sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b9cad726b1455cc95e3f52cb6b517ded26bf1545ade419ffad323a27f376e81a | Jupyter | 18,071 | 414 | # %%
import numpy as np
base = '/home3/ebrahim2/beyond-brainscore/'
import os
# %%
def stack_features(model_dict, filename, saveFolder='/data/LLMs/data_processed/pereira/LLM_acts/', dataset='pereira'):
stored_acts = []
for key, values in model_dict.items():
for value in values:
val = np.lo... |
14d03f92c1536abbd70517d64d91da82a36de6b0529bdf4e483d931775f90c81 | Jupyter | 18,099 | 338 | # %% [markdown]
# # Calculating Error and Correlation Metrics Manually
#
# ``cinnabar``'s scatter plots automatically annotate figures with statistics such as **RMSE** or **MUE** based on the recommend best practices for the observable being plotted as described in the [companion paper](https://livecomsjournal.org/ind... |
4c829055eaff1aca6f86311c6844e402a309c2ee79d685cd79cb0d3851612361 | Jupyter | 18,204 | 566 | # %% [markdown]
# ## Environment Setup
# %%
! pip install kagglehub
# %%
from google.colab import drive
import os
drive.mount('/content/drive')
model_save_dir = '/content/drive/My Drive/Brain_Tumor_Ahmed_Models'
os.makedirs(model_save_dir, exist_ok=True)
print(f"Model weights will be saved in: {model_save_dir}")
# ... |
ffe1ea72efa32de12fc54dd5c2b98f0972f84788ac038cb5ad217e6e2154787e | Jupyter | 18,234 | 558 | # %% [markdown]
# ## Environment Setup
# %%
! pip install kagglehub
# %%
from google.colab import drive
import os
drive.mount('/content/drive')
model_save_dir = '/content/drive/My Drive/Brain_Tumor_Sartaj_Models'
os.makedirs(model_save_dir, exist_ok=True)
print(f"Model weights will be saved in: {model_save_dir}")
... |
d00d67b59ce4fc3392890d9bc1f995b3e918a602647063f1ad825ce82c320784 | Jupyter | 18,309 | 561 | # %% [markdown]
# ## Environment Setup
# %%
! pip install kagglehub
# %%
import kagglehub
path = kagglehub.dataset_download("masoudnickparvar/brain-tumor-mri-dataset")
print("Path to dataset files:", path)
# %%
from google.colab import drive
import os
drive.mount('/content/drive')
model_save_dir = '/content/driv... |
e41d1d17f967ee7848586c97b4e08be7c8c06c52e693f2f33f4b0931fd522640 | Jupyter | 18,414 | 413 | # %% [markdown]
# ### *This file allows to reproduce Fig5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("STG_models.jl") ... |
9cc13fdcb95ecb7903632b6b99ee477bd38b8325d1c8b84d3713e6e35f7c73a6 | Jupyter | 18,486 | 330 | # %% [markdown]
# # <font color=#B2D732> <span style="background-color: #4424D6"> Brain & Spinal Cord fMRI preprocessings </font>
# <hr style="border:1px solid black">
#
# *Project: SpineBrain_Aging*
# *Paper: in prep*
# **@ author:**
# > Caroline Landelle, caroline.landelle@mcgill.ca // landelle.caroline@gmail... |
0230dcd52a83349593fe41c4d388551665e111a31e58e671bdbc5c67be7dd389 | Jupyter | 18,558 | 469 | # %% [markdown]
# ### Calculate coefficient of variation as per Glüer et al. (1995)
#
# Author: Simone Poncioni, MSB, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
#
# Date: 07.2024
#
# Update: 29.04.2025 for evaluating PE as per Schenk et al. (2020)
# %%
from pathlib import Pat... |
852fba2afc60476d52d310090bed6e8b3cafdfd0c4ecbc86d301b4989523581d | Jupyter | 18,570 | 434 | # %% [markdown]
# # <font color=black> Fig 04b Brain and spinal cord functional connectivity </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
#-------- Basics
import sys,json,os, glob, re
import pandas as pd
import numpy as np
#------ load config file
main_dir='/cerebro/cerebro1/datas... |
87b88d89b7c0e07c954f923d66e54c01163a547bebafcf7749a0d8c16cc11f94 | Jupyter | 18,682 | 268 | # %% [markdown]
#
# # Protocol 1: assessment of cell type replicability with unsupervised MetaNeighbor
#
# Protocol 1 demonstrates how to compute and visualize cluster replicability across 4 human pancreas datasets. We will show steps detailing how to install MetaNeighbor, how to compute and interpret MetaNeighbor AU... |
3af368484a8bfce3fc553824974a6af2bda4054a988c76fcdcb953e2ff2c8ba4 | Jupyter | 18,846 | 345 | # %%
base = '/home3/ebrahim2/beyond-brainscore/'
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
from matplotlib import pyplot as plt
import os
from sklearn.metrics import mean_squared_error
import sys
sys.path.append(f'f{base}')
from plotting_functions import plot_test_perf_across_layers, plot_across_subje... |
f966f0bd50455bc6b95c4cc497e1b11ff708a0e26c4f3137b8ec497b6507ac2e | Jupyter | 19,020 | 223 | # %% [markdown]
# # "Photometry FLMM Guide Part I : Data Formating and Binary Variables"
# ## Authors: Gabriel Loewinger, Erjia Cui
# ### rpy2 implementation: Josh Lawrimore
# %% [markdown]
# ## Introduction
#
# `fastFMM` is a fast toolkit for fitting Functional Linear Mixed Models (FLMM). Instead of analyzing summar... |
e4c047fa4c3e9b10fc88d6c54a5e1b82f5a8f2da87a10ca87b234351ed960b1f | Jupyter | 19,152 | 523 | # %% [markdown]
# ### *This file allows to reproduce FigS5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
using Interpolations
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
inc... |
370971bc974deb45d0cc85cb3cc00cce36ff3f295893f07c72e96d3520c8c520 | Jupyter | 19,187 | 475 | # %%
GROUPSTATS_DATE = '2025_07_26'
# %%
"""Computes permutation P values.
