Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid Astatotilapia burtoni.

Code ↔ Paper

3 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 3 matches

[1] § STAR★Methods › Method details › Kinematic tracking and analysis ↔ src/cichlid_omr/fin_tracking.py, lines 1–11 · score 0.76 · swim bladder, DeepLabCut, tail point, mouth, tip, mid
[2] § STAR★Methods › Method details › Kinematic tracking and analysis ↔ notebooks/02_fin_tracking/00_DLC_analyses.ipynb, lines 75–121 · score 0.60 · swim bladder, tail point, tip, mid, tracked
[3] § STAR★Methods › Method details › Optomotor task with varying bar widths ↔ src/cichlid_omr/random_bar_width.py, lines 40–86 · score 0.55 · bar widths, heading angle, rotated, cichlids, Zebrafish, edges

Paper

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The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 217 lines · 7.3 KB · MIT · 1 match

"""Helpers for the DeepLabCut fin-tracking analysis (notebooks/02_fin_tracking).
Coordinate arrays are shaped `(n_frames, n_bodyparts, 2)` with the body-part order
`mouth, swim_bladder, tail_point_mid, tail_point_tip, left_pect_base, left_pect_tip,
right_pect_base, right_pect_tip` (swim bladder at index 5, tail mid at index 2).
"""
import json
import numpy as np
import pandas as pd
def extract_coords_from_results_file(filename):
"""Read a DeepLabCut `.h5` results file into `(bodyparts, coords, likelihoods)`."""
df = pd.read_hdf(filename)
index = df.index
multi_index = df.loc[index[0]].index
scorer_indices = multi_index.levels[0]
bodypart_indices = multi_index.levels[1]
n_frames = len(index)
n_bodyparts = len(bodypart_indices)
coords = np.ones((n_frames, n_bodyparts, 2)) * np.nan
likelihoods = np.ones((n_frames, n_bodyparts)) * np.nan
for i, _idx in enumerate(index):
for j, bodypart in enumerate(bodypart_indices):
coords[i, j, 0] = df.loc[i][scorer_indices[0]][bodypart]["x"]
coords[i, j, 1] = df.loc[i][scorer_indices[0]][bodypart]["y"]
likelihoods[i, j] = df.loc[i][scorer_indices[0]][bodypart]["likelihood"]
return bodypart_indices, coords, likelihoods
def extract_coords_from_centroid_file(filename):
"""Read an `X`/`Y` centroid `.csv` into an `(n_frames, 2)` array."""
df = pd.read_csv(filename)
index = df.index
n_frames = len(index)
coords = np.ones((n_frames, 2)) * np.nan
for i, _idx in enumerate(index):
coords[i, 0] = df.loc[i]["X"]
coords[i, 1] = df.loc[i]["Y"]
return coords
def center_coords(coords):
"""Translate all body parts so the swim bladder (index 5) sits at the origin."""
swim_bladder_coords = coords[:, 5][:, np.newaxis]
coords_centered = coords - swim_bladder_coords
return coords_centered
def calculate_heading_angle(coords, discontinuity_threshold=None):
"""Heading angle (radians, unwrapped) from the tail-mid body part (index 2)."""
heading_angle = np.arctan2(coords[:, 2, 1], coords[:, 2, 0])[:, np.newaxis]
heading_angle = np.unwrap(heading_angle, discont=discontinuity_threshold, axis=0)
return heading_angle
def rotate_coords(coords, rotation_angle):
"""Rotate every frame's coordinates by `rotation_angle` (radians)."""
normalized_coords = np.stack(
[
coords[:, :, 0] * np.cos(rotation_angle) - coords[:, :, 1] * np.sin(rotation_angle),
coords[:, :, 1] * np.cos(rotation_angle) + coords[:, :, 0] * np.sin(rotation_angle),
],
axis=2,
)
return normalized_coords
class NumpyEncoder(json.JSONEncoder):
"""JSON encoder that serialises numpy scalars and arrays."""
def default(self, obj):
if isinstance(
obj,
(
np.int_,
np.intc,
np.intp,
np.int8,
np.int16,
np.int32,
np.int64,
np.uint8,
np.uint16,
np.uint32,
np.uint64,
),
):
return int(obj)
