Qumo/JPMC-quant
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ppt function to replace images

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This commit is contained in:
Luigi Maiorano
2026-01-29 15:36:34 +01:00
parent bc12df28a5
commit 3ee25f9e33
6 changed files with 326 additions and 96 deletions
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176
02_quant_analysis.py
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@@ -29,18 +29,6 @@ def _():
@app.cell(hide_code=True)
def _(mo):
mo.outline(label="Table of Contents")
return
@app.cell
def _():
# Select Dataset
return
@app.cell
def _(mo):
file_browser = mo.ui.file_browser(
initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
@@ -59,12 +47,6 @@ def _(Path, file_browser, mo):
return QSF_FILE, RESULTS_FILE
@app.cell
def _(RESULTS_FILE, mo):
mo.stop(not RESULTS_FILE.name.lower().endswith('labels.csv'), mo.md("**⚠️ Make sure you select a `_Labels.csv` file above**"))
return
@app.cell
def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
@@ -76,12 +58,6 @@ def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
@app.cell
def _():
# check_straight_liners(S.get_ss_green_blue(data_all)[0])
return
@app.cell(hide_code=True)
def _(Path, RESULTS_FILE, mo):
mo.md(f"""
@@ -112,6 +88,26 @@ def _(check_progress, data_all, duration_validation, mo):
return
@app.cell
def _(mo):
mo.md(r"""
### ⚠️ ToDo: "straight-liner" detection and removal
""")
return
@app.cell
def _(mo):
mo.md(r"""
---
# Data Filter
Use to select a subset of the data for the following analysis
""")
return
@app.cell(hide_code=True)
def _(S, mo):
filter_form = mo.md('''
@@ -147,7 +143,7 @@ def _(S, mo):
return (filter_form,)
@app.cell(hide_code=True)
@app.cell
def _(S, data_all, filter_form, mo):
mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
_d = S.filter_data(data_all, 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'])
@@ -173,6 +169,20 @@ def _(S, data, mo):
return (char_rank,)
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _():
# char_rank = S.get_character_ranking(data)[0]
return
@app.cell
def _(S, char_rank, mo):
mo.md(f"""
@@ -218,6 +228,13 @@ def _(S, data, mo):
return (v_18_8_3,)
@app.cell(hide_code=True)
def _():
# print(v_18_8_3.head())
return
@app.cell(hide_code=True)
def _(S, mo, v_18_8_3):
mo.md(f"""
@@ -240,7 +257,7 @@ def _(S, mo, v_18_8_3):
return
@app.cell(hide_code=True)
@app.cell
def _(S, calculate_weighted_ranking_scores, data):
top3_voices = S.get_top_3_voices(data)[0]
top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices)
@@ -284,6 +301,11 @@ def _(S, mo, top3_voices):
@app.cell
def _():
return
@app.cell(hide_code=True)
def _(S, data, mo, utils):
ss_or, choice_map_or = S.get_ss_orange_red(data)
ss_gb, choice_map_gb = S.get_ss_green_blue(data)
@@ -322,12 +344,12 @@ def _(S, mo, pl, ss_long):
return
@app.cell(hide_code=True)
@app.cell
def _():
return
@app.cell
@app.cell(hide_code=True)
def _(S, data, mo):
vscales = S.get_voice_scale_1_10(data)[0]
# plot_average_scores_with_counts(vscales, x_label='Voice', width=1000)
@@ -337,7 +359,7 @@ def _(S, data, mo):
return (vscales,)
@app.cell
@app.cell(hide_code=True)
def _(S, mo, vscales):
mo.md(f"""
### How does each voice score on a scale from 1-10?
@@ -352,7 +374,7 @@ def _():
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
@@ -360,7 +382,7 @@ def _(mo):
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
@@ -400,7 +422,7 @@ def _(choice_map, mo, ss_all, utils, vscales):
return df_style, joined_df
@app.cell
@app.cell(hide_code=True)
def _(S, SPEAKING_STYLES, joined_df, mo):
_content = """### Total Results
@@ -431,34 +453,18 @@ def _(mo):
- [ ] 4 correlation diagrams considering each speaking style (4) and all female voice results.
- [ ] 4 correlation diagrams considering each speaking style (4) and all male voice results.
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Correlations Voice Speaking Styles <-> Voice Ranking Points
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
Let’s show how scoring better on these speaking styles correlates (or not) with better Vocie Ranking results. For each speaking style we show how the traits in these speaking styles correlate with voice ranking points. This gives us a total of 4 correlation diagrams.
Example for speaking style green:
- Trait 1: Friendly | Conversational | Down-to-earth
- Trait 2: Approachable | Familiar | Warm
- Trait 3: Optimistic | Benevolent | Positive | Appreciative
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
### Total Results
- [ ] 4 correlation diagrams
@@ -466,7 +472,31 @@ def _(mo):
return
@app.cell
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
""")
return
@app.cell(hide_code=True)
def _(S, SPEAKING_STYLES, df_style, mo, top3_voices, utils):
df_ranking = utils.process_voice_ranking_data(top3_voices)
joined = df_style.join(df_ranking, on=['_recordId', 'Voice'], how='inner')
@@ -490,7 +520,7 @@ def _(S, SPEAKING_STYLES, df_style, mo, top3_voices, utils):
return
@app.cell(hide_code=True)
@app.cell
def _(mo):
mo.md(r"""
### Female / Male Voices considered seperately
@@ -501,49 +531,5 @@ def _(mo):
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Correlation Heatmap all evaluations <-> voice acoustic data
- [ ] Heatmap for male voices
- [ ] Heatmap for female voices
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
## Most Prominent Character Personality Traits
""")
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
The last question of the survey is about traits for the described character's personality. For each Character personality, we want to display the 8 most chosen character personality traits. This will give us a total of 4 diagrams, one for each character personality included in the test.
- [ ] Bank Teller
- [ ] Familiar Friend
- [ ] The Coach
- [ ] Personal Assistant
""")
return
@app.cell
def _(mo):
mo.md(r"""
---
# Results per subgroup
Use the dropdown selector at the top to filter the data and generate all the plots again
""")
return
if __name__ == "__main__":
app.run()