sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d09577e96555c9e0dc104a197f9e43a3cd196f926117e4c3a5dffdbd4b202755 | Jupyter | 32,326 | 928 | # %%
import pandas as pd
import time
import csv
import random
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn import metrics
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.utils.class_weight import compute_class_weight
import torch
import t... |
e6afc2abeff63a1a86936d42984ca4b497c6b7fc5059410b712e1cfdbb156fd4 | Jupyter | 32,422 | 818 | # %% [markdown]
# # Unit Testing of Time-Mapping Module
# %%
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import plotnine as p9
# %% [markdown]
# ## Set-up Time-Mapping Module
#
# `CumsumMapping` copy-pasted from 'bionetwork.py'... |
d616cea684829506a7febd207e2cdf01498d8fcecf345592f77be2b06b6fccec | Jupyter | 32,607 | 974 | # %%
import os, importlib, sys, time, h5py
# %%
import numpy as np
from scipy import io, linalg, stats, ndimage, signal
from scipy.sparse import diags
# %%
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'], 'size':12})
## for Palatino and other ser... |
07177616a4ee8fa3be7f73d7b9ddccbd4d45bb47023df210fd3702d12eb7b947 | Jupyter | 32,669 | 816 | # %% [markdown]
# # Allen2022 fMRI Benchmark -- Data Preparation Notebooks
#
# These notebooks walk through building the Allen2022 NSD fMRI benchmarks for brain-score,
# from raw NSD data all the way to scored benchmarks. Both volumetric (func1pt8mm) and
# fsaverage surface pipelines are covered.
#
# ## Required Data... |
80014a97e02246638e34b1c6dc01cce96f466b3b7fafc591e08320e2d177c156 | Jupyter | 32,741 | 848 | # %% [markdown]
# ### Initialization
# %%
import os, glob
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib.lines import Line2D
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %%
from network_flow_tracker import F... |
33a9aeb238bc203e473599f48ebda0d9e4264812e500fccc20068e809b64ad9a | Jupyter | 32,798 | 712 | # %% [markdown]
# # Multi-ROI Multi-Repeat Tuning
#
# **Objective:**
# Perform hyperparameter tuning across multiple ROIs (M1, M2, PPC, LEC, vCA1, dCA1) using Optuna-based optimization for selected tree-based models (CatBoost, LightGBM, XGBoost). Generate per-repeat and aggregated performance metrics with multiple i... |
eebafae07edca6f06275cc055d7aaa698510c86945371503836c1999d52b55aa | Jupyter | 32,817 | 804 | # %% [markdown]
# **MAKE SURE THE PICKLE FILE USED HERE IS UP TO DATE!**
#
# The pickle file is created in density3_test_total_neuron_count_pickle.ipynb
#
# The current validation script is here:
# - https://bbpgitlab.epfl.ch/molsys/personal/gene_to_ccfv3/-/blob/main/cell_atlas/validation.py?ref_type=heads
#
# Curr... |
83f0c9f58b877f9d53258e36f5ee3d14ff9f16e17f12aed761a167a8dd710d28 | Jupyter | 32,910 | 676 | # %%
import math
import pickle
import datajoint as dj
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as plticker
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage
import scipy
import seaborn as sns
import torch
fr... |
434b91b84d2a0ebe513bc34635919b63dbd3a2b9ead52734526b3bcc2d00b593 | Jupyter | 32,976 | 848 | # %%
import sys
import scanpy as sc
# setting path
import correlation_base as co
import run
import end2endPLS as pls
import matplotlib.pyplot as plt
import numpy as np
import importlib
from scipy.stats import pearsonr, kendalltau
import gseapy as gp
import pandas as pd
from gseapy import gseaplot
import warnings
from ... |
1f387e9d2a09e3e90b9f1a102bd9729d581eedac0681e14235893b454509fd51 | Jupyter | 32,996 | 854 | # %% [markdown]
# # 06 - Scientific Validation of Allen2022 fMRI Benchmarks
#
# Supplementary analyses addressing scientific validity of the Allen2022 NSD benchmarks.
#
# **Sections:**
# 1. Setup & load assemblies
# 2. IT ROI sensitivity analysis (streams ventral vs Glasser 9-parcel)
# 3. Train/test split stability (... |
7c4297c51eb38cea9a4c2d2a60d2452d55e05d44a349ed21e863720e29a50585 | Jupyter | 33,334 | 758 | # %% [markdown]
# # Synthetic Data Generation
#
# We will use a pre-tuned model to produce data. The model output should be a 3D array with condition, signaling node and time point for dimensions. The model output will be sub-sampled to produce the synthetic dataset.
