OSCR

EthoPy provides an accessible platform for reproducible behavioral neuroscience.

Code ↔ Paper

13 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 13 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Database schema organization › Behavior schema ↔ src/ethopy/behaviors/multi_port.py, the whole file · a weak match · score 0.64 · multi port reward, multi port response, correct response port, beh, proximity, schema
  2. [2] § STAR★Methods › Method details › Database schema organization › Behavior schema ↔ ethopy_package-main.zip/src/ethopy/behaviors/multi_port.py, the whole file · a weak match · score 0.64 · multi port reward, multi port response, correct response port, beh, proximity, schema
  3. [3] § STAR★Methods › Method details › Hardware components › EthoPy controller board and arduino ↔ src/ethopy/core/behavior.py, lines 24–45 · score 0.61 · hardware components, reward delivery, experimental setups, lick ports, interactions, interface
  4. [4] § STAR★Methods › Method details › Database schema organization › Experiment schema ↔ src/ethopy/experiments/free_water.py, lines 23–37 · score 0.57 · free water, trial selection, intertrial, transitions, Reward
  5. [5] § STAR★Methods › Method details › Database schema organization › Stimulus schema ↔ src/ethopy/stimuli/grating.py, lines 66–142 · score 0.57 · grating movie, stim_hash, schema, stimulus
  6. [6] § STAR★Methods › Method details › Calibration ↔ src/ethopy/core/interface.py, lines 535–571 · score 0.56 · air pressure, psi, calibrated, volume, water, liquid
  7. [7] § STAR★Methods › Method details › Calibration ↔ ethopy_package-main.zip/src/ethopy/core/interface.py, lines 520–556 · score 0.56 · air pressure, psi, calibrated, volume, water, liquid
  8. [8] § STAR★Methods › Method details › Synchronization ↔ src/ethopy/core/logger.py, lines 83–126 · score 0.55 · experimental setup, flips, synchronization, internal, interval, machines
  9. [9] § STAR★Methods › Method details › Synchronization ↔ ethopy_package-main.zip/src/ethopy/core/logger.py, lines 81–124 · score 0.55 · experimental setup, flips, synchronization, internal, interval, machines
  10. [10] § STAR★Methods › Method details › Database schema organization › Task setup and execution ↔ src/ethopy/utils/create_ethopy_task.py, lines 113–176 · score 0.54 · stimulus parameters, setup configuration, template, import, customized, execution
  11. [11] § STAR★Methods › Method details › Database schema organization › Task setup and execution ↔ ethopy_package-main.zip/src/ethopy/utils/create_ethopy_task.py, lines 106–169 · score 0.54 · stimulus parameters, setup configuration, template, import, customized, execution
  12. [12] § STAR★Methods › Method details › Database schema organization › Experiment schema ↔ src/ethopy/experiments/match_port.py, lines 9–40 · score 0.53 · match port, trial selection, intertrial, Punish, Reward, schema
  13. [13] § STAR★Methods › Method details › Behavioral data analysis ↔ src/ethopy_analysis/__init__.py, lines 1–27 · score 0.50 · DataJoint, behavioral experiments, pandas, tools, database, EthoPy

