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Fix sample rate handling and improve HTML report #3

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Fix sample rate handling and improve HTML report #3

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1612287
Fix sample rate handling and improve HTML report
solomon23 Feb 23, 2026
110b0c8
Add screenshot of HR Spike Detection Report
solomon23 Feb 23, 2026
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41 changes: 30 additions & 11 deletions analysis/generate_html_report.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,8 @@ def generate_report():

for fpath in csv_files:
fname = os.path.basename(fpath)
if fname == 'detector_results.csv':
continue
label = KNOWN_LABELS.get(fname, fname)
if label == fname:
# Parse 20260217032620_326am_10h_23m.csv
Expand Down Expand Up @@ -251,24 +253,24 @@ def generate_report():
rows.forEach(row => {
let hrs = parseFloat(row.cells[3].dataset.sort) || 0;
totalHrs += hrs;

let evts = parseFloat(row.cells[12].innerText) || 0;
totalEvents += evts;

let pcArr = row.cells[13].innerText.split('/');
totalEvents10 += parseInt(pcArr[0] || 0);
totalEvents15 += parseInt(pcArr[1] || 0);

totalMajorA += parseInt(row.cells[17].innerText) || 0;
totalMajorB += parseInt(row.cells[18].innerText) || 0;
totalMajorC += parseInt(row.cells[19].innerText) || 0;

sumTab += (parseFloat(row.querySelector('.cell-tab').dataset.value) || 0) * hrs;
sumScore += (parseFloat(row.querySelector('.cell-score').dataset.value) || 0) * hrs;

sumDelta += (parseFloat(row.querySelector('.cell-delta').dataset.value) || 0) * evts;
sumP90 += (parseFloat(row.querySelector('.cell-p90').dataset.value) || 0) * evts;

let typeArr = row.cells[11].innerText.split('/');
sumTypeA += (parseFloat(typeArr[0]) || 0) * evts;
sumTypeB += (parseFloat(typeArr[1]) || 0) * evts;
Expand Down Expand Up @@ -316,7 +318,7 @@ def generate_report():
tr.innerHTML = `
<td><input type="checkbox" class="row-checkbox" checked></td>
<td class="left-align" style="white-space: nowrap;">${mergedDate}</td>
<td class="left-align"><span class="editable-label" contenteditable="true">Merged: ${labels.join(' + ')}</span></td>
<td class="left-align" style="font-size:11px;"><span class="editable-label" contenteditable="true">Merged: ${labels.join(' + ')}</span></td>
<td data-sort="${totalHrs}">${hrStr}</td>
<td class="cell-score" data-value="${newScore.toFixed(1)}">${newScore.toFixed(1)}</td>
<td class="cell-tab" data-value="${newTab.toFixed(1)}">${newTab.toFixed(1)}</td>
Expand Down Expand Up @@ -464,7 +466,7 @@ def generate_report():
<tr>
<th class="sorttable_nosort">Inc</th>
<th class="left-align" style="white-space: nowrap;">Date / Time</th>
<th class="left-align">Night Label</th>
<th class="left-align">Notes</th>
<th>Length</th>
<th>Score (0-100)</th>
<th>TAB</th>
Expand Down Expand Up @@ -516,7 +518,7 @@ def cell(metric_key, val):
dt = datetime.datetime.strptime(m_date.group(1), "%Y%m%d%H%M%S")
prev_dt = dt - datetime.timedelta(days=1)
time_str = dt.strftime("%I:%M%p").lstrip("0").lower()
date_str = f"{prev_dt.month}/{prev_dt.day}-{dt.month}/{dt.day} {time_str}"
date_str = f"{prev_dt.month}/{prev_dt.day}-{dt.month}/{dt.day}/{dt.strftime('%y')} {time_str}"
except:
pass

Expand All @@ -530,11 +532,28 @@ def cell(metric_key, val):

type_str = f"{r.get('pct_a', 0):.0f}/{r.get('pct_b', 0):.0f}/{r.get('pct_c', 0):.0f}"

