male/female colored plots

03_quant_report.py

@@ -458,6 +458,12 @@ def _():
     return
 
 
+@app.cell
+def _():
+    COLOR_GENDER = True
+    return (COLOR_GENDER,)
+
+
 @app.cell
 def _():
     mo.md(r"""
@@ -473,8 +479,8 @@ def _(S, data):
 
 
 @app.cell
-def _(S, v_18_8_3):
+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')
+    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
 
 
@@ -487,8 +493,8 @@ def _():
 
 
 @app.cell
-def _(S, v_18_8_3):
+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')
+    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
 
 
@@ -508,8 +514,8 @@ def _(S, data):
 
 
 @app.cell
-def _(S, top3_voices_weighted):
+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)")
+    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
 
 
@@ -524,8 +530,8 @@ def _():
 
 
 @app.cell
-def _(S, top3_voices):
+def _(COLOR_GENDER, S, top3_voices):
-    S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Voice Rankings (1st, 2nd, 3rd)")
+    S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Top 3 Voice Rankings (1st, 2nd, 3rd)", color_gender=COLOR_GENDER)
     return
 
 
@@ -580,8 +586,8 @@ def _():
 
 
 @app.cell
-def _(S, top3_voices):
+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')
+    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
 
 
@@ -594,10 +600,10 @@ def _():
 
 
 @app.cell
-def _(S, data):
+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)")
+    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,)
 
 

04_PPTX_Update_Images.py

@@ -21,8 +21,8 @@ def _():
 
 @app.cell
 def _():
-    TAG_SOURCE = Path('data/reports/Perception-Research-Report_2-2_tagged.pptx')
+    TAG_SOURCE = Path('data/reports/Perception-Research-Report_2-2.pptx')
-    TAG_TARGET = Path('data/reports/Perception-Research-Report_2-2_tagged_2.pptx')
+    TAG_TARGET = Path('data/reports/Perception-Research-Report_2-2_tagged.pptx')
     TAG_IMAGE_DIR = Path('figures/2-2-26')
     return TAG_IMAGE_DIR, TAG_SOURCE, TAG_TARGET
 
@@ -43,8 +43,8 @@ def _():
 
 @app.cell
 def _():
-    REPLACE_SOURCE = Path('data/reports/Perception-Research-Report_2-2_tagged_2.pptx')
+    REPLACE_SOURCE = Path('data/reports/Perception-Research-Report_2-2.pptx')
-    REPLACE_TARGET = Path('data/reports/Perception-Research-Report_2-2.pptx')
+    REPLACE_TARGET = Path('data/reports/Perception-Research-Report_2-2_updated.pptx')
 
     NEW_IMAGES_DIR = Path('figures/2-2-26')
     return NEW_IMAGES_DIR, REPLACE_SOURCE, REPLACE_TARGET

plots.py

@@ -8,6 +8,7 @@ import altair as alt
 import pandas as pd
 import polars as pl
 from theme import ColorPalette
+from reference import VOICE_GENDER_MAPPING
 
 import hashlib
 
@@ -260,6 +261,61 @@ class QualtricsPlotsMixin:
         label = label.replace('_', ' ').strip()
         return label
 
