sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ff73c43ac044bba2c8c2ad1985a9486f809d09a805179569bb20f8e3c53ccee3 | Jupyter | 54,527 | 1,103 | # %% [markdown]
# # Introduction to Medical Image Registration with DeepReg, between Old and New
#
# ### Authors:
# - Nina Montana Brown <sup>1, 2</sup>
# - Yunguan Fu <sup>1, 2, 3</sup>
# - Shaheer Saeed <sup>1, 2</sup>
# - Adrià Casamitjana <sup>2</sup>
# - Zachary Baum <sup>1, 2</sup>
# - Rémi Delaunay... |
26e9edeaeafeb9dde701b4db151671dc14cdd0fdbf2bf6168d4b1f75e65f45be | Jupyter | 54,634 | 1,137 | # %% [markdown]
# # Evaluate and plot the results
# ---
# This notebook loads all the datasets and plots the results. Data is located in `data/` and must be first be downloaded from huggingface: see the `README.md` for instructions. The data is sorted by commodity; each commodity folder in `data/` contains the transpor... |
46dce9f2d47137ec2c68a797390b017c9ceed89a47aaf60ed24d3d4dcff8096f | Jupyter | 55,311 | 1,927 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
815f9fced7df93ff72d592d569d5207ca458edc9c9dbb40115218525415d6bd9 | Jupyter | 55,620 | 1,962 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
c37b48ea95222263bd7951c16a386f43a59a9c9fed63968e5ce95eedc69f0803 | Jupyter | 55,676 | 1,986 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
d1655c5c4072a9ceb1042d974f9783daf86b134a599646713e28ff9d05f18ac1 | Jupyter | 57,626 | 2,081 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
1c39d9457514d20e61352133ae7d409b815245fb48ee00b8a4c144c18128c1d0 | Jupyter | 58,848 | 1,233 | # %%
from staticnet_invariance.segmentation import *
from staticnet_invariance import toy_deis,toy
import matplotlib.pyplot as plt
from scipy.stats import wilcoxon
from numpy.fft import fft2, fftshift, fftfreq
import scipy.stats as stats
import seaborn as sns
# %%
import pandas as pd
from staticnet_pilots import anal... |
07e3ba9df66ba53375ec4216b2018b6410ee2bd7b282217d9ccfa1e539a4d214 | Jupyter | 59,132 | 1,396 | # %%
import os
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib_scalebar.scalebar import ScaleBar
import seaborn as sns
from neuron_morphology.swc_io import morphology_from_swc
from neuron_morphology.constants impo... |
60f45cf5533f68594d88a6edaa031f8c3b12e27b3d22f3a57607c871f1705f0d | Jupyter | 59,291 | 2,243 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
f1ce36a38148ea25c5ebafee450fe6bbfee6f7d531815487aad1c1fd490c0d34 | Jupyter | 61,688 | 1,784 | # %%
%load_ext autoreload
%autoreload 2
%config IPCompleter.use_jedi = False
# %%
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
from matplotlib.lines import Line2D
from mpl_toolkits.axes_grid1 import make_axes_loca... |
48afc7590f7c2d4c33a43e8dd3f7b0fd5f979f69a5cc7782b47cee13fdfc24ed | Jupyter | 61,717 | 2,296 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
b3723cfe0708a59d4d2f5b009bb4db532909c4497ceeabb3fc99c99135339324 | Jupyter | 61,868 | 1,634 | # %%
import os
import numpy as np
import pandas as pd
import math
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt... |
10bc9127b997e15ac71d9c79c5a7b1c6fc1bd889dc9028806838cbe3a5d5daca | Jupyter | 62,127 | 1,634 | # %%
import pandas as pd
import numpy as np
import torch
from torch_geometric.data import Data
import pickle
import seaborn as sns
from torch_geometric.utils import to_networkx
#install required packages
import os
import torch
os.environ['TORCH'] = torch.__version__
print(torch.__version__)
# Helper function for visua... |
af2f691644569eabb11140513626cff5c4975c8ca8a514ab74556b4904e3d2da | Jupyter | 62,747 | 1,774 | # %%
import scanpy as sc
import numpy as np
import pandas as pd
import glob
from tqdm import tqdm
# %%
"""
Utility functions and classes for cross species
analysis
@yhr91
"""
from sklearn.metrics import euclidean_distances
import matplotlib.pyplot as plt
import matplotlib
from collections import defaultdict
from skl... |
865924481014f05fdfaca52a29e7ca17d51bac984f3e78fc163230953d3d2717 | Jupyter | 65,309 | 1,735 | # %% [markdown]
# ## Seizure detection using Hybrid SNN ####
#
# ### Importing Dependencies and Defining Patient Metadata
#
# This initial notebook cell sets up the environment, loads required libraries, and defines the metadata for the subset of CHB-MIT patients used in the pipeline.
