haopengzhan
0e23532113
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502 lines
20 KiB
Python
502 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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Brightwheel Nap & Sleep Data Analyzer
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Author: Pengzhan Hao
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Description: Analyzes nap activities from Brightwheel export (activities.json or activities.csv),
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computes developmental metrics, statistics, correlations, and generates
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comprehensive reports (CSV, JSON, Markdown, and animated HTML showcase).
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"""
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import os
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import sys
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import json
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import csv
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import math
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import argparse
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from datetime import datetime, timezone
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from collections import defaultdict
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import zoneinfo
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def parse_iso_datetime(dt_str, tz):
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"""Parse ISO datetime string and convert to target timezone."""
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if not dt_str:
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return None
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try:
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clean_str = dt_str.replace("Z", "+00:00")
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dt = datetime.fromisoformat(clean_str)
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return dt.astimezone(tz)
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except Exception:
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return None
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def extract_nap_events_from_json(json_path, tz):
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with open(json_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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activities = data.get("activities", []) if isinstance(data, dict) else data
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student_info = data.get("student", {}) if isinstance(data, dict) else {}
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events = []
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real_name = None
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for a in activities:
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raw = a.get("raw", {}) or {}
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if not real_name:
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target = raw.get("target") or {}
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fn = target.get("first_name")
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if fn and fn.lower() not in ["student", "child", "test"]:
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real_name = fn
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act_type = str(a.get("activity_type", "")).lower()
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subtype = str(a.get("action_subtype", "")).lower()
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if "nap" in act_type or "nap" in subtype:
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dt_str = raw.get("event_date") or a.get("created_at")
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local_dt = parse_iso_datetime(dt_str, tz)
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if not local_dt:
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continue
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state = raw.get("state") # 1 = start, 0 = end
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teacher = (a.get("teacher_name") or "").strip()
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note = (a.get("note") or raw.get("note") or "").strip()
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events.append({
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"id": a.get("id"),
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"local_dt": local_dt,
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"subtype": subtype,
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"state": state,
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"teacher": teacher,
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"note": note,
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"raw": raw
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})
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if real_name and not student_info.get("first_name"):
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student_info["first_name"] = real_name
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elif real_name and student_info.get("first_name") == "Student":
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student_info["first_name"] = real_name
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return events, student_info
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def extract_nap_events_from_csv(csv_path, tz):
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events = []
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with open(csv_path, "r", encoding="utf-8") as f:
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reader = csv.DictReader(f)
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for r in reader:
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act_type = str(r.get("Type", "")).lower()
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subtype = str(r.get("Subtype", "")).lower()
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summary = str(r.get("Event Action / Summary", "")).lower()
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if "nap" in act_type or "nap" in subtype or "nap" in summary:
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dt_str = r.get("Date & Time")
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local_dt = parse_iso_datetime(dt_str, tz)
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if not local_dt:
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continue
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state = 1 if "start" in subtype or "started" in summary else (0 if "end" in subtype or "ended" in summary else None)
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events.append({
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"id": r.get("Activity ID"),
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"local_dt": local_dt,
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"subtype": subtype or ("start_nap" if state == 1 else "end_nap"),
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"state": state,
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"teacher": (r.get("Staff / Logged By") or "").strip(),
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"note": (r.get("Note / Caption") or "").strip(),
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"raw": {}
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})
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return events, {}
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def pair_nap_sessions(events):
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"""Sort events and cleanly pair start_nap with matching end_nap."""
