speaking style trait scores vertical
This commit is contained in:
384
plots.py
384
plots.py
@@ -216,22 +216,22 @@ def plot_top3_ranking_distribution(
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return fig
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def plot_character_ranking_distribution(
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def plot_ranking_distribution(
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df: pl.DataFrame,
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title: str = "Character Personality Rankings<br>Distribution of Votes (1st to 4th Place)",
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x_label: str = "Character Personality",
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title: str = "Rankings Distribution<br>(1st to 4th Place)",
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x_label: str = "Item",
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y_label: str = "Number of Votes",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a stacked bar chart showing the distribution of rankings (1st to 4th) for character personalities.
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Sorted by the number of Rank 1 votes to highlight the 'Best' options.
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Create a stacked bar chart showing the distribution of rankings (1st to 4th) for characters or voices.
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Sorted by the number of Rank 1 votes.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing character ranking columns (prefix 'Character_Ranking').
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DataFrame containing ranking columns.
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title : str, optional
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Plot title.
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x_label : str, optional
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@@ -249,8 +249,8 @@ def plot_character_ranking_distribution(
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Plotly figure object.
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"""
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stats = []
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# Identify columns related to Character Ranking (excluding ID)
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ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
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# Identify ranking columns (assume all columns except _recordId)
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ranking_cols = [c for c in df.columns if c != '_recordId']
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for col in ranking_cols:
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# Count occurrences of each rank (1, 2, 3, 4)
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@@ -280,7 +280,7 @@ def plot_character_ranking_distribution(
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# Clean up labels: Remove prefix and underscores
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# e.g. "Character_Ranking_The_Coach" -> "The Coach"
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labels = [
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col.replace('Character_Ranking_', '').replace('_', ' ').strip()
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col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip()
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for col in stats_df['column']
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]
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@@ -354,21 +354,22 @@ def plot_character_ranking_distribution(
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return fig
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def plot_most_ranked_1_character(
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def plot_most_ranked_1(
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df: pl.DataFrame,
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title: str = "Most Popular Character Personality<br>(Number of Times Ranked 1st)",
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x_label: str = "Character Personality",
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title: str = "Most Popular Choice<br>(Number of Times Ranked 1st)",
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x_label: str = "Item",
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y_label: str = "Count of 1st Place Rankings",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a bar chart showing which character personality was ranked #1 the most.
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Create a bar chart showing which item (character/voice) was ranked #1 the most.
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Top 3 items are highlighted.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing character ranking columns.
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DataFrame containing ranking columns.
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title : str, optional
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Plot title.
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x_label : str, optional
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@@ -386,8 +387,8 @@ def plot_most_ranked_1_character(
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Plotly figure object.
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"""
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stats = []
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# Identify columns related to Character Ranking
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ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
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# Identify ranking columns (assume all columns except _recordId)
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ranking_cols = [c for c in df.columns if c != '_recordId']
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for col in ranking_cols:
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# Count occurrences of rank 1
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@@ -403,19 +404,25 @@ def plot_most_ranked_1_character(
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# Clean up labels
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labels = [
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col.replace('Character_Ranking_', '').replace('_', ' ').strip()
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col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip()
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for col in stats_df['column']
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]
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fig = go.Figure()
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# Assign colors: Top 3 get PRIMARY (Blue), others get NEUTRAL (Grey)
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colors = [
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ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL
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for i in range(len(stats_df))
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]
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=labels,
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y=stats_df['count'],
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text=stats_df['count'],
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textposition='inside',
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textfont=dict(size=10, color='white'),
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marker_color=ColorPalette.PRIMARY,
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marker_color=colors,
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hovertemplate='<b>%{x}</b><br>1st Place Votes: %{y}<extra></extra>'
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))
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@@ -444,7 +451,7 @@ def plot_most_ranked_1_character(
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def plot_weighted_ranking_score(
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weighted_df: pl.DataFrame,
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title: str = "Character Popularity Score<br>(Weighted: 1st=3pts, 2nd=2pts, 3rd=1pt)",
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title: str = "Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)",
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x_label: str = "Character Personality",
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y_label: str = "Total Weighted Score",
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color: str = ColorPalette.PRIMARY,
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@@ -508,4 +515,339 @@ def plot_weighted_ranking_score(
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font=dict(size=11)
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)
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return fig
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return fig
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def plot_voice_selection_counts(
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df: pl.DataFrame,
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target_column: str = "8_Combined",
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title: str = "Most Frequently Chosen Voices<br>(Top 8 Highlighted)",
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x_label: str = "Voice",
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y_label: str = "Number of Times Chosen",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a bar plot showing the frequency of voice selections.
