fixed plot alt-text-tag function

02_quant_analysis.py

@@ -61,15 +61,12 @@ def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
 @app.cell
 def _(Path, RESULTS_FILE, data_all, mo):
     mo.md(f"""
-
     ---
     # Load Data
 
     **Dataset:** `{Path(RESULTS_FILE).name}`
 
     **Responses**: `{data_all.collect().shape[0]}`
-
-
     """)
     return
 
@@ -165,8 +162,6 @@ def _(S, mo):
 
     {filter_form}
     ''')
-
-
     return
 
 

03_quant_report.py

@@ -1,7 +1,7 @@
 import marimo
 
 __generated_with = "0.19.2"
-app = marimo.App(width="medium")
+app = marimo.App(width="full")
 
 with app.setup:
     import marimo as mo
@@ -22,7 +22,6 @@ def _():
         initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
     )
     file_browser
-
     return (file_browser,)
 
 
@@ -117,7 +116,7 @@ def _(data_validated):
     data = data_validated
 
     data.collect()
-    return
+    return (data,)
 
 
 @app.cell(hide_code=True)
@@ -130,6 +129,199 @@ def _():
     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
+    """)
+    return
+
+
+@app.cell
+def _(demographics):
+    # Demographics where 'Consumer' is null
+    demographics_no_consumer = demographics.filter(pl.col('Consumer').is_null())['_recordId'].to_list()
+    # demographics_no_consumer
+    return (demographics_no_consumer,)
+
+
+@app.cell
+def _(data_all, demographics_no_consumer):
+    # check if the responses with missing 'Consumer type' in demographics are all business owners as Lucia mentioned
+    assert all(data_all.filter(pl.col('_recordId').is_in(demographics_no_consumer)).collect()['QID4'] == 'Yes'), "Not all respondents with missing 'Consumer' are business owners."
+    return
+
+
+@app.cell
+def _(data_all):
+    # 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."
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Demographic Distributions
+    """)
+    return
+
+
+@app.cell
+def _():
+    demo_plot_cols = [
+        'Age',
+        'Gender',
+        # 'Race/Ethnicity',
+        'Bussiness_Owner',
+        'Consumer'
+    ]
+    return (demo_plot_cols,)
+
+
+@app.cell
+def _(S, demo_plot_cols, demographics):
+    _content = """
+    ## Demographic Distributions
+
+    """
+    for c in demo_plot_cols:
+        _fig = S.plot_demographic_distribution(
+            data=demographics,
+            column=c,
+            title=f"{c.replace('Bussiness', 'Business').replace('_', ' ')} Distribution of Survey Respondents"
+        )
+        _content += f"""{mo.ui.altair_chart(_fig)}\n\n"""
+
+    mo.md(_content)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ---
+
+    # Brand Character Results
+    """)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Best performing: Original vs Refined frankenstein
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    char_refine_rank = S.get_character_refine(data)[0]
+    # print(char_rank.collect().head())
+    # print(char_refine_rank.collect().head())
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Character ranking points
+    """)
+    return
+
+
+@app.cell
+def _(S, char_rank):
+    char_rank_weighted = calculate_weighted_ranking_scores(char_rank)
+    S.plot_weighted_ranking_score(char_rank_weighted, title="Most Popular Character - Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)", x_label='Voice')
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Character ranking 1-2-3
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    char_rank = S.get_character_ranking(data)[0]
+    return (char_rank,)
+
+
+@app.cell
+def _(S, char_rank):
+    S.plot_top3_ranking_distribution(char_rank, x_label='Character Personality', title='Character Personality: Rankings Top 3')
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Character Ranking: times 1st place
+    """)
+    return
+
+
+@app.cell
+def _(S, char_rank):
+    S.plot_most_ranked_1(char_rank, title="Most Popular Character<br>(Number of Times Ranked 1st)", x_label='Character Personality')
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Prominent predefined personality traits wordcloud
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    top8_traits = S.get_top_8_traits(data)[0]
+    S.plot_traits_wordcloud(
+        data=top8_traits,
+        column='Top_8_Traits',
+        title="Most Prominent Personality Traits",
+    )
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Trait frequency per brand character
+    """)
+    return
+
+
+@app.cell
+def _():
+    # Join respondent 
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ---
+
+    # Spoken Voice Results
+    """)
+    return
+
+
 @app.cell(hide_code=True)
 def _():
     mo.md(r"""