#
# #### 1. Inspecting Kaggle I... |
811f14812d4868e9b36081af93d3c94cdbf98b9821ae461d369902bfbe21d1e0 | Jupyter | 65,931 | 1,634 | # %%
import os
import numpy as np
import math
from sklearn.metrics import roc_curve, auc
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as patche... |
f609a844c678dedaa03d915fe26d878115c3876d563c9685cc23332a69a5d17d | Jupyter | 66,099 | 2,134 | # %%
# Standard dependencies
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import cm
import imageio
from scipy import stats
from scipy.stats import spearmanr
from scipy.stats import sem
import pingouin as pg
from time import perf_counter
import altair as al... |
d78f7a1bd9a1fc37f44feb16d6e5c35f51945670ccc5cdde9ccd7878f17dc890 | Jupyter | 67,127 | 1,741 | # %% [markdown]
# # 04-comparing-FS-versions-on-same-dataset.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Last updated: 2024-12-09; 2025-02-10; 2025-02-12 (Fig. 7 revised)
#
#
# Comparing FreeSurfer v 6.0.1 (cross-sectional) and version 7.4.1 (cross-sectional) on the Bergen cohort with 78... |
7fb12a004f9c01b4cc30ab151a55802da452ecdd83e17713c6ec0a10a24975d4 | Jupyter | 67,173 | 1,806 | # %%
import pandas as pd
import numpy as np
import torch
import pickle
import seaborn as sns
import esm
import os
import random
import matplotlib.pyplot as plt
import torch.nn.functional as F
from torch.nn import Linear
import torch.nn as nn
import math
from scipy.stats import pearsonr, spearmanr
import random
from skl... |
05adde0bc9e09a9285f27a6d635d591a5d24ee3791a188f0379ea9910ecb1497 | Jupyter | 69,290 | 1,350 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os
import pickle
from matplotlib.ticker import PercentFormatter
import json
# %%
colors = plt.cm.viridis(np.linspace(0, 1, 20))
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['font.size'] = 8
plt.rcParams['svg.fonttype'] = 'none'
colors_0=['#0B559F','#... |
2a07fc8414135a9b0780741f7251e69ab45e6bb62933a47c7c535952eabf1ec8 | Jupyter | 73,616 | 1,819 | # %% [markdown]
# # Hair-plate axon connectivity analysis and visualization
#
# ### Brandon Pratt
#
# Connectivity analysis for CxHP8 and other hair plates. User needs to request access to the FANC dataset.\
# Dryad repository: DOI: 10.5061/dryad.fxpnvx153
# %%
# Import libraries
import os
viz_method = 'vtk'
'''Thi... |
6dfb6b6729174bc8bd71509e1d4484a0453d8643917113e0db6f898c031f36a8 | Jupyter | 74,338 | 1,386 | # %%
#Copyright (c) 2019 Primoz Ravbar UCSB Licensed under BSD 2-Clause [see LICENSE for details] Written by Primoz Ravbar
##ABRS Label Maker GUI
# Originally written by Primoz Ravbar
# Edited by Venkata Manikanta Muriki
##################
### START CODE ###
##################
import warnings
warnings.filterwarnings... |
849b0abdb000292769cc9c008e6831ad93d1e6b7c4de2a50a7da1b40bc825069 | Jupyter | 74,342 | 1,410 | # %% [markdown]
# # Joint angles and leg movements during optogenetic stimulation
#
# ### Brandon Pratt
#
# Characterize the effects of optogenetics on leg movement and joint angles. Optogentic silencing code is the first-half of the notebook and optogenetic activation code is the last half.
