OSCR

Induction of cortical on/off periods in awake mice fulfills sleep functions.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Statistical analysis ↔ Analysis_code_and_source_data/synchrony/single_units/statistical_source_data/lme_sttc.R, lines 1–49 · score 0.85 · Kenward Roger, lmerTest, lmerMultiMember, lme4, pbkrtest, dyadic
  2. [2] § Methods › SD and optogenetics ↔ Analysis_code_and_source_data/nor_behavior/during_stim/emg_pausing.ipynb, lines 318–366 · score 0.83 · RMS envelope, Detected pauses, scored RMS, Raw EMG, window, thresholding
  3. [3] § Methods › SD and optogenetics ↔ Analysis_code_and_source_data/nor_behavior/during_stim/emg_pausing_ACR.ipynb, lines 322–370 · score 0.83 · RMS envelope, Detected pauses, scored RMS, Raw EMG, window, thresholding
  4. [4] § Methods › Statistical analysis ↔ Analysis_code_and_source_data/swa/full_spg_stats_summary.ipynb, lines 35–73 · score 0.79 · Wilcoxon signed rank, Benjamini Hochberg, discovery rate, bins
  5. [5] § Methods › Electrophysiology acquisition and analysis › Off-period detection and synchrony analyses ↔ Analysis_code_and_source_data/synchrony/field_potentials/halo/lfp_slope__HALO__tonic.ipynb, lines 77–152 · score 0.57 · initiation slope, termination slope, field potential, filter, channel, synchrony
  6. [6] § Methods › FTR task ↔ Analysis_code_and_source_data/nor_behavior/individual_scatter_plots.ipynb, lines 350–389 · score 0.57 · 10–15 min, 10 min
  7. [7] § Methods › FTR task ↔ Analysis_code_and_source_data/nor_behavior/individual_scatter_plots.ipynb, lines 267–342 · score 0.55 · arena position, nodes, tail, nose, DLC, video