04_PPTX_Update_Images.py

@@ -0,0 +1,83 @@
+import marimo
+
+__generated_with = "0.19.2"
+app = marimo.App(width="medium")
+
+with app.setup:
+    import marimo as mo
+    from pathlib import Path
+    import utils
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    # Tag existing images with Alt-Text
+
+    Based on image content
+    """)
+    return
+
+
+@app.cell
+def _():
+    TAG_SOURCE = Path('data/reports/Perception-Research-Report.pptx')
+    TAG_TARGET = Path('data/reports/Perception-Research-Report_tagged.pptx')
+    TAG_IMAGE_DIR = Path('figures/OneDrive_2026-01-28/')
+    return TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET
+
+
+@app.cell
+def _(TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET):
+    utils.update_ppt_alt_text(ppt_path=TAG_SOURCE, image_source_dir=TAG_IMAGE_DIR, output_path=TAG_TARGET)
+    return
+
+
+@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
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    # Replace Images using Alt-Text
+    """)
+    return
+
+
+@app.cell
+def _():
+    REPLACE_SOURCE = Path('data/test_replace_source.pptx')
+    REPLACE_TARGET = Path('data/test_replace_target.pptx')
+    return REPLACE_SOURCE, REPLACE_TARGET
+
+
+app._unparsable_cell(
+    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):
+    utils.pptx_replace_named_image(
+        presentation_path=REPLACE_SOURCE,
+        target_tag=utils.image_alt_text_generator(IMAGE_FILE),
+        new_image_path=IMAGE_FILE,
+        save_path=REPLACE_TARGET)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

99_example_ingest_qualtrics_export.py

@@ -42,14 +42,6 @@ def _(survey):
     return
 
 
-app._unparsable_cell(
-    r"""
-    data.
-    """,
-    name="_"
-)
-
-
 @app.cell
 def _(mo):
     mo.md(r"""

docs/wordcloud-usage.md

@@ -0,0 +1,85 @@
+# Word Cloud for Personality Traits - Usage Example
+
+This example shows how to use the `create_traits_wordcloud` function to visualize the most prominent personality traits from survey data.
+
+## Basic Usage in Jupyter/Marimo Notebook
+
+```python
+from utils import JPMCSurvey, create_traits_wordcloud
+from pathlib import Path
+
+# Load your survey data
+RESULTS_FILE = "data/exports/1-23-26/JPMC_Chase Brand Personality_Quant Round 1_January 23, 2026_Labels.csv"
+QSF_FILE = "data/19-dec_V1_quant_incl_shani_comments.qsf"
+
+S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
+data = S.load_data()
+
+# Get Top 3 Traits data
+top3_traits = S.get_top_3_traits(data)[0]
+
+# Create and display word cloud
+fig = create_traits_wordcloud(
+    data=top3_traits,
+    column='Top_3_Traits',
+    title="Most Prominent Personality Traits",
+    fig_save_dir='figures',  # Will save to figures/All_Respondents/
+    filter_slug='All_Respondents'
+)
+
+# Display in notebook
+fig  # or plt.show()
+```
+
+## With Active Filters
+
+If you're using the survey filter methods, you can pass the filter slug:
+
+```python
+# Apply filters
+S.set_filter_consumer(['Early Professional', 'Established Professional'])
+filtered_data = S.get_filtered_data()
+
+# Get traits from filtered data
+top3_traits = S.get_top_3_traits(filtered_data)[0]
+
+# Get the filter slug for directory naming
+filter_slug = S._get_filter_slug()
+
+# Create word cloud with filtered data
+fig = create_traits_wordcloud(
+    data=top3_traits,
+    column='Top_3_Traits',
+    title="Most Prominent Personality Traits<br>(Early & Established Professionals)",
+    fig_save_dir='figures',
+    filter_slug=filter_slug  # e.g., 'Cons-Early_Professional_Established_Professional'
+)
+
+fig
+```
+
+## Function Parameters
+
+- **data**: Polars DataFrame or LazyFrame with trait data
+- **column**: Column name containing comma-separated traits (default: 'Top_3_Traits')
+- **title**: Title for the word cloud
+- **width**: Width in pixels (default: 1600)
+- **height**: Height in pixels (default: 800)
+- **background_color**: Background color (default: 'white')
+- **fig_save_dir**: Directory to save PNG (default: None - doesn't save)
+- **filter_slug**: Subdirectory name for filtered results (default: 'All_Respondents')
+
+## Colors
+
+The word cloud uses colors from `theme.py`:
+- PRIMARY: #0077B6 (Medium Blue)
+- RANK_1: #004C6D (Dark Blue)
+- RANK_2: #008493 (Teal)
+- RANK_3: #5AAE95 (Sea Green)
+
+## Output
+
+- **Returns**: matplotlib Figure object for display in notebooks
+- **Saves**: PNG file to `{fig_save_dir}/{filter_slug}/{sanitized_title}.png` at 300 DPI
+
+The saved files follow the same naming convention as plots in `plots.py`.