#
# Datasets are found a... |
bb800a87fea78f08034eba36948cc93ebf67fd353592be293156bef5978a6706 | Jupyter | 77,179 | 1,914 | # %% [markdown]
# # Face Validity
#
# ## 1. Noisy Hopfiled Networks reproduce dynamics of th resting state
#
# ## 2. Task-activity maps differently to the Hopfield embeddings than rest
# - energy
# - attractor
# - location/trajectory
#
# ## 3. Hopfield-embeddings explain taks-activations better than... |
4f3011cf772809fd29b3d10b7e250946393cfb76646d5a317e5220b1524f22dd | Jupyter | 77,625 | 2,241 | # %%
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
import pickle
import pandas as pd
%matplotlib inline
import seaborn as sns
# %%
"""
Utility functions and classes for cross species
analysis
@yhr91
"""
from sklearn.metrics import euclidean_distances
import matplotlib.pyplot as plt
import ma... |
c7f043981465144d6ed88431e401edc23ad90a6894473996b215177aae8c433d | Jupyter | 78,980 | 1,983 | # %% [markdown]
# # 05-predicting-IBS-vs-HC-from-morphometric-measures.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Updated: 2024-12-18; 2025-02-01; 2025-02-04 (AUC < 0.5); 2025-02-06 (hyperparameter tuning); 2025-02-12 (Figs. 9 & 10)
#
# This notebook explores the use of morphometric meas... |
ddcd6601986e08defe9e8b3b669d76035748e5eec2b9216184bcccc2e126ceba | Jupyter | 81,568 | 1,639 | # %% [markdown]
# # DATA ANALYSIS NOTEBOOK
#
# This notebook was used to run the analyses and produce most of the images in the paper.
#
# > _Overview:_
# > - Get baseline predictions of the different models:
# > - Bin by bin (pointwise)
# > - Integrating the area of the central gene.
# > - Compare model perfor... |
92460f596e7259f612bce8648a41191a05ea206274c863a8101a3b19268a1ddc | Jupyter | 82,095 | 1,853 | # %%
import numpy as np
import pydicom
import nibabel as nib
import os
import cv2
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import sklearn
from sklearn.metrics import roc_curve, auc
from time import perf_counter
from scipy.stats import wilcoxon, pearsonr
fr... |
b18e5c4daacbb949ca5c6a486a4aadc2f287e69f01a48d79823d85643045d755 | Jupyter | 88,725 | 2,072 | # %% [markdown]
# # The attractor states of the functional brain connectome
# %% [markdown]
# ## Analysis Notebook
#
# Most of the analysis is based the module connattractor, which contains the core Hopfield network model as well as the method of generating the Hopfield embeddings. The Hopfield class provides the ba... |
32db213ea583d8f0cf7502552ace7fdd814c0385f4ff9a955763f6385389643d | Jupyter | 90,584 | 2,151 | # %% [markdown]
# [](https://colab.research.google.com/github/arvidl/psn-ibs/blob/main/notebooks/patient_similarity_networks_ibs_brain_morphometry_cognition.ipynb)
# %% [markdown]
# Version 2025-12-26, A. Lundervold
#
# # Patient Similarity Ne... |
1b8ef3722b172b953dcd09024a1864b66dc02a5627ae4cbcc9b4a4429f7c2fb1 | Jupyter | 91,434 | 2,329 | # %% [markdown]
# # Toy Model Implementation
# %%
import os
import numpy as np
import pandas as pd
import networkx as nx
from scipy.stats import pearsonr
from scipy.interpolate import UnivariateSpline
import torch
import torch.nn.functional as F
import plotnine as p9
import matplotlib.pyplot as plt
import seaborn as... |
c89cec38f2348fa5c1a84965ceaea13b80de535e069ce6c613964c89bc77f210 | Jupyter | 97,459 | 2,416 | # %%
import pandas as pd
import numpy as np
import torch
from torch_geometric.data import Data
import pickle
import seaborn as sns
from torch_geometric.utils import to_networkx
#install required packages
import os
import torch
os.environ['TORCH'] = torch.__version__
print(torch.__version__)
# Helper function for visua... |
55a33a901ced6a60d1123042e9ef0a9122d151dca486f84dc1172422c40079ec | Jupyter | 99,533 | 2,181 | # %%
import os
import numpy as np
import pandas as pd
import math
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt... |
178170b9830f603e59f004589a023053920e749257d63faff001c7f7d779940d | Jupyter | 102,932 | 2,656 | # %% [markdown]
# # 2b. BENCHMARKING CLASTER WITH HYENA-DNA AND ENFORMER
# %% [markdown]
# ## 1. Hyena-DNA:
#
# The following is an adaptation of the open source code provided by the authors of Hyena-DNA, who are to be credited for it.
