sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
aebb309ebde3cb58a0e5a6c0c513d9e066c35bbc977d940c22919856ee70f7ef | Jupyter | 45,583 | 1,309 | # %%
#notebook to reproduce figs 2 e-g
# %%
import nibabel as nb
import numpy as np
import matplotlib.pyplot as plt
import os
import json
from vast import surface_tools
import pandas as pd
import scipy.stats as stats
import subprocess
import sklearn.decomposition # PCA
import sklearn.manifold
import scripts.nb_loader... |
85bdca6ea66138495add91b04aa6a6b10b62035293d19d0683e4dcc9d28d0799 | Jupyter | 49,588 | 1,153 | # %% [markdown]
# # Figure 2 - Cheese3D jitter analysis
# %% [markdown]
# This notebook contains the code to reproduce Figure 2a-f.
#
# To run this notebook, you need the following data:
# 1. Anipose project: `20231013-long-anes-rig2`
# 2. Anipose project: `20240919_long-anes-clips_rig2`
# 3. Facemap project: `202409... |
705995fc1a4541710f22f4a75c3273f1c55bace4c4ddf6af7819da15fedc4a74 | Jupyter | 49,684 | 1,220 | # %% [markdown]
# # Figure 4 - Chewing and whole face kinematics analysis
#
# The following notebook contains the code to reproduce Fig. 4a-f, h-k.
# To run this notebook, you need the following Anipose projects:
# - `omnibus-splitsession-20231031_B6-B20-B21-B26-B31-B32-B33_chew_rig2`
# %% [markdown]
# ## Preliminari... |
e690fdd2ca2de3540ef736bec38bbcf986d30e2697998c6dbe5dc7269d5b11eb | Jupyter | 51,284 | 1,516 | # %% [markdown]
# # Analysis of Children's motion data
#
# ## 1. Import data
# %%
import os
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
from skimage.measure import label, regionprops
from skimage.color import label2rgb
import scipy.io
import pprint
import operator
from matplotlib.pyplot i... |
16dc4b58ebe7a7e680c38c456d537f78df02c40636667a141c9c2871a9aa362e | Jupyter | 53,354 | 1,612 | # %% [markdown]
# # Generate plots from saved runs
# %%
import warnings
import matplotlib
warnings.filterwarnings("ignore", category=matplotlib.MatplotlibDeprecationWarning)
# %%
# %matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
# import pandas as pd
import matplotlib.gridspec as gridspec
from ... |
cb77641c8562ad4d2b212b50ef9f98cf133f807fb64ba4bcdc6242a6ee900466 | Jupyter | 54,718 | 1,374 | # %%
import csv
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from pathlib import Path
import itertools
from typing import Union, Optional
import networkx as nx
from adjustText import adjust_text
from cinnabar import plotlying, stats
import glob
import warnings
import math
import seaborn as sns... |
f88fc1c70b1243995f27579261b47ab30103fc06a4b1b8ce0ce7a172863146ba | Jupyter | 55,143 | 1,096 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_context("talk")
openfe_public_colour = "#8A2283"
openfe_private_colour = "#009384"
# %%
normal_edge_data = pd.read_csv("https://raw.githubusercontent.com/OpenFreeEnergy/IndustryBenchmarks2024/refs/heads/main/indu... |
0ed12690f48ea29fb6a151e1bc3fbba4e4a156b0b3d98db61f3977b97e72d5df | Jupyter | 57,712 | 719 | # %% [markdown]
# # CANDO tutorial
# *Updated January 2025*
#
# This notebook will walk you through the basics of using the Computational Analysis of Novel Drug Opportunities platform, or CANDO. This includes preparing data, setting up a CANDO object, making therapeutic predictions, and benchmarking the platform.
