Outdoor thermistor array for moon observation sessions. Three MF58 10 kΩ NTC sensors read simultaneously by an ADS1115 ADC on an ESP32-C3. Device publishes raw 16-bit ADC counts over MQTT and logs to daily CSVs on NAS (/mnt/nas/rooster/moon-temp-logs/).
Hardware: 3× NTC on ADS1115 (channels 0–2) / ESP32-C3
ADC LSB: 0.13108 mV/count (calibrated; nominal 0.125 mV)
Current offsets (ESP32-C3 flash): ADC0 = 0 · ADC1 = +22 · ADC2 = −64
| Period | Path | VCC | v_rail column |
|---|---|---|---|
| May–Jun 2026 | logs_2.3V/logs/ |
2.3 V (hardware fault) | absent |
| Jul 2026+ | raw_logs/ |
~3.3 V measured per-row | present |
Converted temperature CSVs (pre-computed °C) live in converted_logs/.
%matplotlib inline
import os, platform
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
NAS_BASE = (
"/Volumes/AlphaDoom_6/rooster/moon-temp-logs"
if platform.system() == 'Darwin'
else "/mnt/nas/rooster/moon-temp-logs"
)
plt.rcParams['figure.figsize'] = (14, 4)
plt.rcParams['figure.dpi'] = 110Calibration aligns three channels against ADC0 (reference, offset always 0).
moon_temp_logger --interval 5mosquitto_pub -h 10.0.0.50 -t moon-temp-001/offset/cmd -m '{"adc":1,"offset":16}'sudo systemctl start moon_temp_loggerSensor liveness check: manual heat applied to each thermistor in sequence. Window: 2026-05-27, 09:51–09:58 MDT (~7 minutes) Interval: 1-second sampling Sequence: ADC0 → ADC1 → ADC2, ~1 minute each Response: NTC thermistors decrease resistance when heated → lower ADC counts
TEST_START = '2026-05-27 09:51:00'
TEST_END = '2026-05-27 09:59:00'
df_full = pd.read_csv(
os.path.join(NAS_BASE, "logs_2.3V/logs/2026-05-27.csv"),
parse_dates=['timestamp']
)
df_full = df_full.sort_values('timestamp').reset_index(drop=True)
test = df_full[(df_full['timestamp'] >= TEST_START) & (df_full['timestamp'] <= TEST_END)].copy()
print(f"Rows : {len(test)}")
print(f"Span : {test['timestamp'].iloc[0]} → {test['timestamp'].iloc[-1]}")
print(f"Sample interval (median): {test['timestamp'].diff().dt.total_seconds().median():.0f}s")
print()
for col in ['adc0','adc1','adc2']:
d = test[col]
print(f"{col}: min={d.min()} max={d.max()} mean={d.mean():.1f} std={d.std():.1f}")Rows : 385
Span : 2026-05-27 09:51:26 → 2026-05-27 09:58:15
Sample interval (median): 1s
adc0: min=10880 max=12848 mean=11826.8 std=831.8
adc1: min=11009 max=12769 mean=12122.6 std=757.5
adc2: min=11369 max=12841 mean=12509.9 std=543.2# Per-channel subplots
fig, axes = plt.subplots(3, 1, figsize=(14, 9), sharex=True)
colors = {'adc0': 'steelblue', 'adc1': 'darkorange', 'adc2': 'crimson'}
labels = {'adc0': 'ADC0 (hand first)', 'adc1': 'ADC1 (hand second)', 'adc2': 'ADC2 (hand third)'}
for ax, col in zip(axes, ['adc0', 'adc1', 'adc2']):
ax.plot(test['timestamp'], test[col], color=colors[col], linewidth=1.0, label=labels[col])
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
ax.set_ylabel('ADC counts')
ax.legend(loc='upper left')
ax.set_title(labels[col])
axes[-1].set_xlabel('Time (MDT)')
fig.suptitle('Heat Response Test — 2026-05-27 ~09:51–09:58 MDT', fontsize=13)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
# Overlay — sequential drops clearly visible
fig, ax = plt.subplots(figsize=(14, 5))
for col in ['adc0', 'adc1', 'adc2']:
ax.plot(test['timestamp'], test[col], color=colors[col], linewidth=1.0, label=labels[col], alpha=0.85)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))
ax.set_ylabel('ADC counts')
ax.set_title('Heat Response Test — all channels overlay')
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
Sensors: 3× MF58 10 kΩ NTC, B = 3950 K, R₀ = 10 kΩ @ 25 °C
Series resistor: 10 kΩ fixed
ADC: ADS1115 (I²C 0x48), PGA ±4.096 V, 16-bit
ADC LSB (calibrated): 0.13108 mV/count (nominal 0.125 mV — ~4.9 % reference offset measured against multimeter)
Firmware: ESP32-C3, Embassy async, MQTT 3.1.1
| Stage | Detail |
|---|---|
| Mains | 115 V AC → 5 VDC wall adapter |
| Variable regulator | Adjustable → 5 V rail |
| ESP32-C3 | Powered directly from 5 V rail; 3.3 V pin supplies thermistor VCC |
| Buck converter | 5 V → 3.3 V dedicated supply for ADS1115 VDD |
| Rail monitor | ADS1115 AIN3 wired to ADS VDD (3.3 V from buck) — logged as v_rail |
3.3 V (ESP32 pin) → 10 kΩ → ADS1115 AINx → NTC 10 kΩ (B=3950) → GND
Sensors, ESP32-C3, and power supply — bench setup
Static chamber — 8 ft deep concrete pit (looking down)
Inside the pit — sensor deployment at depth
Pit entrance — July 2026 hardware update
Updated rig — variable regulator and buck converter installed
Auto-loads the most recent log CSV and evaluates inter-channel alignment. Run after any overnight session or after applying new offsets.
Pass criterion: std(adc0 − adc1) ≤ 5 counts AND std(adc0 − adc2) ≤ 5 counts (5 counts = 0.625 mV ≈ 10–15 m°C at pit temperatures)
import glob
LOGS_2_3V = os.path.join(NAS_BASE, "logs_2.3V/logs") # VCC=2.3V era, no v_rail
LOGS_NEW = os.path.join(NAS_BASE, "raw_logs") # Jul 2026+, v_rail present
LOGS_CONV = os.path.join(NAS_BASE, "converted_logs") # pre-computed °C
LOGS_DIR = LOGS_2_3V # calibration analysis uses historical data
ADC_LSB = 0.00013108 # V/count — calibrated against multimeter (nominal 0.000125)
ADC_MV = ADC_LSB * 1000 # mV/count
PASS_STD_THRESHOLD = 4.0 # mV
print(f"NAS base : {NAS_BASE}")
csv_files = [f for f in sorted(glob.glob(os.path.join(LOGS_DIR, "????-??-??.csv")))
if os.path.basename(f) <= "2026-06-28.csv"]
if not csv_files:
raise FileNotFoundError(f"No dated CSV files found in {LOGS_DIR}")
latest_csv = csv_files[-1]
df_cal = pd.read_csv(latest_csv, parse_dates=['timestamp'])
df_cal = df_cal.sort_values('timestamp').reset_index(drop=True)
cal_diff01 = (df_cal['adc0'] - df_cal['adc1']) * ADC_MV
cal_diff02 = (df_cal['adc0'] - df_cal['adc2']) * ADC_MV
print(f"File : {latest_csv}")
print(f"Rows : {len(df_cal)}")
print(f"Span : {df_cal['timestamp'].iloc[0]} → {df_cal['timestamp'].iloc[-1]}")
