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
 
-
-@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.
+    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)
     """)
     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)
-    return choice_map, ss_all
-
-
-@app.cell
-def _(choice_map, ss_all, utl):
+    # print(choice_map)
     ss_long = utl.process_speaking_style_data(ss_all, choice_map)
-    ss_long
     return (ss_long,)
 
 
 @app.cell
-def _(pl, ss_long):
-    target_trait = "Indifferent | Unfocussed | Detached:Attentive | Helpful | Caring | Deliberate"
-    trait_data = ss_long.filter(pl.col("Description") == target_trait)
-    trait_data
-    return target_trait, trait_data
+def _(mo, pl, plts, ss_long):
+    content = """### How does each voice score for each “speaking style labeled trait”?"""
 
+    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
-def _(plts, target_trait, trait_data):
-    plts.plot_speaking_style_trait_scores(
-        trait_data,
-        title=target_trait.replace(":", " ↔ "),
-        # trait_description="Attentive vs Indifferent", # simplified title
-    )
-    return
+        content += f"""
+    ### {i+1}) {trait.replace(":", " ↔ ")}
 
-
-app._unparsable_cell(
+    {mo.ui.plotly(plts.plot_speaking_style_trait_scores(trait_d, title=trait.replace(":", " ↔ "), height=550))}
     """
-    for trait in ss_long.select(\"Description\").unique().to_series().to_list():
-        trait_data = ss_long.filter(pl.col(\"Description\") == trait)
-        mo.md(f\"\"\"
-    """,
-    name="_"
-)
+
+    mo.md(content)
+    return
 
 
 @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"""