other top bc's

XX_quant_report.script.py

@@ -20,8 +20,6 @@ from speaking_styles import SPEAKING_STYLES
 RESULTS_FILE = 'data/exports/debug/JPMC_Chase Brand Personality_Quant Round 1_February 2, 2026_Labels.csv'
 QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
 
-BEST_CHOSEN_CHARACTER = "the_coach"
-
 
 # %%
 # CLI argument parsing for batch automation
@@ -50,6 +48,8 @@ def parse_cli_args():
         parser.add_argument(f'--{filter_name}', type=str, default=None, help=f'JSON list of {filter_name} values')
     
     parser.add_argument('--filter-name', type=str, default=None, help='Name for this filter combination (used for .txt description file)')
+    parser.add_argument('--figures-dir', type=str, default=f'figures/statistical_significance/{Path(RESULTS_FILE).parts[2]}', help='Override the default figures directory')
+    parser.add_argument('--best-character', type=str, default="the_coach", help='Slug of the best chosen character (default: "the_coach")')
     
     # Only parse if running as script (not in Jupyter/interactive)
     try:
@@ -57,7 +57,7 @@ def parse_cli_args():
         get_ipython()  # noqa: F821 # type: ignore
         # Return namespace with all filters set to None
         no_filters = {f: None for f in FILTER_CONFIG}
-        return argparse.Namespace(**no_filters, filter_name=None)
+        return argparse.Namespace(**no_filters, filter_name=None, figures_dir=f'figures/statistical_significance/{Path(RESULTS_FILE).parts[2]}', best_character="the_coach")
     except NameError:
         args = parser.parse_args()
         # Parse JSON strings to lists
@@ -67,11 +67,12 @@ def parse_cli_args():
         return args
 
 cli_args = parse_cli_args()
+BEST_CHOSEN_CHARACTER = cli_args.best_character
 
 
 
 # %%
-S = QualtricsSurvey(RESULTS_FILE, QSF_FILE)
+S = QualtricsSurvey(RESULTS_FILE, QSF_FILE, figures_dir=cli_args.figures_dir)
 try:
     data_all = S.load_data()
 except NotImplementedError as e:

XX_statistical_significance.script.py

@@ -1,12 +1,12 @@
 """Extra statistical significance analyses for quant report."""
 # %% Imports
 
-from utils import QualtricsSurvey
+import utils
 import polars as pl
 import argparse
 import json
 import re
-
+from pathlib import Path
 
 
 
@@ -39,6 +39,7 @@ def parse_cli_args():
         parser.add_argument(f'--{filter_name}', type=str, default=None, help=f'JSON list of {filter_name} values')
     
     parser.add_argument('--filter-name', type=str, default=None, help='Name for this filter combination (used for .txt description file)')
+    parser.add_argument('--figures-dir', type=str, default=f'figures/statistical_significance/{Path(RESULTS_FILE).parts[2]}', help='Override the default figures directory')
     
     # Only parse if running as script (not in Jupyter/interactive)
     try:
@@ -46,7 +47,9 @@ def parse_cli_args():
         get_ipython()  # noqa: F821 # type: ignore
         # Return namespace with all filters set to None
         no_filters = {f: None for f in FILTER_CONFIG}
-        return argparse.Namespace(**no_filters, filter_name=None)
+        # Use the same default as argparse
+        default_fig_dir = f'figures/statistical_significance/{Path(RESULTS_FILE).parts[2]}'
+        return argparse.Namespace(**no_filters, filter_name=None, figures_dir=default_fig_dir)
     except NameError:
         args = parser.parse_args()
         # Parse JSON strings to lists
@@ -59,7 +62,7 @@ cli_args = parse_cli_args()
 
 
 # %%
-S = QualtricsSurvey(RESULTS_FILE, QSF_FILE)
+S = utils.QualtricsSurvey(RESULTS_FILE, QSF_FILE, figures_dir=cli_args.figures_dir)
 data_all = S.load_data()
 
 
@@ -125,4 +128,76 @@ if cli_args.filter_name and S.fig_save_dir:
 data = _d
 data.collect()
 
