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c8a8b27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | """Precompute small aggregates for the dashboard charts.
Long format -> one tiny trends.parquet the app loads for line/bar/donut charts:
kind ∈ {daily, hourly, vehicle, dow, vtype}
label the x-axis label
value the count
order sort key
"""
from collections import Counter
import pandas as pd
from src import config
DOW = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
def build_trends(df):
rows = []
# daily time series (for the line chart)
daily = df.groupby("date").size().reset_index(name="value").sort_values("date")
for i, (d, v) in enumerate(zip(daily["date"], daily["value"])):
rows.append(("daily", str(d), int(v), i))
# hourly profile (bar)
hourly = df.groupby("hour").size()
for h in range(24):
rows.append(("hourly", f"{h:02d}", int(hourly.get(h, 0)), h))
# by vehicle type (top 10 bar)
for i, (k, v) in enumerate(df["vehicle_type"].value_counts().head(10).items()):
rows.append(("vehicle", str(k), int(v), i))
# by day-of-week (bar)
dow = df.groupby("dow").size()
for i in range(7):
rows.append(("dow", DOW[i], int(dow.get(i, 0)), i))
# by violation type (donut) — only parking-relevant labels
vt = Counter()
for lst in df["violations"]:
for v in lst:
if v in config.PARKING_SEVERITY:
vt[v] += 1
for i, (k, c) in enumerate(vt.most_common(8)):
rows.append(("vtype", str(k).title(), int(c), i))
return pd.DataFrame(rows, columns=["kind", "label", "value", "order"])
def build_byday(df):
"""Per-day aggregates so charts can animate cumulatively during replay.
Long format: date, dim ∈ {hour, vehicle}, key, value.
"""
rows = []
h = df.groupby(["date", "hour"]).size().reset_index(name="value")
for _, r in h.iterrows():
rows.append((str(r["date"]), "hour", f'{int(r["hour"]):02d}', int(r["value"])))
v = df.groupby(["date", "vehicle_type"]).size().reset_index(name="value")
for _, r in v.iterrows():
rows.append((str(r["date"]), "vehicle", str(r["vehicle_type"]), int(r["value"])))
return pd.DataFrame(rows, columns=["date", "dim", "key", "value"])
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