sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
5737b46051e0117d2c3265f2b4d97ff308d07b904ce874552b9dcefe67ad037e | Jupyter | 3,785 | 130 | # %% [markdown]
# ## DATA EXPLORATION NOTEBOOK
#
# This notebook is used to explore the data and to get a better understanding of the two monkeys neural recording and stimulus set.
#
# The outline of this notebook is as follows:
#
# 1. Load Oleo and Pico data
# 2. Explore STIMULUS SETS
# %%
# import libraries
# ge... |
5b6f211b4ec81fbbcecb6042823df236b229ac72b527ed8e3a222ac6c398a472 | Jupyter | 3,787 | 143 | # %%
##
#Load Packages
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
import sys
import nibabel as nib
# %%
##
#Define Path to Code Database
DirPath = '/Your/Path/To/Code/'
##
#Define Output Path
OutputPath = '/Your/Path/To/Output/'
#Define Path to HCP1065 MD Map (in FSL this is equivalent ... |
7bed3fc4231bc3935d106dffcf565e2ebb8c45ec8b72bb50dbc1d4ef5e7c3872 | Jupyter | 3,788 | 132 | # %% [markdown]
# # Non-linear features
#
# This notebook demonstrates how to use YASA to calculate epoch-per-epoch non-linear features of a full-night single-channel EEG recording.
#
# Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:
#
# `... |
49199f2f3b255e84410288bd7a78128b1acc8582990ec8688389b7157160c61a | Jupyter | 3,791 | 90 | # %% [markdown]
# # State-based dFC for continuously varying measures
#
# The previous tutorials showed how to (1) estimate continuously varying dFC measures and (2) work with inherently state-based measures. However, Comet also allows users to perform clustering analysis on continuous measures.
#
# We can simply use... |
c10e7681b4bef6279774b4e128ae65cd81540927815d99248f0bd050800334d6 | Jupyter | 3,797 | 62 | # %% [markdown]
# # Make the polygon valid
# Fix self-Intersecting and other invalid polygons as in https://shapely.readthedocs.io/en/stable/manual.html
# %%
import os
import json
import glob
from shapely.geometry import Polygon, MultiPolygon, GeometryCollection
from shapely.validation import explain_validity, make_va... |
551fac8fabb8da106bb04b6d41ad2e7e8b0e9452d460c1c2fb0a16555371d2fd | Jupyter | 3,805 | 132 | # %% [markdown]
# # Laminar resting-state gradients demo
#
# This notebook reproduces the core analysis of the paper on a precomputed subject-averaged multilaminar
# functional-connectivity (FC) matrix using the **Schaefer-400** parcellation.
#
# The two helper modules (`gradients.py`, `surface_maps.py`) sit next to ... |
eb339f73861d3141fc5266b9ea6fd7601eb92b8a9db47356cef8037a35470908 | Jupyter | 3,805 | 111 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import pickle
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RepeatedKFold, RepeatedStrat... |
42127d90847defc22380e90b8f901de9f249851654aa65245c7df378f1b806f5 | Jupyter | 3,812 | 97 | # %%
##
#Load Packages
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
import sys
# %%
##
#Define Path to Code Database
DirPath = '/Your/Path/To/Code/'
##
#Define Output Path
OutputPath = '/Your/Path/To/Output/'
# %%
##
#Load Functions
sys.path.append(''.join([DirPath,'bin']))
from EPGMotion... |
f6ab683c641d0952ffaf0f693777c23b261059f66081cde47e3fa318233de3ef | Jupyter | 3,814 | 141 | # %% [markdown]
# ## Create training datasets for decoder
# %%
import sys
sys.path.append('../utils')
import mat_to_tfrecord
from pathlib import Path
import numpy as np
from T17_SessionArgs import get_session_info
sessions = [
't17.2024.04.15',
't17.2024.04.16',
't17.2024.04.17',
't17.2024.05.06',
... |
fec102dc5398d2033b5a70de3451375a86c67fea4df428bd299a2763e812e697 | Jupyter | 3,815 | 156 | # %% [markdown]
# ## Load libraries
# %%
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import lightgbm as lgb
%matplotlib inline
try:
# To enable interactive mode you should install ipywidgets
# https://github.com/jupyter-widgets/ipywidgets
from ipywidgets import SelectMu... |
ae126ce5075bcc154bb6a73c2515843d5a602dac597359e1e32a1b5ef32eb17d | Jupyter | 3,837 | 190 | # %%
import os
import json
import requests
from requests.exceptions import HTTPError
from requests.auth import HTTPBasicAuth
# %% [markdown]
# ## Parameters
# %%
# this cell is tagged `parameters` and will be overridden by papermill
workflow_name = "asapseq"
workflow_id = "dbc6304d-26bf-42a1-b3bb-4fa65ffaf7a6"
path... |
1e61a55f73e9bef98a5934671c294bf711d77b32b1483e6cfeab9466b1befefe | Jupyter | 3,844 | 125 | # %% [markdown]