# %%
import torch
import numpy
import LEMBAS_gener... |
bf2397fee03e8c50dcfff4901448a10ef88525e8e46a73686f12f56bd60cabfe | Jupyter | 33,342 | 676 | # %%
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','#... |
aeddf1df0eb830ed846686bfad170dd3e0c3cc7788181682fac26abafaf27705 | Jupyter | 33,405 | 1,036 | # %%
# ====== 1. Cell: Data Loading and Settings ======
import os
import pandas as pd
# CSV file path (band_gap column is removed to prevent data leakage/overfitting)
csv_path = "/kaggle/input/features/modelde_kullanlan_Data.csv"
# CIF directory path (extracted from zip, already loaded as input)
cif_dir = "/kaggle/in... |
653e203984fa1d86ef9b63e2f6de995137d681d620cc9640e613bc4b66684900 | Jupyter | 33,495 | 1,232 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import numpy as np
import os
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nilearn.masking import compute_epi_mask
from s... |
52665a62c6652435232a37cfa17527e172f5e11258dca2c65d362b17ffd59f6d | Jupyter | 33,517 | 655 | # %%
from QHT import QHT
from exactqcnn_tensornet import ExactQcnnMPS
from my_tensornetwork import MyFiniteMPS
import numpy as np
import math
import os
import re
import json
from tqdm.notebook import tqdm
# %%
class QHTMPS(QHT):
def __init__(self, exactqcnn:ExactQcnnMPS):
self.n_qubits = exactqcnn.n_qubit... |
6077c6ce586aedac4669da133f7f98e3c9777c90dd37bbc6afd2d3087b84143d | Jupyter | 33,556 | 966 | # %% [markdown]
# ## Introduction:
# %% [markdown]
# This is the first of a series of notebooks whose goal is to interactively show you some techniques to visualize and analyze behavioral and neural data.
#
# We assume some familiarity with Python, but we try to have a progressive complexity so that you can learn as ... |
852e98f2c8a0724c52b318a02951ee25979e3a3b9559e946d1fde9753344fee5 | Jupyter | 33,608 | 919 | # %% [markdown]
# # Toy Model Implementation
# %%
import os
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
import torch
import plotnine as p9
sclembas_path = 'LEMBAS'
import sys
sys.path.insert(1, os.path.join(sclembas_path))
from LEMBAS.model.bionetwork import format_network, SignalingMod... |
b2d289c6be32326797ab5f612175ed2e6a913f6f70c900dbccbed7b7ffaf0737 | Jupyter | 33,702 | 990 | # %%
import os, importlib, sys, time, h5py
# %%
import numpy as np
from scipy import io, linalg, stats, ndimage, signal
from scipy.sparse import diags
# %%
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'], 'size':12})
## for Palatino and other ser... |
125550d45809127f31a928c182d47171b3139f5e4b6a7cc913374d13d6eb807e | Jupyter | 33,704 | 853 | # %%
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
# %% [markdown]
# # load data
# %%
path = 'memristor_simulation'
result_path = os.path.join('./results/', path)
if not os.path.exists(result_path):
... |
8d2897afc1c1d020454cbe65801650fa4e0094fd883f40d2f80e6dcce9c5b401 | Jupyter | 33,824 | 988 | # %% [markdown]
# ## Analysis of Rgl3 signaling network in the developing dopaminergic niche (human)
#
# Luca Fusar Bassini
# %%
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(repr)
options(repr.plot.width=20, repr.plot.height=15)
options(Seurat.object.assay.version = "v5")
# %%
# Seurat o... |
7963d158fd04cdb25faa367f6893c95ab9d61319914f270412ce6714a3aa344d | Jupyter | 33,837 | 634 | # %% [markdown]
# plots
# - nc x h2 (obviously, the effect size drives power; also - PCOR MOSTest behaves as GCOR )
# - dist x t (most is always better than minp; minp power is growing when t increases; cauchy distribution => minp has thesame power as mostest)
# - dist x T (more traits = ... |
eb8ce710f128bb01d97ec746fa5d60589b177cb2f8ef87539cae27b76ad1f974 | Jupyter | 33,867 | 907 | # %% [markdown]
# # Pre-process DecryptM Dataset
#
# **Publication**: Jana Zecha et al. Decrypting drug actions and protein modifications by dose- and time-resolved proteomics.
# %%
import pandas as pd
import numpy as np
import os
import toml
import re
import requests
from unipressed import IdMappingClient
import tim... |
cbf5a0d7a00d36732283fbc8543a10a5bac5a062440a4a373a9590fc658d4a0d | Jupyter | 33,989 | 720 | # %% [markdown]
# # Paper STC Analysis Notebook
#
# This notebook provides an example of how the functions in `sesl` (structural eigenmode source localisation) can be used to perform source localisation on a simulated dataset.