plots.py

@@ -1,6 +1,7 @@
 """Plotting functions for Voice Branding analysis using Altair."""
 
 import re
+import math
 from pathlib import Path
 
 import altair as alt
@@ -728,8 +729,6 @@ class JPMCPlotsMixin:
             },
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
-        ).configure_view(
-            strokeWidth=0  # Remove frame which might obscure labels
         )
 
         chart = self._save_plot(chart, title)
@@ -794,6 +793,101 @@ class JPMCPlotsMixin:
         chart = self._save_plot(chart, title)
         return chart
 
+    def plot_demographic_distribution(
+        self,
+        column: str,
+        data: pl.LazyFrame | pl.DataFrame | None = None,
+        title: str | None = None,
+        height: int | None = None,
+        width: int | str | None = None,
+        show_counts: bool = True,
+    ) -> alt.Chart:
+        """Create a horizontal bar chart showing the distribution of respondents by a demographic column.
+
+        Designed to be compact so multiple charts (approx. 6) can fit on one slide.
+        Uses horizontal bars for better readability with many categories.
+
+        Parameters:
+            column: The column name to analyze (e.g., 'Age', 'Gender', 'Race/Ethnicity').
+            data: Optional DataFrame. If None, uses self.data_filtered.
+            title: Chart title. If None, auto-generates based on column name.
+            height: Chart height in pixels (default: auto-sized based on categories).
+            width: Chart width in pixels (default: 280 for compact layout).
+            show_counts: If True, display count labels on the bars.
+
+        Returns:
+            alt.Chart: An Altair horizontal bar chart showing the distribution.
+        """
+        df = self._ensure_dataframe(data)
+
+        if column not in df.columns:
+            return alt.Chart(pd.DataFrame({'text': [f"Column '{column}' not found"]})).mark_text().encode(text='text:N')
+
+        # Count values in the column, including nulls
+        stats_df = (
+            df.select(pl.col(column))
+            .with_columns(pl.col(column).fill_null("(No Response)"))
+            .group_by(column)
+            .agg(pl.len().alias("count"))
+            .sort("count", descending=True)
+            .to_pandas()
+        )
+
+        if stats_df.empty:
+            return alt.Chart(pd.DataFrame({'text': ['No data']})).mark_text().encode(text='text:N')
+
+        # Calculate percentages
+        total = stats_df['count'].sum()
+        stats_df['percentage'] = (stats_df['count'] / total * 100).round(1)
+
+        # Generate title if not provided
+        if title is None:
+            clean_col = column.replace('_', ' ').replace('/', ' / ')
+            title = f"Distribution: {clean_col}"
+
+        # Calculate appropriate height based on number of categories
+        num_categories = len(stats_df)
+        bar_height = 18  # pixels per bar
+        calculated_height = max(120, num_categories * bar_height + 40)  # min 120px, +40 for title/padding
+
+        # Horizontal bar chart - categories on Y axis, counts on X axis
+        bars = alt.Chart(stats_df).mark_bar(color=ColorPalette.PRIMARY).encode(
+            x=alt.X('count:Q', title='Count', axis=alt.Axis(grid=False)),
+            y=alt.Y(f'{column}:N', title=None, sort='-x', axis=alt.Axis(labelLimit=150)),
+            tooltip=[
+                alt.Tooltip(f'{column}:N', title=column.replace('_', ' ')),
+                alt.Tooltip('count:Q', title='Count'),
+                alt.Tooltip('percentage:Q', title='Percentage', format='.1f')
+            ]
+        )
+
+        # Add count labels at end of bars
+        if show_counts:
+            text = alt.Chart(stats_df).mark_text(
+                align='left',
+                baseline='middle',
+                dx=3,  # Offset from bar end
+                fontSize=9,
+                color=ColorPalette.TEXT
+            ).encode(
+                x='count:Q',
+                y=alt.Y(f'{column}:N', sort='-x'),
+                text='count:Q'
+            )
+            chart = (bars + text)
+        else:
+            chart = bars
+
+        # Compact dimensions for 6-per-slide layout
+        chart = chart.properties(
+            title=self._process_title(title),
+            width=width or 200,
+            height=height or calculated_height
+        )
+
+        chart = self._save_plot(chart, title)
+        return chart
+
     def plot_speaking_style_ranking_correlation(
         self,
         style_color: str,
@@ -849,3 +943,99 @@ class JPMCPlotsMixin:
 