For 6_plotting/compare_correlations_ds.ipynb, 6_plotting/rsa.ipynb, and
6_plotting/subgroups.ipynb, get P values of results. Needs a decent amount of
memory to run (64 GB is sufficient).
"""
import sys
from subprocess import run
import warni... |
b5fb95c7a8e1b88a319b00cb384ac60195c82c790e6ce8011e26161edb8b70a1 | Jupyter | 19,219 | 519 | # %% [markdown]
# # We are assembling all elements of figure 1 of the TwinC paper in this notebook.
# %%
import sys
sys.path.append("../../twinc")
import os
import gzip
import torch
import cooler
import pyBigWig
import argparse
import matplotlib
import numpy as np
import seaborn as sns
import _pickle as pickle
from ... |
7de30d60bcde8e73a39f6ff93d2c7f0e22482916b3fed69dd2ea1d09ba78532d | Jupyter | 19,302 | 514 | # %% [markdown]
# ### *This file allows to reproduce the voltage traces of Fig5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("STG_models... |
a9d57f1ae8250cdc9bb539cb4b4b38f4f43f9a19f61cdacd7465ad0a17ed44a4 | Jupyter | 19,520 | 521 | # %% [markdown]
# ### *This file allows to reproduce the voltage traces of Fig5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("STG_models... |
b457453ac9cf259acc0a9cfb368b09c85ab48414f9b632fdc67c8752733a15c9 | Jupyter | 19,665 | 691 | # %% [markdown]
# ## Extended Data Figure 11
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
import itertools
from tqdm import tqdm
from pathlib import Path
sys.path.insert(0, "./prepare_data/")
import yaml
import numpy as np
import pandas as pd
import networkx ... |
107e6763de0ab3473b0c158e7541d3061ff46d85979beb535ae2bb1bbbdc6fa5 | Jupyter | 20,063 | 345 | # %%
base = '/home3/ebrahim/what-is-brainscore/'
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
base = '/home3/ebrahim/what-is-brainscore/'
from matplotlib import pyplot as plt
import os
from sklearn.metrics import mean_squared_error
import sys
sys.path.append('/home3/ebrahim/what-is-brainscore/')
from plo... |
b06253a7786c0a78f356f31a632fb3e180bfb62ff788634cd52d1d844ca67e2a | Jupyter | 20,079 | 773 | # %% [markdown]
# # Collect META5 Risk Variants Stats From Other GWAS
# - **Author** - Frank Grenn
# - **Date Started** - November 2019
# - **Quick Description:** code to gather summary stats for the meta5 risk loci in other gwases. **Note** that this list is manually edited as the end to make sure maf is consistent
... |
9c46cf7eeaad5db46fe39726e928499c610d69352740c1ce57021b2bff911043 | Jupyter | 20,091 | 561 | # %%
## Imports
import os, sys
import time
import yaml
import h5py
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#from cGANtools.GAN import CGAN
#from keras.models import load_model
from scipy.cluster.hierarchy import linkage, dendrogram
parent_dir = os.path... |
d3f7e88ed796e24f2604976f92f5d3fca05d5494c5563d9db596401e1f0584ab | Jupyter | 20,361 | 544 | # %% [markdown]
# ### Calculate coefficient of variation as per Glüer et al. (1995)
#
# Author: Simone Poncioni, MSB, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
#
# Date: 07.2024
#
# Update: 29.04.2025 for evaluating PE as per Schenk et al. (2020)
# %%
from pathlib import Pat... |
fd2f214c2613e188de72128c97a4050a1ebf140145a2e3edf3f46b41ca553c5e | Jupyter | 20,514 | 549 | # %% [markdown]
# ### *This file allows to reproduce the voltage traces of Fig5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("STG_models... |
db3d480afa43701c1ea49a2df4d4491f120a33e632ffcb616bf0a71c7550fe50 | Jupyter | 20,665 | 521 | # %% [markdown]
# # Extended Data Figure 3: Utility and necessity of six cameras in capturing mouse face
# %% [markdown]
# To run this notebook, you need the following datasets:
# - `/anipose-projects/20231102-3D-structure-rig2`
# - `/anipose-projects/4cam-omnibus-rig2`
# - `/anipose-projects/4cam-centers-omnibus-rig2... |
6ac36f77d26a87a10d8766d4dc29dc5d4eec26834c9a25ee6f86552468cb73ba | Jupyter | 20,733 | 552 | # %% [markdown]
# ### *This file allows to generate homeostasis and sharp neuromodulation traces that are not in the article*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles, Interpolations
include("STG_ki... |
e9b41e54d3662093854039dfd475170425f76dd8398eb396dbcf93125d028de1 | Jupyter | 20,810 | 648 | # %% [markdown]
# # 2D Instance Segmentation with Discriminative Instance Loss
# ---
# Implemntation of paper:
#
# [Semantic Instance Segmentation with a Discriminative Loss Function](https://arxiv.org/abs/1708.02551)
# %%
import os