#
# The paper can be found as:
# Eric Nguyen and Michael Poli and Marjan Faizi a... |
4f2f02e9625a015211b524051733f98979e6f2cd9a4e931721119efe72874c2f | Jupyter | 103,467 | 2,388 | # %% [markdown]
# # Transdiagnostics Figure
# Kai Sandbrink
#
# 2024-04-13
# %%
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import pickle
from scipy.stats import pearsonr
from utils import format_axis
from human_utils_transdiagnostics import get_clean_com... |
eb8aea235bbd1c1439e6049eeec34d2b69b7c1a3b2f3e9d168263a91b2ec7cad | Jupyter | 103,794 | 5,588 | # %% [markdown]
# # 2. CREATE & RUN CLASTER
# %% [markdown]
# CLASTER is, at its core, a deep convolutional neural network aimed to translate a given chromatin landscape and its matching 3D structure to the corresponding nascent RNA landscape.
#
#
# The network consists of:
#
# - **Feature extractors**: two separa... |
4169a0f0ec4f64a5af2415853743be7c98d64b8be24eb24bbbbfb15ff188a8a6 | Jupyter | 108,647 | 1,887 | # %%
import pandas as pd
import numpy as np
from statsmodels.formula.api import mixedlm
from scipy.stats import ttest_1samp, pearsonr
import statsmodels
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.lines import Line2D
import matplotlib.lines as mlines
import warnings
import os
import json
from ... |
ac6e1559390a0218fdbb58fca613e6a7249e4d2ad6168b8fb13151c5d1431272 | Jupyter | 110,550 | 2,516 | # %% [markdown]
# # Treadmill kinematic analysis of walking and posture
# ## Brandon Pratt
#
# Characterize body posture, step kinematics, and interlimb coordination of flies walking on the treadmill.
#
# The datasets analyzed by this analysis script can be access at this Dryad repository: DOI: 10.5061/dryad.fxpnvx15... |
6710e7bf5f23bdd37d95cc341ffd89b1964b349dd0265942ef9002bf821103b9 | Jupyter | 114,922 | 2,432 | # %% [markdown]
# # Individual Differences Figure
# Kai Sandbrink
#
# 2024-03-13
# %%
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import pickle
from utils import format_axis
import statsmodels.api as sm
from statsmodels.stats.multicomp import pairwise_tuk... |
caf313f2a9faaba78b89589922e6084d7d4fbe85d50ebe83151af188894bc1f8 | Jupyter | 125,183 | 3,948 | # %% [markdown]
# # Import pkgs
# %%
import torch
import os
import sys
from pathlib import Path
# --- Start of the robust path modification ---
try:
# This variable is defined in VS Code notebooks
current_file_path = Path(__vsc_ipynb_file__)
except NameError:
# Fallback for other environments or .py scrip... |
3fea9987de5be4314d01e01d115a356c6a5dc88e82e8f54d57b15d01b344b02e | Jupyter | 127,141 | 3,452 | # %%
#!pip install --upgrade pip
!pip install kaggle
#!kaggle datasets create -p /kaggle/working/processed -u alimehrabi -t "Processed CHB-MIT 19ch fixedlen"
!pip install --quiet torch torchvision snntorch mne
# %%
# ============================================================
# Core Libraries
# =====================... |
2bff1be36a06ef09ffaaee3d227593390bc7e957b1313848115a24642bfde130 | Jupyter | 131,508 | 3,004 | # %% [markdown]
# ### imports, directories, load data
# %%
# general imports
import pickle
import numpy as np
import pandas as pd
import sys
import os
import copy
import pdb
import csv
import cv2
sys.path.append('../')
# plotting imports
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors ... |
e2fed53267cc8f5489ce0b3dd2578391af3e684d1986f27aab74e50ba98da75a | Jupyter | 142,648 | 3,323 | # %% [markdown]
# <a href="https://colab.research.google.com/github/ElahehSadeghabadi/Deep-STMIA/blob/main/Baselines.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# This file includes **baselines** compared to the **Deep-STMIA** mo... |
c2faabac98a34b7d1042d54194b6a6639a245143e037114c4f1f6647bc7d4747 | Jupyter | 144,698 | 3,565 | # %% [markdown]