         chart = self._save_plot(chart, title)
         return chart
+
+    def plot_traits_wordcloud(
+        self,
+        data: pl.LazyFrame | pl.DataFrame | None = None,
+        column: str = 'Top_3_Traits',
+        title: str = "Most Prominent Personality Traits",
+        width: int = 1600,
+        height: int = 800,
+        background_color: str = 'white',
+        random_state: int = 23,
+    ):
+        """Create a word cloud visualization of personality traits from survey data.
+        
+        Args:
+            data: Polars DataFrame or LazyFrame containing trait data
+            column: Name of column containing comma-separated traits
+            title: Title for the word cloud
+            width: Width of the word cloud image in pixels
+            height: Height of the word cloud image in pixels
+            background_color: Background color for the word cloud
+            random_state: Random seed for reproducible word cloud generation (default: 23)
+            
+        Returns:
+            matplotlib.figure.Figure: The word cloud figure for display in notebooks
+        """
+        import matplotlib.pyplot as plt
+        from wordcloud import WordCloud
+        from collections import Counter
+        import random
+        
+        df = self._ensure_dataframe(data)
+        
+        # Extract and split traits
+        traits_list = []
+        for row in df[column].drop_nulls():
+            # Split by comma and clean whitespace
+            traits = [trait.strip() for trait in row.split(',')]
+            traits_list.extend(traits)
+        
+        # Create frequency dictionary
+        trait_freq = Counter(traits_list)
+        
+        # Set random seed for color selection
+        random.seed(random_state)
+        
+        # Color function using JPMC colors
+        def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
+            colors = [
+                ColorPalette.PRIMARY,
+                ColorPalette.RANK_1,
+                ColorPalette.RANK_2,
+                ColorPalette.RANK_3,
+            ]
+            return random.choice(colors)
+        
+        # Generate word cloud
+        wordcloud = WordCloud(
+            width=width,
+            height=height,
+            background_color=background_color,
+            color_func=color_func,
+            relative_scaling=0.5,
+            min_font_size=10,
+            prefer_horizontal=0.7,
+            collocations=False,  # Treat each word independently
+            random_state=random_state  # Seed for reproducible layout
+        ).generate_from_frequencies(trait_freq)
+        
+        # Create matplotlib figure
+        fig, ax = plt.subplots(figsize=(width/100, height/100), dpi=100)
+        ax.imshow(wordcloud, interpolation='bilinear')
+        ax.axis('off')
+        ax.set_title(title, fontsize=16, pad=20, color=ColorPalette.TEXT)
+        
+        plt.tight_layout(pad=0)
+        
+        # Save figure if directory specified (using same pattern as other plots)
+        if hasattr(self, 'fig_save_dir') and self.fig_save_dir:
+            save_path = Path(self.fig_save_dir)
+            
+            # Add filter slug subfolder
+            filter_slug = self._get_filter_slug()
+            save_path = save_path / filter_slug
+            
+            if not save_path.exists():
+                save_path.mkdir(parents=True, exist_ok=True)
+            
+            # Use _sanitize_filename for consistency
+            filename = f"{self._sanitize_filename(title)}.png"
+            filepath = save_path / filename
+            
+            # Save as PNG at high resolution
+            fig.savefig(filepath, dpi=300, bbox_inches='tight', facecolor='white')
+            print(f"Word cloud saved to: {filepath}")
+        
+        return fig

pyproject.toml

@@ -6,6 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "altair>=6.0.0",
+    "imagehash>=4.3.1",
     "marimo>=0.18.0",
     "matplotlib>=3.10.8",
     "modin[dask]>=0.37.1",
@@ -14,6 +15,7 @@ dependencies = [
     "openai>=2.9.0",
     "openpyxl>=3.1.5",
     "pandas>=2.3.3",
+    "pillow>=11.0.0",
     "polars>=1.37.1",
     "pyarrow>=23.0.0",
     "pysqlite3>=0.6.0",

theme.py

@@ -24,6 +24,20 @@ class ColorPalette:
     GRID = "lightgray"
     BACKGROUND = "white"
 