#
#... |
676f1f38289bc29f2786942ff2da63f9fef4e2362bf37427e00abc0e2c7f3c25 | Jupyter | 60,649 | 1,226 | # %%
"""
21 MARCH 2024
Theo Gauvrit
Testing if the responsivity pattern are different between WT and KO groups
"""
import percephone.core.recording as pc
import percephone.plts.behavior as pbh
import percephone.plts.stats as ppt
import percephone.plts.style as sty
from percephone.analysis.utils import get_zscore
im... |
c4bf1a4be3f8f653b09e65be0375c7f4c92cbd8b3478a59fdd94e508c56963cf | Jupyter | 61,153 | 1,306 | # %%
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.stats.multitest import fdrcorrection
from scipy.spatial.distance import squareform, pdist, cdist, cosine
from scipy.stats import zscore, ttest_ind, ttest_rel
import scipy.integrate as spint
if not hasattr(spint, 'simps'):
sp... |
85fe6b60dedf69533b70fbec12c1074abfe36b2de3be1d3910194f945e822fb3 | Jupyter | 63,924 | 1,297 | # %% [markdown]
# # Figure 5 - Synchronized Cheese3D and electrophysiology recording analysis
#
# This notebook contains the code to reproduce Fig5g-h.
# To run this code, you need the following Anipose projects:
# - `20240823_ephys-all_rig1_score_0_75`
# %% [markdown]
# Import video and ephys data
# %%
import os
im... |
1abb8aaf6c7521b46b072e63cc61f6b707cece9face04c50584a254ab897eea7 | Jupyter | 66,071 | 1,578 | # %% [markdown]
# # FIGURE 3 (Feb 2025 submission)
# %% [markdown]
# This notebook gathers the code to make all the panels in Figure 3. In order to run this notebook, you need the following anipose projects:
# 1. 20231013-long-anes-rig2
# 2. 202408-eeg-emg-all
# %% [markdown]
# ## Prep the notebook
# %% [markdown]
#... |
3c8b4bc0920693fac5b67f676efdd0087b89d5b4cab4b251e70ba42dab57833f | Jupyter | 70,579 | 1,425 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import glob, os, yaml, sparse, itertools, subprocess, sys, pickle, re, collections, shutil
from functools import reduce
from matplotlib.patches import Patch
import Bio.SeqUtils
import Bio.Data
from Bio import SeqIO
from B... |
720867d26e855b0bfee7fe57894c9c06865e2da8fa4cafb0a37f32ecef4f3ed7 | Jupyter | 70,874 | 1,461 | # %%
subject_folder = r"C:/dummy/path/sub-XX"
#subject_folder = "C:/Yoyi/BIDS Dataset/sourcedata/sub-29"
# %% [markdown]
# # Import Libraries
# %%
from BCI2kReader import BCI2kReader as b2k # Library for reading BCI2000 files
import os # Library to read file directories
import numpy... |
ae8905ce4febc5cbd456ecdb85e1aab09f023b8a6e3f2242a0cc4614a25aa84d | Jupyter | 72,156 | 1,717 | # %% [markdown]
# # Figure 3h-i - Prediction of EEG from facial features
# %% [markdown]
# This notebook contains the code to reproduce Fig. 3h-i and Extended Data Fig. 7b-d. To run this code, you need the following Anipose projects:
# - `202408-eeg-emg-all`
# - `202505-eeg-redose`
#
# as well as corresponding EEG da... |
48cd43124c8ff702f8cf26c95e02f2f67e5b097d1c1476c14da6a1473b94e13c | Jupyter | 72,174 | 1,844 | # %% [markdown]
# # Figure 5i-k - Prediction of neural activity from facial features
# %% [markdown]
# This notebook contains the code to reproduce Fig 5i-k. To run this code, you need the following Anipose projects:
# - `20240823_ephys-all_rig1_score_0_75_frozen`
# - `202505-ephys-record`
#
# as well as correspondin... |
594fc6cdc07d0296a2725e164ead9455e3c163f7cd0a4ef56b41253e07dc640c | Jupyter | 72,499 | 1,219 | # %% [markdown]
# # DMSO & BMS analysis
# **14th June 2024**
#
# *Célien Vandromme*
#
# ---
# %%
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import matplotlib