elif isinstance(obj, (np.float16, np.float32, np.float64)):
return float(obj)
elif isinstance(obj, (np.ndarray,)):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
def detect_bouts(data, bout_threshold, window_size):
"""Return a boolean mask marking frames within a swim bout.
A bout starts when the signal deviates by more than `bout_threshold` from a running
extremum and ends after `window_size` frames without a further deviation.
"""
min_value = np.inf
max_value = -np.inf
find_max = True
bout_detected = np.zeros(len(data)).astype(bool)
bout_counter = 0
prev_value = None
bout = False
for index, value in enumerate(data):
if value > max_value:
max_value = value
if value < min_value:
min_value = value
if bout:
if not find_max and value > min_value + bout_threshold:
max_value = value
if not find_max:
find_max = True
elif find_max and value < max_value - bout_threshold:
min_value = value
if find_max:
find_max = False
if np.abs(value - prev_value) > bout_threshold:
bout_counter = 0
else:
bout_counter += 1
else:
if value > min_value + bout_threshold or value < max_value - bout_threshold:
bout = True
bout_counter = 0
if value < max_value - bout_threshold:
find_max = False
if bout_counter > window_size:
min_value = np.inf
max_value = -np.inf
bout = False
find_max = True
bout_counter = 0
prev_value = value
bout_detected[index] = bout
return bout_detected
def detect_peaks2(data, delta, x=None):
"""Peak/trough detection (Billauer's algorithm); returns `(minima, maxima)` tables."""
maxtab = []
mintab = []
if x is None:
x = np.arange(len(data))
data = np.asarray(data)
mn, mx = np.inf, -np.inf
mnpos, mxpos = np.nan, np.nan
lookformax = True
for i in np.arange(len(data)):
this = data[i]
if this > mx:
mx = this
mxpos = x[i]
if this < mn:
mn = this
mnpos = x[i]
if lookformax:
if this < mx - delta:
maxtab.append((mxpos, mx))
mn = this
mnpos = x[i]
lookformax = False
else:
if this > mn + delta:
mintab.append((mnpos, mn))
mx = this
mxpos = x[i]
lookformax = True
maxtab = np.array(maxtab)
mintab = np.array(mintab)
return mintab, maxtab
def calculate_frequency_from_peaks(data, data_length, framerate, half_cycles=False):
"""Expand peak indices `data` into a per-frame frequency trace (Hz)."""
new_data = np.zeros(data_length)
for peak0, peak1 in zip(data[:-1], data[1:], strict=False):
frequency = framerate / ((1 + int(half_cycles)) * (peak1 - peak0))
new_data[peak0:peak1] = frequency
new_data[-1] = frequency
return new_data
def calculate_continuous_inter_peak_interval(data, data_length):
"""Expand peak indices `data` into a per-frame inter-peak-interval trace."""
new_data = np.zeros(data_length)
for peak0, peak1 in zip(data[:-1], data[1:], strict=False):
ipi = peak1 - peak0
new_data[peak0:peak1] = ipi
new_data[data[-1] :] = ipi
return new_data
def check_bout_starts_ends(bouts, bout_starts, bout_ends):
"""Pad `bout_starts`/`bout_ends` so they pair up when a bout runs past a signal edge."""
if len(bout_starts) != len(bout_ends):
if bout_starts[0] > bout_ends[0]:
bout_starts = np.insert(bout_starts, 0, 0)
bouts, bout_starts, bout_ends = check_bout_starts_ends(bouts, bout_starts, bout_ends)
if bout_starts[-1] > bout_ends[-1]:
bout_ends = np.append(bout_ends, len(bouts))
bouts, bout_starts, bout_ends = check_bout_starts_ends(bouts, bout_starts, bout_ends)
return bouts, bout_starts, bout_ends

fin_tracking.py at commit 3672765, under MIT · at the source

Overview

Authors: Nicholas C. Guilbeault^1,2, Molly E. Westbrook³, Venkatesh S. Krishna¹, Rida Ansari^1,2, Alexander Keth³, Aristides B. Arrenberg⁴, Scott A. Juntti³, Tod R. Thiele^1,2,5