# It runs on one example simulation, uses fsaverage, and demonstrates how the figures in ... |
32e037f2cced72532991c0e048188f4701519d0970a10776f24c5ef3a114fa98 | Jupyter | 34,327 | 728 | # %% [markdown]
# ## 4d
# %%
import scipy
import seaborn as sns
import matplotlib.pyplot as plt
from staticnet_invariance.deis_reconfigured import *
from staticnet_pilots.analyses import scatterplot_helper
from sklearn.decomposition import PCA
from scipy.spatial.distance import cosine,pdist,squareform,cdist
# %%
cla... |
d8bb35fac64890e2453ebee1a80ad62f85cc9d6b7dff4ab54683a99065a332a0 | Jupyter | 34,349 | 927 | # %%
# All imports for MBNN (Multi-Branch Neural Network) for PFAS efficiency prediction
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset, Dataset
from torch_geometric.data import Data, Batch
from torch_ge... |
ebbea047ca745e9d1a4046e00acd94a05b6959a03b689228e2e45b2086950afb | Jupyter | 34,550 | 728 | # %%
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Subset
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns
import random
import time
from tqdm import tqdm #
import os
from pathli... |
80ca10ea538beaf27e6048fcc8941112e488fd6b7290c901f589052d2e889c9d | Jupyter | 34,750 | 729 | # %%
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Subset
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns
import random
import time
from tqdm import tqdm #
import os
from pathli... |
c91d1c0839a78cacb5033839c5b690630901778f1f0d7e654c36eae70c01691b | Jupyter | 35,066 | 793 | # %% [markdown]
# # 06-morphometry-cognition-exploration.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Last updated: 2024-12-18; 2025-02-10; 2025-02-12 (Fig. 11)
# %%
import os
import numpy as np
import pandas as pd
import pyreadstat
import matplotlib.pyplot as plt
import seaborn as sns
imp... |
212e6f9843019ec64c164755935f423acc52a88339bffad196bd23802b2dcd85 | Jupyter | 35,102 | 999 | # %% [markdown]
# # Prot T5 Finetuning
# # per residue regression
#
# This notebook allows you to finetune ProtT5 to your own datasets
#
# The protein language model ProtT5 was first published [here](https://ieeexplore.ieee.org/document/9477085) and is available on [github](https://github.com/agemagician/ProtTrans). ... |
585db22e32bbc62ed6a58e5e71f84070ad78fda87dd1ad0384562ae857d4b332 | Jupyter | 35,231 | 545 | # %% [markdown]
# # The Effects of Marketing Decisions using the Bank Marketing Dataset
# %% [markdown]
# The [UCI Bank Marketing dataset](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing) comes from a real-world bank's direct marketing campaign. It is a popular dataset most commonly treated as a classification ... |
c698c666775e75a0fef1b7e2ccca8658d4a875244abf376cdf83df1ad08b26e8 | Jupyter | 35,333 | 985 | # %% [markdown]
# ## Analysis of Rgl3 signaling network in the developing dopaminergic niche (mouse)
#
# Luca Fusar Bassini
# %%
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(repr)
options(repr.plot.width=20, repr.plot.height=15)
options(Seurat.object.assay.version = "v5")
# %%
# %% [ma... |
3f13d0aaa73cbb35796023bae2a44deff89fd5b350dc5671ffe1127efec29090 | Jupyter | 35,363 | 757 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
dbea2eb7943595213acc8028b4669e9f5b713e02086cba96064af4d978dd4afa | Jupyter | 35,565 | 866 | # %%
# import relevant packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.stats as ss
import seaborn as sns
import torch
import torch_geometric
from torch_geometric.loader import DataLoader
# set figure parameters
sc.settings.set_figure_params(dpi=100)
# ... |
10ba5f84b3345c267f9f9762db633f5db5d1f5eb726b238a093d425099ac4a7e | Jupyter | 35,631 | 997 | # %% [markdown]
# ### Code for producing Figure 2, 3a and Supplemental Figure 4 for CHyMErA-seq manuscript
# %%
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
library(scales)
library(tibble)
library(data.table)
library(Seurat)
library(edgeR)
library(mgcv)
library(gprofiler2)
libra... |
0937116fd54cebece6cab6c6d1e3ccd040f5806608ee2bbfa3e900151a9b823d | Jupyter | 35,750 | 963 | # %%
import numpy as np
import pandas as pd
from scipy.spatial import distance_matrix
import scanpy as sc
from sklearn.neighbors import kneighbors_graph
from scipy.stats import pearsonr
import scipy
import ot
import squidpy as sq
import matplotlib.pyplot as plt
# %%
ad_sc=sc.read_h5ad('merfish_sc_test.h5ad')
ad_st=sc.... |
9ed12dcf3138fd296e74589b9fe94bbacb69b3947d8eab1f1dd01dca1296f6fd | Jupyter | 35,791 | 875 | # %%
# import relevant packages
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scanpy as sc
import scipy.stats as ss
import seaborn as sns
import torch
import torch_geometric
from torch_geometric.loader import DataLoader
# set figure parameters
sc.settings.set_figure_params(dpi=100)
# ... |
72c9f1e252195607f038753ec0a24dbca5dac68ed3b7cc9dd28ef0ad556fde5f | Jupyter | 35,795 | 706 | # %%
import pandas as pd
import numpy as np
from scipy.cluster import hierarchy
from scipy.cluster.hierarchy import dendrogram, linkage
from matplotlib import pyplot as plt
import seaborn as sns
import collections
import itertools
# %%
# Define methods
def read_values(parent_folder, min_num_shared_clones, da... |
94d8a3aeeb2ef77d67337ed1a4c6e5c68df153c99947f7534e70792296fd0b36 | Jupyter | 35,867 | 910 | # %% [markdown]
# <span style="color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;">An Exception was encountered at '<a href="#papermill-error-cell">In [20]</a>'.</span>
# %%
from ase import Atoms
from ase.io import Trajectory, write
from ase.build import bulk, surface, molecule, add_a... |
342fa9470a0b65e244a2d3bb5512175e51ad45140a8d6007fed50d941c62e13e | Jupyter | 35,886 | 1,000 | # %%
import os
import json
import pandas as pd
import numpy as np
from statsmodels.formula.api import mixedlm
from sklearn.preprocessing import StandardScaler
from statsmodels.formula.api import ols
from statsmodels.stats.multitest import multipletests
import glob
from utils.setup_paths_and_configs import setup_paths_... |
144971afcb05c586fd1b0f7835a2ba2248efb7746bd2ee475d23ea7f0e4694ee | Jupyter | 36,602 | 1,005 | # %% [markdown]
# # Figures for Wingert, Parida et al. 2026
# %% [markdown]
# Code to generate figures in Wingert et al. (2026) Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex. *Nature Neuroscience*. In press.
