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|---|---|---|---|---|
b1e7a51097b6baef54f8f0bc4990362788174205d95bbb889817f1711a34a08b | Jupyter | 74,277 | 1,644 | # %% [markdown]
# # Welcome to the AIMS Jupyter Notebook!
# # As a refresher, hit ctrl + enter to run each cell
# I tried to add comments and other markdown cells like this one where appropriate to help with interpretations
# %% [markdown]
# # Section 0: Loading in Modules and Defining Figure Formatting
# This first c... |
6570f0699136d8915fde17e7cfc00e3a1ccae9b6304733d89a9f35da5611e4ad | Jupyter | 74,755 | 2,091 | # %% [markdown]
# # Training from CV folds
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVI... |
c39eb8111aad18fb31a736526645b3aa67b473809c8458378828fdabd1dcbab5 | Jupyter | 76,504 | 2,054 | # %%
import pickle
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import tqdm
import scipy
from scipy import signal
from tqdm import tnrange
import seaborn as sns
from scipy.stats import norm,entropy,linregress
from scipy.optimize import minimize, curve_fit
from scipy.io import sa... |
12ca02ad6f4590cd2cd1db87cf2eb44e488f7b52258509aea2e4eb90053b6fdc | Jupyter | 76,849 | 2,167 | # %% [markdown]
# # ONESHOT test
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="1"
... |
895e6c9f7e759687ffa3aaa9ae40741a22f9fd251bd6cde53171dd9a7be9a227 | Jupyter | 76,880 | 2,158 | # %% [markdown]
# # Training from CV folds
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVI... |
3059385ce57ab8cc87e1b259fa86bf3f27156374ce5c4980daebdfebfacc5491 | Jupyter | 76,963 | 2,766 | # %%
# Import library
import glob, json, os, pickle
import numpy as np
import mne
import pandas as pd
from mne.viz import plot_topomap
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import mat73
import fnmatch
import seaborn as sns
from collections import Counter
from scipy.stats import t... |
c5159ff312e3bcdf8b9609b63a0124a990645f1ae78b62403eda0c8a0972ca99 | Jupyter | 77,013 | 2,151 | # %% [markdown]
# # Training from CV folds
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVI... |
8f9fed0edd234658cc12fe0ed50d04de1c6b9ac81fde397cd574d980466aa4e6 | Jupyter | 80,636 | 2,311 | # %% [markdown]
# # Training from CV folds
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVI... |
79e5ea1aed5216bfe70156b04b4e5acf118543cafefab439f2feee891edbc9b0 | Jupyter | 84,836 | 3,203 | # %%
import pandas as pd
import numpy as np
import os
# %%
Semi_processed = '/storage/Arushi/090526_EvoAge/kg_formation/processed_data/Monarch/'
Processed = '/storage/Arushi/090526_EvoAge/kg_formation/processed_data/Monarch/Monarch_final/'
# %%
# %%
# %%
import os
import re
import numpy as np
import pandas as pd
... |
88180e477822774ba9f9868a873edf834d855a964ac75c57820f27d11af0c72c | Jupyter | 85,458 | 1,395 | # %% [markdown]