print(f"v_rail : {'present' if 'v_rail' in df_cal.columns else 'absent (2.3 V era)'}")
print()
print(f"adc0 − adc1 : mean={cal_diff01.mean():+.3f} mV std={cal_diff01.std():.3f} mV")
print(f"adc0 − adc2 : mean={cal_diff02.mean():+.3f} mV std={cal_diff02.std():.3f} mV")NAS base : /mnt/nas/rooster/moon-temp-logs
File : /mnt/nas/rooster/moon-temp-logs/logs_2.3V/logs/2026-06-28.csv
Rows : 880
Span : 2026-06-28 00:00:54 → 2026-06-28 14:39:54
v_rail : absent (2.3 V era)
adc0 − adc1 : mean=-0.055 mV std=1.005 mV
adc0 − adc2 : mean=+0.913 mV std=1.146 mVfig, axes = plt.subplots(2, 1, figsize=(14, 7), sharex=True)
for ax, diff, label, color in [
(axes[0], cal_diff01, 'adc0 − adc1', 'darkorange'),
(axes[1], cal_diff02, 'adc0 − adc2', 'crimson'),
]:
ax.plot(df_cal['timestamp'], diff, color=color, linewidth=0.7, alpha=0.85)
ax.axhline(0, color='gray', linestyle='--', linewidth=1.0, alpha=0.5)
ax.axhline( PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.axhline(-PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
ax.set_title(f'{label} (mean={diff.mean():+.3f} mV std={diff.std():.3f} mV)')
ax.set_ylabel('Δ mV')
axes[-1].set_xlabel('Time')
fig.suptitle(f'Inter-channel alignment — {os.path.basename(latest_csv)}', fontsize=12)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
# Calibration history — mean inter-channel difference per day (± 1 std, mV)
all_csvs = [f for f in sorted(glob.glob(os.path.join(LOGS_DIR, "????-??-??.csv")))
if "2026-05-26.csv" <= os.path.basename(f) <= "2026-06-28.csv"]
rows = []
for path in all_csvs:
d = pd.read_csv(path, parse_dates=['timestamp'])
d = d.sort_values('timestamp').reset_index(drop=True)
label = os.path.basename(path).replace('.csv', '')
if '2026-05-27' in label:
d = d[~((d['timestamp'] >= '2026-05-27 09:51:00') & (d['timestamp'] <= '2026-05-27 09:58:15'))]
diff01 = (d['adc0'] - d['adc1']) * ADC_MV
diff02 = (d['adc0'] - d['adc2']) * ADC_MV
rows.append({
'day': label,
'd01_mean': diff01.mean(),
'd02_mean': diff02.mean(),
'd01_std': diff01.std(),
'd02_std': diff02.std(),
})
hist = pd.DataFrame(rows).set_index('day')
print("Inter-channel alignment history (mV, heat test excluded from May 27)")
print()
print(hist.round(3).to_string())
fig, ax = plt.subplots(figsize=(14, 6))
x = range(len(rows))
for col, color, lbl in [
('d01_mean', 'darkorange', 'adc0 − adc1'),
('d02_mean', 'crimson', 'adc0 − adc2'),
]:
means = hist[col].values
stds = hist[col.replace('mean', 'std')].values
ax.errorbar(x, means, yerr=stds, fmt='o-', color=color, label=lbl,
capsize=4, linewidth=1.5)
ax.axhline(0, color='gray', linestyle='--', linewidth=1, alpha=0.5)
ax.axhline( PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.axhline(-PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
# Offset adjustment markers
adj_markers = [
('2026-06-15', 'cyan', 'ADC2 −80→−64'),
('2026-06-18', 'yellow', 'ADC1 +16→+10'),
('2026-06-20', 'magenta', 'ADC1 +10→+22'),
]
idx_list = list(hist.index)
for day, color, lbl in adj_markers:
if day in idx_list:
xi = idx_list.index(day)
ax.axvline(xi, color=color, linestyle='--', linewidth=1.2, alpha=0.85, label=lbl)
ax.text(xi + 0.15, 0.97, lbl, transform=ax.get_xaxis_transform(),
color=color, fontsize=8, va='top', ha='left')
ax.set_xticks(list(x))
ax.set_xticklabels(hist.index, fontsize=9, rotation=45, ha='right')
ax.set_ylabel('Δ mV')
ax.set_title('Inter-channel alignment per day — mean ± 1 std (mV)')
ax.legend()
plt.tight_layout()
plt.show()Inter-channel alignment history (mV, heat test excluded from May 27)
d01_mean d02_mean d01_std d02_std
day
2026-05-26 0.854 -4.275 9.612 5.422
2026-05-27 10.913 0.580 0.977 2.248
2026-05-28 3.376 -1.346 7.446 3.690
2026-05-29 0.153 -0.750 1.474 1.327
2026-05-30 1.450 0.082 0.969 0.880
2026-05-31 0.733 -0.086 1.000 1.139
2026-06-01 0.383 -0.396 0.811 1.056
2026-06-02 0.303 -0.297 1.894 3.592
2026-06-03 0.435 1.439 0.855 1.219
2026-06-04 0.852 1.691 1.033 1.402
2026-06-05 1.308 1.957 1.029 1.148
2026-06-06 0.744 1.748 1.007 1.066
2026-06-07 0.859 1.705 1.035 1.279
2026-06-08 0.785 1.675 1.045 1.206
2026-06-09 0.832 1.743 1.070 1.154
2026-06-10 0.929 1.722 1.051 1.151
2026-06-11 0.736 1.598 1.004 1.248
2026-06-12 0.998 1.762 1.093 1.223
2026-06-13 0.469 1.557 0.881 1.162
2026-06-14 0.847 1.766 1.073 1.074
2026-06-15 0.961 0.792 1.048 1.543
2026-06-16 1.200 -0.230 1.050 1.181
2026-06-17 0.486 -0.425 0.889 1.115
2026-06-18 1.194 -0.182 1.174 1.133
2026-06-19 1.569 -0.058 1.035 1.056
2026-06-20 1.118 -0.160 1.075 1.054
2026-06-21 -0.100 0.017 1.006 1.114
2026-06-22 -0.162 0.229 0.963 0.867
2026-06-23 -0.093 0.291 0.993 1.005
2026-06-24 0.269 0.504 1.061 1.015
2026-06-25 0.165 0.585 1.044 1.077
2026-06-26 -0.286 0.414 0.911 1.101
2026-06-27 -0.226 0.590 0.928 1.083
2026-06-28 -0.055 0.913 1.005 1.146
results = []
for label, diff in [('adc0 − adc1', cal_diff01), ('adc0 − adc2', cal_diff02)]:
results.append((label, diff.mean(), diff.std(), diff.std() <= PASS_STD_THRESHOLD))
print("=" * 58)
print(f" Calibration Confirmation — {os.path.basename(latest_csv)}")
print(f" Pass threshold: std ≤ {PASS_STD_THRESHOLD} mV (16 counts = 2 mV minimum step)")
print("=" * 58)
for label, mean_v, std_v, passed in results:
print(f" {label:14s}: mean={mean_v:+.3f} mV std={std_v:.3f} mV [{'PASS' if passed else 'FAIL'}]")
print("=" * 58)
if all(r[3] for r in results):
print(" >> CALIBRATION OK")
else:
print(" >> ADC offset may need adjustment — use burst alignment (Step 1)")==========================================================
Calibration Confirmation — 2026-06-28.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.055 mV std=1.005 mV [PASS]
adc0 − adc2 : mean=+0.913 mV std=1.146 mV [PASS]
==========================================================
>> CALIBRATION OKPass/fail snapshot for every logged day (May 26 → Jun 28). Heat-test window excluded from May 27.
