voice plots

03_quant_report.py

@@ -255,7 +255,7 @@ def _():
     mo.md(r"""
     ---
 
-    # Brand Character Results (overall)
+    # Brand Character Results
     """)
     return
 
@@ -276,7 +276,7 @@ def _(S, data):
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Character ranking points
@@ -311,7 +311,7 @@ def _(S, char_rank):
     return
 
 
-@app.cell(disabled=True)
+@app.cell
 def _():
     mo.md(r"""
     ### Statistical Significance Character Ranking
@@ -335,7 +335,7 @@ def _(S, char_rank):
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Character Ranking: times 1st place
@@ -438,7 +438,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     return
 
@@ -461,20 +461,34 @@ def _():
 @app.cell
 def _():
     mo.md(r"""
-    ## 8 Most Chosen
+    ## Top 8 Most Chosen out of 18
     """)
     return
 
 
 @app.cell
 def _(S, data):
-    v_18_8_3 = S.get_18_8_3(data)[0].collect()
+    v_18_8_3 = S.get_18_8_3(data)[0]
     return (v_18_8_3,)
 
 
 @app.cell
 def _(S, v_18_8_3):
-    S.plot_voice_selection_counts(v_18_8_3)
+    S.plot_voice_selection_counts(v_18_8_3, title="Top 8 Voice Selection from 18 Voices", x_label='Voice')
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Top 3 most chosen out of 8
+    """)
+    return
+
+
+@app.cell
+def _(S, v_18_8_3):
+    S.plot_top3_selection_counts(v_18_8_3, title="Top 3 Voice Selection Counts from 8 Voices", x_label='Voice')
     return
 
 
@@ -497,10 +511,7 @@ def _(S, data):
 
 @app.cell
 def _(S, top3_voices):
-    _plot = S.plot_ranking_distribution(top3_voices, x_label='Voice')
-    mo.md(f"""
-    {mo.ui.altair_chart(_plot)}
-    """)
+    S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Voice Rankings (1st, 2nd, 3rd)")
     return
 
 
@@ -548,7 +559,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _():
     mo.md(r"""
     ## Weighted Popularity Scores
@@ -568,6 +579,7 @@ def _(S, top3_voices_weighted):
 
 @app.cell
 def _():
+    ## Voice Ranked 1st the most
     return
 
 
@@ -578,7 +590,8 @@ def _(top3_voices_weighted):
 
 
 @app.cell
-def _():
+def _(S, top3_voices):
+    S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice')
     return
 
 
@@ -597,13 +610,13 @@ def _(S, data):
     return (voice_1_10,)
 
 
-@app.cell(hide_code=True)
+@app.cell(disabled=True)
 def _(S, voice_1_10):
-    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10)")
+    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)")
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ### Statistical Significance (Scale 1-10)
@@ -687,5 +700,60 @@ def _(S, voice_1_10):
     return
 
 
+@app.cell
+def _(S, data):
+    # Get your voice scale data (from notebook)
+    voice_1_10, _ = S.get_voice_scale_1_10(data)
+    return (voice_1_10,)
+
+
+@app.cell
+def _(S, voice_1_10):
+    S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)")
+    return
+
+
+@app.cell(disabled=True)
+def _():
+    mo.md(r"""
+    ### Statistical Significance (Scale 1-10)
+    """)
+    return
+
+
+@app.cell(disabled=True)
+def _(S, voice_1_10):
+    # Compute pairwise significance tests
+    pairwise_df, metadata = S.compute_pairwise_significance(
+        voice_1_10,
+        test_type="mannwhitney",  # or "ttest", "chi2", "auto"
+        alpha=0.05,
+        correction="bonferroni"   # or "holm", "none"
+    )
+
+    # View significant pairs
+    # print(pairwise_df.filter(pl.col('significant') == True))
+
+    # Create heatmap visualization
+    _heatmap = S.plot_significance_heatmap(
+        pairwise_df, 
+        metadata=metadata,
+        title="Voice Rating Significance<br>(Pairwise Comparisons)"
+    )
+
+    # Create summary bar chart
+    _summary = S.plot_significance_summary(
+        pairwise_df,
+        metadata=metadata
+    )
+
+    mo.md(f"""
+    {mo.ui.altair_chart(_heatmap)}
+
+    {mo.ui.altair_chart(_summary)}
+    """)
+    return
+
+
 if __name__ == "__main__":
     app.run()