commit 4e6a6c999d41d80a2c30f20937ee163050e31c73 Author: tom.hempel Date: Wed Sep 24 16:58:38 2025 +0200 initial upload diff --git a/Plots/Aggregate/HR_average_aligned.csv b/Plots/Aggregate/HR_average_aligned.csv new file mode 100644 index 0000000..b968fd3 --- /dev/null +++ b/Plots/Aggregate/HR_average_aligned.csv @@ -0,0 +1,1001 @@ +normalized_time,aligned_seconds,hr_mean,hr_std,n +0.0,0.0,81.66666666666667,5.637178175095921,9 +0.001001001001001001,0.8146866866866869,82.1929176006883,6.072589783481929,9 +0.002002002002002002,1.6293733733733737,82.14296350849303,5.9386254398884235,9 +0.003003003003003003,2.4440600600600604,81.98358238094531,5.855563660572661,9 +0.004004004004004004,3.2587467467467475,81.66317930252033,5.479040435733054,9 +0.005005005005005005,4.073433433433435,81.3024091593219,5.220527934138474,9 +0.006006006006006006,4.888120120120121,81.00772162609945,5.087004361835697,9 +0.007007007007007007,5.702806806806808,80.62048376714917,5.029633643184294,9 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`SingleRecordings//{hr.csv, rr.csv, timestamps.csv}` +- Scripts: + - `plot_meditation_data.py`: per-recording plots (raw + moving averages + segment boxplots) + - `aggregate_segments_analysis.py`: aggregate metrics/plots across recordings with exactly 4 marks (conditions) + +## How calculations are done + +- HR and RR loading + - Read `timestamp` (ms) → convert to datetime; set as index. + - HR column: `hr` (beats per minute). RR column: `rr_ms` (milliseconds). + +- Moving averages (per recording) + - Time-based rolling means over irregular timestamps using windows: 5 s, 10 s, 30 s, 60 s. + - X-axis is seconds from each recording’s start. + +- Mark handling (per recording) + - Vertical lines at marks from `timestamps.csv`. + - Segment boxplots use values in: each interval between consecutive marks and the final interval (last mark → end). + +- RMSSD (HRV) + - Definition: RMSSD = sqrt(mean(diff(RR_ms)^2)) using successive RR intervals (ms). + - Aggregate per-condition RMSSD: computed over all RR samples within each condition’s time window (recording-level); requires ≥2 RR samples. + - Aligned time-series RMSSD: first compute a 30 s time-based rolling RMSSD per recording, then align and average across recordings (see below). + +- Aggregate metrics across recordings (exactly 4 marks) + - Conditions (English labels): + - Breathing Scene 1 (pre-first mark) + - Spring Scene (1st–2nd mark) + - Summer Scene (2nd–3rd mark) + - Autumn Scene (3rd–4th mark) + - Breathing Scene 2 (post-last mark) + - Per recording: compute medians for HR and RR within each condition; compute RMSSD within each condition. + - Summary CSV aggregates these per-recording values; boxplots show distributions. The mean of each condition is marked by a black dot on the boxplots. + +- Aligned average curves (HR, RR, RMSSD) + - Only recordings with exactly 4 marks are used. + - For each recording, durations of the five segments are measured; median segment proportions across recordings define a normalized 0–1 time axis with aligned boundaries. + - Each recording’s series is piecewise-linearly time-normalized to this axis, interpolated to a common grid, and then averaged (mean ± 1 SD). For RMSSD, a 30 s rolling RMSSD is used before alignment. + +# Results + +| Condition | HR (bpm) | RR (ms) | RMSSD (ms) | +|-------------------|----------|---------|------------| +| Breathing Scene 1 | 72.61 | 843.33 | 52.70 | +| Spring Scene | 72.33 | 860.78 | 56.95 | +| Summer Scene | 72.50 | 862.00 | 41.15 | +| Autumn Scene | 72.89 | 850.44 | 41.89 | +| Breathing Scene 2 | 73.78 | 839.94 | 42.59 | + +## Plots + +### Aggregate Boxplots + +![HR Boxplot Across Conditions](Plots/Aggregate/HR_boxplot_conditions.png) +![RR Boxplot Across Conditions](Plots/Aggregate/RR_boxplot_conditions.png) +![RMSSD Boxplot Across Conditions](Plots/Aggregate/RMSSD_boxplot_conditions.png) + +### Aligned Average Curves + +![Aligned Average Heart Rate (HR)](Plots/Aggregate/HR_average_aligned.png) +![Aligned Average RR Interval](Plots/Aggregate/RR_average_aligned.png) +![Aligned Average RMSSD](Plots/Aggregate/RMSSD_average_aligned.png) diff --git a/SingleRecordings/01/hr.csv b/SingleRecordings/01/hr.csv new 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+1757862298259,1115 +1757862299213,1129 +1757862300174,1100 +1757862301136,1081 +1757862302092,982 +1757862303053,900 +1757862304494,870 +1757862304980,899 +1757862305933,1045 +1757862306893,1120 +1757862307859,1119 +1757862309293,1114 +1757862310255,1042 +1757862311212,924 +1757862312175,870 +1757862313133,882 +1757862314093,978 +1757862315049,1098 +1757862316012,1142 +1757862316973,1093 +1757862317932,981 +1757862318894,901 +1757862318894,844 +1757862319849,828 +1757862321292,903 +1757862321292,923 +1757862322252,446 diff --git a/SingleRecordings/11/timestamps.csv b/SingleRecordings/11/timestamps.csv new file mode 