speaking style trait scores

02_quant_analysis.py

@@ -73,7 +73,6 @@ def _(check_progress, data_all, duration_validation, mo):
 
 
     {duration_validation(data_all)}
-
     """)
     return
 
@@ -263,23 +262,14 @@ def _(mo, plot_most_ranked_1, top3_voices):
 def _(mo):
     mo.md(r"""
     ## Voice Speaking Style - Perception Traits
-    """)
-    return
 
-
+    Here you can find the speaking styles and traits: [Speaking Style Traits Quantitative test design.docx](https://voicebranding-my.sharepoint.com/:w:/g/personal/phoebe_voicebranding_ai/IQBfM_Z8PF98Qalz4lzIbJ3RAUCdc7waB32HZXCj7k3xfo0?e=rtFd27)
-@app.cell(hide_code=True)
-def _(mo):
-    mo.md(r"""
-    How does each voice score for each “speaking style labeled trait”? Here you can find the speaking styles and traits: [Speaking Style Traits Quantitative test design.docx](https://voicebranding-my.sharepoint.com/:w:/g/personal/phoebe_voicebranding_ai/IQBfM_Z8PF98Qalz4lzIbJ3RAUCdc7waB32HZXCj7k3xfo0?e=rtFd27)
-
-    - There are 4 speaking styles: Green, Blue, Orange, Red.
-    - There are 16 traits distributed across the 4 speaking styles.
     """)
     return
 
 
 @app.cell
-def _(data, survey):
+def _(data, survey, utl):
     ss_or, choice_map_or = survey.get_ss_orange_red(data)
     ss_gb, choice_map_gb = survey.get_ss_green_blue(data)
 
@@ -289,43 +279,26 @@ def _(data, survey):
 
     choice_map = {**choice_map_or, **choice_map_gb}
     # print(_d.head())
-    print(choice_map)
+    # print(choice_map)
-    return choice_map, ss_all
-
-
-@app.cell
-def _(choice_map, ss_all, utl):
     ss_long = utl.process_speaking_style_data(ss_all, choice_map)
-    ss_long
     return (ss_long,)
 
 
 @app.cell
-def _(pl, ss_long):
+def _(mo, pl, plts, ss_long):
-    target_trait = "Indifferent | Unfocussed | Detached:Attentive | Helpful | Caring | Deliberate"
+    content = """### How does each voice score for each “speaking style labeled trait”?"""
-    trait_data = ss_long.filter(pl.col("Description") == target_trait)
-    trait_data
-    return target_trait, trait_data
 
+    for i, trait in enumerate(ss_long.select("Description").unique().to_series().to_list()):
+        trait_d = ss_long.filter(pl.col("Description") == trait)
 
-@app.cell
+        content += f"""
-def _(plts, target_trait, trait_data):
+    ### {i+1}) {trait.replace(":", " ↔ ")}
-    plts.plot_speaking_style_trait_scores(
-        trait_data,
-        title=target_trait.replace(":", " ↔ "),
-        # trait_description="Attentive vs Indifferent", # simplified title
-    )
-    return
 
-
+    {mo.ui.plotly(plts.plot_speaking_style_trait_scores(trait_d, title=trait.replace(":", " ↔ "), height=550))}
-app._unparsable_cell(
     """
-    for trait in ss_long.select(\"Description\").unique().to_series().to_list():
+
-        trait_data = ss_long.filter(pl.col(\"Description\") == trait)
+    mo.md(content)
-        mo.md(f\"\"\"
+    return
-    """,
-    name="_"
-)
 
 
 @app.cell(hide_code=True)
@@ -343,21 +316,13 @@ def _(data, mo, plot_average_scores_with_counts, survey):
 
     mo.md(f"""
 
-    How does each voice score on a scale from 1-10?
+    ### How does each voice score on a scale from 1-10?
 
     {mo.ui.plotly(plot_average_scores_with_counts(vscales, x_label='Voice', width=1000))}
     """)
     return
 
 
-@app.cell
-def _(mo):
-    mo.md(r"""
- 
-    """)
-    return
-
-
 @app.cell(hide_code=True)
 def _(mo):
     mo.md(r"""

plots.py

@@ -768,7 +768,7 @@ def plot_speaking_style_trait_scores(
             pl.col("score").mean().alias("mean_score"),
             pl.col("score").count().alias("count")
         ])
-        .sort("mean_score", descending=True) # Descending for Left-to-Right
+        .sort("mean_score", descending=False) # Ascending for display bottom-to-top
     )
 
     # Attempt to extract anchors from DF if not provided
@@ -795,33 +795,36 @@ def plot_speaking_style_trait_scores(
     fig = go.Figure()
 
     fig.add_trace(go.Bar(
-        x=stats["Voice"], # X is Voice
+        y=stats["Voice"], # Y is Voice
-        y=stats["mean_score"], # Y is Score
+        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>%{x}</b><br>Average: %{y:.2f}<br>Count: %{text}<extra></extra>'
+        hovertemplate='<b>%{y}</b><br>Average: %{x:.2f}<br>Count: %{text}<extra></extra>'
     ))
 
     # Add annotations for anchors
     annotations = []
     
-    # Place anchors on the right side
+    # Place anchors at the bottom
     if left_anchor:
         annotations.append(dict(
-            xref='paper', yref='y',
+            xref='x', yref='paper',
-            x=1.01, y=1,
+            x=1, y=-0.2, # Below axis
-            xanchor='left', yanchor='middle',
+            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='paper', yref='y',
+            xref='x', yref='paper',
-            x=1.01, y=5,
+            x=5, y=-0.2, # Below axis
-            xanchor='left', yanchor='middle',
+            xanchor='right', yanchor='top',
             text=f"<b>5: {right_anchor.split('|')[0]}</b>",
             showarrow=False,
             font=dict(size=10, color='gray')
@@ -832,21 +835,21 @@ def plot_speaking_style_trait_scores(
             text=f"{title}<br><sub>{trait_description}</sub><br><sub>(Numbers on bars indicate respondent count)</sub>",
             y=0.92
         ),
-        xaxis_title="Voice",
+        xaxis_title="Average Score (1-5)",
-        yaxis_title="Average Score (1-5)",
+        yaxis_title="Voice",
         height=height,
         width=width,
         plot_bgcolor=ColorPalette.BACKGROUND,
-        yaxis=dict(
+        xaxis=dict(
             range=[1, 5],
             showgrid=True,
             gridcolor=ColorPalette.GRID,
             zeroline=False
         ),
-        xaxis=dict(
+        yaxis=dict(
             showgrid=False
         ),
-        margin=dict(r=150),
+        margin=dict(b=120),
         annotations=annotations,
         font=dict(size=11)
     )