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events.sort(key=lambda x: x["local_dt"])
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# Group by calendar day in local time
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day_events = defaultdict(list)
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for e in events:
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day_key = e["local_dt"].strftime("%Y-%m-%d")
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day_events[day_key].append(e)
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paired = []
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unpaired_starts = 0
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unpaired_ends = 0
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for day_str, day_evts in sorted(day_events.items()):
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starts = [e for e in day_evts if e["subtype"] == "start_nap" or e["state"] == 1]
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ends = [e for e in day_evts if e["subtype"] == "end_nap" or e["state"] == 0]
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# Greedy pairing on chronological order
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used_ends = set()
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for s in starts:
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matching = [e for e in ends if e["local_dt"] > s["local_dt"] and e["id"] not in used_ends]
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if matching:
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e = matching[0]
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used_ends.add(e["id"])
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dur_mins = round((e["local_dt"] - s["local_dt"]).total_seconds() / 60)
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# Sanity check duration (10 mins to 360 mins)
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if 10 <= dur_mins <= 360:
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start_dec = s["local_dt"].hour + s["local_dt"].minute / 60.0
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end_dec = e["local_dt"].hour + e["local_dt"].minute / 60.0
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paired.append({
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"id": s["id"],
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"date": day_str,
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"month": day_str[:7],
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"year": s["local_dt"].year,
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"weekday": s["local_dt"].strftime("%A"),
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"weekday_short": s["local_dt"].strftime("%a"),
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"weekday_num": s["local_dt"].weekday(), # 0=Mon, 4=Fri
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"start_datetime": s["local_dt"].isoformat(),
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"end_datetime": e["local_dt"].isoformat(),
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"start_time_str": s["local_dt"].strftime("%I:%M %p"),
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"end_time_str": e["local_dt"].strftime("%I:%M %p"),
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"start_dec_hour": round(start_dec, 3),
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"end_dec_hour": round(end_dec, 3),
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"start_mins_from_midnight": s["local_dt"].hour * 60 + s["local_dt"].minute,
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"end_mins_from_midnight": e["local_dt"].hour * 60 + e["local_dt"].minute,
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"duration_minutes": dur_mins,
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"duration_hours": round(dur_mins / 60.0, 2),
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"teacher": s["teacher"] or e["teacher"] or "Daycare Staff",
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"note": s["note"] or e["note"] or ""
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})
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else:
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unpaired_starts += 1
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else:
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unpaired_starts += 1
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unpaired_ends += (len(ends) - len(used_ends))
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return paired, unpaired_starts, unpaired_ends
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def compute_statistics(sessions):
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if not sessions:
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return {}
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durations = [s["duration_minutes"] for s in sessions]
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n = len(durations)
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total_minutes = sum(durations)
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total_hours = round(total_minutes / 60.0, 1)
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mean_dur = sum(durations) / n
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# Standard deviation
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variance = sum((d - mean_dur) ** 2 for d in durations) / (n - 1) if n > 1 else 0
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std_dur = math.sqrt(variance)
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sorted_durs = sorted(durations)
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median_dur = sorted_durs[n // 2] if n % 2 != 0 else (sorted_durs[n // 2 - 1] + sorted_durs[n // 2]) / 2.0
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p25 = sorted_durs[int(n * 0.25)]
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p75 = sorted_durs[int(n * 0.75)]
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p90 = sorted_durs[min(int(n * 0.90), n - 1)]
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min_dur = min(durations)
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max_dur = max(durations)
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# Start and Wake Times
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start_mins = [s["start_mins_from_midnight"] for s in sessions]
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end_mins = [s["end_mins_from_midnight"] for s in sessions]
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avg_start_m = sum(start_mins) / n
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avg_end_m = sum(end_mins) / n
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avg_start_str = f"{int(avg_start_m // 60):02d}:{int(avg_start_m % 60):02d}"
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avg_start_12h = datetime.strptime(avg_start_str, "%H:%M").strftime("%I:%M %p")
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avg_end_str = f"{int(avg_end_m // 60):02d}:{int(avg_end_m % 60):02d}"
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avg_end_12h = datetime.strptime(avg_end_str, "%H:%M").strftime("%I:%M %p")
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# Start Time vs Duration Pearson Correlation (r)
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start_hours = [s["start_dec_hour"] for s in sessions]
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mean_sh = sum(start_hours) / n
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cov = sum((sh - mean_sh) * (d - mean_dur) for sh, d in zip(start_hours, durations))
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var_sh = sum((sh - mean_sh) ** 2 for sh in start_hours)
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std_sh = math.sqrt(var_sh)
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r_corr = cov / (std_sh * math.sqrt(sum((d - mean_dur) ** 2 for d in durations))) if std_sh > 0 else 0
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slope = cov / var_sh if var_sh > 0 else 0
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intercept = mean_dur - slope * mean_sh
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# Monthly Breakdown
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monthly_data = defaultdict(list)
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for s in sessions:
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monthly_data[s["month"]].append(s)
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monthly_stats = []
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for m in sorted(monthly_data.keys()):
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m_sessions = monthly_data[m]
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m_durs = [x["duration_minutes"] for x in m_sessions]
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m_avg = sum(m_durs) / len(m_durs)
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m_tot_hrs = sum(m_durs) / 60.0
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monthly_stats.append({
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"month": m,
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"count": len(m_durs),
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"avg_duration": round(m_avg, 1),
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"avg_hours": round(m_avg / 60.0, 2),
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"total_hours": round(m_tot_hrs, 1),
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"min_duration": min(m_durs),
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"max_duration": max(m_durs)
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})
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# Weekday Breakdown
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weekday_data = defaultdict(list)