import errno
import numpy as np
import deepcell
# %% [markdown]
# ## Load the ... |
133993624792b7c97debf1e71c6ba2eb69fdc8c561ca1f05277beddc3690a969 | Jupyter | 20,926 | 736 | # %% [markdown]
# ## Figure 4
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from tqdm import tqdm
from pathlib import Path
sys.path.insert(0, "./prepare_data/")
import yaml
import numpy as np
import pandas as pd
import networkx as nx
import seaborn as sns
from ma... |
010002695c9a7114fe35bcf5dbf5a63f60d9527991e72b96b3893de517e53130 | Jupyter | 21,137 | 644 | # %%
subject_folder = r"C:/dummy/path/sub-XX"
#subject_folder = "C:/Users/jorge/OneDrive/Documents/Doctorado en Tec. Monterrey/Data Motor Task without tES/BIDS/Data Motor Task without tES/sourcedata/sub-16"
# %% [markdown]
# # Import Libraries
# %%
from BCI2kReader import BCI2kReader as b2k # Library for reading BCI... |
eab6d1ac345ba415dfc5f8071cc6cb8dbb1851ebb7ba5e6785bc95d56079a416 | Jupyter | 21,299 | 802 | # %% [markdown]
# ## Figure 2
#
# 
# %%
%load_ext autoreload
%autoreload 2
import os
import copy
import logging
from pathlib import Path
import scipy
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.stats.multitest
from comm... |
035aa9448405e1dd0926f0fdd0c6e2d95e31533f3f1f4cb42dedcccd9303ee42 | Jupyter | 21,300 | 564 | # %% [markdown]
# <a href="https://colab.research.google.com/github/xinformatics/alphafold_embeddings/blob/main/Representations_AlphaFold2PredictStructure.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# #Protein structure predictio... |
66933f76b423e542e51c412ba43995e84571a9f1d5da1c7ace1598aaf04bd9fe | Jupyter | 21,316 | 594 | # %% [markdown]
# ### *This file allows to reproduce FigS6*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
using Interpolations
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
inc... |
06b2f9cc23fade7bde323f73440cef44e71ffec13dc21940f643ad41e934324f | Jupyter | 21,636 | 558 | # %% [markdown]
# ### *This file allows to reproduce Fig3AB and D*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
using Interpolations
include("STG_kinetics.jl") # Loading of STG kinetics of gating variab... |
83252a1b0b39f50daba978bcfcdac09eb8dd92719dfe40b6f1d7204da3594d27 | Jupyter | 22,071 | 438 | # %% [markdown]
# # Working with the ``Cinnabar`` API
#
# ## Passing data to Cinnabar using ``FEMap``
#
# The ``FEMap`` object is the central datastructure in ``cinnabar``. It represents free energy information as a graph:
#
# - **Nodes** are ligands.
# - **Edges** are relative free energy differences (ΔΔG) between ... |
6f97869a991e6a5bf4652121036caf9272687479946079fd89137fe8dd31f0f6 | Jupyter | 22,177 | 549 | # %%
import os
import numpy as np
import pickle
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from scipy.spatial.distance import cdist
from utils import STIM_INFO_PATH, COCO_IMAGES_DIR, SUBJECTS, FMRI_DATA_DIR, FMRI_BIDS_DATA_DIR, RESULTS_DI... |
91111250b66676a1b45005dfcd3e2630c3140b083ffffbc83ff255665deaf559 | Jupyter | 22,434 | 587 | # %% [markdown]
# This notebook is part of the `deepcell-tf` documentation: https://deepcell.readthedocs.io/.
# %% [markdown]
# # Training a segmentation model
#
# `deepcell-tf` leverages [Jupyter Notebooks](https://jupyter.org) in order to train models. Example notebooks are available for most model architectures in... |
4a170a7bbc67db24abd628f11d3586a00b28a1cb4101641a88020ee3e320fd9b | Jupyter | 22,538 | 686 | # %% [markdown]
# ### *This file allows to reproduce FigS3 using a custom package NmodController*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
using Interpolations
using NmodController
include("DA_kinet... |
8cf1ea74020dacd6299e13e8f2e03370cd691c537e39b0d1b41951b3076cc6a1 | Jupyter | 22,835 | 572 | # %% [markdown]
# # Linear Mixed-Effects (LME) Analysis
# **Study:** APOE ε4 × Hippocampal Volume × Cognitive Decline
#
# Primary analysis addressing reviewer comments:
# - Hippocampal volume treated as **continuous** (not median-split)
# - Full covariate set including GDS (depression) — **new per Reviewer 2**
# - Mod... |
56da1dc986753288ac03415e277fdad6f816b4792eb08ca36e6b018caad90bbe | Jupyter | 22,866 | 493 | # %% [markdown]
# # Pre-stimulus baseline comparison
#
# ___
# %%
"""
04 MARCH 2024
Theo Gauvrit
Testing the higher baseline hypothesis to explain the no detection of tactile stimulus on KO mice.
