knb_stats: validate PC interpretations + rank-3 reconstruction + writeup PC sections

pca_svd: exclude torn-down powersurge from the PCA, trim attractions.io
category names (trailing-space gotcha), time-side validation of PC1-PC3
interpretations, per-ride R^2 of the rank-3 reconstruction, blog-ready
PC2xPC3 scatter. writeup: PC1/PC2/PC3 narrative sections.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

.gitignore

@@ -5,3 +5,5 @@ __pycache__/
 venv/
 historical_data/
 *.log
+*.xapk
+*.apk

.streamlit/config.toml

@@ -0,0 +1,6 @@
+[theme]
+primaryColor = "#2E8B57"
+backgroundColor = "#1A1C18"
+secondaryBackgroundColor = "#2D302A"
+textColor = "#E6E6E6"
+font = "sans serif"

Dockerfile

@@ -2,9 +2,14 @@ FROM python:3.13-slim
 
 WORKDIR /app
 
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
+
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
 
-COPY attractions_api_logger.py .
+COPY . .
 
+# Default to scraper, but can be overridden in docker-compose
 ENTRYPOINT ["python", "-u", "attractions_api_logger.py"]

app.py

@@ -0,0 +1,891 @@
+# Knoebels Queue Dashboard v1.1 - Scaled wait times fix
+import streamlit as st
+import pandas as pd
+import numpy as np
+import psycopg2
+from psycopg2.extras import RealDictCursor
+import plotly.express as px
+import plotly.graph_objects as go
+import os
+from datetime import datetime, timedelta
+from dotenv import load_dotenv
+
+# Load environment variables from .env if it exists
+load_dotenv(os.path.join(os.path.dirname(__file__), '.env'))
+
+# --- Configuration ---
+st.set_page_config(
+    page_title="Knoebels Queue Dashboard",
+    page_icon="🎢",
+    layout="wide",
+)
+
+# --- Database Connection ---
+def get_db_connection():
+    try:
+        conn = psycopg2.connect(
+            host=os.environ.get('DB_HOST', '192.168.88.9'),
+            port=os.environ.get('DB_PORT', '5432'),
+            database=os.environ.get('DB_NAME', 'gp0'),
+            user=os.environ.get('DB_USER', 'kuhnobowls'),
+            password=os.environ.get('DB_PASSWORD'),
+            options="-c search_path=knoebels"
+        )
+        return conn
+    except Exception as e:
+        st.error(f"Database connection failed: {e}")
+        return None
+
+@st.cache_data(ttl=300)
+def fetch_latest_status():
+    conn = get_db_connection()
+    if not conn: return pd.DataFrame()
+    query = "SELECT * FROM dm_latest_ride_info"
+    df = pd.read_sql(query, conn)
+    conn.close()
+    if not df.empty:
+        # Safely convert and scale wait times to minutes
+        df['queue_time'] = pd.to_numeric(df['queue_time'], errors='coerce')
+        df['queue_time'] = (df['queue_time'] / 60.0).round(0)
+        # Ensure coordinates are numeric
+        df['lattitude'] = pd.to_numeric(df['lattitude'], errors='coerce')
+        df['longitude'] = pd.to_numeric(df['longitude'], errors='coerce')
+    return df
+
+@st.cache_data(ttl=600)
+def fetch_historical_trends(days=2):
+    conn = get_db_connection()
+    if not conn: return pd.DataFrame()
+    query = f"""
+    SELECT time_stamp, name, queue_time, is_operational, category
+    FROM dm_knb_live_data 
+    WHERE time_stamp > NOW() - INTERVAL '{days} days'
+    ORDER BY time_stamp ASC
+    """
+    df = pd.read_sql(query, conn)
+    conn.close()
+    if not df.empty:
+        # Safely convert and scale wait times to minutes
+        df['queue_time'] = pd.to_numeric(df['queue_time'], errors='coerce')
+        df['queue_time'] = (df['queue_time'] / 60.0).round(1)
+    return df
+
+@st.cache_data(ttl=3600)
+def fetch_categories():
+    conn = get_db_connection()
+    if not conn: return {}
+    query = 'SELECT _id, name FROM "LZ_attractions_io_categories"'
+    with conn.cursor() as cur:
+        cur.execute(query)
+        cats = {row[0]: row[1] for row in cur.fetchall()}
+    conn.close()
+    return cats
+
+# --- PCA: "reading the park's mind" ----------------------------------------
+# Reproduces the analysis from the blog post (Reading a Theme Park's Mind with
+# PCA). Standardize the time x ride wait matrix, take the top 3 components, and
+# read them as: PC1 = overall busyness, PC2 = Kiddieland vs. main midway
+# (spatial), PC3 = daytime school-group rides vs. the evening crowd.
+
+PC_LABELS = {
+    "PC1": "Overall busyness",
+    "PC2": "Kiddieland ↔ main midway",
+    "PC3": "Daytime crowd ↔ evening crowd",
+    "PC4": "Off-peak spread ↔ headliner pull",
+}
+PC_BLURB = {
+    "PC1": "How many people are in the park. Every ride loads the same sign — "
+           "when it's packed, everything spikes together.",
+    "PC2": "Which half of the park they're in. A contrast: Kiddieland corner "
+           "(negative) vs. the main midway (positive). It's geography, not the "
+           "park's own category tags.",
+    "PC3": "What time of day it is. Daytime family flat-rides the school/daycare "
+           "groups hit at open (positive) vs. the evening coaster & date-night "
+           "crowd (negative).",
+    "PC4": "The faint fourth axis (~5% of variance — diminishing returns). A "
+           "contrast between a few secondary rides (super round-up, tumbling "
+           "timbers, flying tigers — positive) and the headliners (Phoenix, "
+           "Impulse, Flying Turns — negative). Its score drifts with the "
+           "calendar: positive in quiet spring weekday mornings, negative on "
+           "busy summer/fall weekend evenings when demand piles onto the "
+           "marquee rides. Real, but mostly where the clean signal ends.",
+}
+CATEGORY_COLORS = {"Family": "#4C9BE8", "Kiddie": "#E8A33D", "Thrill": "#E0524E"}
+
+# --- Busyness tiers ("How busy is it?" tab) --------------------------------
+# Four ordinal levels of crowding, learned from the data (k-means on the
+# park-wide mean wait), low -> high. Names are deliberately playful.
+TIER_NAMES = ["Walk right on", "Pleasant", "Packed", "What were you thinking?!"]
+TIER_EMOJI = ["🚶", "😎", "😤", "🥵"]
+TIER_COLORS = ["#2E8B57", "#E8C53D", "#E8843D", "#E0524E"]  # green→gold→orange→red
+
+
+def _context_features(ts):
+    """Calendar/clock features for the forecast model. Fixed column set so
+    training and live inference always line up, regardless of which months or
+    days happen to be present."""
+    ts = pd.DatetimeIndex(ts)
+    F = pd.DataFrame(index=range(len(ts)))
+    F["hour_sin"] = np.sin(2 * np.pi * ts.hour / 24)
+    F["hour_cos"] = np.cos(2 * np.pi * ts.hour / 24)
+    F["weekend"] = (ts.weekday >= 5).astype(int)
+    for d in range(7):
+        F[f"dow_{d}"] = (ts.weekday == d).astype(int)
+    for mo in range(4, 11):  # Knoebels' April–October season
+        F[f"mo_{mo}"] = (ts.month == mo).astype(int)
+    return F
+
+
+@st.cache_data(ttl=21600)  # 6h — underlying history grows slowly, PCA is heavy
+def compute_pca():
+    """Pull the full wait history, build the standardized time x ride matrix,
+    and return (loadings, scores, explained_variance_ratio, ride_meta).
+
+    ride_meta carries each ride's category tag (trailing space trimmed) and GPS
+    coordinates, so the same DataFrame drives the behavioral map."""
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.decomposition import PCA
+    from sklearn.cluster import KMeans
+
+    conn = get_db_connection()
+    if not conn:
+        return None
+
+    waits = pd.read_sql(
+        """
+        SELECT a.time_stamp, r.ride AS ride_name, a.queue_time AS queue_time_sec
+          FROM knoebels."LZ_attractions_io" a
+          JOIN knoebels.ride_master r ON r.attractions_dot_io_id = a._id
+         WHERE a.queue_time IS NOT NULL
+        """,
+        conn,
+    )
+    meta = pd.read_sql(
+        """
+        SELECT r.ride AS ride_name,
+               trim(c.name) AS category,
+               (p.location)[0]::float AS lat,
+               (p.location)[1]::float AS lon
+          FROM knoebels.ride_master r
+          JOIN knoebels."LZ_attractions_io_poi" p ON p._id = r.attractions_dot_io_id
+          LEFT JOIN knoebels."LZ_attractions_io_categories" c ON c._id = p.category
+        """,
+        conn,
+    ).set_index("ride_name")
+    conn.close()
+
+    if waits.empty:
+        return None
+
+    waits["time_stamp"] = pd.to_datetime(waits["time_stamp"])
+    waits["queue_time_min"] = pd.to_numeric(waits["queue_time_sec"], errors="coerce") / 60.0
+
+    # long -> (time x ride) matrix: mean wait per 5-min bin, ffill then zero
+    matrix = (
+        waits.assign(ts=waits["time_stamp"].dt.floor("5min"))
+        .pivot_table(index="ts", columns="ride_name",
+                     values="queue_time_min", aggfunc="mean")
