sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
7a25220400f102cb03f906b904772c8ece7f88825ae8d7d7d587b0c85e728859 | Jupyter | 11,994 | 315 | # %% [markdown]
# # **Libraries and Paths**
# %%
import re
import wordninja
import numpy as np
import pandas as pd
import seaborn as sns
from shap.plots import colors
import matplotlib.pyplot as plt
from gensim.models import KeyedVectors
from sklearn.model_selection import train_test_split
from sklearn.feature_extract... |
50db99077ee04e579d9ae4ba8ab8b8308275e8e9ceec0c46b62b886f547e8331 | Jupyter | 12,003 | 333 | # %% [markdown]
# ### *This file allows to reproduce Fig2D-F*
# %% [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
i... |
7a8a648bee5db08784081888a155e8ea771b258a4e757ea724eebadb1187ce30 | Jupyter | 12,231 | 465 | # %% [markdown]
# # Extended Data 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
... |
17efa7493652e1b4e30332ced7c7c12d50c44f8fe7c1de54d02f5309d003906d | Jupyter | 12,353 | 146 | # %% [markdown]
# # Photometry FLMM Guide Part III: Association with continuous variables – akin to FLMM version of a correlation
# ## Authors: Gabriel Loewinger, Erjia Cui
# ### 2024-09-07
# ### rpy2 implementation: Josh Lawrimore
#
# ## Part III: Associations with continuous variables – Akin to FLMM version of a cor... |
356adf8d1c5bbd921b6b1ff896e26c484c8dda8719e56b3dcf3c2302fed1c27c | Jupyter | 12,400 | 376 | # %% [markdown]
# # Installation
# %% [markdown]
# In order to run Scenic, we had to specify several package versions. This combination made it work for us:
#
# conda create -n pyscenic python==3.10
# conda activate pyscenic
# pip install pyscenic==0.12.1 numpy==1.23.4 distributed==2024.2.1 dask-expr==0.5.3 ipykernel... |
07939de50ec021b572c21e6f8ed6fed6622e55c82340431009b3f922c3e9824d | Jupyter | 12,406 | 248 | # %%
import os
import torch
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import wilcoxon
os.chdir('/data/users4/xli/interpolation')
from models.vae import VAE
from data.utils import load_sz_score, load_sfnc
os.chdir('/data/users4/xli/interpolation/visuali... |
c14c0ba501057ffd61da9ab39ba5309791df11b1597649614f5de22445c9e5c8 | Jupyter | 12,464 | 225 | # %% [markdown]
# # Results comparison framework for benchmarking using ``Cinnabar``
#
# Comparing two sets of free energy predictions by eye is deceptively difficult. Two methods can show different RMSE or MUE values on a plot yet be statistically indistinguishable given the size of the dataset. ``Cinnabar`` addresse... |
b3b634244d1dd5f3c776f00d1abda70170251ea01603e8e31b359a04fa03cbe6 | Jupyter | 12,471 | 314 | # %%
#!/usr/bin/env python3
"""
05_detailed_counts.py — Detailed breakdown of conversion, GDS, age, and follow-up
Run: python notebooks2/05_detailed_counts.py
"""
import pandas as pd
import numpy as np
import os
BASE = '/media/faizaan/4TB/1_DATA_PROJECTS/Projects/Multimodel_study'
REPORTS = os.path.join(BASE, 'rep... |
df5f66d4eedf4b05faf5785f0ff6174fa335034a847d5e37e9f036dee7fcdab7 | Jupyter | 12,554 | 138 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
from utils import plot_dwell_time, plot_transition_matrix
# %%
n_window_sz = 137
n_window_asd = 168
suptitle_fontsize = 20
fig = plt.figure(constrained_layout=True, figsize=(21, 14))
subfigs = fig.subfigures(4, 2)
(ax11, ax12, ax13) = subfigs[0,0].sub... |
3ad7cb278c0025ff4cd33ec12f86f8df6059b7db4088d053c592c0ff856e27aa | Jupyter | 12,611 | 311 | # %% [markdown]
# # Extended Data Figure 2
# %% [markdown]
# This notebook includes the analysis code to compare measurements taken from 3D scans to measurement from the Rig2 Cheese3D keypoint estimations.