# # Monarch KG → Knowledge Graph (KG) Builder
#
# **Source:** [Monarch Initiative KG](https://monarchinitiative.org/) —
#
# ## What this notebook does
#
# 1. Loads all reference dictionaries (NCBI, PubChem, ChEBI, UniProt, DO, MONDO).
# 2. Loads the full Monarch KG edge pickle, strips NCBITaxon prefi... |
32698085fb45767c9d752f832aa16a13c92f3c598bc9aaed76c41f5bd68761ec | Jupyter | 90,838 | 2,598 | # %% [markdown]
# # imports
# %%
def wrap_labels(label, max_length=20, ignore=[]):
if len(label) > max_length:
for i in ignore:
if i in label:
return label
last_space = label.rfind(' ', 0, max_length)
if last_space > 0:
if len(label[last_space+1:]) > ... |
6bafefb98a4facccc94cfb3456c8424a36ce417fbd0ffca09be3251fee12bdef | Jupyter | 95,770 | 2,650 | # %%
import sys
import warnings
warnings.filterwarnings('ignore')
import os
import pickle
import pandas as pd
import numpy as np
import h5py
import os
import tqdm
from tqdm import tnrange as trange
import seaborn as sns
import matplotlib as mpl
from matplotlib import rcParams
import matplotlib.pyplot as plt
mpl.rcPar... |
061ec492c1660e2625a94c8866900f7c802720fd819e0891af8911a4753648b4 | Jupyter | 100,409 | 2,804 | # %% [markdown]
# # Training from CV folds
#
# Loads folds saved either as `cv_folds.pkl` or as per-fold pickles in a directory named `folds_interaction-type`. Trains an MLP per fold and reports fold-wise metrics and mean ± std.
# %%
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVI... |
b002f1b1c3f02da7142cb9777762020653448bd3966fd6ec53f7b2ad319af6d1 | Jupyter | 103,439 | 2,782 | # %%
import os
import sys
# Get the absolute path of the current notebook's directory
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
sys.path.append(parent_dir) # Add parent directory to sys.path
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
impo... |
56d9c0afac6422e86c7c5786f6b17025e81ee0fe5c6d616213074e6569e50600 | Jupyter | 104,211 | 2,824 | # %%
import os
import sys
# Get the absolute path of the current notebook's directory
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
sys.path.append(parent_dir) # Add parent directory to sys.path
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
impo... |
b262106fd6a0b86836b82267f4037e4796f3cc3becf394584f45b123702503aa | Jupyter | 106,064 | 2,816 | # %% [markdown]
# # [Amygdala GABA Neurons: Gatekeepers of Stress and Reproduction in Female Mice ](https://www.biorxiv.org/content/10.1101/2025.01.06.631361v4). Analysis pipeline + Figures
# %% [markdown]
# # 🧰 Preparation Block for Analysis
# %%
# Check the current Python version for a stable connection
!python --... |
99b1897647b97cda3f32b2ad438be09942bc37868264d92fd0bca8ea34ac4c3e | Jupyter | 106,096 | 2,848 | # %%
import os
import sys
# Get the absolute path of the current notebook's directory
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
sys.path.append(parent_dir) # Add parent directory to sys.path
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
impo... |
1db5c84e1c41447752037c3b9b82bbe057c997b1fa182ae328fe1e24b8636e5f | Jupyter | 122,423 | 3,581 | # %%
import os
import pydicom
import numpy as np
import matplotlib.pyplot as plt
# %%
import nibabel as nib
import numpy as np
import os
# Parameters
case_name = "L067"
nifti_path = f"/mnt/nas7/data/maria/final_features/nifti_new_dataset/{case_name}_volume_hu2.nii"
center = np.array([505, 247, 125])
radius_mm = 15.... |
e1657509e520406128f0bd2d98b47c198a8affb6a451af65441c12a7ba78a48a | Jupyter | 124,933 | 3,996 | # %%
import os
from datetime import datetime
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.formula.api as smf
# Local module imports
from helper_analysis import (
load_experiment_data,
summarize_data,
load_probe_data,
map_cluster_depths_to_real_dimensions,
no... |
a1891835b1b2e3681a8dc2a229a2d67179b13a24599459b2a9470caae00c6066 | Jupyter | 129,355 | 4,088 | # %%
import os
from datetime import datetime
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.formula.api as smf
# Local module imports
from helper_analysis import (
load_experiment_data,
summarize_data,
load_probe_data,
map_cluster_depths_to_real_dimensions,
no... |
fe2b7a98965b49baa1eed8eb555e0b4563aa7d5b3a118c8a2da022bd08150649 | Jupyter | 129,862 | 4,107 | # %%
import os
from datetime import datetime
import numpy as np
import pandas as pd
import scipy.stats as stats
import statsmodels.formula.api as smf
# Local module imports
from helper_analysis import (
load_experiment_data,
summarize_data,
load_probe_data,
map_cluster_depths_to_real_dimensions,
no... |
8937c2fed43c9c0f6ef695c01d6e9c262aa06915fb616b0491f6260d5f4d5947 | Jupyter | 143,961 | 3,484 | # %% [markdown]
# # This notebook will analyze raw spike data from recorded sessions in the experiments outlined in Rolon-Martinez, et. al (2024).