ORCID iDs: Scott A. Juntti

Department of Biological Sciences, University of Toronto Scarborough, Scarborough, ON, Canada
Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
Department of Biology, University of Maryland, College Park, MD, USA
Werner Reichardt Centre for Integrative Neuroscience, University of Tübigen, Tübigen, Germany
Natural Science Division, Pepperdine University, Malibu, CA, USA

Institutions: University of Toronto (Canada); University of Toronto Scarborough (Canada); University of Maryland, College Park (United States); University of Tübingen (Germany); Pepperdine University (United States)

Journal: iScience, volume 29, issue 9, article 117136

Dates: received 12 May 2026; accepted 24 July 2026; published online 18 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.117136 · PMID 42662783 · PMCID PMC13520728 · OpenAlex W7203726305

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: other (organism), zebrafish (organism), systems (subfield)

Methods: Statistics, Machine learning

Keywords: zebrafish, cichlid, optomotor, visual systems, evolution, neural circuits

Topic: Biomimetic flight and propulsion mechanisms (Aerospace Engineering, Engineering), according to OpenAlex

Funding: NSERC; Human Frontier Science Program (RGY0079/201); Canadian Foundation for Innovation and the Ontario Research Fund; NIH (R35GM142872)

Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

The optomotor response (OMR) is a conserved visuomotor reflex that stabilizes position during optic flow. The OMR has been extensively studied in zebrafish but examined in detail in only a small number of other fish species. Here, we characterize the OMR in larval cichlids (Astatotilapia burtoni). Using high-resolution behavioral assays, we reveal striking species differences in alignment accuracy, speed matching, and swimming kinematics. While zebrafish employ discrete tail-driven swims and stepwise turns, cichlids exhibit continuous swimming, dynamic pivot maneuvers, and backward locomotion powered by complex pectoral fin movements (labriform swimming). Local contrast analyses indicate inverted, species-specific visual feature tuning for the OMR. These results, together with a prior report of similar prey-capture kinematics between these species, highlight the modularity of evolved visuomotor specializations. Together, our results establish larval cichlid optomotor behavior as a tractable system for elucidating how evolutionary history and ecological demands shape neural circuits to produce diverse solutions to a highly conserved visuomotor behavior.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Zenodo 21463359

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: NumPy (22 files), pandas (16 files), Matplotlib (15 files), SciPy (9 files), Pingouin (4 files), scikit-learn (2 files), DeepLabCut (1 file), ggpubr (1 file), OpenCV (1 file), rstatix (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

33 files

dlc/analyze_videos.py, Python, 55 lines
dlc/convert_mp4_to_avi.py, Python, 33 lines
notebooks/00_standard_OMR/00_process_raw_data.ipynb, Jupyter, 179 lines
notebooks/00_standard_OMR/01_process_trial_data.ipynb, Jupyter, 539 lines
notebooks/00_standard_OMR/02_plot_direction_over_trial_data.ipynb, Jupyter, 260 lines
notebooks/00_standard_OMR/03_plot_speed_ratio_from_trial_data.ipynb, Jupyter, 818 lines
notebooks/00_standard_OMR/04_plot_trial_duration_data.ipynb, Jupyter, 483 lines
notebooks/00_standard_OMR/05_plot_thigmotaxis_data.ipynb, Jupyter, 188 lines
notebooks/00_standard_OMR/06_plot_exit_entrance_pdf.ipynb, Jupyter, 1,112 lines
notebooks/00_standard_OMR/07_plot_individual_fish_trajectory_and_data.ipynb, Jupyter, 526 lines
notebooks/00_standard_OMR/08_cycles_per_degree_calc.ipynb, Jupyter, 35 lines
notebooks/01_random_bar_widths/00_random_bar_widths_stimulus_over_fish.ipynb, Jupyter, 650 lines
notebooks/02_fin_tracking/00_DLC_analyses.ipynb, Jupyter, 3,471 lines
notebooks/02_fin_tracking/01_DLC_analysis_total_video_time_analyzed.ipynb, Jupyter, 68 lines
notebooks/02_fin_tracking/02_cross_correlations.ipynb, Jupyter, 185 lines
notebooks/03_habituation/00_process_raw_data.ipynb, Jupyter, 149 lines
notebooks/03_habituation/01_process_trial_data.ipynb, Jupyter, 285 lines
notebooks/03_habituation/02_process_individual_plots.ipynb, Jupyter, 100 lines
notebooks/03_habituation/03_plot_habituation_data.ipynb, Jupyter, 1,171 lines
notebooks/03_habituation/04_plot_individual_fish_trajectory_and_data.ipynb, Jupyter, 289 lines
src/cichlid_omr/__init__.py, Python, 18 lines
src/cichlid_omr/config.py, Python, 46 lines
src/cichlid_omr/figures.py, Python, 329 lines
src/cichlid_omr/fin_tracking.py, Python, 217 lines
src/cichlid_omr/io.py, Python, 144 lines
src/cichlid_omr/plotting.py, Python, 43 lines
src/cichlid_omr/random_bar_width.py, Python, 1,691 lines
src/cichlid_omr/signal.py, Python, 151 lines
stats/anova_common.R, R, 97 lines
stats/swim_gain_three-way-anova.R, R, 8 lines
stats/trial_duration_three-way-anova.R, R, 8 lines
LICENSE, License, 9 lines
README.md, Text, 83 lines