W = 58 # box width
all_csvs = [f for f in sorted(glob.glob(os.path.join(LOGS_DIR, "????-??-??.csv")))
if "2026-05-26.csv" <= os.path.basename(f) <= "2026-06-28.csv"]
for path in all_csvs:
fname = os.path.basename(path)
d = pd.read_csv(path, parse_dates=['timestamp'])
d = d.sort_values('timestamp').reset_index(drop=True)
# Exclude heat test window from May 27
if '2026-05-27' in fname:
d = d[~((d['timestamp'] >= '2026-05-27 09:51:00') &
(d['timestamp'] <= '2026-05-27 09:58:15'))]
diff01 = (d['adc0'] - d['adc1']) * ADC_MV
diff02 = (d['adc0'] - d['adc2']) * ADC_MV
results = [
('adc0 − adc1', diff01.mean(), diff01.std(), diff01.std() <= PASS_STD_THRESHOLD),
('adc0 − adc2', diff02.mean(), diff02.std(), diff02.std() <= PASS_STD_THRESHOLD),
]
all_pass = all(r[3] for r in results)
print('=' * W)
print(f" Calibration Confirmation — {fname}")
print(f" Pass threshold: std ≤ {PASS_STD_THRESHOLD} mV (16 counts = 2 mV minimum step)")
print('=' * W)
for label, mean_v, std_v, passed in results:
print(f" {label:12s}: mean={mean_v:+.3f} mV std={std_v:.3f} mV [{'PASS' if passed else 'FAIL'}]")
print('=' * W)
print(f" >> {'CALIBRATION OK' if all_pass else 'CALIBRATION FAIL'}")
print('=' * W)
print()==========================================================
Calibration Confirmation — 2026-05-26.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.854 mV std=9.612 mV [FAIL]
adc0 − adc2 : mean=-4.275 mV std=5.422 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-05-27.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+10.913 mV std=0.977 mV [PASS]
adc0 − adc2 : mean=+0.580 mV std=2.248 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-05-28.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+3.376 mV std=7.446 mV [FAIL]
adc0 − adc2 : mean=-1.346 mV std=3.690 mV [PASS]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-05-29.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.153 mV std=1.474 mV [PASS]
adc0 − adc2 : mean=-0.750 mV std=1.327 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-05-30.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.450 mV std=0.969 mV [PASS]
adc0 − adc2 : mean=+0.082 mV std=0.880 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-05-31.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.733 mV std=1.000 mV [PASS]
adc0 − adc2 : mean=-0.086 mV std=1.139 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-01.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.383 mV std=0.811 mV [PASS]
adc0 − adc2 : mean=-0.396 mV std=1.056 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-02.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.303 mV std=1.894 mV [PASS]
adc0 − adc2 : mean=-0.297 mV std=3.592 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-03.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.435 mV std=0.855 mV [PASS]
adc0 − adc2 : mean=+1.439 mV std=1.219 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-04.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.852 mV std=1.033 mV [PASS]
adc0 − adc2 : mean=+1.691 mV std=1.402 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-05.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.308 mV std=1.029 mV [PASS]
adc0 − adc2 : mean=+1.957 mV std=1.148 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-06.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.744 mV std=1.007 mV [PASS]
adc0 − adc2 : mean=+1.748 mV std=1.066 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-07.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.859 mV std=1.035 mV [PASS]
adc0 − adc2 : mean=+1.705 mV std=1.279 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-08.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.785 mV std=1.045 mV [PASS]
adc0 − adc2 : mean=+1.675 mV std=1.206 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-09.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.832 mV std=1.070 mV [PASS]
adc0 − adc2 : mean=+1.743 mV std=1.154 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-10.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.929 mV std=1.051 mV [PASS]
adc0 − adc2 : mean=+1.722 mV std=1.151 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-11.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.736 mV std=1.004 mV [PASS]
adc0 − adc2 : mean=+1.598 mV std=1.248 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-12.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.998 mV std=1.093 mV [PASS]
adc0 − adc2 : mean=+1.762 mV std=1.223 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-13.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.469 mV std=0.881 mV [PASS]
adc0 − adc2 : mean=+1.557 mV std=1.162 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-14.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.847 mV std=1.073 mV [PASS]
adc0 − adc2 : mean=+1.766 mV std=1.074 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-15.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.961 mV std=1.048 mV [PASS]
adc0 − adc2 : mean=+0.792 mV std=1.543 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-16.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.200 mV std=1.050 mV [PASS]
adc0 − adc2 : mean=-0.230 mV std=1.181 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-17.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.486 mV std=0.889 mV [PASS]
adc0 − adc2 : mean=-0.425 mV std=1.115 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-18.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.194 mV std=1.174 mV [PASS]
adc0 − adc2 : mean=-0.182 mV std=1.133 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-19.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.569 mV std=1.035 mV [PASS]
adc0 − adc2 : mean=-0.058 mV std=1.056 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-20.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+1.118 mV std=1.075 mV [PASS]
adc0 − adc2 : mean=-0.160 mV std=1.054 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-21.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.100 mV std=1.006 mV [PASS]
adc0 − adc2 : mean=+0.017 mV std=1.114 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-22.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.162 mV std=0.963 mV [PASS]
adc0 − adc2 : mean=+0.229 mV std=0.867 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-23.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.093 mV std=0.993 mV [PASS]
adc0 − adc2 : mean=+0.291 mV std=1.005 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-24.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.269 mV std=1.061 mV [PASS]
adc0 − adc2 : mean=+0.504 mV std=1.015 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-25.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=+0.165 mV std=1.044 mV [PASS]
adc0 − adc2 : mean=+0.585 mV std=1.077 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-26.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.286 mV std=0.911 mV [PASS]
adc0 − adc2 : mean=+0.414 mV std=1.101 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-27.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.226 mV std=0.928 mV [PASS]
adc0 − adc2 : mean=+0.590 mV std=1.083 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-06-28.csv
Pass threshold: std ≤ 4.0 mV (16 counts = 2 mV minimum step)
==========================================================
adc0 − adc1 : mean=-0.055 mV std=1.005 mV [PASS]
adc0 − adc2 : mean=+0.913 mV std=1.146 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
Analysis of Jun 12–14 data showed ADC2 reading consistently ~13 counts low (median gap adc0 − adc2 = +16 counts).
mosquitto_pub -h 10.0.10.20 -t moon-temp-001/offset/cmd -m '{"adc":2,"offset":-64}'ADC2 offset: −80 → −64
Three-day post-adjustment analysis (Jun 15–17) confirms ADC2 correction. ADC1 (offset +16) had a median gap of +6.8 counts (0.85 mV) — within spec but drifting.
ADC1 was reading low (+6.8 counts below ADC0), but offset was reduced:
mosquitto_pub -h 10.0.10.20 -t moon-temp-001/offset/cmd -m '{"adc":1,"offset":10}'ADC1 offset: +16 → +10 — this was the wrong direction. Gap widened to +11.3 counts (+1.41 mV) over Jun 18–19.
mosquitto_pub -h 10.0.10.20 -t moon-temp-001/offset/cmd -m '{"adc":1,"offset":22}'ADC1 offset: +10 → +22
Jun 21–22 verification: ADC1 mean gap = −0.20 mV, std = 0.91 mV — PASS.
All three channels are within specification. Sensors ready for deployment.
| Channel | Final Offset | Status |
|---|---|---|
| ADC0 | 0 (reference) | — |
| ADC1 | +22 counts (+2.75 mV) | PASS |
| ADC2 | −64 counts (−8.00 mV) | PASS |
Calibration period: May 26 → June 28, 2026 (33 days) Pass criterion: std(adc0 − adcN) ≤ 4.0 mV Pit temperature drift: +2.1 °C over the calibration period (seasonal ground warming)
Inter-channel spread stabilised to < 0.05 °C by mid-June. The residual ±5–7 count diurnal swing on ADC1 is a thermal gradient between sensor placements — a DC offset cannot eliminate it, but it is centred around zero.
Next step: Reprogram ESP32-C3 MQTT broker address for field deployment.
Loads all 33 days of valid data. Shows the pit temperature rising ~2.1 °C as summer heat diffuses into the ground, and confirms inter-channel offsets held stable throughout.
# Load all valid log days — both 2.3 V era and new v_rail era
R_SERIES = 10_000; R0 = 10_000; B = 3950; T0_K = 298.15
ADC_LSB = 0.00013108 # calibrated (applies to all channels on this ADS1115)
def counts_to_celsius(counts, v_rail=None):
"""Convert ADS1115 raw count to °C.