#
# ## Requirements
#
# * python >= 3.9
# * NEMS (N... |
5235c08eea4bf3e709f2832d8a8e7480251647a14272bb0ce97fe658c1939c95 | Jupyter | 36,806 | 875 | # %%
from pegasusio import UnimodalData, MultimodalData
import pegasus as pg
import pandas as pd
import glob
import itertools
import sys
sys.path.append('../utils/')
from utils import *
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
# %% [markdown]
# # Save h5_celltype
# %%
# data =... |
1e37bbc92392af3201bf21698fbee73e0abae79574f97a9d2035858f1753be8a | Jupyter | 37,002 | 915 | # %% [markdown]
# ## Figures 6EF, S4, S7, S8B
# %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import mne
import glob
import os
import sys
from scipy.io import savemat, loadmat
from scipy.stats import pearsonr, spearmanr, sem
from statsmodels.stats.multitest import fd... |
94ac2dc7c5dec600298f96641e82973e4a16756739fc016999fdba7b02c1a771 | Jupyter | 37,464 | 966 | # %% [markdown]
# # Pre-process EGF Dataset
#
# **Publication**: Song Feng et al. A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks
# %%
import pandas as pd
import numpy as np
import os
import re
from pycombat import Combat
import plotnine as p9
import matplotlib.pyplot as plt... |
ce0f916a88dc2609a5067b748689baeb8559084ef6ba6c6da13251680522ff6a | Jupyter | 37,473 | 1,030 | # %% [markdown]
# # TUTORIAL
# **_Training and validating CLASTER_**
#
# *Authors:*
#
# Marc Pielies Avelli ([marc.pielies-avelli@cpr.ku.dk](mailto:marc.pielies-avelli@cpr.ku.dk), [mpielies@broadinstitute.org](mailto:mpielies@broadinstitute.org) )
#
# Arnor Sigurdsson ([arnor.sigurdsson@sund.ku.dk](mailto:arnor.sig... |
3dc3675587f30d7bf14b6907c0c2e924bd1c8b3e1cb88137cd9e4ee2e3f42625 | Jupyter | 38,071 | 904 | # %%
!which python
import pandas as pd
print(pd.__version__)
import sys
print(sys.version)
import inspect
print(inspect.getfile(pd))
# %%
import os, sys, copy
import pandas as pd
from voxcell.nexus.voxelbrain import RegionMap
import numpy as np
import pickle
import nrrd
import re
import multiprocessing as mp
sys.pat... |
32755dd8baa51342edeed87f81dd25a0554bffc65046a9af8a34b48fa5d29629 | Jupyter | 38,265 | 1,032 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
import os
from cluster_surface import cluster_surface, visualize_clusters
surf = os.path.join("/Users/dchaimow/data/test_cluster_surface",
"colin.cerebral.L.flat.164k_fs_LR.surf.gii")
metric = os.path.join("/Users/dchaimow/data/test_c... |
ba97b04545278e0bf834c54666eb7106cb30470b74685a3d3292356e9c3f64d6 | Jupyter | 38,297 | 690 | # %%
# Basic imports
import numpy as np
import pandas as pd
# Plotting tools
import bokeh
from bokeh.io import show
import cellseg.plot
bokeh.io.output_notebook()
# %% [markdown]
# ## Figure plotting
# The purpose of this code is to generate the figure panels for publication from the data collected by `in_vitro_cel... |
c596e6ba742c02d51dfce41c1b6cfeb83e0fad6aa1727e618dc98b3dff8781e4 | Jupyter | 38,355 | 1,188 | # %%
import scanpy as sc #important note: the source code for scanpy heatmap was modified locally to suite the visual preferences (Ziadoon Al-Akashi)
import anndata as ad
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
import ... |
5e7bab9566058614161ee93b10162dc81867e4aef63019ee316443c7ef59234f | Jupyter | 38,742 | 951 | # %% [markdown]
# ## LASSO select features
# %%
## LASSO
from sklearn.linear_model import LassoCV#导入Lasso工具包LassoCV
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def Lasso_pack(lassox,lassoy,alpha_range,WARNING =False,lineup1 = 0.08, lineup2 = 0.01, dpi = 200):
if WARNING == False:
... |
c8bb99bb7957d75b2f58ba5fb303007b3df57e9f83b60f223d663afb89d5bf6e | Jupyter | 39,074 | 1,423 | # %% [markdown]
# # Figure 3
# %%
#Import relevant packages
import numpy as np
import pandas as pd
from matplotlib import rcParams
import os
import scanpy as sc
import matplotlib as mpl
import matplotlib.pyplot as plt
import cmocean
import seaborn as sns
from scipy.stats import median_abs_deviation
import anndata... |
4622ec94447583edc9a06803eabb35d4e03ad42fb79a1fdb6e030bd6e2fcc6c0 | Jupyter | 39,286 | 946 | # %% [markdown]
# ### Initialization
# %%
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %%
import network_flow_tracker.linking as NFTLinking
import network_flow_tracker.FlowGraph as FG
import networ... |