+    # Extended palette for categorical charts (e.g., pie charts with many categories)
+    CATEGORICAL = [
+        "#0077B6",  # PRIMARY - Medium Blue
+        "#004C6D",  # RANK_1 - Dark Blue
+        "#008493",  # RANK_2 - Teal
+        "#5AAE95",  # RANK_3 - Sea Green
+        "#9E9E9E",  # RANK_4 - Grey
+        "#D3D3D3",  # NEUTRAL - Light Grey
+        "#003049",  # Dark Navy
+        "#669BBC",  # Light Steel Blue
+        "#A8DADC",  # Pale Cyan
+        "#457B9D",  # Steel Blue
+    ]
+
 
 def jpmc_altair_theme():
     """JPMC brand theme for Altair charts."""

utils.py

@@ -6,6 +6,11 @@ import json
 import re
 import hashlib
 import os
+from io import BytesIO
+
+import imagehash
+from PIL import Image
+
 from plots import JPMCPlotsMixin
 
 
@@ -13,8 +18,12 @@ from pptx import Presentation
 from pptx.enum.shapes import MSO_SHAPE_TYPE
 
 
-def image_alt_text_generator(fpath):
+def image_alt_text_generator(fpath, include_dataset_dirname=False) -> str:
     """convert image file path to alt text
+    
+    Args:
+        fpath (str or Path): path to image file, must start with 'figures/'
+        include_dataset_dirname (bool): whether to include the dataset directory name in the alt text. Recommended to keep False, so that the images do not get tied to a specific dataset export. (Defeats the purpose of assigning alt text to be able to update images when new datasets are exported.)
     """
 
     if not isinstance(fpath, Path):
@@ -23,7 +32,10 @@ def image_alt_text_generator(fpath):
     fparts = fpath.parts
     assert fparts[0] == 'figures', "Image file path must start with 'figures'"
 
-    return Path('/'.join(fparts[2:])).as_posix()
+    if include_dataset_dirname:
+        return Path('/'.join(fparts[1:])).as_posix()
+    else:
+        return Path('/'.join(fparts[2:])).as_posix()
 
 def pptx_replace_named_image(presentation_path, target_tag, new_image_path, save_path):
     """
@@ -117,16 +129,54 @@ def _calculate_file_sha1(file_path: Union[str, Path]) -> str:
     return sha1.hexdigest()
 
 
-def _build_image_hash_map(root_dir: Union[str, Path]) -> dict:
+def _calculate_perceptual_hash(image_source: Union[str, Path, bytes]) -> str:
     """
-    Recursively walk the directory and build a map of SHA1 hashes to file paths.
+    Calculate perceptual hash of an image based on visual content.
+    
+    Uses pHash (perceptual hash) which is robust against:
+    - Metadata differences
+    - Minor compression differences
+    - Small color/contrast variations
+    
+    Args:
+        image_source: File path to image or raw image bytes.
+        
+    Returns:
+        str: Hexadecimal string representation of the perceptual hash.
+    """
+    if isinstance(image_source, bytes):
+        img = Image.open(BytesIO(image_source))
+    else:
+        img = Image.open(image_source)
+    
+    # Convert to RGB if necessary (handles RGBA, P mode, etc.)
+    if img.mode not in ('RGB', 'L'):
+        img = img.convert('RGB')
+    
+    # Use pHash (perceptual hash) - robust against minor differences
+    phash = imagehash.phash(img)
+    return str(phash)
+
+
+def _build_image_hash_map(root_dir: Union[str, Path], use_perceptual_hash: bool = True) -> dict:
+    """
+    Recursively walk the directory and build a map of image hashes to file paths.
     Only includes common image extensions.
+    
+    Args:
+        root_dir: Root directory to scan for images.
+        use_perceptual_hash: If True, uses perceptual hashing (robust against metadata
+            differences). If False, uses SHA1 byte hashing (exact match only).
+    
+    Returns:
+        dict: Mapping of hash strings to file paths.
     """
     hash_map = {}
     valid_extensions = {'.png', '.jpg', '.jpeg', '.tiff', '.bmp', '.gif'}
     
     root = Path(root_dir)
-    print(f"Building image hash map from {root}...")
+    hash_type = "perceptual" if use_perceptual_hash else "SHA1"
+    print(f"Building image hash map from {root} using {hash_type} hashing...")
     