- v_rail present (Jul 2026+): use measured rail voltage
- v_rail absent (pre-Jul 2026, 2.3 V era): use VCC=2.3
"""
vcc = v_rail if (v_rail is not None and np.isscalar(v_rail) and v_rail > 0.5) else 2.3
V = counts * ADC_LSB
if V <= 0 or V >= vcc:
return np.nan
R = V * R_SERIES / (vcc - V)
return 1 / (1/T0_K + (1/B) * np.log(R / R0)) - 273.15
# Collect CSVs from both eras
old_csvs = [f for f in sorted(glob.glob(os.path.join(LOGS_2_3V, "????-??-??.csv")))
if "2026-05-26.csv" <= os.path.basename(f) <= "2026-06-28.csv"]
new_csvs = [f for f in sorted(glob.glob(os.path.join(LOGS_NEW, "????-??-??.csv")))
if os.path.basename(f) <= "2026-06-28.csv"]
all_csvs = old_csvs + new_csvs
daily = {}
for path in all_csvs:
date = os.path.basename(path).replace('.csv','')
d = pd.read_csv(path, parse_dates=['timestamp'])
d = d.sort_values('timestamp').reset_index(drop=True)
has_rail = 'v_rail' in d.columns
for ch in ['adc0','adc1','adc2']:
col = ch.replace('adc','temp')
if has_rail:
d[col] = d.apply(lambda r, c=ch: counts_to_celsius(r[c], r['v_rail']), axis=1)
else:
d[col] = d[ch].apply(counts_to_celsius) # falls back to VCC=2.3
if date == '2026-05-27':
d = d[~((d['timestamp'] >= '2026-05-27 09:51:00') & (d['timestamp'] <= '2026-05-27 09:58:15'))]
daily[date] = d
rows = []
for date, d in daily.items():
rows.append({
'date': pd.Timestamp(date),
't0': d['temp0'].mean(),
't1': d['temp1'].mean(),
't2': d['temp2'].mean(),
'd01': (d['adc0'] - d['adc1']).mean() * ADC_MV,
'd02': (d['adc0'] - d['adc2']).mean() * ADC_MV,
'd01_std': (d['adc0'] - d['adc1']).std() * ADC_MV,
'd02_std': (d['adc0'] - d['adc2']).std() * ADC_MV,
'era': 'v_rail' if 'v_rail' in d.columns else '2.3V',
})
prog = pd.DataFrame(rows).set_index('date')
print(f"Loaded {len(prog)} days: {prog.index[0].date()} → {prog.index[-1].date()}")
print(f" 2.3V era : {(prog['era']=='2.3V').sum()} days")
print(f" v_rail era: {(prog['era']=='v_rail').sum()} days")
print(prog[['t0','t1','t2','d01','d02','era']].round(3).to_string())Loaded 34 days: 2026-05-26 → 2026-06-28
2.3V era : 34 days
v_rail era: 0 days
t0 t1 t2 d01 d02 era
date
2026-05-26 3.989 4.025 3.804 0.854 -4.275 2.3V
2026-05-27 4.136 4.604 4.162 10.913 0.580 2.3V
2026-05-28 4.139 4.283 4.081 3.376 -1.346 2.3V
2026-05-29 4.472 4.479 4.440 0.153 -0.750 2.3V
2026-05-30 4.686 4.748 4.690 1.450 0.082 2.3V
2026-05-31 4.822 4.853 4.818 0.733 -0.086 2.3V
2026-06-01 4.762 4.779 4.746 0.383 -0.396 2.3V
2026-06-02 4.808 4.821 4.796 0.303 -0.297 2.3V
2026-06-03 4.591 4.610 4.652 0.435 1.439 2.3V
2026-06-04 4.653 4.689 4.725 0.852 1.691 2.3V
2026-06-05 4.719 4.774 4.802 1.308 1.957 2.3V
2026-06-06 4.779 4.810 4.853 0.744 1.748 2.3V
2026-06-07 4.810 4.846 4.882 0.859 1.705 2.3V
2026-06-08 4.968 5.002 5.039 0.785 1.675 2.3V
2026-06-09 5.096 5.131 5.170 0.832 1.743 2.3V
2026-06-10 5.101 5.141 5.174 0.929 1.722 2.3V
2026-06-11 5.304 5.335 5.372 0.736 1.598 2.3V
2026-06-12 5.429 5.471 5.504 0.998 1.762 2.3V
2026-06-13 5.486 5.506 5.552 0.469 1.557 2.3V
2026-06-14 5.609 5.644 5.683 0.847 1.766 2.3V
2026-06-15 5.701 5.742 5.735 0.961 0.792 2.3V
2026-06-16 5.829 5.879 5.819 1.200 -0.230 2.3V
2026-06-17 6.001 6.022 5.984 0.486 -0.425 2.3V
2026-06-18 6.244 6.294 6.237 1.194 -0.182 2.3V
2026-06-19 6.572 6.637 6.569 1.569 -0.058 2.3V
2026-06-20 6.714 6.761 6.708 1.118 -0.160 2.3V
2026-06-21 6.746 6.742 6.747 -0.100 0.017 2.3V
2026-06-22 6.791 6.784 6.800 -0.162 0.229 2.3V
2026-06-23 6.801 6.798 6.813 -0.093 0.291 2.3V
2026-06-24 6.912 6.923 6.932 0.269 0.504 2.3V
2026-06-25 7.005 7.012 7.029 0.165 0.585 2.3V
2026-06-26 7.150 7.139 7.167 -0.286 0.414 2.3V
2026-06-27 7.236 7.227 7.260 -0.226 0.590 2.3V
2026-06-28 7.277 7.275 7.315 -0.055 0.913 2.3V# Pit temperature trend — daily mean, all 3 channels
fig, ax = plt.subplots(figsize=(16, 5))
for col, color, label in [('t0','steelblue','ch0'), ('t1','darkorange','ch1'), ('t2','crimson','ch2')]:
ax.plot(prog.index, prog[col], 'o-', color=color, label=label, linewidth=1.5, markersize=4)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax.set_ylabel('°C')
ax.set_title('Daily mean pit temperature — May 26 → Jun 28 [MF58 10K NTC, B=3950 K]')
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
rise = prog['t0'].iloc[-1] - prog['t0'].iloc[0]
days = (prog.index[-1] - prog.index[0]).days
print(f'Total rise: {rise:+.3f} °C over {days} days ({rise/days*7:.3f} °C/week)')
Total rise: +3.289 °C over 33 days (0.698 °C/week)# Inter-channel offset drift — daily mean ± 1 std (mV)
fig, axes = plt.subplots(2, 1, figsize=(16, 6), sharex=True)
for ax, col, std_col, label, color in [
(axes[0], 'd01', 'd01_std', 'adc0 − adc1 (mV)', 'darkorange'),
(axes[1], 'd02', 'd02_std', 'adc0 − adc2 (mV)', 'crimson'),
]:
ax.fill_between(prog.index, prog[col]-prog[std_col], prog[col]+prog[std_col], color=color, alpha=0.2)
ax.plot(prog.index, prog[col], 'o-', color=color, linewidth=1.2, markersize=4)
ax.axhline(0, color='black', linestyle='--', linewidth=0.8)
ax.axhline( PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.7, label='±pass threshold')
ax.axhline(-PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.7)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax.set_ylabel('mV')
ax.set_title(f'{label} (shaded = ±1 std)')
ax.legend(loc='upper right')
fig.suptitle('Inter-channel offset drift — full calibration period', fontsize=12)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
Hardware updated July 2026: variable regulator + dedicated buck converter for ADS1115 VDD, rail voltage monitor on AIN3. Sensors returned to the static thermal chamber for post-update calibration verification.