"""
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import scipy.stats as ss
im... |
a9dbe6e7a6765dc9a5b01bb91d2cac3e69e0b279466925ca3f0456d9a9dc19c7 | Jupyter | 22,867 | 671 | # %% [markdown]
# ### HR-pQCT parameters: correlation matrices
#
# Author: Simone Poncioni, MSB
#
# Date: 31.03.2025
#
# Data: HR-pQCT database of the University of Bern, Switzerland
# %%
# Create a user library directory if it doesn't exist
user_lib <- "~/R/library"
dir.create(user_lib, recursive = TRUE, showWarni... |
d78ecf614f7e1ad3bd1e63de8281501aacf02aba2d92307b5f2afc4862d1a09d | Jupyter | 23,618 | 630 | # %% [markdown]
# # ADNI Data Integration Pipeline
# **Study:** APOE ε4 × Hippocampal Volume × Cognitive Decline
#
# This notebook implements the full data pipeline addressing all reviewer comments:
# - Expands sample from 133 → 2,386+ subjects
# - Hippocampal volume as **continuous** (not median-split)
# - Adds GDS (... |
b6385e1778e756243b4aa203751c28987252d3e3672c8ddf47f63716ed5fbd88 | Jupyter | 23,767 | 850 | # %% [markdown]
# # Figure 1
#
# 
# %%
%load_ext autoreload
%autoreload 2
import os
import sys
import logging
from pathlib import Path
logging.getLogger("matplotlib.font_manager").disabled = True
import scipy
import numpy as np
import pandas as pd
import seaborn as sns
import networkx ... |
66a990e0626b78f0b088e201076ec00865348135215af601e3ecd1bcc4df4671 | Jupyter | 23,864 | 570 | # %% [markdown]
# # Survival Analysis & Sensitivity Analyses
# **Study:** APOE ε4 × Hippocampal Volume × Cognitive Decline
#
# This notebook covers:
# 1. **Survival/Conversion Analysis** — Cox PH model for CN → MCI/AD conversion
# 2. **Kaplan-Meier Curves** by APOE dose
# 3. **Sensitivity Analysis 1** — Stratified by ... |
50163e72b196514457b4e6581c0851d4d98b15a4b7a5a81a1061376bfa984859 | Jupyter | 24,180 | 908 | # %% [markdown]
# ## Figure 6
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
import itertools
from tqdm import tqdm
from pathlib import Path
sys.path.insert(0, "./prepare_data/")
import yaml
import numpy as np
import pandas as pd
import networkx as nx
import seabo... |
0f7cd504c3bf3bdb13416e0792fcc1e5fe8a395714e2b6ebd02177f1dc7a54b3 | Jupyter | 24,363 | 782 | # %%
import pandas as pd
import numpy as np
import glob, os, vcf, itertools, subprocess, shutil
import matplotlib.pyplot as plt
import seaborn as sns
from Bio import Entrez, Seq, SeqIO
cc_df = pd.read_csv("~/who-analysis/data/drug_CC.csv")
cc_df_internal = pd.read_csv("/n/data1/hms/dbmi/farhat/Sanjana/MIC_data/critic... |
9bd787ad7d2df9bceb7deb81e4c142e5f885d3476464ec1e1ad6256adb1f4286 | Jupyter | 24,445 | 700 | # %%
# import necessary packages
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from statsmodels.stats.multitest import multipletests
from itertools import combinations
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-seri... |
69c21bf9ecc5f522b15074537b2d28cc0ac57c3f20dace03cf9542166a931635 | Jupyter | 25,016 | 663 | # %% [markdown]
# # Demo - Using `alchemiscale` to evaluate a relative binding free energy network
# %% [markdown]
# This notebook details the process of running a relative binding free energy calculation using the [openfe](https://github.com/OpenFreeEnergy/openfe/) toolkit and the execution platform [alchemiscale](ht... |
a65d8504e80e9a9fc8b98da27b0b2c7cff2d0d4378616004a4f2c9e9728b8b54 | Jupyter | 25,425 | 522 | # %% [markdown]
# # Trial by trial variability analysis
#
# **11th March 2024 (edited the 6th of May 2024)**
#
# *Théo Gauvrit & Célien Vandromme*
#
# ---
# %% [markdown]
# ## Modules and data import
# %%
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import scipy.stats as ss
import ... |
c4c62faef10ca766e1bbcd2fc50abea9cc934b1d8531dbfa6c36a95009a11618 | Jupyter | 25,804 | 832 | # %% [markdown]
# The purpose of this Jupyter notebook is to analyze Fiber Photometry Data recorded by a TDT system
#
# The notebook is adapted from Thoam Akam & Lauren Burgeno by referring to Simpson et al. 2023
#
# The preprocessing consists of the following steps:
#
# 1. Lowpass filtering to reduce noise (10 ... |
98e4f27f7486d8975c3fbc4ba901847073fbb0f0e28abbfb0ae3dc43cac1d8fe | Jupyter | 25,931 | 758 | # %%
#reproduces all panels of Figure 4, and Supp 5
# %%
import numpy as np
import nibabel as nb
import os
import matplotlib.pyplot as plt
from scripts.wgcna_module_enrichments import WGCNApostprocessing
concat= np.load('/data1/allen_surfaces/hcp_surfs_2/all_subs_smoothed_z.npy')
base_dir = '/data1/allen_surfaces/'
... |
fe19b9f776b966a402bda9442175d5220fa6c88dba809c2a1fc4393c38cd4735 | Jupyter | 26,010 | 835 | # %%
import anndata as ad
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
# %%
#Read the MAPMYCELLS results file
PLI_mapped = pd.read_csv('./MAPMYCELLS/PLI_mapmycell_results.csv', comment="#")
PLI_mapped
# %%
#Save the dataframe
PLI_mapped.to_... |
6d513a6b5371688cd921c13c540f07b132afe2530241bcb4965c5fcc519a36f7 | Jupyter | 26,247 | 437 | # %% [markdown]