+        .sort_index()
+        .ffill()
+        .fillna(0)
+        .drop(columns=["powersurge"], errors="ignore")  # ~90% imputed, noise
+    )
+
+    scaled = StandardScaler().fit_transform(matrix)  # each ride mean 0, var 1
+    pca = PCA(n_components=4)  # PC4 is faint (~5%) — exposed for exploration only
+    sc = pca.fit_transform(scaled)
+
+    pc_cols = ["PC1", "PC2", "PC3", "PC4"]
+    scores = pd.DataFrame(sc, index=matrix.index, columns=pc_cols)
+    loadings = pd.DataFrame(pca.components_.T, index=matrix.columns, columns=pc_cols)
+    loadings = loadings.join(meta)
+
+    # Spatial "areas" from GPS alone — k-means on standardized lat/lon, the same
+    # 5 clusters the write-up uses. Names are derived, not hard-coded to a
+    # cluster index, so they survive re-clustering as the history grows:
+    #  - Kiddieland = the cluster whose rides lean most negative on PC2.
+    #  - the rest are named for the marquee ride that lands in each.
+    located = loadings.dropna(subset=["lat", "lon"])
+    if len(located) >= 5:
+        km = KMeans(n_clusters=5, n_init=10, random_state=0).fit_predict(
+            StandardScaler().fit_transform(located[["lat", "lon"]]))
+        area = pd.Series(km, index=located.index)
+        kiddie = loadings.loc[area.index].assign(a=area).groupby("a")["PC2"].mean().idxmin()
+        names = {kiddie: "Kiddieland"}
+        anchors = [("phoenix", "The Woodies"),
+                   ("impulse", "The Grand Midway"),
+                   ("flying_turns", "Flying Turns Grove"),
+                   ("giant_wheel", "Giant Wheel Corner")]
+        for ride, label in anchors:
+            if ride in area.index:
+                names.setdefault(area[ride], label)  # don't overwrite Kiddieland
+        loadings["area"] = area.map(lambda a: names.get(a, f"Area {a + 1}"))
+    else:
+        loadings["area"] = "Untagged"
+
+    return {
+        "loadings": loadings,
+        "scores": scores,
+        "evr": pca.explained_variance_ratio_,
+    }
+
+
+@st.cache_resource(ttl=21600)  # 6h — heavy; holds fitted sklearn models
+def compute_busyness_model():
+    """Learn 4 busyness tiers from the data, then train two classifiers:
+
+    - **board-reading**: per-ride waits -> tier. Near-perfect by construction
+      (the tier is derived from the mean of those very waits); it's the live
+      gauge and its coefficients name the 'bellwether' rides.
+    - **calendar forecast**: hour/day/month/weekend -> tier. The honest,
+      non-circular model — weak on the everyday tiers but it pins the rare
+      'zoo' tier, because those days are locked to specific festival weekends.
+    """
+    from sklearn.preprocessing import StandardScaler
+    from sklearn.cluster import KMeans
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.model_selection import cross_val_predict, GroupKFold, StratifiedKFold
+    from sklearn.metrics import accuracy_score, confusion_matrix
+
+    conn = get_db_connection()
+    if not conn:
+        return None
+    waits = pd.read_sql(
+        """
+        SELECT a.time_stamp, r.ride AS ride_name, a.queue_time AS queue_time_sec
+          FROM knoebels."LZ_attractions_io" a
+          JOIN knoebels.ride_master r ON r.attractions_dot_io_id = a._id
+         WHERE a.queue_time IS NOT NULL
+        """,
+        conn,
+    )
+    conn.close()
+    if waits.empty:
+        return None
+
+    waits["time_stamp"] = pd.to_datetime(waits["time_stamp"])
+    waits["queue_time_min"] = pd.to_numeric(waits["queue_time_sec"], errors="coerce") / 60.0
+    M = (
+        waits.assign(ts=waits["time_stamp"].dt.floor("5min"))
+        .pivot_table(index="ts", columns="ride_name", values="queue_time_min", aggfunc="mean")
+        .sort_index().ffill().fillna(0)
+        .drop(columns=["powersurge"], errors="ignore")
+    )
+    M = M[(M.index.hour >= 10) & (M.index.hour <= 22)]  # operating hours
+    if len(M) < 500:
+        return None
+
+    busy = M.mean(axis=1)  # busyness scalar = park-wide mean wait per snapshot
+
+    # --- learn the tiers: k-means on the 1-D busyness scalar, ordered low->high
+    km = KMeans(n_clusters=4, n_init=10, random_state=0).fit(busy.values.reshape(-1, 1))
+    rank = np.zeros(4, int)
+    rank[np.argsort(km.cluster_centers_.ravel())] = np.arange(4)
+    y = rank[km.labels_]
+    centers = np.sort(km.cluster_centers_.ravel())
+    bounds = [(centers[i] + centers[i + 1]) / 2 for i in range(3)]
+
+    # --- board-reading model: per-ride waits -> tier
+    bscaler = StandardScaler().fit(M.values)
+    Xb = bscaler.transform(M.values)
+    board = LogisticRegression(max_iter=2000, class_weight="balanced")
+    yb = cross_val_predict(board, Xb, y, cv=StratifiedKFold(5, shuffle=True, random_state=0))
+    board.fit(Xb, y)
+    bell = pd.Series(board.coef_[3], index=M.columns).sort_values()  # top-tier drivers
+
+    # --- calendar forecast model: time context -> tier (grouped by date)
+    F = _context_features(M.index)
+    cscaler = StandardScaler().fit(F.values)
+    Xc = cscaler.transform(F.values)
+    cal = LogisticRegression(max_iter=3000, class_weight="balanced")
+    yc = cross_val_predict(cal, Xc, y, cv=GroupKFold(5),
+                           groups=pd.DatetimeIndex(M.index).date)
+    cal.fit(Xc, y)
+    cal_cm = confusion_matrix(y, yc)
+
+    return {
+        "rides": list(M.columns),
+        "centers": centers, "bounds": bounds,
+        "busy_min": float(busy.min()), "busy_max": float(busy.max()),
+        "tier_share": (np.bincount(y, minlength=4) / len(y)),
+        "n": int(len(M)),
+        "board_scaler": bscaler, "board_clf": board,
+        "board_acc": float(accuracy_score(y, yb)),
+        "board_cm": confusion_matrix(y, yb),
+        "bellwether": bell,
+        "cal_scaler": cscaler, "cal_clf": cal, "cal_cols": list(F.columns),
+        "cal_acc": float(accuracy_score(y, yc)), "cal_cm": cal_cm,
+        "cal_within1": float(np.mean(np.abs(y - yc) <= 1)),
+        "baseline": float(np.mean(y == np.bincount(y).argmax())),
+        "zoo_recall": float(cal_cm[3, 3] / cal_cm[3].sum()) if cal_cm[3].sum() else 0.0,
+    }
+
+
+@st.cache_data(ttl=300)  # 5 min — the live snapshot
+def latest_ride_waits():
+    """Most recent non-null wait (minutes) per ride, plus the reading's time."""
+    conn = get_db_connection()
+    if not conn:
+        return None
+    df = pd.read_sql(
+        """
+        SELECT DISTINCT ON (r.ride) r.ride AS ride_name,
+               a.queue_time AS queue_time_sec, a.time_stamp
+          FROM knoebels."LZ_attractions_io" a
+          JOIN knoebels.ride_master r ON r.attractions_dot_io_id = a._id
+         WHERE a.queue_time IS NOT NULL
+         ORDER BY r.ride, a.time_stamp DESC
+        """,
+        conn,
+    )
+    conn.close()
+    if df.empty:
+        return None
+    waits = pd.to_numeric(df["queue_time_sec"], errors="coerce") / 60.0
+    return (pd.Series(waits.values, index=df["ride_name"]),
+            pd.to_datetime(df["time_stamp"]).max())
+
+# --- Header ---
+st.title("🎢 Knoebels Queue Time Dashboard")
+st.markdown("---")
+
+# --- Sidebar Filters ---
+st.sidebar.header("Dashboard Filters")
+latest_df = fetch_latest_status()
+categories = fetch_categories()
+
+if not latest_df.empty:
+    all_cats = sorted(latest_df['category'].dropna().unique())
+    cat_options = {c: categories.get(c, f"Category {c}") for c in all_cats}
+    selected_cats = st.sidebar.multiselect(
+        "Filter by Category", 
+        options=all_cats, 
+        format_func=lambda x: cat_options.get(x),
+        default=all_cats
+    )
+    
+    filtered_latest = latest_df[latest_df['category'].isin(selected_cats)]
+else:
+    st.warning("No data found in the database. Please check your connection and .env file.")
+    st.stop()
+
+# --- Tabs ---
+tab1, tab2, tab3, tab4, tab5 = st.tabs(
+    ["📍 Live Status", "📈 Historical Trends", "🔍 Ride Explorer",
+     "🧠 Park Mind (PCA)", "🎟️ How Busy?"]
+)
+
+with tab1:
+    # KPI Row
+    col1, col2, col3, col4 = st.columns(4)
+    open_rides = filtered_latest[filtered_latest['is_open'] == True]
+    avg_wait = open_rides['queue_time'].mean()
+    max_wait_row = open_rides.loc[open_rides['queue_time'].idxmax()] if not open_rides.empty else None
+    
+    col1.metric("Rides Open", len(open_rides))
+    col2.metric("Avg Wait Time", f"{int(avg_wait)} min" if not pd.isna(avg_wait) else "N/A")
+    if max_wait_row is not None:
+        col3.metric("Longest Queue", f"{int(max_wait_row['queue_time'])} min", f"on {max_wait_row['name']}")
+    else:
+        col3.metric("Longest Queue", "N/A")
+    col4.metric("Operational %", f"{int(len(open_rides)/len(filtered_latest)*100)}%" if len(filtered_latest) > 0 else "0%")
+
+    # Map and Table
+    mcol1, mcol2 = st.columns([2, 1])
+    
+    with mcol1:
+        st.subheader("Park Map")
+        
+        # Prepare data for map: drop rows without valid coordinates
+        map_df = filtered_latest.dropna(subset=['lattitude', 'longitude']).copy()
+        
+        if not map_df.empty:
+            # Handle NaN wait times for sizing: fill with 0 and set minimum size
+            map_df['wait_size'] = map_df['queue_time'].fillna(0).apply(lambda x: max(float(x), 5.0))
+            
+            # Plotly map for better control than st.map
+            fig_map = px.scatter_mapbox(
+                map_df,
+                lat="lattitude",
+                lon="longitude",
+                color="queue_time",
+                size="wait_size",
+                hover_name="name",
+                hover_data=["queue_status_message", "price"],
+                color_continuous_scale="Viridis",
+                zoom=15,
+                height=600,
+                mapbox_style="carto-darkmatter"
+            )
+            fig_map.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+            st.plotly_chart(fig_map, use_container_width=True)
+        else:
+            st.info("No location data available to display map.")
+
+    with mcol2:
+        st.subheader("Live Board")
+        board_df = filtered_latest[['name', 'queue_time', 'is_open', 'price']].sort_values('queue_time', ascending=False)
+        st.dataframe(
+            board_df, 
+            column_config={
+                "name": "Ride",
+                "queue_time": st.column_config.NumberColumn("Wait (min)", format="%d ⏱️"),
+                "is_open": "Status",
+                "price": st.column_config.NumberColumn("Price", format="$%.2f")
+            },
+            hide_index=True,
+            use_container_width=True
+        )
+
+with tab2:
+    st.subheader("Wait Time Trends (Last 48 Hours)")
+    hist_df = fetch_historical_trends(days=2)
+    
+    if not hist_df.empty:
+        # Filter historical data by selected categories
+        hist_df = hist_df[hist_df['category'].isin(selected_cats)]
+        
+        # Aggregate by timestamp
+        avg_hist = hist_df.groupby('time_stamp')['queue_time'].mean().reset_index()
+        
+        fig_trend = px.line(
+            avg_hist, 
+            x='time_stamp', 
+            y='queue_time', 
+            title="Park-wide Average Wait Time",
+            labels={'queue_time': 'Avg Wait (min)', 'time_stamp': 'Time'}
+        )
+        fig_trend.update_traces(line_color="#2E8B57")
+        st.plotly_chart(fig_trend, use_container_width=True)
+        
+        # Heatmap
+        st.subheader("Busiest Times of Day")
+        hist_df['hour'] = pd.to_datetime(hist_df['time_stamp']).dt.hour
+        hist_df['day'] = pd.to_datetime(hist_df['time_stamp']).dt.day_name()
+        
+        heatmap_data = hist_df.groupby(['day', 'hour'])['queue_time'].mean().unstack().fillna(0)
+        # Order days
+        days_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+        heatmap_data = heatmap_data.reindex(days_order)
+        
+        fig_heat = px.imshow(
+            heatmap_data,
+            labels=dict(x="Hour of Day", y="Day of Week", color="Avg Wait (min)"),
+            x=heatmap_data.columns,
+            y=heatmap_data.index,
+            color_continuous_scale="YlGn"
+        )
+        st.plotly_chart(fig_heat, use_container_width=True)
+    else:
+        st.info("No historical data available for the selected period.")
+
+with tab3:
+    st.subheader("Ride Explorer")
+    ride_names = sorted(latest_df['name'].unique())
+    selected_ride = st.selectbox("Select a Ride to Explore", options=ride_names)
+    
+    ride_info = latest_df[latest_df['name'] == selected_ride].iloc[0]
+    
+    ecol1, ecol2 = st.columns([1, 2])
+    
+    with ecol1:
+        st.markdown(f"### {ride_info['name']}")
+        st.write(f"**Price:** ${ride_info['price']:.2f}")
+        st.write(f"**Capacity:** {ride_info['capacity']} pph")
+        st.write(f"**Duration:** {ride_info['duration']} sec")
+        st.write(f"**Handstamp Included:** {'Yes' if ride_info['hand_stamp_included'] else 'No'}")
+        st.write(f"**Height Requirement:** {ride_info['minimum_height_requirement']}\"")
+        st.markdown("---")
+        st.write(f"**Summary:** {ride_info['summary']}")
+        if ride_info['restriction_summary']:
+            st.warning(f"**Restrictions:** {ride_info['restriction_summary']}")
+
+    with ecol2:
+        st.subheader(f"7-Day History: {selected_ride}")
+        # Fetch longer history for specific ride
+        conn = get_db_connection()
+        ride_id = ride_info['_id']
+        ride_hist_query = f"SELECT time_stamp, queue_time FROM dm_knb_live_data WHERE _id = {ride_id} AND time_stamp > NOW() - INTERVAL '7 days' ORDER BY time_stamp ASC"
+        ride_hist_df = pd.read_sql(ride_hist_query, conn)
+        conn.close()
+        
+        if not ride_hist_df.empty:
+            # Convert to minutes
+            ride_hist_df['queue_time'] = pd.to_numeric(ride_hist_df['queue_time'], errors='coerce')
+            ride_hist_df['queue_time'] = (ride_hist_df['queue_time'] / 60.0).round(1)
+            
+            fig_ride_hist = px.area(
+                ride_hist_df, 
+                x='time_stamp', 
+                y='queue_time', 
+                labels={'queue_time': 'Wait Time (min)', 'time_stamp': 'Time'}
+            )
+            fig_ride_hist.update_traces(line_color="#2E8B57", fillcolor="rgba(46, 139, 87, 0.3)")
+            st.plotly_chart(fig_ride_hist, use_container_width=True)
+        else:
+            st.info("No historical data found for this ride.")
+
+with tab4:
+    st.subheader("🧠 What two seasons of wait times reveal about the park")
+    st.markdown(
+        "Feed every ride's wait-time history into **Principal Component Analysis** "
+        "— with *no* labels for what any ride is or where it sits — and three "
+        "hidden axes fall out on their own. They turn out to be **how busy the "
+        "park is**, **which half of it you're in**, and **what time of day it is**. "
+        "[Full write-up here.](https://blog.c0smere.net/reading-a-theme-parks-mindwith-pca/)"
+    )
+
+    try:
+        pca = compute_pca()
+    except ImportError:
+        st.error("`scikit-learn` isn't installed in this image yet — rebuild the "
+                 "dashboard container to enable the PCA tab.")
+        pca = None
+
+    if pca is None:
+        st.info("Not enough data to compute the components right now.")
+    else:
+        loadings, scores, evr = pca["loadings"], pca["scores"], pca["evr"]
+
+        # --- the three axes, with their explained variance ---
+        st.markdown("#### The three axes PCA found")
+        cols = st.columns(3)
+        for col, pc in zip(cols, ["PC1", "PC2", "PC3"]):
+            col.metric(f"{pc} · {PC_LABELS[pc]}", f"{evr[int(pc[-1]) - 1]:.1%}",
+                       help=PC_BLURB[pc])
+        st.caption(f"Just these 3 axes (out of {loadings.shape[0]}) capture "
+                   f"**{evr[:3].sum():.1%}** of all the variation in park wait times.")
+
+        st.markdown("---")
+
+        # --- the behavioral map: any two components, freely colored ---
+        st.markdown("#### The map of the park, drawn from wait times alone")
+        st.markdown(
+            "Each ride placed by two of its component loadings — its *behavioral* "
+            "position, drawn purely from how its queue moves over time. The "
+            "default (**PC2 × PC3**, colored by category) is the write-up's view: "
+            "Kiddieland huddles left, evening thrill rides sink bottom-right, "
+            "daytime family rides float to the top. **Swap either axis** to any "
+            "component, or **recolor by a third component's loading**, to hunt for "
+            "structure the default hides."
+        )
+        pcs = ["PC1", "PC2", "PC3", "PC4"]
+        axis_labels = {p: f"{p} · {PC_LABELS[p]}" for p in pcs}
+        axis_labels.update({"category": "Park's tag", "area": "Spatial area"})
+        mc1, mc2, mc3 = st.columns(3)
+        with mc1:
+            xpc = st.selectbox("X axis", pcs, index=1, key="map_x",
+                               format_func=lambda p: f"{p} — {PC_LABELS[p]}")
+        with mc2:
+            ypc = st.selectbox("Y axis", pcs, index=2, key="map_y",