At the source: doi.org/10.5281/zenodo.21463359

ncguilbeault/cichlid_omr_analysis

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 36727650834d019e61e3d06f11f1bff93c1f2f68, 20 July 2026

Languages: Jupyter (18), Python (10), R (3)

Size: 44 files, 31 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, license file, CITATION.cff, environment (pyproject.toml, uv.lock), 18 notebooks

Not found: tests, continuous integration, documentation

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

33 files

dlc/analyze_videos.py, Python, 55 lines
dlc/convert_mp4_to_avi.py, Python, 33 lines
notebooks/00_standard_OMR/00_process_raw_data.ipynb, Jupyter, 179 lines
notebooks/00_standard_OMR/01_process_trial_data.ipynb, Jupyter, 539 lines
notebooks/00_standard_OMR/02_plot_direction_over_trial_data.ipynb, Jupyter, 260 lines
notebooks/00_standard_OMR/03_plot_speed_ratio_from_trial_data.ipynb, Jupyter, 818 lines
notebooks/00_standard_OMR/04_plot_trial_duration_data.ipynb, Jupyter, 483 lines
notebooks/00_standard_OMR/05_plot_thigmotaxis_data.ipynb, Jupyter, 188 lines
notebooks/00_standard_OMR/06_plot_exit_entrance_pdf.ipynb, Jupyter, 1,112 lines
notebooks/00_standard_OMR/07_plot_individual_fish_trajectory_and_data.ipynb, Jupyter, 526 lines
notebooks/00_standard_OMR/08_cycles_per_degree_calc.ipynb, Jupyter, 35 lines
notebooks/01_random_bar_widths/00_random_bar_widths_stimulus_over_fish.ipynb, Jupyter, 650 lines
notebooks/02_fin_tracking/00_DLC_analyses.ipynb, Jupyter, 3,471 lines, 1 match
notebooks/02_fin_tracking/01_DLC_analysis_total_video_time_analyzed.ipynb, Jupyter, 68 lines
notebooks/02_fin_tracking/02_cross_correlations.ipynb, Jupyter, 185 lines
notebooks/03_habituation/00_process_raw_data.ipynb, Jupyter, 149 lines
notebooks/03_habituation/01_process_trial_data.ipynb, Jupyter, 285 lines
notebooks/03_habituation/02_process_individual_plots.ipynb, Jupyter, 100 lines
notebooks/03_habituation/03_plot_habituation_data.ipynb, Jupyter, 1,171 lines
notebooks/03_habituation/04_plot_individual_fish_trajectory_and_data.ipynb, Jupyter, 289 lines
src/cichlid_omr/__init__.py, Python, 18 lines
src/cichlid_omr/config.py, Python, 46 lines
src/cichlid_omr/figures.py, Python, 329 lines
src/cichlid_omr/fin_tracking.py, Python, 217 lines, 1 match
src/cichlid_omr/io.py, Python, 144 lines
src/cichlid_omr/plotting.py, Python, 43 lines
src/cichlid_omr/random_bar_width.py, Python, 1,691 lines, 1 match
src/cichlid_omr/signal.py, Python, 151 lines
stats/anova_common.R, R, 97 lines
stats/swim_gain_three-way-anova.R, R, 8 lines
stats/trial_duration_three-way-anova.R, R, 8 lines
LICENSE, License, 9 lines
README.md, Text, 85 lines