# # <font color=#B2D732> <span style="background-color: #4424D6"> Brain and spinal cord fMRI denoising </font>
# %% [markdown]
# @ author of the script: <font color=#B2D732> Caroline Landelle </font>, caroline.landelle@mcgill.ca // landelle.caroline@gmail.com
# @ Contribution and adjustements: <fo... |
7311d68e75c53526a78c6d5cecd75e578d43b78e5689361485a1bea26bbaf0ee | Jupyter | 26,322 | 665 | # %%
## Imports
import os, sys
import time
import yaml
import h5py
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#from cGANtools.GAN import CGAN
#from keras.models import load_model
parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
sys.path... |
ed956387e35d6e750974537d9bcb00501eefec5d966321ae8507a6314f98d9d5 | Jupyter | 26,335 | 746 | # %%
import gc
import scanpy as sc
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import random
# this line should go before importing cell2location
import os
os.environ["THEANO_FLAGS"] = 'device=cuda,floatX=float32,force_device=True'
import cell2location
import cell2lo... |
a137437c9e6fa95a43d5ba538f9d5bb4d756c85e00fcdafdf75cdd6f03c672ca | Jupyter | 26,446 | 873 | # %%
from google.colab import drive
drive.mount('/content/drive')
# %%
import pathlib
import tensorflow as tf
import pandas as pd
from PIL import Image
import random
from skimage.measure import label, regionprops
from skimage.filters import threshold_otsu
import shutil
from sklearn.metrics import roc_curve, roc_auc_sc... |
7f9926d34b8f9c044bb13b20b6d9d14357cbeca46fbce3389292411f337433ce | Jupyter | 26,468 | 668 | # %% [markdown]
# # SLURM Resource Efficiency Analysis
#
# Aggregates per-job SLURM efficiency reports from MosaiCatcher pipeline runs to identify
# resource optimization opportunities. Analyzes CPU efficiency, memory usage, and runtime
# across all rules to suggest better resource allocations.
# %%
import glob
impor... |
b8c5160f41637b3919aa15cf6d1e13873e5311f14460da1caa931595be49c824 | Jupyter | 26,626 | 529 | # %%
import pandas as pd
import numpy as np
import os
import h5py
from sklearn import linear_model
import matplotlib.pyplot as plt
import deepdish as dd
import string
try:
os.chdir('/data/MoL_clean/scripts')
except:
pass
import util
# util has some variables in them
# import GLM_helper as gh
import scipy.stat... |
03f073d2bdc5782b34189a79355ec60f9f538873e931374a4488ad1108fa1186 | Jupyter | 26,835 | 753 | # %%
import sys, glob, os, yaml, sparse, tracemalloc, vcf, subprocess, collections
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import scipy.stats as st
from sklearn.metrics import roc_auc_score, average_precision_score, confusion_matrix
from sklearn.model_selection impo... |
c8902673807abf87fd799803e99facb2e13aba634c8814de09f79bebc32ac0dc | Jupyter | 26,896 | 592 | # %% [markdown]
# # <font color=black> Figure 1 Spinal cord morphometry </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import glob, os, sys, json
import pandas as pd
import numpy as np
import seaborn as sns
import pickle
main_dir="/cerebro/cerebro1/dataset/bmpd/derivatives/Aging_pro... |
42d5b0cbc397b1ade4b740784ebc48ca20c9df62d32df9e104e418c8a396da65 | Jupyter | 26,980 | 592 | # %% [markdown]
# # AlphaFold Colab
#
# This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of [AlphaFold v2.0](https://doi.org/10.1038/s41586-021-03819-2).
#
# **Differences to AlphaFold v2.0**
#
# In comparison to AlphaFold v2.0, this Colab notebook uses... |
89b1f5fc4a021cadf99fd51d8748794d92cacb92db2b19659272174dd37d1432 | Jupyter | 27,143 | 628 | # %% [markdown]
# # Decoding Responsivity (imbalanced learn)
# **Can we predict whether a stimulus will be detected or not based on neuron's responsivity ?**
#
# Célien Vandromme
# 18/04/2024
#
# ---
# %%
from unittest import result
import numpy as np
import pandas as pd
import cebra
import percephone.core.recordi... |
2a04496f04593c2024bc267244f09caee8509aff112b202149145bb10cdb08e8 | Jupyter | 27,184 | 1,077 | # %% [markdown]
# Phase space plots
# %%
for i in range(84):
if i in ez:
plt.plot(pstr_samples_1['x'][-1,:,i], pstr_samples_1['z'][-1,:,i], color='red')
elif i in pz:
plt.plot(pstr_samples_1['x'][-1,:,i], pstr_samples_1['z'][-1,:,i], color='orange')
else:
plt.plot(pstr_samples_1['x... |
5b0163b186f55aeb3c7e2e3aed876d8f6014b2bfc51026c5f74d777944776415 | Jupyter | 27,426 | 766 | # %% [markdown]
# # Stereo-seq Region Selection
# %% [markdown]
# ## 1. Envrionment
# %%
import scanpy as sc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import json
import gzip
import shutil
from pathlib import Path
from scipy.sparse i... |
149967cdb74d0a427c4cc8da1998323b44a6e1a9b075743575b8d1a44cc368b2 | Jupyter | 27,622 | 777 | # %% [markdown]
# # Stereo-seq Region Selection
# %% [markdown]
# ## 1. Envrionment
# %%
import scanpy as sc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import json
import gzip
import shutil
from pathlib import Path
from scipy.sparse i... |
1093923becb6c519565c5a7a9ae0aea970f71eee2f5a6627b4c4d87032865cd2 | Jupyter | 27,795 | 800 | # %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from scipy.stats import mode, entropy
from scipy import interpolate
from scipy.spatial.distance import jensenshannon
import helper as hp
import warnings
warnings.fil... |
3c629cf0d601d2f2dd37ef0609dd60b73a2e8be6a4a1c09a8a6fb83b08004601 | Jupyter | 28,624 | 639 | # %% [markdown]