+                               format_func=lambda p: f"{p} — {PC_LABELS[p]}")
+        with mc3:
+            color_opt = st.selectbox(
+                "Color by", ["Category tag", "Spatial area"] + [f"{p} loading" for p in pcs],
+                key="map_color")
+
+        if xpc == ypc:
+            st.info("Pick two *different* components for the axes.")
+        else:
+            plot_df = loadings.reset_index().copy()
+            plot_df["category"] = plot_df["category"].fillna("Untagged")
+            common = dict(x=xpc, y=ypc, text="ride_name", hover_name="ride_name",
+                          height=620, labels=axis_labels)
+            if color_opt == "Category tag":
+                fig_map_pca = px.scatter(plot_df, color="category",
+                    color_discrete_map={**CATEGORY_COLORS, "Untagged": "#888"}, **common)
+            elif color_opt == "Spatial area":
+                fig_map_pca = px.scatter(plot_df, color="area", **common)
+            else:
+                cpc = color_opt.split()[0]  # "PC3 loading" -> "PC3"
+                lim = plot_df[cpc].abs().max()  # symmetric scale → white at 0
+                fig_map_pca = px.scatter(plot_df, color=cpc, color_continuous_scale="RdBu",
+                    range_color=[-lim, lim], **common)
+            fig_map_pca.update_traces(textposition="top center",
+                                      textfont=dict(size=9, color="#9aa0a6"),
+                                      marker=dict(size=11, line=dict(width=1, color="#1A1C18")))
+            fig_map_pca.add_hline(y=0, line_width=0.5, line_color="grey")
+            fig_map_pca.add_vline(x=0, line_width=0.5, line_color="grey")
+            fig_map_pca.update_layout(legend=dict(orientation="h", yanchor="bottom", y=1.02))
+            st.plotly_chart(fig_map_pca, use_container_width=True)
+
+        st.markdown("---")
+
+        # --- the same idea in 3D: add PC1 as a third axis -------------------
+        st.markdown("#### The full 3-D picture")
+        st.markdown(
+            "The map above flattens out **PC1** (overall busyness) to fit on a "
+            "page. Here are both clouds in their native 3-D — click-drag to spin, "
+            "scroll to zoom. Two different things get plotted:"
+        )
+
+        rides_tab, moments_tab = st.tabs(
+            ["🎢 Rides in PC-space", "⏱️ Moments in PC-space"])
+
+        # ---- A) each RIDE as a point (the loadings) ----
+        with rides_tab:
+            st.caption(
+                "Every **ride** placed by how its queue behaves. Notice the cloud "
+                "is nearly flat along **PC1** — almost every ride loads the same "
+                "way on overall-busyness, so it barely separates them. All the "
+                "real structure lives in the PC2/PC3 plane you saw above.")
+            ride_color = st.selectbox(
+                "Color the rides by",
+                ["Park category tag", "Spatial area (GPS k-means)",
+                 "Overall busyness (PC1 loading)"],
+                key="ride_color3d")
+            rdf = loadings.reset_index().copy()
+            rdf["category"] = rdf["category"].fillna("Untagged")
+            common = dict(x="PC1", y="PC2", z="PC3", hover_name="ride_name", height=640)
+            if ride_color == "Park category tag":
+                fig3d_r = px.scatter_3d(rdf, color="category",
+                    color_discrete_map={**CATEGORY_COLORS, "Untagged": "#888"}, **common)
+            elif ride_color == "Spatial area (GPS k-means)":
+                fig3d_r = px.scatter_3d(rdf, color="area", **common)
+            else:
+                fig3d_r = px.scatter_3d(rdf, color="PC1",
+                    color_continuous_scale="Viridis", **common)
+            fig3d_r.update_traces(marker=dict(size=5, line=dict(width=0.5, color="#1A1C18")))
+            fig3d_r.update_layout(
+                margin=dict(l=0, r=0, t=0, b=0),
+                scene=dict(xaxis_title="PC1 · busyness",
+                           yaxis_title="PC2 · Kiddieland↔midway",
+                           zaxis_title="PC3 · evening↔daytime"))
+            st.plotly_chart(fig3d_r, use_container_width=True)
+
+        # ---- B) each MOMENT as a point (the scores) ----
+        with moments_tab:
+            st.caption(
+                "Every **5-minute snapshot** of the park as one point — this is the "
+                "direct analog of an embedding cloud, but each dot is a *moment*, "
+                "not a note. Unlike the rides, the moments spread on all three "
+                "axes: a packed evening sits far out on PC1, a kiddie-heavy morning "
+                "pulls toward the PC2/PC3 corners.")
+            mom_color = st.selectbox(
+                "Color the moments by",
+                ["Hour of day", "Day of week", "Weekend vs. weekday", "Month"],
+                key="mom_color3d")
+            sdf = scores.copy()
+            idx = pd.DatetimeIndex(sdf.index)
+            sdf["Hour of day"] = idx.hour
+            sdf["Day of week"] = idx.day_name()
+            sdf["Weekend vs. weekday"] = np.where(idx.weekday >= 5, "Weekend", "Weekday")
+            sdf["Month"] = idx.month_name()
+            # keep it responsive — sample if the history is large
+            cap = 15000
+            note = ""
+            if len(sdf) > cap:
+                sdf = sdf.sample(cap, random_state=0)
+                note = f"  *(showing a random {cap:,} of {len(scores):,} snapshots)*"
+            common_m = dict(x="PC1", y="PC2", z="PC3", height=640, opacity=0.55)
+            if mom_color == "Hour of day":
+                fig3d_m = px.scatter_3d(sdf, color="Hour of day",
+                    color_continuous_scale="Turbo", **common_m)
+            elif mom_color == "Month":
+                order = [pd.Timestamp(2020, m, 1).month_name() for m in range(1, 13)]
+                fig3d_m = px.scatter_3d(sdf, color="Month",
+                    category_orders={"Month": order}, **common_m)
+            elif mom_color == "Day of week":
+                order = ["Monday", "Tuesday", "Wednesday", "Thursday",
+                         "Friday", "Saturday", "Sunday"]
+                fig3d_m = px.scatter_3d(sdf, color="Day of week",
+                    category_orders={"Day of week": order}, **common_m)
+            else:
+                fig3d_m = px.scatter_3d(sdf, color="Weekend vs. weekday",
+                    color_discrete_map={"Weekday": "#4C9BE8", "Weekend": "#E0524E"},
+                    **common_m)
+            fig3d_m.update_traces(marker=dict(size=2.5))
+            fig3d_m.update_layout(
+                margin=dict(l=0, r=0, t=0, b=0),
+                scene=dict(xaxis_title="PC1 · busyness",
+                           yaxis_title="PC2 · Kiddieland↔midway",
+                           zaxis_title="PC3 · evening↔daytime"))
+            st.plotly_chart(fig3d_m, use_container_width=True)
+            if note:
+                st.caption(note.strip(" *"))
+
+        st.markdown("---")
+
+        # --- loadings explorer: which rides drive a chosen component ---
+        lcol, rcol = st.columns([1, 2])
+        with lcol:
+            st.markdown("#### Read an axis")
+            pick = st.radio("Component", ["PC1", "PC2", "PC3", "PC4"],
+                            format_func=lambda p: f"{p} — {PC_LABELS[p]}")
+            st.info(PC_BLURB[pick])
+        with rcol:
+            ranked = loadings[[pick]].copy().sort_values(pick)
+            ranked["ride"] = ranked.index
+            lim = ranked[pick].abs().max()  # symmetric so the scale's white sits at 0
+            fig_load = px.bar(
+                ranked, x=pick, y="ride", orientation="h",
+                color=pick, color_continuous_scale="RdBu", range_color=[-lim, lim],
+                labels={pick: f"{pick} loading", "ride": ""},
+                height=max(420, 16 * len(ranked)),
+            )
+            fig_load.update_layout(coloraxis_showscale=False,
+                                   margin={"l": 0, "r": 0, "t": 10, "b": 0})
+            st.plotly_chart(fig_load, use_container_width=True)
+
+        st.markdown("---")
+
+        # --- the clock: each axis' average score by hour of day ---
+        st.markdown("#### How each axis moves over a day")
+        st.markdown(
+            "Average score for each component by hour. **PC1** climbs into the "
+            "evening as crowds build; **PC3** humps at midday (the school-group "
+            "window) then falls — the fingerprint of *time of day*."
+        )