# # Synthetic Skull Filtering w/ Normalized MSE
#
# 1. We first trim the data to get rid of the large blank space in the lower half of the skulls.
# 2. Second, we define a function, MSE_norm(a,b), that ravels matrices a and b, computes the global maximum of the two, normalizes each vector according... |
31aa132f515af2f2fab3eddf624b1022f1a337c8928d2dda8ace450541d78fd1 | Jupyter | 3,856 | 108 | # %% [markdown]
# # Feedforward ANN (Backprop)
# %% [markdown]
# Here we will show the analysis for a single example seed of a network described in [Galloni et al. 2025.](https://www.biorxiv.org/content/10.1101/2025.05.22.655599v2)
# %%
import EIANN as eiann
from EIANN import utils as ut
eiann.update_plot_defaults()
... |
388ff6d1d8eb46ea56057a8998e2bcf8e5e7f764d4e6232b7e2994b75291eb0a | Jupyter | 3,857 | 104 | # %% [markdown]
# # Behavior Analysis: % Total Frames Per Behavior
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_3WeekMorphine'
# Select group and condition, see meta.py to see how you previously defined it for you... |
6bf76e0e7aeb0f87a3e2a5df7e64dacfa95cf85440530aa860efe3f793120214 | Jupyter | 3,872 | 168 | # %% [markdown]
# # Data Loader for the Cell atlas of aqueous humor outflow
# %% [markdown]
# Paper link: https://www.pnas.org/doi/10.1073/pnas.2001250117
#
# For data links use these or go to the subseries section on https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi with GSE146188
#
# Mouse
# https://ftp.ncbi.nlm.nih... |
172aa09037b55c0949bf3074b336ffb75866016db3aff341cc727637a4353c8d | Jupyter | 3,884 | 106 | # %% [markdown]
# # Behavior Analysis: % Total Frames Per Behavior
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_Study4_NociFormalinResponse'
# Select group and condition, see meta.py to see how you previously defined it for yo... |
181319dc89390fe4c359fd8f0ee5ee16389effa355d87598069f040e832ac4cf | Jupyter | 3,891 | 104 | # %%
import os
import csv
from pathlib import Path
base_path = 'new2_particleCorrec_all_noAreaCap/19032025' #### Adjust this path as per step 2
# Create list to store subfolder and image paths
data_list = []
# Walk through all subfolders
for subfolder in os.listdir(base_path):
subfolder_path = os.path.join(base_... |
34e2eb2450565e7f64bb36078af79e8f4c3f9c72f1fe9a4c1d67dfc189885f32 | Jupyter | 3,910 | 90 | # %% [markdown]
# # Data harmonization
#
# Data from different batches may have different characteristics, and in order to make sense of the entirety of the data, those characteristics have to be brought into agreement, which is what we call harmonization. When a model is fitted to data from different batches with the... |
c10bfa101e5fcadcc451a3bef6e73aec8f9abc41825da1b01ad87e07098b59ed | Jupyter | 3,911 | 106 | # %% [markdown]
# # Behavior Analysis: % Total Frames Per Behavior
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# Select group and condition, see meta.py to see how you previously defined it for your proje... |
5dda8c9401c75b5435ca5925ecbd10290aaea6f783c63e2b76e23db521f3f479 | Jupyter | 3,912 | 120 | # %% [markdown]
# <a href="https://colab.research.google.com/github/CPernet/OpenNeuro_MetaPrivAssessment/blob/main/EEGAlcoholImageryReinforcementLearningTask.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Link to the repo and im... |
f53fd12377139cc4cdf25dab02f29523e5e39b38cace562f4e07daaec432e72b | Jupyter | 3,917 | 106 | # %% [markdown]
# # Behavior Analysis: % Total Frames Per Behavior
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# Select group and condition, see meta.py to see how you previously defined it for your proje... |
879b60acda7969ca08ec67ce97ab7cedc13f760ef280ba4c91a3569d8091e407 | Jupyter | 3,922 | 124 | # %%
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split, RandomizedSearchCV, KFold
from sklearn.metrics import mean_absolute_error, r2_score
import matplotlib.pyplot as plt
from scipy.stats import uniform, randint
from BorutaShap import BorutaShap
import s... |
de75f526f0d980cfd481f7cadd2917dd7f0f2f3c83b479a1ca8189432eda6279 | Jupyter | 3,923 | 112 | # %% [markdown]
# # Preparing fMRIprep data for connectivity analysis
#
# This tutorial will use the same dataset and analysis as the GUI tutorial, so they can be seen as complimentary.
# If you have already downloaded the data, you can skip the following steps and simply modify the path to the `BIDSLayout` if neces... |
b93e0f7b13c94c1f89399da17df49296cf7cba276b9f26cd9e72b0bb112d1cdc | Jupyter | 3,924 | 149 | # %%
import scanpy as sc
import numpy as np
import pandas as pd
import os
import scipy.io
import seaborn as sb
import anndata
import matplotlib.pyplot as plt
%matplotlib inline
save_path = "/home/ubuntu/mouse_lymph_node/nova/"
# %%
nova_data = sc.read_h5ad("nova_processed_data.h5ad")
# %% [markdown]
# ## visualize ... |
cae0def07849403f192c32a44b732cbdc2aed105e869674f3e0e109366d2f396 | Jupyter | 3,928 | 148 | # %% [markdown]
# # HumanBreastCancer (Visium) Analysis Tutorial
# %% [markdown]
# <p style="color:#1E90FF;">
# <b>Data availability:</b> All processed spatial transcriptomics datasets are publicly available on Zenodo:
# <a href="https://doi.org/10.5281/zenodo.17522268" style="color:#1E90FF; text-decoration:underline;... |
016f10c21d4719e031e9db3e1c8a9cafaffff6022b403454a361ec1e449fdc63 | Jupyter | 3,936 | 110 | # %% [markdown]
# # Approximate single value decomposition
#
# The aproximate implementation works by:
# * binned the movie in 1 s bins (per channel)
# * Estimating U from the binned movie.
# * Iterating over the raw data to estimate S.VT
#
# Details in wfield-tools/decomposition.py
#
# The first example subtracts... |
34aed173bd51c1371d23b3d88e346f894afc7b4f580ffd38acbba85f66d80f8a | Jupyter | 3,939 | 114 | # %%
import pandas as pd
from scipy.stats import fisher_exact
import re
import sys
sys.path.append('../utils/')
from utils import *
from plot_utils import *
# %% [markdown]
# ## Fisher's exact test
# %%
## DEG
deg = pd.read_csv(f"/gpfs/gibbs/pi/girgenti/JZhang/CL/multiome/data_AUD/fig3-DEG/unique-all.txt", header=No... |
3578475b643793ff8a0119e1d83b245e4af26243674b67ddd6081ee7dbd1ec01 | Jupyter | 3,940 | 135 | # %%
# Use this file to find spikes and correlation in GCaMP videos (upward spiking)
file = 'dcre_nb_003_data.xlsx' ## Must include .xlsx
video_duration = 120 # In seconds
trace_outputs = True # False is default.
