────────┼────────────────┤     Cannot do significance tests!
+│ V14       │ 209            │
+│ V04       │ 180            │
+```
+
+**Solution:** Go back to the raw data before aggregation.
+
+---
+
+## Available Tests
+
+### 1. Mann-Whitney U Test (Default for Continuous)
+**Use when:** Comparing scores/ratings between groups  
+**Assumes:** Nothing about distribution shape (non-parametric)  
+**Best for:** Most survey data, Likert scales, ratings
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    voice_data, 
+    test_type="mannwhitney"  # This is the default
+)
+```
+
+**Pros:**
+- Works with any distribution shape
+- Robust to outliers
+- Safe choice when unsure
+
+**Cons:**
+- Slightly less powerful than t-test when data IS normally distributed
+
+---
+
+### 2. Independent t-Test
+**Use when:** Comparing means between groups  
+**Assumes:** Data is approximately normally distributed  
+**Best for:** Large samples (n > 30 per group), truly continuous data
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    voice_data, 
+    test_type="ttest"
+)
+```
+
+**Pros:**
+- Most powerful when assumptions are met
+- Well-understood, commonly reported
+
+**Cons:**
+- Can give misleading results if data is skewed
+- Sensitive to outliers
+
+---
+
+### 3. Chi-Square Test
+**Use when:** Comparing frequency distributions  
+**Assumes:** Expected counts ≥ 5 in each cell  
+**Best for:** Count data, categorical comparisons
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    count_data, 
+    test_type="chi2"
+)
+```
+
+**Pros:**
+- Designed for count/frequency data
+- Tests if distributions differ
+
+**Cons:**
+- Needs sufficient sample sizes
+- Less informative about direction of difference
+
+---
+
+### 4. Two-Proportion Z-Test (For Rankings)
+**Use when:** Comparing ranking vote proportions  
+**Automatically used by:** `compute_ranking_significance()`
+
+```python
+pairwise_df, meta = S.compute_ranking_significance(ranking_data)
+```
+
+**What it tests:** "Does Voice A get a significantly different proportion of Rank 1 votes than Voice B?"
+
+---
+
+## Multiple Comparison Corrections
+
+### Why Do We Need Corrections?
+
+When you compare many groups, you're doing many tests. Each test has a 5% chance of a false positive (if α = 0.05). With 17 voices:
+
+| Comparisons | Expected False Positives (no correction) |
+|-------------|------------------------------------------|
+| 136 pairs   | ~7 false "significant" results!          |
+
+**Corrections adjust p-values to account for this.**
+
+---
+
+### Bonferroni Correction (Conservative)
+**Formula:** `p_adjusted = p_value × number_of_comparisons`
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    data, 
+    correction="bonferroni"  # This is the default
+)
+```
+
+**Use when:**
+- You want to be very confident about significant results
+- False positives are costly (publishing, major decisions)
+- You have few comparisons (< 20)
+
+**Trade-off:** May miss real differences (more false negatives)
+
+---
+
+### Holm-Bonferroni Correction (Less Conservative)
+**Formula:** Step-down procedure that's less strict than Bonferroni
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    data, 
+    correction="holm"
+)
+```
+
+**Use when:**
+- You have many comparisons
+- You want better power to detect real differences
+- Exploratory analysis where missing a real effect is costly
+
+**Trade-off:** Slightly higher false positive risk than Bonferroni
+
+---
+
+### No Correction
+**Not recommended for final analysis**, but useful for exploration.
+
+```python
+pairwise_df, meta = S.compute_pairwise_significance(
+    data, 
+    correction="none"
+)
+```
+
+**Use when:**
+- Initial exploration only
+- You'll follow up with specific hypotheses
+- You understand and accept the inflated false positive rate
+
+---
+
+### Correction Method Comparison
+
+| Method | Strictness | Best For | Risk |
+|--------|------------|----------|------|
+| Bonferroni | Most strict | Few comparisons, high stakes | Miss real effects |
+| Holm | Moderate | Many comparisons, balanced approach | Slightly more false positives |
+| None | No control | Exploration only | Many false positives |
+
+**Recommendation for Voice Branding:** Use **Holm** for exploratory analysis, **Bonferroni** for final reporting.
+
+---
+
+## Interpreting Results
+
+### Key Output Columns
+
+| Column | Meaning |
+|--------|---------|
+| `p_value` | Raw probability this difference happened by chance |
+| `p_adjusted` | Corrected p-value (use this for decisions!) |
+| `significant` | TRUE if p_adjusted < alpha (usually 0.05) |
+| `effect_size` | How big is the difference (practical significance) |
+
+### What the p-value Means
+
+| p-value | Interpretation |
+|---------|----------------|
+| < 0.001 | Very strong evidence of difference |
+| < 0.01 | Strong evidence |
+| < 0.05 | Moderate evidence (traditional threshold) |
+| 0.05 - 0.10 | Weak evidence, "trending" |
+| > 0.10 | No significant evidence |
+
+### Statistical vs Practical Significance
+
+**Statistical significance** (p < 0.05) means the difference is unlikely due to chance.
+
+**Practical significance** (effect size) means the difference matters in the real world.
+
+| Effect Size (Cohen's d) | Interpretation |
+|-------------------------|----------------|
+| < 0.2 | Small (may not matter practically) |
+| 0.2 - 0.5 | Medium |
+| 0.5 - 0.8 | Large |
+| > 0.8 | Very large |
+
+**Example:** A p-value of 0.001 with effect size of 0.1 means "we're confident there's a difference, but it's tiny."
+
+---
+
+## Code Examples
+
+### Example 1: Voice Scale Ratings
+
+```python
+# Get the raw rating data
+voice_data, _ = S.get_voice_scale_1_10(data)
+
+# Test for significant differences
+pairwise_df, meta = S.compute_pairwise_significance(
+    voice_data,
+    test_type="mannwhitney",  # Safe default for ratings
+    alpha=0.05,
+    correction="bonferroni"
+)
+
+# Check overall test first
+print(f"Overall test: {meta['overall_test']}")
+print(f"Overall p-value: {meta['overall_p_value']:.4f}")
+
+# If overall is significant, look at pairwise
+if meta['overall_p_value'] < 0.05:
+    sig_pairs = pairwise_df.filter(pl.col('significant') == True)
+    print(f"Found {sig_pairs.height} significant pairwise differences")
+
+# Visualize
+S.plot_significance_heatmap(pairwise_df, metadata=meta)
+```
+
+### Example 2: Top 3 Voice Rankings
+
+```python
+# Get the raw ranking data (NOT the weighted scores!)
+ranking_data, _ = S.get_top_3_voices(data)
+
+# Test for significant differences in Rank 1 proportions
+pairwise_df, meta = S.compute_ranking_significance(
+    ranking_data,
+    alpha=0.05,
+    correction="holm"  # Less conservative for many comparisons
+)
+
+# Check chi-square test
+print(f"Chi-square p-value: {meta['chi2_p_value']:.4f}")
+
+# View contingency table (Rank 1, 2, 3 counts per voice)
+for voice, counts in meta['contingency_table'].items():
+    print(f"{voice}: R1={counts[0]}, R2={counts[1]}, R3={counts[2]}")
+
+# Find significant pairs
+sig_pairs = pairwise_df.filter(pl.col('significant') == True)
+print(sig_pairs)
+```
+
+### Example 3: Comparing Demographic Subgroups
+
+```python
+# Filter to specific demographics
+S.filter_data(data, consumer=['Early Professional'])
+early_pro_data, _ = S.get_voice_scale_1_10(data)
+
+S.filter_data(data, consumer=['Established Professional'])
+estab_pro_data, _ = S.get_voice_scale_1_10(data)
+
+# Test each group separately, then compare results qualitatively
+# (For direct group comparison, you'd need a different test design)
+```
+
+---
+
+## Common Mistakes to Avoid
+
+### ❌ Using Aggregated Data
+```python
+# WRONG - already summarized, lost individual variance
+weighted_scores = calculate_weighted_ranking_scores(ranking_data)
+S.compute_pairwise_significance(weighted_scores)  # Will fail!
+```
+
+### ✅ Use Raw Data
+```python
+# RIGHT - use raw data before aggregation
+ranking_data, _ = S.get_top_3_voices(data)
+S.compute_ranking_significance(ranking_data)
+```
+
+### ❌ Ignoring Multiple Comparisons
+```python
+# WRONG - 7% of pairs will be "significant" by chance alone!
+S.compute_pairwise_significance(data, correction="none")
+```
+
+### ✅ Apply Correction
+```python
+# RIGHT - corrected p-values control false positives
+S.compute_pairwise_significance(data, correction="bonferroni")
+```
+
+### ❌ Only Reporting p-values
+```python
+# WRONG - statistical significance isn't everything
+print(f"p = {p_value}")  # Missing context!
+```
+
+### ✅ Report Effect Sizes Too
+```python
+# RIGHT - include practical significance
+print(f"p = {p_value}, effect size = {effect_size}")
+print(f"Mean difference: {mean1 - mean2:.2f} points")
+```
+
+---
+
+## Quick Reference Card
+
+| Data Type | Function | Default Test | Recommended Correction |
+|-----------|----------|--------------|------------------------|
+| Ratings (1-10) | `compute_pairwise_significance()` | Mann-Whitney U | Bonferroni |
+| Rankings (1st/2nd/3rd) | `compute_ranking_significance()` | Proportion Z | Holm |
+| Count frequencies | `compute_pairwise_significance(test_type="chi2")` | Chi-square | Bonferroni |
+
+| Scenario | Correction |
+|----------|------------|
+| Publishing results | Bonferroni |
+| Client presentation | Bonferroni |
+| Exploratory analysis | Holm |
+| Quick internal check | Holm or None |
+
+---
+
+## Further Reading
+
+- [Statistics for Dummies Cheat Sheet](https://www.dummies.com/article/academics-the-arts/math/statistics/statistics-for-dummies-cheat-sheet-208650/)
+- [Choosing the Right Statistical Test](https://stats.oarc.ucla.edu/other/mult-pkg/whatstat/)
+- [Multiple Comparisons Problem (Wikipedia)](https://en.wikipedia.org/wiki/Multiple_comparisons_problem)

