added filter disabled broken cells and starting spoken voice generic results

03_quant_report.py

@@ -154,10 +154,48 @@ def _():
     return
 
 
+@app.cell
+def _(S):
+    filter_form = mo.md('''
+
+
+
+    {age}
+
+    {gender}
+
+    {ethnicity}
+
+    {income}
+
+    {consumer}
+    '''
+    ).batch(
+        age=mo.ui.multiselect(options=S.options_age, value=S.options_age, label="Select Age Group(s):"),
+        gender=mo.ui.multiselect(options=S.options_gender, value=S.options_gender, label="Select Gender(s):"),
+        ethnicity=mo.ui.multiselect(options=S.options_ethnicity, value=S.options_ethnicity, label="Select Ethnicities:"),
+        income=mo.ui.multiselect(options=S.options_income, value=S.options_income, label="Select Income Group(s):"),
+        consumer=mo.ui.multiselect(options=S.options_consumer, value=S.options_consumer, label="Select Consumer Groups:")
+    ).form()
+    mo.md(f'''
+    ---
+
+    # Data Filter
+
+    {filter_form}
+    ''')
+    return
+
+
 @app.cell
 def _(data_validated):
-    # drop rows where 'consumer' is null
+    # mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
-    # data = data_validated.filter(pl.col('Consumer').is_not_null())
+    # _d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
+
+    # # Stop execution and prevent other cells from running if no data is selected
+    # mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
+    # data = _d
+
     data = data_validated
     data.collect()
     return (data,)
@@ -222,7 +260,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Best performing: Original vs Refined frankenstein
@@ -230,7 +268,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _(S, data):
     char_refine_rank = S.get_character_refine(data)[0]
     # print(char_rank.collect().head())
@@ -273,7 +311,7 @@ def _(S, char_rank):
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ### Statistical Significance Character Ranking
@@ -281,7 +319,7 @@ def _():
     return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _(S, char_rank):
     _pairwise_df, _meta = S.compute_ranking_significance(char_rank)
 
@@ -385,10 +423,10 @@ def _(S, char_df):
     """
 
     mo.md(_content)
-    return character_colors, consistent_sort_order
+    return
 
 
-@app.cell
+@app.cell(disabled=True)
 def _():
     mo.md(r"""
     ## Statistical significance best characters
@@ -400,176 +438,6 @@ def _():
     return
 
