857 lines
24 KiB
Python
857 lines
24 KiB
Python
"""Plotting functions for Voice Branding analysis."""
|
|
|
|
import plotly.graph_objects as go
|
|
import polars as pl
|
|
from theme import ColorPalette
|
|
|
|
|
|
|
|
def plot_average_scores_with_counts(
|
|
df: pl.DataFrame,
|
|
title: str = "General Impression (1-10)<br>Per Voice with Number of Participants Who Rated It",
|
|
x_label: str = "Stimuli",
|
|
y_label: str = "Average General Impression Rating (1-10)",
|
|
color: str = ColorPalette.PRIMARY,
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a bar plot showing average scores and count of non-null values for each column.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing numeric columns to analyze.
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
color : str, optional
|
|
Bar color (hex code or named color).
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
# Calculate average and count of non-null values for each column
|
|
# Exclude _recordId column
|
|
stats = []
|
|
for col in [c for c in df.columns if c != '_recordId']:
|
|
avg_score = df[col].mean()
|
|
non_null_count = df[col].drop_nulls().len()
|
|
stats.append({
|
|
'column': col,
|
|
'average': avg_score,
|
|
'count': non_null_count
|
|
})
|
|
|
|
# Sort by average score in descending order
|
|
stats_df = pl.DataFrame(stats).sort('average', descending=True)
|
|
|
|
# Extract voice identifiers from column names (e.g., "V14" from "Voice_Scale_1_10__V14")
|
|
labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
|
|
|
|
# Create the plot
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=labels,
|
|
y=stats_df['average'],
|
|
text=stats_df['count'],
|
|
textposition='inside',
|
|
textfont=dict(size=10, color='black'),
|
|
marker_color=color,
|
|
hovertemplate='<b>%{x}</b><br>Average: %{y:.2f}<br>Count: %{text}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
range=[0, 10],
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
font=dict(size=11)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_top3_ranking_distribution(
|
|
df: pl.DataFrame,
|
|
title: str = "Top 3 Rankings Distribution<br>Count of 1st, 2nd, and 3rd Place Votes per Voice",
|
|
x_label: str = "Voices",
|
|
y_label: str = "Number of Mentions in Top 3",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a stacked bar chart showing how often each voice was ranked 1st, 2nd, or 3rd.
|
|
|
|
The total height of the bar represents the popularity (frequency of being in Top 3),
|
|
while the segments show the quality of those rankings.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing ranking columns (values 1, 2, 3).
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
# Exclude _recordId column
|
|
stats = []
|
|
for col in [c for c in df.columns if c != '_recordId']:
|
|
# Count occurrences of each rank (1, 2, 3)
|
|
# We ensure we're just counting the specific integer values
|
|
rank1 = df.filter(pl.col(col) == 1).height
|
|
rank2 = df.filter(pl.col(col) == 2).height
|
|
rank3 = df.filter(pl.col(col) == 3).height
|
|
total = rank1 + rank2 + rank3
|
|
|
|
# Only include if it received at least one vote (optional, but keeps chart clean)
|
|
if total > 0:
|
|
stats.append({
|
|
'column': col,
|
|
'Rank 1': rank1,
|
|
'Rank 2': rank2,
|
|
'Rank 3': rank3,
|
|
'Total': total
|
|
})
|
|
|
|
# Sort by Total count descending (Most popular overall)
|
|
# Tie-break with Rank 1 count
|
|
stats_df = pl.DataFrame(stats).sort(['Total', 'Rank 1'], descending=[True, True])
|
|
|
|
# Extract voice identifiers from column names
|
|
labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
|
|
|
|
fig = go.Figure()