# ### imports, directories, load data
# %%
figDataSaveDir = r"C:\code\miniscope-analysis-mek\InterneuronInhibNAc\InterneuronInhibNAc\figs\paper_figs\fig_data"
dryadDir = r"C:\code\miniscope-analysis-mek\InterneuronInhibNAc\InterneuronInhibNAc\dryad\preprocessedData"
# %%
# general imports
import os
i... |
5ec1a51b1a7d5988a87d9a4e5223d6cf9f9d929848d4ecdb428a513b1f95aada | Jupyter | 146,063 | 2,832 | # %% [markdown]
# # Individual Differences Figure
# Kai Sandbrink
#
# 2024-03-13
# %%
import os
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import pickle
from utils import format_axis
import statsmodels.api as sm
from statsmodels.stats.multicomp import pairwise_tuk... |
c7f32f2acfe34d6ace22841b6573fe23ad19e6ff3a8b0831061cad177707b25a | Jupyter | 157,627 | 3,809 | # %%
import os
import pandas as pd
import numpy as np
import json
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib_scalebar.scalebar import ScaleBar
import seaborn as sns
from neuron_morphology.swc_io import morphology_from_swc
from neuron_morphology.constants impo... |
6bde5077694a3cf6bec85dfdb5aa70318717ab17bf958012ca0120ba575bd21d | Jupyter | 157,718 | 3,859 | # %% [markdown]
# # Initialization
# %%
import numpy as np
import pandas as pd
import seaborn as sns
import os
import ast
from matplotlib.lines import Line2D
import matplotlib.lines as mlines
from scipy import stats
from scipy.stats import mannwhitneyu
import matplotlib.pyplot as plt
# Set font sizes for all text elem... |
cf7213f48613d7ce1ca3e5fbdeea582f8dd0492517ea4214895b501ed4f4bf85 | Jupyter | 160,801 | 4,358 | # %%
#!pip install --upgrade pip
!pip install kaggle
#!kaggle datasets create -p /kaggle/working/processed -u alimehrabi -t "Processed CHB-MIT 19ch fixedlen"
!pip install --quiet torch torchvision snntorch mne
# %%
# ============================================================
# Core Libraries
# =====================... |
775f1e5f00ed084b29c0270378c25eb07e7fa7feff96d0074f09a7c642d9901b | Jupyter | 167,025 | 4,148 | # %%
save_path = '/Users/maori2/Documents/revision_stuff/All_dataset/sessions_files'
umap_path = '/Users/maori2/Documents/revision_stuff/All_dataset/umap_files'
fig_path = "/Users/maori2/Documents/revision_stuff/new_figures"
umap_path_2save ="/Users/maori2/Documents/revision_stuff/All_dataset/umap_files/new_run"
geom_p... |
873e2ba1954ae374b3592bf08715890ed4b666ef4251b26ec6cc969f5a531cdd | Jupyter | 169,794 | 3,393 | # %% [markdown]
# # Visualize treadmill walking kinematics and resting posture
#
# ### Brandon Pratt
#
# Visualize intraleg kinematics and interleg coordination, and resting posture in flies with CxHP8 neurons silenced.
#
# Dyrad Repository: DOI: 10.5061/dryad.fxpnvx153
# %%
# python libraries
import os
from os imp... |
bf6e671cb3c5e3ef811270447574f1a6fe4a7b7c8bc6aec17cca848303e3d5c6 | Jupyter | 195,976 | 4,974 | # %%
save_path = '/Users/maori2/Documents/revision_stuff/All_dataset/sessions_files'
fig_path='/Users/maori2/Documents/revision_stuff/new_figures'
import scipy.io as sio
import scipy.stats as sts
import numpy as np
import pandas as pd
import os
from matplotlib import pyplot as plt
import seaborn as sns
import pickle
f... |
595f47e75e103b428c61be095ecd924c8662b8eaa680e5abfa302a05d8059e00 | Jupyter | 200,000 | 66 | # %% [markdown]
# # Neural Architecture Search Lab
#
# ## Introduction
# In this lab, we will apply hardware-aware neural architecture search to search for an accurate, robust, and noise-resilient architecture for analog in-memory computing using analogai-nas.