# %% [markdown]
# ## Functions and Plotting Parameters
# %% [markdown]
# ### Import packages you will need for running this script ##
# %%
import os
import numpy as np
im... |
0535efcf89c155c18ea3efc9ede0837d1d37c180f366f2dc57f7059fcd8091b4 | Jupyter | 144,080 | 4,067 | # %% [markdown]
# # Cytocraft's performance on Platforms
# %% [markdown]
# ## imports
# %%
import pickle, copy, importlib, math, os
import seaborn as sns
import numpy as np
import pandas as pd
from tqdm import tqdm
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from cytocraft.craft imp... |
2a55d00b18bf4f8895dadc607364be9dfd33dfbbdee5300a1d64cc49e5888f71 | Jupyter | 149,720 | 3,623 | # %% [markdown]
# # This notebook will analyze raw spike data from recorded sessions in the experiments outlined in Rolon-Martinez, et. al (2024).
# %% [markdown]
# ## Functions and Plotting Parameters
# %% [markdown]
# ### Import packages you will need for running this script ##
# %%
import os
import numpy as np
im... |
d61006175c9784135e3a5f6f29ab870bc91b41288c5cdcb6da0dec9540008c97 | Jupyter | 159,581 | 3,906 | # %% [markdown]
# # This notebook will analyze raw spike data from recorded sessions in the experiments outlined in Rolon-Martinez, et. al (2024).
# %% [markdown]
# ## Functions and Plotting Parameters
# %% [markdown]
# ### Import packages you will need for running this script ##
# %%
import os
import numpy as np
im... |
188c129441173413645f615fce0b090dd7277a18bad69e4b799639fd0f362018 | Jupyter | 200,022 | 6,335 | # %% [markdown]
# # Cytocraft Analysis Notebook - Reproducibility
#
# This notebook contains the downstream analysis code for the cytocraft project.
#
# ## Data Requirements
# Before running this notebook, please ensure you have downloaded the required datasets and placed them in the appropriate directories:
#
# - `... |
297154e895a2fd51a185955d80704cf097bf8b036b1d571e233f7856f39659ac | Jupyter | 204,154 | 5,312 | # %%
import pandas as pd
# %% [markdown]
# ## Main Figure 1
# %% [markdown]
# ## 1b
# %%
"""
EvoAge KG — Figure 1b: single-ring sunburst/donut of data sources,
wedge size = log10(Total edges), colored by KG_Type category.
Reads figure1b.csv (columns: Source, KG_Type, Total, log) and reproduces
evoage_sunburst_1b.sv... |
64100ca628de40fd3f23aa7a2872eb93d31922cef8687cdfae9ab9de23716a59 | License | 379 | 5 | ###NODDI-GBSS License
NODDI-GBSS (c) by Arash Nazeri, Jon Pipitone, and Tina Roostaei Kimel Family Translational Imaging-Genetics Research Lab. NODDI-GBSS is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
You should have received a copy of the license along with this work. If ... |
c36a8a35ac4a7b6fc1a13c5538aa89dc57a362ca27c2e7db247623818eb5cf4c | License | 380 | 6 | Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Copyright (c) 2026 Laboratory of Cheminformatics (LCi), Faculty of pharmacy, Federal University of Goiás, Brazil.
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
To view a copy of this lic... |
52f72e12efaaf6b94d7c444a21d0f3010dce52aff0f465a461ec752a742a474e | License | 381 | 6 | Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Copyright (c) 2025 Laboratory of Cheminformatics (LCi), Faculty of pharmacy, Federal University of Goiás, Brazil.