pyproject.toml

@@ -22,6 +22,7 @@ dependencies = [
     "python-pptx>=1.0.2",
     "pyzmq>=27.1.0",
     "requests>=2.32.5",
+    "scipy>=1.14.0",
     "taguette>=1.5.1",
     "vl-convert-python>=1.9.0.post1",
     "wordcloud>=1.9.5",

reference.py

@@ -0,0 +1,59 @@
+ORIGINAL_CHARACTER_TRAITS = {
+    "the_familiar_friend": [
+        "Warm",
+        "Friendly",
+        "Approachable",
+        "Familiar",
+        "Casual",
+        "Appreciative",
+        "Benevolent",
+    ],
+    "the_coach": [
+        "Empowering",
+        "Encouraging",
+        "Caring",
+        "Positive",
+        "Optimistic",
+        "Guiding",
+        "Reassuring",
+    ],
+    "the_personal_assistant": [
+        "Forward-thinking",
+        "Progressive",
+        "Cooperative",
+        "Intentional",
+        "Resourceful",
+        "Attentive",
+        "Adaptive",
+    ],
+    "the_bank_teller": [
+        "Patient",
+        "Grounded",
+        "Down-to-earth",
+        "Stable",
+        "Formal",
+        "Balanced",
+        "Efficient",
+    ]
+}
+
+VOICE_GENDER_MAPPING = {
+    "V14": "Female",
+    "V04": "Female",
+    "V08": "Female",
+    "V77": "Female",
+    "V48": "Female",
+    "V82": "Female",
+    "V89": "Female",
+    "V91": "Female",
+    "V34": "Male",
+    "V69": "Male",
+    "V45": "Male",
+    "V46": "Male",
+    "V54": "Male",
+    "V74": "Male",
+    "V81": "Male",
+    "V86": "Male",
+    "V88": "Male",
+    "V16": "Male",
+}

theme.py

@@ -19,11 +19,32 @@ class ColorPalette:
     # Neutral color for unhighlighted comparison items
     NEUTRAL = "#D3D3D3"  # Light Grey
 