13ed3a8a1bfc7f12982a99b9188c510e5807a58adef05760f9fda746f91bef9c | Jupyter | 39,568 | 1,087 | # %%
import pyabf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import sem
import os, re
import seaborn as sns
import re
import scipy.stats as stats
from mpl_toolkits.mplot3d import Axes3D
import statsmodels.formula.api as smf
%matplotlib widget
# %%
... |
7bc51ffc359f74f877438522403c936a4b3d5f9f2a06530f3ab08423d2377437 | Jupyter | 39,650 | 923 | # %%
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
# %%
sns.reset_orig()
sc.settings._vector_friendly = True
# p9.theme_set(p9.theme_classic)
plt.rcParams["sv... |
08fa29361d8dc9e8ce5790071a28322c383a52dec5330def1c78ab4730ec030d | Jupyter | 39,769 | 962 | # %% [markdown]
# # Normative Modelling: Hierarchical Bayesian Regression with SHASH likelihood
#
# Welcome to this tutorial notebook that will go through the fitting and evaluation of Normative models with a Hierarchical Bayesian Regression model using a SHASH likelihood.
#
# Let's jump right in.
#
# This notebook... |
acd4ef787b067250600347d7a63efe70180b5f5be7e921c625fd65e15807e6f1 | Jupyter | 40,095 | 1,140 | # %%
import os, importlib, sys, time, h5py
# %%
import numpy as np
from scipy import io, linalg, stats, ndimage
from scipy.sparse import diags
# %%
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['DejaVu Sans'], 'size':13})
## for Palatino and other serif fon... |
fd65882cb9426c96c4cc3a253f505ba9d671b511c2ae3a65c8ccb98e641159b0 | Jupyter | 40,556 | 1,126 | # %%
import math
import os
import pickle
import joblib
import random
import warnings
from functools import partial
from itertools import product
import numpy as np
import pandas as pd
import seaborn as sns
import torch
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sc... |
6f9b6863db782007fda449f861f724fdca147a3bf92643e1ac155e9166dab675 | Jupyter | 40,748 | 1,039 | # %%
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... |
11bad593848bdfd531370bf3fb02d50dc8a8388a4a8383628e227d75ca0e39b1 | Jupyter | 40,830 | 825 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from Utilities_and_Loading import *
from Global_parameters import *
from Plotting import *
from Preprocessing import *
from General import *
from Manifolds_analysis import *
fr... |
746c5f58f882edf4568b49ecc87ba32a04b8da90f5fafb60a0c966eef6b631d1 | Jupyter | 41,343 | 841 | # %%
import nrrd
import pandas as pd
import numpy as np
from voxcell import RegionMap
import re
import multiprocessing as mp
import sys
sys.path.append('/gpfs/bbp.cscs.ch/data/project/proj84/csaba/aibs_10x_mouse_wholebrain/notebooks/scripts/')
from helper_functions import get_all_filenames, get_csv_filenames, extrac... |
9e1a397466e180c219026cce068e5b62da7181480bc01dea57989a9c16e580d9 | Jupyter | 42,240 | 864 | # %% [markdown]
# # **Assessing correlation analysis for MRI metrics and GO pathways in the DLPFC**
# ## **No superficial white matter**
# *Notebook by Melissa Grant-Peters, 06/2025*
#
# In this notebook I have aimed to assess the results of the correlative analysis between MRI metrics and GO pathways in the control D... |
03c5643bbd0920a7d81b6f6acf876b383101d7c2b1e22a6bf7a548cadb78b9b4 | Jupyter | 42,263 | 946 | # %%
import numpy as np
import matplotlib.pyplot as plt
import itertools
import copy
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torchvision
from torchvision import transforms
# 防止内核挂掉
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
from torchv... |
2bb5a5027f8a758e1a6ea05028809d0e22ce9eab4c2c1cd938d8dcdc4d6f80e5 | Jupyter | 42,972 | 1,094 | # %%
import os
import numpy as np
import pandas as pd
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from skimage.morphology import remove_small_objects
from scipy.spatial.distance import directed_hausdorff
from sklearn.metrics import roc_curve, auc
import matplotlib
import matplotlib.pyplo... |
f068e674fa863f7bb770df8c0618f1723230f77061a9bf2aa8e3a245628b6e5b | Jupyter | 43,624 | 1,101 | # %% [markdown]
# ## LASSO select features
# %%
## LASSO
from sklearn.linear_model import LassoCV#导入Lasso工具包LassoCV
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def Lasso_pack(lassox,lassoy,alpha_range,WARNING =False,lineup1 = 0.08, lineup2 = 0.01, dpi = 200):
if WARNING == False:
... |