#
# Before running the notebook, first make sure the anipose project is downloaded (the `ignore_videos=true` fla... |
042aeaf35c71aa63cafdf593ccc886847b4b6c0c40cf2281f00b8669b022c8c9 | Jupyter | 12,676 | 407 | # %% [markdown]
# # Fully Convolutional Interior/Edge Segmentation for 2D Data
#
# ---
#
# Classifies each pixel as either Cell Edge, Cell Interior, or Background.
#
# There are 2 different Cell Edge classes (Cell-Cell Boundary and Cell-Background Boundary)
# %%
import os
import errno
import numpy as np
import de... |
aa20221d23ecb555201e82967784d1f59f25b1953e7d2a549d27cccb5d00a1c5 | Jupyter | 12,723 | 258 | # %% [markdown]
# # Visualizing Error Distributions with ECDF Plots
#
# Aggregate error statistics such as RMSE or MUE provide a useful summary of prediction quality, but they can hide important information about the **distribution** of errors. An **Empirical Cumulative Distribution Function (ECDF)** plot addresses th... |
281e518d37e31d8b97d43ad3d109b0f63b631e5ab32953433bb4b973c1fb1be9 | Jupyter | 12,743 | 325 | # %% [markdown]
# # Session Analysis Example
# This notebook demonstrates detailed analysis functions for single session behavioral data
# %%
import matplotlib.pyplot as plt
from ethopy_analysis.data.utils import get_setup, find_consecutive_runs, add_column_by_key
from ethopy_analysis.data.loaders import (
get_ses... |
5aa66d36fc6c5b462fdc7ed8a092f1ab89730fb4f29d0addc751fcdb959ae7e6 | Jupyter | 12,775 | 344 | # %% [markdown]
# ### *This file allows to reproduce Fig3A-C*
# %% [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
i... |
0a02ae0188b4154f379f7a4d1f61d19da7acb12e6a2910870b65762698b086d9 | Jupyter | 12,786 | 374 | # %% [markdown]
# ### Reverse correlation tutorial
# %% [markdown]
# #### Helper functions
# The way MATLAB works through jupyter is by running the eval function, so you can't define functions here locally, they need to be called from somewhere else. For demonstration purposes that is not optimal, therefore I am using... |
6a5f229dc47e5c42bfa42ec61aee0ad19ef3b0dc33a9796f9d63833cec016920 | Jupyter | 12,794 | 415 | # %% [markdown]
# This file combines z-score data from several animals
# %%
import pandas as pd
import numpy as np
import os
import matplotlib
import tdt
import scipy.stats as stats
import matplotlib.pyplot as plt
from scipy.signal import butter
import matplotlib.pyplot as plt
from scipy.signal import medfilt
from sci... |
88357911bb9e5800e6aecc6ebd2553a00cbaca25157b134f48bf7768a4c2f6f9 | Jupyter | 12,812 | 334 | # %% [markdown]
# ### *This file allows to reproduce FigS1*
# %% [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... |
ced7077c92dbd8b91eab905d5d944f86692e9a5c81132d32e29aa96103be1d90 | Jupyter | 12,890 | 392 | # %%
import os
import numpy as np
import pickle
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from utils import STIM_INFO_PATH, COCO_IMAGES_DIR
from data import get_fmri_data_paths, get_latent_features, LatentFeatsConfig
from eval import get_distance_matrix, dist_mat_to_pairwise_... |
f84b1eafc11cb6968364ca9df8f288917829e2dd48d220d349ff302d4e0274ae | Jupyter | 12,953 | 385 | # %% [markdown]
# # Response characterization
# **9th april 2024 (edited the 6th May 2024)**
#
# *Célien Vandromme*
#
# ---
# %%
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import matplotlib
import percephone.plts.stats as ppt
import matplotlib.pyplot as plt
from multiproc... |
2f5759458638cb95fc59202f7dc101034678eeb21f1e1c00e920da5737d0a63c | Jupyter | 13,014 | 285 | # %% [markdown]
# # <i> Retraining models to predict age in new datasets with missing genes</i>
# <b> This notebook uses the same training data as other models, but retrains a new model based on the genes present in your new dataset where you want age predictions. We will use 3 additional fetal brain datasets to test t... |
d117f8f68c41c76c8ea47683f5a3289a3e155e0367526c52acca20ccda7c14f0 | Jupyter | 13,043 | 389 | # %% [markdown]
# ### Imports
# %%
import os
import sys
sys.path.append('..')
import helper as hp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import umap
from scipy import interpolate
#from sklearn.decomposition import PCA
#from sklearn.metrics import mean_squared_error as rmse
#from skle... |
b458b515977e10edfb171e2aa96b8ef4effd6a7549b18a3428ff555a3d975d23 | Jupyter | 13,061 | 324 | # %%
"""Makes plots used in high-level overview schematics (found in figures 1 and
2).
"""
import sys
import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.stats import norm,multivariate_normal,linregress
import matplotlib as mpl
from matplotlib import pyplot as plt
import seab... |
bccb052ae0f393f73e7fd524a209562d6853a66c5da0a5d249cf53f77cbdec6f | Jupyter | 13,083 | 362 | # %%
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/... |
92c65e0f2d58335e323e5316a319caf7059841f9aa38e491ae9c925a495d8e2e | Jupyter | 13,167 | 418 | # %% [markdown]
