694 lines
18 KiB
Python
694 lines
18 KiB
Python
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__generated_with = "0.19.7"
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# %%
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import marimo as mo
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import polars as pl
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from pathlib import Path
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import argparse
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import json
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from validation import check_progress, duration_validation, check_straight_liners
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from utils import QualtricsSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores
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import utils
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from speaking_styles import SPEAKING_STYLES
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# %%
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# CLI argument parsing for batch automation
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# When run as script: python 03_quant_report.script.py --age '["18 to 21 years"]' --consumer '["Starter"]'
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# When run in Jupyter: args will use defaults (all filters = None = all options selected)
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# Central filter configuration - add new filters here only
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# Format: 'cli_arg_name': 'QualtricsSurvey.options_* attribute name'
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FILTER_CONFIG = {
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'age': 'options_age',
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'gender': 'options_gender',
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'ethnicity': 'options_ethnicity',
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'income': 'options_income',
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'consumer': 'options_consumer',
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'business_owner': 'options_business_owner',
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'ai_user': 'options_ai_user',
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'investable_assets': 'options_investable_assets',
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'industry': 'options_industry',
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}
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def parse_cli_args():
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parser = argparse.ArgumentParser(description='Generate quant report with optional filters')
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# Dynamically add filter arguments from config
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for filter_name in FILTER_CONFIG:
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parser.add_argument(f'--{filter_name}', type=str, default=None, help=f'JSON list of {filter_name} values')
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parser.add_argument('--filter-name', type=str, default=None, help='Name for this filter combination (used for .txt description file)')
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# Only parse if running as script (not in Jupyter/interactive)
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try:
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# Check if running in Jupyter by looking for ipykernel
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get_ipython() # noqa: F821
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# Return namespace with all filters set to None
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return argparse.Namespace(**{f: None for f in FILTER_CONFIG}, filter_name=None)
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except NameError:
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args = parser.parse_args()
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# Parse JSON strings to lists
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for filter_name in FILTER_CONFIG:
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val = getattr(args, filter_name)
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setattr(args, filter_name, json.loads(val) if val else None)
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return args
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cli_args = parse_cli_args()
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# %%
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# file_browser = mo.ui.file_browser(
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# initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
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# )
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# file_browser
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# # %%
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# mo.stop(file_browser.path(index=0) is None, mo.md("**⚠️ Please select a `_Labels.csv` file above to proceed**"))
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# RESULTS_FILE = Path(file_browser.path(index=0))
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RESULTS_FILE = 'data/exports/2-3-26_Copy-2-2-26/JPMC_Chase Brand Personality_Quant Round 1_February 2, 2026_Labels.csv'
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QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
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# %%
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S = QualtricsSurvey(RESULTS_FILE, QSF_FILE)
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try:
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data_all = S.load_data()
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except NotImplementedError as e:
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mo.stop(True, mo.md(f"**⚠️ {str(e)}**"))
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# %%
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BEST_CHOSEN_CHARACTER = "the_coach"
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# # %%
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# filter_form = mo.md('''
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# {age}
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# {gender}
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# {ethnicity}
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# {income}
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# {consumer}
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# '''
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# ).batch(
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# age=mo.ui.multiselect(options=S.options_age, value=S.options_age, label="Select Age Group(s):"),
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# gender=mo.ui.multiselect(options=S.options_gender, value=S.options_gender, label="Select Gender(s):"),
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# ethnicity=mo.ui.multiselect(options=S.options_ethnicity, value=S.options_ethnicity, label="Select Ethnicities:"),
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# income=mo.ui.multiselect(options=S.options_income, value=S.options_income, label="Select Income Group(s):"),
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# consumer=mo.ui.multiselect(options=S.options_consumer, value=S.options_consumer, label="Select Consumer Groups:")
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# ).form()
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# mo.md(f'''
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# ---
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# # Data Filter
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# {filter_form}
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# ''')
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# %%
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# mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
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# CLI args: None means "all options selected" (use S.options_* defaults)
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# Build filter values dict dynamically from FILTER_CONFIG
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_active_filters = {}
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for filter_name, options_attr in FILTER_CONFIG.items():
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cli_value = getattr(cli_args, filter_name)
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all_options = getattr(S, options_attr)
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_active_filters[filter_name] = cli_value if cli_value is not None else all_options
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_d = S.filter_data(data_all, **_active_filters)
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# Write filter description file if filter-name is provided
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if cli_args.filter_name and S.fig_save_dir:
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# Get the filter slug (e.g., "All_Respondents", "Cons-Starter", etc.)