+    # Character-specific colors (for individual character plots)
+    # Each character has a main color and a lighter highlight for original traits
+    CHARACTER_BANK_TELLER = "#004C6D"              # Dark Blue
+    CHARACTER_BANK_TELLER_HIGHLIGHT = "#669BBC"    # Light Steel Blue
+    
+    CHARACTER_FAMILIAR_FRIEND = "#008493"          # Teal
+    CHARACTER_FAMILIAR_FRIEND_HIGHLIGHT = "#A8DADC" # Pale Cyan
+    
+    CHARACTER_COACH = "#5AAE95"                    # Sea Green
+    CHARACTER_COACH_HIGHLIGHT = "#A8DADC"          # Pale Cyan
+    
+    CHARACTER_PERSONAL_ASSISTANT = "#457B9D"       # Steel Blue
+    CHARACTER_PERSONAL_ASSISTANT_HIGHLIGHT = "#669BBC" # Light Steel Blue
+
     # General UI elements
     TEXT = "black"
     GRID = "lightgray"
     BACKGROUND = "white"
 
+    # Statistical significance colors (for heatmaps/annotations)
+    SIG_STRONG = "#004C6D"       # p < 0.001 - Dark Blue (highly significant)
+    SIG_MODERATE = "#0077B6"     # p < 0.01 - Medium Blue (significant)
+    SIG_WEAK = "#5AAE95"         # p < 0.05 - Sea Green (marginally significant)
+    SIG_NONE = "#E8E8E8"         # p >= 0.05 - Light Grey (not significant)
+    SIG_DIAGONAL = "#FFFFFF"     # White for diagonal (self-comparison)
+
     # Extended palette for categorical charts (e.g., pie charts with many categories)
     CATEGORICAL = [
         "#0077B6",  # PRIMARY - Medium Blue
@@ -38,6 +59,30 @@ class ColorPalette:
         "#457B9D",  # Steel Blue
     ]
 
+    # Gender-based colors (Male = Blue tones, Female = Pink tones)
+    # Primary colors by gender
+    GENDER_MALE = "#0077B6"       # Medium Blue (same as PRIMARY)
+    GENDER_FEMALE = "#B6007A"     # Medium Pink
+
+    # Ranking colors by gender (Darkest -> Lightest)
+    GENDER_MALE_RANK_1 = "#004C6D"    # Dark Blue
+    GENDER_MALE_RANK_2 = "#0077B6"    # Medium Blue
+    GENDER_MALE_RANK_3 = "#669BBC"    # Light Steel Blue
+
+    GENDER_FEMALE_RANK_1 = "#6D004C"  # Dark Pink
+    GENDER_FEMALE_RANK_2 = "#B6007A"  # Medium Pink
+    GENDER_FEMALE_RANK_3 = "#BC669B"  # Light Pink
+
+    # Neutral colors by gender (for non-highlighted items)
+    GENDER_MALE_NEUTRAL = "#B8C9D9"   # Grey-Blue
+    GENDER_FEMALE_NEUTRAL = "#D9B8C9" # Grey-Pink
+
+    # Speaking Style Colors (named after the style quadrant colors)
+    STYLE_GREEN = "#2E7D32"   # Forest Green
+    STYLE_BLUE = "#1565C0"    # Strong Blue
+    STYLE_ORANGE = "#E07A00"  # Burnt Orange
+    STYLE_RED = "#C62828"     # Deep Red
+
 
 def jpmc_altair_theme():
     """JPMC brand theme for Altair charts."""

utils.py

@@ -37,16 +37,232 @@ def image_alt_text_generator(fpath, include_dataset_dirname=False) -> str:
     else:
         return Path('/'.join(fparts[2:])).as_posix()
 