+        by_hour = scores[["PC1", "PC2", "PC3"]].groupby(scores.index.hour).mean()
+        by_hour.index.name = "hour"
+        hour_long = by_hour.reset_index().melt("hour", var_name="Component",
+                                               value_name="Mean score")
+        fig_hour = px.line(
+            hour_long, x="hour", y="Mean score", color="Component", markers=True,
+            labels={"hour": "Hour of day (local)"},
+            color_discrete_map={"PC1": "#2E8B57", "PC2": "#E8A33D", "PC3": "#4C9BE8"},
+        )
+        fig_hour.add_hline(y=0, line_width=0.5, line_color="grey")
+        st.plotly_chart(fig_hour, use_container_width=True)
+
+with tab5:
+    st.subheader("🎟️ How busy is Knoebels right now?")
+    st.markdown(
+        "Two seasons of wait times sort the park into **four levels of crowding** "
+        "(k-means on the park-wide average wait), from a quiet walk-on day to the "
+        "rare zoo. A model rates the **current** moment — and a second model asks "
+        "the harder question: *how busy* should *it be right now?*"
+    )
+
+    try:
+        B = compute_busyness_model()
+    except ImportError:
+        st.error("`scikit-learn` isn't installed in this image yet — rebuild the "
+                 "dashboard container to enable this tab.")
+        B = None
+
+    if B is None:
+        st.info("Not enough data to train the busyness model right now.")
+    else:
+        # tier scale legend
+        edges = [B["busy_min"], *B["bounds"], B["busy_max"]]
+        legend = "  ·  ".join(
+            f"{TIER_EMOJI[i]} **{TIER_NAMES[i]}** ({edges[i]:.0f}–{edges[i+1]:.0f} min, "
+            f"{B['tier_share'][i]:.0%})" for i in range(4))
+        st.caption("The four levels (avg wait across rides, share of all snapshots):  " + legend)
+
+        live = latest_ride_waits()
+        st.markdown("---")
+        gcol, ecol = st.columns([3, 2])
+
+        if live is None:
+            gcol.info("No recent readings to rate.")
+        else:
+            waits, ts = live
+            vec = waits.reindex(B["rides"]).fillna(0.0)  # closed/missing = no line
+            cur = float(vec.mean())
+            p = np.zeros(4)
+            proba = B["board_clf"].predict_proba(
+                B["board_scaler"].transform(vec.values.reshape(1, -1)))[0]
+            for c, pp in zip(B["board_clf"].classes_, proba):
+                p[int(c)] = pp
+            tier = int(p.argmax())
+
+            with gcol:
+                gauge = go.Figure(go.Indicator(
+                    mode="gauge+number", value=round(cur, 1),
+                    number={"suffix": " min", "font": {"size": 40}},
+                    gauge={
+                        "axis": {"range": [B["busy_min"], B["busy_max"]]},
+                        "bar": {"color": "rgba(0,0,0,0)"},
+                        "steps": [{"range": [edges[i], edges[i + 1]], "color": TIER_COLORS[i]}
+                                  for i in range(4)],
+                        "threshold": {"line": {"color": "white", "width": 5}, "value": cur},
+                    },
+                    title={"text": f"{TIER_EMOJI[tier]}  <b>{TIER_NAMES[tier]}</b>",
+                           "font": {"size": 26}},
+                ))
+                gauge.update_layout(height=320, margin=dict(l=30, r=30, t=60, b=0))
+                st.plotly_chart(gauge, use_container_width=True)
+                st.caption(f"Reading from **{ts:%b %d, %-I:%M %p}** · park-wide average "
+                           f"wait **{cur:.1f} min** · model confidence **{p[tier]:.0%}**")
+
+            with ecol:
+                st.markdown("##### Model's call")
+                conf = pd.DataFrame({"Level": TIER_NAMES, "Probability": p})
+                fig_conf = px.bar(conf, x="Probability", y="Level", orientation="h",
+                                  color="Level", color_discrete_sequence=TIER_COLORS,
+                                  range_x=[0, 1])
+                fig_conf.update_layout(showlegend=False, height=200,
+                                       yaxis={"categoryorder": "array",
+                                              "categoryarray": TIER_NAMES[::-1]},
+                                       margin=dict(l=0, r=0, t=0, b=0))
+                st.plotly_chart(fig_conf, use_container_width=True)
+
+                # expected for this day/time, from the calendar model
+                Fn = _context_features(pd.DatetimeIndex([ts])).reindex(
+                    columns=B["cal_cols"], fill_value=0)
+                exp = int(B["cal_clf"].predict(B["cal_scaler"].transform(Fn.values))[0])
+                verdict = ("about what you'd expect" if exp == tier else
+                           "**busier** than usual" if tier > exp else "**quieter** than usual")
+                st.markdown(
+                    f"##### Expected vs. actual\n"
+                    f"For a **{ts:%A in %B}**, a typical moment is "
+                    f"**{TIER_NAMES[exp]}** {TIER_EMOJI[exp]}. Right now it's "
+                    f"**{TIER_NAMES[tier]}** {TIER_EMOJI[tier]} — {verdict}.")
+
+        st.markdown("---")
+
+        # --- bellwether rides + the honest model cards ---
+        bcol, mcol = st.columns([2, 3])
+        with bcol:
+            st.markdown("##### Bellwether rides")
+            st.caption("Lines that most signal a *zoo* day, from the board model's "
+                       "coefficients. (Loadings are noisy under collinearity — read "
+                       "as a leaderboard, not exact weights.)")
+            top = B["bellwether"].tail(10).iloc[::-1]
+            fig_bell = px.bar(x=top.values, y=top.index, orientation="h",
+                              labels={"x": "coefficient (→ Zoo tier)", "y": ""},
+                              color=top.values, color_continuous_scale="OrRd")
+            fig_bell.update_layout(coloraxis_showscale=False, height=360,
+                                   yaxis={"categoryorder": "total ascending"},
+                                   margin=dict(l=0, r=0, t=0, b=0))
+            st.plotly_chart(fig_bell, use_container_width=True)
+
+        with mcol:
+            st.markdown("##### Two models, two honest stories")
+            m1, m2 = st.columns(2)
+            m1.metric("Board-reading accuracy", f"{B['board_acc']:.1%}",
+                      help="Per-ride waits → tier. Near-perfect by construction — the "
+                           "tier is the mean of those very waits, so this just 'reads "
+                           "the board'. Great gauge, trivial as prediction.")
+            m2.metric("Calendar-forecast accuracy", f"{B['cal_acc']:.1%}",
+                      delta=f"{B['cal_acc'] - B['baseline']:+.1%} vs. guessing",
+                      help="Hour/day/month/weekend → tier. The non-circular model.")
+            st.markdown(
+                f"**The chaos has a schedule.** From the calendar alone you *can't* "
+                f"tell a quiet day from a pleasant one — crowd noise (weather, events, "
+                f"luck) swamps it, so the forecast model ({B['cal_acc']:.0%}) barely "
+                f"beats always-guessing ({B['baseline']:.0%}). **But it flags the zoo "
+                f"days {B['zoo_recall']:.0%} of the time** — because Knoebels' worst "
+                f"crowds are locked to specific October festival weekends. The everyday "
+                f"is unpredictable; the extreme is on the calendar.")
+            with st.expander("Calendar-model confusion matrix (where it succeeds & fails)"):
+                cm = B["cal_cm"]
+                cmn = cm / cm.sum(axis=1, keepdims=True)
+                fig_cm = px.imshow(cmn, x=TIER_NAMES, y=TIER_NAMES, text_auto=".0%",
+                                   color_continuous_scale="Blues", zmin=0, zmax=1,
+                                   labels={"x": "predicted", "y": "actual",
+                                           "color": "row share"})
+                fig_cm.update_layout(height=380, margin=dict(l=0, r=0, t=10, b=0))
+                st.plotly_chart(fig_cm, use_container_width=True)
+                st.caption("Rows sum to 100%. Note the bottom-right cell: the rare "
+                           "'What were you thinking?!' tier is caught almost every time, "
+                           "while the three everyday tiers bleed into each other.")
+
+# --- Footer ---
+st.sidebar.markdown("---")
+st.sidebar.caption(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
+if st.sidebar.button("Force Refresh Data"):
+    st.cache_data.clear()
+    st.rerun()