Paper

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The authors' code

Jupyter notebook · 430 lines · 17 KB · MIT · 2 matches

  1. # %%
  2. %reload_ext autoreload
  3. %autoreload 2
  4. import pandas as pd
  5. import os
  6. pd.options.mode.chained_assignment = None
  7. import matplotlib.pyplot as plt
  8. import seaborn as sns
  9. import sys
  10. from acr.nor import *
  11. import acr
  12. from acr.utils import PAPER_FIGURE_ROOT
  13. import numpy as np
  14. from acr.utils import NREM_RED, NNXR_GRAY
  16. # %%
  17. acr.plots.lrg()
  19. # %%
  20. def display_colormap(cmap):
  21. """Display the colormap"""
  22. gradient = np.linspace(0, 1, 256)
  23. gradient = np.vstack((gradient, gradient))
  25. fig, ax = plt.subplots(figsize=(30, 2))
  26. ax.imshow(gradient, aspect='auto', cmap=cmap)
  27. ax.set_axis_off()
  28. return fig, ax
  30. # %%
  31. import numpy as np
  32. import matplotlib.pyplot as plt
  33. import matplotlib.colors as mcolors
  34. from matplotlib.colors import LinearSegmentedColormap
  35. import colorsys
  36. from acr.utils import SOM_BLUE
  37. import matplotlib as mpl
  39. # your base color
  40. base_hex = SOM_BLUE
  41. base_rgb = mcolors.hex2color(base_hex)
  43. # helper to adjust lightness (factor >1 → lighter; <1 → darker)
  44. def adjust_lightness(rgb, factor):
  45. h, l, s = colorsys.rgb_to_hls(*rgb)
  46. l = max(0, min(1, l * factor))
  47. return colorsys.hls_to_rgb(h, l, s)
  49. # make a lighter and a darker variant
  50. low_rgb = adjust_lightness(base_rgb, 2.3) # light end
  51. high_rgb = adjust_lightness(base_rgb, 0.2) # dark end
  53. # build the colormap: low → base (mid) → high
  54. colors = [low_rgb, base_rgb, high_rgb]
  55. custom_diverging = LinearSegmentedColormap.from_list(
  56. "custom_divergent", colors, N=256
  57. )
  58. mpl.colormaps.register(custom_diverging, name="som", force=True)
  60. # usage:
  61. # plt.scatter(x, y, c=values, cmap="custom_divergent")
  62. # plt.colorbar(); plt.show()
  63. f, ax = display_colormap(custom_diverging)
  64. plt.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/som_colormap.png', dpi=600, bbox_inches='tight', transparent=True)
  66. # %%
  67. # your base color
  68. base_hex = '#f24f4f'
  69. base_rgb = mcolors.hex2color(base_hex)
  71. # helper to adjust lightness (factor >1 → lighter; <1 → darker)
  72. def adjust_lightness(rgb, factor):
  73. h, l, s = colorsys.rgb_to_hls(*rgb)
  74. l = max(0, min(1, l * factor))
  75. return colorsys.hls_to_rgb(h, l, s)
  77. # make a lighter and a darker variant
  78. low_rgb = adjust_lightness(base_rgb, 1.3) # light end
  79. high_rgb = adjust_lightness(base_rgb, 0.2) # dark end
  81. # build the colormap: low → base (mid) → high
  82. colors = [low_rgb, base_rgb, high_rgb]
  83. custom_diverging = LinearSegmentedColormap.from_list(
  84. "custom_divergent", colors, N=256
  85. )
  86. mpl.colormaps.register(custom_diverging, name="nrem_red_map", force=True)
  88. # usage:
  89. # plt.scatter(x, y, c=values, cmap="custom_divergent")
  90. # plt.colorbar(); plt.show()
  91. f, ax = display_colormap(custom_diverging)
  92. plt.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/nrem_red_colormap.png', dpi=600, bbox_inches='tight', transparent=True)
  94. # %%
  95. # your base color
  96. base_hex = '#7f7f7f'
  97. base_rgb = mcolors.hex2color(base_hex)
  99. # helper to adjust lightness (factor >1 → lighter; <1 → darker)
  100. def adjust_lightness(rgb, factor):
  101. h, l, s = colorsys.rgb_to_hls(*rgb)
  102. l = max(0, min(1, l * factor))
  103. return colorsys.hls_to_rgb(h, l, s)
  105. # make a lighter and a darker variant
  106. low_rgb = adjust_lightness(base_rgb, 1.5) # light end
  107. high_rgb = adjust_lightness(base_rgb, 0.1) # dark end
  109. # build the colormap: low → base (mid) → high
  110. colors = [low_rgb, base_rgb, high_rgb]