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.
To view a copy of this lic... |
f3aaa8534a6338b24e00dc412a08368bae7e24c8c83543cfbea82198b862bfdf | License | 451 | 7 | Copyright (c) 2005-2016 Galaxy Contributors (see https://github.com/galaxyproject/galaxy/blob/dev/CONTRIBUTORS.md)
Du Novo is licensed under GPLv2 (see the file "COPYING").
The files "swalign.c" and "swalign.h" contain software copyrighted by Nicolaus Lance Hepler and released under the MIT license, which can be foun... |
0b02cb02193fcd2ce0fc75cf02c10f0ae205b88732323690b93bc39f369f59ce | License | 531 | 12 | MIT License
Copyright (c) 2026
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, subli... |
9298b85328926e57829a3a1968450316850a8e8a8f21f930787b0670dd7e8748 | License | 566 | 13 | Copyright 2020-2021 Tao Cui, Tingting Wang
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, ... |
bddcde75e8e5519d04ce426d411ddafd020e931e441cfae7d380ff4f993eb839 | License | 566 | 13 | Copyright 2020-2023 Tao Cui, Tingting Wang
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, ... |
cb36bee1bb7f363a1f2947b81002e21fa4e666771b2e3537948ee18b35b4af55 | License | 566 | 13 | Copyright 2020-2024 Tao Cui, Tingting Wang
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, ... |
0dd79a9aaa4bc5698cc600e45c69fe0525efcf30ed525eb412dab38b5cc18b7b | License | 630 | 3 | Creative Commons Attribution-ShareAlike 4.0 International
Creative Commons Corporation ("Creative Commons") is not a law firm and does not provide legal services or legal advice. Distribution of Creative Commons public licenses does not create a lawyer-client or other relationship. Creative Commons makes its licenses ... |
8990c551671a51765c4dfe9428f3e1d58dc327f49aca346a32d28cc0fcc7aa88 | License | 675 | 16 | Copyright 2022 Robyn Speer
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, software
distri... |
a51efeafe3469cfccba3401e028c9ad69ab444960a402c0a7723879dc17b2e92 | License | 1,050 | 7 | Copyright 2020 Scott Prahl
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, sublicense... |
ad861db67c318f9636d546c27341f226671d7a9f568af476dcddc5ff514f47ab | License | 1,051 | 7 | MIT License
Copyright (c) 2025 [Liping Fu]
Permission is hereby granted, free of charge, to any person obtaining a copy of this Brain Atlas - FDG and associated documentation files (the "Atlas"), to deal in the Atlas without restriction, including without limitation the rights to use, copy, modify, merge, publish, di... |
49f7fa084c507162838387d73ec691995f13112b6e37452079d43381fb9cfc0c | License | 1,061 | 21 | MIT License
Copyright (c) 2021 Adam
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, ... |
cdf40857d75c89355be94808bf02faecdec29c57871f4c9cca36ad03945be76c | License | 1,061 | 21 | MIT License
Copyright (c) 2024 wobc
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, ... |
04873764307db9c5498467d95d7d1fb7cbec0a8ad974bc6a1e5ed7cad840ad4a | License | 1,062 | 21 | MIT License
Copyright (c) 2024 PyFox
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
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462fdfac6e04748dde5e4551e51f0eb43ecb863ff1dbeef17e29518214250f9e | License | 1,062 | 21 | MIT License
Copyright (c) 2024 Binxu
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
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707610726892f56822784922287f21c6c1810d58518795d1fc09c8a77bbb681f | License | 1,062 | 21 | MIT License
Copyright (c) 2024 paran
Permission is hereby granted, free of charge, to any person obtaining a copy
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a8500f2eb53ecc916746f9caeee740e2161886a74ca8d04d615336af7236f794 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 Ellen
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30911edb110025f7b393c40dba54798baa160e5dc06b34d56e037e2a1d3e2640 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 Eun517
Permission is hereby granted, free of charge, to any person obtaining a copy