# Change to True if you want a file containing normalized values.
# Useful for making an example plot in ... |
220a2a4511e57a6c97026866152ce537a31360a209aa61c82a017c77061be0d4 | Jupyter | 3,945 | 122 | # %% [markdown]
# # Supplementary: Baseline-corrected individual line profiles - ID16A
#
# This notebook renders the multi-panel figure for the paper’s supplemen, Supp Figure S6:
# - **Epon 1** (blue)
# - **CPD 1–2** (red)
# %%
# ==== ID16A supplementary figure: baseline-corrected individual profiles (1×3) ====
impo... |
5d0bab80415c31b166544e14a5deb09a06970f52625e140b82e991974b4e3cbe | Jupyter | 3,958 | 197 | # %% [markdown]
# # Sankey Plot
# %%
##################################
# loading data
import pandas as pd
data = pd.read_csv('E:/wochong/s40. MetaCell/sankey/strong.csv',sep='\t',header=0)
data['label'] = data.apply(lambda row : row['From'].split('_')[1],axis=1)
data['time'] = data.apply(lambda row : row['From'].spl... |
42054af94731a28671bec7c6b080713ee6e429dd3a2b0f5837dc029d0d05c16f | Jupyter | 3,964 | 133 | # %% [markdown]
# ## Figure 5C
# %%
import numpy as np
import pandas as pd
# import mido
import ast
import scipy
from scipy.stats import ttest_rel, wilcoxon
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statannotations.Annotator import Annotator
import matplotlib.pyplot as plt
import matplo... |
2c8cec20ebf7b1849ae3e3741b388f8f76b02fa21a025624e094115308d2f6fe | Jupyter | 3,965 | 119 | # %% [markdown]
# # 1. INIT - Import packages
# %%
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 environment... |
e4017639ef2d3bca9f246ac993ac37db1ce9e0a550a8e3fef6d2546d3c44f481 | Jupyter | 3,965 | 109 | # %%
import os
import json
import pyreadr
import time
from pathlib import Path
import seaborn as sns
import pandas as pd
import numpy as np
import pingouin as pg
import matplotlib.pyplot as plt
from joblib import Parallel, delayed
from utils.setup_paths_and_configs import setup_paths_and_config
# %%
dataset = 'AIB... |
50040082d6ab4a5f371158a255efb7f600e7ec547f2821b45d98123bfd3daf87 | Jupyter | 3,979 | 114 | # %% [markdown]
# # AnalogNAS Tutorial
# %% [markdown]
# [AnalogAINAS](https://github.com/IBM/analog-nas) is a framework that aims at building analog-aware efficient deep learning models. AnalogNAS is built on top of the [AIHWKIT](https://github.com/IBM/aihwkit). IBM Analog Hardware Acceleration Kit (AIHWKIT) is an op... |
1ab8b81311ad6ca07981b7f61203cd92e38616d8fa951f88d43ef2cb954d3455 | Jupyter | 3,990 | 115 | # %%
from wfield import *
# load data and references
localdisk = '/home/joao/dual/temp_folder/'
frames_average = np.load(pjoin(localdisk,'frames_average.npy'))
resolution = 0.0194 # mm per pixel
image = frames_average[0] # load a frame from the references
bregma_offset = (np.array(image.shape[::-1])/2).astype('int')
... |
256aee24faf455967a493893cbd3f3b0e382d8f70393272f08e77e2052ce613f | Jupyter | 4,004 | 119 | # %% [markdown]