+# %% Character coach significatly higher than others
+
+
+char_rank = S.get_character_ranking(data)[0]
+
+
+
+_pairwise_df, _meta = S.compute_ranking_significance(
+    char_rank,
+    alpha=0.05,
+    correction="none",
+    )
+
+# %% [markdown]
+"""
+### Methodology Analysis
+
+**Input Data (`char_rank`)**:
+*   Generated by `S.get_character_ranking(data)`.
+*   Contains the ranking values (1st, 2nd, 3rd, 4th) assigned by each respondent to the four options ("The Coach", etc.).
+*   Columns represent the characters; rows represent individual respondents; values are the numerical rank (1 = Top Choice).
+
+**Processing**:
+*   The function `compute_ranking_significance` aggregates these rankings to find the **"Rank 1 Share"** (the percentage of respondents who picked that character as their #1 favorite).
+*   It builds a contingency table of how many times each character was ranked 1st vs. not 1st (or 1st v 2nd v 3rd).
+
+**Statistical Test**:
+*   **Test Used**: Pairwise Z-test for two proportions (uncorrected).
+*   **Comparison**: It compares the **Rank 1 Share** of every pair of characters.
+    *   *Example*: "Is the 42% of people who chose 'Coach' significantly different from the 29% who chose 'Familiar Friend'?"
+*   **Significance**: A result of `p < 0.05` means the difference in popularity (top-choice preference) is statistically significant and not due to random chance.
+"""
+
+
+# %% Plot heatmap of pairwise significance
+S.plot_significance_heatmap(_pairwise_df, metadata=_meta, title="Statistical Significance: Character Top Choice Preference")
+
+# %% Plot summary of significant differences (e.g., which characters are significantly higher than others)
+# S.plot_significance_summary(_pairwise_df, metadata=_meta)
+
+# %% [markdown]
+"""
+# Analysis: Significance of "The Coach"
+
+**Parameters**: `alpha=0.05`, `correction='none'`
+*   **Rationale**: No correction was applied to allow for detection of all potential pairwise differences (uncorrected p < 0.05). If strict control for family-wise error rate were required (e.g., Bonferroni), the significance threshold would be lower (p < 0.0083).
+
+**Results**:
+"The Coach" is the top-ranked option (42.0% Rank 1 share) and shows strong separation from the field.
+
+*   **Vs. Bottom Two**: "The Coach" is significantly higher than both "The Bank Teller" (26.9%, p < 0.001) and "Familiar Friend" (29.4%, p < 0.001).
+*   **Vs. Runner-Up**: "The Coach" is widely preferred over "The Personal Assistant" (33.4%). The difference of **8.6 percentage points** is statistically significant (p = 0.017) at the standard 0.05 level.
+    *   *Note*: While p=0.017 is significant in isolation, it would not meet the stricter Bonferroni threshold (0.0083). However, the effect size (+8.6%) is commercially meaningful.
+
+**Conclusion**:
+Yes, "The Coach" can be considered statistically more significant than the other options. It is clearly superior to the bottom two options and holds a statistically significant lead over the runner-up ("Personal Assistant") in direct comparison.
+"""
+
+
+# %% voices analysis
+top3_voices = S.get_top_3_voices(data)[0]
+
+
+_pairwise_df_voice, _metadata = S.compute_ranking_significance(
+    top3_voices,alpha=0.05,correction="none")
+
+
+S.plot_significance_heatmap(
+    _pairwise_df_voice, 
+    metadata=_metadata,
+    title="Statistical Significance: Voice Top Choice Preference"
+)
 # %%

plots.py

@@ -2308,9 +2308,9 @@ class QualtricsPlotsMixin:
         # Base heatmap
         heatmap = alt.Chart(heatmap_df).mark_rect(stroke='white', strokeWidth=1).encode(
             x=alt.X('col:N', title=None, sort=all_groups,
-                   axis=alt.Axis(labelAngle=-45, labelLimit=150)),
+                   axis=alt.Axis(labelAngle=-45, labelLimit=150, grid=False)),
             y=alt.Y('row:N', title=None, sort=all_groups,
-                   axis=alt.Axis(labelLimit=150)),
+                   axis=alt.Axis(labelLimit=150, grid=False)),
             color=alt.Color('sig_category:N',
                            scale=alt.Scale(domain=sig_domain, range=sig_range),
                            legend=alt.Legend(

utils.py

@@ -762,7 +762,7 @@ def normalize_global_values(df: pl.DataFrame, target_cols: list[str]) -> pl.Data
 class QualtricsSurvey(QualtricsPlotsMixin):
     """Class to handle Qualtrics survey data."""
     
-    def __init__(self, data_path: Union[str, Path], qsf_path: Union[str, Path]):
+    def __init__(self, data_path: Union[str, Path], qsf_path: Union[str, Path], figures_dir: Union[str, Path] = None):
         if isinstance(data_path, str):
             data_path = Path(data_path)
         
@@ -774,8 +774,12 @@ class QualtricsSurvey(QualtricsPlotsMixin):
         self.qid_descr_map = self._extract_qid_descr_map()
         self.qsf:dict = self._load_qsf()
         
+        if figures_dir:
+            self.fig_save_dir = Path(figures_dir)
+        else:
             # get export directory name for saving figures ie if data_path='data/exports/OneDrive_2026-01-21/...' should be 'figures/OneDrive_2026-01-21'
             self.fig_save_dir = Path('figures') / self.data_filepath.parts[2]
+            
         if not self.fig_save_dir.exists():
             self.fig_save_dir.mkdir(parents=True, exist_ok=True)