100644 index 0000000..caf9c5c --- /dev/null +++ b/SingleRecordings/11/timestamps.csv @@ -0,0 +1,4 @@ +timestamp,event_type +1757861705304,manual_mark +1757862054280,manual_mark +1757862204233,manual_mark diff --git a/aggregate_segments_analysis.py b/aggregate_segments_analysis.py new file mode 100644 index 0000000..ec8a9b6 --- /dev/null +++ b/aggregate_segments_analysis.py @@ -0,0 +1,648 @@ +import math +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + + +def ensure_dir(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + + +def read_signal_csv(csv_path: Path, value_column: str) -> pd.DataFrame: + if not csv_path.exists(): + return pd.DataFrame(columns=[value_column]) + df = pd.read_csv(csv_path) + if 'timestamp' not in df.columns or value_column not in df.columns: + return pd.DataFrame(columns=[value_column]) + df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', errors='coerce') + df = df.dropna(subset=['timestamp']).set_index('timestamp').sort_index() + df[value_column] = pd.to_numeric(df[value_column], errors='coerce') + df = df.dropna(subset=[value_column]) + return df + + +def read_marks(csv_path: Path) -> pd.Series: + if not csv_path.exists(): + return pd.Series([], dtype='datetime64[ns]') + df = pd.read_csv(csv_path) + if 'timestamp' not in df.columns: + return pd.Series([], dtype='datetime64[ns]') + ts = pd.to_datetime(df['timestamp'], unit='ms', errors='coerce').dropna().sort_values() + return ts + + +def get_recording_bounds(hr_df: pd.DataFrame, rr_df: pd.DataFrame) -> tuple[pd.Timestamp, pd.Timestamp] | tuple[None, None]: + if (hr_df is None or hr_df.empty) and (rr_df is None or rr_df.empty): + return None, None + starts = [] + ends = [] + if hr_df is not None and not hr_df.empty: + starts.append(hr_df.index.min()) + ends.append(hr_df.index.max()) + if rr_df is not None and not rr_df.empty: + starts.append(rr_df.index.min()) + ends.append(rr_df.index.max()) + return min(starts), max(ends) + + +def segment_slices(marks: list[pd.Timestamp], start_ts: pd.Timestamp, end_ts: pd.Timestamp) -> dict: + """Return dict with segments: pre, betweens (list of (a,b)), post. + + Assumes exactly 4 marks provided. + """ + assert len(marks) == 4 + a, b, c, d = marks + pre = (start_ts, a) + betweens = [(a, b), (b, c), (c, d)] + post = (d, end_ts) + return {'pre': pre, 'betweens': betweens, 'post': post} + + +def rmssd(rr_ms_series: pd.Series) -> float | None: + """Compute RMSSD (ms) for a series of RR values in milliseconds. + + Returns None if insufficient data. + """ + if rr_ms_series is None or len(rr_ms_series) < 2: + return None + rr = rr_ms_series.values.astype(float) + diff = np.diff(rr) + if diff.size == 0: + return None + return float(np.sqrt(np.mean(np.square(diff)))) + + +def summarize_segments_for_recording(rec_name: str, hr_df: pd.DataFrame, rr_df: pd.DataFrame, marks: pd.Series) -> dict | None: + # Only include recordings with exactly 4 marks + if marks is None or len(marks) != 4: + return None + + start_ts, end_ts = get_recording_bounds(hr_df, rr_df) + if start_ts is None or end_ts is None: + return None + + segs = segment_slices(list(marks), start_ts, end_ts) + + # Helper to subset by time range + def subset(df: pd.DataFrame, value_col: str, a: pd.Timestamp, b: pd.Timestamp) -> pd.Series: + if df is None or df.empty: + return pd.Series([], dtype=float) + return df.loc[(df.index >= a) & (df.index < b), value_col] + + # Pre segment + pre_hr = subset(hr_df, 'hr', *segs['pre']) + pre_rr = subset(rr_df, 'rr_ms', *segs['pre']) + pre_rmssd = rmssd(pre_rr) if len(pre_rr) >= 2 else None + + # Three between segments: compute individually + between_vals = [] # list of dicts with hr, rr, rmssd + for a, b in segs['betweens']: + bh = subset(hr_df, 'hr', a, b) + br = subset(rr_df, 'rr_ms', a, b) + between_vals.append({ + 'hr': bh.values if len(bh) > 0 else None, + 'rr': br.values if len(br) > 0 else None, + 'rmssd': rmssd(br) if len(br) >= 2 else None, + }) + + # Post segment + post_hr = subset(hr_df, 'hr', *segs['post']) + post_rr = subset(rr_df, 'rr_ms', *segs['post']) + post_rmssd = rmssd(post_rr) if len(post_rr) >= 2 else None + + # Build summary dict (use medians for HR/RR to be robust) for five named conditions + def safe_median(arr_like) -> float | None: + if arr_like is None: + return None + vals = np.asarray(arr_like, dtype=float) + vals = vals[~np.isnan(vals)] + if vals.size == 0: + return None + return float(np.median(vals)) + + summary = { + 'recording': rec_name, + # HR medians + 'hr_breathing1': safe_median(pre_hr.values if len(pre_hr) > 0 else None), + 'hr_spring': safe_median(between_vals[0]['hr'] if between_vals and between_vals[0]['hr'] is not None else None), + 'hr_summer': safe_median(between_vals[1]['hr'] if len(between_vals) > 1 and between_vals[1]['hr'] is not None else None), + 'hr_autumn': safe_median(between_vals[2]['hr'] if len(between_vals) > 2 and between_vals[2]['hr'] is