Paper

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The authors' code

Python · 102 lines · 3.5 KB · MIT · 1 match

  1. import datajoint as dj
  2. import numpy as np
  4. from ethopy.core.behavior import Behavior
  5. from ethopy.core.logger import behavior
  8. @behavior.schema
  9. class MultiPort(Behavior, dj.Manual):
  10. definition = """
  11. # This class handles the behavior variables for RP
  12. ->behavior.BehCondition
  13. """
  15. class Response(dj.Part):
  16. definition = """
  17. # Lick response condition
  18. -> MultiPort
  19. response_port : tinyint # response port id
  20. """
  22. class Reward(dj.Part):
  23. definition = """
  24. # reward port conditions
  25. -> MultiPort
  26. ---
  27. reward_port : tinyint # reward port id
  28. reward_amount=0 : float # reward amount
  29. reward_type : varchar(16) # reward type
  30. """
  32. def __init__(self):
  33. super().__init__()
  34. self.cond_tables = ["MultiPort", "MultiPort.Response", "MultiPort.Reward"]
  35. self.required_fields = ["response_port", "reward_port", "reward_amount"]
  36. self.default_key = {"reward_type": "water"}
  38. def is_ready(self, duration, since=False):
  39. position, ready_time, tmst = self.interface.in_position()
  40. if duration == 0:
  41. return True
  42. elif position == 0 or position.ready == 0:
  43. return False
  44. elif not since:
  45. return ready_time > duration # in position for specified duration
  46. elif tmst >= since:
  47. # has been in position for specified duration since timepoint
  48. return ready_time > duration
  49. else:
  50. # has been in position for specified duration since timepoint
  51. return (ready_time + tmst - since) > duration
  53. def is_correct(self):
  54. """Check if the response port is correct.
  56. if current response port is -1, then any response port is correct
  57. otherwise if the response port is equal to the current response port/ports,
  58. then it is correct
  60. Returns:
  61. bool: True if correct, False otherwise
  63. """
  64. return self.curr_cond['response_port'] == -1 or \
  65. np.any(np.equal(self.response.port, self.curr_cond['response_port']))
  67. def is_off_proximity(self):
  68. return self.interface.off_proximity()
  70. def reward(self, tmst=0):
  71. """Give reward at latest licked port.
  73. After the animal has made a correct response, give the reward at the
  74. first port that animal has licked and is definded as reward.
  76. Args:
  77. tmst (int, optional): Time in milliseconds. Defaults to 0.
  79. Returns:
  80. bool: True if rewarded, False otherwise
  82. """
  83. # if response and reward ports are the same no need of tmst
  84. if self.response.reward:
  85. tmst = 0
  86. # check that the last licked port is also a reward port
  87. licked_port = self.is_licking(since=tmst, reward=True)
  88. rewarded_port = self.curr_cond["reward_port"]
  89. if licked_port and (rewarded_port == -1 or licked_port == rewarded_port):
  90. self.interface.give_liquid(licked_port)
  91. self.log_reward(self.reward_amount[self.licked_port])
  92. self.update_history(self.response.port, self.reward_amount[self.licked_port])
  93. return True
  94. return False
  96. def exit(self):
  97. super().exit()
  98. self.interface.cleanup()
  100. def punish(self):
  101. port = self.response.port if self.response.port > 0 else np.nan
  102. self.update_history(port, punish=True)

multi_port.py at commit b478cf9, under MIT · at the source

Overview

Authors: Alexandros Evangelou1,2, Maria Diamantaki1,2, Konstantina Georgelou1,2, Zoi Drakaki1,2, Lydia Ntanavara1,2, Gerasimos Gerardos1,2, Sofia Morou1,2, Nikolaos Chatziris1,2, Zoi Dogani1,2, Elissavet Anna Petsalaki1,2, Odysseas Nikolaos Raos1,2, Anastasios Gratsakis1,2, Stamatios Aliprantis1,2, Athanasia Papoutsi1,2, Emmanouil Froudarakis1,2
ORCID iDs: Alexandros Evangelou, Maria Diamantaki, Konstantina Georgelou, Lydia Ntanavara, Sofia Morou, Nikolaos Chatziris, Zoi Dogani, Elissavet Anna Petsalaki, Odysseas Nikolaos Raos, Anastasios Gratsakis, Stamatios Aliprantis, Athanasia Papoutsi, Emmanouil Froudarakis
  1. Institute of Molecular Biology & Biotechnology, Foundation of Research & Technology - Hellas, Heraklion, Crete, Greece
  2. Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Crete, Greece
Institutions: University of Crete (Greece); Foundation for Research and Technology Hellas (Greece); FORTH Institute of Molecular Biology and Biotechnology (Greece)
Journal: Cell reports methods, volume 6, issue 6, article 101421
Dates: received 21 November 2025; accepted 30 March 2026; published online 23 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.crmeth.2026.101421 · PMID 42030950 · PMCID PMC13282661 · OpenAlex W7155410608
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), mouse (organism), methods / tools (subfield)
Keywords: behavioral training, high-throughput training, home-cage training, Python, open source, automated behavioral tasks, modular architecture, data reproducibility, brain-behavior integration, mouse
MeSH: Behavior, Animal*, Neurosciences*, Software*, Animals, Mice, Reproducibility of Results (* major topic)
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: European Research Council (ERC-2022-STG); NEURACT (101076710); Hellenic Foundation for Research and Innovation; HFRI (4049); Funding of Basic Research; National Recovery and Resilience Plan; European Union – NextGenerationEU (016552); European Union’s Horizon 2020; Marie Skłodowska-Curie Actions (101025482); FlexBe
Citations: not cited yet (Europe PMC); 82 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 13 matches between paragraphs and lines of code.

figshare 30111118

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 8 files
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.6084/m9.figshare.30111118