import percephone.plts.stats as ppt
import matplotlib.pyplot as plt
from multiprocessing import Pool, cpu_count, p... |
a12d1aa7fb7833eb2a87653a107f782325ae9dbae05dcbe251fa8a45b1f78666 | Jupyter | 75,079 | 1,877 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob, os, sparse, sys, warnings, yaml, vcf, pickle, shutil, subprocess
import scipy.optimize
os.chdir("../")
sys.path.append("utils")
from data_utils import *
from inSilicoMut_utils import *
import seaborn as sns
import scipy.stats as ... |
3000ebc289601f1702d89f4bf235a978ddd11d6f3ad253bb81d2d3bc7fcafa68 | Jupyter | 78,446 | 1,548 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from cinnabar.plotting import _master_plot
from cinnabar.stats import bootstrap_statistic
import seaborn as sns
import numpy as np
sns.set_context("talk")
# setup the colours to use in the plots
openfe_color = '#8A2283'
fep_plus_color = '#50CAF5'
fep_plus_text_c... |
2af20149e000e6c4eb6537ccf4e8fb4bc2c1c292a4bc20cc8fc104616d552b33 | Jupyter | 79,643 | 1,614 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import glob, os, yaml, sparse, itertools, subprocess, sys, pickle, re, collections, shutil
from functools import reduce
import Bio.SeqUtils
import Bio.Data
from Bio import SeqIO
from Bio.Seq import Seq
plt.rcParams['figu... |
24d255c8ca70cb3d03902b51191b0eadf127eec56209078ac4053feb3650e821 | Jupyter | 89,840 | 165 | # %% [markdown]
# # Facemap 1.0 Keypoints Prediction in colab with a GPU
#
# Framework for predicting neural activity from mouse orofacial movements tracked using a pose estimation model. Package also includes singular value decomposition (SVD) of behavioral videos.
#
# ***Facemap code: Carsen Stringer & Atika Syeda*... |
3315ffa184ba56034a6e21a01ed393de9def8d88dab785797614beff3ffec345 | Jupyter | 102,891 | 2,446 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import glob, os, yaml, itertools, subprocess, sys, shutil,sparse
import Bio.SeqUtils
import Bio.Data
from Bio import SeqIO
from Bio.Seq import Seq
plt.rcParams['figure.dpi'] = 150
plt.... |
64803950f92e8493e56bb78302dcaa16081b4862ef614dbeb7d85b7e39801bb7 | Jupyter | 108,232 | 140 | # %% [markdown]
# # Facemap 1.0 - Keypoints model refinement in colab using a GPU
#
# Facemap is a framework for predicting neural activity from mouse orofacial movements tracked using a pose estimation model. Package also includes singular value decomposition (SVD) of behavioral videos.
#
# ***Facemap code: Carsen S... |
c1bbcc50971c32ef550fc190a2c603379abac6a66257062c43c7c3aafac12d19 | Jupyter | 137,192 | 3,251 | # %%
from pathlib import Path
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
base_dir = Path("/kaggle/input/datasets/ma7bakr/tep-dataset/TEP_data")
output_dir = Path("/kaggle/working/phase1_data_governance")
output_dir.mkdir(parents... |
17039712062f32cadb8b20efe5bdb3d6c6ddfd5b0257b9ed4cf7dc36edf6c1f2 | Jupyter | 141,817 | 4,064 | # %% [markdown]
# # CORnet E/I Imbalance: Face Recognition under Simulated ASD Pathology
#
# This notebook runs the full experimental pipeline:
#
# 1. **Setup** — install dependencies, mount Drive, import modules
# 2. **Post-hoc analysis** — inspect previously saved CORnet results
# 3. **Dataset preparation** — build... |
cefccb6828eb9eb9fa33bf08d37d3a0a4d73ddeeafea2a268873f0aa06b48009 | License | 295 | 1 | The code is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) for NonCommercial use only. Any commercial use should get formal permission first (Email: VAGRANTLYUN@GMAIL.COM).