|
|
|
|
# Add traces for Rank 1, 2, and 3.
|
|
# Stack order: Rank 1 at bottom (Base) -> Rank 2 -> Rank 3
|
|
# This makes it easy to compare the "First Choice" volume across bars.
|
|
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 1 (1st Choice)',
|
|
x=labels,
|
|
y=stats_df['Rank 1'],
|
|
marker_color=ColorPalette.RANK_1,
|
|
hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 2 (2nd Choice)',
|
|
x=labels,
|
|
y=stats_df['Rank 2'],
|
|
marker_color=ColorPalette.RANK_2,
|
|
hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 3 (3rd Choice)',
|
|
x=labels,
|
|
y=stats_df['Rank 3'],
|
|
marker_color=ColorPalette.RANK_3,
|
|
hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
barmode='stack',
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
legend=dict(
|
|
orientation="h",
|
|
yanchor="bottom",
|
|
y=1.02,
|
|
xanchor="right",
|
|
x=1,
|
|
traceorder="normal"
|
|
),
|
|
font=dict(size=11)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_ranking_distribution(
|
|
df: pl.DataFrame,
|
|
title: str = "Rankings Distribution<br>(1st to 4th Place)",
|
|
x_label: str = "Item",
|
|
y_label: str = "Number of Votes",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a stacked bar chart showing the distribution of rankings (1st to 4th) for characters or voices.
|
|
Sorted by the number of Rank 1 votes.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing ranking columns.
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
stats = []
|
|
# Identify ranking columns (assume all columns except _recordId)
|
|
ranking_cols = [c for c in df.columns if c != '_recordId']
|
|
|
|
for col in ranking_cols:
|
|
# Count occurrences of each rank (1, 2, 3, 4)
|
|
# Using height/len to count rows in the filtered frame
|
|
r1 = df.filter(pl.col(col) == 1).height
|
|
r2 = df.filter(pl.col(col) == 2).height
|
|
r3 = df.filter(pl.col(col) == 3).height
|
|
r4 = df.filter(pl.col(col) == 4).height
|
|
total = r1 + r2 + r3 + r4
|
|
|
|
if total > 0:
|
|
stats.append({
|
|
'column': col,
|
|
'Rank 1': r1,
|
|
'Rank 2': r2,
|
|
'Rank 3': r3,
|
|
'Rank 4': r4
|
|
})
|
|
|
|
if not stats:
|
|
return go.Figure()
|
|
|
|
# Sort by Rank 1 (Most "Best" votes) descending to show the winner first
|
|
# Secondary sort by Rank 2
|
|
stats_df = pl.DataFrame(stats).sort(['Rank 1', 'Rank 2'], descending=[True, True])
|
|
|
|
# Clean up labels: Remove prefix and underscores
|
|
# e.g. "Character_Ranking_The_Coach" -> "The Coach"
|
|
labels = [
|
|
col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip()
|
|
for col in stats_df['column']
|
|
]
|
|
|
|
fig = go.Figure()
|
|
|
|
# Rank 1 (Best)
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 1 (Best)',
|
|
x=labels,
|
|
y=stats_df['Rank 1'],
|
|
marker_color=ColorPalette.RANK_1,
|
|
hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
|
|
))
|
|
|
|
# Rank 2
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 2',
|
|
x=labels,
|
|
y=stats_df['Rank 2'],
|
|
marker_color=ColorPalette.RANK_2,
|
|
hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
|
|
))
|
|
|
|
# Rank 3
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 3',
|
|
x=labels,
|
|
y=stats_df['Rank 3'],
|
|
marker_color=ColorPalette.RANK_3,
|
|
hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
|
|
))
|
|
|
|
# Rank 4 (Worst)
|
|
# Using a neutral grey as a fallback for the lowest rank to keep focus on top ranks
|
|
fig.add_trace(go.Bar(
|
|
name='Rank 4 (Worst)',
|
|
x=labels,
|
|
y=stats_df['Rank 4'],
|
|
marker_color=ColorPalette.RANK_4,
|
|
hovertemplate='<b>%{x}</b><br>Rank 4: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
barmode='stack',
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
legend=dict(
|
|
orientation="h",
|
|
yanchor="bottom",
|
|
y=1.02,
|
|
xanchor="right",
|
|
x=1,
|
|
traceorder="normal"
|
|
),
|
|
font=dict(size=11)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_most_ranked_1(
|
|
df: pl.DataFrame,
|
|
title: str = "Most Popular Choice<br>(Number of Times Ranked 1st)",
|
|
x_label: str = "Item",
|
|
y_label: str = "Count of 1st Place Rankings",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a bar chart showing which item (character/voice) was ranked #1 the most.
|
|
Top 3 items are highlighted.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing ranking columns.