Current device state (flash config):
{"offset_adc0":0,"offset_adc1":49,"offset_adc2":-73,"avg_samples":64}Averaging time: 4 channels × 64 samples × 9 ms = 2,304 ms ≈ 2.3 seconds per published reading
Offset history: | Applied | ADC1 | ADC2 | Notes | |—|—|—|—| | Jul 11 (initial) | +22 | −64 | Post-hardware-update starting point | | Jul 13 ~10:30 MDT | +49 | −73 | Corrected from 33-day calibration analysis |
Hardware change notes: - Single ADC sample per minute (pre-averaging firmware) produced noise spikes of ±1–2 °C — not actual temperature events; seen during field experimental runs - 64-sample averaging per publish implemented in firmware to suppress these spikes - Rail voltage (AIN3) averaged identically; v_rail tracks ADS1115 VDD
# Load all converted_logs CSVs (post-update calibration era)
field_csvs = sorted(glob.glob(os.path.join(LOGS_CONV, "????-??-??.csv")))
if not field_csvs:
raise FileNotFoundError(f"No CSVs found in {LOGS_CONV}")
dfs = []
for path in field_csvs:
d = pd.read_csv(path, parse_dates=['timestamp'])
d = d.sort_values('timestamp').reset_index(drop=True)
dfs.append(d)
field = pd.concat(dfs, ignore_index=True).sort_values('timestamp').reset_index(drop=True)
field['d01_mV'] = (field['adc0'] - field['adc1']) * ADC_MV
field['d02_mV'] = (field['adc0'] - field['adc2']) * ADC_MV
field['dt01'] = field['t0_c'] - field['t1_c']
field['dt02'] = field['t0_c'] - field['t2_c']
field['date'] = field['timestamp'].dt.date
# Offset changeover: ADC1 +22→+49, ADC2 -64→-73 applied 2026-07-13 ~10:30 MDT
OFFSET_CUTOVER = pd.Timestamp('2026-07-13 11:00:00')
field_old = field[field['timestamp'] < OFFSET_CUTOVER].copy() # old offsets
field_new = field[field['timestamp'] >= OFFSET_CUTOVER].copy() # new offsets
print(f"Full span : {field['timestamp'].iloc[0]} → {field['timestamp'].iloc[-1]} ({len(field)} rows)")
print(f"Old offsets: {len(field_old)} rows (ADC1=+22 ADC2=−64)")
print(f"New offsets: {len(field_new)} rows (ADC1=+49 ADC2=−73)")
print(f"v_rail : mean={field['v_rail'].mean():.4f} V std={field['v_rail'].std():.4f} V")Full span : 2026-07-11 10:00:33 → 2026-07-31 19:08:02 (53395 rows)
Old offsets: 2948 rows (ADC1=+22 ADC2=−64)
New offsets: 50447 rows (ADC1=+49 ADC2=−73)
v_rail : mean=3.5178 V std=0.0309 V# Time series — inter-channel difference in mV
fig, axes = plt.subplots(2, 1, figsize=(16, 7), sharex=True)
for ax, col, label, color in [
(axes[0], 'd01_mV', 'adc0 − adc1', 'darkorange'),
(axes[1], 'd02_mV', 'adc0 − adc2', 'crimson'),
]:
ax.plot(field['timestamp'], field[col], color=color, linewidth=0.6, alpha=0.8)
ax.axhline(0, color='gray', linestyle='--', linewidth=1, alpha=0.5)
ax.axhline( PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.axhline(-PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d %H:%M'))
ax.set_title(f'{label} mean={field[col].mean():+.3f} mV std={field[col].std():.3f} mV')
ax.set_ylabel('Δ mV')
fig.suptitle('Field deployment — ADC inter-channel alignment (mV)', fontsize=12)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
# Daily mean ADC alignment
daily_adc = field.groupby('date').agg(
d01_mean=('d01_mV', 'mean'), d01_std=('d01_mV', 'std'),
d02_mean=('d02_mV', 'mean'), d02_std=('d02_mV', 'std'),
).reset_index()
print("Daily ADC alignment (mV):")
print(daily_adc.round(3).to_string(index=False))
fig, ax = plt.subplots(figsize=(12, 5))
x = range(len(daily_adc))
for col_m, col_s, color, lbl in [
('d01_mean', 'd01_std', 'darkorange', 'adc0 − adc1'),
('d02_mean', 'd02_std', 'crimson', 'adc0 − adc2'),
]:
ax.errorbar(x, daily_adc[col_m], yerr=daily_adc[col_s], fmt='o-', color=color,
label=lbl, capsize=4, linewidth=1.5)
ax.axhline(0, color='gray', linestyle='--', linewidth=1, alpha=0.5)
ax.axhline( PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6, label='±pass threshold')
ax.axhline(-PASS_STD_THRESHOLD, color='lime', linestyle=':', linewidth=0.8, alpha=0.6)
ax.set_xticks(list(x))
ax.set_xticklabels([str(d) for d in daily_adc['date']], rotation=30, ha='right')
ax.set_ylabel('Δ mV')
ax.set_title('Daily mean ADC inter-channel alignment — field deployment')
ax.legend()
plt.tight_layout()
plt.show()Daily ADC alignment (mV):
date d01_mean d01_std d02_mean d02_std
2026-07-11 3.924 1.922 -0.876 1.153
2026-07-12 3.401 0.252 -1.308 0.151
2026-07-13 1.348 1.782 -0.633 0.609
2026-07-14 -0.079 0.212 -0.079 0.174
2026-07-15 -0.056 0.199 0.099 0.180
2026-07-16 -0.184 0.200 0.084 0.171
2026-07-17 -0.114 0.216 0.075 0.164
2026-07-18 0.142 0.181 0.116 0.186
2026-07-19 0.072 0.175 0.237 0.188
2026-07-20 0.146 0.151 0.220 0.174
2026-07-21 0.040 0.120 0.198 0.169
2026-07-22 0.058 0.124 0.359 0.170
2026-07-23 0.131 0.148 0.666 0.162
2026-07-24 0.276 0.137 0.836 0.132
2026-07-25 0.151 0.127 0.988 0.127
2026-07-26 0.218 0.134 0.944 0.160
2026-07-27 3.468 9.873 15.710 17.637
2026-07-28 0.308 9.271 9.668 18.376
2026-07-29 -2.835 13.297 2.365 14.722
2026-07-30 -5.128 21.094 -0.271 21.265
2026-07-31 -9.898 40.104 -0.912 60.225
# Full time series in °C
fig, ax = plt.subplots(figsize=(16, 5))
for col, color, label in [
('t0_c', 'steelblue', 'ch0'),
('t1_c', 'darkorange', 'ch1'),
('t2_c', 'crimson', 'ch2'),
]:
ax.plot(field['timestamp'], field[col], color=color, linewidth=0.8, alpha=0.85, label=label)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d %H:%M'))
ax.set_ylabel('°C')
ax.set_title('Field deployment — sensor temperatures (°C)')
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
# Daily mean temperature in °C
daily_temp = field.groupby('date').agg(
t0_mean=('t0_c', 'mean'), t0_std=('t0_c', 'std'),
t1_mean=('t1_c', 'mean'), t1_std=('t1_c', 'std'),
t2_mean=('t2_c', 'mean'), t2_std=('t2_c', 'std'),
).reset_index()
print("Daily mean temperature (°C):")
print(daily_temp.round(3).to_string(index=False))
fig, ax = plt.subplots(figsize=(12, 5))
x = range(len(daily_temp))
for col_m, col_s, color, lbl in [
('t0_mean', 't0_std', 'steelblue', 'ch0'),
('t1_mean', 't1_std', 'darkorange', 'ch1'),
('t2_mean', 't2_std', 'crimson', 'ch2'),
]:
ax.errorbar(x, daily_temp[col_m], yerr=daily_temp[col_s], fmt='o-', color=color,
label=lbl, capsize=4, linewidth=1.5)
ax.set_xticks(list(x))
ax.set_xticklabels([str(d) for d in daily_temp['date']], rotation=30, ha='right')
ax.set_ylabel('°C')
ax.set_title('Daily mean field temperature — July 2026')
ax.legend()
plt.tight_layout()
plt.show()Daily mean temperature (°C):
date t0_mean t0_std t1_mean t1_std t2_mean t2_std
2026-07-11 12.928 1.338 13.027 1.375 12.905 1.364
2026-07-12 12.319 0.020 12.406 0.023 12.286 0.021
2026-07-13 12.319 0.026 12.354 0.037 12.303 0.037
2026-07-14 12.374 0.019 12.372 0.018 12.371 0.022
2026-07-15 12.432 0.034 12.430 0.034 12.434 0.033
2026-07-16 12.534 0.032 12.529 0.035 12.536 0.030
2026-07-17 12.623 0.024 12.620 0.028 12.625 0.025
2026-07-18 12.719 0.034 12.723 0.031 12.722 0.036
2026-07-19 12.779 0.026 12.781 0.028 12.785 0.027
2026-07-20 12.851 0.017 12.855 0.015 12.856 0.019
2026-07-21 12.915 0.025 12.916 0.025 12.920 0.027
2026-07-22 12.978 0.024 12.980 0.024 12.987 0.025
2026-07-23 13.047 0.022 13.051 0.024 13.064 0.025
2026-07-24 13.117 0.033 13.124 0.032 13.138 0.033
2026-07-25 13.214 0.032 13.217 0.033 13.239 0.031
2026-07-26 13.296 0.032 13.302 0.032 13.320 0.034
2026-07-27 14.501 3.408 14.586 3.266 14.896 3.580
2026-07-28 20.858 8.603 20.862 8.464 21.100 8.193
2026-07-29 20.651 8.725 20.575 8.675 20.702 8.502
2026-07-30 21.214 9.274 21.073 9.135 21.190 8.876
2026-07-31 13.901 10.006 13.605 8.950 13.833 8.480
# Inter-channel spread in °C
fig, axes = plt.subplots(2, 1, figsize=(16, 7), sharex=True)
for ax, col, label, color in [
(axes[0], 'dt01', 'ch0 − ch1 (°C)', 'darkorange'),
(axes[1], 'dt02', 'ch0 − ch2 (°C)', 'crimson'),
]:
ax.plot(field['timestamp'], field[col], color=color, linewidth=0.6, alpha=0.8)
ax.axhline(0, color='gray', linestyle='--', linewidth=1, alpha=0.5)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d %H:%M'))
ax.set_title(f'{label} mean={field[col].mean():+.4f} °C std={field[col].std():.4f} °C')
ax.set_ylabel('Δ °C')
fig.suptitle('Field deployment — inter-channel spread (°C)', fontsize=12)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
Sensors sit in a high-thermal-mass concrete pit: temperature should be extremely stable (< 0.05 °C variation per sample under normal conditions). Any sample deviating > 1.5 °C from its 5-sample rolling median is flagged as a suspect read.