#
# ### Analog In-memory Computing
#
# Analog in-memory ... |
a88dd4a55a34125e41b13bb57ef68e4d2289ac556ad3d395f3d14eb498487245 | Jupyter | 200,000 | 32 | # %% [markdown]
# <a href="https://colab.research.google.com/github/ElahehSadeghabadi/Deep-STMIA/blob/main/Deep_STMIA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# This file includes simulations of the **Deep-STMIA** model, an E2... |
0a57b5bc365b87b49192f49cd62ec9de72c514f21af6e17c9a0b1dc3beecd6d2 | Jupyter | 200,002 | 9,402 | # %% [markdown]
# # <center> REVISIONS: CODE ADDED <center>
#
# **Summary:**
#
# This notebook contains the analyses that were added during the reviewing process. It borrows elements from the rest of the notebooks, but it was made so that it can be run completely independently from the others. Some cells can be run l... |
107af80bb3695736c14e791d00209aa683a4406d003b9dd74cebb6b65b6a3f77 | Jupyter | 200,011 | 4,524 | # %% [markdown]
# ### imports, directories, load data
# %%
import sys
sys.path.append('../')
# %%
# custom imports
from general_utils import get_transient_timestamps_mod, calculate_auROC, significant_modulation_proportion, generate_labels_from_cv_filtered, calculate_auROC_with_permutation
import assemblies
from ass... |
d1973a77abe995ef6927353de754d3ec3518e1beff69c7c26294abf3a7d51ad1 | Jupyter | 202,244 | 5,014 | # %% [markdown]
# # load the stuck position
# %%
import sys
sys.path.insert(0, './..')
import os
import numpy as np
import matplotlib.pyplot as plt
from HopfiledNetwork import *
from utils import *
import json
# %%
matrix = np.load("data/stuck.npz")
# %%
stuck_0 = matrix['stuck0']
plt.imshow(stuck_0)
# plt.colorbar(... |
d8902a29b2ee2387c1d3ae16f6bcac9e0dcf06c8979864255612f9f1177275f0 | License | 251 | 1 | This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
|
eae9fab2fa7ecff3567781f5b9c0464ace52a94832ca25526db0dca59caae1bc | License | 331 | 8 | This dataset is released under the Creative Commons Zero (CC0) 1.0 Universal
Public Domain Dedication.
You may use, modify, and distribute the data without any restrictions.
To view the full legal text, visit:
https://creativecommons.org/publicdomain/zero/1.0/
THE DATA IS PROVIDED "AS IS" WITHOUT WARRANTY O... |
77cbc3d387d63dadfd861948cf018efcbd7f9bc4c4a675535cc2ac8f5c313c92 | License | 387 | 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 thi... |
8754206af6d2e4ccb4e7376f30734467eba838e4f88ae9fbe9bccbfa9e4d0da8 | License | 425 | 9 | NeuroFM - Foundation model for individualized brain health estimation
Copyright (c) 2026 Austin Dibble
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
You should have received a copy of the license along with this work.
If not, see https://creativecommo... |
85e3db4a482cf530c46a7eee14a094584f37aff2f6a2b11945b5fd6b51cb315d | License | 556 | 13 | Copyright 2019 Neurodata Group
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
di... |
4ec0ef60fef35c220b88ab2e77babf60314d2ce5d4db594638869eb73dde6eea | License | 577 | 7 | Copyright (c) 2026 Willem B. Bruin
Use of this text as a reference material or as an operational text for digital platforms is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).