At the source: github.com/ncguilbeault/cichlid_OMR_analysis/tree/v0.1.0

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
62 scripts, each with its path and the digest of its content;
3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

• All data reported in this study will be shared by the lead contact upon request. • All original code has been deposited at Zenodo and is publicly available at https://doi.org/10.5281/zenodo.21463359. • Any additional information required to reanalyze the data reported in this study is available from the lead contact upon request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Authors: added Scott A. Juntti (0000-0001-7634-1395); removed Scott A. Juntti

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 6 keywords, 4 funders, 67 references.

Cite

This paper

Guilbeault, N. C., Westbrook, M. E., Krishna, V. S., Ansari, R., Keth, A., Arrenberg, A. B., Juntti, S. A., & Thiele, T. R. (2026). Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid Astatotilapia burtoni. iScience, 29(9), 117136. https://doi.org/10.1016/j.isci.2026.117136

BibTeX

@article{guilbeault2026complex, author = {Guilbeault, Nicholas C. and Westbrook, Molly E. and Krishna, Venkatesh S. and Ansari, Rida and Keth, Alexander and Arrenberg, Aristides B. and Juntti, Scott A. and Thiele, Tod R.}, title = {{Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid \<i\>Astatotilapia burtoni\</i\>}}, journal = {iScience}, year = {2026}, month = aug, volume = {29}, number = {9}, pages = {117136}, publisher = {Elsevier}, issn = {2589-0042}, doi = {10.1016/j.isci.2026.117136}, url = {https://doi.org/10.1016/j.isci.2026.117136}, pmid = {42662783}, pmcid = {PMC13520728} }

RIS

TY - JOUR AU - Guilbeault, Nicholas C. AU - Westbrook, Molly E. AU - Krishna, Venkatesh S. AU - Ansari, Rida AU - Keth, Alexander AU - Arrenberg, Aristides B. AU - Juntti, Scott A. AU - Thiele, Tod R. TI - Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid Astatotilapia burtoni T2 - iScience J2 - iScience PY - 2026 DA - 2026/08/18 VL - 29 IS - 9 SP - 117136 SN - 2589-0042 PB - Elsevier DO - 10.1016/j.isci.2026.117136 UR - https://doi.org/10.1016/j.isci.2026.117136 LA - en ER -

CSL-JSON

{ "id": "10.1016/j.isci.2026.117136", "type": "article-journal", "title": "Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid Astatotilapia burtoni", "container-title": "iScience", "author": [ { "family": "Guilbeault", "given": "Nicholas C." }, { "family": "Westbrook", "given": "Molly E." }, { "family": "Krishna", "given": "Venkatesh S." }, { "family": "Ansari", "given": "Rida" }, { "family": "Keth", "given": "Alexander" }, { "family": "Arrenberg", "given": "Aristides B." }, { "family": "Juntti", "given": "Scott A." }, { "family": "Thiele", "given": "Tod R." } ], "container-title-short": "iScience", "volume": "29", "issue": "9", "page": "117136", "DOI": "10.1016/j.isci.2026.117136", "PMID": "42662783", "PMCID": "PMC13520728", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.117136", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 18 ] ] } }

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Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid <i>Astatotilapia burtoni</i>.

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Zenodo 21463359

ncguilbeault/cichlid_omr_analysis

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Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid &lt;i&gt;Astatotilapia burtoni&lt;/i&gt;.

Code ↔ Paper

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Abstract

Repositories

Zenodo 21463359

ncguilbeault/cichlid_omr_analysis

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Data

Data and code availability

Versions

Version 2, 28 September 2026

Version 1, 27 September 2026: the first record

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Discussion, reproductions, activity

Complex pectoral-fin-driven locomotion underlies refined visuomotor behavior in the cichlid <i>Astatotilapia burtoni</i>.