  111. custom_diverging = LinearSegmentedColormap.from_list(
  112. "custom_divergent", colors, N=256
  113. )
  114. mpl.colormaps.register(custom_diverging, name="sd_gray", force=True)
  116. # usage:
  117. # plt.scatter(x, y, c=values, cmap="custom_divergent")
  118. # plt.colorbar(); plt.show()
  119. f, ax = display_colormap(custom_diverging)
  120. plt.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/sd_gray_colormap.png', dpi=600, bbox_inches='tight', transparent=True)
  122. # %%
  123. for sub in sleep_subjects:
  124. t = get_subject_type(sub)
  125. df = load_subject_node(sub, 'nose', conds=['test'], type=t)
  126. arena_df = acr.nor.get_arena_df(sub, 'test')
  127. plt.rcdefaults()
  128. acr.plots.lrg()
  129. plt.rcParams['axes.spines.left'] = False
  130. plt.rcParams['axes.spines.bottom'] = False
  131. plt.rcParams['xtick.bottom'] = False
  132. plt.rcParams['ytick.left'] = False
  133. f, ax = acr.nor.create_arena_plot_streamlined(arena_df, buffer=20)
  134. sns.scatterplot(x='x', y='y', data=df, hue='side', hue_order=['familiar', 'novel'], palette=['black', 'green'], ax=ax, alpha=0.5, s=300, c='b')
  135. ax.set_title(f'{sub}')
  137. # %%
  138. sub = 'NOR_54'
  139. t = get_subject_type(sub)
  140. df = load_subject_node(sub, 'nose', conds=['test'], type=t)
  141. arena_df = acr.nor.get_arena_df(sub, 'test')
  142. plt.rcdefaults()
  143. acr.plots.lrg()
  144. plt.rcParams['axes.spines.left'] = False
  145. plt.rcParams['axes.spines.bottom'] = False
  146. plt.rcParams['xtick.bottom'] = False
  147. plt.rcParams['ytick.left'] = False
  148. f, ax, bounds = acr.nor.create_arena_plot_streamlined(arena_df, buffer_lr=20, buffer_ud=20, return_bounds=True)
  149. sns.scatterplot(x='x', y='y', data=df,
  150. hue='frame', palette='som',
  151. #style='side', style_order=['familiar', 'novel'], markers=['X', 'o'],
  152. ax=ax, alpha=0.75, s=300
  153. )
  154. # Remove the legend from the plot
  155. ax.get_legend().remove()
  156. ax.set_xlim(bounds[0]-2, bounds[1]+2)
  157. ax.set_ylim(bounds[2]-2, bounds[3]+2)
  158. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR54-test-scatter__novel-left.png', dpi=600, bbox_inches='tight', transparent=True)
  160. # %%
  161. # X HISTOGRAM
  162. midline_x = (arena_df.loc[arena_df['node']=='midline_upper']['x'].values[0] + arena_df.loc[arena_df['node']=='midline_lower']['x'].values[0]) / 2
  163. f, ax = plt.subplots(figsize=(30, 3))
  164. sns.histplot(df, x='x', bins=200, ax=ax, color=SOM_BLUE)
  165. plt.tight_layout()
  166. ax.set_xlim(df['x'].min(), df['x'].max())
  167. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR54-Xhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  170. # Y HISTOGRAM
  171. upper_arena_y = arena_df.loc[arena_df['node']=='midline_upper']['y'].values[0]
  172. lower_arena_y = arena_df.loc[arena_df['node']=='midline_lower']['y'].values[0]
  173. midline_y = (upper_arena_y + lower_arena_y) / 2
  174. f, ax = plt.subplots(figsize=(30, 4.95))
  175. sns.histplot(df, x='y', bins=200, ax=ax, color=SOM_BLUE)
  176. plt.tight_layout()
  177. ax.set_xlim(df['y'].min(), df['y'].max())
  178. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR54-Yhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  180. # %%
  181. sub = 'NOR_18'
  182. t = get_subject_type(sub)
  183. df = load_subject_node(sub, 'nose', conds=['test'], type=t)
  184. arena_df = acr.nor.get_arena_df(sub, 'test')
  185. plt.rcdefaults()
  186. acr.plots.lrg()
  187. plt.rcParams['axes.spines.left'] = False
  188. plt.rcParams['axes.spines.bottom'] = False
  189. plt.rcParams['xtick.bottom'] = False
  190. plt.rcParams['ytick.left'] = False
  191. f, ax, bounds = acr.nor.create_arena_plot_streamlined(arena_df, buffer_lr=20, buffer_ud=20, return_bounds=True)
  192. sns.scatterplot(x='x', y='y', data=df,
  193. hue='frame', palette='nrem_red_map',