You are free to use, copy, modify, and redistribute the contents of t... |
741ff060f1ab7a731edb6376fc43717139267e3841b82f4ab5ec402a6994b773 | License | 634 | 6 | Melbourne Subcortex Atlas License
Permission is hereby granted, free of charge, to any person obtaining a copy of Melbourne Subcortex Atlas and associated documentation files, to use the atlas without restriction, including without limitation the rights to use, copy, modify, merge, publish and distribute, subject to t... |
a26e6fea6c3c7ce2b7ae78e6d1d554dda053296975d801213ef389cedfae97a4 | License | 645 | 16 |
Copyright 2023 yatengLG
https://github.com/yatengLG/ISAT_with_segment_anything
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... |
94e06d764b6183d4a26f277564bb531d77cbff87725d48ef7550f3b4123d7e91 | License | 813 | 16 | Creative Commons Attribution 4.0 International (CC BY 4.0)
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
- Attribution — You must give appropriate credit, prov... |
426b42d60a9f5e1f98eb2c01a2173d1d5a42e69ae3a96b2df8a83da510f0007d | License | 845 | 15 | Creative Commons Attribution 4.0 International (CC BY 4.0)
This work is licensed under the Creative Commons Attribution 4.0 International License.
To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
You are free to:
- Share — copy and redistribute the material in any medium or format.
-... |
d6a4c0b61af4557dd0748b3e9d111844a509a2a7edc5fb31b5147b9e9a538776 | License | 877 | 22 | # THE DATASET LICENSE
## A. SAMPLE SUBSET (PUBLIC)
Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0). Full terms: https://creativecommons.org/licenses/by-nc-nd/4.0/
## B. FULL DATASET (UPON REQUEST)
Subject to additional restrictions:
1. **Research Use Only*... |
bc1b52095147eb9fe583652a39983490f582256e75edd2a2358b1a0f5f297393 | License | 934 | 20 | Authors : Larry Zweifel and lab members
(at the University of Washington, Seattle)
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any ... |
da0934a8f9c967aa6201b3eb267b8a39c503ffaceeaccb629820a4dd774d0c8a | License | 1,020 | 11 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRE... |
0a0b4d0b10cb1f7ed9ab2993ef93defc03447e6eba9daca1315dd32dae4877e3 | License | 1,056 | 19 | Copyright 2014-2024 Numenta Inc.
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, subl... |
4d2a71b4a2198f5042094b07c714fe37a3abd91833579dc3a47eb8a27bd314eb | License | 1,057 | 19 | Copyright (c) 2024 by Marc Canela
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... |
562ddc22d91b96be9f67019b365a7f1e705a305c1749660f92b5502d75f72c2b | License | 1,057 | 22 | MIT License
Copyright (c) 2025
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... |
bdc2d4dbffff92d84edfa75d050759aa1546a59106be0afbb3abdfdf2862852f | License | 1,057 | 7 | Copyright (c) 2018 Karolina Finc
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, subl... |
8d11bd5d4382c88d5f3056e285dca2456f5c13f4cb3a2d9d13733a7a9b1eb578 | License | 1,060 | 21 | MIT License
Copyright (c) 2025 pyj
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, s... |
01a50b2a7818e73e00b3084ac6f4c39643d6be23195d472a9ad7079c1c53d51d | License | 1,061 | 21 | MIT License
Copyright (c) 2024 vlad
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, ... |
5be3782db30e9fdc1f35bce9c0b05640e81c9a426952f1fe7a5cb1aad4a2d87d | License | 1,061 | 21 | MIT License
Copyright (c) 2024 p-koo
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,... |
62d6d7003207edf2a5124577732f19d2a8e874cb91ca239a4fc9adcfdcbccc33 | License | 1,062 | 21 | MIT License
Copyright (c) 2026 ngohv
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,... |
794ef507356957d9cf67e4b61d89d5e2d3020513566ce2dd5efde661d8d57d24 | License | 1,062 | 21 | MIT License
Copyright (c) 2025 Meraj
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,... |
aa1f5ae4688777f7ca4f8d53966a382da874d2646dd79e29ea231eb40f9bc007 | License | 1,062 | 21 | MIT License
Copyright (c) 2023 ToFec
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,... |