|
f6a4501214da571024b852cc2763ac0e557b5a2ac161c189507defdd084074a7 | License | 564 | 13 | Copyright 2017 Aix-Marseille University
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, so... |
0a386ab91daac204f5ff0875d569432bd7caedcf427ea54a374708e5adcad2e8 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 ceadm
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
9d870614ce0250f917eb475923ca510c7230aec7890a9d72ee38fc139035e3d5 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 ZYyli
Permission is hereby granted, free of charge, to any person obtaining a copy
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5b4a9387a1f9b78869bd765d92f0a5b96ff3e20e0516780efbb1b807071f0125 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 SNUFML
Permission is hereby granted, free of charge, to any person obtaining a copy
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b5a45f38581197c1f310f50fcaa734a8191ea9106c2658aa88e1715419aa9fe7 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 ef-lab
Permission is hereby granted, free of charge, to any person obtaining a copy
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151126604e3c38782a272222187a3b67b51095c6965e67e5e36bce03ee8b9a18 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 wpham17
Permission is hereby granted, free of charge, to any person obtaining a copy
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178b17f8894dce6fec78ea8bf6ff88fb4f735cbf2e59bdc579e3c07b759815b0 | License | 1,064 | 19 | Copyright (c) 2017-2020, the mripy team
Permission is hereby granted, free of charge, to any person obtaining a copy
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Copyright (c) 2026 jinming
Permission is hereby granted, free of charge, to any person obtaining a copy
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6233c2902ac62ad5f0eb1279739f3052fd58491ba3467abdb7e539aa71caf682 | License | 1,064 | 21 | MIT License
Copyright (c) 2025 Minh Le
Permission is hereby granted, free of charge, to any person obtaining a copy
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87608a4197924bfc22b95ee9c98c9aafb73933256da3fd07809003a273fae591 | License | 1,064 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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e21da18b656bee3bb2f0c3fc3ed0af2eb71e65bdd13d064fed1da7ae6c9cbd21 | License | 1,064 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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101e6af3947495c67182bcf447774a771a1b51b807ca5fd40c06e4c262c02218 | License | 1,065 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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12430d16884afc98e3bbe1eb195c2946e67f78c3cab913f7c2fb9cb3ee24a373 | License | 1,065 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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Copyright (c) 2025 Yuzhi Xu
Permission is hereby granted, free of charge, to any person obtaining a copy
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014eb05ff5bb2acf6b3fbf3a4b00ffb68ccd4746b271380cd61b35b1fa4a8324 | License | 1,066 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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783924bc69b7e22bd8d759f1f23a1e27d517bdaf28d96f8a4356a2462ff4e5b1 | License | 1,066 | 21 | MIT License
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974c013244c2d2d632ee40fc2e119cb64b7f046c25b3db4b4849fcfe05139c20 | License | 1,066 | 21 | MIT License
Copyright (c) 2023 huBioinfo
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e47e077a0e8d72d92a127fdf5301594bf183920aa41e66f77b683f97824c420f | License | 1,066 | 7 | Copyright © 2025 Colin Bredenberg
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sub... |
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Copyright (c) 2022 Lily Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
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2000a3b0d702ea53adafc330b13129b59a14bd6c32bc19001f6b6d938af1a804 | License | 1,067 | 21 | MIT License
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21b335efc201db5130fc589f8425eb41ff7921701bb8f0dc2b15b4f96736218c | License | 1,067 | 21 | MIT License
Copyright (c) 2026 Hongfu Sun