     count = 0
     for root_path, dirs, files in os.walk(root):
@@ -134,9 +184,12 @@ def _build_image_hash_map(root_dir: Union[str, Path]) -> dict:
             file_path = Path(root_path) / file
             if file_path.suffix.lower() in valid_extensions:
                 try:
-                    file_sha1 = _calculate_file_sha1(file_path)
+                    if use_perceptual_hash:
+                        file_hash = _calculate_perceptual_hash(file_path)
+                    else:
+                        file_hash = _calculate_file_sha1(file_path)
                     # We store the absolute path for reference, but we might just need the path relative to project for alt text
-                    hash_map[file_sha1] = file_path
+                    hash_map[file_hash] = file_path
                     count += 1
                 except Exception as e:
                     print(f"Error hashing {file_path}: {e}")
@@ -161,22 +214,25 @@ def _iter_picture_shapes(shapes):
             yield shape
 
 
-def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str, Path], output_path: Union[str, Path] = None):
+def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str, Path], output_path: Union[str, Path] = None, use_perceptual_hash: bool = True):
     """
     Updates the alt text of images in a PowerPoint presentation by matching
-    their content (SHA1 hash) with images in a source directory.
+    their content with images in a source directory.
 
     Args:
         ppt_path (str/Path): Path to the PowerPoint file.
         image_source_dir (str/Path): Directory containing source images to match against.
         output_path (str/Path, optional): Path to save the updated presentation. 
                                           If None, overwrites the input file.
+        use_perceptual_hash (bool): If True (default), uses perceptual hashing which
+            matches images based on visual content (robust against metadata differences,
+            re-compression, etc.). If False, uses SHA1 byte hashing (exact file match only).
     """
     if output_path is None:
         output_path = ppt_path
 
-    # 1. Build lookup map of {sha1: file_path} from the source directory
+    # 1. Build lookup map of {hash: file_path} from the source directory
-    image_hash_map = _build_image_hash_map(image_source_dir)
+    image_hash_map = _build_image_hash_map(image_source_dir, use_perceptual_hash=use_perceptual_hash)
 
     # 2. Open Presentation
     try:
@@ -186,6 +242,7 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
         return
 
     updates_count = 0
+    unmatched_images = []  # Collect unmatched images to report at the end
     slides = list(prs.slides)
     total_slides = len(slides)
 
@@ -197,11 +254,16 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
         
         for shape in picture_shapes:
             try:
-                # shape.image.sha1 returns the SHA1 hash of the image blob
+                # Get image hash based on selected method
-                current_sha1 = shape.image.sha1
+                if use_perceptual_hash:
+                    # Use perceptual hash of the image blob for visual content matching
+                    current_hash = _calculate_perceptual_hash(shape.image.blob)
+                else:
+                    # Use SHA1 hash from python-pptx (exact byte match)
+                    current_hash = shape.image.sha1
                 
-                if current_sha1 in image_hash_map:
+                if current_hash in image_hash_map:
-                    original_path = image_hash_map[current_sha1]
+                    original_path = image_hash_map[current_hash]
                     
                     # Generate Alt Text
                     try:
@@ -246,17 +308,39 @@ def update_ppt_alt_text(ppt_path: Union[str, Path], image_source_dir: Union[str,
                     except Exception as e:
                         print(f"Error updating alt text for {original_path}: {e}")
                 
+                else:
+                    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,
+                        'hash_type': hash_type,
+                        'hash': current_hash
+                    })
+                            
             except AttributeError:
                 continue
             except Exception as e:
                 print(f"Error processing shape on slide {i+1}: {e}")
 
+    # Print summary
+    print("\n" + "="*80)
     if updates_count > 0:
         prs.save(output_path)
-        print(f"Saved updated presentation to {output_path} with {updates_count} updates.")
+        print(f"✓ Saved updated presentation to {output_path} with {updates_count} updates.")
     else:
         print("No images matched or required updates.")
     