+def _get_shape_alt_text(shape) -> str:
+    """
+    Extract alt text from a PowerPoint shape.
+    
+    Args:
+        shape: A python-pptx shape object.
+        
+    Returns:
+        str: The alt text (descr attribute) or empty string if not found.
+    """
+    try:
+        # Check for common property names used by python-pptx elements to store non-visual props
+        # nvPicPr (Picture), nvSpPr (Shape/Placeholder), nvGrpSpPr (Group), 
+        # nvGraphicFramePr (GraphicFrame), nvCxnSpPr (Connector)
+        nvPr = None
+        for attr in ['nvPicPr', 'nvSpPr', 'nvGrpSpPr', 'nvGraphicFramePr', 'nvCxnSpPr']:
+            if hasattr(shape._element, attr):
+                nvPr = getattr(shape._element, attr)
+                break
+        
+        if nvPr is not None and hasattr(nvPr, 'cNvPr'):
+            return nvPr.cNvPr.get("descr", "")
+    except Exception:
+        pass
+    return ""
+
+
+def pptx_replace_images_from_directory(
+    presentation_path: Union[str, Path],
+    image_source_dir: Union[str, Path],
+    save_path: Union[str, Path] = None
+) -> dict:
+    """
+    Replace all images in a PowerPoint presentation using images from a directory
+    where subdirectory/filename paths match the alt_text of each image.
+    
+    This function scans all images in the presentation, extracts their alt_text,
+    and looks for a matching image file in the source directory. The alt_text
+    should be a relative path (e.g., "All_Respondents/chart_name.png") that
+    corresponds to the directory structure under image_source_dir.
+    
+    Args:
+        presentation_path (str/Path): Path to the source .pptx file.
+        image_source_dir (str/Path): Root directory containing replacement images.
+            The directory structure should mirror the alt_text paths.
+            Example: if alt_text is "All_Respondents/voice_scale.png", the
+            replacement image should be at image_source_dir/All_Respondents/voice_scale.png
+        save_path (str/Path, optional): Path to save the modified presentation.
+            If None, overwrites the input file.
+    
+    Returns:
+        dict: Summary with keys:
+            - 'replaced': List of dicts with slide number, shape name, and matched path
+            - 'not_found': List of dicts with slide number, shape name, and alt_text
+            - 'no_alt_text': List of dicts with slide number and shape name
+            - 'total_images': Total number of picture shapes processed
+    
+    Example:
+        >>> pptx_replace_images_from_directory(
+        ...     "presentation.pptx",
+        ...     "figures/2-2-26/",
+        ...     "presentation_updated.pptx"
+        ... )
+        
+    Notes:
+        - Alt text should be set using update_ppt_alt_text() or image_alt_text_generator()
+        - Images without alt_text are skipped
+        - Original image position, size, and aspect ratio are preserved
+    """
+    presentation_path = Path(presentation_path)
+    image_source_dir = Path(image_source_dir)
+    
+    if save_path is None:
+        save_path = presentation_path
+    else:
+        save_path = Path(save_path)
+    
+    if not presentation_path.exists():
+        raise FileNotFoundError(f"Presentation not found: {presentation_path}")
+    if not image_source_dir.exists():
+        raise FileNotFoundError(f"Image source directory not found: {image_source_dir}")
+    
+    # Build a lookup of all available images in the source directory
+    available_images = {}
+    for img_path in image_source_dir.rglob("*"):
+        if img_path.is_file() and img_path.suffix.lower() in {'.png', '.jpg', '.jpeg', '.gif', '.bmp', '.tiff', '.webp'}:
+            # Store relative path from image_source_dir as key
+            rel_path = img_path.relative_to(image_source_dir).as_posix()
+            available_images[rel_path] = img_path
+    
+    print(f"Found {len(available_images)} images in source directory")
+    
+    # Open presentation
+    prs = Presentation(presentation_path)
+    
+    # Track results
+    results = {
+        'replaced': [],
+        'not_found': [],
+        'no_alt_text': [],
+        'total_images': 0
+    }
+    
+    total_slides = len(prs.slides)
+    print(f"Processing {total_slides} slides...")
+    
+    for slide_idx, slide in enumerate(prs.slides):
+        slide_num = slide_idx + 1
+        
+        # Use recursive iterator to find all pictures including those in groups
+        picture_shapes = list(_iter_picture_shapes(slide.shapes))
+        
+        for shape in picture_shapes:
+            results['total_images'] += 1
+            shape_name = shape.name or f"Unnamed (ID: {getattr(shape, 'shape_id', 'unknown')})"
+            
+            # Get alt text
+            alt_text = _get_shape_alt_text(shape)
+            
+            if not alt_text:
+                results['no_alt_text'].append({
+                    'slide': slide_num,
+                    'shape_name': shape_name
+                })
+                continue
+            
+            # Look for matching image in source directory
+            # Try the alt_text as-is, and also with common extensions if not present
+            matched_path = None
+            
+            if alt_text in available_images:
+                matched_path = available_images[alt_text]
+            else:
+                # Try adding common extensions if alt_text doesn't have one
+                alt_text_path = Path(alt_text)
+                if not alt_text_path.suffix:
+                    for ext in ['.png', '.jpg', '.jpeg', '.gif']:
+                        test_key = f"{alt_text}{ext}"
+                        if test_key in available_images:
+                            matched_path = available_images[test_key]
+                            break
+            
+            if matched_path is None:
+                results['not_found'].append({
+                    'slide': slide_num,
+                    'shape_name': shape_name,
+                    'alt_text': alt_text
+                })
+                continue
+            
+            # Replace the image
+            try:
+                # Record coordinates
+                left, top, width, height = shape.left, shape.top, shape.width, shape.height
+                
+                # Remove old shape from XML
+                old_element = shape._element
+                old_element.getparent().remove(old_element)
+                
+                # Add new image at the same position/size
+                new_shape = slide.shapes.add_picture(str(matched_path), left, top, width, height)
+                
+                # Preserve the alt text on the new shape
+                new_nvPr = None
+                for attr in ['nvPicPr', 'nvSpPr', 'nvGrpSpPr', 'nvGraphicFramePr', 'nvCxnSpPr']:
+                    if hasattr(new_shape._element, attr):
+                        new_nvPr = getattr(new_shape._element, attr)
+                        break
+                if new_nvPr and hasattr(new_nvPr, 'cNvPr'):
+                    new_nvPr.cNvPr.set("descr", alt_text)
+                
+                results['replaced'].append({
+                    'slide': slide_num,
+                    'shape_name': shape_name,
+                    'matched_path': str(matched_path)
+                })
+                print(f"Slide {slide_num}: Replaced '{alt_text}'")
+                    
+            except Exception as e:
+                results['not_found'].append({
+                    'slide': slide_num,
+                    'shape_name': shape_name,
+                    'alt_text': alt_text,
+                    'error': str(e)
+                })
+    
+    # Save presentation
+    prs.save(save_path)
+    
+    # Print summary
+    print("\n" + "=" * 80)
+    if results['replaced']:
+        print(f"✓ Saved updated presentation to {save_path} with {len(results['replaced'])} replacements.")
+    else:
+        print("No images matched or required updates.")
+    
+    if results['not_found']:
+        print(f"\n⚠ {len(results['not_found'])} image(s) not found in source directory:")
+        for item in results['not_found']:
+            print(f"  • Slide {item['slide']}: '{item.get('alt_text', 'N/A')}'")
+    
+    if results['no_alt_text']:
+        print(f"\n⚠ {len(results['no_alt_text'])} image(s) without alt text (skipped):")
+        for item in results['no_alt_text']:
+            print(f"  • Slide {item['slide']}: '{item['shape_name']}'")
+    
+    if not results['not_found'] and not results['no_alt_text']:
+        print("\n✓ All images replaced successfully!")
+    print("=" * 80)
+    
+    return results
+
+
 def pptx_replace_named_image(presentation_path, target_tag, new_image_path, save_path):
     """
-    Finds and replaces specific images in a PowerPoint presentation while 
+    Finds and replaces a specific image in a PowerPoint presentation while 
-    preserving their original position, size, and aspect ratio.
+    preserving its original position, size, and aspect ratio.
 
     This function performs a 'surgical' replacement: it records the coordinates 
     of the existing image, removes it from the slide's XML, and inserts a 
     new image into the exact same bounding box. It identifies the target 
     image by searching for a specific string within the Shape Name 
     (Selection Pane) or Alt Text.
+    
+    Note: For batch replacement of all images using a directory structure,
+    use pptx_replace_images_from_directory() instead.
 