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_filter_slug = S._get_filter_slug()
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_filter_slug_dir = S.fig_save_dir / _filter_slug
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_filter_slug_dir.mkdir(parents=True, exist_ok=True)
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# Build filter description
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_filter_desc_lines = [
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f"Filter: {cli_args.filter_name}",
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"",
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"Applied Filters:",
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]
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_short_desc_parts = []
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for filter_name, options_attr in FILTER_CONFIG.items():
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all_options = getattr(S, options_attr)
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values = _active_filters[filter_name]
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display_name = filter_name.replace('_', ' ').title()
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if values != all_options:
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_short_desc_parts.append(f"{display_name}: {', '.join(values)}")
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_filter_desc_lines.append(f" {display_name}: {', '.join(values)}")
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else:
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_filter_desc_lines.append(f" {display_name}: All")
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# Write detailed description INSIDE the filter-slug directory
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_filter_file = _filter_slug_dir / f"{cli_args.filter_name}.txt"
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_filter_file.write_text('\n'.join(_filter_desc_lines))
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# Append to summary index file at figures/<export_date>/filter_index.txt
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_summary_file = S.fig_save_dir / "filter_index.txt"
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_short_desc = "; ".join(_short_desc_parts) if _short_desc_parts else "All Respondents"
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_summary_line = f"{_filter_slug} | {cli_args.filter_name} | {_short_desc}\n"
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# Append or create the summary file
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if _summary_file.exists():
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_existing = _summary_file.read_text()
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# Avoid duplicate entries for same slug
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if _filter_slug not in _existing:
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with _summary_file.open('a') as f:
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f.write(_summary_line)
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else:
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_header = "Filter Index\n" + "=" * 80 + "\n\n"
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_header += "Directory | Filter Name | Description\n"
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_header += "-" * 80 + "\n"
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_summary_file.write_text(_header + _summary_line)
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# Stop execution and prevent other cells from running if no data is selected
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# mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
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data = _d
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# data = data_validated
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data.collect()
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# %%
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# %%
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# Check if all business owners are missing a 'Consumer type' in demographics
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# assert all([a is None for a in data_all.filter(pl.col('QID4') == 'Yes').collect()['Consumer'].unique()]) , "Not all business owners are missing 'Consumer type' in demographics."
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# %%
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mo.md(r"""
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# Demographic Distributions
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""")
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# %%
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demo_plot_cols = [
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'Age',
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'Gender',
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# 'Race/Ethnicity',
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'Bussiness_Owner',
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'Consumer'
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]
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# %%
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_content = """
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"""
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for c in demo_plot_cols:
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_fig = S.plot_demographic_distribution(
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data=S.get_demographics(data)[0],
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column=c,
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title=f"{c.replace('Bussiness', 'Business').replace('_', ' ')} Distribution of Survey Respondents"
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)
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_content += f"""{mo.ui.altair_chart(_fig)}\n\n"""
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mo.md(_content)
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# %%
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mo.md(r"""
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---
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# Brand Character Results
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""")
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# %%
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mo.md(r"""
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## Best performing: Original vs Refined frankenstein
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""")
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# %%
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char_refine_rank = S.get_character_refine(data)[0]
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# print(char_rank.collect().head())
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print(char_refine_rank.collect().head())
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# %%
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mo.md(r"""
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## Character ranking points
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""")
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# %%
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mo.md(r"""
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## Character ranking 1-2-3
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""")
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# %%
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char_rank = S.get_character_ranking(data)[0]
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# %%
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char_rank_weighted = calculate_weighted_ranking_scores(char_rank)
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S.plot_weighted_ranking_score(char_rank_weighted, title="Most Popular Character - Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)", x_label='Voice')
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# %%
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S.plot_top3_ranking_distribution(char_rank, x_label='Character Personality', title='Character Personality: Rankings Top 3')
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# %%
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mo.md(r"""
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### Statistical Significance Character Ranking
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""")
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# %%