docker-compose.yml

@@ -3,12 +3,57 @@ services:
     build: .
     image: kuh-no-bowls:latest
     container_name: kuh-no-bowls-scraper
+    profiles: ["oneshot"]
     env_file: .env
     environment:
       LOG_PATH: /var/log/kuh-no-bowls/kuh-no-bowls.log
       TZ: America/New_York
     volumes:
       - knb_logs:/var/log/kuh-no-bowls
+    networks:
+      - services_net
+
+  # Hourly Open-Meteo weather -> knoebels."LZ_open_meteo_hourly".
+  # Oneshot, run from cron. Default mode does the recent sync; pass --backfill
+  # once to load history: docker compose --profile oneshot run --rm weather --backfill
+  weather:
+    build: .
+    image: kuh-no-bowls:latest
+    container_name: kuh-no-bowls-weather
+    profiles: ["oneshot"]
+    entrypoint: ["python", "-u", "weather_logger.py"]
+    env_file: .env
+    environment:
+      LOG_PATH: /var/log/kuh-no-bowls/weather.log
+      TZ: America/New_York
+    volumes:
+      - knb_logs:/var/log/kuh-no-bowls
+    networks:
+      - services_net
+
+  dashboard:
+    build: .
+    image: kuh-no-bowls:latest
+    container_name: kuh-no-bowls-dashboard
+    restart: unless-stopped
+    env_file: .env
+    entrypoint: ["streamlit", "run", "app.py", "--server.port=8502", "--server.address=0.0.0.0"]
+    ports:
+      - "8502:8502"
+    volumes:
+      - ./app.py:/app/app.py:ro
+      - ./.streamlit:/app/.streamlit:ro
+    healthcheck:
+      test: ["CMD", "curl", "-fsS", "http://localhost:8502/_stcore/health"]
+      interval: 30s
+      timeout: 5s
+      retries: 3
+    networks:
+      - services_net
 
 volumes:
   knb_logs:
+
+networks:
+  services_net:
+    external: true

import_poi_data.py

@@ -1,118 +1,166 @@
-'''
-Importing POI data from attractions.io api
-'''
-#!/usr/bin/env python3
-
-from datetime import datetime, date
-import json, requests, psycopg2
-from psycopg2 import sql
-
-db_params = {
-    'dbname': 'gp0',
-    'user': 'kuhnobowls',
-    'password': 'saddog095',
-    'host': '192.168.88.5',
-    'port': '5432',
-    'options': '-c search_path=knoebels'
-}
-
-def log_to_database(table_name, data):
-    with psycopg2.connect(**db_params) as conn:
-        with conn.cursor() as cursor:
-            columns = len(data[0])
-            query = sql.SQL("INSERT INTO {table} VALUES ({values})").format(
-            table=sql.Identifier(table_name),
-            values=sql.SQL(', ').join(sql.Placeholder() * columns)
-            )
-            
-            cursor.executemany(query, data)
-        conn.commit()
-
-def generic_query(q):
-    ret = []
-    with psycopg2.connect(**db_params) as conn:
-        with conn.cursor() as cursor:
-            cursor.execute(q)
-            rows = cursor.fetchall()
-            col_names = [desc[0] for desc in cursor.description]
-            
-            for row in rows:
-                ret.append(row)
-                    
-    return ret
-
-data = None
-
-with open('records.json', 'r', encoding='utf-8') as file:
-    data = json.load(file)
-
-rows = []
-
-for poi in data['Item']:
-    aoi_id = poi['_id']
-    Name = poi['Name']
-    AbbreviatedName = poi['AbbreviatedName']
-    Summary = poi['Summary']
-    Keywords = poi['Keywords']
-    DefaultImage = poi['DefaultImage']
-    Location = poi['Location']
-    Featured = poi['Featured']
-    WayfindingEnabled = poi['WayfindingEnabled']    
-    VisibleOnMap = poi['VisibleOnMap']    
-    Category = poi['Category']    
-    Parent = poi['Parent']
-
-    try:
-        MenuURL = poi['MenuURL']
-    except:
-        MenuURL = None
-
-    try:
-        MinimumHeightRequirement = poi['MinimumHeightRequirement']
-    except:
-        MinimumHeightRequirement = None
-
-    try:
-        MinimumUnaccompaniedHeightRequirement = poi['MinimumUnaccompaniedHeightRequirement']
-    except:
-        MinimumUnaccompaniedHeightRequirement = None
-
-    try:
-        MaximumHeightRequirement = poi['MaximumHeightRequirement']
-    except:
-        MaximumHeightRequirement = None
-
-    try:
-        MinimumAgeRequirement = poi['MinimumAgeRequirement']
-    except:
-        MinimumAgeRequirement = None
-
-    try:
-        MinimumUnaccompaniedAgeRequirement = poi['MinimumUnaccompaniedAgeRequirement']
-    except:
-        MinimumUnaccompaniedAgeRequirement = None
-
-    try:
-        MaximumAgeRequirement = poi['MaximumAgeRequirement']
-    except:
-        MaximumAgeRequirement = None
-
-    try:
-        RestrictionSummary = poi['RestrictionSummary']
-    except:
-        RestrictionSummary = None
-
-    row = [aoi_id, Name, AbbreviatedName, Summary, Keywords, DefaultImage, Location, Featured, WayfindingEnabled, VisibleOnMap, Category, Parent, MenuURL, MinimumHeightRequirement, MinimumUnaccompaniedHeightRequirement, MaximumHeightRequirement, MinimumAgeRequirement, MinimumUnaccompaniedAgeRequirement, MaximumAgeRequirement, RestrictionSummary]
-    rows.append(row)
-
-cats = []
-
-for cat in data['Category']:
-    aoi_id = cat['_id']
-    Name = cat['Name']
-    Parent = cat['Parent']
-    row = [aoi_id, Name, Parent]
-    cats.append(row)
-
-log_to_database('LZ_attractions_io_categories', cats)
-#log_to_database('LZ_attractions_io_poi', rows)
+#!/usr/bin/env python3
+"""Import POI and category data from the attractions.io records bundle."""
+
+import io
+import json
+import logging
+import os
+import sys
+import uuid
+import zipfile
+from datetime import datetime, timezone
+
+import psycopg2
+import requests
+from psycopg2.extras import execute_values
+
+API_BASE = 'https://api.attractions.io/v1'
+API_KEY = '72f0ea9e-d196-508a-bee8-cce62c3228c7'  # Knoebels app id (BuildConfig.API_KEY)
+APP_VERSION = '1.3.2'   # bump if a newer xapk reveals drift
+APP_BUILD = 70
+NATIVE_VERSION = '1.0.129+6c346f2'
+
+DB_PASSWORD = os.environ.get('DB_PASSWORD')
+if not DB_PASSWORD:
+    sys.stderr.write("ERROR: DB_PASSWORD env var is required\n")
+    sys.exit(1)
+
+db_params = {
+    'dbname': 'gp0',
+    'user': os.environ.get('DB_USER', 'kuhnobowls'),
+    'password': DB_PASSWORD,
+    'host': os.environ.get('DB_HOST', '192.168.88.9'),
+    'port': os.environ.get('DB_PORT', '5432'),
+    'options': '-c search_path=knoebels',
+}
+
+CATEGORY_COLUMNS = ('_id', 'name', 'parent')
+
+POI_COLUMNS = (
+    '_id', 'name', 'abbreviated_name', 'summary', 'keywords', 'default_image',
+    'location', 'featured', 'wayfinding_enabled', 'visible_on_map', 'category',
+    'parent', 'menu_url', 'minimum_height_requirement',
+    'minimum_unaccompanied_height_requirement', 'maximum_height_requirement',
+    'minimum_age_requirement', 'minimum_unaccompanied_age_requirement',
+    'maximum_age_requirement', 'restriction_summary',
+)
+
+
+def _occasio_headers(installation_token=None):
+    auth = f'Attractions-Io api-key="{API_KEY}"'
+    if installation_token:
+        auth += f', installation-token="{installation_token}"'
+    return {
+        'Authorization': auth,
+        'X-Idempotency-Key': str(uuid.uuid4()),
+        'Date': datetime.now(timezone.utc).strftime('%Y-%m-%dT%H:%M:%SZ'),
+        'Occasio-Platform': 'Android',
+        'Occasio-Platform-Version': '14',
+        'Occasio-App-Version': APP_VERSION,
+        'Occasio-App-Build': str(APP_BUILD),
+        'Occasio-Native-Version': NATIVE_VERSION,
+    }
+
+
+def fetch_records_bundle():
+    """Two-step Occasio data fetch: register an installation, then pull the data zip."""
+    install_resp = requests.post(
+        f'{API_BASE}/installation',
+        headers=_occasio_headers(),
+        files={
+            'device_identifier': (None, '123'),
+            'user_identifier': (None, str(uuid.uuid4())),
+            'app_build': (None, str(APP_BUILD)),
+            'app_version': (None, APP_VERSION),
+        },
+    )
+    install_resp.raise_for_status()
+    token = install_resp.json()['token']
+
+    data_resp = requests.get(
+        f'{API_BASE}/data',
+        headers=_occasio_headers(installation_token=token),
+    )
+    data_resp.raise_for_status()
+
+    with zipfile.ZipFile(io.BytesIO(data_resp.content)) as zf:
+        with zf.open('records.json') as f:
+            return json.load(f)
+
+
+def _location_to_point(loc):
+    # Location arrives as 'lat,lon'; Postgres point input wants '(x,y)'.
+    if not loc:
+        return None
+    return f'({loc})'
+
+
+def _category_row(cat):
+    return (cat['_id'], cat['Name'], cat.get('Parent'))
+
+
+def _poi_row(poi):
+    return (
+        poi['_id'],
+        poi.get('Name'),
+        poi.get('AbbreviatedName'),
+        poi.get('Summary'),
+        poi.get('Keywords'),
+        poi.get('DefaultImage'),
+        _location_to_point(poi.get('Location')),
+        poi.get('Featured'),
+        poi.get('WayfindingEnabled'),
+        poi.get('VisibleOnMap'),
+        poi.get('Category'),
+        poi.get('Parent'),
+        poi.get('MenuURL'),
+        poi.get('MinimumHeightRequirement'),
+        poi.get('MinimumUnaccompaniedHeightRequirement'),
+        poi.get('MaximumHeightRequirement'),
+        poi.get('MinimumAgeRequirement'),
+        poi.get('MinimumUnaccompaniedAgeRequirement'),
+        poi.get('MaximumAgeRequirement'),
+        poi.get('RestrictionSummary'),
+    )
+
+
+def upsert_rows(conn, table, columns, rows, *, location_column=None):
+    if not rows:
+        return 0
+    cols_sql = ', '.join(f'"{c}"' for c in columns)
+    update_sql = ', '.join(f'"{c}" = EXCLUDED."{c}"' for c in columns if c != '_id')
+    # Force the 'point' cast on the location column so multi-row VALUES doesn't
+    # fall back to inferring the type as text.
+    placeholders = ', '.join(
+        '%s::point' if c == location_column else '%s' for c in columns
+    )
+    template = f'({placeholders})'
+    query = (
+        f'INSERT INTO knoebels."{table}" ({cols_sql}) VALUES %s '
+        f'ON CONFLICT (_id) DO UPDATE SET {update_sql}'
+    )
+    with conn.cursor() as cur:
+        execute_values(cur, query, rows, template=template)
+    return len(rows)
+
+
+def main():
+    logging.basicConfig(format='%(asctime)s %(levelname)s %(message)s', level=logging.INFO)
+
+    logging.info('Fetching records bundle from attractions.io...')
+    data = fetch_records_bundle()
+    logging.info('Got %d Items, %d Categories', len(data['Item']), len(data['Category']))
+
+    cats = [_category_row(c) for c in data['Category']]
+    pois = [_poi_row(p) for p in data['Item']]
+
+    with psycopg2.connect(**db_params) as conn:
+        n_cats = upsert_rows(conn, 'LZ_attractions_io_categories', CATEGORY_COLUMNS, cats)
+        n_pois = upsert_rows(conn, 'LZ_attractions_io_poi', POI_COLUMNS, pois,
+                             location_column='location')
+        conn.commit()
+    logging.info('Upserted %d categories, %d POIs', n_cats, n_pois)
+
+
+if __name__ == '__main__':
+    main()