     Args:
         presentation_path (str): The file path to the source .pptx file.
@@ -73,24 +289,7 @@ def pptx_replace_named_image(presentation_path, target_tag, new_image_path, save
             print(f"Checking shape: {shape.name} of type {shape.shape_type}...")
             
             shape_name = shape.name or ""
-            alt_text = ""
+            alt_text = _get_shape_alt_text(shape)
-            
-            # More robust strategy: Check for alt text in ANY valid element property
-            # This allows replacing Pictures, Placeholders, GraphicFrames, etc.
-            try:
-                # Check for common property names used by python-pptx elements to store non-visual props
-                # nvPicPr (Picture), nvSpPr (Shape/Placeholder), nvGrpSpPr (Group), 
-                # nvGraphicFramePr (GraphicFrame), nvCxnSpPr (Connector)
-                nvPr = None
-                for attr in ['nvPicPr', 'nvSpPr', 'nvGrpSpPr', 'nvGraphicFramePr', 'nvCxnSpPr']:
-                    if hasattr(shape._element, attr):
-                        nvPr = getattr(shape._element, attr)
-                        break
-                
-                if nvPr is not None and hasattr(nvPr, 'cNvPr'):
-                    alt_text = nvPr.cNvPr.get("descr", "")
-            except Exception:
-                pass
             
             print(f"Alt Text for shape '{shape_name}': {alt_text}")
 
@@ -309,9 +508,16 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
                         print(f"Error updating alt text for {original_path}: {e}")
                 
                 else:
+                    # Check if image already has alt text set - if so, skip reporting as unmatched
+                    existing_alt = _get_shape_alt_text(shape)
+                    if existing_alt:
+                        # Image already has alt text, no need to report as unmatched
+                        continue
+                    
                     shape_id = getattr(shape, 'shape_id', getattr(shape, 'id', 'Unknown ID'))
                     shape_name = shape.name if shape.name else f"Unnamed Shape (ID: {shape_id})"
                     hash_type = "pHash" if use_perceptual_hash else "SHA1"
+                    
                     unmatched_images.append({
                         'slide': i+1,
                         'shape_name': shape_name,
@@ -515,7 +721,7 @@ def normalize_global_values(df: pl.DataFrame, target_cols: list[str]) -> pl.Data
 
 
 class QualtricsSurvey(QualtricsPlotsMixin):
-    """Class to handle JPMorgan Chase survey data."""
+    """Class to handle Qualtrics survey data."""
     
     def __init__(self, data_path: Union[str, Path], qsf_path: Union[str, Path]):
         if isinstance(data_path, str):
@@ -873,6 +1079,442 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         
         return self._get_subset(q, QIDs, rename_cols=True), None
 