# Flag samples that deviate more than 1.5 °C from a 5-point rolling median
SPIKE_THRESH = 0.25 # °C
field_s = field.sort_values('timestamp').reset_index(drop=True)
spike_rows = pd.DataFrame()
for ch in ['t0_c', 't1_c', 't2_c']:
roll = field_s[ch].rolling(5, center=True, min_periods=1).median()
mask = (field_s[ch] - roll).abs() > SPIKE_THRESH
if mask.any():
bad = field_s[mask][['timestamp', 'adc0', 'adc1', 'adc2', 'v_rail', ch]].copy()
bad['channel'] = ch
bad['deviation_C'] = (field_s[ch] - roll)[mask]
spike_rows = pd.concat([spike_rows, bad])
if spike_rows.empty:
print(f"No spikes detected (threshold: >{SPIKE_THRESH} °C from 5-sample rolling median)")
print("Signal is stable — consistent with high-thermal-mass static chamber environment.")
else:
print(f"{len(spike_rows)} suspect samples found:")
print(spike_rows[['timestamp','channel','deviation_C']].to_string(index=False))
# Plot all channels with spike threshold bands
fig, ax = plt.subplots(figsize=(16, 5))
for col, color, label in [('t0_c','steelblue','ch0'), ('t1_c','darkorange','ch1'), ('t2_c','crimson','ch2')]:
ax.plot(field_s['timestamp'], field_s[col], color=color, linewidth=0.7, alpha=0.85, label=label)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d %H:%M'))
ax.set_ylabel('°C')
ax.set_title(f'Stability check — all channels (spike threshold ±{SPIKE_THRESH} °C, none detected)')
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()132 suspect samples found:
timestamp channel deviation_C
2026-07-30 11:39:52 t0_c 0.44
2026-07-30 11:40:02 t0_c 0.48
2026-07-30 19:42:32 t0_c 0.95
2026-07-30 20:53:52 t0_c 0.77
2026-07-30 20:54:02 t0_c 2.74
2026-07-31 00:01:52 t0_c 1.01
2026-07-31 00:02:02 t0_c 2.59
2026-07-31 00:09:12 t0_c 0.29
2026-07-31 00:11:32 t0_c -0.37
2026-07-31 00:12:12 t0_c 0.41
2026-07-31 00:13:52 t0_c 0.79
2026-07-31 00:14:02 t0_c 1.11
2026-07-31 00:14:12 t0_c -0.35
2026-07-31 00:14:22 t0_c 0.35
2026-07-31 00:44:52 t0_c 0.28
2026-07-31 00:45:22 t0_c 0.27
2026-07-31 00:46:22 t0_c -0.26
2026-07-31 00:48:12 t0_c 0.35
2026-07-31 00:48:22 t0_c -0.30
2026-07-31 00:48:42 t0_c 0.30
2026-07-31 00:48:52 t0_c -0.38
2026-07-31 00:50:52 t0_c 0.28
2026-07-31 00:51:02 t0_c 5.74
2026-07-31 07:18:02 t0_c 0.39
2026-07-31 07:56:02 t0_c 1.50
2026-07-31 07:57:02 t0_c 0.37
2026-07-31 07:58:02 t0_c -2.09
2026-07-31 07:59:02 t0_c 3.50
2026-07-31 08:43:02 t0_c 2.16
2026-07-31 08:44:02 t0_c 1.24
2026-07-31 08:57:02 t0_c 0.31
2026-07-31 09:41:02 t0_c 1.03
2026-07-31 09:42:02 t0_c 1.42
2026-07-31 09:47:02 t0_c -0.27
2026-07-31 10:12:02 t0_c 0.27
2026-07-31 10:16:02 t0_c 0.78
2026-07-31 10:21:02 t0_c 0.74
2026-07-31 12:15:02 t0_c 0.94
2026-07-31 13:12:02 t0_c 0.37
2026-07-31 13:13:02 t0_c 0.44
2026-07-31 14:04:02 t0_c -0.45
2026-07-31 14:10:02 t0_c 0.42
2026-07-31 14:20:02 t0_c 0.28
2026-07-31 14:24:02 t0_c 0.44
2026-07-31 15:14:02 t0_c 0.48
2026-07-30 11:39:52 t1_c 0.46
2026-07-30 11:40:02 t1_c 0.52
2026-07-30 19:42:32 t1_c 0.94
2026-07-30 20:53:52 t1_c 0.76
2026-07-30 20:54:02 t1_c 2.72
2026-07-31 00:01:52 t1_c 0.98
2026-07-31 00:02:02 t1_c 2.59
2026-07-31 00:09:12 t1_c 0.28
2026-07-31 00:11:32 t1_c -0.37
2026-07-31 00:12:12 t1_c 0.40
2026-07-31 00:13:52 t1_c 0.77
2026-07-31 00:14:02 t1_c 1.11
2026-07-31 00:14:12 t1_c -0.35
2026-07-31 00:14:22 t1_c 0.35
2026-07-31 00:44:52 t1_c 0.29
2026-07-31 00:45:22 t1_c 0.26
2026-07-31 00:48:12 t1_c 0.35
2026-07-31 00:48:22 t1_c -0.31
2026-07-31 00:48:42 t1_c 0.28
2026-07-31 00:48:52 t1_c -0.39
2026-07-31 00:50:52 t1_c 0.27
2026-07-31 00:51:02 t1_c 5.70
2026-07-31 07:18:02 t1_c 0.38
2026-07-31 07:56:02 t1_c 1.50
2026-07-31 07:57:02 t1_c 0.34
2026-07-31 07:58:02 t1_c -2.10
2026-07-31 07:59:02 t1_c 3.50
2026-07-31 08:43:02 t1_c 2.16
2026-07-31 08:44:02 t1_c 1.25
2026-07-31 08:57:02 t1_c 0.29
2026-07-31 09:41:02 t1_c 1.05
2026-07-31 09:42:02 t1_c 1.46
2026-07-31 09:47:02 t1_c -0.26
2026-07-31 10:12:02 t1_c 0.28
2026-07-31 10:16:02 t1_c 0.77
2026-07-31 10:21:02 t1_c 0.73
2026-07-31 12:15:02 t1_c 0.90
2026-07-31 13:12:02 t1_c 0.34
2026-07-31 13:13:02 t1_c 0.37
2026-07-31 14:10:02 t1_c 0.44
2026-07-31 14:24:02 t1_c 0.38
2026-07-31 15:14:02 t1_c 0.52
2026-07-30 11:39:52 t2_c 0.45
2026-07-30 11:40:02 t2_c 0.50
2026-07-30 19:42:32 t2_c 0.97
2026-07-30 20:53:52 t2_c 0.78
2026-07-30 20:54:02 t2_c 2.73
2026-07-31 00:01:52 t2_c 1.00
2026-07-31 00:02:02 t2_c 2.59
2026-07-31 00:09:12 t2_c 0.28
2026-07-31 00:11:32 t2_c -0.36
2026-07-31 00:12:12 t2_c 0.41
2026-07-31 00:13:52 t2_c 0.77
2026-07-31 00:14:02 t2_c 1.10
2026-07-31 00:14:12 t2_c -0.35
2026-07-31 00:14:22 t2_c 0.35
2026-07-31 00:44:52 t2_c 0.29
2026-07-31 00:45:22 t2_c 0.27
2026-07-31 00:46:22 t2_c -0.26
2026-07-31 00:48:12 t2_c 0.32
2026-07-31 00:48:22 t2_c -0.32
2026-07-31 00:48:42 t2_c 0.29
2026-07-31 00:48:52 t2_c -0.39
2026-07-31 00:50:52 t2_c 0.29
2026-07-31 00:51:02 t2_c 5.70
2026-07-31 07:18:02 t2_c 0.40
2026-07-31 07:56:02 t2_c 1.48
2026-07-31 07:57:02 t2_c 0.38
2026-07-31 07:58:02 t2_c -2.03
2026-07-31 07:59:02 t2_c 3.50
2026-07-31 08:43:02 t2_c 2.11
2026-07-31 08:44:02 t2_c 1.17
2026-07-31 08:57:02 t2_c 0.31
2026-07-31 09:41:02 t2_c 1.06
2026-07-31 09:42:02 t2_c 1.44
2026-07-31 09:47:02 t2_c -0.27
2026-07-31 10:12:02 t2_c 0.29
2026-07-31 10:16:02 t2_c 0.77
2026-07-31 10:21:02 t2_c 0.73
2026-07-31 11:53:02 t2_c 0.29
2026-07-31 12:14:02 t2_c 0.28
2026-07-31 12:15:02 t2_c 0.99
2026-07-31 13:12:02 t2_c 0.37
2026-07-31 13:13:02 t2_c 0.39
2026-07-31 14:10:02 t2_c 0.44
2026-07-31 14:24:02 t2_c 0.39
2026-07-31 15:14:02 t2_c 0.53
ADC0 is the reference (offset always 0).