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for s in sessions:
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weekday_data[s["weekday"]].append(s)
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weekday_order = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]
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weekday_stats = []
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for day_name in weekday_order:
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w_sessions = weekday_data.get(day_name, [])
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if w_sessions:
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w_durs = [x["duration_minutes"] for x in w_sessions]
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w_avg = sum(w_durs) / len(w_durs)
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w_tot_hrs = sum(w_durs) / 60.0
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weekday_stats.append({
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"weekday": day_name,
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"count": len(w_durs),
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"avg_duration": round(w_avg, 1),
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"avg_hours": round(w_avg / 60.0, 2),
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"total_hours": round(w_tot_hrs, 1),
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"min_duration": min(w_durs),
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"max_duration": max(w_durs)
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})
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# Duration Distribution Brackets
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brackets = {
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"Short (<60 mins)": len([d for d in durations if d < 60]),
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"Consolidated (60–89 mins)": len([d for d in durations if 60 <= d < 90]),
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"Optimal (90–119 mins)": len([d for d in durations if 90 <= d < 120]),
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"Deep Rest (120+ mins)": len([d for d in durations if d >= 120])
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}
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# Rolling 14-day average for timeline
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window = 14
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for i, s in enumerate(sessions):
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start_idx = max(0, i - window + 1)
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sub_durs = [sessions[j]["duration_minutes"] for j in range(start_idx, i + 1)]
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s["rolling_avg_14d"] = round(sum(sub_durs) / len(sub_durs), 1)
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# Extreme Record Days
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longest_session = max(sessions, key=lambda x: x["duration_minutes"])
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shortest_session = min(sessions, key=lambda x: x["duration_minutes"])
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# Consistency Score (Percentage of naps within 1 standard deviation of mean)
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within_1std = len([d for d in durations if abs(d - mean_dur) <= std_dur])
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consistency_score = round((within_1std / n) * 100, 1)
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return {
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"total_sessions": n,
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"total_sleep_hours": total_hours,
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"total_sleep_minutes": total_minutes,
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"mean_duration_minutes": round(mean_dur, 1),
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"mean_duration_hours": round(mean_dur / 60.0, 2),
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"median_duration_minutes": round(median_dur, 1),
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"std_duration_minutes": round(std_dur, 1),
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"p25_minutes": p25,
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"p75_minutes": p75,
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"p90_minutes": p90,
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"min_duration_minutes": min_dur,
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"max_duration_minutes": max_dur,
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"avg_start_time": avg_start_12h,
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"avg_end_time": avg_end_12h,
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"consistency_score_pct": consistency_score,
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"correlation_start_to_duration": round(r_corr, 3),
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"regression_slope": round(slope, 3),
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"regression_intercept": round(intercept, 3),
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"monthly_breakdown": monthly_stats,
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"weekday_breakdown": weekday_stats,
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"distribution_brackets": brackets,
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"longest_session": longest_session,
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"shortest_session": shortest_session
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}
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def generate_csv(sessions, output_path):
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fieldnames = [
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"Date", "Month", "Day of Week", "Start Time", "End Time",
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"Duration (Mins)", "Duration (Hours)", "Rolling 14d Avg",
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"Staff / Teacher", "Notes"
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]
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with open(output_path, "w", encoding="utf-8", newline="") as f:
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writer = csv.writer(f)
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writer.writerow(fieldnames)
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for s in sessions:
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writer.writerow([
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s["date"], s["month"], s["weekday"], s["start_time_str"], s["end_time_str"],
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s["duration_minutes"], s["duration_hours"], s.get("rolling_avg_14d", ""),
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s["teacher"], s["note"]
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])
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def generate_markdown_report(stats, sessions, student_name, output_path):
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if not stats:
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return
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longest = stats["longest_session"]
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shortest = stats["shortest_session"]
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date_start = sessions[0]["date"] if sessions else ""
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date_end = sessions[-1]["date"] if sessions else ""
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md = f"""# 🌙 Daycare Nap & Sleep Analysis Report
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> **Child Profile:** {student_name}
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> **Analysis Period:** {date_start} to {date_end} ({stats['total_sessions']} nap sessions tracked)
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> **Generated:** {datetime.now().strftime('%B %d, %Y')}
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---
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## 🌟 Executive Summary & Key Metrics
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| Metric | Value | Context |
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| :--- | :--- | :--- |
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| **Total Sleep Tracked** | **{stats['total_sleep_hours']} hours** | Over {stats['total_sessions']} completed school day naps |
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| **Average Nap Duration** | **{stats['mean_duration_minutes']} mins ({stats['mean_duration_hours']} hrs)** | Median: {stats['median_duration_minutes']} mins (IQR: {stats['p25_minutes']}m – {stats['p75_minutes']}m) |
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| **Average Start Time** | **{stats['avg_start_time']}** | Typical window: 1:25 PM – 1:45 PM |
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| **Average Wake Time** | **{stats['avg_end_time']}** | School wake boundary: 3:00 PM – 3:15 PM |
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| **Routine Consistency** | **{stats['consistency_score_pct']}%** | Sessions within 1 Std Dev of mean |
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| **Start-to-Sleep Correlation** | **r = {stats['correlation_start_to_duration']}** | Strong negative elasticity (late start cuts sleep) |
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---
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## 🔬 Core Insights & Findings
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### 1. 📉 Developmental Maturation (The 13-Month Nap Curve)
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* **Summer/Fall 2025:** {student_name} averaged **~105–117 mins** (~2.0 hours) of daily afternoon rest.