knb_stats/.gitignore

@@ -0,0 +1,8 @@
+.env
+.venv/
+__pycache__/
+*.pyc
+.ipynb_checkpoints/
+.ghost
+post_assets/
+preview.html

queries/dm_knb_queue_weather.sql

@@ -0,0 +1,61 @@
+-- Full queue-time time series joined to hourly Open-Meteo weather.
+-- Queue readings (sub-hour, every ~5 min) are bucketed to the hour and matched
+-- to knoebels."LZ_open_meteo_hourly". Both sides are naive America/New_York, so
+-- the hour buckets line up directly with no timezone math. LEFT JOIN keeps every
+-- queue row even if an hour's weather is missing (e.g. the once-a-year DST hour).
+SELECT a.time_stamp,
+       a._id                  AS ride_id,
+       TRIM(r.name)           AS ride_name,
+       TRIM(c."name")         AS category,
+       ROUND((r.minimum_height_requirement * 39.37)::numeric, 0)                AS minimum_height_requirement_inches,
+       ROUND((r.minimum_unaccompanied_height_requirement * 39.37)::numeric, 0)  AS minimum_unaccompanied_height_requirement_inches,
+       ROUND((r.maximum_height_requirement * 39.37)::numeric, 0)                AS maximum_height_requirement_inches,
+       r."location"           AS coordinates,
+       a.queue_time           AS queue_time_sec,
+       w.temperature_2m       AS temperature_f,
+       w.apparent_temperature AS feels_like_f,
+       w.relative_humidity_2m AS humidity_pct,
+       w.precipitation        AS precipitation_in,
+       w.rain                 AS rain_in,
+       w.weather_code         AS wmo_weather_code,
+       CASE w.weather_code
+           WHEN 0  THEN 'Clear sky'
+           WHEN 1  THEN 'Mainly clear'
+           WHEN 2  THEN 'Partly cloudy'
+           WHEN 3  THEN 'Overcast'
+           WHEN 45 THEN 'Fog'
+           WHEN 48 THEN 'Depositing rime fog'
+           WHEN 51 THEN 'Light drizzle'
+           WHEN 53 THEN 'Moderate drizzle'
+           WHEN 55 THEN 'Dense drizzle'
+           WHEN 56 THEN 'Light freezing drizzle'
+           WHEN 57 THEN 'Dense freezing drizzle'
+           WHEN 61 THEN 'Slight rain'
+           WHEN 63 THEN 'Moderate rain'
+           WHEN 65 THEN 'Heavy rain'
+           WHEN 66 THEN 'Light freezing rain'
+           WHEN 67 THEN 'Heavy freezing rain'
+           WHEN 71 THEN 'Slight snowfall'
+           WHEN 73 THEN 'Moderate snowfall'
+           WHEN 75 THEN 'Heavy snowfall'
+           WHEN 77 THEN 'Snow grains'
+           WHEN 80 THEN 'Slight rain showers'
+           WHEN 81 THEN 'Moderate rain showers'
+           WHEN 82 THEN 'Violent rain showers'
+           WHEN 85 THEN 'Slight snow showers'
+           WHEN 86 THEN 'Heavy snow showers'
+           WHEN 95 THEN 'Thunderstorm'
+           WHEN 96 THEN 'Thunderstorm with slight hail'
+           WHEN 99 THEN 'Thunderstorm with heavy hail'
+       END                    AS weather_description,
+       w.cloud_cover          AS cloud_cover_pct,
+       w.wind_speed_10m       AS wind_mph,
+       w.wind_gusts_10m       AS wind_gust_mph
+  FROM knoebels."LZ_attractions_io" a
+  JOIN knoebels."LZ_attractions_io_poi" r
+    ON r._id = a._id
+  JOIN knoebels."LZ_attractions_io_categories" c
+    ON r.category = c._id
+  LEFT JOIN knoebels."LZ_open_meteo_hourly" w
+    ON w."time" = date_trunc('hour', a.time_stamp)
+ WHERE a.queue_time IS NOT NULL;

requirements.txt

@@ -1,2 +1,8 @@
 requests
 psycopg2-binary
+streamlit
+pandas
+numpy
+plotly
+python-dotenv
+scikit-learn

