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male/female colored plots

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This commit is contained in:
Luigi Maiorano
2026-02-03 00:40:51 +01:00
parent 77fdd6e8f6
commit acf9c45844
5 changed files with 394 additions and 111 deletions
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442
plots.py
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@@ -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)
@@ -293,16 +356,33 @@ class QualtricsPlotsMixin:
domain = [stats_df['average'].min(), stats_df['average'].max()]
# Base bar chart - use y2 to explicitly start bars at domain minimum
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)),
y2=alt.datum(domain[0]), # Bars start at domain minimum (bottom edge)
tooltip=[
alt.Tooltip('voice:N', title='Voice'),
alt.Tooltip('average:Q', title='Average', format='.2f'),
alt.Tooltip('count:Q', title='Count')
]
)
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)),
y2=alt.datum(domain[0]), # Bars start at domain minimum (bottom edge)
tooltip=[
alt.Tooltip('voice:N', title='Voice'),
alt.Tooltip('average:Q', title='Average', format='.2f'),
alt.Tooltip('count:Q', title='Count')
]
)
# Text overlay for counts
text = alt.Chart(stats_df).mark_text(
@@ -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})
stats.append({'item': label, 'rank': 'Rank 2', 'count': r2, 'rank1': r1})
stats.append({'item': label, 'rank': 'Rank 3', 'count': r3, 'rank1': r1})
# stats.append({'item': label, 'rank': 'Rank 4 (Worst)', 'count': r4, 'rank1': r1})
gender = self._get_voice_gender(label) if color_gender else None
stats.append({'item': label, 'rank': 'Rank 1 (Best)', 'count': r1, 'total': total, 'gender': gender, 'rank_order': 1})
stats.append({'item': label, 'rank': 'Rank 2', 'count': r2, 'total': total, 'gender': gender, 'rank_order': 2})
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')
@@ -419,25 +506,59 @@ class QualtricsPlotsMixin:
# Interactive legend selection - click to filter
selection = alt.selection_point(fields=['rank'], bind='legend')
chart = alt.Chart(stats_df).mark_bar().encode(
x=alt.X('item:N', title=x_label, sort=alt.EncodingSortField(field='rank1', 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'),
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')
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'
]
).add_params(selection).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
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='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_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')
]
).add_params(selection).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
chart = self._save_plot(chart, title)
return chart
@@ -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)
@@ -474,23 +602,51 @@ class QualtricsPlotsMixin:
.alias('category')
).to_pandas()
# Bar chart with conditional color
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('category:N',
scale=alt.Scale(domain=['Top 3', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip('item:N', title='Item'),
alt.Tooltip('count:Q', title='1st Place Votes')
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
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
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'),
y=alt.Y('count:Q', title=y_label),
color=alt.Color('category:N',
scale=alt.Scale(domain=['Top 3', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip('item:N', title='Item'),
alt.Tooltip('count:Q', title='1st Place Votes')
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
chart = self._save_plot(chart, title)
return chart
@@ -504,19 +660,43 @@ 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()
# Bar chart
bars = alt.Chart(weighted_df).mark_bar(color=color).encode(
x=alt.X('Character:N', title=x_label, sort='-y'),
y=alt.Y('Weighted Score:Q', title=y_label),
tooltip=[
alt.Tooltip('Character:N'),
alt.Tooltip('Weighted Score:Q', title='Score')
]
)
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'),
y=alt.Y('Weighted Score:Q', title=y_label),
tooltip=[
alt.Tooltip('Character:N'),
alt.Tooltip('Weighted Score:Q', title='Score')
]
)
# Text overlay
text = bars.mark_text(
@@ -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:
@@ -573,22 +759,52 @@ class QualtricsPlotsMixin:
.to_pandas()
)
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('category:N',
scale=alt.Scale(domain=['Top 8', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip(f'{target_column}:N', title='Voice'),
alt.Tooltip('count:Q', title='Selections')
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
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
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),
color=alt.Color('category:N',
scale=alt.Scale(domain=['Top 8', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip(f'{target_column}:N', title='Voice'),
alt.Tooltip('count:Q', title='Selections')
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
chart = self._save_plot(chart, title)
return chart
@@ -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:
@@ -629,22 +851,52 @@ class QualtricsPlotsMixin:
.to_pandas()
)
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('category:N',
scale=alt.Scale(domain=['Top 3', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip(f'{target_column}:N', title='Voice'),
alt.Tooltip('count:Q', title='In Top 3')
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
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
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),
color=alt.Color('category:N',
scale=alt.Scale(domain=['Top 3', 'Other'],
range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
legend=None),
tooltip=[
alt.Tooltip(f'{target_column}:N', title='Voice'),
alt.Tooltip('count:Q', title='In Top 3')
]
).properties(
title=self._process_title(title),
width=width or 800,
height=height or getattr(self, 'plot_height', 400)
)
chart = self._save_plot(chart, title)
return chart