9d98ed40da538a87b1e366338812e4bd983b61fca1ab236046bf1f4629ac5734 | Jupyter | 43,630 | 1,638 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import numpy as np
import os
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nilearn.masking import compute_epi_mask
from s... |
3b11f0248ffded9486025cf4344b999d2c8daf9fb0024f143eebd9a3f9cbb575 | Jupyter | 44,298 | 999 | # %%
import re, time, warnings, os, sys, copy
from typing import Union, Dict
import gzip, pickle
import numpy as np
import pandas as pd
import gzip, pickle
import scipy, sklearn, mne
import auxiliary as aux
import data, rsa
sys.path.append('../../spaces/')
import embeddings as emb
import pubplot as pub
import matplot... |
064fd3036cda6daebfe9d7925e3b2da01be84701136d1ae2f5e22e936b9d1f10 | Jupyter | 44,307 | 979 | # %%
import torch
import torchvision
import torchvision.transforms as transforms
# %%
transform = transforms.Compose(
[torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))])
batch_size = 6000
trainset ... |
43245e5db3d7463a1a03ec6d54e41c3377cf718c8bf5a184f54fb0aa8fb82940 | Jupyter | 44,700 | 1,031 | # %%
import numpy as np
import networkx as nx
import webbrowser
import os
import pickle
import math
import keras
import time
from datetime import timedelta
import datetime
import pandas as pd
import random
from src import utils, trnvaltst_sigmoid_oned, TGCNN_layer, whole_model, whole_model_demographics, create_fake_pat... |
4831eb2b2f65e47b1a3d0c4524af2cd43e2694d1e4534bffbd34c2d63dc79578 | Jupyter | 44,777 | 1,232 | # %% [markdown]
# # Add Libraries
# %%
import os
import time
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from core import core_global as core
from core.spike_monitor import SpikeMonitor
from core.voltage_monitor import VoltageMonito... |
c90f4a7ef6f21aff1ba58e2850d25063f6d7700d34d28481f1fb94455cb92163 | Jupyter | 44,837 | 925 | # %% [markdown]
# # Notebook 3: Movement Primitive Interneurons
#
# ### General Overview
#
# The movement primitive layer contains 672 movement primitive (MP) interneurons (INs) that act as coincidence detectors, firing whenever two or three inputs overlap. Each input is a velocity or position spike train from a diff... |
00335e340f985e4c846fe558f900932df54b2ac9128baa522199160eab524b4c | Jupyter | 45,108 | 1,669 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import numpy as np
import os
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nilearn.masking import compute_epi_mask
from s... |
dfa95283a482bf7798148fdad757b541dcd29fe91a8969512bc50a2b28bbf7af | Jupyter | 45,945 | 1,290 | # %% [markdown]
# In this notebook we create csv files which will contain estimated densities for all brain regions.
#
# I highly recommend to switch here to the annotation_ccfv3_l23split_barrelsplit_validated.nrrd and hierarchy_ccfv3_l23split_barrelsplit.json file which belong together.
#
# Otherwise, feel free to... |
5f9590831d07e2eb9f4ce5454c6994b141ef0609df196bc8f7fc794ec0c27f97 | Jupyter | 46,125 | 1,281 | # %%
#Experiment 1 - Deletion Frame difference
import os
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.m... |
54c4edb2bc9927efb38280347c6886f3c40d1e11f07e9e1e496dc1d64266c58e | Jupyter | 46,605 | 1,185 | # %%
#Newer pickle versions are not compatible
!which python
import pandas as pd
print(pd.__version__)
import sys
print(sys.version)
import inspect
print(inspect.getfile(pd))
# %%
import numpy as np
import pandas as pd
import pickle
import nrrd
import multiprocessing as mp
import re, os, copy
from voxcell import Regio... |
4a4f8e930228bbf46f2786d25da63d351fcbed0ef379005d0a3d62dba11a6a4c | Jupyter | 47,007 | 1,212 | # %%
from scipy import signal
from scipy.signal import firwin, lfilter
import mne
import numpy as np
from sklearn.metrics import classification_report, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from scipy.signal import butter, so... |
4307779a03e5083f2ea45ad963a9b9385de3fa207431ee7268bbe24fef52298d | Jupyter | 47,372 | 1,465 | # %% [markdown]
# # Generate Spectral Datasets with Surrogate Models
# %%
import os
from typing import Any
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from dotenv import load_dotenv
from rich.progress import track
from torch.utils.data import DataLoader, TensorDataset
from mcm... |
b3be4b919f821848ca82054f896d6f4ae7fda882ed49b8a26cbc142443f8bc6d | Jupyter | 47,555 | 1,156 | # %% [markdown]