+    def transform_character_trait_frequency(
+        self, 
+        char_df: pl.LazyFrame | pl.DataFrame,
+        character_column: str,
+    ) -> tuple[pl.DataFrame, dict | None]:
+        """Transform character refine data to trait frequency counts for a single character.
+        
+        Original use-case: "I need a bar plot that shows the frequency of the times 
+        each trait is chosen per brand character."
+        
+        This function takes a DataFrame with comma-separated trait selections per 
+        character, explodes traits, and counts their frequency for a single character.
+        
+        Args:
+            char_df: Pre-fetched data 
+                Expected columns: '_recordId', '<character_column>' (with comma-separated traits)
+            character_column: Name of the character column to analyze (e.g., 'Bank Teller')
+        
+        Returns:
+            tuple: (DataFrame with columns ['trait', 'count', 'is_original'], None)
+                   - 'trait': individual trait name
+                   - 'count': frequency count
+                   - 'is_original': boolean indicating if trait is in the original definition
+        """
+        from reference import ORIGINAL_CHARACTER_TRAITS
+        
+        if isinstance(char_df, pl.LazyFrame):
+            char_df = char_df.collect()
+        
+        # Map display names to reference keys
+        character_key_map = {
+            'Bank Teller': 'the_bank_teller',
+            'Familiar Friend': 'the_familiar_friend',
+            'The Coach': 'the_coach',
+            'Personal Assistant': 'the_personal_assistant',
+        }
+        
+        # Get original traits for this character
+        ref_key = character_key_map.get(character_column)
+        original_traits = set(ORIGINAL_CHARACTER_TRAITS.get(ref_key, []))
+        
+        # Filter to rows where this character has a value (not null)
+        char_data = char_df.filter(pl.col(character_column).is_not_null())
+        
+        # Split comma-separated traits and explode
+        exploded = (
+            char_data
+            .select(
+                pl.col(character_column)
+                .str.split(',')
+                .alias('traits')
+            )
+            .explode('traits')
+            .with_columns(
+                pl.col('traits').str.strip_chars().alias('trait')
+            )
+            .filter(pl.col('trait') != '')
+        )
+        
+        # Count trait frequencies
+        freq_df = (
+            exploded
+            .group_by('trait')
+            .agg(pl.len().alias('count'))
+            .sort('count', descending=True)
+        )
+        
+        # Add is_original flag
+        freq_df = freq_df.with_columns(
+            pl.col('trait').is_in(list(original_traits)).alias('is_original')
+        )
+        
+        return freq_df, None
+
+    def compute_pairwise_significance(
+        self,
+        data: pl.LazyFrame | pl.DataFrame,
+        test_type: str = "auto",
+        alpha: float = 0.05,
+        correction: str = "bonferroni",
+    ) -> tuple[pl.DataFrame, dict]:
+        """Compute pairwise statistical significance tests between columns.
+        
+        Original use-case: "I need to test for statistical significance and present 
+        this in a logical manner. It should be a generalized function to work on 
+        many dataframes."
+        
+        This function performs pairwise statistical tests between all numeric columns
+        (excluding '_recordId') to determine which groups differ significantly.
+        
+        Args:
+            data: Pre-fetched data with numeric columns to compare.
+                Expected format: rows are observations, columns are groups/categories.
+                Example: Voice_Scale_1_10__V14, Voice_Scale_1_10__V04, etc.
+            test_type: Statistical test to use:
+                - "auto": Automatically chooses based on data (default)
+                - "mannwhitney": Mann-Whitney U test (non-parametric, for continuous)
+                - "ttest": Independent samples t-test (parametric, for continuous)
+                - "chi2": Chi-square test (for count/frequency data)
+            alpha: Significance level (default 0.05)
+            correction: Multiple comparison correction method:
+                - "bonferroni": Bonferroni correction (conservative)
+                - "holm": Holm-Bonferroni (less conservative)
+                - "none": No correction
+        
+        Returns:
+            tuple: (pairwise_df, metadata)
+                - pairwise_df: DataFrame with columns ['group1', 'group2', 'p_value', 
+                  'p_adjusted', 'significant', 'effect_size', 'mean1', 'mean2', 'n1', 'n2']
+                - metadata: dict with 'test_type', 'alpha', 'correction', 'n_comparisons',
+                  'overall_test_stat', 'overall_p_value'
+        """
+        from scipy import stats as scipy_stats
+        import numpy as np
+        
+        if isinstance(data, pl.LazyFrame):
+            df = data.collect()
+        else:
+            df = data
+        
+        # Get numeric columns (exclude _recordId and other non-data columns)
+        value_cols = [c for c in df.columns if c != '_recordId' and df[c].dtype in [pl.Float64, pl.Float32, pl.Int64, pl.Int32]]
+        
+        if len(value_cols) < 2:
+            raise ValueError(f"Need at least 2 numeric columns for comparison, found {len(value_cols)}")
+        
+        # Auto-detect test type based on data characteristics
+        if test_type == "auto":
+            # Check if data looks like counts (integers, small range) vs continuous
+            sample_col = df[value_cols[0]].drop_nulls()
+            if len(sample_col) > 0:
+                is_integer = sample_col.dtype in [pl.Int64, pl.Int32]
+                unique_ratio = sample_col.n_unique() / len(sample_col)
+                if is_integer and unique_ratio < 0.1:
+                    test_type = "chi2"
+                else:
+                    test_type = "mannwhitney"  # Default to non-parametric
+            else:
+                test_type = "mannwhitney"
+        
+        # Extract data as lists (dropping nulls for each column)
+        group_data = {}
+        for col in value_cols:
+            group_data[col] = df[col].drop_nulls().to_numpy()
+        
+        # Compute overall test (Kruskal-Wallis for non-parametric, ANOVA for parametric)
+        all_groups = [group_data[col] for col in value_cols if len(group_data[col]) > 0]
+        if test_type in ["mannwhitney", "auto"]:
+            overall_stat, overall_p = scipy_stats.kruskal(*all_groups)
+            overall_test_name = "Kruskal-Wallis"
+        elif test_type == "ttest":
+            overall_stat, overall_p = scipy_stats.f_oneway(*all_groups)
+            overall_test_name = "One-way ANOVA"
+        else:
+            overall_stat, overall_p = None, None
+            overall_test_name = "N/A (Chi-square)"
+        
+        # Compute pairwise tests
+        results = []
+        n_comparisons = len(value_cols) * (len(value_cols) - 1) // 2
+        
+        for i, col1 in enumerate(value_cols):
+            for col2 in value_cols[i+1:]:
+                data1 = group_data[col1]
+                data2 = group_data[col2]
+                
+                n1, n2 = len(data1), len(data2)
+                mean1 = float(np.mean(data1)) if n1 > 0 else None
+                mean2 = float(np.mean(data2)) if n2 > 0 else None
+                
+                # Skip if either group has no data
+                if n1 == 0 or n2 == 0:
+                    results.append({
+                        'group1': self._clean_voice_label(col1),
+                        'group2': self._clean_voice_label(col2),
+                        'p_value': None,
+                        'effect_size': None,
+                        'mean1': mean1,
+                        'mean2': mean2,
+                        'n1': n1,
+                        'n2': n2,
+                    })
+                    continue
+                
+                # Perform the appropriate test
+                if test_type == "mannwhitney":
+                    stat, p_value = scipy_stats.mannwhitneyu(data1, data2, alternative='two-sided')
+                    # Effect size: rank-biserial correlation
+                    effect_size = 1 - (2 * stat) / (n1 * n2)
+                elif test_type == "ttest":
+                    stat, p_value = scipy_stats.ttest_ind(data1, data2)
+                    # Effect size: Cohen's d
+                    pooled_std = np.sqrt(((n1-1)*np.std(data1)**2 + (n2-1)*np.std(data2)**2) / (n1+n2-2))
+                    effect_size = (mean1 - mean2) / pooled_std if pooled_std > 0 else 0
+                elif test_type == "chi2":
+                    # Create contingency table from the two distributions
+                    # Bin the data for chi-square
+                    all_data = np.concatenate([data1, data2])
+                    bins = np.histogram_bin_edges(all_data, bins='auto')
+                    counts1, _ = np.histogram(data1, bins=bins)
+                    counts2, _ = np.histogram(data2, bins=bins)
+                    contingency = np.array([counts1, counts2])
+                    # Remove zero columns
+                    contingency = contingency[:, contingency.sum(axis=0) > 0]
+                    if contingency.shape[1] > 1:
+                        stat, p_value, _, _ = scipy_stats.chi2_contingency(contingency)
+                        effect_size = np.sqrt(stat / (contingency.sum() * (min(contingency.shape) - 1)))
+                    else:
+                        p_value, effect_size = 1.0, 0.0
+                else:
+                    raise ValueError(f"Unknown test_type: {test_type}")