systemd/knb-weather.service

@@ -0,0 +1,14 @@
+[Unit]
+Description=Knoebels Open-Meteo hourly weather sync (one-shot)
+After=docker.service network-online.target
+Wants=network-online.target
+
+[Service]
+Type=oneshot
+WorkingDirectory=/home/nox/docker/kuh-no-bowls
+# `compose run` activates the service regardless of its compose profile.
+# Default (no args) = recent sync (Forecast API, past_days=7, self-healing).
+# One-time history load was done manually with: ... run --rm weather --backfill
+ExecStart=/usr/bin/docker compose run --rm weather
+StandardOutput=append:/home/nox/docker/kuh-no-bowls/cron-logs/weather-cron.log
+StandardError=append:/home/nox/docker/kuh-no-bowls/cron-logs/weather-cron.log

systemd/knb-weather.timer

@@ -0,0 +1,12 @@
+[Unit]
+Description=Run the Knoebels weather sync every 6 hours
+
+[Timer]
+# Every 6 hours (00/06/12/18 ET). past_days=7 means each run refreshes the
+# last week, so a missed run self-heals and provisional values get refined.
+OnCalendar=*-*-* 00/6:00:00
+Persistent=true
+RandomizedDelaySec=300
+
+[Install]
+WantedBy=timers.target

weather_logger.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""Fetch hourly weather for Knoebels (Elysburg, PA) from Open-Meteo and upsert
+into knoebels."LZ_open_meteo_hourly".
+
+Two modes:
+  (default) recent  -- Forecast API with past_days; keeps the table current and
+                       self-heals recent gaps. This is what the daily cron runs.
+  --backfill        -- Archive (ERA5 reanalysis) API back to --start
+                       (default 2024-05-15, when collection began).
+
+Timestamps are stored as naive America/New_York to match how the rest of the
+knoebels schema records time. Upserts are idempotent via ON CONFLICT (time),
+so rerunning overwrites provisional values with better data as it arrives.
+"""
+
+import argparse
+import logging
+import os
+import sys
+from datetime import date, datetime
+
+import psycopg2
+import requests
+from psycopg2.extras import execute_values
+
+# Knoebels Amusement Resort, Elysburg PA
+LATITUDE = 40.7906
+LONGITUDE = -76.4847
+COLLECTION_START = "2024-05-15"  # first day of queue-time collection
+TIMEZONE = "America/New_York"
+
+ARCHIVE_ENDPOINT = "https://archive-api.open-meteo.com/v1/archive"
+FORECAST_ENDPOINT = "https://api.open-meteo.com/v1/forecast"
+
+# Hourly variables available identically in both the archive and forecast APIs.
+HOURLY_VARS = [
+    "temperature_2m",
+    "relative_humidity_2m",
+    "apparent_temperature",
+    "precipitation",
+    "rain",
+    "weather_code",
+    "cloud_cover",
+    "wind_speed_10m",
+    "wind_gusts_10m",
+]
+
+COMMON_PARAMS = {
+    "latitude": LATITUDE,
+    "longitude": LONGITUDE,
+    "hourly": ",".join(HOURLY_VARS),
+    "temperature_unit": "fahrenheit",
+    "precipitation_unit": "inch",
+    "wind_speed_unit": "mph",
+    "timezone": TIMEZONE,
+}
+
+LOG_PATH = os.environ.get("LOG_PATH")  # unset -> log to stderr
+
+DB_PASSWORD = os.environ.get("DB_PASSWORD")
+if not DB_PASSWORD:
+    sys.stderr.write("ERROR: DB_PASSWORD env var is required\n")
+    sys.exit(1)
+
+db_params = {
+    "dbname": os.environ.get("DB_NAME", "gp0"),
+    "user": os.environ.get("DB_USER", "kuhnobowls"),
+    "password": DB_PASSWORD,
+    "host": os.environ.get("DB_HOST", "192.168.88.9"),
+    "port": os.environ.get("DB_PORT", "5432"),
+    "options": "-c search_path=knoebels",
+}
+
+CREATE_TABLE = """
+CREATE TABLE IF NOT EXISTS knoebels."LZ_open_meteo_hourly" (
+    "time"               timestamp without time zone PRIMARY KEY,
+    temperature_2m       real,
+    relative_humidity_2m smallint,
+    apparent_temperature real,
+    precipitation        real,
+    rain                 real,
+    weather_code         smallint,
+    cloud_cover          smallint,
+    wind_speed_10m       real,
+    wind_gusts_10m       real,
+    inserted_at          timestamp without time zone DEFAULT now()
+)
+"""
+
+UPSERT = """
+INSERT INTO knoebels."LZ_open_meteo_hourly"
+    ("time", temperature_2m, relative_humidity_2m, apparent_temperature,
+     precipitation, rain, weather_code, cloud_cover, wind_speed_10m, wind_gusts_10m)
+VALUES %s
+ON CONFLICT ("time") DO UPDATE SET
+    temperature_2m       = EXCLUDED.temperature_2m,
+    relative_humidity_2m = EXCLUDED.relative_humidity_2m,
+    apparent_temperature = EXCLUDED.apparent_temperature,
+    precipitation        = EXCLUDED.precipitation,
+    rain                 = EXCLUDED.rain,
+    weather_code         = EXCLUDED.weather_code,
+    cloud_cover          = EXCLUDED.cloud_cover,
+    wind_speed_10m       = EXCLUDED.wind_speed_10m,
+    wind_gusts_10m       = EXCLUDED.wind_gusts_10m,
+    inserted_at          = now()
+"""
+
+
+def fetch(endpoint, extra):
+    params = dict(COMMON_PARAMS, **extra)
+    resp = requests.get(endpoint, params=params, timeout=60)
+    resp.raise_for_status()
+    return resp.json()
+
+
+def rows_from_payload(payload):
+    """Turn an Open-Meteo payload into row tuples, dropping all-null hours
+    (the archive API pads the last few days with nulls until ERA5 lands)."""
+    hourly = payload["hourly"]
+    times = hourly["time"]
+    cols = [hourly[var] for var in HOURLY_VARS]
+    rows = []
+    for i, t in enumerate(times):
+        values = [col[i] for col in cols]
+        if all(v is None for v in values):
+            continue
+        rows.append((datetime.fromisoformat(t), *values))
+    return rows
+
+
+def upsert(conn, rows):
+    if not rows:
+        return 0
+    with conn.cursor() as cur:
+        execute_values(cur, UPSERT, rows, page_size=1000)
+    conn.commit()
+    return len(rows)
+
+
+def run_recent(conn, past_days, forecast_days):
+    payload = fetch(FORECAST_ENDPOINT,
+                    {"past_days": past_days, "forecast_days": forecast_days})
+    rows = rows_from_payload(payload)
+    n = upsert(conn, rows)
+    span = f"{rows[0][0]} .. {rows[-1][0]}" if rows else "(none)"
+    logging.info("recent: upserted %d hours [%s]", n, span)
+    return n
+
+
+def run_backfill(conn, start):
+    end = date.today().isoformat()
+    payload = fetch(ARCHIVE_ENDPOINT, {"start_date": start, "end_date": end})
+    rows = rows_from_payload(payload)
+    n = upsert(conn, rows)
+    span = f"{rows[0][0]} .. {rows[-1][0]}" if rows else "(none)"
+    logging.info("backfill: upserted %d hours [%s]", n, span)
+    return n
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Open-Meteo -> knoebels weather logger")
+    parser.add_argument("--backfill", action="store_true",
+                        help="pull the full ERA5 archive history before the recent sync")
+    parser.add_argument("--start", default=COLLECTION_START,
+                        help="backfill start date (YYYY-MM-DD)")
+    parser.add_argument("--past-days", type=int, default=7,
+                        help="forecast-API lookback for the recent sync (max 92)")
+    parser.add_argument("--forecast-days", type=int, default=1,
+                        help="forecast-API lookahead for the recent sync (0-16)")
+    args = parser.parse_args()
+
+    logging.basicConfig(
+        format="%(asctime)s %(levelname)s %(message)s",
+        level=logging.INFO,
+        filename=LOG_PATH,
+        filemode="a" if LOG_PATH else None,
+    )
+    logging.info("-" * 80)
+
+    with psycopg2.connect(**db_params) as conn:
+        with conn.cursor() as cur:
+            cur.execute(CREATE_TABLE)
+        conn.commit()
+
+        if args.backfill:
+            run_backfill(conn, args.start)
+        # Always finish with a recent sync so the ERA5 gap (last ~5 days) is
+        # filled and the table is current.
+        run_recent(conn, args.past_days, args.forecast_days)
+
+
+if __name__ == "__main__":
+    main()