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
stats = []
|
|
# Identify ranking columns (assume all columns except _recordId)
|
|
ranking_cols = [c for c in df.columns if c != '_recordId']
|
|
|
|
for col in ranking_cols:
|
|
# Count occurrences of rank 1
|
|
count_rank_1 = df.filter(pl.col(col) == 1).height
|
|
|
|
stats.append({
|
|
'column': col,
|
|
'count': count_rank_1
|
|
})
|
|
|
|
# Sort by count descending
|
|
stats_df = pl.DataFrame(stats).sort('count', descending=True)
|
|
|
|
# Clean up labels
|
|
labels = [
|
|
col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip()
|
|
for col in stats_df['column']
|
|
]
|
|
|
|
# Assign colors: Top 3 get PRIMARY (Blue), others get NEUTRAL (Grey)
|
|
colors = [
|
|
ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL
|
|
for i in range(len(stats_df))
|
|
]
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=labels,
|
|
y=stats_df['count'],
|
|
text=stats_df['count'],
|
|
textposition='inside',
|
|
textfont=dict(size=10, color='white'),
|
|
marker_color=colors,
|
|
hovertemplate='<b>%{x}</b><br>1st Place Votes: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
font=dict(size=11)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
|
|
def plot_weighted_ranking_score(
|
|
weighted_df: pl.DataFrame,
|
|
title: str = "Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)",
|
|
x_label: str = "Character Personality",
|
|
y_label: str = "Total Weighted Score",
|
|
color: str = ColorPalette.PRIMARY,
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a bar chart showing the weighted ranking score for each character.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing ranking columns.
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
color : str, optional
|
|
Bar color.
|
|
height : int, optional
|
|
Plot height.
|
|
width : int, optional
|
|
Plot width.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=weighted_df['Character'],
|
|
y=weighted_df['Weighted Score'],
|
|
text=weighted_df['Weighted Score'],
|
|
textposition='inside',
|
|
textfont=dict(size=11, color='white'),
|
|
marker_color=color,
|
|
hovertemplate='<b>%{x}</b><br>Score: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
font=dict(size=11)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_voice_selection_counts(
|
|
df: pl.DataFrame,
|
|
target_column: str = "8_Combined",
|
|
title: str = "Most Frequently Chosen Voices<br>(Top 8 Highlighted)",
|
|
x_label: str = "Voice",
|
|
y_label: str = "Number of Times Chosen",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Create a bar plot showing the frequency of voice selections.
|
|
Takes a column containing comma-separated values (e.g. "Voice 1, Voice 2..."),
|
|
counts occurrences, and highlights the top 8 most frequent voices.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing the selection column.
|
|
target_column : str, optional
|
|
Name of the column containing comma-separated voice selections.
|
|
Defaults to "8_Combined".
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
if target_column not in df.columns:
|
|
return go.Figure()
|
|
|
|
# Process the data:
|
|
# 1. Select the relevant column and remove nulls
|
|
# 2. Split the comma-separated string into a list
|
|
# 3. Explode the list so each voice gets its own row
|
|
# 4. Strip whitespace ensuring "Voice 1" and " Voice 1" match
|
|
# 5. Count occurrences
|
|
stats_df = (
|
|
df.select(pl.col(target_column))
|
|
.drop_nulls()
|
|
.with_columns(pl.col(target_column).str.split(","))
|
|
.explode(target_column)
|
|
.with_columns(pl.col(target_column).str.strip_chars())
|
|
.filter(pl.col(target_column) != "")
|
|
.group_by(target_column)
|
|
.agg(pl.len().alias("count"))
|
|
.sort("count", descending=True)
|
|
)
|
|
|
|
# Define colors: Top 8 get PRIMARY, rest get NEUTRAL
|
|
colors = [
|
|
ColorPalette.PRIMARY if i < 8 else ColorPalette.NEUTRAL
|
|
for i in range(len(stats_df))
|
|
]
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=stats_df[target_column],
|
|
y=stats_df['count'],
|
|
text=stats_df['count'],
|
|
textposition='outside',
|
|
marker_color=colors,
|
|
hovertemplate='<b>%{x}</b><br>Selections: %{y}<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
font=dict(size=11),
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_top3_selection_counts(
|
|
df: pl.DataFrame,
|
|
target_column: str = "3_Ranked",
|
|
title: str = "Most Frequently Chosen Top 3 Voices<br>(Top 3 Highlighted)",
|
|
x_label: str = "Voice",
|
|
y_label: str = "Count of Mentions in Top 3",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Question: Which 3 voices are chosen the most out of 18?