  194. #style='side', style_order=['familiar', 'novel'], markers=['X', 'o'],
  195. ax=ax, alpha=0.75, s=300
  196. )
  197. # Remove the legend from the plot
  198. ax.get_legend().remove()
  200. ax.set_xlim(bounds[0]-2, bounds[1]+2)
  201. ax.set_ylim(bounds[2]-2, bounds[3]+2)
  202. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR18-test-scatter__novel-right.png', dpi=600, bbox_inches='tight', transparent=True)
  204. # %%
  205. # X HISTOGRAM
  206. midline_x = (arena_df.loc[arena_df['node']=='midline_upper']['x'].values[0] + arena_df.loc[arena_df['node']=='midline_lower']['x'].values[0]) / 2
  207. f, ax = plt.subplots(figsize=(30, 3))
  208. sns.histplot(df, x='x', bins=200, ax=ax, color=NREM_RED)
  209. plt.tight_layout()
  210. ax.set_xlim(df['x'].min(), df['x'].max())
  211. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR18-Xhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  214. # Y HISTOGRAM
  215. upper_arena_y = arena_df.loc[arena_df['node']=='midline_upper']['y'].values[0]
  216. lower_arena_y = arena_df.loc[arena_df['node']=='midline_lower']['y'].values[0]
  217. midline_y = (upper_arena_y + lower_arena_y) / 2
  218. f, ax = plt.subplots(figsize=(30, 4.95))
  219. sns.histplot(df, x='y', bins=200, ax=ax, color=NREM_RED)
  220. plt.tight_layout()
  221. ax.set_xlim(df['y'].min(), df['y'].max())
  222. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR18-Yhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  224. # %%
  225. sub = 'NOR_19'
  226. t = get_subject_type(sub)
  227. df = load_subject_node(sub, 'nose', conds=['test'], type=t)
  228. arena_df = acr.nor.get_arena_df(sub, 'test')
  229. plt.rcdefaults()
  230. acr.plots.lrg()
  231. plt.rcParams['axes.spines.left'] = False
  232. plt.rcParams['axes.spines.bottom'] = False
  233. plt.rcParams['xtick.bottom'] = False
  234. plt.rcParams['ytick.left'] = False
  235. f, ax, bounds = acr.nor.create_arena_plot_streamlined(arena_df, buffer_lr=20, buffer_ud=20, return_bounds=True)
  236. sns.scatterplot(x='x', y='y', data=df,
  237. hue='frame', palette='sd_gray',
  238. #style='side', style_order=['familiar', 'novel'], markers=['X', 'o'],
  239. ax=ax, alpha=0.75, s=300
  240. )
  241. # Remove the legend from the plot
  242. ax.get_legend().remove()
  243. ax.set_xlim(bounds[0]-2, bounds[1]+2)
  244. ax.set_ylim(bounds[2]-2, bounds[3]+2)
  245. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR19-test-scatter__novel-left.png', dpi=600, bbox_inches='tight', transparent=True)
  247. # %%
  248. # X HISTOGRAM
  249. midline_x = (arena_df.loc[arena_df['node']=='midline_upper']['x'].values[0] + arena_df.loc[arena_df['node']=='midline_lower']['x'].values[0]) / 2
  250. f, ax = plt.subplots(figsize=(30, 3))
  251. sns.histplot(df, x='x', bins=200, ax=ax, color=NNXR_GRAY)
  252. plt.tight_layout()
  253. ax.set_xlim(df['x'].min(), df['x'].max())
  254. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR19-Xhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  257. # Y HISTOGRAM
  258. upper_arena_y = arena_df.loc[arena_df['node']=='midline_upper']['y'].values[0]
  259. lower_arena_y = arena_df.loc[arena_df['node']=='midline_lower']['y'].values[0]
  260. midline_y = (upper_arena_y + lower_arena_y) / 2
  261. f, ax = plt.subplots(figsize=(30, 4.95))
  262. sns.histplot(df, x='y', bins=200, ax=ax, color=NNXR_GRAY)
  263. plt.tight_layout()
  264. ax.set_xlim(df['y'].min(), df['y'].max())
  265. f.savefig(f'{PAPER_FIGURE_ROOT}/nor_behavior/NOR19-Yhisto.png', dpi=600, bbox_inches='tight', transparent=True)
  267. # %% [markdown]
  268. # # DEPRECATED
  270. # %%
  272. arena_nodes = ['midline_upper',
  273. 'midline_lower',
  274. 'arena_upper_left',
  275. 'arena_upper_right',
  276. 'arena_lower_left',
  277. 'arena_lower_right']
  278. conditions = ['acq', 'test']
  279. subject = 'NOR_13'
  280. ni = load_nor_info()