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# _pairwise_df, _meta = S.compute_ranking_significance(char_rank)
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# # print(_pairwise_df.columns)
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# mo.md(f"""
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# {mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))}
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# {mo.ui.altair_chart(S.plot_significance_summary(_pairwise_df, metadata=_meta))}
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# """)
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# %%
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mo.md(r"""
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## Character Ranking: times 1st place
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""")
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# %%
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S.plot_most_ranked_1(char_rank, title="Most Popular Character<br>(Number of Times Ranked 1st)", x_label='Character Personality')
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# %%
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mo.md(r"""
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## Prominent predefined personality traits wordcloud
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""")
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# %%
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top8_traits = S.get_top_8_traits(data)[0]
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S.plot_traits_wordcloud(
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data=top8_traits,
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column='Top_8_Traits',
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title="Most Prominent Personality Traits",
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)
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# %%
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mo.md(r"""
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## Trait frequency per brand character
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""")
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# %%
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char_df = S.get_character_refine(data)[0]
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# %%
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from theme import ColorPalette
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# Assuming you already have char_df (your data from get_character_refine or similar)
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characters = ['Bank Teller', 'Familiar Friend', 'The Coach', 'Personal Assistant']
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character_colors = {
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'Bank Teller': (ColorPalette.CHARACTER_BANK_TELLER, ColorPalette.CHARACTER_BANK_TELLER_HIGHLIGHT),
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'Familiar Friend': (ColorPalette.CHARACTER_FAMILIAR_FRIEND, ColorPalette.CHARACTER_FAMILIAR_FRIEND_HIGHLIGHT),
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'The Coach': (ColorPalette.CHARACTER_COACH, ColorPalette.CHARACTER_COACH_HIGHLIGHT),
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'Personal Assistant': (ColorPalette.CHARACTER_PERSONAL_ASSISTANT, ColorPalette.CHARACTER_PERSONAL_ASSISTANT_HIGHLIGHT),
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}
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# Build consistent sort order (by total frequency across all characters)
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all_trait_counts = {}
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for char in characters:
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freq_df, _ = S.transform_character_trait_frequency(char_df, char)
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for row in freq_df.iter_rows(named=True):
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all_trait_counts[row['trait']] = all_trait_counts.get(row['trait'], 0) + row['count']
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consistent_sort_order = sorted(all_trait_counts.keys(), key=lambda x: -all_trait_counts[x])
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_content = """"""
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# Generate 4 plots (one per character)
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for char in characters:
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freq_df, _ = S.transform_character_trait_frequency(char_df, char)
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main_color, highlight_color = character_colors[char]
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chart = S.plot_single_character_trait_frequency(
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data=freq_df,
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character_name=char,
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bar_color=main_color,
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highlight_color=highlight_color,
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trait_sort_order=consistent_sort_order,
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)
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_content += f"""
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{mo.ui.altair_chart(chart)}
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"""
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mo.md(_content)
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# %%
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mo.md(r"""
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## Statistical significance best characters
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zie chat
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> voorbeeld: als de nr 1 en 2 niet significant verschillen maar wel van de nr 3 bijvoorbeeld is dat ook top. Beetje meedenkend over hoe ik het kan presenteren weetje wat ik bedoel?:)
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>
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""")
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# %%
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# %%
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# %%
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mo.md(r"""
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---
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# Spoken Voice Results
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""")
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# %%
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COLOR_GENDER = True
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# %%
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mo.md(r"""
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## Top 8 Most Chosen out of 18
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""")
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# %%
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v_18_8_3 = S.get_18_8_3(data)[0]
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# %%
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S.plot_voice_selection_counts(v_18_8_3, title="Top 8 Voice Selection from 18 Voices", x_label='Voice', color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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## Top 3 most chosen out of 8
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""")
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# %%
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S.plot_top3_selection_counts(v_18_8_3, title="Top 3 Voice Selection Counts from 8 Voices", x_label='Voice', color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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## Voice Ranking Weighted Score
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""")
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# %%
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top3_voices = S.get_top_3_voices(data)[0]
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top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
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# %%
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S.plot_weighted_ranking_score(top3_voices_weighted, title="Most Popular Voice - Weighted Popularity Score<br>(1st = 3pts, 2nd = 2pts, 3rd = 1pt)", color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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## Which voice is ranked best in the ranking question for top 3?