# # 1. DATA OBTENTION & PREPROCESSING
#
# Hi!
# This notebook is a guide to obtain and preprocess the data used to train and test CLASTER. It is meant to be run sequentially, i.e. one cell after the other, and it mainly contains:
# - Markdown cells with descriptions of the data or the code
# - Code cel... |
21c46bc81024ac8a6166f684b7bc715861a5ad15f24cb8bc4a4be277a5eae47f | Jupyter | 47,700 | 1,318 | # %%
# kinescope imports
# Install once from repo root: pip install -e ../
import warnings, sys, os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from tqdm import tqdm
import kinescope
from kinescope.pipeline.pose_pipeline import PoseProcessingPipeline
from kinescope.pose.io import read_coco_... |
f707e8b663b00535313dfaa542e56aba7f473c361873f07362e35f6ee49825a0 | Jupyter | 48,447 | 1,259 | # %%
from scipy import signal
from scipy.signal import firwin, lfilter
import mne
import numpy as np
from sklearn.metrics import classification_report, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from scipy.signal import butter, so... |
33abad8638bfe1eac33716a434e71da644628f1313d99542406f7b625c810d11 | Jupyter | 48,548 | 1,748 | # %%
#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... |
60c213a1c20c742a9bf10d4e401a46c22623a97f9670f5032335f3921ed9c411 | Jupyter | 48,594 | 1,748 | # %%
#python resample.py $IMAGE_TO_RESAMPLE $REFERENCE_IMAGE
# %%
import nibabel as nib
from nilearn.input_data import NiftiMasker , MultiNiftiMasker
# %%
import nilearn as nil
# %%
import numpy as np
import os
import os.path
import scipy.io
import nibabel as nib
from nilearn.input_data import NiftiMasker
from nil... |
8bdf1c341a80a946a2428c068d2880d545817ef2a9bed0498c80e6f5319e39e8 | Jupyter | 48,965 | 1,353 | # %% [markdown]
# %%
# ============================================================
# Core Libraries
# ============================================================
import os
import time
import math
import gc
import re
import warnings
from contextlib import nullcontext
from typing import Tuple, Optional, Callable, Dic... |
605f95c6bde5263fb63e589b100d939be927035fc9d67e486c18bdda335fac2d | Jupyter | 49,267 | 1,362 | # %% [markdown]
# ## Introduction:
# %% [markdown]
# In this notebook we'll try to understand brain activity relates to external signals, by analysing the results of an experiment where a fish was exposed to a visual stimulus while we recorded its brain activity and tail movements.
# As a reminder this is a schematic ... |
a017868788a9b9a0e0465ccd65f7658c7cd99a0ff0bc944fae8af07ecf2c6543 | Jupyter | 49,302 | 1,725 | # %% [markdown]
# # Tissue oxygenation s$_t$O$_2$ inference using Monte Carlo-simulated and surrogate model-generated reflectances
#
# Note: To ensure data access and correct path resolution, run this notebook with a kernel that uses the internal version of the `htc` package.
# %%
import os
import pickle
import sys
i... |
236c783884614bbbd20a88335c952684ae6a14cf66ff791d3d28611bf0d80876 | Jupyter | 49,547 | 1,223 | # %% [markdown]
# # Linear track demo
# %% [markdown]
# Here we load sorted unit data obtained from MatClust to identify putative place cells. The session has about 15 minutes of linear track running, followed by 15 minutes of rest.
#
# Data was recorded by _Joshua Chu_, with a Spikegadgets wireless headstage, on Ju... |
b09508a29cba2f95be7e1955f9b6392f50ab7ee46d89dad3d898245262d50b02 | Jupyter | 49,686 | 1,374 | # %%
!pip install pandas==1.5.3
import warnings
import sys, os, cv2, glob, json, gc, shutil
import multiprocessing
from multiprocessing import Pool
sys.path.append('./utils')
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib.animation import FuncAnimation
from moviepy.editor import Vid... |
77525f43d52d1a0890cfda4fe80f886adbb2eac067fa5d8452f522d8f97c6a60 | Jupyter | 49,820 | 1,301 | # %% [markdown]
# Welcome to Day 4 of the Practicals !!
#
# Today we will investicate how to study sequences of behaviours. In perticular, how to use Markov Chains (MCs) and Hidden Markov Models (HMMs) to analyse the statistics of how zebrafish reorient themselves during free swim.
#
# The data we will be using comes... |
90d24bb79aafab6d8c7c9536aa0d3682f91ed634c364732a807ff56d74048251 | Jupyter | 49,933 | 1,270 | # %% [markdown]
# # Unit Testing of Phosphosite-Mapping Module
# %%
import os
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
from scipy.integrate import solve_ivp
import torch
import torch.nn as nn
import torch.nn.functional as F
import plotnine as p9
from IPython.display import display, clear... |
3e4e7cfda3013a8a10bad1de66e01e3ef47044c9ab234e69087f334ad8593da7 | Jupyter | 50,366 | 1,416 | # %% [markdown]
# <a href="https://colab.research.google.com/github/yiruchen4/Chen2026/blob/main/SocialDoorInstructions/doordata_process.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# Authour: Yiru Chen
#
# Last modified date: Fe... |
6eb1ff5f693db4f5a15cfdc30ca5577d5ffe894475e4737445971641a7a54967 | Jupyter | 50,584 | 1,489 | # %%
%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... |
027b58ad26e4746c0b87b6c16a3003bd292f2a2135f5e3a396792b0c3ea37382 | Jupyter | 50,611 | 886 | # %% [markdown]
# # Main Workflow
#
# This notebook describes the main workflow of normative modelling (NM). It contains the accompanying code for the manuscript: ___, but can also be used as a standalone learning tool for NM.