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Copyright (c) 2026 Yurika Doi
Permission is hereby granted, free of charge, to any person obtaining a copy
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3dc95c28caad6da197815aff4d1bf33877b43d5497ee5c3192aa07cc168a0163 | License | 1,067 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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60328261b9b52a020eec5995a8e0ded536d21ed967304718b57ad2343821a98a | License | 1,067 | 21 | MIT License
Copyright (c) 2025 JeniaOle13
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Copyright (c) 2026 Marlin Lab
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ae81e6807ee347fb2e8818b02718811fd34ae49bf9523ab7a3f7ccb10651d140 | License | 1,067 | 21 | MIT License
Copyright (c) 2025 Nova Zhang
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d8f1fef03d95d025beea4b4d4345aca9bccf359510616941c05b3125d4e10336 | License | 1,067 | 21 | MIT License
Copyright (c) 2026 Dongho Kim
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e6c3f08dd06515892fd60f4bb08c32af238599bc6ae8f04b8c8751a14d66d9a3 | License | 1,067 | 21 | MIT License
Copyright (c) 2025 Chaofan Li
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0d966c4ce1e4c70ed2d9015032b9df98ff75f4f50b52c90369c29584fc4a63b1 | License | 1,068 | 21 | MIT License
Copyright (c) 2026 ZuoLab@UCSC
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1ba4786655be8a9762807108d5eccd16ace4f9c0e160e4389ac9e4221a672594 | License | 1,068 | 21 | MIT License
Copyright (c) 2025 xthomaswang
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2563ced049a4340c2473afb788f24042163b0dbd33d8651b5f54c5e3f839c0f5 | License | 1,068 | 21 | MIT License
Copyright (c) 2026 Abuzar Khan
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2d2abb0122bc799cedb25581dced7e75b341b4b1c74f23f6686721b5e4506ffc | License | 1,068 | 21 | MIT License
Copyright (c) 2023 melanieganz
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35b405e7d91c2a824215e530a5555387d64b9ab377dc5bfe76255c1397a7deb7 | License | 1,068 | 21 | MIT License
Copyright (c) 2019 Ruben Dries
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52a73cc199013d295b808f2d0fb5fa9df5656a5d1981ae3579058b1bd10e7789 | License | 1,068 | 21 | MIT License
Copyright (c) 2025 Markus Drag
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9729d6c019fef779e2b9d38094471534f8bc16192cb770209454b1b4897ff88f | License | 1,068 | 21 | MIT License
Copyright (c) 2025 Masseck Lab
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c2771463575fc951164c9aed9f2f71d821c611d984bdf12c257cb455dd0526c6 | License | 1,068 | 21 | MIT License
Copyright (c) 2025 Ryan Gorzek
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d82e42aa01a194344ed740e004576fca5902bf29c00e5312c369a1ba1e72fd9b | License | 1,068 | 21 | MIT License
Copyright (c) 2024 Arthur Fyon
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0aa247578fc36c759ed4dca094e0b41fe8084b34b58502816c9f290b168b5695 | License | 1,069 | 21 | MIT License
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1000cc3f67ec457dc0972a6d9e4167da47157e3bf739999046745964c64b5621 | License | 1,069 | 21 | MIT License
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1b622dc6ac17d1ea847d2d774cf756c11bd38f2956cacf6ddd4967c584403437 | License | 1,069 | 21 | MIT License
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8699c934344c93736e7122139c5243e462c2977188a56b9a9fe1a8dd176392f4 | License | 1,069 | 21 | MIT License
Copyright (c) 2026 RaresCandrea
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8818973fc1112278ae91d31c61e2102e5b2f292ade4bd7731b24729018206a12 | License | 1,069 | 21 | MIT License
Copyright (c) 2021 Roger Sodré
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8f31e5296d9bb19b62e7eacb0403f2156f54e6b39f43ffdaed5d058a4bed8ae1 | License | 1,069 | 21 | MIT License
Copyright (c) 2025 Omid Sharafi
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b2fe4dbf8ae7fc9df156b52eb9bc73f6e26bffef0b250723eb310df4372de8f1 | License | 1,069 | 21 | MIT License
Copyright (c) 2025 Yuanchen Sun
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c005c03f9eec4a765baaa8d5e6e5b1ca453d17d800ebdae32897265b1fc8c195 | License | 1,069 | 21 | MIT License