+                
+                results.append({
+                    'group1': self._clean_voice_label(col1),
+                    'group2': self._clean_voice_label(col2),
+                    'p_value': float(p_value),
+                    'effect_size': float(effect_size),
+                    'mean1': mean1,
+                    'mean2': mean2,
+                    'n1': n1,
+                    'n2': n2,
+                })
+        
+        # Create DataFrame and apply multiple comparison correction
+        results_df = pl.DataFrame(results)
+        
+        # Apply correction
+        p_values = results_df['p_value'].to_numpy()
+        valid_mask = ~np.isnan(p_values.astype(float))
+        p_adjusted = np.full_like(p_values, np.nan, dtype=float)
+        
+        if correction == "bonferroni":
+            p_adjusted[valid_mask] = np.minimum(p_values[valid_mask] * n_comparisons, 1.0)
+        elif correction == "holm":
+            # Holm-Bonferroni step-down procedure
+            valid_p = p_values[valid_mask]
+            sorted_idx = np.argsort(valid_p)
+            sorted_p = valid_p[sorted_idx]
+            m = len(sorted_p)
+            adjusted = np.zeros(m)
+            for j in range(m):
+                adjusted[j] = sorted_p[j] * (m - j)
+            # Ensure monotonicity
+            for j in range(1, m):
+                adjusted[j] = max(adjusted[j], adjusted[j-1])
+            adjusted = np.minimum(adjusted, 1.0)
+            # Restore original order
+            p_adjusted[valid_mask] = adjusted[np.argsort(sorted_idx)]
+        elif correction == "none":
+            p_adjusted = p_values.astype(float)
+        
+        results_df = results_df.with_columns([
+            pl.Series('p_adjusted', p_adjusted),
+            pl.Series('significant', p_adjusted < alpha),
+        ])
+        
+        metadata = {
+            'test_type': test_type,
+            'alpha': alpha,
+            'correction': correction,
+            'n_comparisons': n_comparisons,
+            'overall_test': overall_test_name,
+            'overall_stat': overall_stat,
+            'overall_p_value': overall_p,
+        }
+        
+        return results_df, metadata
+
+    def compute_ranking_significance(
+        self,
+        data: pl.LazyFrame | pl.DataFrame,
+        alpha: float = 0.05,
+        correction: str = "bonferroni",
+    ) -> tuple[pl.DataFrame, dict]:
+        """Compute statistical significance for ranking data (e.g., Top 3 Voices).
+        
+        Original use-case: "Test whether voices are ranked significantly differently
+        based on the distribution of 1st, 2nd, 3rd place votes."
+        
+        This function takes raw ranking data (rows = respondents, columns = voices,
+        values = rank 1/2/3 or null) and performs:
+        1. Overall chi-square test on the full contingency table
+        2. Pairwise proportion tests comparing Rank 1 vote shares
+        
+        Args:
+            data: Pre-fetched ranking data from get_top_3_voices() or get_character_ranking().
+                Expected format: rows are respondents, columns are voices/characters,
+                values are 1, 2, 3 (rank) or null (not ranked).
+            alpha: Significance level (default 0.05)
+            correction: Multiple comparison correction method:
+                - "bonferroni": Bonferroni correction (conservative)
+                - "holm": Holm-Bonferroni (less conservative)
+                - "none": No correction
+        
+        Returns:
+            tuple: (pairwise_df, metadata)
+                - pairwise_df: DataFrame with columns ['group1', 'group2', 'p_value', 
+                  'p_adjusted', 'significant', 'rank1_count1', 'rank1_count2', 
+                  'rank1_pct1', 'rank1_pct2', 'total1', 'total2']
+                - metadata: dict with 'alpha', 'correction', 'n_comparisons',
+                  'chi2_stat', 'chi2_p_value', 'contingency_table'
+        
+        Example:
+            >>> ranking_data, _ = S.get_top_3_voices(data)
+            >>> pairwise_df, meta = S.compute_ranking_significance(ranking_data)
+            >>> # See which voices have significantly different Rank 1 proportions
+            >>> print(pairwise_df.filter(pl.col('significant') == True))
+        """
+        from scipy import stats as scipy_stats
+        import numpy as np
+        
+        if isinstance(data, pl.LazyFrame):
+            df = data.collect()
+        else:
+            df = data
+        
+        # Get ranking columns (exclude _recordId)
+        ranking_cols = [c for c in df.columns if c != '_recordId']
+        
+        if len(ranking_cols) < 2:
+            raise ValueError(f"Need at least 2 ranking columns, found {len(ranking_cols)}")
+        
+        # Build contingency table: rows = ranks (1, 2, 3), columns = voices
+        # Count how many times each voice received each rank
+        contingency_data = {}
+        for col in ranking_cols:
+            label = self._clean_voice_label(col)
+            r1 = df.filter(pl.col(col) == 1).height
+            r2 = df.filter(pl.col(col) == 2).height
+            r3 = df.filter(pl.col(col) == 3).height
+            contingency_data[label] = [r1, r2, r3]
+        
+        # Create contingency table as numpy array
+        labels = list(contingency_data.keys())
+        contingency_table = np.array([contingency_data[l] for l in labels]).T  # 3 x n_voices
+        
+        # Overall chi-square test on contingency table
+        # Tests whether rank distribution is independent of voice
+        chi2_stat, chi2_p, chi2_dof, _ = scipy_stats.chi2_contingency(contingency_table)
+        
+        # Pairwise proportion tests for Rank 1 votes
+        # We use a two-proportion z-test to compare rank 1 proportions
+        results = []
+        n_comparisons = len(labels) * (len(labels) - 1) // 2
+        
+        # Total respondents who ranked any voice in top 3
+        total_respondents = df.height
+        
+        for i, label1 in enumerate(labels):
+            for label2 in labels[i+1:]:
+                r1_count1 = contingency_data[label1][0]  # Rank 1 votes for voice 1
+                r1_count2 = contingency_data[label2][0]  # Rank 1 votes for voice 2
+                
+                # Total times each voice was ranked (1st + 2nd + 3rd)
+                total1 = sum(contingency_data[label1])
+                total2 = sum(contingency_data[label2])
+                
+                # Calculate proportions of Rank 1 out of all rankings for each voice
+                pct1 = r1_count1 / total1 if total1 > 0 else 0
+                pct2 = r1_count2 / total2 if total2 > 0 else 0
+                
+                # Two-proportion z-test
+                # H0: p1 = p2 (both voices have same proportion of Rank 1)
+                if total1 > 0 and total2 > 0 and (r1_count1 + r1_count2) > 0:
+                    # Pooled proportion
+                    p_pooled = (r1_count1 + r1_count2) / (total1 + total2)
+                    
+                    # Standard error
+                    se = np.sqrt(p_pooled * (1 - p_pooled) * (1/total1 + 1/total2))
+                    
+                    if se > 0:
+                        z_stat = (pct1 - pct2) / se
+                        p_value = 2 * (1 - scipy_stats.norm.cdf(abs(z_stat)))  # Two-tailed
+                    else:
+                        p_value = 1.0
+                else:
+                    p_value = 1.0
+                
+                results.append({
+                    'group1': label1,
+                    'group2': label2,
+                    'p_value': float(p_value),
+                    'rank1_count1': r1_count1,
+                    'rank1_count2': r1_count2,
+                    'rank1_pct1': round(pct1 * 100, 1),
+                    'rank1_pct2': round(pct2 * 100, 1),
+                    'total1': total1,
+                    'total2': total2,
+                })
+        
+        # Create DataFrame and apply correction
+        results_df = pl.DataFrame(results)
+        
+        p_values = results_df['p_value'].to_numpy()
+        p_adjusted = np.full_like(p_values, np.nan, dtype=float)
+        
+        if correction == "bonferroni":
+            p_adjusted = np.minimum(p_values * n_comparisons, 1.0)
+        elif correction == "holm":
+            sorted_idx = np.argsort(p_values)
+            sorted_p = p_values[sorted_idx]
+            m = len(sorted_p)
+            adjusted = np.zeros(m)
+            for j in range(m):
+                adjusted[j] = sorted_p[j] * (m - j)
+            for j in range(1, m):
+                adjusted[j] = max(adjusted[j], adjusted[j-1])
+            adjusted = np.minimum(adjusted, 1.0)
+            p_adjusted = adjusted[np.argsort(sorted_idx)]
+        elif correction == "none":
+            p_adjusted = p_values.astype(float)
+        
+        results_df = results_df.with_columns([
+            pl.Series('p_adjusted', p_adjusted),
+            pl.Series('significant', p_adjusted < alpha),
+        ])
+        
+        # Sort by p_value for easier inspection
+        results_df = results_df.sort('p_value')
+        
+        metadata = {
+            'test_type': 'proportion_z_test',
+            'alpha': alpha,
+            'correction': correction,
+            'n_comparisons': n_comparisons,
+            'chi2_stat': chi2_stat,
+            'chi2_p_value': chi2_p,
+            'chi2_dof': chi2_dof,
+            'overall_test': 'Chi-square',
+            'overall_stat': chi2_stat,
+            'overall_p_value': chi2_p,
+            'contingency_table': {label: contingency_data[label] for label in labels},
+        }
+        
+        return results_df, metadata
+
 
