| { |
| "format": "oscr-script-manifest/1", |
| "repository": "zenodo:21463359", |
| "url": "https://doi.org/10.5281/zenodo.21463359", |
| "host": "zenodo.org", |
| "commit": "", |
| "license": "MIT", |
| "license_confirmed_by": "license file LICENSE", |
| "redistribution": "yes", |
| "files": [ |
| { |
| "path": "LICENSE", |
| "sha256": "6f60b4d187a0a0c4b1394d9c74c6c5418003f93ca219be6fc158255e58a51330", |
| "language": "License", |
| "lines": 9, |
| "truncated": false, |
| "block": 3, |
| "row": 8061 |
| }, |
| { |
| "path": "README.md", |
| "sha256": "a5cb9459cdab129ebf46a3b1f287eeff66fe0bb0d49e5e74a90ff9b01b3c4ea2", |
| "language": "Text", |
| "lines": 83, |
| "truncated": false, |
| "block": 4, |
| "row": 2510 |
| }, |
| { |
| "path": "dlc/analyze_videos.py", |
| "sha256": "26d6886768b943fc974fc112036b276eba60ec05593ada66142ef7614311ee9d", |
| "language": "Python", |
| "lines": 55, |
| "truncated": false, |
| "block": 3, |
| "row": 20061 |
| }, |
| { |
| "path": "dlc/convert_mp4_to_avi.py", |
| "sha256": "19926fc6bb936047cd95321ea83b6503877beab6708b10b11eb53e82b9d9dca1", |
| "language": "Python", |
| "lines": 33, |
| "truncated": false, |
| "block": 3, |
| "row": 18777 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/00_process_raw_data.ipynb", |
| "sha256": "69b73d2df08c410b20cf6ec6d53d7458ca44bfe98318d6b223fcbde7a1a1aaf2", |
| "language": "Jupyter", |
| "lines": 179, |
| "truncated": false, |
| "block": 3, |
| "row": 7377 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/01_process_trial_data.ipynb", |
| "sha256": "5d6121acfbf901aace483a59a516c0efdb77fa5979a64af6c821ad85a73cbb6e", |
| "language": "Jupyter", |
| "lines": 539, |
| "truncated": false, |
| "block": 3, |
| "row": 7750 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/02_plot_direction_over_trial_data.ipynb", |
| "sha256": "908c6f381fc2e39994536b87f5bd4ec032bab5d379fa375eb3d675706fc0fc25", |
| "language": "Jupyter", |
| "lines": 260, |
| "truncated": false, |
| "block": 3, |
| "row": 7517 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/03_plot_speed_ratio_from_trial_data.ipynb", |
| "sha256": "6c9da6ef4ffa8a18574ae87972c0c5b66c3a1ee761dd1db830ecc299ec41d9f2", |
| "language": "Jupyter", |
| "lines": 818, |
| "truncated": false, |
| "block": 3, |
| "row": 7771 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/04_plot_trial_duration_data.ipynb", |
| "sha256": "995e8f2a40e2bf8903ffe34bfa7216239633c076282ed059cd2b579aaacc8efd", |
| "language": "Jupyter", |
| "lines": 483, |
| "truncated": false, |
| "block": 3, |
| "row": 7636 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/05_plot_thigmotaxis_data.ipynb", |
| "sha256": "7c6f52c4c84b017c13682d961953b849559acde7379d3c8b3e320be295ba5627", |
| "language": "Jupyter", |
| "lines": 188, |
| "truncated": false, |
| "block": 3, |
| "row": 7315 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/06_plot_exit_entrance_pdf.ipynb", |