+    def _get_voice_gender(self, voice_label: str) -> str:
+        """Get the gender of a voice from its label.
+        
+        Parameters:
+            voice_label: Voice label (e.g., 'V14', 'Voice 14', etc.)
+            
+        Returns:
+            'Male' or 'Female', defaults to 'Male' if not found
+        """
+        # Extract voice code (e.g., 'V14' from 'Voice 14' or 'V14')
+        voice_code = None
+        
+        # Try to find VXX pattern
+        match = re.search(r'V(\d+)', voice_label)
+        if match:
+            voice_code = f"V{match.group(1)}"
+        else:
+            # Try to extract number and prepend V
+            match = re.search(r'(\d+)', voice_label)
+            if match:
+                voice_code = f"V{match.group(1)}"
+        
+        if voice_code and voice_code in VOICE_GENDER_MAPPING:
+            return VOICE_GENDER_MAPPING[voice_code]
+        
+        return "Male"  # Default to Male if unknown
+
+    def _get_gender_color(self, gender: str, color_type: str = "primary") -> str:
+        """Get the appropriate color based on gender.
+        
+        Parameters:
+            gender: 'Male' or 'Female'
+            color_type: One of 'primary', 'rank_1', 'rank_2', 'rank_3', 'neutral'
+            
+        Returns:
+            Hex color string
+        """
+        color_map = {
+            "Male": {
+                "primary": ColorPalette.GENDER_MALE,
+                "rank_1": ColorPalette.GENDER_MALE_RANK_1,
+                "rank_2": ColorPalette.GENDER_MALE_RANK_2,
+                "rank_3": ColorPalette.GENDER_MALE_RANK_3,
+                "neutral": ColorPalette.GENDER_MALE_NEUTRAL,
+            },
+            "Female": {
+                "primary": ColorPalette.GENDER_FEMALE,
+                "rank_1": ColorPalette.GENDER_FEMALE_RANK_1,
+                "rank_2": ColorPalette.GENDER_FEMALE_RANK_2,
+                "rank_3": ColorPalette.GENDER_FEMALE_RANK_3,
+                "neutral": ColorPalette.GENDER_FEMALE_NEUTRAL,
+            }
+        }
+        return color_map.get(gender, color_map["Male"]).get(color_type, ColorPalette.PRIMARY)
+
     def plot_average_scores_with_counts(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
@@ -270,8 +326,13 @@ class QualtricsPlotsMixin:
         height: int | None = None,
         width: int | str | None = None,
         domain: list[float] | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Create a bar plot showing average scores and count of non-null values for each column."""
+        """Create a bar plot showing average scores and count of non-null values for each column.
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender (blue=male, pink=female).
+        """
         df = self._ensure_dataframe(data)
 
         # Calculate stats for each column (exclude _recordId)
@@ -280,10 +341,12 @@ class QualtricsPlotsMixin:
             avg_score = df[col].mean()
             non_null_count = df[col].drop_nulls().len()
             label = self._clean_voice_label(col)
+            gender = self._get_voice_gender(label) if color_gender else None
             stats.append({
                 'voice': label,
                 'average': avg_score,
-                'count': non_null_count
+                'count': non_null_count,
+                'gender': gender
             })
 
         # Convert to pandas for Altair (sort by average descending)
@@ -293,6 +356,23 @@ class QualtricsPlotsMixin:
             domain = [stats_df['average'].min(), stats_df['average'].max()]
 
         # Base bar chart - use y2 to explicitly start bars at domain minimum
+        if color_gender:
+            bars = alt.Chart(stats_df).mark_bar().encode(
+                x=alt.X('voice:N', title=x_label, sort='-y'),
+                y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=domain)),
+                y2=alt.datum(domain[0]),  # Bars start at domain minimum (bottom edge)
+                color=alt.Color('gender:N',
+                               scale=alt.Scale(domain=['Male', 'Female'],
+                                             range=[ColorPalette.GENDER_MALE, ColorPalette.GENDER_FEMALE]),
+                               legend=alt.Legend(orient='top', direction='horizontal', title='Gender')),
+                tooltip=[
+                    alt.Tooltip('voice:N', title='Voice'),
+                    alt.Tooltip('average:Q', title='Average', format='.2f'),
+                    alt.Tooltip('count:Q', title='Count'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            )
+        else:
             bars = alt.Chart(stats_df).mark_bar(color=color).encode(
                 x=alt.X('voice:N', title=x_label, sort='-y'),
                 y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=domain)),
@@ -390,8 +470,14 @@ class QualtricsPlotsMixin:
         y_label: str = "Number of Votes",
         height: int | None = None,
         width: int | str | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Create a stacked bar chart showing the distribution of rankings (1st to 3rd)."""
+        """Create a stacked bar chart showing the distribution of rankings (1st to 3rd).
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender with rank intensity
+                         (blue shades=male, pink shades=female).
+        """
         df = self._ensure_dataframe(data)
 
         stats = []
@@ -406,10 +492,11 @@ class QualtricsPlotsMixin:
 
