added streamlit v1

viz/prototype_charts.py

@@ -0,0 +1,134 @@
+'''
+    prototype_charts.py
+    generate test charts for later addition to streamlit
+'''
+   
+
+from pathlib import Path
+import pandas as pd
+import plotly.express as px
+import numpy as np
+
+inp = Path(r"c:/users/moses/projects/vath/analysis/jobs/f452-1/review.csv")
+out = Path("viz/")
+out.mkdir(parents=True, exist_ok=True)
+
+stance_order = ["support", "oppose", "neutral", "unknown"]
+
+# tone_order = ["positive", "negative", "neutral", "mixed", "unknown", "unclear"]
+# default order was actually better - unclear/negative/neutral/mixed/positive vs unknown/oppose/neutral/support
+# same for pct w/in stance
+df = pd.read_csv(inp)
+df["date"] = pd.to_datetime(df["date"], errors="coerce")
+df["date_day"] = df["date"].dt.date
+df["stance"] = df["stance"].fillna("unknown")
+df["tone"] = df["tone"].fillna("unknown")
+
+# 1. stance share
+counts = df["stance"].value_counts().reindex(stance_order, fill_value=0).reset_index()
+counts.columns = ["stance", "count"]
+fig = px.bar(counts, x="count", y="stance", orientation="h", text="count")
+fig.write_html(out / "stance_share.html")
+
+# 2. stance over time
+daily = df.groupby(["date_day", "stance"]).size().reset_index(name="count")
+fig = px.bar(daily, x="date_day", y="count", color="stance", category_orders={"stance": stance_order})
+fig.write_html(out / "stance_over_time.html")
+
+# 3. stance x tone
+heat = df.groupby(["stance", "tone"]).size().reset_index(name="count")
+fig = px.density_heatmap(heat, x="tone", y="stance", z="count", category_orders={"stance": stance_order})
+fig.write_html(out / "stance_tone_heatmap.html")
+
+# 4. confidence by stance
+fig = px.box(df, x="stance", y="stance_confidence", category_orders={"stance": stance_order}, points="outliers")
+fig.write_html(out / "confidence_by_stance.html")
+
+# 5. cumulative stance and share over time
+daily = (
+    df.groupby(["date_day", "stance"])
+      .size()
+      .unstack(fill_value=0)
+      .reindex(columns=stance_order, fill_value=0)
+      .sort_index()
+)
+
+cum = daily.cumsum()
+cum_long = cum.reset_index().melt(id_vars="date_day", var_name="stance", value_name="cumulative_count")
+
+fig = px.area(
+    cum_long,
+    x="date_day",
+    y="cumulative_count",
+    color="stance",
+    category_orders={"stance": stance_order},
+    title="cumulative comments by stance over time",
+)
+fig.write_html(out / "cumulative_stance_area.html")
+
+cum_pct = cum.div(cum.sum(axis=1), axis=0).reset_index().melt(
+    id_vars="date_day", var_name="stance", value_name="cumulative_share"
+)
+
+fig = px.line(
+    cum_pct,
+    x="date_day",
+    y="cumulative_share",
+    color="stance",
+    category_orders={"stance": stance_order},
+    title="cumulative stance share over time",
+)
+fig.update_yaxes(tickformat=".0%")
+fig.write_html(out / "cumulative_stance_share.html")
+
+# 7. diverging h-bar
+stance_counts = df["stance"].value_counts().reindex(stance_order, fill_value=0)
+
+div = pd.DataFrame({
+    "stance": ["oppose", "support", "neutral", "unknown"],
+    "count": [
+        -stance_counts.get("oppose", 0),
+         stance_counts.get("support", 0),
+         stance_counts.get("neutral", 0),
+         stance_counts.get("unknown", 0),
+    ],
+})
+
+fig = px.bar(
+    div,
+    x="count",
+    y="stance",
+    orientation="h",
+    text=div["count"].abs(),
+    title="support vs oppose",
+)
+fig.update_xaxes(title="comments", zeroline=True)
+fig.update_traces(textposition="outside")
+fig.write_html(out / "stance_diverging_bar.html")
+
+# 8. Stance x Tone labels
+heat = pd.crosstab(df["stance"], df["tone"]).reindex(
+    index=stance_order,
+    columns=[c for c in tone_order if c in df["tone"].unique()],
+    fill_value=0,
+)
+
+fig = px.imshow(
+    heat,
+    text_auto=True,
+    aspect="auto",
+    title="stance x tone, count",
+)
+fig.write_html(out / "stance_tone_counts.html")
+
+rowpct = heat.div(heat.sum(axis=1).replace(0, np.nan), axis=0)
+
+fig = px.imshow(
+    rowpct,
+    text_auto=".0%",
+    aspect="auto",
+    title="stance x tone, percent within stance",
+)
+fig.write_html(out / "stance_tone_rowpct.html")
+
+