| "sha256": "0d98ef8b135de9906717130190fc4e27ac623bb63bad46811a488134169cee95", |
| "language": "Jupyter", |
| "lines": 1112, |
| "truncated": false, |
| "block": 3, |
| "row": 7831 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/07_plot_individual_fish_trajectory_and_data.ipynb", |
| "sha256": "dde843b46ec7bae597c21ecd682cec7a7dc5fb5270db809c31d27768e8438e14", |
| "language": "Jupyter", |
| "lines": 526, |
| "truncated": false, |
| "block": 3, |
| "row": 7716 |
| }, |
| { |
| "path": "notebooks/00_standard_OMR/08_cycles_per_degree_calc.ipynb", |
| "sha256": "a98c6483e94e5739b09b5d48790462ea0045f85407244ce35cf1ad89d6188c32", |
| "language": "Jupyter", |
| "lines": 35, |
| "truncated": false, |
| "block": 3, |
| "row": 7051 |
| }, |
| { |
| "path": "notebooks/01_random_bar_widths/00_random_bar_widths_stimulus_over_fish.ipynb", |
| "sha256": "ebeca22d7bfbe615ab761b7f967201a668d9e04177eb6790e12b07bd6da15512", |
| "language": "Jupyter", |
| "lines": 650, |
| "truncated": false, |
| "block": 3, |
| "row": 7761 |
| }, |
| { |
| "path": "notebooks/02_fin_tracking/00_DLC_analyses.ipynb", |
| "sha256": "afa8150e796f6e40a4a40e82b6af5625c9d81f8388529c92fc0dbafef991a1d7", |
| "language": "Jupyter", |
| "lines": 3471, |
| "truncated": false, |
| "block": 3, |
| "row": 7891 |
| }, |
| { |
| "path": "notebooks/02_fin_tracking/01_DLC_analysis_total_video_time_analyzed.ipynb", |
| "sha256": "8e8ead818b92e24d29d49085a59f99738a473033297a6f0cef6fcbb24f312a7e", |
| "language": "Jupyter", |
| "lines": 68, |
| "truncated": false, |
| "block": 3, |
| "row": 7152 |
| }, |
| { |
| "path": "notebooks/02_fin_tracking/02_cross_correlations.ipynb", |
| "sha256": "3da19759c0026774c494787e38318c3b476289d3a842c157b5f13ae8858beda4", |
| "language": "Jupyter", |
| "lines": 185, |
| "truncated": false, |
| "block": 3, |
| "row": 7429 |
| }, |
| { |
| "path": "notebooks/03_habituation/00_process_raw_data.ipynb", |
| "sha256": "a05cece84a4f415cd360f9cbd8d71d030f80b191a32a7f742b049dd58ece3590", |
| "language": "Jupyter", |
| "lines": 149, |
| "truncated": false, |
| "block": 3, |
| "row": 7385 |
| }, |
| { |
| "path": "notebooks/03_habituation/01_process_trial_data.ipynb", |
| "sha256": "e408e41d360dd125ddb978f8a0fb79fb1f03e373a92cb424c8c9b2d237562e72", |
| "language": "Jupyter", |
| "lines": 285, |
| "truncated": false, |
| "block": 3, |
| "row": 7550 |
| }, |
| { |
| "path": "notebooks/03_habituation/02_process_individual_plots.ipynb", |
| "sha256": "c1a0f3c55cc5c3e75888ccba1a0c5531e3ccdcde91857787d5dbb3cb4e1fd394", |
| "language": "Jupyter", |
| "lines": 100, |
| "truncated": false, |
| "block": 3, |
| "row": 7155 |
| }, |
| { |
| "path": "notebooks/03_habituation/03_plot_habituation_data.ipynb", |
| "sha256": "864ba8812ef235521cca40bc46938582423fc8407b4dbe9e45d716d549c25271", |
| "language": "Jupyter", |
| "lines": 1171, |
| "truncated": false, |
| "block": 3, |
| "row": 7837 |
| }, |