# # Multiverse integration
#
# Comet includes multiverse integration methods based on the weighting schemes introduced in:
#
# > Cantone, G. G., & Tomaselli, V. (2025). Characterisation and calibration of multiversal methods.
# > Advances in Data Analysis and Classification, 19(4), 989-1021. https:/... |
f5988bbfe2cc2e67626600df7bda6a4d3051ef070f2b003c22c2824c77197ea0 | Jupyter | 4,004 | 147 | # %%
import os
import wget
import tarfile
import numpy as np
import pandas as pd
import h5py
import obspy as obs
# %%
DASH_data_folder = os.path.join("data", "DASH")
nodal_data_folder = os.path.join("data", "nodal")
meta_folder = os.path.join("data", "meta")
for folder in (DASH_data_folder, nodal_data_folder, meta_f... |
187a686abe7cf757f1429835eca575431af744b23b813a98621de87c5d1619a1 | Jupyter | 4,011 | 128 | # %%
import pandas as pd
import numpy as np
import scanpy as sc
import anndata as ad
import scFates as scf
import palantir
import matplotlib.pyplot as plt
import scipy.stats as stats
from matplotlib import rcParams
plt.rcParams.update(plt.rcParamsDefault)
%matplotlib inline
rcParams['font.family'] = 'arial'
sc.setting... |
1d951790d3c608f81dd72de80460de7eeb382e3bc1cb48c61041e90ac4e3f375 | Jupyter | 4,021 | 112 | # %%
import pandas as pd
import numpy as np
import tensorflow as tf
from src import create_fake_patients, whole_model_demographics_gradcam, graph_plot, plot_feature_value, ga, gc, utils
# %%
second_TGCNN_layer = True
demo = True
stride = 1
filter_size = 4
num_filters = 16
years_in_advance = '5'
hip_or_knee='hip'
max... |
8f96b49578372e9d72dfa2855f1137c9f0f73f1d18d0c905c7ab46e56b037d73 | Jupyter | 4,028 | 124 | # %% [markdown]
# <a href="https://colab.research.google.com/github/CPernet/OpenNeuro_MetaPrivAssessment/blob/main/TheMidnightScanClub.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Link to the repo and import local data and lib... |
05e3d220d06e4fe50bc5e1e157c79aaa5223c67009e8e4d77cf66b52d52f2f9e | Jupyter | 4,030 | 147 | # %%
import pickle
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
import pingouin as pg
# %%
files = pickle.load(open("./data/emnlp_scores_coha.p", "rb"))
files.keys()
df1 = files["moral_relevance"]
df1["property"] = ["previous_link"] * len(df1)
df2 = files["moral_polarity"... |
3879a162c79ae11ecc54bd9e4141aae321abe7d53b88bf172361c5f72f94da0f | Jupyter | 4,030 | 112 | # %% [markdown]
# # 1. Import CellRefiner and packages
# %%
import seaborn as sns
from matplotlib import pyplot as plt
import scanpy as sc
import cellrefiner as cr
# %% [markdown]
# # 2. Load Data
# %% [markdown]
# The source data of human SCC Visium and scRNA-seq datasets can be found at the NCBI Gene Expression Om... |
57301d5088b69bb52f0cccfbe6f01ef79e19e243ee8ec729dd515577213038de | Jupyter | 4,030 | 110 | # %%
from organelle_transport_analysis import kymographs, directions, velocities, plotting
import matplotlib.pyplot as plt
import json
# %%
# Custom time and distance parameters
time_c = 0.1563
distance_c = 0.092
# %%
# Importing distances to soma for each kymograph
f = open('head_distances_ida1.json')
head_distances... |
cdafb35ed55f3421d8f2e514be32f4948ed44d2d46f28f6d818b9692eb30ade9 | Jupyter | 4,034 | 141 | # %%
##
#Load Packages
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
import sys
# %%
##
#Define Path to Code Database
DirPath = '/Your/Path/To/Code/'
##
#Define Output Path
OutputPath = '/Your/Path/To/Output/'
# %%
##
#Load Functions
sys.path.append(''.join([DirPath,'bin']))
from EPGMotion... |
695f9cd654d8eefbbfe8bd6b4bcecf4950b6c29e6d2a5c4be4aeed251bf5edb8 | Jupyter | 4,042 | 124 | # %% [markdown]
# This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
#
# -----
#
# # Connectome Manipulation : Connection Removal
#
# ## Introduction
#
# Welcome to this demonstration on manipulating connectomes within SONATA circuit mo... |
210ae8c82a703280000fd5ad63a6579e0cde1af8a25542c32a1c62e69967c396 | Jupyter | 4,065 | 119 | # %%
import torch
import os
import sys
sys.path.append('/hdd/yang/projects/glomeruli_segmentation/2025-zhou-hipct-hierarchical-segmentation')
from segmentation.models.kidney_vnet.net.ResUnet import ResUNet
def load_model(checkpoint_path, model_name):
checkpoint = torch.load(os.path.join(checkpoint_path, model_name... |
16f9ab376b71fb028329b0178d85ac1f2c9e52e36fa6311ed2295f35b3f9581d | Jupyter | 4,067 | 114 | # %% [markdown]
# # Behavior Analysis: % Total Frames Per Behavior
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_acc_MiniscopeCap'
# Select group and condition, see meta.py to see how you previously defined it for your project
sele... |
1d7becf03477eb930c415803ed3560a2ce033bdcb7ed076b2fb7e6a6b60e3198 | Jupyter | 4,094 | 171 | # %% [markdown]