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(not best 3 out of 8 question)
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""")
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# %%
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S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Top 3 Voice Rankings (1st, 2nd, 3rd)", color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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### Statistical significance for voice ranking
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""")
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# %%
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# print(top3_voices.collect().head())
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# %%
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# _pairwise_df, _metadata = S.compute_ranking_significance(
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# top3_voices,alpha=0.05,correction="none")
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# # View significant pairs
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# # print(pairwise_df.filter(pl.col('significant') == True))
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# # Create heatmap visualization
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# _heatmap = S.plot_significance_heatmap(
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# _pairwise_df,
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# metadata=_metadata,
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# title="Weighted Voice Ranking Significance<br>(Pairwise Comparisons)"
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# )
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# # Create summary bar chart
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# _summary = S.plot_significance_summary(
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# _pairwise_df,
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# metadata=_metadata
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# )
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# mo.md(f"""
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# {mo.ui.altair_chart(_heatmap)}
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# {mo.ui.altair_chart(_summary)}
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# """)
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# %%
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## Voice Ranked 1st the most
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# %%
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S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice', color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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## Voice Scale 1-10
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""")
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# %%
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# Get your voice scale data (from notebook)
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voice_1_10, _ = S.get_voice_scale_1_10(data)
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S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)", color_gender=COLOR_GENDER)
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# %%
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mo.md(r"""
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### Statistical Significance (Scale 1-10)
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""")
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# %%
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# Compute pairwise significance tests
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# pairwise_df, metadata = S.compute_pairwise_significance(
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# voice_1_10,
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# test_type="mannwhitney", # or "ttest", "chi2", "auto"
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# alpha=0.05,
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# correction="bonferroni" # or "holm", "none"
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# )
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# # View significant pairs
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# # print(pairwise_df.filter(pl.col('significant') == True))
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# # Create heatmap visualization
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# _heatmap = S.plot_significance_heatmap(
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# pairwise_df,
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# metadata=metadata,
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# title="Voice Rating Significance<br>(Pairwise Comparisons)"
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# )
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# # Create summary bar chart
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# _summary = S.plot_significance_summary(
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# pairwise_df,
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# metadata=metadata
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# )
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# mo.md(f"""
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# {mo.ui.altair_chart(_heatmap)}
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# {mo.ui.altair_chart(_summary)}
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# """)
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# %%
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# %%
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mo.md(r"""
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## Ranking points for Voice per Chosen Brand Character
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**missing mapping**
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""")
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# %%
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mo.md(r"""
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## Correlation Speaking Styles
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""")
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# %%
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ss_or, choice_map_or = S.get_ss_orange_red(data)
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ss_gb, choice_map_gb = S.get_ss_green_blue(data)
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# Combine the data
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ss_all = ss_or.join(ss_gb, on='_recordId')
|
|
_d = ss_all.collect()
|
|
|
|
choice_map = {**choice_map_or, **choice_map_gb}
|
|
# print(_d.head())
|
|
# print(choice_map)
|
|
ss_long = utils.process_speaking_style_data(ss_all, choice_map)
|
|
|
|
df_style = utils.process_speaking_style_data(ss_all, choice_map)
|
|
|
|
vscales = S.get_voice_scale_1_10(data)[0]
|
|
df_scale_long = utils.process_voice_scale_data(vscales)
|
|
|
|
joined_scale = df_style.join(df_scale_long, on=["_recordId", "Voice"], how="inner")
|
|
|
|
df_ranking = utils.process_voice_ranking_data(top3_voices)
|
|
joined_ranking = df_style.join(df_ranking, on=['_recordId', 'Voice'], how='inner')
|
|
|
|
# %%
|
|
joined_ranking.head()
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Colors vs Scale 1-10
|
|
""")
|
|
|
|
# %%
|
|
# Transform to get one row per color with average correlation
|
|
color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, SPEAKING_STYLES)
|
|
S.plot_speaking_style_color_correlation(
|
|
data=color_corr_scale,
|
|
title="Correlation: Speaking Style Colors and Voice Scale 1-10"
|
|
)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Colors vs Ranking Points
|
|
""")
|
|
|
|
# %%
|
|
color_corr_ranking, _ = utils.transform_speaking_style_color_correlation(
|
|
joined_ranking,
|
|
SPEAKING_STYLES,
|
|
target_column="Ranking_Points"
|
|
)
|
|
S.plot_speaking_style_color_correlation(
|
|
data=color_corr_ranking,
|
|
title="Correlation: Speaking Style Colors and Voice Ranking Points"
|
|
)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Individual Traits vs Scale 1-10
|
|
""")
|
|
|
|
# %%
|
|
_content = """"""
|
|
|
|
for _style, _traits in SPEAKING_STYLES.items():