d85b781d53bce1c25d9672bdbd4c7d1e68146f284631deeb18db9d3f68852d9e | License | 1,062 | 19 | Copyright (c) 2018 Impyute Developers
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,... |
fe0cc65dcadd4873966c1efa557e19034ce3a79eaf1062a73ba6cf138f97ecdf | License | 1,062 | 21 | MIT License
Copyright (c) 2025 acv132
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... |
ff72835f9d11d2d692e45529942d15ce0cf67e422e5421acde459ce60764fe70 | License | 1,062 | 21 | MIT License
Copyright (c) 2025 mzlab
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,... |
021b98a4692e24a062d005306d74a0443c11bc4df8331f3538bd1dad036eee07 | License | 1,063 | 21 | MIT License
Copyright (c) 2020 JianHu
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... |
0ff108b11dfbaaa19c985182089dd5f2ac66b3fbf5dcfe6e5924ce9eb89923d8 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 jocpae
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... |
147eec8eaf59febbfb0363c1024cffa0ca88118f32cfed20a8d7f2cfeab67ee6 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 Javier
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... |
1b091013cacc12802a4f0e5f2bb11bb9190ab5ad3d5fb221df8032285c17bd66 | License | 1,063 | 21 | MIT License
Copyright (c) 2022 Jackn0
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... |
44e92f7e1a202bf715f6fddd2fe3b3ebf6cf1fb866a4c4e535d7c9fe71fc6dd8 | License | 1,063 | 7 | Copyright 2024 Micha Burkhardt
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, sublic... |
6ba618fc5c68dc9c8e36c3a157f890904b89f47d59a182f8ea70db2ed06c13da | License | 1,063 | 21 | MIT License
Copyright (c) 2025 abhi_g
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... |
e137dfcef930d4bc8eea9fc503c118f2cdcfa8b6437277a2754c4a25aed88b90 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 JazLab
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... |
e87d5baa5e60bdd709ae28a8ff29717790802d4b567a33ad039f24a8a8bb3295 | License | 1,063 | 21 | MIT License
Copyright (c) 2024 sjpi22
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... |
fd0ca8b3c3fb7296ded6afcb6244a2c8e6cdbec5837b371112441fc22a3428a9 | License | 1,063 | 21 | MIT License
Copyright (c) 2024 TigerZ
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... |
fde9da713ff14a9086c5d78ec24fe5f25ed3c406146d70ef8e7866c1b41bbd6e | License | 1,063 | 21 | MIT License
Copyright (c) 2025 willn2
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... |
0b68c96f987efa7d1049c240a00d9577768e4763c7cbb599758816ee0ca390a5 | License | 1,064 | 21 | MIT License
Copyright (c) 2023 Joy Roy
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, distribut... |
13086fe631ea59c87d890f6aac212f316556ef48cdb72a40e48c013e3ede30cb | License | 1,064 | 21 | MIT License
Copyright (c) 2025 XU Yitao
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, distribu... |
2a96b17464f1b66834d42f14b69b786bb8b63c8c42e9562d54b08f9d0189e012 | License | 1,064 | 21 | MIT License
Copyright (c) 2026 jn feng
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, distribut... |
2b9cd599b2d13a8af7bcfb57cf4adc75f14d61c56c94a91db331be1f5877283b | License | 1,064 | 21 | MIT License
Copyright (c) 2022 ysl-lab
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, distribut... |
42365a4e378f5f07a98e192a9f2de6f215dbbc81ff6bf66d9ee2a324b77e3908 | License | 1,064 | 21 | MIT License
Copyright (c) 2020 CPP lab
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, distribut... |
4dcec3f38d80c126ddc6973368142db8c3b490a877d7a0b847ea0f3333f0b60c | License | 1,064 | 21 | MIT License
Copyright (c) 2025 Giladsc
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, distribut... |
55150b2ca985f37c0b5c4c397692f0410a046feb661559f4ecf9359ea0f6cd96 | License | 1,064 | 21 | MIT License
Copyright (c) 2025 btjaden
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, distribut... |
594f98f7b1d7b62c3ff189b9f490a8540c6bfc64fe88e871aa3aae3ba6cbcc5a | License | 1,064 | 21 | MIT License
Copyright (c) 2025 SAAAHco
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, distribut... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.