 
-# @app.cell(hide_code=True)
-# def _():
-#     mo.md(r"""
-#     # BC per Consumer
-#     """)
-#     return
-
-
-# @app.cell
-# def _():
-#     split_group = 'Consumer'
-#     return (split_group,)
-
-
-# @app.cell
-# def _(split_group):
-#     mo.md(rf"""
-#     ## Character Ranking Points (per {split_group} segment)
-#     """)
-#     return
-
-
-# @app.cell
-# def _(S, data):
-#     _content = ""
-#     for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
-
-#         _char_rank = S.get_character_ranking(_consumer_df)[0]
-#         _char_rank_weighted = calculate_weighted_ranking_scores(_char_rank)
-
-#         _plot = S.plot_weighted_ranking_score(
-#             _char_rank_weighted, 
-#             title=f'Most Popular Character - Weighted Popularity Score - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", 'Worth')}"<br>(1st=3pts, 2nd=2pts, 3rd=1pt)',
-#             x_label='Voice'
-#         )
-
-#         _content += f"""
-#         {mo.ui.altair_chart(_plot)}
-
-#         """
-
-#     mo.md(_content)
-#     return
-
-
-# @app.cell
-# def _(split_group):
-#     mo.md(rf"""
-#     ## Character Ranking Place 1-2-3 in one (per {split_group})
-#     """)
-#     return
-
-
-# @app.cell
-# def _(S, data):
-#     _content = ""
-#     for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
-
-#         _char_rank = S.get_character_ranking(_consumer_df)[0]
-
-
-#         _plot = S.plot_top3_ranking_distribution(_char_rank, x_label='Character Personality', title='Character Personality: Rankings Top 3 - CONSUMER: "'+_consumer_group.replace("_", " ").replace("Woth", 'Worth')+'"')
-
-#         _content += f"""
-#         {mo.ui.altair_chart(_plot)}
-
-#         """
-
-#     mo.md(_content)
-#     return
-
-
-# @app.cell
-# def _(split_group):
-#     mo.md(rf"""
-#     ## Character Ranking times 1st place (per {split_group})
-#     """)
-#     return
-
-
-# @app.cell
-# def _(S, data):
-#     _content = ""
-#     for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
-
-#         _char_rank = S.get_character_ranking(_consumer_df)[0]
-
-#         _plot = S.plot_most_ranked_1(_char_rank, title=f'Most Popular Character - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", 'Worth')}"<br>(Number of Times Ranked 1st)', x_label='Character Personality')
-
-#         _content += f"""
-#         {mo.ui.altair_chart(_plot)}
-
-#         """
-
-#     mo.md(_content)
-#     return
-
-
-# @app.cell
-# def _(split_group):
-#     mo.md(rf"""
-#     ## Predefined personality traits WordClouds per {split_group}
-#     """)
-#     return
-
-
-# @app.cell
-# def _(S, data):
-#     _content = ""
-#     for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
-#         _top8_traits = S.get_top_8_traits(_consumer_df)[0]
-
-#         _plot = S.plot_traits_wordcloud(
-#             data=_top8_traits,
-#             column='Top_8_Traits',
-#             title=f'Most Prominent Personality Traits - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", "Worth")}"',
-#         )
-
-#         _content += f"""
-#         {_plot}
-
-#         """
-
-#     mo.md(_content)
-#     return
-
-
-# @app.cell
-# def _(split_group):
-#     mo.md(rf"""
-#     ## Frequency traits chosen - per {split_group} segment
-#     """)
-#     return
-
-
-# @app.cell
-# def _(S, character_colors, consistent_sort_order, data):
-#     top_char = "The Coach"
-
-#     _content = ""
-#     for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
-
-#         _char_df = S.get_character_refine(_consumer_df)[0]
-
-#         _freq_df, _ = S.transform_character_trait_frequency(_char_df, top_char)
-#         _main_color, _highlight_color = character_colors[top_char]
-#         _chart = S.plot_single_character_trait_frequency(
-#             data=_freq_df,
-#             character_name=top_char,
-#             bar_color=_main_color,
-#             highlight_color=_highlight_color,
-#             trait_sort_order=consistent_sort_order,
-#             title=f"""Top Personality Traits for '{top_char}' - CONSUMER: "{_consumer_group.replace('_', ' ').replace("Woth", "Worth")}"""
-#         )
-#         _content += f"""
-#         {mo.ui.altair_chart(_chart)}
-
-#         """
-#     mo.md(_content)
-#     return
-
-
-# @app.cell
-# def _():
-#     mo.md(r"""
-#     # BC per Gender
-#     """)
-#     return
-
-
 @app.cell
 def _():
     return
@@ -590,11 +458,24 @@ def _():
     return
 
 
+@app.cell
+def _():
+    mo.md(r"""
+    ## 8 Most Chosen
+    """)
+    return
+
+
 @app.cell
 def _(S, data):
-    top3_voices = S.get_top_3_voices(data)[0]
+    v_18_8_3 = S.get_18_8_3(data)[0].collect()
-    top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
+    return (v_18_8_3,)
-    return top3_voices, top3_voices_weighted
+
+
+@app.cell
+def _(S, v_18_8_3):
+    S.plot_voice_selection_counts(v_18_8_3)
+    return
 
 
 @app.cell(hide_code=True)
@@ -607,6 +488,13 @@ def _():
     return
 
 
+@app.cell
+def _(S, data):
+    top3_voices = S.get_top_3_voices(data)[0]
+    top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
+    return top3_voices, top3_voices_weighted
+
+
 @app.cell
 def _(S, top3_voices):
     _plot = S.plot_ranking_distribution(top3_voices, x_label='Voice')