             if total > 0:
                 label = self._clean_voice_label(col)
-                stats.append({'item': label, 'rank': 'Rank 1 (Best)', 'count': r1, 'rank1': r1})
+                gender = self._get_voice_gender(label) if color_gender else None
-                stats.append({'item': label, 'rank': 'Rank 2', 'count': r2, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 1 (Best)', 'count': r1, 'total': total, 'gender': gender, 'rank_order': 1})
-                stats.append({'item': label, 'rank': 'Rank 3', 'count': r3, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 2', 'count': r2, 'total': total, 'gender': gender, 'rank_order': 2})
-                # stats.append({'item': label, 'rank': 'Rank 4 (Worst)', 'count': r4, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 3', 'count': r3, 'total': total, 'gender': gender, 'rank_order': 3})
+                # stats.append({'item': label, 'rank': 'Rank 4 (Worst)', 'count': r4, 'total': total, 'gender': gender, 'rank_order': 4})
 
         if not stats:
             return alt.Chart(pd.DataFrame({'text': ['No data']})).mark_text().encode(text='text:N')
@@ -419,14 +506,48 @@ class QualtricsPlotsMixin:
         # Interactive legend selection - click to filter
         selection = alt.selection_point(fields=['rank'], bind='legend')
 
+        if color_gender:
+            # Add gender_rank column for combined color encoding
+            stats_df['gender_rank'] = stats_df['gender'] + ' - ' + stats_df['rank']
+            
+            # Define combined domain and range for gender + rank
+            domain = [
+                'Male - Rank 1 (Best)', 'Male - Rank 2', 'Male - Rank 3',
+                'Female - Rank 1 (Best)', 'Female - Rank 2', 'Female - Rank 3'
+            ]
+            range_colors = [
+                ColorPalette.GENDER_MALE_RANK_1, ColorPalette.GENDER_MALE_RANK_2, ColorPalette.GENDER_MALE_RANK_3,
+                ColorPalette.GENDER_FEMALE_RANK_1, ColorPalette.GENDER_FEMALE_RANK_2, ColorPalette.GENDER_FEMALE_RANK_3
+            ]
+            
             chart = alt.Chart(stats_df).mark_bar().encode(
-            x=alt.X('item:N', title=x_label, sort=alt.EncodingSortField(field='rank1', order='descending')),
+                x=alt.X('item:N', title=x_label, sort=alt.EncodingSortField(field='total', order='descending')),
+                y=alt.Y('count:Q', title=y_label, stack='zero'),
+                color=alt.Color('gender_rank:N',
+                               scale=alt.Scale(domain=domain, range=range_colors),
+                               legend=alt.Legend(orient='top', direction='horizontal', title=None, columns=3)),
+                order=alt.Order('rank_order:Q', sort='ascending'),
+                opacity=alt.condition(selection, alt.value(1), alt.value(0.2)),
+                tooltip=[
+                    alt.Tooltip('item:N', title='Item'),
+                    alt.Tooltip('rank:N', title='Rank'),
+                    alt.Tooltip('count:Q', title='Count'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            ).add_params(selection).properties(
+                title=self._process_title(title),
+                width=width or 800,
+                height=height or getattr(self, 'plot_height', 400)
+            )
+        else:
+            chart = alt.Chart(stats_df).mark_bar().encode(
+                x=alt.X('item:N', title=x_label, sort=alt.EncodingSortField(field='total', order='descending')),
                 y=alt.Y('count:Q', title=y_label, stack='zero'),
                 color=alt.Color('rank:N',
                                scale=alt.Scale(domain=['Rank 1 (Best)', 'Rank 2', 'Rank 3'],
                                              range=[ColorPalette.RANK_1, ColorPalette.RANK_2, ColorPalette.RANK_3]),
                                legend=alt.Legend(orient='top', direction='horizontal', title=None)),
-            order=alt.Order('rank:N', sort='ascending'),
+                order=alt.Order('rank_order:Q', sort='ascending'),
                 opacity=alt.condition(selection, alt.value(1), alt.value(0.2)),
                 tooltip=[
                     alt.Tooltip('item:N', title='Item'),
@@ -450,8 +571,14 @@ class QualtricsPlotsMixin:
         y_label: str = "Count of 1st Place Rankings",
         height: int | None = None,
         width: int | str | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Create a bar chart showing which item was ranked #1 the most. Top 3 highlighted."""
+        """Create a bar chart showing which item was ranked #1 the most. Top 3 highlighted.
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender with highlight/neutral intensity
+                         (blue shades=male, pink shades=female).
+        """
         df = self._ensure_dataframe(data)
 