#
# ## Setting up the environment
#
# -----
#
# <details>
# <summary><b>System requiremen... |
5453495453635cbaa7893deb586ea65733ef3352e213e4ce5af80d6abedaa799 | Jupyter | 51,164 | 1,404 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
from HopfiledNetwork import *
from utils import *
import json
# %%
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['font.size'] = 8
plt.rcParams['svg.fonttype'] = 'none'
# %%
def plot_patterns(patterns, title, path):
num_patterns = len(patterns)... |
6e3279a4f6e354c91cf3a6145df3ef2b08d958b373b41b7dcf8cd8d40f9a3863 | Jupyter | 51,587 | 1,463 | # %% [markdown]
# <a href="https://colab.research.google.com/github/yiruchen4/Chen2026/blob/main/SocialDoorInstructions/doordata_process.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# Authour: Yiru Chen
#
# Last modified date: Ma... |
1d47925ebf8e3080d793501d859d47702f6ae9174ff4d363cd3d8e750e0b3afe | Jupyter | 51,902 | 1,352 | # %% [markdown]
# Welcome to Day 4 of the Practicals !!
#
# Today we will investicate how to study sequences of behaviours. In perticular, how to use Markov Chains (MCs) and Hidden Markov Models (HMMs) to analyse the statistics of how zebrafish reorient themselves during free swim.
#
# The data we will be using comes... |
52568796dd8a1891e78f60c877d4d5321bf67f274907c457c1077b5ca60d4152 | Jupyter | 52,254 | 1,316 | # %% [markdown]
# Welcome to Day 3 of the Practicals !!
#
# The goal of today is to use Dimentionality Reduction technics on whole-brain data.
#
# **What is Dimentionality Reduction ?**
# The goal of Dimentionality Reduction (DR) is to transform data which has $D$ dimentions into a new space of dimention $d$ with $... |
3a342626764b86dc0cb4f2c696dd2085f76fcd675361d2fdcca06c85110e585b | Jupyter | 52,501 | 1,407 | # %%
# Frame insertion logic updated November 22, 2025
# Adds random seed, metadata logging, and verification
import os
import subprocess
import shutil
import random
import pandas as pd
import json
# Paths
input_videos_folder = r"E:\USB Data\Data sets\BDD-A\BDDA13Frames Randomly Inserted"
output_videos_folder = r"E:\... |
500e8d03bca37e2203d3a6573d857b2ba0d3d25ceec7b46ccf2ed5d7f1a87f8b | Jupyter | 52,816 | 1,388 | # %% [markdown]
# # Mean FR and beta power in healthy and PD condition without DBS
# %%
import numpy as np
import pandas as pd
import statistics
from scipy import signal
from scipy.integrate import simps
from scipy.stats import sem
base_dir='PATH TO DIRECTORY THAT CONTAINS BG DATA IN HEALTHY AND PD WITHOUT DBS CONDIT... |
8f5ab40f4d38d993b00f2da48cb08a3ba3f2249e5a17f0de8b40df4f29136f94 | Jupyter | 52,885 | 1,426 | # %% [markdown]
# ## Introduction:
# %% [markdown]
# In this notebook we'll try to understand brain activity relates to external signals, by analysing the results of an experiment where a fish was exposed to a visual stimulus while we recorded its brain activity and tail movements.
# As a reminder this is a schematic ... |
cebd70f3719c1adfb4c6a0d958f3cad316b4e3915ac42e2e203c1b06dd5dc1f5 | Jupyter | 52,901 | 1,889 | # %%
#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... |
9ce791b0391086b2611dcda37b856fb113e826952114956f7c0b376379c1c4d7 | Jupyter | 53,403 | 1,323 | # %% [markdown]
# Welcome to Day 3 of the Practicals !!
#
# The goal of today is to use Dimentionality Reduction technics on whole-brain data.
#
# **What is Dimentionality Reduction ?**
# The goal of Dimentionality Reduction (DR) is to transform data which has $D$ dimentions into a new space of dimention $d$ with $... |
f26e0d83b54c83762306ffa186fcf2e37336f48cbb774b3a1586fa5fc6eb40fe | Jupyter | 53,840 | 1,373 | # %% [markdown]
# # 07-predicting-IBS-vs-HC-from-morphometry-and-cognition.ipynb
#
# Using `ibs-brain` conda environment
#
# Arvid Lundervold
#
# Updated: 2024-12-18; 2025-02-01; 2025-02-10; 2025-02-12 (Figs. 12 & 13 dpi=300)
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# This notebook explores the use of morphometric measures and cognition (RBANS) to pred... |
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