# # Extended Data Figure 6
#
# Reduction in keypoint tracking jitter from Facemap to Cheese3D by facial region.
# %% [markdown]
# Before running this notebook, make sure you have:
# - `anipose-projects/20230919_long-anes-clips_rig2`
#
# And the Facemap results:
# - `facemap-projects/20240919_long-ane... |
aa0a574ed89c5dbede9e128627366145e5ea7b8b161c82456585b30787f936e6 | Jupyter | 28,700 | 926 | # %% [markdown]
# This notebook contains the code used to generate figures related to the transcriptomic type-based spatial patterns, but relies on data and libraries that are on internal systems, so it is provided for reference (rather than being able to be run on its own). The notebook shows the results for the CP sp... |
c47c9b47cf5015f43afe45aa1f139e84f6979dc3d1fd67f232e5f125bfecbc16 | Jupyter | 28,816 | 826 | # %% [markdown]
# ### Author: Hannah E. Aichelman
#
# Analysis of Xenium data for Mariani et al. composition manuscript
#
# All slides and tissues in this experiment were done with the human 5K Pan Tissue & Pathways panel
#
# This notebook reads in all samples, saves raw zarr bundles, crops all tissue to area where ... |
72856ef4fcf646b1caa1ba2bfc6d90cadbeca3e3044fb502961b1f69ea47a7d6 | Jupyter | 29,186 | 895 | # %% [markdown]
# # GSEApy Tutorial
#
# [**GSEApy**](https://github.com/zqfang/GSEApy) is a Python/Rust toolkit for
# **Gene Set Enrichment Analysis** and related methods. This notebook walks through
# every public entry point with small, runnable demos using the data bundled in the
# repository's `tests/` folder.
#
... |
ff85841ea482968279375f190b63dc8c1a26c4b286bc81129c631d7bf43bda94 | Jupyter | 29,478 | 672 | # %% [markdown]
# # Figure 3
# %% [markdown]
# In order to run this notebook, you need the following Anipose project:
# - `202505-eeg-redose`
#
# For a swifter run, you can download a local version of the already generated facial features:
# - `redose-eeg-slow-drift.pkl`
#
# Even though the EEG signal did not make i... |
7dfbe936ac810bd99f2db336c1867ba2142508899aa0ebf39c2f85caa133df3f | Jupyter | 29,609 | 855 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import glob, os, sparse, sys, warnings, yaml, vcf, pickle, shutil, subprocess, re
import scipy.optimize
os.chdir("../")
who_variants = pd.read_csv("./data_processing/data_utils/WHO_catalog_V2.csv", header=[2]).reset_index(drop=True)
coll_2014... |
45a1c4d3430c9d04b6cee30c78bb0c2a91a076a7b36602cf7eeee34f5b6957f8 | Jupyter | 29,771 | 819 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from utils import RESULTS_DIR, SUBJECTS, COCO_IMAGES_DIR, STIM_INFO_PATH
from data import MODALITY_AGNOSTIC, TRAINING_MODES, CAPTION, IMAGE, DEFAULT_VISION_FEATURE... |
9c4c8877a86a33f08deb5a31f0ef587fbf2233d57c7c75789d82e6efcb7cb6db | Jupyter | 30,063 | 1,035 | # %%
# %% [markdown]
# NOTE ON PARAMETER SCALING
#
#
# - When we generated 500 parameter sets, the code automatically saved the latent vectors used and their corresponding maximum eigenvalues.
# - These eigenvalues were computed after batch-scaling all 500 parameter sets together, so that the resulting Km values fi... |
c3404e71e831615723b284043243915629ac40353559ec384e3cdc7ac82af3e5 | Jupyter | 30,911 | 880 | # %%
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 matplotlib.pylab as plt
import numpy as np
import networkx as nx
from adjustText import adjust_text
from cinnabar import plotlying, stats
import glob... |
0cb2ddfd4e646a9dde9b08db66ef42c998826fc9c9ff772d376be994f25c82f3 | Jupyter | 31,244 | 970 | # %%
import gc
import pandas as pd
import numpy as np
import scanpy as sc
import anndata as ad
import scvi
import torch
import anndata
import copy
from rich import print
from scib_metrics.benchmark import Benchmarker
from scvi.model.utils import mde
from scvi_colab import install
#import scrublet as scr
import matpl... |
4ecba29d0687122817c44b6dc8a56ceb532a5948493c55ca0252d1d3624b0bbb | Jupyter | 32,059 | 762 | # %%
# import packages
import os
import lap
import copy
import torch
from torch import optim
from torch.utils.data import DataLoader
import numpy as np
from scipy.stats import norm
from scipy.stats import pearsonr
from scipy.spatial.distance import cdist
import seaborn as sns
import matplotlib as mpl
import matplotlib.... |
2c34c1da48a8ed67dd25eff4a5440f1151e2e0210d4fdabb335ad7fbccee6efd | Jupyter | 32,067 | 691 | # %% [markdown]