Pre-adjustment (Jul 11–12, ADC1=+22, ADC2=−64):
| Channel | Mean gap (counts) | Mean gap (mV) | Assessment |
|---|---|---|---|
| ADC1 | +26.9 (reads low) | +3.52 mV | Under-corrected |
| ADC2 | −9.0 (reads high) | −1.18 mV | Over-corrected |
Adjustment applied 2026-07-13: ADC1 +22→+49, ADC2 −64→−73
{"adc":1,"offset":49}
{"adc":2,"offset":-73}# Offset summary table
rows = [
('ADC0', 0, 0.0, 0.0, 0),
('ADC1', 22, +26.89, +3.524, 49),
('ADC2', -64, -8.97, -1.176, -73),
]
print(f" {'Ch':5s} {'Old':>6s} {'Gap (cts)':>10s} {'Gap (mV)':>9s} {'New':>6s}")
for ch, old, gc, gm, new in rows:
print(f" {ch:5s} {old:>+6d} {gc:>+10.2f} {gm:>+9.3f} {new:>+6d}") Ch Old Gap (cts) Gap (mV) New
ADC0 +0 +0.00 +0.000 +0
ADC1 +22 +26.89 +3.524 +49
ADC2 -64 -8.97 -1.176 -73Analysis of the new offset era only (2026-07-13 11:00 MDT onwards). Mirrors the 33-day calibration format for direct comparison.
Sensors entered Ready For Experiment mode 2026-07-26 — calibration confirmed stable across all prior days. First night session: Moon-Temp-002 (full moon 99.2%).
# Inter-channel alignment history — post-adjustment era, daily mean ± std
import datetime as _dt
hist_new = field_new.groupby("date").agg(
d01_mean=("d01_mV", "mean"), d01_std=("d01_mV", "std"),
d02_mean=("d02_mV", "mean"), d02_std=("d02_mV", "std"),
).reset_index()
print("Post-adjustment inter-channel alignment (mV):")
print(hist_new.round(3).to_string(index=False))
fig, ax = plt.subplots(figsize=(12, 5))
x = range(len(hist_new))
for col_m, col_s, color, lbl in [
("d01_mean", "d01_std", "darkorange", "adc0 − adc1"),
("d02_mean", "d02_std", "crimson", "adc0 − adc2"),
]:
ax.errorbar(x, hist_new[col_m], yerr=hist_new[col_s], fmt="o-", color=color,
label=lbl, capsize=4, linewidth=1.5)
ax.axhline(0, color="gray", linestyle="--", linewidth=1, alpha=0.5)
ax.axhline( PASS_STD_THRESHOLD, color="lime", linestyle=":", linewidth=0.8, alpha=0.6, label="±pass threshold")
ax.axhline(-PASS_STD_THRESHOLD, color="lime", linestyle=":", linewidth=0.8, alpha=0.6)
_exp_date = _dt.date(2026, 7, 28)
_dates = list(hist_new["date"])
if _exp_date in _dates:
_xi = _dates.index(_exp_date)
ax.axvline(_xi, color="limegreen", linestyle="--", linewidth=1.5, alpha=0.9,
label="Ready For Experiment (Jul 26)")
ax.text(_xi + 0.1, 0.97, "Ready For\nExperiment", transform=ax.get_xaxis_transform(),
color="limegreen", fontsize=8, va="top")
ax.set_xticks(list(x))
ax.set_xticklabels([str(d) for d in hist_new["date"]], rotation=30, ha="right")
ax.set_ylabel("Δ mV")
ax.set_title("Post-adjustment ADC alignment — daily mean ± 1 std (mV)")
ax.legend()
plt.tight_layout()
plt.show()Post-adjustment inter-channel alignment (mV):
date d01_mean d01_std d02_mean d02_std
2026-07-13 -0.282 0.218 -0.088 0.157
2026-07-14 -0.079 0.212 -0.079 0.174
2026-07-15 -0.056 0.199 0.099 0.180
2026-07-16 -0.184 0.200 0.084 0.171
2026-07-17 -0.114 0.216 0.075 0.164
2026-07-18 0.142 0.181 0.116 0.186
2026-07-19 0.072 0.175 0.237 0.188
2026-07-20 0.146 0.151 0.220 0.174
2026-07-21 0.040 0.120 0.198 0.169
2026-07-22 0.058 0.124 0.359 0.170
2026-07-23 0.131 0.148 0.666 0.162
2026-07-24 0.276 0.137 0.836 0.132
2026-07-25 0.151 0.127 0.988 0.127
2026-07-26 0.218 0.134 0.944 0.160
2026-07-27 3.468 9.873 15.710 17.637
2026-07-28 0.308 9.271 9.668 18.376
2026-07-29 -2.835 13.297 2.365 14.722
2026-07-30 -5.128 21.094 -0.271 21.265
2026-07-31 -9.898 40.104 -0.912 60.225
# Per-day pass/fail confirmation — post-adjustment era
W = 58
for date, g in field_new.groupby('date'):
diff01 = g['d01_mV']; diff02 = g['d02_mV']
results = [
('adc0 − adc1', diff01.mean(), diff01.std(), diff01.std() <= PASS_STD_THRESHOLD),
('adc0 − adc2', diff02.mean(), diff02.std(), diff02.std() <= PASS_STD_THRESHOLD),
]
all_pass = all(r[3] for r in results)
print('=' * W)
print(f" Calibration Confirmation — {date}")
print(f" Pass threshold: std ≤ {PASS_STD_THRESHOLD} mV")
print('=' * W)
for label, mean_v, std_v, passed in results:
print(f" {label:12s}: mean={mean_v:+.3f} mV std={std_v:.3f} mV [{'PASS' if passed else 'FAIL'}]")
print('=' * W)
print(f" >> {'CALIBRATION OK' if all_pass else 'CALIBRATION FAIL'}")
print('=' * W)
print()==========================================================
Calibration Confirmation — 2026-07-13
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-0.282 mV std=0.218 mV [PASS]
adc0 − adc2 : mean=-0.088 mV std=0.157 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-14
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-0.079 mV std=0.212 mV [PASS]
adc0 − adc2 : mean=-0.079 mV std=0.174 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-15
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-0.056 mV std=0.199 mV [PASS]
adc0 − adc2 : mean=+0.099 mV std=0.180 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-16
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-0.184 mV std=0.200 mV [PASS]
adc0 − adc2 : mean=+0.084 mV std=0.171 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-17
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-0.114 mV std=0.216 mV [PASS]
adc0 − adc2 : mean=+0.075 mV std=0.164 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-18
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.142 mV std=0.181 mV [PASS]
adc0 − adc2 : mean=+0.116 mV std=0.186 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-19
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.072 mV std=0.175 mV [PASS]
adc0 − adc2 : mean=+0.237 mV std=0.188 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-20
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.146 mV std=0.151 mV [PASS]
adc0 − adc2 : mean=+0.220 mV std=0.174 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-21
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.040 mV std=0.120 mV [PASS]
adc0 − adc2 : mean=+0.198 mV std=0.169 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-22
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.058 mV std=0.124 mV [PASS]
adc0 − adc2 : mean=+0.359 mV std=0.170 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-23
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.131 mV std=0.148 mV [PASS]
adc0 − adc2 : mean=+0.666 mV std=0.162 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-24
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.276 mV std=0.137 mV [PASS]
adc0 − adc2 : mean=+0.836 mV std=0.132 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-25