| { |
| "path": "notebooks/03_habituation/04_plot_individual_fish_trajectory_and_data.ipynb", |
| "sha256": "243ff2e2f07ed65a1cba3912310bccffbea0ee61e4f6d9709238b1b1291f9dd5", |
| "language": "Jupyter", |
| "lines": 289, |
| "truncated": false, |
| "block": 3, |
| "row": 7555 |
| }, |
| { |
| "path": "src/cichlid_omr/__init__.py", |
| "sha256": "12d7c1e5a422256d91a1d66e02241fab13f830dcfd8cbc628138c054737504f3", |
| "language": "Python", |
| "lines": 18, |
| "truncated": false, |
| "block": 3, |
| "row": 18712 |
| }, |
| { |
| "path": "src/cichlid_omr/config.py", |
| "sha256": "e5a7505c35f35cf3eee03707ada37e6df24a20bd171ae232288750b90a10db35", |
| "language": "Python", |
| "lines": 46, |
| "truncated": false, |
| "block": 3, |
| "row": 19627 |
| }, |
| { |
| "path": "src/cichlid_omr/figures.py", |
| "sha256": "e11dadf59355728eb12f2feecfe6d083a66e5fdbe13bc8a7e7aa433e8c6aca2b", |
| "language": "Python", |
| "lines": 329, |
| "truncated": false, |
| "block": 3, |
| "row": 25645 |
| }, |
| { |
| "path": "src/cichlid_omr/fin_tracking.py", |
| "sha256": "4d55fe1650b9f1ee882bc2c81b063c69eac4f1a346101f4441eaa284120f5b47", |
| "language": "Python", |
| "lines": 217, |
| "truncated": false, |
| "block": 3, |
| "row": 24294 |
| }, |
| { |
| "path": "src/cichlid_omr/io.py", |
| "sha256": "4d8b418698cbb4fb6cb0ee92526af7e0b866fcfd067c7f1115b853cd32f1d402", |
| "language": "Python", |
| "lines": 144, |
| "truncated": false, |
| "block": 3, |
| "row": 23271 |
| }, |
| { |
| "path": "src/cichlid_omr/plotting.py", |
| "sha256": "2645f8af3b7335211fcfc62a3013db296b778088ab4a69fd927e88e1d354bf5c", |
| "language": "Python", |
| "lines": 43, |
| "truncated": false, |
| "block": 3, |
| "row": 19468 |
| }, |
| { |
| "path": "src/cichlid_omr/random_bar_width.py", |
| "sha256": "5d8c369cf64ba50d071372dd5f491acbbf2856a1b63595bd4cecfc6fd605027f", |
| "language": "Python", |
| "lines": 1691, |
| "truncated": false, |
| "block": 3, |
| "row": 28020 |
| }, |
| { |
| "path": "src/cichlid_omr/signal.py", |
| "sha256": "cedba9505139462384e939b2ea6a11ee74c91f15a9cd739b7e1df0b745447d2e", |
| "language": "Python", |
| "lines": 151, |
| "truncated": false, |
| "block": 3, |
| "row": 23296 |
| }, |
| { |
| "path": "stats/anova_common.R", |
| "sha256": "dd0d818634e5511b9b9af9f3ea4bd73e3173a734a3978a5d1d5213bae6abaaf4", |
| "language": "R", |
| "lines": 97, |
| "truncated": false, |
| "block": 4, |
| "row": 624 |
| }, |
| { |
| "path": "stats/swim_gain_three-way-anova.R", |
| "sha256": "014c1f953297ba435630def10adfd908dd15a21e79321cac3b1a6a6e93bb8e2b", |
| "language": "R", |
| "lines": 8, |
| "truncated": false, |
| "block": 4, |
| "row": 180 |
| }, |
| { |
| "path": "stats/trial_duration_three-way-anova.R", |
| "sha256": "0cfee86cc908a0ec7d248332877f57e296d190e559a67280482aba7f6a785cd9", |
| "language": "R", |
| "lines": 8, |
| "truncated": false, |
| "block": 4, |
| "row": 181 |
| } |
| ] |
| } |