|
|
|
|
How many times does each voice end up in the top 3?
|
|
(this is based on the survey question where participants need to choose 3 out
|
|
of the earlier selected 8 voices). So how often each of the 18 stimuli ended
|
|
up in participants' Top 3, after they first selected 8 out of 18.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing the ranking column (comma-separated strings).
|
|
target_column : str, optional
|
|
Name of the column containing comma-separated Top 3 voice elections.
|
|
Defaults to "3_Ranked".
|
|
title : str, optional
|
|
Plot title.
|
|
x_label : str, optional
|
|
X-axis label.
|
|
y_label : str, optional
|
|
Y-axis label.
|
|
height : int, optional
|
|
Plot height in pixels.
|
|
width : int, optional
|
|
Plot width in pixels.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
if target_column not in df.columns:
|
|
return go.Figure()
|
|
|
|
# Process the data:
|
|
# Same logic as plot_voice_selection_counts: explode comma-separated string
|
|
stats_df = (
|
|
df.select(pl.col(target_column))
|
|
.drop_nulls()
|
|
.with_columns(pl.col(target_column).str.split(","))
|
|
.explode(target_column)
|
|
.with_columns(pl.col(target_column).str.strip_chars())
|
|
.filter(pl.col(target_column) != "")
|
|
.group_by(target_column)
|
|
.agg(pl.len().alias("count"))
|
|
.sort("count", descending=True)
|
|
)
|
|
|
|
# Define colors: Top 3 get PRIMARY, rest get NEUTRAL
|
|
colors = [
|
|
ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL
|
|
for i in range(len(stats_df))
|
|
]
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
x=stats_df[target_column],
|
|
y=stats_df['count'],
|
|
text=stats_df['count'],
|
|
textposition='outside',
|
|
marker_color=colors,
|
|
hovertemplate='<b>%{x}</b><br>In Top 3: %{y} times<extra></extra>'
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=title,
|
|
xaxis_title=x_label,
|
|
yaxis_title=y_label,
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
tickangle=-45
|
|
),
|
|
yaxis=dict(
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID
|
|
),
|
|
font=dict(size=11),
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
def plot_speaking_style_trait_scores(
|
|
df: pl.DataFrame,
|
|
trait_description: str = None,
|
|
left_anchor: str = None,
|
|
right_anchor: str = None,
|
|
title: str = "Speaking Style Trait Analysis",
|
|
height: int = 500,
|
|
width: int = 1000,
|
|
) -> go.Figure:
|
|
"""
|
|
Plot scores for a single speaking style trait across multiple voices.
|
|
|
|
The plot shows the average score per Voice, sorted by score.
|
|
It expects the DataFrame to contain 'Voice' and 'score' columns,
|
|
typically filtered for a single trait/description.
|
|
|
|
Parameters
|
|
----------
|
|
df : pl.DataFrame
|
|
DataFrame containing at least 'Voice' and 'score' columns.
|
|
Produced by utils.process_speaking_style_data and filtered.
|
|
trait_description : str, optional
|
|
Description of the trait being analyzed (e.g. "Indifferent : Attentive").
|
|
If not provided, it will be constructed from annotations.
|
|
left_anchor : str, optional
|
|
Label for the lower end of the scale (e.g. "Indifferent").
|
|
If not provided, attempts to read 'Left_Anchor' column from df.
|
|
right_anchor : str, optional
|
|
Label for the upper end of the scale (e.g. "Attentive").
|
|
If not provided, attempts to read 'Right_Anchor' column from df.
|
|
title : str, optional
|
|
Plot title.
|
|
height : int, optional
|
|
Plot height.