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.151 mV std=0.127 mV [PASS]
adc0 − adc2 : mean=+0.988 mV std=0.127 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-26
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.218 mV std=0.134 mV [PASS]
adc0 − adc2 : mean=+0.944 mV std=0.160 mV [PASS]
==========================================================
>> CALIBRATION OK
==========================================================
==========================================================
Calibration Confirmation — 2026-07-27
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+3.468 mV std=9.873 mV [FAIL]
adc0 − adc2 : mean=+15.710 mV std=17.637 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-07-28
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=+0.308 mV std=9.271 mV [FAIL]
adc0 − adc2 : mean=+9.668 mV std=18.376 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-07-29
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-2.835 mV std=13.297 mV [FAIL]
adc0 − adc2 : mean=+2.365 mV std=14.722 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-07-30
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-5.128 mV std=21.094 mV [FAIL]
adc0 − adc2 : mean=-0.271 mV std=21.265 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
==========================================================
Calibration Confirmation — 2026-07-31
Pass threshold: std ≤ 4.0 mV
==========================================================
adc0 − adc1 : mean=-9.898 mV std=40.104 mV [FAIL]
adc0 − adc2 : mean=-0.912 mV std=60.225 mV [FAIL]
==========================================================
>> CALIBRATION FAIL
==========================================================
# Temperature trend — post-adjustment era
daily_t = field_new.groupby("date").agg(
t0=("t0_c","mean"), t1=("t1_c","mean"), t2=("t2_c","mean"),
t0s=("t0_c","std"), t1s=("t1_c","std"), t2s=("t2_c","std"),
).reset_index()
daily_t["date"] = pd.to_datetime(daily_t["date"])
fig, ax = plt.subplots(figsize=(14, 5))
for col, col_s, color, label in [
("t0","t0s","steelblue","ch0"), ("t1","t1s","darkorange","ch1"), ("t2","t2s","crimson","ch2")
]:
ax.errorbar(daily_t["date"], daily_t[col], yerr=daily_t[col_s],
fmt="o-", color=color, label=label, capsize=4, linewidth=1.5)
_exp_ts = pd.Timestamp("2026-07-26")
ax.axvline(_exp_ts, color="limegreen", linestyle="--", linewidth=1.5, alpha=0.9,
label="Ready For Experiment (Jul 26)")
ax.text(_exp_ts, 0.97, "Ready For\nExperiment", transform=ax.get_xaxis_transform(),
color="limegreen", fontsize=8, va="top")
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d"))
ax.set_ylabel("°C")
ax.set_title("Daily mean pit temperature — post-adjustment era [ADC1=+49 ADC2=−73]")
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
for col, label in [("t0","ch0"),("t1","ch1"),("t2","ch2")]:
d = field_new[col+"_c"] if col+"_c" in field_new.columns else field_new[{"t0":"t0_c","t1":"t1_c","t2":"t2_c"}[col]]
print(f" {label}: mean={d.mean():.3f}°C std={d.std():.3f}°C min={d.min():.3f}°C max={d.max():.3f}°C")
ch0: mean=17.088°C std=7.914°C min=6.830°C max=42.790°C
ch1: mean=17.037°C std=7.755°C min=7.010°C max=38.540°C
ch2: mean=17.149°C std=7.620°C min=7.340°C max=36.720°C# Inter-channel spread in °C — post-adjustment era
_exp_ts = pd.Timestamp("2026-07-26")
fig, axes = plt.subplots(2, 1, figsize=(16, 7), sharex=True)
for i, (ax, col, label, color) in enumerate([
(axes[0], "dt01", "ch0 − ch1 (°C)", "darkorange"),
(axes[1], "dt02", "ch0 − ch2 (°C)", "crimson"),
]):
ax.plot(field_new["timestamp"], field_new[col], color=color, linewidth=0.6, alpha=0.8)
ax.axhline(0, color="gray", linestyle="--", linewidth=1, alpha=0.5)
ax.axvline(_exp_ts, color="limegreen", linestyle="--", linewidth=1.5, alpha=0.9,
label="Ready For Experiment (Jul 26)")
ax.text(_exp_ts, 0.97, "Ready For\nExperiment" if i == 0 else "",
transform=ax.get_xaxis_transform(), color="limegreen", fontsize=8, va="top")
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b %d %H:%M"))
ax.set_title(f"{label} mean={field_new[col].mean():+.4f}°C std={field_new[col].std():.4f}°C max_abs={field_new[col].abs().max():.3f}°C")
ax.set_ylabel("Δ°C")
ax.legend(loc="upper left")
fig.suptitle("Post-adjustment inter-channel spread (°C) — ADC1=+49 ADC2=−73", fontsize=12)
fig.autofmt_xdate()
plt.tight_layout()
plt.show()
# Noise spike analysis — post-adjustment era
SPIKE_THRESH_NEW = 0.25 # °C
fn = field_new.sort_values('timestamp').reset_index(drop=True)
spike_summary = []
for ch, col in [('ch0','t0_c'), ('ch1','t1_c'), ('ch2','t2_c')]:
roll = fn[col].rolling(5, center=True, min_periods=1).median()
dev = (fn[col] - roll).abs()
spikes = dev > SPIKE_THRESH_NEW
spike_summary.append((ch, spikes.sum(), dev.max(), dev.mean()))
print(f"Spike detection threshold: >{SPIKE_THRESH_NEW}°C from 5-sample rolling median")
print("Post-adjustment era:", len(fn), "samples")
all_clear = all(s[1] == 0 for s in spike_summary)
for ch, n, max_dev, mean_dev in spike_summary:
status = 'CLEAR' if n == 0 else f'{n} SPIKES'
print(f" {ch}: max_dev={max_dev:.4f}°C mean_dev={mean_dev:.4f}°C [{status}]")
print()
print(f" >> {'ALL CLEAR — no noise spikes detected' if all_clear else 'SPIKES PRESENT — review above'}")
# Plot max deviation per day
fn['date'] = fn['timestamp'].dt.date
fig, ax = plt.subplots(figsize=(12, 4))
for ch, col, color in [('ch0','t0_c','steelblue'),('ch1','t1_c','darkorange'),('ch2','t2_c','crimson')]:
roll = fn[col].rolling(5, center=True, min_periods=1).median()
fn[f'dev_{ch}'] = (fn[col] - roll).abs()
daily_max = fn.groupby('date')[f'dev_{ch}'].max()
ax.plot(pd.to_datetime(list(daily_max.index)), daily_max.values, 'o-', color=color, label=ch, linewidth=1.5)
ax.axhline(SPIKE_THRESH_NEW, color='red', linestyle='--', linewidth=1, label=f'threshold {SPIKE_THRESH_NEW}°C')
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
ax.set_ylabel('Max deviation (°C)')
ax.set_title('Daily max sample deviation from 5-point rolling median — post-adjustment era')
ax.legend()
fig.autofmt_xdate()
plt.tight_layout()
plt.show()Spike detection threshold: >0.25°C from 5-sample rolling median
Post-adjustment era: 50447 samples
ch0: max_dev=5.7400°C mean_dev=0.0031°C [45 SPIKES]
ch1: max_dev=5.7000°C mean_dev=0.0027°C [42 SPIKES]
ch2: max_dev=5.7000°C mean_dev=0.0027°C [45 SPIKES]
>> SPIKES PRESENT — review above