# This example demonstrates the MUSE reconstruction.
#
# Author:
#
# * Zhengguo Tan <zhengguo.tan@gmail.com>
# %%
import h5py
import os
import torch
import matplotlib.pyplot as plt
import numpy as np
from sigpy.mri import retro, sms
import torch.nn as nn
import torch.optim as optim
from deepdwi.mo... |
62e409aa855239f62217b5750901f4b7f68bc978e3e224f45ef4c4b829a9b64d | Jupyter | 4,103 | 84 | # %%
import datajoint as dj
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
import pandas as pd
import scipy
from tqdm import tqdm
from staticnet_analyses import gabors
from staticnet_pilots.analyses i... |
7e8b8b057d6b96026e8e5010c1134094ba6b53701730a5a6010dcf16059fc7a8 | Jupyter | 4,109 | 165 | # %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# %%
from nltk.stem import WordNetLemmatizer
# %%
ts_df = pd.read_csv("./data/SWOW_prediction/eval/time_series/ts_df.csv")
ts_df = (
ts_df.groupby(["words", "year", "property"])[["outputs_z", "count"]]
.mean()
.reset_index()
)
ts... |
c1049137b9873204d42f2816a3e7b8dabf270465bea508ee5e3d74fa8e26923e | Jupyter | 4,125 | 112 | # %%
import nibabel as nib
import cv2
import matplotlib.animation as animation
import matplotlib.pyplot as plt
from matplotlib import patches as ptc
import numpy as np
from scipy import optimize
import os
import pickle
from pathlib import Path
import src.contour as con
import src.contextract as ce
# %% [markdown]
# ##... |
06f16da834c90dc704436eafc98b82cfc3b5bc8e797aacf62f114d4ead19a215 | Jupyter | 4,126 | 101 | # %% [markdown]
# ### plot the average memory uasge and running time of 12 DLPFC samples for each algorithm
# %%
mean_run_time = c(0.42, 0.62, 1.36, 1.86, 4.66, 15.86, 46.73)
sd_run_time = c(0.02, 0.09, 0.2, 0.3, 0.52, 5.65, 9.49)
method = c('Leiden','SpaSEG','SpaGCN','SEDR','stLearn', 'Giotto',
'BayesSpace... |
f1593faf96801101e9422bb62e7f610540474d47e38fc12af128d9c6fd055629 | Jupyter | 4,134 | 143 | # %% [markdown]
# # Test spectral initialization
# - generate data
# - use spectral initialization to get embedding
# - compare the embedding with real value
# %%
import sys
sys.path.append('../')
from src_simulate_data.sim_data import Simulated
import matplotlib.pyplot as plt
from dlim.dataset import Data_model... |
521de05b1837c8dd22981d12c87893761f5e3d890bc1ab40b85b91b6746389ad | Jupyter | 4,141 | 111 | # %% [markdown]
# # Multiverse basics
#
# To conduct a multiverse analysis, the forking paths must be specified in a dictionary. Options can contain:
#
# * strings
# * numerical values
# * boolean values
# * comet dFC methods
# * comet and bct graph measures
# * any kind of function
# %%
from comet.multiverse import... |
c78a5290a72e672fefbe57cd374230e9342137023ced111d4de224949db228e1 | Jupyter | 4,141 | 115 | # %% [markdown]
# # Params description
# %%
import pandas as pd
# %%
PARAMS = \
{'preprocess':
{
# We only recommend modifying the following 2 hyperparameter including path and dataset_file.
'path': './dataset/',
'dataset_file': pd.DataFrame(
[['raw-live... |
3abb670ececf510a219f141308ed52249cb0007043b90b8f3d5130a6953bec37 | Jupyter | 4,143 | 117 | # %%
%matplotlib inline
import spikeinterface.full as si
import numpy as np
from os.path import join, split
from glob import glob
import matplotlib.pyplot as plt
from pathlib import Path
# %%
spikeglx_folder = Path(r'/path/to/bin/file/')
if len(glob(join(spikeglx_folder, '*.cbin'))) > 0:
rec = si.read_cbin_ibl(sp... |
077dac124fd5f90fd369c26980e8f7b47e357a117e916fe1097dc4020dd1d34f | Jupyter | 4,160 | 108 | # %% [markdown]
# # Create MEIs
# %%
from neuro_data.static_images import data_schemas as data, stats
from neuro_data.static_images.data_schemas import meso, experiment
import datajoint as dj
from staticnet_experiments import models, configs
from staticnet_analyses import multi_mei, closed_loop
import numpy as np
impo... |
71b2c8d404b595635cab938f1b40544832b4346387754f177d8caf0c18d2b22c | Jupyter | 4,168 | 147 | # %%
import sys
sys.path.append('../utils')
import mat_to_tfrecord
from pathlib import Path
import numpy as np
import os
from sklearn.metrics import f1_score
session = 't17.2024.03.26'
blocks = [10,14,15,16,17,19,20,21,22]
trialsToRemove = []
dataDir = str(Path('../Data', 't17', session, 'brainToText'))
data = mat_to_... |
909ba033c0d6f0f4b116db7d851d0b74089e822ad8b3f6b8f3801a2a2a14a721 | Jupyter | 4,177 | 116 | # %%
from wfield import *
# This will create a landmarks file and a mask in the "session_folder" folder
template_folder = '/home/data/JC111/20230628_171517/wfield/'
session_folder = '/home/data/JC111/20230520_164209/wfield/'
transform, nlmarks, nmask = prepare_multisession_match_files(session_folder, template_fol... |
6e0589fa57fa4313421e4177a06d7f2b731c1e713ec562fade3ad2b10ad215c6 | Jupyter | 4,186 | 142 | # %%
import os.path as op
import nibabel as nib
import numpy as np
import scipy as sp
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
# %%
work_dir = "/Users/jmt/sandbox/epi2resp"
dphi_pname = op.join(work_dir, "dphi.nii.gz")
wmask_pname = op.join(work_dir, "wmask.nii.gz")
ts... |
c107bee5d62b745b689c4cd3d54a47dfee03792357927bc2661435bdc84dfef9 | Jupyter | 4,205 | 94 | # %%