not None else None), + 'hr_breathing2': safe_median(post_hr.values if len(post_hr) > 0 else None), + # RR medians + 'rr_breathing1': safe_median(pre_rr.values if len(pre_rr) > 0 else None), + 'rr_spring': safe_median(between_vals[0]['rr'] if between_vals and between_vals[0]['rr'] is not None else None), + 'rr_summer': safe_median(between_vals[1]['rr'] if len(between_vals) > 1 and between_vals[1]['rr'] is not None else None), + 'rr_autumn': safe_median(between_vals[2]['rr'] if len(between_vals) > 2 and between_vals[2]['rr'] is not None else None), + 'rr_breathing2': safe_median(post_rr.values if len(post_rr) > 0 else None), + # RMSSD ms + 'rmssd_breathing1_ms': pre_rmssd if pre_rmssd is not None and not math.isnan(pre_rmssd) else None, + 'rmssd_spring_ms': between_vals[0]['rmssd'] if between_vals and between_vals[0]['rmssd'] is not None and not math.isnan(between_vals[0]['rmssd']) else None, + 'rmssd_summer_ms': between_vals[1]['rmssd'] if len(between_vals) > 1 and between_vals[1]['rmssd'] is not None and not math.isnan(between_vals[1]['rmssd']) else None, + 'rmssd_autumn_ms': between_vals[2]['rmssd'] if len(between_vals) > 2 and between_vals[2]['rmssd'] is not None and not math.isnan(between_vals[2]['rmssd']) else None, + 'rmssd_breathing2_ms': post_rmssd if post_rmssd is not None and not math.isnan(post_rmssd) else None, + } + + return summary + + +def boxplot_five_categories(values_df: pd.DataFrame, value_cols: tuple[str, str, str, str, str], title: str, ylabel: str, out_path: Path) -> dict: + """Create a boxplot comparing five named conditions across recordings. + + value_cols is a tuple of (breathing1, spring, summer, autumn, breathing2) + """ + b1, sp, su, au, b2 = value_cols + df = values_df[[b1, sp, su, au, b2]].dropna(how='all') + if df.empty: + return {} + data = [df[b1].dropna().values, df[sp].dropna().values, df[su].dropna().values, df[au].dropna().values, df[b2].dropna().values] + labels = ['Breathing Scene 1', 'Spring Scene', 'Summer Scene', 'Autumn Scene', 'Breathing Scene 2'] + + fig, ax = plt.subplots(figsize=(10, 5)) + ax.boxplot(data, tick_labels=labels, vert=True, patch_artist=True) + # Overlay small dot for the mean per category + means = [float(np.mean(d)) if len(d) > 0 else np.nan for d in data] + ax.scatter(range(1, 6), means, color='black', s=20, zorder=3) + ax.set_title(title) + ax.set_ylabel(ylabel) + ax.grid(True, axis='y', alpha=0.3) + fig.tight_layout() + fig.savefig(out_path, dpi=150) + plt.close(fig) + # Return mapping of label -> mean + return {label: mean for label, mean in zip(labels, means)} + + +def compute_and_plot_aligned_hr(recordings_root: Path, out_root: Path) -> None: + """Compute an across-recordings averaged HR curve aligned by the 4 timestamps. + + Approach: + - Use recordings with exactly 4 marks and available HR + - Compute median durations for the 5 segments (pre, 3 betweens, post) + - Map each recording piecewise-linearly so marks align to the same normalized positions + based on the median segment-length proportions + - Interpolate each recording to a common grid and average + """ + recs: list[dict] = [] + seg_durations: list[tuple[float, float, float, float, float]] = [] + + for rec_dir in sorted([p for p in recordings_root.iterdir() if p.is_dir()]): + rec_name = rec_dir.name + hr_csv = rec_dir / 'hr.csv' + ts_csv = rec_dir / 'timestamps.csv' + if not hr_csv.exists() or not ts_csv.exists(): + continue + hr_df = read_signal_csv(hr_csv, 'hr') + marks = read_marks(ts_csv) + if hr_df.empty or marks is None or len(marks) != 4: + continue + + # Determine bounds + start_ts = hr_df.index.min() + end_ts = hr_df.index.max() + a, b, c, d = list(marks) + # Ensure marks fall within HR bounds + if not (start_ts < a < b < c < d < end_ts): + # Skip if pathological + continue + + # Segment durations in seconds + pre_d = (a - start_ts).total_seconds() + d12 = (b - a).total_seconds() + d23 = (c - b).total_seconds() + d34 = (d - c).total_seconds() + post_d = (end_ts - d).total_seconds() + seg_durations.append((pre_d, d12, d23, d34, post_d)) + + # Store for second pass + recs.append({'name': rec_name, 'hr': hr_df, 'start': start_ts, 'marks': (a, b, c, d), 'end': end_ts}) + + if not recs: + return + + # Median segment durations and normalized cumulative boundaries + med = np.nanmedian(np.array(seg_durations), axis=0) + total = float(np.sum(med)) + if total <= 0: + return + proportions = med / total + boundaries = np.concatenate(([0.0], np.cumsum(proportions))) # length 6, last == 1.0 + + # Common grid (normalized [0,1]) and scaled seconds by median total duration + N = 1000 + x_norm = np.linspace(0.0, 1.0, N) + x_sec = x_norm * total + + # Helper: piecewise-linear map original timestamp to normalized coordinate + def map_times_to_norm(hr_df: pd.DataFrame, start: pd.Timestamp, marks: tuple[pd.Timestamp, pd.Timestamp, pd.Timestamp, pd.Timestamp], end: pd.Timestamp) -> tuple[np.ndarray, np.ndarray]: + a, b, c, d = marks + segments = [ + (start, a, boundaries[0], boundaries[1]), + (a, b, boundaries[1], boundaries[2]), + (b, c, boundaries[2], boundaries[3]), + (c, d, boundaries[3], boundaries[4]), + (d, end, boundaries[4], boundaries[5]), + ] + u_list = [] + y_list = [] + for seg_start, seg_end, u0, u1 in segments: + seg = hr_df.loc[(hr_df.index >= seg_start) & (hr_df.index <= seg_end), 'hr'] + if seg.empty: + continue + dur_ns = (seg_end.value - seg_start.value) + if dur_ns <= 0: + continue + rel = (seg.index.view('int64') - np.int64(seg_start.value)) / np.float64(dur_ns) + rel = np.clip(rel.astype(float), 0.0, 1.0) + u = u0 + rel * (u1 - u0) + u_list.append(u) + y_list.append(seg.values.astype(float)) + if not u_list: + return np.array([]), np.array([]) + u_all = np.concatenate(u_list) + y_all = np.concatenate(y_list) + order = np.argsort(u_all) + u_sorted = u_all[order] + y_sorted = y_all[order] + uniq_u, idx_start = np.unique(u_sorted, return_index=True) + counts = np.diff(np.append(idx_start, len(u_sorted))) + y_avg = [] + for i, cnt in enumerate(counts): + start_idx = idx_start[i] + y_avg.append(np.mean(y_sorted[start_idx:start_idx + cnt])) + y_avg = np.asarray(y_avg) + return uniq_u, y_avg + + curves = [] + for rec in recs: + u, y = map_times_to_norm(rec['hr'], rec['start'], rec['marks'], rec['end']) + if u.size < 2: + continue + y_grid = np.interp(x_norm, u, y, left=np.nan, right=np.nan) + y_grid[x_norm < u.min()] = np.nan + y_grid[x_norm > u.max()] = np.nan + curves.append(y_grid) + + if not curves: + return + + Y = np.vstack(curves) + mean = np.nanmean(Y, axis=0) + std = np.nanstd(Y, axis=0) + n = np.sum(~np.isnan(Y), axis=0) + + # Plot + fig, ax = plt.subplots(figsize=(12, 5)) + ax.plot(x_sec, mean, color='tab:blue', label='Mean HR') + ax.fill_between(x_sec, mean - std, mean + std, color='tab:blue', alpha=0.2, label='±1 SD') + for xb in boundaries * total: + ax.axvline(xb, color='k', linestyle='--', linewidth=0.8, alpha=0.5) + ax.set_xlabel('Aligned time (s)') + ax.set_ylabel('HR (bpm)') + ax.set_title('Aligned Average HR Across Recordings (4 marks)') + ax.grid(True, alpha=0.3) + ax.legend(loc='upper right') + fig.tight_layout() + fig.savefig(out_root / 'HR_average_aligned.png', dpi=150) + plt.close(fig) + + out_csv = out_root / 'HR_average_aligned.csv' + out_df = pd.DataFrame({ + 'normalized_time': x_norm, + 'aligned_seconds': x_sec, + 'hr_mean': mean, + 'hr_std': std, + 'n': n, + }) + out_df.to_csv(out_csv, index=False) + + +def compute_and_plot_aligned_rr(recordings_root: Path, out_root: Path) -> None: + """Aligned average RR (ms) across recordings with 4 marks (same method as HR).""" + recs: list[dict] = [] + seg_durations: list[tuple[float, float, float, float, float]] = [] + + for rec_dir in sorted([p for p in recordings_root.iterdir() if p.is_dir()]): + rr_csv = rec_dir / 'rr.csv' + ts_csv = rec_dir / 'timestamps.csv' + if not rr_csv.exists() or not ts_csv.exists(): + continue + rr_df = read_signal_csv(rr_csv, 'rr_ms') + marks = read_marks(ts_csv) + if rr_df.empty or marks is None or len(marks) != 4: + continue + start_ts = rr_df.index.min() + end_ts = rr_df.index.max() + a, b, c, d = list(marks) + if not (start_ts < a < b < c < d < end_ts): + continue + + pre_d = (a - start_ts).total_seconds() + d12 = (b - a).total_seconds() + d23 = (c - b).total_seconds() + d34 = (d - c).total_seconds() + post_d = (end_ts - d).total_seconds() + seg_durations.append((pre_d, d12, d23, d34, post_d)) + recs.append({'df': rr_df, 'start': start_ts, 'marks': (a, b, c, d), 'end': end_ts}) + + if not recs: + return + + med = np.nanmedian(np.array(seg_durations), axis=0) + total = float(np.sum(med)) + if total <= 0: + return + boundaries = np.concatenate(([0.0], np.cumsum(med / total))) + N = 1000 + x_norm = np.linspace(0.0, 1.0, N) + x_sec = x_norm * total + + def map_times_to_norm(df: pd.DataFrame, start: pd.Timestamp, marks: tuple[pd.Timestamp, pd.Timestamp, pd.Timestamp, pd.Timestamp], end: pd.Timestamp) -> tuple[np.ndarray, np.ndarray]: + a, b, c, d = marks + segments = [ + (start, a, boundaries[0], boundaries[1]), + (a, b, boundaries[1], boundaries[2]), + (b, c, boundaries[2], boundaries[3]), + (c, d, boundaries[3], boundaries[4]), + (d, end, boundaries[4], boundaries[5]), + ] + u_list = [] + y_list = [] + for seg_start, seg_end, u0, u1 in segments: + seg = df.loc[(df.index >= seg_start) & (df.index <= seg_end), 'rr_ms'] + if seg.empty: + continue + dur_ns = (seg_end.value - seg_start.value) + if dur_ns <= 0: + continue + rel = (seg.index.view('int64') - np.int64(seg_start.value)) / np.float64(dur_ns) + rel = np.clip(rel.astype(float), 0.0, 1.0) + u = u0 + rel * (u1 - u0) + u_list.append(u) + y_list.append(seg.values.astype(float)) + if not u_list: + return np.array([]), np.array([]) + u_all = np.concatenate(u_list) + y_all = np.concatenate(y_list) + order = np.argsort(u_all) + u_sorted = u_all[order] + y_sorted = y_all[order] + uniq_u, idx_start = np.unique(u_sorted, return_index=True) + counts = np.diff(np.append(idx_start, len(u_sorted))) + y_avg = [] + for i, cnt in enumerate(counts): + start_idx = idx_start[i] + y_avg.append(np.mean(y_sorted[start_idx:start_idx + cnt])) + y_avg = np.asarray(y_avg) + return uniq_u, y_avg + + curves = [] + for rec in recs: + u, y = map_times_to_norm(rec['df'], rec['start'], rec['marks'], rec['end']) + if u.size < 2: + continue + y_grid = np.interp(x_norm, u, y, left=np.nan, right=np.nan) + y_grid[x_norm < u.min()] = np.nan + y_grid[x_norm > u.max()] = np.nan + curves.append(y_grid) + + if not curves: + return + + Y = np.vstack(curves) + mean = np.nanmean(Y, axis=0) + std = np.nanstd(Y, axis=0) + n = np.sum(~np.isnan(Y), axis=0) + + fig, ax = plt.subplots(figsize=(12, 5)) + ax.plot(x_sec, mean, color='tab:green', label='Mean RR') + ax.fill_between(x_sec, mean - std, mean + std, color='tab:green', alpha=0.2, label='±1 SD') + for xb in boundaries * total: + ax.axvline(xb, color='k', linestyle='--', linewidth=0.8, alpha=0.5) + ax.set_xlabel('Aligned time (s)') + ax.set_ylabel('RR (ms)') + ax.set_title('Aligned Average RR Across Recordings (4 marks)') + ax.grid(True, alpha=0.3) + ax.legend(loc='upper right') + fig.tight_layout() + fig.savefig(out_root / 'RR_average_aligned.png', dpi=150) + plt.close(fig) + + out_csv = out_root / 'RR_average_aligned.csv' + pd.DataFrame({'normalized_time': x_norm, 'aligned_seconds': x_sec, 'rr_mean': mean, 'rr_std': std, 'n': n}).to_csv(out_csv, index=False) + + +def compute_and_plot_aligned_rmssd(recordings_root: Path, out_root: Path, window_seconds: int = 30) -> None: + """Aligned average rolling RMSSD (ms) across recordings with 4 marks. + + RMSSD is computed as a time-based rolling window over RR (default 30s). + """ + def rolling_rmssd(rr: pd.Series) -> pd.Series: + def rmssd_func(x: np.ndarray) -> float: + if x.size < 2: + return np.nan + d = np.diff(x.astype(float)) + if d.size == 0: + return np.nan + return float(np.sqrt(np.mean(d * d))) + return rr.rolling(f'{window_seconds}s', min_periods=5).apply(rmssd_func, raw=True) + + recs: list[dict] = [] + seg_durations: list[tuple[float, float, float, float, float]] = [] + + for rec_dir in sorted([p for p in recordings_root.iterdir() if p.is_dir()]): + rr_csv = rec_dir / 'rr.csv' + ts_csv = rec_dir / 'timestamps.csv' + if not rr_csv.exists() or not ts_csv.exists(): + continue + rr_df = read_signal_csv(rr_csv, 'rr_ms') + marks = read_marks(ts_csv) + if rr_df.empty or marks is None or len(marks) != 4: + continue + # Compute rolling RMSSD series + rmssd_series = rolling_rmssd(rr_df['rr_ms']).dropna() + if rmssd_series.empty: + continue + rmssd_df = rmssd_series.to_frame(name='rmssd') + + start_ts = rmssd_df.index.min() + end_ts = rmssd_df.index.max() + a, b, c, d = list(marks) + if not (start_ts < a < b < c < d < end_ts): + continue + + pre_d = (a - start_ts).total_seconds() + d12 = (b - a).total_seconds() + d23 = (c - b).total_seconds() + d34 = (d - c).total_seconds() + post_d = (end_ts - d).total_seconds() + seg_durations.append((pre_d, d12, d23, d34, post_d)) + recs.append({'df': rmssd_df, 'start': start_ts, 'marks': (a, b, c, d), 'end': end_ts}) + + if not recs: + return + + med = np.nanmedian(np.array(seg_durations), axis=0) + total = float(np.sum(med)) + if total <= 0: + return + boundaries = np.concatenate(([0.0], np.cumsum(med / total))) + N = 1000 + x_norm = np.linspace(0.0, 1.0, N) + x_sec = x_norm * total + + def map_times_to_norm(df: pd.DataFrame, start: pd.Timestamp, marks: tuple[pd.Timestamp, pd.Timestamp, pd.Timestamp, pd.Timestamp], end: pd.Timestamp) -> tuple[np.ndarray, np.ndarray]: + a, b, c, d = marks + segments = [ + (start, a, boundaries[0], boundaries[1]), + (a, b, boundaries[1], boundaries[2]), + (b, c, boundaries[2], boundaries[3]), + (c, d, boundaries[3], boundaries[4]), + (d, end, boundaries[4], boundaries[5]), + ] + u_list = [] + y_list = [] + for seg_start, seg_end, u0, u1 in segments: + seg = df.loc[(df.index >= seg_start) & (df.index <= seg_end), 'rmssd'] + if seg.empty: + continue + dur_ns = (seg_end.value - seg_start.value) + if dur_ns <= 0: + continue + rel = (seg.index.view('int64') - np.int64(seg_start.value)) / np.float64(dur_ns) + rel = np.clip(rel.astype(float), 0.0, 1.0) + u = u0 + rel * (u1 - u0) + u_list.append(u) + y_list.append(seg.values.astype(float)) + if not u_list: + return np.array([]), np.array([]) + u_all = np.concatenate(u_list) + y_all = np.concatenate(y_list) + order = np.argsort(u_all) + u_sorted = u_all[order] + y_sorted = y_all[order] + uniq_u, idx_start = np.unique(u_sorted, return_index=True) + counts = np.diff(np.append(idx_start, len(u_sorted))) + y_avg = [] + for i, cnt in enumerate(counts): + start_idx = idx_start[i] + y_avg.append(np.mean(y_sorted[start_idx:start_idx + cnt])) + y_avg = np.asarray(y_avg) + return uniq_u, y_avg + + curves = [] + for rec in recs: + u, y = map_times_to_norm(rec['df'], rec['start'], rec['marks'], rec['end']) + if u.size < 2: + continue + y_grid = np.interp(x_norm, u, y, left=np.nan, right=np.nan) + y_grid[x_norm < u.min()] = np.nan + y_grid[x_norm > u.max()] = np.nan + curves.append(y_grid) + + if not curves: + return + + Y = np.vstack(curves) + mean = np.nanmean(Y, axis=0) + std = np.nanstd(Y, axis=0) + n = np.sum(~np.isnan(Y), axis=0) + + fig, ax = plt.subplots(figsize=(12, 5)) + ax.plot(x_sec, mean, color='tab:red', label=f'Mean RMSSD ({window_seconds}s window)') + ax.fill_between(x_sec, mean - std, mean + std, color='tab:red', alpha=0.2, label='±1 SD') + for xb in boundaries * total: + ax.axvline(xb, color='k', linestyle='--', linewidth=0.8, alpha=0.5) + ax.set_xlabel('Aligned time (s)') + ax.set_ylabel('RMSSD (ms)') + ax.set_title('Aligned Average RMSSD Across Recordings (4 marks)') + ax.grid(True, alpha=0.3) + ax.legend(loc='upper right') + fig.tight_layout() + fig.savefig(out_root / 'RMSSD_average_aligned.png', dpi=150) + plt.close(fig) + + out_csv = out_root / 'RMSSD_average_aligned.csv' + pd.DataFrame({'normalized_time': x_norm, 'aligned_seconds': x_sec, 'rmssd_mean': mean, 'rmssd_std': std, 'n': n}).to_csv(out_csv, index=False) + + +def main() -> None: + root = Path(__file__).resolve().parent + recordings_root = root / 'SingleRecordings' + out_root = root / 'Plots' / 'Aggregate' + ensure_dir(out_root) + + summaries: list[dict] = [] + for rec_dir in sorted([p for p in recordings_root.iterdir() if p.is_dir()]): + rec_name = rec_dir.name + hr_df = read_signal_csv(rec_dir / 'hr.csv', 'hr') + rr_df = read_signal_csv(rec_dir / 'rr.csv', 'rr_ms') + marks = read_marks(rec_dir / 'timestamps.csv') + + if marks is None or len(marks) != 4: + # Ignore recordings not having exactly 4 timestamps + continue + + s = summarize_segments_for_recording(rec_name, hr_df, rr_df, marks) + if s is not None: + summaries.append(s) + + if not summaries: + print('No qualifying recordings (exactly 4 timestamps) found for aggregation.') + return + + df = pd.DataFrame(summaries) + df.to_csv(out_root / 'summary_metrics.csv', index=False) + + # Generate aggregate boxplots for HR, RR, RMSSD with five categories + hr_means = boxplot_five_categories( + df, + ('hr_breathing1', 'hr_spring', 'hr_summer', 'hr_autumn', 'hr_breathing2'), + 'HR Median Across Conditions (All Recordings)', + 'HR (bpm)', + out_root / 'HR_boxplot_conditions.png', + ) + rr_means = boxplot_five_categories( + df, + ('rr_breathing1', 'rr_spring', 'rr_summer', 'rr_autumn', 'rr_breathing2'), + 'RR Median Across Conditions (All Recordings)', + 'RR (ms)', + out_root / 'RR_boxplot_conditions.png', + ) + rmssd_means = boxplot_five_categories( + df, + ('rmssd_breathing1_ms', 'rmssd_spring_ms', 'rmssd_summer_ms', 'rmssd_autumn_ms', 'rmssd_breathing2_ms'), + 'RMSSD Across Conditions (All Recordings)', + 'RMSSD (ms)', + out_root / 'RMSSD_boxplot_conditions.png', + ) + + # Write means to a txt file + means_txt = out_root / 'condition_means.txt' + with means_txt.open('w', encoding='utf-8') as f: + def write_block(name: str, mapping: dict): + if not mapping: + return + f.write(f'{name} means by condition\n') + for label, val in mapping.items(): + if val is None or np.isnan(val): + f.write(f' {label}: NA\n') + else: + f.write(f' {label}: {val:.2f}\n') + f.write('\n') + + write_block('HR (bpm)', hr_means) + write_block('RR (ms)', rr_means) + write_block('RMSSD (ms)', rmssd_means) + + print(f'Aggregation complete. Outputs at: {out_root}') + + # Also compute HR average over aligned time across recordings with 4 marks + compute_and_plot_aligned_hr(recordings_root, out_root) + compute_and_plot_aligned_rr(recordings_root, out_root) + compute_and_plot_aligned_rmssd(recordings_root, out_root) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/plot_meditation_data.py b/plot_meditation_data.py new file mode 100644 index 0000000..f713e7d --- /dev/null +++ b/plot_meditation_data.py @@ -0,0 +1,328 @@ +import os +from pathlib import Path + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + + +def ensure_dir(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + + +def read_signal_csv(csv_path: Path, value_column: str) -> pd.DataFrame: + """Read a CSV with columns ['timestamp', value_column] into a DataFrame with datetime index. + + Returns empty DataFrame if file does not exist. + """ + if not csv_path.exists(): + return pd.DataFrame(columns=[value_column]) + + df = pd.read_csv(csv_path) + if 'timestamp' not in df.columns or value_column not in df.columns: + return pd.DataFrame(columns=[value_column]) + + # Convert timestamp (ms) to datetime and set as index + df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', errors='coerce') + df = df.dropna(subset=['timestamp']) + df = df.set_index('timestamp').sort_index() + # Coerce values to