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901d147cf9eebbd366dbf2246ed408f156ad4e7349d22992d65b6cc7bf4ee8c8 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 OpenMM
Permission is hereby granted, free of charge, to any person obtaining a copy
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a8f014feab7dc0beed87c90837693acbe03c7ae05eea3cf36cdb29bb4eb75c41 | License | 1,063 | 21 | MIT License
Copyright (c) 2024 JBauer
Permission is hereby granted, free of charge, to any person obtaining a copy
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ac44116e8f05d98bd88d1f1431c61d414850138bbeb66c329c002265d2a2ca26 | License | 1,063 | 21 | MIT License
Copyright (c) 2022 pressm
Permission is hereby granted, free of charge, to any person obtaining a copy
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e933b49510a28c381dce354488f81210b38e74bd64b017aa65c33b3c0acff269 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 QA4IQI
Permission is hereby granted, free of charge, to any person obtaining a copy
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42ebbdf86c57fa5dcc7cbb05eb43def03dac25e66d97a1a2991340ae6ebe3df3 | License | 1,064 | 21 | MIT License
Copyright (c) 2025 mirivar
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4ed3e393db0c27b912910fa724ce7258ca50654ce8af8b2e6e56487ba0c9bd7e | License | 1,064 | 21 | MIT License
Copyright (c) 2025 WeiYang
Permission is hereby granted, free of charge, to any person obtaining a copy
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7878b27f9d3d83be5d85c2918b71b87952566a5b4203061d4374f6c8c9fb7752 | License | 1,064 | 21 | MIT License
Copyright (c) 2025 Zeyu Fu
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920bb293ef4a8be2b5bb430c2682f30d3d38e56801c92c0325b9061cc4b2764e | License | 1,064 | 21 | MIT License
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952f064d3087a6dc165710dc59b93552f42087533bb006c99196d4240657db21 | License | 1,064 | 21 | MIT License
Copyright (c) 2025 Lin Cai
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a2cdacf5ad1e41a332575716b5f1f59b18c2f4bb2764287fd82d843ab58254e3 | License | 1,064 | 21 | MIT License
Copyright (c) 2026 Jie Lin
Permission is hereby granted, free of charge, to any person obtaining a copy
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db1c1a4f35cfcee2238ab974cb0ec91f28c29fe6329b5157445b567dfabd3a8d | License | 1,064 | 21 | MIT License
Copyright (c) 2018 Tian Ge
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230d3277517ddc2987f0c2f7cb7bd60deddf0c9a2834ae5f7719f0415279f3ed | License | 1,065 | 21 | MIT License
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2a118c39d0a998b187e29980104da859b2c1ae5fff08c522d089a7831d357fc4 | License | 1,065 | 21 | MIT License
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35d81d7e469bc16065e2b1cd37b47c35f94b9ceab12735bd48abcdaa497ecb43 | License | 1,065 | 21 | MIT License
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580e35a37ce36eb1f7cf850902d300f5c1084cbe16f490fb40d534af19a3a98f | License | 1,065 | 21 | MIT License
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744c453cfd4013941aca6a8ebf76041c31ddb6bae4fccf00abd5afa375595592 | License | 1,065 | 21 | MIT License
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81df8084a7929e91ad37dac77f30fa66c6a296de6d641056cebd41d03d57e9ac | License | 1,065 | 21 | MIT License
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ae2e0a9591833d3b5b554cc62a8bd4ee6921b996d320c6b125eeea8e0a25dd54 | License | 1,065 | 21 | MIT License
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0531977e63ecb97b1460d21e4bdd217075ee945f001f5e73a81442ff7e8c8d61 | License | 1,066 | 21 | MIT License
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09416e64042fb5e0e84bb3a1cc12cd9fd0950e13ee0c31f063ae8be8d6477992 | License | 1,066 | 21 | MIT License
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0f29368de9327866cd96e57849eb7e11af65564651c09839bca053f5aaed58f3 | License | 1,066 | 21 | MIT License