import warnings
warnings.filterwarnings('ignore',
message='.*Attempting to register factory for plugin.*')
warnings.filterwarnings('ignore',
message='.*computation placer already registered.*')
import os
os.chdir('/app/NBT-Classifier/')
import glob
import numpy as np
import pandas as pd
import openslide
... |
fa7315e4a73d8c8b69f89d43e6f76e708965827938540fe3dd1d025df6fdd20d | Jupyter | 4,220 | 81 | # %% [markdown]
# ```
# This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
# ```
# %% [markdown]
# # Correlation between physiological synapse parameters
#
# By default, during rewiring, physiological synapse parameter values are drawn in... |
da4b791ad0aed7d52d670927a83b39afdef83ba30cc507d375e41e0dd2c59256 | Jupyter | 4,227 | 204 | # %%
import os
from time import time
import jax
from jax import random
from PIL import Image
from splotch import get_input_data, run_nuts, run_svi
os.environ["XLA_FLAGS"] = "--xla_force_host_platform_device_count=8"
Image.MAX_IMAGE_PIXELS = 1000000000
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure... |
bb869891f40eed7280e69517a0ce21240690d5cce558151efd29e917fae7e633 | Jupyter | 4,236 | 136 | # %%
import numpy as np
from nilearn.maskers import NiftiMasker, MultiNiftiMasker
from nilearn.image import math_img, concat_imgs, smooth_img
from nilearn.regions import RegionExtractor
from nilearn.plotting import plot_prob_atlas
import matplotlib.pyplot as plt
# %%
def post_process(img, threshold=95):
extractor... |
f1306dfde16b0e2561b94c46f891d91dbfe145de5a85bf95070a262676f65f6d | Jupyter | 4,242 | 141 | # %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from adjustText import adjust_text
from scipy.stats import pearsonr
# %%
test_results_path = "./data/SWOW_prediction/eval/"
properties = ["previous_link", "polarity"]
model_name = "bert-base-uncased"
reduces = ["forward",... |
0f50192ad6d69511e66bdeaa1f7ad06c9256895225c27949b05fb47b15095447 | Jupyter | 4,243 | 129 | # %%
import os,csv,re
import pandas as pd
import numpy as np
import scanpy as sc
import math
import SpaGCN as spg
from scipy.sparse import issparse
import random, torch
import warnings
warnings.filterwarnings("ignore")
import matplotlib.colors as clr
import matplotlib.pyplot as plt
import SpaGCN as spg
import cv2
# %%... |
2499b7eb794322a5e4d74af080e4306e2c0899b564d2920f5468583790542aaf | Jupyter | 4,263 | 138 | # %%
import numpy as np
import mne
import matplotlib.pyplot as plt
from eeglib import eegprep
from configs import FS, DIR, EPOCH_RANGE, CH_SET
# set logging level
mne.set_log_level('ERROR')
# %%
filename = 'S14_S2.fif' #select the file to visualize
proc_before_ins, proc_after_ins, ica_before_ins, ica_after_ins = ee... |
d76a3fbdccabb2ad6910b801b890ce12e67712d3924dfc8772bf04ce06a24815 | Jupyter | 4,268 | 162 | # %% [markdown]
# This is a minimal interactive example for trying demonstrating different models trained on a variety of datasets on a few typical examples.
# %%
import seisbench.models as sbm
import numpy as np
import matplotlib.pyplot as plt
from obspy.clients.fdsn import Client
from obspy import UTCDateTime
from i... |
07d6bb22d768d2c93ed72f19e14daa2ae8834fc0a8bff2f3211602f4548a4948 | Jupyter | 4,271 | 131 | # %%
import pandas as pd
import matplotlib
import matplotlib.font_manager as fm
fm.fontManager.addfont('/usr/share/fonts/truetype/msttcorefonts/Arial.ttf')
matplotlib.rcParams["font.family"] = "Arial"
import matplotlib.pyplot as plt
import seaborn as sns
# sns.set_theme(style = "ticks", font = "Arial")
from adjustText ... |
e4f62f546309ea29e6368ed2552f8e378b1ae56202a9057541a2da500e207039 | Jupyter | 4,277 | 138 | # %% [markdown]
# ### Imports
# %%
from importlib import reload
import numpy as np
import networkx as nx
import pandas as pd
from src import create_fake_patients, whole_model_demographics_gradcam, graph_plot, plot_feature_value, gc, utils
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
# ... |
31a9666591fe87b0a3e9cb8f6347a5d8a7e11c0d7a9233fb84b70897538d221e | Jupyter | 4,282 | 112 | # %% [markdown]
# # EI network - Backprop (Dale's Law, learned inhibition)
# %% [markdown]
# Here we will show the analysis for a single example seed of a network described in [Galloni et al. 2025.](https://www.biorxiv.org/content/10.1101/2025.05.22.655599v2)
# %%
import EIANN as eiann
from EIANN import utils as ut
e... |
168548d5a326552e462c5674914f433d946da3434912c8ed4082fb16febd82e4 | Jupyter | 4,288 | 118 | # %%
import sys
sys.path.append('../utils')
import scipy.io
import mat_to_tfrecord
from pathlib import Path
import numpy as np
import os
from sklearn.metrics import f1_score
session = 't17.2024.03.25'
blocks = [14,15,16,17,18]
trialsToRemove = []
dataDir = str(Path('../Data', 't17', session, 'brainToText'))
data = mat... |
879a4debcdb52e8f52fb56df54dedafde14b672c24e3a7b38bd4e72897492e67 | Jupyter | 4,297 | 151 | # %%
import scanpy as sc
import pandas as pd
import anndata as ad
import yaml
import socket
import os
import warnings
# %%
outdir="saved/scanpy/"
Branch_adata = sc.read_h5ad(outdir+"mlo_with_new_annot_wPaga_foetal.h5ad")