# # Analysis pipeline for questionnaire, behavioral and LC data of ADHD experiment with fMRI
#
# Leonhard H. Drescher, Ghent University, 2022-2025
#
# #### Short description of the experiment:
# Participants: Adults with ADHD (n = 27) and adults without any psychiatric diagnosis (n = 28).
#
# Questio... |
ba36387829589d7d5ee5d38a274aa38302461f0018370fab5a98ad617696a2ad | Jupyter | 32,186 | 687 | # %%
import pandas as pd
import numpy as np
import os
import h5py
from sklearn import linear_model
import matplotlib.pyplot as plt
import deepdish as dd
import string
try:
os.chdir('/data/MoL_clean/scripts')
except:
pass
import util
# util has some variables in them
# import GLM_helper as gh
import scipy.sta... |
b8c32aaa16321f605ca7e315da99bfe24dab5ce9a149e28a9a073f8448701b5d | Jupyter | 32,394 | 1,399 | # %% [markdown]
# # Installation
#
# https://github.com/theislab/scCODA
#
# conda create -n sccoda python=3.8
# pip install sccoda
# pip install ipykernel
# %%
# Setup
import importlib
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pickle as pkl
import matplotlib.pyplot as plt
import t... |
12399d66bf7f7f9f5c77f21bf54927d6c917483a792143ea9f0c759a484c7af8 | Jupyter | 32,927 | 775 | # %%
# import packages
import os
import lap
import copy
import torch
from torch import optim
from torch.utils.data import DataLoader
import numpy as np
from scipy.stats import pearsonr
from scipy.spatial.distance import cdist
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib... |
f9d7883be76bb374acfadce9b14936ffbc2bba06622b16e762f57b0e0ea9e723 | Jupyter | 33,663 | 722 | # %%
# load libraries
import os
import torch
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from torch import optim
from torch.utils.data import DataLoader
os.chdir("..")
from data.dataset import ConditionalDataset
from data.utils import to_one_hot, lo... |
cae321851e172cfaadbde0962846e706a350174160ec2a8ba433edcc4b3cd087 | Jupyter | 33,771 | 731 | # %%
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
base = '/home3/ebrahim2/beyond-brainscore/'
from matplotlib import pyplot as plt
from sklearn.metrics import mean_squared_error
import sys
sys.path.append(base)
from plotting_functions import plot_across_subjects
from trained_untrained_results_funcs impor... |
2bfaad20af947b1aef3fbbc82c6e2bca78ea25aec124b2edb8ab73b5e17d7655 | Jupyter | 34,570 | 812 | # %% [markdown]
# #AlphaFold2 w/ MMseqs2
# Easy to use version of AlphaFold 2 [(Jumper et al. 2021, Nature)](https://www.nature.com/articles/s41586-021-03819-2) a protein structure prediction pipeline, with an API hosted at the Södinglab based on the MMseqs2 server [(Mirdita et al. 2019, Bioinformatics)](https://academ... |
2992df54fca68e1284c60faea2dcd8a6099d4296a49c37a102f5394b06cae274 | Jupyter | 35,438 | 746 | # %%
# load libraries
import os
import torch
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from torch import optim
from torch.utils.data import DataLoader
os.chdir("..")
from data.dataset import ConditionalDataset
from data.utils import to_one_hot, lo... |
4d8029c97b24d95e206d8d0dc04c2d966b606a11c43d7a04738d7c333c0fd4cb | Jupyter | 35,439 | 988 | # %% [markdown]
# Carries out gene-area boundary analysis and generates figure panels for fig 1e&f and supp figure 2
# %%
import numpy as np
import pandas as pd
import os
import nibabel as nb
import seaborn as sns
from matplotlib import pyplot as plt
import matplotlib_surface_plotting as msp
from scripts.prepare_gene_... |
82500d0fc798d2fa3df23320a4e160ca472c185db14bbb529143e38d039cc99e | Jupyter | 35,965 | 1,510 | # %% [markdown]
# # Installation
#
# https://github.com/theislab/scCODA
#
# conda create -n sccoda python=3.8
# pip install sccoda
# pip install ipykernel
# %%
# Setup
import importlib
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import pickle as pkl
import matplotlib.pyplot as plt
import t... |
fbf1d33ea2b4e466dbce95628e857eae46afc6e6626fa72db21fbbfcbeda5fdd | Jupyter | 36,797 | 738 | # %%
import pandas as pd
import numpy as np
import os
import h5py
from sklearn import linear_model
import matplotlib.pyplot as plt
import deepdish as dd
import string
try:
os.chdir('/data/MoL_clean/scripts')
except:
pass
import util
# util has some variables in them
# import GLM_helper as gh
import random
im... |
27dc14c5e847d50dce2bb9994d994a796f597fac4640b63cd2a1feecb9036369 | Jupyter | 37,981 | 875 | # %% [markdown]
# # TRUST MIC Predictions for First-Line Drugs
#
# ## First combine data from genotypic samples and patients
# %%
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... |
0d11722ed2ffa8f1948d2d12eea04bf79ba082006fc2d26b6a31e3764f6073e3 | Jupyter | 38,321 | 976 | # %% [markdown]
# # Saliency Plots
# %%
from dna_features_viewer import BiopythonTranslator, GraphicFeature, GraphicRecord
from dna_features_viewer.biotools import annotate_biopython_record
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import glob, os, yaml, sparse, itert... |
d1c2f7eeef6421a382822b6f0ffccd77c293e5adc0246616daf65cea7b7c5808 | Jupyter | 41,341 | 1,159 | # %%
import sys
import os
import numpy as np
import pandas as pd
# %%
eye_data_path = '/data/pt_02747/action_hippo/data/derivatives/eyetracker/'
event_data_path = '/data/pt_02747/action_hippo/data/'
subs_path = '/data/pt_02747/action_hippo/data/derivatives/'
# subs are all subjects in event_data_path
subs = os.listdi... |
6d5d66b07cc1a8c190a7687f944f342adf2816c6ef24886dbbbc2ea754821ee1 | Jupyter | 41,540 | 965 | # %% [markdown]
# # Further tests
# ##### In this notebook, we run some data quality checks, prepare initial tests, and make couple of figures. Some of this notebook's results are used in the paper.