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21184479a584e11fabc858ec2b0dd05382fcaed628e72b1d34aa25a45fc81c8e | License | 1,066 | 21 | MIT License
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67dbdba71ecd8c41fa95d87f532c20fb281ae1e81de890dc874efb1c555d3114 | License | 1,066 | 21 | MIT License
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a84a9b71ad77d73a6e469cfd56797957f52d9d6085336db16157c70e78614873 | License | 1,066 | 21 | MIT License
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b66ecd10e05bcb9b79792277b978016a51d41825edfc1d3db2a42ec3a706c63c | License | 1,066 | 21 | MIT License
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c71e6447ebb71f81fb1cec5bd18bb633330452117d468807ffdc82bd3abaa6fa | License | 1,066 | 21 | MIT License
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ca50d26b9487cd839659e2a080ce69037edf6edd0cd5d48f95c71cb04c488646 | License | 1,066 | 21 | MIT License
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caf51c90d7a0db2d590656e77875bd9d9eb45c1df1d1e880aae29aa211c0d96e | License | 1,066 | 21 | MIT License
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d2eb65a23c30730e530697e58d27283a86d621ffdeff530d7c4e2c24f95f6dc6 | License | 1,066 | 21 | MIT License
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dd742186bbaeae32ad863751b5e1308a5edc41c49fa473ba0367ac66b0eff502 | License | 1,066 | 21 | MIT License
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fa95444ad902855b2823bcb87fb4ae8ad54ad13100f2cf48a6d13c172aa739d9 | License | 1,066 | 21 | MIT License
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0e8c2e6279f662f3250b6d75cca5961c6f13f2a4d705f0634345d1df0ed64a6a | License | 1,067 | 17 | MIT License
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447db15915acab930ad88ec969a62d81f996b9876b3f0dc5639b8f7cd7553490 | License | 1,067 | 21 | MIT License
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4b5efed41fa6bbde7a9fb8bf82dd6d663d6fe339705869f826307237600021f0 | License | 1,067 | 19 | Copyright (c) 2026 Christopher T. Boughter
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521292d1066595b393898b72ec4db5378ffd376af918ff6a6a5d84a2b4997172 | License | 1,067 | 21 | MIT License
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545df4733c6cd12723315caad5fffe976a35cc2a44afff412e50b9e952b3b51a | License | 1,067 | 9 | MIT License
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80d54b8b14e75f6b964b241c3ffa604745048ba5f1627efac5143e9c38430955 | License | 1,067 | 21 | MIT License
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9453acfda937533e7d6f0386e1e68e2c3e29c24f4d2a773a6013e93df4bcf7ee | License | 1,067 | 21 | MIT License
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997635de03eb6675e7001dfd242579090c78c430e1f710334eccd873e75bdccc | License | 1,067 | 21 | MIT License
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c045be646b42fb7fde7fe03b17655a90722337dc48597f6736b8ea4c83c61b66 | License | 1,067 | 21 | MIT License
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c30d452775f05e9ac00929d78761797e05c1fea013d9d6c9d04fa35741d4417f | License | 1,067 | 21 | MIT License
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e3314494081832d2f874ab2ed00d69ef4704a3649746c1845a62457456f684d8 | License | 1,067 | 21 | MIT License
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f490b360b5969efff6a9c1f04e1a5e9e239e6b45f17c7e4ecddf53c3185f11e9 | License | 1,067 | 21 | MIT License
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197f689932b8e1b5a5b4973ddb90f58a6f14e0e4aced514ffbd02196d2fc3b09 | License | 1,068 | 21 | MIT License
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2268d5a9efb4ec837a49799055c0fb521d4e0047a349f45ff90743e5e7562b81 | License | 1,068 | 21 | MIT License
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43b207bc8d32b8495ccc412af58915391adc8e172174ddac331190bc6e8a75ab | License | 1,068 | 21 | MIT License
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