# %%
BranchBCs = Branch_adata.obs_names[Branch_adata.obs["endpoint_Astrocytes"] == 1].tolist()
B... |
42bf34dd7353ad3ac0be3c5ad2ed129e72388817e319bea0d1543d54760ecf7e | Jupyter | 4,307 | 100 | # %%
import pyranges as pr
# %%
dm6 = pr.read_gtf('Drosophila_melanogaster.BDGP6.46.113.gtf.gz')
# %%
# Filter the data for cannonical chromosomes
chromosomes = ['2L', '2R', '3L', '3R', '4', 'X', 'Y']
# Filter the data for the specified chromosomes
dm6 = dm6[dm6.Chromosome.isin(chromosomes)]
# Define positive and n... |
31195279c46d70c9bf27a9dd14a5f5f0c2851400238328ba2dfe393652ed352c | Jupyter | 4,310 | 150 | # %%
import scanpy as sc
import pandas as pd
import anndata as ad
import yaml
import socket
import os
import warnings
# %%
outdir="saved/scanpy/"
Branch_adata = sc.read_h5ad(outdir+"mlo_with_new_annot_wPaga_foetal.h5ad")
# %%
BranchBCs = Branch_adata.obs_names[Branch_adata.obs["endpoint_Mature Neurons"] == 1].tolist(... |
4c48a49bf549a1c502b1237a2d7570067a9fc767f7994adeb1d42104ac43cb20 | Jupyter | 4,323 | 127 | # %% [markdown]
# This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
#
# -----
#
# # Connectome Manipulation : Connection Removal
#
# ## Introduction
#
# Welcome to this demonstration on manipulating connectomes within SONATA circuit mo... |
f4b13d68dbccd37fa4f29e264e37dc8413fd16d941f06ece59bd62a300610251 | Jupyter | 4,337 | 85 | # %% [markdown]
# # Subselecting nouns and visualise psycholinguistic properties
# %% [markdown]
# We now have a list of concrete-abstract word pairs generated using LexOPS (Taylor et al., 2020) and stored as `word_list_seed42_2023-04-14.csv`. In this notebook, we want to (1) remove some nouns because they are not app... |
a6c39f3f19943a20fbec6c2cf97d2160e130f94bb75aea60562c35071247218b | Jupyter | 4,353 | 115 | # %% [markdown]
# # Brain-behaviour association simulation example
#
# A multiverse is defined as a dictionary of decision/option pairs. For example, consider a hypothetical analysis pipeline which correlates global efficiency of static brain networks with general cognitive abiity. Such an analysis pipeline involves m... |
898b94f2bd5ed4f74a5bae2a03793620f6a8d7d20f12174719ff4844c7da28aa | Jupyter | 4,363 | 139 | # %%
import os
from scipy import stats
import tempfile
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
from matplotlib.colors import rgb2hex
import pickle as pkl
from sklearn.model_selection import train_test_split
from sklearn.metrics import Confus... |
d801551308e3145c1f5e43ab578b70ab8d42cb888f2b02f93fe5bbf42fc94806 | Jupyter | 4,379 | 114 | # %% [markdown]
# # EI network - Backprop (Dale's Law, fixed inhibition)
# %% [markdown]
# Here we will show the analysis for a single example seed of a network described in [Galloni et al. 2025.](https://www.biorxiv.org/content/10.1101/2025.05.22.655599v2)
# %%
import EIANN as eiann
from EIANN import utils as ut
eia... |
c9e862ba510bdafcd55505fc7c5872a39bc92c9f32316d5c5a442350207eaedc | Jupyter | 4,380 | 140 | # %%
import os
import re
from scipy import stats
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
import seaborn as sns
from matplotlib.colors import rgb2hex
import pickle as pkl
import sys
sys.path.append('../../CEBRA_analysis')
from dataset_load im... |
0da33ce66acf58a1e2e826cb71c452a79b2a0e4194d2bb407f570a3ca0746e22 | Jupyter | 4,385 | 125 | # %% [markdown]
# # Automatic sleep staging
#
# This notebook demonstrates how to perform automatic sleep staging of polysomnography data in YASA. For more details, make sure to read the [eLife publication](https://elifesciences.org/articles/70092).
#
# Please install the latest version of YASA first with: `pip insta... |
dfe96625571efca604304f4ada787dc28df2c6d481626dc810bee34a883f2aea | Jupyter | 4,386 | 137 | # %% [markdown]
# # Average hemodynamic response
# %%
from wfield import *
folder = 'd:/data/DemoRec_new/'
SVT = np.load(pjoin(folder,'SVT.npy'))
SVT_470 = SVT[:,::2]
SVT_405 = SVT[:,1::2]
SVT_corr = np.load(pjoin(folder,'SVTcorr.npy'))
U = np.load(pjoin(folder,'U.npy'))
T = np.load(pjoin(folder,'T.npy'))
# filter a... |
75130f4d8d1e74e8dc0013be7ec7a3c6560f4fc2d3a83eb0963a8b841ea65fc4 | Jupyter | 4,407 | 152 | # %% [markdown]
# # LPS vs RAS conventions in tractography data
#
# This notebook demonstrates the issues when dealing with LPS vs RAS conventions in tractography files.
# %% [markdown]
# We begin downloading the data.
# %%
import os
from pathlib import Path
import requests
output_path = Path(os.getcwd())
def dow... |
8b5841b09fb830a40ceb742bf28df05c41f990efb2b3c846a368cd897bd5a778 | Jupyter | 4,413 | 172 | # %% [markdown]
# # DeBCR API tutorial
# ## Deblur data using trained DeBCR model
# %% [markdown]
# This notebook shows how to restore low-quality microscopy data using DeBCR.
#
# To achieve that you would need:
# - **pre-processed input data** by normalization and patching;
# - **trained DeBCR model** on the same-si... |
91a7205462b3bcce3476852d88e2508f0550e28e433fc9d02dd7b7ec511d427c | Jupyter | 4,413 | 129 | # %% [markdown]
# ## sim_rewiring_ex2.py
# %%
#!/usr/bin/env python
"""Shows effectiveness of post-spike plasticity."""
