plotting_guide

# 绘图指南 从 Neuropixels 数据创建出版级可视化图表的综合指南。 ## 设置 python import matplotlib.pyplot as plt import numpy as np import spikeinterface.full as si import spikeinterface.widgets as sw import neuropixels_analysis as npa # 高质量设置 plt.rcParams['figure.dpi'] = 150 plt.rcParams['savefig.dpi'] = 300 plt.rcParams['font.size'] = 10 plt.rcParams['font.family'] = 'sans-serif' ## 漂移和运动图表 ### 基础漂移图 python # 使用 npa npa.plot_drift(recording, output='drift_map.png') # 使用 SpikeInterface 工具 from spikeinterface.preprocessing import detect_peaks, localize_peaks peaks = detect_peaks(recording, method='locally_exclusive') peak_locations = localize_peaks(recording, peaks, method='center_of_mass') sw.plot_drift_raster_map( peaks=peaks, peak_locations=peak_locations, recording=recording, clim=(-50, 50), ) plt.savefig('drift_raster.png', bbox_inches='tight') ### 运动估算可视化 python motion_info = npa.estimate_motion(recording) fig, axes = plt.subplots(1, 2, figsize=(12, 5)) # 随时间变化的运动 ax = axes[0] for i in range(motion_info['motion'].shape[1]): ax.plot(motion_info['temporal_bins'], motion_info['motion'][:, i], alpha=0.5) ax.set_xlabel('Time (s)') ax.set_ylabel('Motion (um)') ax.set_title('Estimated Motion') # 运动直方图 ax = axes[1] ax.hist(motion_info['motion'].flatten(), bins=50, edgecolor='black') ax.set_xlabel('Motion (um)') ax.set_ylabel('Count') ax.set_title('Motion Distribution') plt.tight_layout() plt.savefig('motion_analysis.png', dpi=300) ## 波形图表 ### 单单元波形 python unit_id = 0 # 基础波形 sw.plot_unit_waveforms(analyzer, unit_ids=[unit_id]) plt.savefig(f'unit_{unit_id}_waveforms.png') # 带密度图 sw.plot_unit_waveform_density_map(analyzer, unit_ids=[unit_id]) plt.savefig(f'unit_{unit_id}_density.png') ### 模板比较 python # 比较多个单元 unit_ids = [0, 1, 2, 3] sw.plot_unit_templates(analyzer, unit_ids=unit_ids) plt.savefig('template_comparison.png') ### 探针上的波形 python # 在探针上空间展示波形 sw.plot_unit_waveforms_on_probe( analyzer, unit_ids=[unit_id], plot_channels=True, ) plt.savefig(f'unit_{unit_id}_probe.png') ## 质量指标可视化 ### 指标概览 python npa.plot_quality_metrics(analyzer, metrics, output='quality_overview.png') ``