Zenodo 19110086

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 4 files
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 (24 files), pandas (7 files), Matplotlib (6 files), OpenCV (2 files), SciPy (2 files), h5py (1 file), imageio (1 file), Neurodata Without Borders (PyNWB, MatNWB) (1 file), Plotly (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
102 files
At the source: doi.org/10.5281/zenodo.19110086

ef-lab/ethopy_package

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b478cf90a25c06301fc7fd5fd62dce9ae3250182, 10 September 2026
Languages: Python (61)
Size: 131 files, 61 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, environment (pyproject.toml, requirements-lock.txt), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (19 files), OpenCV (2 files), SciPy (2 files), h5py (1 file), imageio (1 file), Neurodata Without Borders (PyNWB, MatNWB) (1 file), pandas (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
63 files
At the source: github.com/ef-lab/ethopy_package

ef-lab/ethopy_control

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 8bf7f82ed1b1d529015768b1dca703e480fc88e5, 2 September 2026
Languages: Python (14), JavaScript (1)
Size: 52 files, 15 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, environment (docker-compose.yml, Dockerfile, pyproject.toml), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: Plotly (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
17 files
At the source: github.com/ef-lab/ethopy_control

ef-lab/ethopy_analysis

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: b02138b105ea2490b1628b73c8bfbc12051e9a53, 10 September 2026
Languages: Python (17), Jupyter (3)
Size: 46 files, 20 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, environment (constraints.txt, pyproject.toml), continuous integration, documentation, 3 notebooks
Not found: CITATION.cff, tests
Tools: Matplotlib (6 files), NumPy (6 files), pandas (6 files), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
22 files
At the source: github.com/ef-lab/ethopy_analysis

ef-lab/ethopy_hardware

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: fd1c74230a7615d9238df146f265667c360ba2d2, 20 February 2026
Size: 111 files, 0 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
2 files
At the source: github.com/ef-lab/ethopy_hardware

ef-lab/ethopy_plugins

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: d7e08e3e140e7177352edde9ae9268b06749a560, 1 September 2026
Languages: Python (30)
Size: 62 files, 30 scripts
Software Heritage: not archived
Found in: the text, “EthoPy plugins”
Holds: README, license file, environment (openfield/requirements.txt), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (13 files), h5py (3 files), Neurodata Without Borders (PyNWB, MatNWB) (3 files), PsychoPy (2 files), SciPy (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
32 files
At the source: github.com/ef-lab/ethopy_plugins/

Code availability statement

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Read it in the paper: doi.org/10.1016/j.crmeth.2026.101421.

Tracing map

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  • 7 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 220 scripts, each with its path and the digest of its content;
  • 13 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Code and data availability statement

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Read it in the paper: doi.org/10.1016/j.crmeth.2026.101421.

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 10 keywords, 6 MeSH terms, 10 funders, 78 references.

Cite

This paper

Evangelou, A., Diamantaki, M., Georgelou, K., Drakaki, Z., Ntanavara, L., Gerardos, G., Morou, S., Chatziris, N., Dogani, Z., Petsalaki, E. A., Raos, O. N., Gratsakis, A., Aliprantis, S., Papoutsi, A., & Froudarakis, E. (2026). EthoPy provides an accessible platform for reproducible behavioral neuroscience. Cell reports methods, 6(6), 101421. https://doi.org/10.1016/j.crmeth.2026.101421

BibTeX

@article{evangelou2026ethopy,
author = {Evangelou, Alexandros and Diamantaki, Maria and Georgelou, Konstantina and Drakaki, Zoi and Ntanavara, Lydia and Gerardos, Gerasimos and Morou, Sofia and Chatziris, Nikolaos and Dogani, Zoi and Petsalaki, Elissavet Anna and Raos, Odysseas Nikolaos and Gratsakis, Anastasios and Aliprantis, Stamatios and Papoutsi, Athanasia and Froudarakis, Emmanouil},
title = {{EthoPy provides an accessible platform for reproducible behavioral neuroscience}},
journal = {Cell reports methods},
year = {2026},
month = apr,
volume = {6},
number = {6},
pages = {101421},
publisher = {Elsevier},
issn = {2667-2375},
doi = {10.1016/j.crmeth.2026.101421},
url = {https://doi.org/10.1016/j.crmeth.2026.101421},
pmid = {42030950},
pmcid = {PMC13282661}
}

RIS

TY - JOUR
AU - Evangelou, Alexandros
AU - Diamantaki, Maria
AU - Georgelou, Konstantina
AU - Drakaki, Zoi
AU - Ntanavara, Lydia
AU - Gerardos, Gerasimos
AU - Morou, Sofia
AU - Chatziris, Nikolaos
AU - Dogani, Zoi
AU - Petsalaki, Elissavet Anna
AU - Raos, Odysseas Nikolaos
AU - Gratsakis, Anastasios
AU - Aliprantis, Stamatios
AU - Papoutsi, Athanasia
AU - Froudarakis, Emmanouil
TI - EthoPy provides an accessible platform for reproducible behavioral neuroscience
T2 - Cell reports methods
J2 - Cell Rep Methods
PY - 2026
DA - 2026/04/23
VL - 6
IS - 6
SP - 101421
SN - 2667-2375
PB - Elsevier
DO - 10.1016/j.crmeth.2026.101421
UR - https://doi.org/10.1016/j.crmeth.2026.101421
LA - en
ER -

CSL-JSON

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