  281. data = {}
  282. data_root = '/Volumes/neuropixel_archive/Data/acr_archive/NOR_videos/DLC_project_files/NOR_ROUND_2--analysis_results'
  283. for condition in conditions:
  284. path = f"{data_root}/{subject}-{condition}DLC_Resnet50_nor_single_subJun25shuffle0_snapshot_250_filtered.csv"
  285. data[condition] = pd.read_csv(path)
  286. # Orgainze data into dataframes for each node
  287. arena_dfs = {}
  288. nose_dfs = {}
  289. base_dfs = {}
  290. center_dfs = {}
  291. for condition in conditions:
  292. print(condition)
  293. arena_dfs[condition] = []
  294. nose_dfs[condition] = []
  295. base_dfs[condition] = []
  296. center_dfs[condition] = []
  297. for node in arena_nodes:
  298. df = data[condition].loc[:, data[condition].loc[0] == node]
  299. df = clean_df(df)
  300. arena_dfs[condition].append(df)
  301. nose = data[condition].loc[:, data[condition].loc[0] == 'nose']
  302. nose = clean_df(nose)
  303. nose_dfs[condition] = nose
  304. base = data[condition].loc[:, data[condition].loc[0] == 'tail_base']
  305. base = clean_df(base)
  306. base_dfs[condition] = base
  307. center = data[condition].loc[:, data[condition].loc[0] == 'mid_body']
  308. center = clean_df(center)
  309. center_dfs[condition] = center
  310. arena_dfs[condition] = pd.concat(arena_dfs[condition])
  311. # get the median position for each arena node in each condition
  312. arena_positions = {}
  313. for condition in conditions:
  314. arena_positions[condition] = arena_dfs[condition].groupby('node').median(numeric_only=True)
  315. def create_arena_plot(arena_positions, condition='acq'):
  316. f, ax = plt.subplots(figsize=(25, 15))
  317. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  318. upper_arena_y = arena_positions[condition].loc['midline_upper']['y']
  319. lower_arena_y = arena_positions[condition].loc['midline_lower']['y']
  320. left_arena_x = (arena_positions[condition].loc['arena_upper_left']['x'] + arena_positions[condition].loc['arena_lower_left']['x']) / 2
  321. right_arena_x = (arena_positions[condition].loc['arena_upper_right']['x'] + arena_positions[condition].loc['arena_lower_right']['x']) / 2
  322. ax.axvline(midline_x, color='r', linestyle='--')
  323. ax.axhline(upper_arena_y, color='r', linestyle='--')
  324. ax.axhline(lower_arena_y, color='r', linestyle='--')
  325. ax.axvline(left_arena_x, color='r', linestyle='--')
  326. ax.axvline(right_arena_x, color='r', linestyle='--')
  327. sns.scatterplot(x='x', y='y', data=arena_positions[condition], ax=ax, s=100, c='r')
  328. return f, ax
  329. condition = 'acq'
  330. novel_side = ni[subject]['novel']
  331. locdf = nose_dfs[condition]
  332. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  333. if novel_side == 'left':
  334. locdf.loc[locdf['x']>midline_x, 'side'] = 'familiar'
  335. locdf.loc[locdf['x']<midline_x, 'side'] = 'novel'
  336. else:
  337. locdf.loc[locdf['x']>midline_x, 'side'] = 'novel'
  338. locdf.loc[locdf['x']<midline_x, 'side'] = 'familiar'
  340. locdf = locdf.loc[locdf['frame']<6000]
  341. f, ax = create_arena_plot(arena_positions, 'acq')
  342. sns.scatterplot(x='x', y='y', data=locdf, hue='side', hue_order=['familiar', 'novel'], palette=['black', 'green'], ax=ax, alpha=0.5, s=30, c='b')
  344. # %%
  345. 2236/6000
  347. # %%
  348. locdf.groupby('side').count()
  350. # %%
  351. sz = 20
  352. alpha = 0.4
  354. f, ax = plt.subplots(3, 1, figsize=(12, 27))
  355. condition = 'acq'
  356. novel_side = ni[subject]['novel']
  357. test_df = center_dfs[condition]
  358. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  359. if novel_side == 'left':
  360. test_df.loc[test_df['x']>midline_x, 'side'] = 'familiar'
  361. test_df.loc[test_df['x']<midline_x, 'side'] = 'novel'
  362. else:
  363. test_df.loc[test_df['x']>midline_x, 'side'] = 'novel'
  364. test_df.loc[test_df['x']<midline_x, 'side'] = 'familiar'