# Uncheck daytime sessions by default (start hour before 8pm / after 6am)
is_daytime = False
m_hour = re.match(r'^\d{8}(\d{2})', fname)
if m_hour:
hour = int(m_hour.group(1))
if 6 <= hour < 20:
is_daytime = True
checked_attr = "" if is_daytime else "checked"

html.append(f"<tr data-filename='{fname}'>")
html.append(f'<td><input type="checkbox" class="row-checkbox" checked></td>')
html.append(f'<td><input type="checkbox" class="row-checkbox" {checked_attr}></td>')
chart_fname = fname.replace('.csv', '_chart.html')
html.append(f'<td class="left-align" style="white-space: nowrap;"><a href="javascript:openChart(\'charts/{chart_fname}\');" style="text-decoration:none; color:#0366d6;">{date_str}</a></td>')
html.append(f'<td class="left-align"><span class="editable-label" contenteditable="true">{r["label"]}</span></td>')
# Split label into display label and notes
label_text = r["label"]
# Strip date prefix and time/duration suffix, whatever remains is notes
tmp = label_text
tmp = re.sub(r'^\d{4}-\d{2}-\d{2}\s*', '', tmp)
tmp = re.sub(r'^\d+/\d+-\d+/\d+\s*', '', tmp)
tmp = re.sub(r'\d+[ap]m\s+\d+h\s+\d+m\s*$', '', tmp)
notes_text = tmp.strip()
html.append(f'<td class="left-align" style="font-size:11px; max-width:250px;"><span class="editable-label" contenteditable="true">{notes_text}</span></td>')
html.append(f'<td data-sort="{hrs_exact}">{hr_str}</td>')
html.append(cell('score', r['score']))
html.append(cell('tab', r['tab']))
Expand Down
18 changes: 16 additions & 2 deletions analysis/hr_spike_detector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -894,7 +894,7 @@ def load_data(filepath: str, source: str = 'auto') -> np.ndarray:

if hr_cols:
hr_col = hr_cols[0]

hr = df[hr_col].values.astype(float)
elif df.shape[1] == 1:
hr = df.iloc[:, 0].values.astype(float)
Expand All @@ -907,7 +907,21 @@ def load_data(filepath: str, source: str = 'auto') -> np.ndarray:
else:
print(f"Available columns: {list(df.columns)}")
raise ValueError("Could not identify HR column. Please specify.")


# Resample to 1Hz if timestamps indicate non-1s intervals
time_cols = [c for c in df.columns if c.lower() in ['time', 'timestamp']]
if time_cols and len(hr) >= 2:
try:
t = pd.to_datetime(df[time_cols[0]])
interval_s = (t.iloc[1] - t.iloc[0]).total_seconds()
if interval_s > 1.5: # Not already 1Hz
# Resample by repeating each value for its interval
hr_1hz = np.repeat(hr, int(round(interval_s)))
print(f" Resampled from {interval_s:.0f}s intervals: {len(hr)} -> {len(hr_1hz)} samples")
return hr_1hz
except Exception:
pass

return hr

except ImportError:
Expand Down
15 changes: 9 additions & 6 deletions analysis/run_detector_batch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,18 +42,21 @@ def generate_session_chart(fpath, hr_smooth, baseline, valid, events, summary, c

os.makedirs(chart_dir, exist_ok=True)

# Read the timestamp column
# Read the timestamp column and generate 1Hz time axis matching resampled data
try:
df = pd.read_csv(fpath)
if 'Time' in df.columns:
t_axis = pd.to_datetime(df['Time'])
start_time_str = t_axis.iloc[0].strftime("%Y-%m-%d %H:%M:%S")
start_ts = t_axis.iloc[0].timestamp()
csv_times = pd.to_datetime(df['Time'])
start_time = csv_times.iloc[0]
start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S")
start_ts = start_time.timestamp()
# Generate 1Hz time axis to match resampled hr_smooth length
t_axis = pd.date_range(start=start_time, periods=len(hr_smooth), freq='1s')
else:
t_axis = np.arange(len(hr_smooth)) / 3600.0
start_time_str = ""
start_ts = 0

# Extract CSV content for in-browser export
csv_content = df.to_csv(index=False).replace('\\n', '\\\\n').replace('\\"', '\\\\"')
csv_js_string = f'`{csv_content}`'
Expand Down Expand Up @@ -386,7 +389,7 @@ def analyze_night(fpath, label, generate_chart=False, chart_dir=None):
out_lines.append(f"{'='*80}")

try:
# Load using the detector's own loader
# Load using the detector's own loader (resamples to 1Hz)
hr_raw = load_data(fpath, source='auto')
n = len(hr_raw)
hrs = n / 3600
Expand Down
Binary file added screenshot.png
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