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Takes a column containing comma-separated values (e.g. "Voice 1, Voice 2..."),
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counts occurrences, and highlights the top 8 most frequent voices.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing the selection column.
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target_column : str, optional
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Name of the column containing comma-separated voice selections.
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Defaults to "8_Combined".
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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if target_column not in df.columns:
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return go.Figure()
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# Process the data:
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# 1. Select the relevant column and remove nulls
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# 2. Split the comma-separated string into a list
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# 3. Explode the list so each voice gets its own row
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# 4. Strip whitespace ensuring "Voice 1" and " Voice 1" match
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# 5. Count occurrences
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stats_df = (
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df.select(pl.col(target_column))
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.drop_nulls()
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.with_columns(pl.col(target_column).str.split(","))
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.explode(target_column)
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.with_columns(pl.col(target_column).str.strip_chars())
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.filter(pl.col(target_column) != "")
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.group_by(target_column)
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.agg(pl.len().alias("count"))
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.sort("count", descending=True)
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)
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# Define colors: Top 8 get PRIMARY, rest get NEUTRAL
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colors = [
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ColorPalette.PRIMARY if i < 8 else ColorPalette.NEUTRAL
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for i in range(len(stats_df))
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]
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=stats_df[target_column],
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y=stats_df['count'],
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text=stats_df['count'],
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textposition='outside',
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marker_color=colors,
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hovertemplate='<b>%{x}</b><br>Selections: %{y}<extra></extra>'
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))
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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font=dict(size=11),
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)
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return fig
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def plot_top3_selection_counts(
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df: pl.DataFrame,
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target_column: str = "3_Ranked",
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title: str = "Most Frequently Chosen Top 3 Voices<br>(Top 3 Highlighted)",
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x_label: str = "Voice",
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y_label: str = "Count of Mentions in Top 3",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Question: Which 3 voices are chosen the most out of 18?
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How many times does each voice end up in the top 3?
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(this is based on the survey question where participants need to choose 3 out
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of the earlier selected 8 voices). So how often each of the 18 stimuli ended
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up in participants' Top 3, after they first selected 8 out of 18.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing the ranking column (comma-separated strings).
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target_column : str, optional
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Name of the column containing comma-separated Top 3 voice elections.
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Defaults to "3_Ranked".
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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if target_column not in df.columns:
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return go.Figure()
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# Process the data:
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# Same logic as plot_voice_selection_counts: explode comma-separated string
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stats_df = (
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df.select(pl.col(target_column))
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.drop_nulls()
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.with_columns(pl.col(target_column).str.split(","))
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.explode(target_column)
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.with_columns(pl.col(target_column).str.strip_chars())
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.filter(pl.col(target_column) != "")
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.group_by(target_column)
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.agg(pl.len().alias("count"))
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.sort("count", descending=True)
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)
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# Define colors: Top 3 get PRIMARY, rest get NEUTRAL
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colors = [
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ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL
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for i in range(len(stats_df))
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]
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=stats_df[target_column],
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y=stats_df['count'],
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text=stats_df['count'],
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textposition='outside',
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marker_color=colors,
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hovertemplate='<b>%{x}</b><br>In Top 3: %{y} times<extra></extra>'
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))
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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font=dict(size=11),
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)
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return fig
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def plot_speaking_style_trait_scores(
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df: pl.DataFrame,
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trait_description: str = None,
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left_anchor: str = None,
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right_anchor: str = None,
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title: str = "Speaking Style Trait Analysis",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Plot scores for a single speaking style trait across multiple voices.