  365. for i, border in enumerate([0, 6000, 12000]):
  366. data_to_plot = test_df.loc[(test_df['frame']>border) & (test_df['frame']<border+6000)]
  367. sns.scatterplot(x='x', y='y', data=data_to_plot, hue='side', hue_order=['familiar', 'novel'], palette=['black', 'green'], ax=ax[i], alpha=alpha, s=sz, c='b')
  368. sns.scatterplot(x='x', y='y', data=arena_positions[condition], ax=ax[i], s=sz*3, c='r')
  370. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  371. upper_arena_y = arena_positions[condition].loc['midline_upper']['y']
  372. lower_arena_y = arena_positions[condition].loc['midline_lower']['y']
  373. left_arena_x = (arena_positions[condition].loc['arena_upper_left']['x'] + arena_positions[condition].loc['arena_lower_left']['x']) / 2
  374. right_arena_x = (arena_positions[condition].loc['arena_upper_right']['x'] + arena_positions[condition].loc['arena_lower_right']['x']) / 2
  375. if i == 0:
  376. tit = 'ACQ PERIOD, 0-5 min'
  377. elif i ==1:
  378. tit = 'ACQ PERIOD, 5-10 min'
  379. else:
  380. tit = 'ACQ PERIOD, 10-15 min'
  381. ax[i].set_title(tit, fontsize=20)
  382. ax[i].axvline(midline_x, color='r', linestyle='--')
  383. ax[i].axhline(upper_arena_y, color='r', linestyle='--')
  384. ax[i].axhline(lower_arena_y, color='r', linestyle='--')
  385. ax[i].axvline(left_arena_x, color='r', linestyle='--')
  386. ax[i].axvline(right_arena_x, color='r', linestyle='--')
  387. f.suptitle(f'{subject} | Body (center) positions and arena boundaries')
  388. f.tight_layout(rect=[0, 0.03, 1, 0.97])
  389. #plt.savefig(f'./plots/{subject}_BODY--scatter_all_frames-ACQ.png')
  391. # %%
  392. sz = 20
  393. alpha = 0.4
  395. f, ax = plt.subplots(3, 1, figsize=(12, 27))
  396. condition = 'test'
  397. novel_side = ni[subject]['novel']
  398. test_df = center_dfs[condition]
  399. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  400. if novel_side == 'left':
  401. test_df.loc[test_df['x']>midline_x, 'side'] = 'familiar'
  402. test_df.loc[test_df['x']<midline_x, 'side'] = 'novel'
  403. else:
  404. test_df.loc[test_df['x']>midline_x, 'side'] = 'novel'
  405. test_df.loc[test_df['x']<midline_x, 'side'] = 'familiar'
  406. for i, border in enumerate([0, 6000, 12000]):
  407. data_to_plot = test_df.loc[(test_df['frame']>border) & (test_df['frame']<border+6000)]
  408. sns.scatterplot(x='x', y='y', data=data_to_plot, hue='side', hue_order=['familiar', 'novel'], palette=['black', 'green'], ax=ax[i], alpha=alpha, s=sz, c='b')
  409. sns.scatterplot(x='x', y='y', data=arena_positions[condition], ax=ax[i], s=sz*3, c='r')
  411. midline_x = (arena_positions[condition].loc['midline_upper']['x'] + arena_positions[condition].loc['midline_lower']['x']) / 2
  412. upper_arena_y = arena_positions[condition].loc['midline_upper']['y']
  413. lower_arena_y = arena_positions[condition].loc['midline_lower']['y']
  414. left_arena_x = (arena_positions[condition].loc['arena_upper_left']['x'] + arena_positions[condition].loc['arena_lower_left']['x']) / 2
  415. right_arena_x = (arena_positions[condition].loc['arena_upper_right']['x'] + arena_positions[condition].loc['arena_lower_right']['x']) / 2
  416. if i == 0:
  417. tit = 'TESTING PERIOD, 0-5 min'
  418. elif i ==1:
  419. tit = 'TESTING PERIOD, 5-10 min'
  420. else:
  421. tit = 'TESTING PERIOD, 10-15 min'
  422. ax[i].set_title(tit, fontsize=20)
  423. ax[i].axvline(midline_x, color='r', linestyle='--')
  424. ax[i].axhline(upper_arena_y, color='r', linestyle='--')
  425. ax[i].axhline(lower_arena_y, color='r', linestyle='--')
  426. ax[i].axvline(left_arena_x, color='r', linestyle='--')
  427. ax[i].axvline(right_arena_x, color='r', linestyle='--')
  428. f.suptitle(f'{subject} | Body (center) positions and arena boundaries')
  429. f.tight_layout(rect=[0, 0.03, 1, 0.97])
  430. #plt.savefig(f'./plots/{subject}_BODY--scatter_all_frames-TEST.png')