|
|
# print(f"Correlation plot for {style}...")
|
|
_fig = S.plot_speaking_style_correlation(
|
|
data=joined_scale,
|
|
style_color=_style,
|
|
style_traits=_traits,
|
|
title=f"Correlation: Speaking Style {_style} and Voice Scale 1-10",
|
|
)
|
|
_content += f"""
|
|
#### Speaking Style **{_style}**:
|
|
|
|
{mo.ui.altair_chart(_fig)}
|
|
|
|
"""
|
|
mo.md(_content)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Individual Traits vs Ranking Points
|
|
""")
|
|
|
|
# %%
|
|
_content = """"""
|
|
|
|
for _style, _traits in SPEAKING_STYLES.items():
|
|
# print(f"Correlation plot for {style}...")
|
|
_fig = S.plot_speaking_style_ranking_correlation(
|
|
data=joined_ranking,
|
|
style_color=_style,
|
|
style_traits=_traits,
|
|
title=f"Correlation: Speaking Style {_style} and Voice Ranking Points",
|
|
)
|
|
_content += f"""
|
|
#### Speaking Style **{_style}**:
|
|
|
|
{mo.ui.altair_chart(_fig)}
|
|
|
|
"""
|
|
mo.md(_content)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
## Correlations when "Best Brand Character" is chosen
|
|
|
|
Select only the traits that fit with that character
|
|
""")
|
|
|
|
# %%
|
|
from reference import ORIGINAL_CHARACTER_TRAITS
|
|
chosen_bc_traits = ORIGINAL_CHARACTER_TRAITS[BEST_CHOSEN_CHARACTER]
|
|
|
|
# %%
|
|
STYLES_SUBSET = utils.filter_speaking_styles(SPEAKING_STYLES, chosen_bc_traits)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Individual Traits vs Ranking Points
|
|
""")
|
|
|
|
# %%
|
|
_content = ""
|
|
for _style, _traits in STYLES_SUBSET.items():
|
|
_fig = S.plot_speaking_style_ranking_correlation(
|
|
data=joined_ranking,
|
|
style_color=_style,
|
|
style_traits=_traits,
|
|
title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Ranking Points"""
|
|
)
|
|
_content += f"""
|
|
{mo.ui.altair_chart(_fig)}
|
|
|
|
"""
|
|
mo.md(_content)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Individual Traits vs Scale 1-10
|
|
""")
|
|
|
|
# %%
|
|
_content = """"""
|
|
|
|
for _style, _traits in STYLES_SUBSET.items():
|
|
# print(f"Correlation plot for {style}...")
|
|
_fig = S.plot_speaking_style_correlation(
|
|
data=joined_scale,
|
|
style_color=_style,
|
|
style_traits=_traits,
|
|
title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Scale 1-10""",
|
|
)
|
|
_content += f"""
|
|
{mo.ui.altair_chart(_fig)}
|
|
|
|
"""
|
|
mo.md(_content)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Colors vs Scale 1-10 (Best Character)
|
|
""")
|
|
|
|
# %%
|
|
# Transform to get one row per color with average correlation
|
|
_color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, STYLES_SUBSET)
|
|
S.plot_speaking_style_color_correlation(
|
|
data=_color_corr_scale,
|
|
title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Scale 1-10"""
|
|
)
|
|
|
|
# %%
|
|
mo.md(r"""
|
|
### Colors vs Ranking Points (Best Character)
|
|
""")
|
|
|
|
# %%
|
|
_color_corr_ranking, _ = utils.transform_speaking_style_color_correlation(
|
|
joined_ranking,
|
|
STYLES_SUBSET,
|
|
target_column="Ranking_Points"
|
|
)
|
|
S.plot_speaking_style_color_correlation(
|
|
data=_color_corr_ranking,
|
|
title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Ranking Points"""
|
|
) |