 def process_speaking_style_data(
     df: Union[pl.LazyFrame, pl.DataFrame],
@@ -1034,6 +1676,69 @@ def join_voice_and_style_data(
         how="inner"
     )
 
+
+def transform_speaking_style_color_correlation(
+    joined_df: pl.LazyFrame | pl.DataFrame,
+    speaking_styles: dict[str, list[str]],
+    target_column: str = "Voice_Scale_Score"
+) -> tuple[pl.DataFrame, dict | None]: 
+    """Aggregate speaking style correlation by color (Green, Blue, Orange, Red).
+    
+    Original use-case: "I want to create high-level correlation plots between
+    'green, blue, orange, red' speaking styles and the 'voice scale scores'.
+    I want to go to one plot with one bar for each color."
+    
+    This function calculates the mean correlation per speaking style color by
+    averaging the correlations of all traits within each color.
+    
+    Parameters
+    ----------
+    joined_df : pl.LazyFrame or pl.DataFrame
+        Pre-fetched data from joining speaking style data with target data.
+        Must have columns: Right_Anchor, score, and the target_column
+    speaking_styles : dict
+        Dictionary mapping color names to their constituent traits.
+        Typically imported from speaking_styles.SPEAKING_STYLES
+    target_column : str
+        The column to correlate against speaking style scores.
+        Default: "Voice_Scale_Score" (for voice scale 1-10)
+        Alternative: "Ranking_Points" (for top 3 voice ranking)
+    
+    Returns
+    -------
+    tuple[pl.DataFrame, dict | None]
+        (DataFrame with columns [Color, correlation, n_traits], None)
+    """
+    if isinstance(joined_df, pl.LazyFrame):
+        joined_df = joined_df.collect()
+    
+    color_correlations = []
+    
+    for color, traits in speaking_styles.items():
+        trait_corrs = []
+        for trait in traits:
+            # Filter to this specific trait
+            subset = joined_df.filter(pl.col("Right_Anchor") == trait)
+            valid_data = subset.select(["score", target_column]).drop_nulls()
+            
+            if valid_data.height > 1:
+                corr_val = valid_data.select(pl.corr("score", target_column)).item()
+                if corr_val is not None:
+                    trait_corrs.append(corr_val)
+        
+        # Average across all traits for this color
+        if trait_corrs:
+            avg_corr = sum(trait_corrs) / len(trait_corrs)
+            color_correlations.append({
+                "Color": color,
+                "correlation": avg_corr,
+                "n_traits": len(trait_corrs)
+            })
+    
+    result_df = pl.DataFrame(color_correlations)
+    return result_df, None
+
+
 def process_voice_ranking_data(
     df: Union[pl.LazyFrame, pl.DataFrame]
 ) -> pl.DataFrame:
@@ -1081,3 +1786,57 @@ def process_voice_ranking_data(
     ])
     
     return result.collect()
+
+
+def split_consumer_groups(df: Union[pl.LazyFrame, pl.DataFrame], col: str = "Consumer") -> dict[str, pl.DataFrame]:
+    """
+    Split dataframe into groups based on a column.
+    
+    If col is 'Consumer', it combines A/B subgroups (e.g. Mass_A + Mass_B -> Mass).
+    For other columns, it splits by unique values as-is.
+    """
+    if isinstance(df, pl.LazyFrame):
+        df = df.collect()
+        
+    if col not in df.columns:
+        raise ValueError(f"Column '{col}' not found in DataFrame")
+
+    group_col_alias = f"{col}_Group"
+    
+    if col == "Consumer":
+        # Clean Consumer column by removing _A or _B suffix
+        # Using regex replacement for trailing _A or _B
+        df_clean = df.with_columns(
+            pl.col(col)
+            .str.replace(r"_[AB]$", "")
+            .alias(group_col_alias)
+        )
+    else:
+        # Use values as is
+        df_clean = df.with_columns(
+            pl.col(col).alias(group_col_alias)
+        )
+    
+    # Split into dict
+    groups = {}
+    unique_groups = df_clean[group_col_alias].drop_nulls().unique().to_list()
+    
+    for group in unique_groups:
+        groups[group] = df_clean.filter(pl.col(group_col_alias) == group)
+        
+    return groups
+
+
+
+# Filter SPEAKING_STYLES to only include traits containing any keyword
+def filter_speaking_styles(speaking_styles: dict, keywords: list[str]) -> dict:
+    """Filter speaking styles to only include traits matching any keyword."""
+    filtered = {}
+    for color, traits in speaking_styles.items():
+        matching_traits = [
+            trait for trait in traits 
+            if any(kw.lower() in trait.lower() for kw in keywords)
+        ]
+        if matching_traits:
+            filtered[color] = matching_traits
+    return filtered

uv.lock

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+    { url = "https://files.pythonhosted.org/packages/e5/23/d39ccc4ed76222db31530b0a7d38876fdb7673e23f838e8d8f0ed4651a4f/dask-2026.1.2-py3-none-any.whl", hash = "sha256:46a0cf3b8d87f78a3d2e6b145aea4418a6d6d606fe6a16c79bd8ca2bb862bc91", size = 1482084, upload-time = "2026-01-30T21:04:18.363Z" },
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