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60b21d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | #!/usr/bin/env python3
# SPDX-FileCopyrightText: 2025 Stanford University, ETH Zurich, and the project authors (see CONTRIBUTORS.md)
# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
"""
Plot SleepEDF time series samples from sleep_cot_data.csv.
Each sample is plotted as a PNG with EEG data and the full_prediction as text.
"""
import os
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
CSV_PATH = "sleep_cot_data.csv"
OUTPUT_DIR = "sleep_cot_plots"
# Publication style
plt.style.use("seaborn-v0_8")
sns.set_palette("colorblind")
# Create output directory
os.makedirs(OUTPUT_DIR, exist_ok=True)
display_label_map = {
"W": "Wake",
"N1": "Non-REM stage 1",
"N2": "Non-REM stage 2",
"N3": "Non-REM stage 3",
"N4": "Non-REM stage 4",
"REM": "REM sleep",
"M": "Movement",
"Unknown": "Unknown",
}
def plot_sample(row, idx):
eeg_data = np.array(json.loads(row["eeg_data"]))
full_pred = row["full_prediction"]
gt_label = row["ground_truth_label"]
pred_label = row["predicted_label"]
sample_idx = row["sample_index"]
series_length = row["series_length"]
# Map labels to pretty names
pretty_gt = display_label_map.get(gt_label, gt_label)
pretty_pred = display_label_map.get(pred_label, pred_label)
# Normalize text length to exactly 800 characters
text_length = 900
if len(full_pred) < text_length:
# Pad with whitespace if shorter
full_pred = full_pred + " " * (text_length - len(full_pred))
elif len(full_pred) > text_length:
# Truncate if longer
full_pred = full_pred[:text_length]
# Add extra newlines to ensure consistent text box height
full_pred = full_pred + "\n"
# Normalize EEG data for plotting
mean = np.mean(eeg_data)
std = np.std(eeg_data) if np.std(eeg_data) > 0 else 1.0
eeg_plot = (eeg_data - mean) / std
fig, ax1 = plt.subplots(figsize=(12, 7))
t = np.arange(len(eeg_plot))
# Use the same blue color as PAMAP2 plots (first color from colorblind palette)
ax1.plot(t, eeg_plot, linewidth=2.5, color="#0173B2", alpha=0.8, label="EEG")
ax1.set_xlabel("Time Step", fontsize=26)
ax1.set_ylabel("Normalized EEG Amplitude", fontsize=26)
ax1.set_title(
f"Sample {sample_idx} | GT: {pretty_gt} | Pred: {pretty_pred}",
fontsize=22,
fontweight="bold",
)
ax1.legend(fontsize=13, loc="upper right")
ax1.grid(True, alpha=0.3)
ax1.tick_params(axis="both", which="major", labelsize=26)
ax1.set_ylim(-3, 3)
ax1.set_yticks(np.linspace(-3, 3, 7))
# Add full_prediction as a text box below the plot (same as PAMAP2)
plt.gcf().text(
0.01,
-0.02,
f"Prediction:\n{full_pred}",
fontsize=30,
ha="left",
va="top",
wrap=True,
bbox=dict(
boxstyle="round", facecolor="whitesmoke", alpha=0.9, edgecolor="gray"
),
)
plt.tight_layout(rect=[0, 0.05, 1, 1])
fname = f"sample_{idx+1:03d}_gt_{pretty_gt.lower().replace(' ', '_').replace('-', '_')}.png"
plt.savefig(os.path.join(OUTPUT_DIR, fname), dpi=300, bbox_inches="tight")
plt.close()
print(f"Saved {fname}")
def main():
df = pd.read_csv(CSV_PATH)
print(f"Loaded {len(df)} samples from {CSV_PATH}")
for idx, row in df.iterrows():
plot_sample(row, idx)
print(f"All plots saved to {OUTPUT_DIR}/")
if __name__ == "__main__":
main()
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