Copyright (c) 2025 caseypaquola
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e12927467f3e724b8dd0417c54a95c6703e987baa32dd108c16a4ba567b02228 | License | 1,069 | 21 | MIT License
Copyright (c) 2019 Eli Cornblath
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07c2c2e2eeb0ca55f7c944b0457f32313b212d5a3ce27c21140e4c1eccc3c66d | License | 1,070 | 21 | MIT License
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512df160c3b4a7725b407e1814be5eb12ae1afb0078fbf822f28efb03751fa8f | License | 1,070 | 21 | MIT License
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897a84e6c3f7e591bef370d76d2fc6aedcb369ad183a72f4dc80ffad2e3c41a8 | License | 1,070 | 21 | MIT License
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9c090f295f2fa60f8a0f797d2781693b965bf7e43460e550fc264cb1397d463b | License | 1,070 | 21 | MIT License
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a4fdece1f2fe1cd302867c4e3b964dac00179c39a93d7a85601d9e585eb6aba5 | License | 1,070 | 21 | MIT License
Copyright (c) 2025 ebrahimfeghhi
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abe3901fa092b2d99a46ba56b3125ab6b57e23e52a44fd28d78f0a8cf1a2c97b | License | 1,070 | 21 | MIT License
Copyright (c) 2023 Space Physics
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dc522488ce31166e700df0c9afe2f5a1f07149e51813bdbe92ad193a990a4cf0 | License | 1,070 | 21 | MIT License
Copyright (c) 2025 Jianlong Zhao
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0c7613a6278638e56e7c935d13e8912f29087d9a80c02b3f4cd4a2150038867f | License | 1,071 | 21 | MIT License
Copyright (c) 2025 Hou Lab @ CSHL
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3dc59d360e4781b593cc6abfdb893ecfca0931d670776dc430f5a8340b850916 | License | 1,071 | 21 | MIT License
Copyright (c) 2026 Jason Semprini
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663ee3982388616f463b4e5b3202ccd28cedb994ff34a1f8338410bcda7eae0a | License | 1,071 | 21 | MIT License
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9e6291c1c4cb6d4c2a332aa9f4bd9d7617dce4bd7a9d8a9473fdb50c841b70a3 | License | 1,071 | 21 | MIT License
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afc79ab0a169bf7bea2f31ef9e1f2f12211a4a57e0d8126853b23766ceab1b6e | License | 1,071 | 21 | MIT License
Copyright (c) 2021 Raj Lab @ UCSF
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d5714e107e8352712ecd843fcb2edb44cc63c7ae6c880184bd8fe203717ca800 | License | 1,071 | 21 | MIT License
Copyright (c) 2026 mitja nikolaus
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f7be16f2e93af361e160a662846dd25a56ade07bbf11c1cd448d3b7a81c87768 | License | 1,071 | 21 | MIT License
Copyright (c) 2020 Raj Lab @ UCSF
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0ae221db835d1aadff0c30c53493803b1a5a6cb260dc723fb6c73153ae356367 | License | 1,072 | 21 | MIT License
Copyright (c) 2025 Esau De la Vega
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2007d43a9ee2091bbf79ac4f356cea359bfca19aaa2e2ef400b17ecc7198674b | License | 1,072 | 21 | MIT License
Copyright (c) 2018 Patrick Walters
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8f9f6208950895057a8a8d4611a4b1cb89f5de1e9629e312c20bc22ce3e2e112 | License | 1,072 | 21 | MIT License
Copyright (c) 2023 Diedrichsen Lab
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983ebd5ec811a5e0b3633f753e0f0f847c4dbf52db98ac933c7a8794f9106252 | License | 1,072 | 22 | MIT License
Copyright (c) 2017 Renan Valieris
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a25a5a2e897c8de2ff359704dc37617059c2d33c6aa0f5e97d8e14f4214a65e0 | License | 1,072 | 21 | MIT License
Copyright (c) 2026 Geodaisics-code
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262f33c6a7c6d50536d583c1521bb229994e75b44749f45165a6de2c0da7558a | License | 1,073 | 21 | MIT License
Copyright (c) 2024 Musaddiq K. Lodi
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477395e98c386a79de3c68407576e8c0e6140fe7ae1dade751c73d9333f71f79 | License | 1,073 | 21 | MIT License
Copyright (c) 2024 Open Free Energy
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580963b4d05914efd1f69b997b62882c7e558b2c8fc3b8711fc8e0744c2cd1fa | License | 1,073 | 21 | MIT License
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