         stats = []
@@ -460,7 +587,8 @@ class QualtricsPlotsMixin:
         for col in ranking_cols:
             count_rank_1 = df.filter(pl.col(col) == 1).height
             label = self._clean_voice_label(col)
-            stats.append({'item': label, 'count': count_rank_1})
+            gender = self._get_voice_gender(label) if color_gender else None
+            stats.append({'item': label, 'count': count_rank_1, 'gender': gender})
 
         # Convert and sort
         stats_df = pl.DataFrame(stats).sort('count', descending=True)
@@ -474,6 +602,34 @@ class QualtricsPlotsMixin:
             .alias('category')
         ).to_pandas()
 
+        if color_gender:
+            # Add gender_category column for combined color encoding
+            stats_df['gender_category'] = stats_df['gender'] + ' - ' + stats_df['category']
+            
+            # Define combined domain and range for gender + category
+            domain = ['Male - Top 3', 'Male - Other', 'Female - Top 3', 'Female - Other']
+            range_colors = [
+                ColorPalette.GENDER_MALE, ColorPalette.GENDER_MALE_NEUTRAL,
+                ColorPalette.GENDER_FEMALE, ColorPalette.GENDER_FEMALE_NEUTRAL
+            ]
+            
+            chart = alt.Chart(stats_df).mark_bar().encode(
+                x=alt.X('item:N', title=x_label, sort='-y'),
+                y=alt.Y('count:Q', title=y_label),
+                color=alt.Color('gender_category:N',
+                               scale=alt.Scale(domain=domain, range=range_colors),
+                               legend=alt.Legend(orient='top', direction='horizontal', title=None)),
+                tooltip=[
+                    alt.Tooltip('item:N', title='Item'),
+                    alt.Tooltip('count:Q', title='1st Place Votes'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            ).properties(
+                title=self._process_title(title),
+                width=width or 800,
+                height=height or getattr(self, 'plot_height', 400)
+            )
+        else:
             # Bar chart with conditional color
             chart = alt.Chart(stats_df).mark_bar().encode(
                 x=alt.X('item:N', title=x_label, sort='-y'),
@@ -504,10 +660,34 @@ class QualtricsPlotsMixin:
         color: str = ColorPalette.PRIMARY,
         height: int | None = None,
         width: int | str | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Create a bar chart showing the weighted ranking score for each character."""
+        """Create a bar chart showing the weighted ranking score for each character.
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender (blue=male, pink=female).
+        """
         weighted_df = self._ensure_dataframe(data).to_pandas()
 
+        if color_gender:
+            # Add gender column based on Character name
+            weighted_df['gender'] = weighted_df['Character'].apply(self._get_voice_gender)
+            
+            # Bar chart with gender coloring
+            bars = alt.Chart(weighted_df).mark_bar().encode(
+                x=alt.X('Character:N', title=x_label, sort='-y'),
+                y=alt.Y('Weighted Score:Q', title=y_label),
+                color=alt.Color('gender:N',
+                               scale=alt.Scale(domain=['Male', 'Female'],
+                                             range=[ColorPalette.GENDER_MALE, ColorPalette.GENDER_FEMALE]),
+                               legend=alt.Legend(orient='top', direction='horizontal', title='Gender')),
+                tooltip=[
+                    alt.Tooltip('Character:N'),
+                    alt.Tooltip('Weighted Score:Q', title='Score'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            )
+        else:
             # Bar chart
             bars = alt.Chart(weighted_df).mark_bar(color=color).encode(
                 x=alt.X('Character:N', title=x_label, sort='-y'),
@@ -545,8 +725,14 @@ class QualtricsPlotsMixin:
         y_label: str = "Number of Times Chosen",
         height: int | None = None,
         width: int | str | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Create a bar plot showing the frequency of voice selections."""
+        """Create a bar plot showing the frequency of voice selections.
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender with highlight/neutral intensity
+                         (blue shades=male, pink shades=female).
+        """
         df = self._ensure_dataframe(data)
 
         if target_column not in df.columns:
@@ -573,6 +759,36 @@ class QualtricsPlotsMixin:
             .to_pandas()
         )
 