individual_scatter_plots.ipynb at commit d62bc21, under MIT · at the source

Overview

Authors: Kort Driessen1,2, Fabio Squarcio1, Giulio Tononi1, Chiara Cirelli1
ORCID iDs: Kort Driessen, Fabio Squarcio, Giulio Tononi, Chiara Cirelli
  1. Department of Psychiatry, University of Wisconsin–Madison,Madison, WI USA
  2. Neuroscience Training Program, University of Wisconsin–Madison,Madison, WI USA
Institutions: University of Wisconsin–Madison (United States)
Journal: Nature neuroscience, volume 29, issue 8, pages 1954-1965
Dates: received 24 September 2025; accepted 23 April 2026; published online 8 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41593-026-02318-9 · PMID 42260219 · PMCID PMC13433317 · OpenAlex W4414870360
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.04.680459
Status: code verified
Categories: mouse (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Circadian rhythms and sleep, Non-REM sleep, Slow-wave sleep, Sleep deprivation, Neuroscience
MeSH: Cerebral Cortex*, Sleep*, Sleep, Slow-Wave*, Wakefulness*, Animals, Male, Memory Consolidation, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neurons, Optogenetics, Sleep Deprivation (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS (R01NS131389)
Citations: cited by 3 papers (Europe PMC); 86 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

CSC-UW/OFF_PERIOD_INDUCTION_MATERIALS

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d62bc2112a74efa7b389ff7593bacda7791b769c, 24 February 2026
Languages: Jupyter (75), Python (2), R (1)
Size: 235 files, 78 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 75 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (77 files), pandas (76 files), seaborn (76 files), SciPy (64 files), Pingouin (63 files), NumPy (53 files), xarray (29 files), statsmodels (7 files), SpikeInterface (3 files), Plotly (2 files), YASA (2 files), data.table (1 file), lme4 (1 file), lmerTest (1 file), OpenCV (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
80 files
At the source: github.com/CSC-UW/OFF_PERIOD_INDUCTION_MATERIALS

CSC-UW/sleepscore

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2d63d1814a54c266a74d41df65fad17ddb783a47, 27 January 2022
Languages: Python (11)
Size: 16 files, 11 scripts
Software Heritage: not archived
Found in: the text, “Sleep scoring”
Holds: README, environment (environment.yml, requirements.txt, setup.py)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (5 files), Matplotlib (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
12 files
At the source: github.com/CSC-UW/sleepscore

Code availability statement

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Read it in the paper: doi.org/10.1038/s41593-026-02318-9.

Tracing map

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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41593-026-02318-9.

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 13 MeSH terms, 1 funder, 86 references.

Cite

This paper

Driessen, K., Squarcio, F., Tononi, G., & Cirelli, C. (2026). Induction of cortical on/off periods in awake mice fulfills sleep functions. Nature neuroscience, 29(8), 1954-1965. https://doi.org/10.1038/s41593-026-02318-9

BibTeX

@article{driessen2026induction,
author = {Driessen, Kort and Squarcio, Fabio and Tononi, Giulio and Cirelli, Chiara},
title = {{Induction of cortical on/off periods in awake mice fulfills sleep functions}},
journal = {Nature neuroscience},
year = {2026},
month = jun,
volume = {29},
number = {8},
pages = {1954--1965},
publisher = {Nature Portfolio},
issn = {1097-6256},
doi = {10.1038/s41593-026-02318-9},
url = {https://doi.org/10.1038/s41593-026-02318-9},
pmid = {42260219},
pmcid = {PMC13433317}
}

RIS

TY - JOUR
AU - Driessen, Kort
AU - Squarcio, Fabio
AU - Tononi, Giulio
AU - Cirelli, Chiara
TI - Induction of cortical on/off periods in awake mice fulfills sleep functions
T2 - Nature neuroscience
J2 - Nat Neurosci
PY - 2026
DA - 2026/06/08
VL - 29
IS - 8
SP - 1954
EP - 1965
SN - 1097-6256
PB - Nature Portfolio
DO - 10.1038/s41593-026-02318-9
UR - https://doi.org/10.1038/s41593-026-02318-9
LA - en
ER -

CSL-JSON

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