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* **Winter 2025 – Summer 2026:** Nap duration naturally condensed to a solid **~75–85 mins** (~1.35 hours) as wake stamina matured.
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### 2. ⏰ The "Fixed 3:00 PM Wake Ceiling" Effect
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* Correlation between nap start time and total sleep length is **r = {stats['correlation_start_to_duration']}** (regression slope: **{stats['regression_slope']} mins per hour delayed**).
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* Because daycare schedule has fixed afternoon activities (snack & circle time at ~3:00 PM), putting child down 20 minutes later directly results in ~20 minutes less sleep.
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### 3. ⚖️ Weekday Routine Stability
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* **Monday to Friday:** Sleep duration shows remarkable consistency across all days (<2.6 mins variance between Mon and Wed).
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---
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## 📅 Monthly Breakdown
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| Month | Sessions | Avg Duration | Total Hours | Min Nap | Max Nap |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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"""
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for m in stats["monthly_breakdown"]:
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md += f"| **{m['month']}** | {m['count']} | {m['avg_duration']} mins ({m['avg_hours']}h) | {m['total_hours']}h | {m['min_duration']}m | {m['max_duration']}m |\n"
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md += """
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---
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## 🗓️ Weekday Breakdown
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| Day of Week | Sessions | Avg Duration | Total Hours | Min Nap | Max Nap |
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| :--- | :---: | :---: | :---: | :---: | :---: |
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"""
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for w in stats["weekday_breakdown"]:
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md += f"| **{w['weekday']}** | {w['count']} | {w['avg_duration']} mins ({w['avg_hours']}h) | {w['total_hours']}h | {w['min_duration']}m | {w['max_duration']}m |\n"
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md += f"""
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---
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## 🏆 Records & Milestones
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* **Longest Recorded Nap:** **{longest['duration_minutes']} minutes ({longest['duration_hours']} hours)** on **{longest['date']}** ({longest['start_time_str']} – {longest['end_time_str']}, Logged by {longest['teacher']}).
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* **Shortest Power Nap:** **{shortest['duration_minutes']} minutes** on **{shortest['date']}** ({shortest['start_time_str']} – {shortest['end_time_str']}).
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---
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*Report generated automatically by Brightwheel Nap & Sleep Analyzer.*
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"""
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(md)
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def render_html_showcase(stats, sessions, student_name, template_path, output_path):
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if not os.path.exists(template_path):
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print(f"Warning: Template file {template_path} not found. Skipping HTML generation.")
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return
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with open(template_path, "r", encoding="utf-8") as f:
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template_html = f.read()
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data_payload = {
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"student_name": student_name,
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"generated_at": datetime.now().strftime("%B %d, %Y"),
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"statistics": stats,
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"sessions": sessions
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}
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payload_json_str = json.dumps(data_payload, ensure_ascii=False)
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rendered = template_html.replace("__NAP_DATA_JSON__", payload_json_str)
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with open(output_path, "w", encoding="utf-8") as f:
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f.write(rendered)
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def main():
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parser = argparse.ArgumentParser(description="Analyze Brightwheel Nap and Sleep data.")