+        if color_gender:
+            # Add gender column based on voice label
+            stats_df['gender'] = stats_df[target_column].apply(self._get_voice_gender)
+            # Add gender_category column for combined color encoding
+            stats_df['gender_category'] = stats_df['gender'] + ' - ' + stats_df['category']
+            
+            # Define combined domain and range for gender + category
+            domain = ['Male - Top 8', 'Male - Other', 'Female - Top 8', 'Female - Other']
+            range_colors = [
+                ColorPalette.GENDER_MALE, ColorPalette.GENDER_MALE_NEUTRAL,
+                ColorPalette.GENDER_FEMALE, ColorPalette.GENDER_FEMALE_NEUTRAL
+            ]
+            
+            chart = alt.Chart(stats_df).mark_bar().encode(
+                x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
+                y=alt.Y('count:Q', title=y_label),
+                color=alt.Color('gender_category:N',
+                               scale=alt.Scale(domain=domain, range=range_colors),
+                               legend=alt.Legend(orient='top', direction='horizontal', title=None)),
+                tooltip=[
+                    alt.Tooltip(f'{target_column}:N', title='Voice'),
+                    alt.Tooltip('count:Q', title='Selections'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            ).properties(
+                title=self._process_title(title),
+                width=width or 800,
+                height=height or getattr(self, 'plot_height', 400)
+            )
+        else:
             chart = alt.Chart(stats_df).mark_bar().encode(
                 x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
                 y=alt.Y('count:Q', title=y_label),
@@ -602,8 +818,14 @@ class QualtricsPlotsMixin:
         y_label: str = "Count of Mentions in Top 3",
         height: int | None = None,
         width: int | str | None = None,
+        color_gender: bool = False,
     ) -> alt.Chart:
-        """Question: Which 3 voices are chosen the most out of 18?"""
+        """Question: Which 3 voices are chosen the most out of 18?
+        
+        Parameters:
+            color_gender: If True, color bars by voice gender with highlight/neutral intensity
+                         (blue shades=male, pink shades=female).
+        """
         df = self._ensure_dataframe(data)
 
         if target_column not in df.columns:
@@ -629,6 +851,36 @@ class QualtricsPlotsMixin:
             .to_pandas()
         )
 
+        if color_gender:
+            # Add gender column based on voice label
+            stats_df['gender'] = stats_df[target_column].apply(self._get_voice_gender)
+            # Add gender_category column for combined color encoding
+            stats_df['gender_category'] = stats_df['gender'] + ' - ' + stats_df['category']
+            
+            # Define combined domain and range for gender + category
+            domain = ['Male - Top 3', 'Male - Other', 'Female - Top 3', 'Female - Other']
+            range_colors = [
+                ColorPalette.GENDER_MALE, ColorPalette.GENDER_MALE_NEUTRAL,
+                ColorPalette.GENDER_FEMALE, ColorPalette.GENDER_FEMALE_NEUTRAL
+            ]
+            
+            chart = alt.Chart(stats_df).mark_bar().encode(
+                x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
+                y=alt.Y('count:Q', title=y_label),
+                color=alt.Color('gender_category:N',
+                               scale=alt.Scale(domain=domain, range=range_colors),
+                               legend=alt.Legend(orient='top', direction='horizontal', title=None)),
+                tooltip=[
+                    alt.Tooltip(f'{target_column}:N', title='Voice'),
+                    alt.Tooltip('count:Q', title='In Top 3'),
+                    alt.Tooltip('gender:N', title='Gender')
+                ]
+            ).properties(
+                title=self._process_title(title),
+                width=width or 800,
+                height=height or getattr(self, 'plot_height', 400)
+            )
+        else:
             chart = alt.Chart(stats_df).mark_bar().encode(
                 x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
                 y=alt.Y('count:Q', title=y_label),

theme.py

@@ -59,6 +59,24 @@ 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
+
 
 def jpmc_altair_theme():
     """JPMC brand theme for Altair charts."""

utils.py

@@ -508,9 +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,