|
|
width : int, optional
|
|
Plot width.
|
|
|
|
Returns
|
|
-------
|
|
go.Figure
|
|
Plotly figure object.
|
|
"""
|
|
if df.is_empty():
|
|
return go.Figure()
|
|
|
|
required_cols = ["Voice", "score"]
|
|
if not all(col in df.columns for col in required_cols):
|
|
return go.Figure()
|
|
|
|
# Calculate stats: Mean, Count
|
|
stats = (
|
|
df.filter(pl.col("score").is_not_null())
|
|
.group_by("Voice")
|
|
.agg([
|
|
pl.col("score").mean().alias("mean_score"),
|
|
pl.col("score").count().alias("count")
|
|
])
|
|
.sort("mean_score", descending=False) # Ascending for display bottom-to-top
|
|
)
|
|
|
|
# Attempt to extract anchors from DF if not provided
|
|
if (left_anchor is None or right_anchor is None) and "Left_Anchor" in df.columns:
|
|
head = df.filter(pl.col("Left_Anchor").is_not_null()).head(1)
|
|
if not head.is_empty():
|
|
if left_anchor is None: left_anchor = head["Left_Anchor"][0]
|
|
if right_anchor is None: right_anchor = head["Right_Anchor"][0]
|
|
|
|
if trait_description is None:
|
|
if left_anchor and right_anchor:
|
|
trait_description = f"{left_anchor.split('|')[0]} vs. {right_anchor.split('|')[0]}"
|
|
else:
|
|
# Try getting from Description column
|
|
if "Description" in df.columns:
|
|
head = df.filter(pl.col("Description").is_not_null()).head(1)
|
|
if not head.is_empty():
|
|
trait_description = head["Description"][0]
|
|
else:
|
|
trait_description = ""
|
|
else:
|
|
trait_description = ""
|
|
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(go.Bar(
|
|
y=stats["Voice"], # Y is Voice
|
|
x=stats["mean_score"], # X is Score
|
|
orientation='h',
|
|
text=stats["count"],
|
|
textposition='inside',
|
|
textangle=0,
|
|
textfont=dict(size=16, color='white'),
|
|
texttemplate='%{text}', # Count on bar
|
|
marker_color=ColorPalette.PRIMARY,
|
|
hovertemplate='<b>%{y}</b><br>Average: %{x:.2f}<br>Count: %{text}<extra></extra>'
|
|
))
|
|
|
|
# Add annotations for anchors
|
|
annotations = []
|
|
|
|
# Place anchors at the bottom
|
|
if left_anchor:
|
|
annotations.append(dict(
|
|
xref='x', yref='paper',
|
|
x=1, y=-0.2, # Below axis
|
|
xanchor='left', yanchor='top',
|
|
text=f"<b>1: {left_anchor.split('|')[0]}</b>",
|
|
showarrow=False,
|
|
font=dict(size=10, color='gray')
|
|
))
|
|
if right_anchor:
|
|
annotations.append(dict(
|
|
xref='x', yref='paper',
|
|
x=5, y=-0.2, # Below axis
|
|
xanchor='right', yanchor='top',
|
|
text=f"<b>5: {right_anchor.split('|')[0]}</b>",
|
|
showarrow=False,
|
|
font=dict(size=10, color='gray')
|
|
))
|
|
|
|
fig.update_layout(
|
|
title=dict(
|
|
text=f"{title}<br><sub>{trait_description}</sub><br><sub>(Numbers on bars indicate respondent count)</sub>",
|
|
y=0.92
|
|
),
|
|
xaxis_title="Average Score (1-5)",
|
|
yaxis_title="Voice",
|
|
height=height,
|
|
width=width,
|
|
plot_bgcolor=ColorPalette.BACKGROUND,
|
|
xaxis=dict(
|
|
range=[1, 5],
|
|
showgrid=True,
|
|
gridcolor=ColorPalette.GRID,
|
|
zeroline=False
|
|
),
|
|
yaxis=dict(
|
|
showgrid=False
|
|
),
|
|
margin=dict(b=120),
|
|
annotations=annotations,
|
|
font=dict(size=11)
|
|
)
|
|
return fig
|