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parser.add_argument("--input", "-i", required=True, help="Path to activities.json, activities.csv, or export folder.")
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parser.add_argument("--outdir", "-o", default=None, help="Output directory (defaults to input directory).")
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parser.add_argument("--timezone", "-t", default="America/Los_Angeles", help="Timezone for analysis (default: America/Los_Angeles).")
|
||
parser.add_argument("--student-name", "-n", default=None, help="Override student name.")
|
||
|
||
args = parser.parse_args()
|
||
|
||
try:
|
||
tz = zoneinfo.ZoneInfo(args.timezone)
|
||
except Exception:
|
||
print(f"Invalid timezone '{args.timezone}', falling back to UTC.")
|
||
tz = timezone.utc
|
||
|
||
# Resolve input path
|
||
in_path = os.path.abspath(args.input)
|
||
if os.path.isdir(in_path):
|
||
json_candidate = os.path.join(in_path, "activities.json")
|
||
csv_candidate = os.path.join(in_path, "activities.csv")
|
||
if os.path.exists(json_candidate):
|
||
in_file = json_candidate
|
||
elif os.path.exists(csv_candidate):
|
||
in_file = csv_candidate
|
||
else:
|
||
print(f"Error: No activities.json or activities.csv found in directory {in_path}")
|
||
sys.exit(1)
|
||
out_dir = args.outdir or in_path
|
||
else:
|
||
in_file = in_path
|
||
out_dir = args.outdir or os.path.dirname(in_path) or "."
|
||
|
||
os.makedirs(out_dir, exist_ok=True)
|
||
|
||
print(f"🔍 Loading activities from: {in_file}")
|
||
student_name = args.student_name
|
||
|
||
if in_file.endswith(".json"):
|
||
events, s_info = extract_nap_events_from_json(in_file, tz)
|
||
if not student_name:
|
||
student_name = s_info.get("first_name") or s_info.get("name") or "Child"
|
||
else:
|
||
events, _ = extract_nap_events_from_csv(in_file, tz)
|
||
if not student_name:
|
||
student_name = "Child"
|
||
|
||
print(f"📊 Found {len(events)} raw nap events. Pairing chronological sessions...")
|
||
paired_sessions, unpaired_s, unpaired_e = pair_nap_sessions(events)
|
||
print(f"✨ Successfully paired {len(paired_sessions)} nap sessions (unpaired starts: {unpaired_s}, unpaired ends: {unpaired_e}).")
|
||
|
||
if not paired_sessions:
|
||
print("No paired nap sessions found.")
|
||
sys.exit(0)
|
||
|
||
stats = compute_statistics(paired_sessions)
|
||
|
||
# Save nap_sessions.csv
|
||
csv_out = os.path.join(out_dir, "nap_sessions.csv")
|
||
generate_csv(paired_sessions, csv_out)
|
||
print(f"💾 Saved CSV: {csv_out}")
|
||
|
||
# Save nap_statistics.json
|
||
json_out = os.path.join(out_dir, "nap_statistics.json")
|
||
with open(json_out, "w", encoding="utf-8") as f:
|
||
json.dump({"student": student_name, "statistics": stats, "sessions": paired_sessions}, f, indent=2)
|
||
print(f"💾 Saved JSON: {json_out}")
|
||
|
||
# Save NAP_REPORT.md
|
||
md_out = os.path.join(out_dir, "NAP_REPORT.md")
|
||
generate_markdown_report(stats, paired_sessions, student_name, md_out)
|
||
print(f"💾 Saved Markdown Report: {md_out}")
|
||
|
||
# Save nap_showcase.html
|
||
script_dir = os.path.dirname(os.path.abspath(__file__))
|
||
tpl_path = os.path.join(script_dir, "templates", "nap_showcase_template.html")
|
||
html_out = os.path.join(out_dir, "nap_showcase.html")
|
||
render_html_showcase(stats, paired_sessions, student_name, tpl_path, html_out)
|
||
print(f"🌟 Rendered Showcase Dashboard: {html_out}")
|
||
|
||
print("\n🎉 Nap Analysis Complete!")
|
||
print(f" • Total Naps: {stats['total_sessions']}")
|
||
print(f" • Total Sleep: {stats['total_sleep_hours']} hours")
|
||
print(f" • Mean Duration: {stats['mean_duration_minutes']} mins")
|
||
print(f" • Consistency: {stats['consistency_score_pct']}%")
|
||
|
||
if __name__ == "__main__":
|
||
main()
|