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The plot shows the average score per Voice, sorted by score.
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It expects the DataFrame to contain 'Voice' and 'score' columns,
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typically filtered for a single trait/description.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing at least 'Voice' and 'score' columns.
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Produced by utils.process_speaking_style_data and filtered.
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trait_description : str, optional
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Description of the trait being analyzed (e.g. "Indifferent : Attentive").
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If not provided, it will be constructed from annotations.
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left_anchor : str, optional
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Label for the lower end of the scale (e.g. "Indifferent").
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If not provided, attempts to read 'Left_Anchor' column from df.
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right_anchor : str, optional
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Label for the upper end of the scale (e.g. "Attentive").
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If not provided, attempts to read 'Right_Anchor' column from df.
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title : str, optional
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Plot title.
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height : int, optional
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Plot height.
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width : int, optional
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Plot width.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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if df.is_empty():
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return go.Figure()
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required_cols = ["Voice", "score"]
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if not all(col in df.columns for col in required_cols):
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return go.Figure()
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# Calculate stats: Mean, Count
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stats = (
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df.filter(pl.col("score").is_not_null())
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.group_by("Voice")
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.agg([
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pl.col("score").mean().alias("mean_score"),
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pl.col("score").count().alias("count")
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])
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.sort("mean_score", descending=True) # Descending for Left-to-Right
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)
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# Attempt to extract anchors from DF if not provided
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if (left_anchor is None or right_anchor is None) and "Left_Anchor" in df.columns:
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head = df.filter(pl.col("Left_Anchor").is_not_null()).head(1)
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if not head.is_empty():
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if left_anchor is None: left_anchor = head["Left_Anchor"][0]
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if right_anchor is None: right_anchor = head["Right_Anchor"][0]
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if trait_description is None:
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if left_anchor and right_anchor:
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trait_description = f"{left_anchor.split('|')[0]} vs. {right_anchor.split('|')[0]}"
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else:
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# Try getting from Description column
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if "Description" in df.columns:
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head = df.filter(pl.col("Description").is_not_null()).head(1)
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if not head.is_empty():
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trait_description = head["Description"][0]
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else:
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trait_description = ""
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else:
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trait_description = ""
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=stats["Voice"], # X is Voice
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y=stats["mean_score"], # Y is Score
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text=stats["count"],
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textposition='inside',
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texttemplate='%{text}', # Count on bar
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marker_color=ColorPalette.PRIMARY,
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hovertemplate='<b>%{x}</b><br>Average: %{y:.2f}<br>Count: %{text}<extra></extra>'
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))
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# Add annotations for anchors
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annotations = []
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# Place anchors on the right side
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if left_anchor:
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annotations.append(dict(
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xref='paper', yref='y',
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x=1.01, y=1,
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xanchor='left', yanchor='middle',
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text=f"<b>1: {left_anchor.split('|')[0]}</b>",
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showarrow=False,
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font=dict(size=10, color='gray')
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))
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if right_anchor:
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annotations.append(dict(
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xref='paper', yref='y',
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x=1.01, y=5,
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xanchor='left', yanchor='middle',
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text=f"<b>5: {right_anchor.split('|')[0]}</b>",
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showarrow=False,
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font=dict(size=10, color='gray')
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))
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fig.update_layout(
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title=dict(
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text=f"{title}<br><sub>{trait_description}</sub><br><sub>(Numbers on bars indicate respondent count)</sub>",
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y=0.92
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),
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xaxis_title="Voice",
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yaxis_title="Average Score (1-5)",
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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yaxis=dict(
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range=[1, 5],
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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zeroline=False
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),
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xaxis=dict(
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showgrid=False
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),
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margin=dict(r=150),
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annotations=annotations,
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font=dict(size=11)
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)
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return fig
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Block a user