+    # List unmatched images at the end
+    if unmatched_images:
+        print(f"\n⚠ {len(unmatched_images)} image(s) not found in source directory:")
+        for img in unmatched_images:
+            print(f"  • Slide {img['slide']}: '{img['shape_name']}' ({img['hash_type']}: {img['hash']})")
+    else:
+        print("\n✓ All images matched successfully!")
+    print("="*80)
+
 
 def extract_voice_label(html_str: str) -> str:
     """
@@ -605,7 +689,7 @@ class JPMCSurvey(JPMCPlotsMixin):
         
         Renames columns using qid_descr_map if provided.
         """
-        QIDs = ['QID1', 'QID2', 'QID3', 'QID4', 'QID13', 'QID14', 'QID15', 'QID16', 'QID17', 'Consumer']
+        QIDs = ['QID1', 'QID2', 'QID3', 'QID4', 'QID7', 'QID13', 'QID14', 'QID15', 'QID16', 'QID17', 'Consumer']
         return self._get_subset(q, QIDs), None
 
 

uv.lock

@@ -556,6 +556,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" },
 ]
 
+[[package]]
+name = "imagehash"
+version = "4.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+    { name = "pillow" },
+    { name = "pywavelets" },
+    { name = "scipy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/cd/de/5c0189b0582e21583c2a213081c35a2501c0f9e51f21f6a52f55fbb9a4ff/ImageHash-4.3.2.tar.gz", hash = "sha256:e54a79805afb82a34acde4746a16540503a9636fd1ffb31d8e099b29bbbf8156", size = 303190, upload-time = "2025-02-01T08:45:39.328Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/31/2c/5f0903a53a62029875aaa3884c38070cc388248a2c1b9aa935632669e5a7/ImageHash-4.3.2-py2.py3-none-any.whl", hash = "sha256:02b0f965f8c77cd813f61d7d39031ea27d4780e7ebcad56c6cd6a709acc06e5f", size = 296657, upload-time = "2025-02-01T08:45:36.102Z" },
+]
+
 [[package]]
 name = "importlib-resources"
 version = "6.5.2"
@@ -1404,6 +1419,7 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
     { name = "altair" },
+    { name = "imagehash" },
     { name = "marimo" },
     { name = "matplotlib" },
     { name = "modin", extra = ["dask"] },
@@ -1412,6 +1428,7 @@ dependencies = [
     { name = "openai" },
     { name = "openpyxl" },
     { name = "pandas" },
+    { name = "pillow" },
     { name = "polars" },
     { name = "pyarrow" },
     { name = "pysqlite3" },
@@ -1426,6 +1443,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
     { name = "altair", specifier = ">=6.0.0" },
+    { name = "imagehash", specifier = ">=4.3.1" },
     { name = "marimo", specifier = ">=0.18.0" },
     { name = "matplotlib", specifier = ">=3.10.8" },
     { name = "modin", extras = ["dask"], specifier = ">=0.37.1" },
@@ -1434,6 +1452,7 @@ requires-dist = [
     { name = "openai", specifier = ">=2.9.0" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=2.3.3" },
+    { name = "pillow", specifier = ">=11.0.0" },
     { name = "polars", specifier = ">=1.37.1" },
     { name = "pyarrow", specifier = ">=23.0.0" },
     { name = "pysqlite3", specifier = ">=0.6.0" },
@@ -1820,6 +1839,57 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/c4/34e93fe5f5429d7570ec1fa436f1986fb1f00c3e0f43a589fe2bbcd22c3f/pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00", size = 509225, upload-time = "2025-03-25T02:24:58.468Z" },
 ]
 
+[[package]]
+name = "pywavelets"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5a/75/50581633d199812205ea8cdd0f6d52f12a624886b74bf1486335b67f01ff/pywavelets-1.9.0.tar.gz", hash = "sha256:148d12203377772bea452a59211d98649c8ee4a05eff019a9021853a36babdc8", size = 3938340, upload-time = "2025-08-04T16:20:04.978Z" }
+wheels = [
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+
 [[package]]
 name = "sentry-sdk"
 version = "2.49.0"

wordclouds.py

@@ -0,0 +1,18 @@
+"""Word cloud utilities for Voice Branding analysis.
+
+The main wordcloud function is available as a method on JPMCSurvey:
+    S.plot_traits_wordcloud(data, column='Top_3_Traits', title='...')
+
+This module provides standalone imports for backwards compatibility.
+"""
+import numpy as np
+from os import path
+from PIL import Image, ImageDraw
+from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
+import matplotlib.pyplot as plt
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+