numeric + df[value_column] = pd.to_numeric(df[value_column], errors='coerce') + df = df.dropna(subset=[value_column]) + return df + + +def compute_time_based_moving_averages(df: pd.DataFrame, value_col: str) -> pd.DataFrame: + """Add 5s, 10s, 30s, 60s time-based rolling means for an irregularly sampled series. + + Requires a DatetimeIndex. + """ + if df.empty: + return df + + # Ensure index is datetime for time-based rolling windows + if not isinstance(df.index, pd.DatetimeIndex): + raise ValueError('DataFrame index must be a DatetimeIndex for time-based rolling windows') + + df = df.copy() + for seconds in (5, 10, 30, 60): + window = f'{seconds}s' + df[f'{value_col}_ma_{seconds}s'] = ( + df[value_col].rolling(window=window, min_periods=1).mean() + ) + return df + + +def add_seconds_from_start(df: pd.DataFrame, start_ts: pd.Timestamp) -> pd.DataFrame: + if df.empty: + return df + df = df.copy() + df['seconds'] = (df.index - start_ts).total_seconds() + return df + + +def read_marks(csv_path: Path) -> pd.Series: + """Read marked timestamps as a pandas Series of pd.Timestamp, sorted ascending. + + Returns empty Series if file missing or no valid rows. + """ + if not csv_path.exists(): + return pd.Series([], dtype='datetime64[ns]') + + df = pd.read_csv(csv_path) + if 'timestamp' not in df.columns: + return pd.Series([], dtype='datetime64[ns]') + # Convert to datetime + ts = pd.to_datetime(df['timestamp'], unit='ms', errors='coerce').dropna().sort_values() + return ts + + +def segment_bounds_from_marks(marks: pd.Series, start_ts: pd.Timestamp, end_ts: pd.Timestamp) -> list[tuple[pd.Timestamp, pd.Timestamp]]: + """Create segments between consecutive marks, plus the final segment from last mark to end. + + Does NOT include the pre-first mark segment. + If there are no marks, returns a single segment from start to end. + """ + segments: list[tuple[pd.Timestamp, pd.Timestamp]] = [] + if marks is None or len(marks) == 0: + return [(start_ts, end_ts)] + + # Between consecutive marks + marks_list = list(marks) + for i in range(len(marks_list) - 1): + a = marks_list[i] + b = marks_list[i + 1] + if a < b and a < end_ts: + segments.append((max(a, start_ts), min(b, end_ts))) + + # From last mark to end + last = marks_list[-1] + if last < end_ts: + segments.append((max(last, start_ts), end_ts)) + return segments + + +def plot_timeseries_and_mas(out_dir: Path, rec_name: str, hr_df: pd.DataFrame, rr_df: pd.DataFrame, marks_sec: list[float]) -> None: + ensure_dir(out_dir) + + fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True) + + # HR plot + ax = axes[0] + if not hr_df.empty: + ax.plot(hr_df['seconds'], hr_df['hr'], label='HR', color='tab:blue', linewidth=0.8, alpha=0.7) + for seconds, color in zip((5, 10, 30, 60), ('tab:orange', 'tab:green', 'tab:red', 'tab:purple')): + col = f'hr_ma_{seconds}s' + if col in hr_df.columns: + ax.plot(hr_df['seconds'], hr_df[col], label=f'HR MA {seconds}s', color=color, linewidth=1.5) + ax.set_ylabel('HR (bpm)') + ax.grid(True, alpha=0.3) + + # RR plot + ax = axes[1] + if not rr_df.empty: + ax.plot(rr_df['seconds'], rr_df['rr_ms'], label='RR (ms)', color='tab:blue', linewidth=0.8, alpha=0.7) + for seconds, color in zip((5, 10, 30, 60), ('tab:orange', 'tab:green', 'tab:red', 'tab:purple')): + col = f'rr_ms_ma_{seconds}s' + if col in rr_df.columns: + ax.plot(rr_df['seconds'], rr_df[col], label=f'RR MA {seconds}s', color=color, linewidth=1.5) + ax.set_ylabel('RR (ms)') + ax.grid(True, alpha=0.3) + + # Vertical lines for marks on both subplots + for ax in axes: + for s in marks_sec: + ax.axvline(s, color='k', linestyle='--', linewidth=0.8, alpha=0.7) + ax.legend(loc='upper right', ncol=2, fontsize=8) + + axes[-1].set_xlabel('Time (s)') + fig.suptitle(f'{rec_name} - HR and RR with Moving Averages') + fig.tight_layout(rect=(0, 0, 1, 0.97)) + + out_path = out_dir / f'{rec_name}_timeseries.png' + fig.savefig(out_path, dpi=150) + plt.close(fig) + + +def plot_separate_moving_averages(out_dir: Path, rec_name: str, hr_df: pd.DataFrame, rr_df: pd.DataFrame, marks_sec: list[float]) -> None: + """Save separate plots for each moving average window (5s, 10s, 30s, 60s) + into a dedicated subdirectory per recording. + + Each figure contains two subplots (HR MA and RR MA) sharing the x-axis in seconds. + """ + if (hr_df is None or hr_df.empty) and (rr_df is None or rr_df.empty): + return + + ma_dir = out_dir / 'MovingAverages' + ensure_dir(ma_dir) + + windows = (5, 10, 30, 60) + for seconds in windows: + fig, axes = plt.subplots(2, 1, figsize=(14, 8), sharex=True) + + # HR MA subplot + ax = axes[0] + plotted_any = False + if hr_df is not None and not hr_df.empty: + col = f'hr_ma_{seconds}s' + if col in hr_df.columns: + ax.plot(hr_df['seconds'], hr_df[col], label=f'HR MA {seconds}s', color='tab:orange', linewidth=1.5) + plotted_any = True + if plotted_any: + for s in marks_sec: + ax.axvline(s, color='k', linestyle='--', linewidth=0.8, alpha=0.7) + ax.set_ylabel('HR (bpm)') + ax.grid(True, alpha=0.3) + ax.legend(loc='upper right', fontsize=9) + + # RR MA subplot + ax = axes[1] + plotted_any = False + if rr_df is not None and not rr_df.empty: + col = f'rr_ms_ma_{seconds}s' + if col in rr_df.columns: + ax.plot(rr_df['seconds'], rr_df[col], label=f'RR MA {seconds}s', color='tab:green', linewidth=1.5) + plotted_any = True + if plotted_any: + for s in marks_sec: + ax.axvline(s, color='k', linestyle='--', linewidth=0.8, alpha=0.7) + ax.set_ylabel('RR (ms)') + ax.grid(True, alpha=0.3) + ax.legend(loc='upper right', fontsize=9) + + axes[-1].set_xlabel('Time (s)') + fig.suptitle(f'{rec_name} - Moving Average {seconds}s') + fig.tight_layout(rect=(0, 0, 1, 0.96)) + + out_path = ma_dir / f'{rec_name}_ma_{seconds}s.png' + fig.savefig(out_path, dpi=150) + plt.close(fig) + + +def plot_segment_boxplots(out_dir: Path, rec_name: str, signal_df: pd.DataFrame, value_col: str, segments: list[tuple[pd.Timestamp, pd.Timestamp]], label_prefix: str) -> None: + """Create boxplots of values within each [start, end) segment. + + If no non-empty segments, skips plotting. + """ + if signal_df.empty or not segments: + return + + # Collect data per segment + data = [] + labels = [] + start_ts = signal_df.index.min() + for (a, b) in segments: + seg = signal_df.loc[(signal_df.index >= a) & (signal_df.index < b), value_col] + if seg.empty: + continue + data.append(seg.values) + labels.append(f'{label_prefix} {int((a - start_ts).total_seconds())}-{int((b - start_ts).total_seconds())}s') + + if not data: + return + + fig, ax = plt.subplots(figsize=(max(8, len(data) * 1.2), 5)) + ax.boxplot(data, tick_labels=labels, vert=True, patch_artist=True) + ax.set_ylabel(value_col) + ax.set_title(f'{rec_name} - {value_col} Boxplots by Marked Segments') + ax.grid(True, axis='y', alpha=0.3) + fig.tight_layout() + + out_path = out_dir / f'{rec_name}_{value_col}_boxplots.png' + fig.savefig(out_path, dpi=150) + plt.close(fig) + + +def process_recording(rec_dir: Path, plots_root: Path) -> None: + rec_name = rec_dir.name + + hr_csv = rec_dir / 'hr.csv' + rr_csv = rec_dir / 'rr.csv' + ts_csv = rec_dir / 'timestamps.csv' + + hr_df = read_signal_csv(hr_csv, 'hr') + rr_df = read_signal_csv(rr_csv, 'rr_ms') + marks = read_marks(ts_csv) + + if hr_df.empty and rr_df.empty: + return + + # Establish global start and end for the recording (across HR and RR) + candidates = [] + if not hr_df.empty: + candidates.append(hr_df.index.min()) + if not rr_df.empty: + candidates.append(rr_df.index.min()) + start_ts = min(candidates) + + end_candidates = [] + if not hr_df.empty: + end_candidates.append(hr_df.index.max()) + if not rr_df.empty: + end_candidates.append(rr_df.index.max()) + end_ts = max(end_candidates) + + # Compute moving averages + if not hr_df.empty: + hr_df = compute_time_based_moving_averages(hr_df, 'hr') + hr_df = add_seconds_from_start(hr_df, start_ts) + if not rr_df.empty: + rr_df = compute_time_based_moving_averages(rr_df, 'rr_ms') + rr_df = add_seconds_from_start(rr_df, start_ts) + + # Marks in seconds relative to start + marks_sec = [] + if marks is not None and len(marks) > 0: + marks_sec = [max(0.0, (t - start_ts).total_seconds()) for t in marks] + + # Output directory for this recording + out_dir = plots_root / rec_name + ensure_dir(out_dir) + + # Time series plots + plot_timeseries_and_mas(out_dir, rec_name, hr_df, rr_df, marks_sec) + + # Separate moving average plots per window + plot_separate_moving_averages(out_dir, rec_name, hr_df, rr_df, marks_sec) + + # Segments and boxplots (by instruction: between marked timestamps, plus last->end) + segments = segment_bounds_from_marks(marks, start_ts, end_ts) + + if not hr_df.empty: + plot_segment_boxplots(out_dir, rec_name, hr_df, 'hr', segments, label_prefix='t') + if not rr_df.empty: + plot_segment_boxplots(out_dir, rec_name, rr_df, 'rr_ms', segments, label_prefix='t') + + +def main() -> None: + root = Path(__file__).resolve().parent + recordings_root = root / 'SingleRecordings' + plots_root = root / 'Plots' / 'SingleRecordings' + ensure_dir(plots_root) + + if not recordings_root.exists(): + print(f'No SingleRecordings directory found at: {recordings_root}') + return + + # Iterate through subdirectories that look like recordings + rec_dirs = sorted([p for p in recordings_root.iterdir() if p.is_dir()]) + if not rec_dirs: + print('No recording subdirectories found in SingleRecordings/') + return + + for rec_dir in rec_dirs: + # Only process if at least one of the expected CSVs exists + if any((rec_dir / f).exists() for f in ('hr.csv', 'rr.csv')): + print(f'Processing {rec_dir.name}...') + try: + process_recording(rec_dir, plots_root) + except Exception as e: + print(f'Failed to process {rec_dir.name}: {e}') + + print(f'All done. Plots saved under: {plots_root}') + + +if __name__ == '__main__': + main() + +