import os
import time
from core import core_global as core
from core.spike_monitor import SpikeMonitor
from core.voltage_monitor import VoltageMonitor
from core.weight_matrix_monitor import WeightM... |
43ce998248681c3fa7a03edb0fde343b3492bda442de2a7b637aa57a9fe9b931 | Jupyter | 4,418 | 141 | # %% [markdown]
# # Tutorial 0: get the phenotype and fitness landscape
# %% [markdown]
# If you didn't install dlim: !pip install dlim
#
# dlim includes:
# - DLIM: model
# - utils: for data and training
# - plot_figure: for plotting figures
# - sim_data: simulate data points
# %%
# Standard imports
from numpy ... |
bb2178d8699e4ed5e223f32d9cdc549e70b3a0cc85d5fd7d0a465b2b43049f67 | Jupyter | 4,420 | 128 | # %%
import os
os.chdir('/app/NBT-Classifier/')
import random
import numpy as np
import pandas as pd
from PIL import Image
import staintools
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
# %%
features_df = pd.read_csv("../examples/NBTClassifier_512px_externaltesting.csv")
# %... |
43ab5434fcdd0fd48697979b3e224a13d0cec0fed52e501322c64634d39285bd | Jupyter | 4,431 | 139 | # %%
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# %%
from utils import get_mean_normal, find_location_index_cell_type, metrics_vector, discrete_histogram
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
i... |
e79fbdabb8ec15c77ac67f0f9dc0de4fcf1695130d1bfa48003813395c3c54d3 | Jupyter | 4,444 | 160 | # %% [markdown]
# # Plot Metrics
# %%
from pathlib import Path
from collections import defaultdict
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from torch.jit import load
# %%
model_identifier = 'bi_lambda-30_lb-10'
split = 'test'
df... |
5ce323c29a0aa5260912e7fbe3063579ea1c7eea573a62c5cb857444e1184f30 | Jupyter | 4,455 | 145 | # %%
import numpy as np
import pandas as pd
import pegasus as pg
import json
import os
import pegasusio
from pegasusio import UnimodalData, MultimodalData
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import itertools
import random
import matplotlib.cm
import glob
from collections import Counte... |
0a9a0690badc0fb6f830d619d52aaaa4addbd97aff08edbfa82f354f1e9f089f | Jupyter | 4,458 | 133 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
def hjorth_parameters(signal):
"""
Compute Hjorth Activity, Mobility, and Complexity.
Activity = variance of the signal.
Mobility = sqrt(variance(derivative) / variance(signal)).
Complexity = (sqrt(variance(second_derivative)/variance(d... |
0be4aa7db6b7f9860c86e7674cd80092a71c387c1951d5f5e3ac48909a4ebe51 | Jupyter | 4,463 | 101 | # %%
import json
import pathlib
# reading the 5-fold json file
fold = 4
json_path = '/hdd/yang/projects/glomeruli_segmentation/2025-zhou-hipct-hierarchical-segmentation/data/nnUNet_preprocessed/Dataset010_25-08Glom_search_w_fat_label_partly_filtered/splits_final.json'
with open(json_path, 'r') as f:
data = json.loa... |
a776cc52fa20db741eb70bfe1062a02e6b6ec29e4e112f48b3beef2bcabdb662 | Jupyter | 4,466 | 117 | # %%
import pandas as pd
pd.set_option('display.max_rows', 500)
# %%
data_artificial = pd.read_csv("data/20210205_all_expt_data_no_duplicates_solvent_calcs.csv")
# %%
# several molecules have 0 in experimetnal absorption results
data_artificial = data_artificial[data_artificial["peakwavs_max"] != 0]
# %%
# convert ... |
4e98de7e472f9b5df14fffa80a071a5894b56b5d740aa84edc9363bbf69d2c31 | Jupyter | 4,469 | 141 | # %% [markdown]
# # AVPENet — Quick-Start Demo
#
# This notebook demonstrates how to:
# 1. Load a pretrained AVPENet model
# 2. Run inference on a single audio-visual segment
# 3. Visualise cross-modal attention weights
# 4. Reproduce the key evaluation metrics from Table 2 of the paper
# %%
import sys
sys.path.inser... |
40e5e3c1a0feccbc91cc0b6387147eb3d38d596191a1ba9ed65e62d10a10f2c7 | Jupyter | 4,494 | 85 | # %% [markdown]
# This script takes stimulus materials created for our three experimental manipulations and put them into one stimulus file, to be taken as input to the stimulus delivery script.
# %%
import numpy as np
import pandas as pd
from random import sample
import matplotlib.pyplot as plt
import seaborn as sns... |
b94fa543652daea5722c0eae63570c9d2031bb9a5291a9394fc04157790cddf9 | Jupyter | 4,497 | 127 | # %%
import pandas as pd
import numpy as np
from scipy.stats import pearsonr, spearmanr
import seaborn as sns
import matplotlib.pyplot as plt
import pickle
from sklearn.metrics import root_mean_squared_error
from statsmodels.nonparametric.smoothers_lowess import lowess
# %%
#LIST OF PDB INFO FOR EACH PROTEIN
#N.B. D... |
672b988d8f52db1da453c418e8e8ec40415f3a4646d03b94e7dc13c23eaafd5d | Jupyter | 4,503 | 113 | # %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relative; top: 3px; left: 8px;">S2 Project: DNA Classification - (part0... |
ca67b237446b29d99466c242013d6f28abd11f72622732c43c95118beab0a401 | Jupyter | 4,528 | 114 | # %%
import pandas as pd
import numpy as np
import scanpy as sc
import os
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import homo... |
4ea1429ae1737f0777f2014bd5e9e32932a544002e2c4b849086dfb8cc9c913d | Jupyter | 4,534 | 158 | # %% [markdown]
# # JuSpyce API test: cross validation
# %%
import sys
import os
from glob import glob
import pathlib
import numpy as np
import pandas as pd
from IPython.display import display
import seaborn as sns
import matplotlib.pyplot as plt
from nilearn.image import load_img
# current path
wd = pathlib.Path().r... |
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