# %%
import os
# Imports
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import pandas as pd
impor... |
d2b0e7cd86c8e4d92d306f735a17ce1b812319126fa31d4fa1ec6182373d2f69 | Jupyter | 41,650 | 776 | # %% [markdown]
# <h1 style="color: #1e88e5; font-weight: bold; margin-bottom: 5px;">NeuroBED_ML: DATA EXPLORATION NOTEBOOK</h1>
# <hr style="border: 2px solid #cfd8dc; margin-top: 0; margin-bottom: 20px;">
# %% [markdown]
# <div style="color: #37474f; font-size: 16px;">
# <p>
# This notebook performs data explo... |
b26775966a4d75360e2ae809c7f44fa369b70e32dd0ebf19fed4273a3368d7b5 | Jupyter | 42,587 | 1,187 | # %% [markdown]
# # Notebook for fine mapping based on the PD meta5v2 summary results
# %% [markdown]
# ## Jan 27, 2020
# ## **Author** - Raph Gibbs
# %% [markdown]
# #### set global variables and import libraries
# %%
#set up notebook global variables
WRKDIR = '/path/to/finemap/meta5v2'
AUTOSOMES = [str(x) for x in... |
902f2e3e0bebb7f6464ffc1b53b39d53e2226558393077c86a3f128caa53ae77 | Jupyter | 42,596 | 1,187 | # %% [markdown]
# # Notebook for fine mapping based on the PD meta5v2 summary results
# %% [markdown]
# ## Jan 27, 2020
# ## **Author** - Raph Gibbs
# %% [markdown]
# #### set global variables and import libraries
# %%
#set up notebook global variables
WRKDIR = '$PATH/spd/finemap/meta5v2'
AUTOSOMES = [str(x) for x i... |
6ee69da01a608b598be40e9b111643a042076a75b39140ab4dcd2ec5234ddd0d | Jupyter | 42,652 | 1,529 | # %% [markdown]
# # Figure 3
#
# 
# %%
%load_ext autoreload
%autoreload 2
import os
import pickle
import logging
import itertools
from pathlib import Path
import sys
sys.path.insert(0, './prepare_data')
import Figure3_prepare_data as prepare_data
import scipy
import numpy as np
import p... |
f9731c7ae054c71f880f073c1db5e779599c06a5402ad62cd000848bcb5983fb | Jupyter | 43,021 | 897 | # %%
library(Seurat)
library(caret)
library(dplyr)
library(Matrix)
library(readr)
library(ggplot2)
library(stringr)
library(ggpubr)
library(tidyr)
library(ComplexHeatmap)
library(circlize)
# %%
sem <- function(x) {
sd(x, na.rm = TRUE) / sqrt(length(na.omit(x)))
}
# %%
load("/home/sridevi/inkwell03_sridevi/metadevor... |
440be8cef5d33ced7c6feb3eea1311a229d355dfa4c591e60af33fb5c9021b0a | Jupyter | 43,052 | 1,268 | # %% [markdown]
# # Souporcell + Vireo Donor mapping
# %% [markdown]
# ## Import modules
# %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from cyvcf2 import VCF
import vireoSNP
import glob
import subprocess
import tempfile
import json
# %% [markdown]
# ## Da... |
cce4e0c24589ef7c7e5b6fdc2f7781a75aed1315ae1985267011f0fcb0a11ad5 | Jupyter | 43,582 | 138 | # %%
import re
def count_words_skip_citations_and_punctuations(text):
# Remove \citet and \citep citations from the text
text_without_citations = re.sub(r'\\cite[t|p]*\{[^}]*\}', '', text)
# Remove all punctuation using regex
text_without_punctuation = re.sub(r'[^\w\s]', '', text_without_citations)... |
2ca32478dcbe24864c8905d0bb76945d46152a5199de300dfaa909d895d1d854 | Jupyter | 43,870 | 864 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from cinnabar.plotting import _master_plot
from cinnabar import stats
import seaborn as sns
import numpy as np
sns.set_context("talk")
# %%
# load the basic edge data
cumulative_data = pd.read_csv("https://raw.githubusercontent.com/OpenFreeEnergy/IndustryBenchm... |
98f4b028094bdd8d3b6b414b5708fb8aa4c9e42ffd915ca4513537ca01bc1bc8 | Jupyter | 44,302 | 1,038 | # %% [markdown]
# # Extended Data Figure 5: Tracking jitter by 3D facial feature
# %% [markdown]
# To run this notebook, you need the following datasets:
# - `/anipose-projects/20231013-long-anes-rig2`
# %%
%load_ext autoreload
%autoreload 2
# Update path as if notebook was run from top-level repo directory
import o... |
e3f07ee7e6fbbb90c954e72941a6afbc8c20b47851e9d813d2d6589ad99e2a62 | Jupyter | 44,631 | 1,227 | # %% [markdown]
# ### Author: Hannah E. Aichelman
#
# This notebook identifies human cells in chimeric samples and cell types those human cells
# %%
# import needed libraries
import scanpy as sc
import pandas as pd
import numpy as np
import h5py
import anndata as ad
import matplotlib.pyplot as plt
import warnings
imp... |
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