# # Sample Based Interior/Edge Segmentation for 2D Data
#
# ---
#
# Classifies each pixel as either Cell Edge, Cell Interior, or Background.
#
# There are 2 different Cell Edge classes (Cell-Cell Boundary and Cell-Background Boundary)
# %%
import os
import errno
import numpy as np
import deepcell
... |
82844da731b85900ed2e33add79e481edce33914340c67a33cb0bacc309b827a | Jupyter | 13,194 | 131 | # %%
import pandas as pd
import os
l = [
"/g/korbel/Costea/Computational/StrandSeq/P6_Rel/TAllPDX6340p6REL/scNOVA_result/count_reads_CREs/PDX6340rel2PE20303_CREs_2kb.tab",
"/g/korbel/Costea/Computational/StrandSeq/P6_Rel/TAllPDX6340p6REL/scNOVA_result/count_reads_CREs/PDX6340rel2PE20305_CREs_2kb.tab",
"/g/... |
3bb70f54b61806b88f1254fb7ceaf11f04eb65e65bda137be38b429149648647 | Jupyter | 13,213 | 362 | # %% [markdown]
# # With JAX
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/compose_with_jax.ipynb)
#
# ## About this tutorial
#
# JAX is a machine learning librar... |
bce61325522dfed66ff8c1d7ee2dea415808eef856fe68b2d422a37db6c52f7f | Jupyter | 13,283 | 384 | # %%
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
import seaborn as sns
from rich import print
from scib_metrics.benchmark import Benchmarker
from scvi.model.utils import mde
from scvi_colab import install
import matplotl... |
dedf504b8e12613f859b2096106c8fad5f10901660c1ca2f6fc32a3f7b5f4312 | Jupyter | 13,330 | 420 | # %%
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal as signal
import lib.io.stan
from lib.preprocess.envelope import *
import os
# %% [markdown]
# # Patient AC
# %%
# Load the simulated data
sim_data = np.load('datasets/id001_ac/AC_syn_tvb_ez=59_pz=82-74.npz')
# Plot the da... |
1fa506552e1a4947ff74eeee55c164ff971b0c5def70b6bc1d5bb1f9914dbfe4 | Jupyter | 13,356 | 227 | # %% [markdown]
# # <font color=black> Figure 3 Spinal cord func / morphometry coupling </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import sys,json
import glob, os
import pandas as pd
import numpy as np
main_dir="/cerebro/cerebro1/dataset/bmpd/derivatives/Aging_project/2025_brsc_... |
50f6a24bce3b4bb9b5a7de4e554feb96407cb8540033ad60860b637a131e2733 | Jupyter | 13,394 | 336 | # %%
GROUPSTATS_DATE = '2025_07_26'
# %%
"""Makes polar plots comparing between-subjects correlations and
within-subjects modulations."""
import sys
import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.gridsp... |
8fa8031bc2a798846155f1335bcc0ed1638702d5ab028ee8b51eb6e8450d4de7 | Jupyter | 13,479 | 330 | # %% [markdown]
# # With TensorFlow
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/compose_with_tf.ipynb)
#
# ## About this tutorial
#
# This tutorial shows how to... |
636c6e8889fa78d99448352257f525faf10b69c2b35bfbe8a5997c34a7a72213 | Jupyter | 13,584 | 425 | # %% [markdown]
# # Sample Based Interior/Edge Segmentation for 3D Data
#
# ---
#
# Classifies each pixel as either Cell Edge, Cell Interior, or Background.
#
# There are 2 different Cell Edge classes (Cell-Cell Boundary and Cell-Background Boundary)
# %%
import os
import errno
import numpy as np
import deepcell
... |
2ad777f7df62402dfc76e56bc594fbe3a05441da4877e92270488f836ca95a3c | Jupyter | 13,585 | 434 | # %% [markdown]
# # Fully Convolutional Watershed Distance Transform for 2D Data
# ---
# Implementation of papers:
#
# [Deep Watershed Transform for Instance Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bai_Deep_Watershed_Transform_CVPR_2017_paper.pdf)
#
# [Learn to segment single cells with de... |
1ea0d167b8b18924eaf838ee3ca73dedc4245294b2ebb276597093207ed3e5b2 | Jupyter | 13,600 | 437 | # %% [markdown]
# # Fully Convolutional Interior/Edge Segmentation for 2D Data
#
# ---
#
# Classifies each pixel as either Cell Edge, Cell Interior, or Background.
#
# There are 2 different Cell Edge classes (Cell-Cell Boundary and Cell-Background Boundary)
# %%
import os
import errno
import numpy as np
import de... |
061e500af252f77b6dad140332ab325e87ad47853e4ed709058a51b1a44d5b66 | Jupyter | 13,606 | 410 | # %% [markdown]
# ### *This file allows to generate voltage traces at different times in different conditions*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx
include("STG_kinetics.jl") # Loading of STG kinetics of gating... |
04c7eddbef06e553827c3ea64fae654111ffab947ed6b6b384a017b798685bf5 | Jupyter | 13,665 | 357 | # %% [markdown]
# # Session Analysis Example
# This notebook demonstrates detailed analysis functions for single session behavioral data
# %%
import matplotlib.pyplot as plt
from ethopy_analysis.data.utils import get_setup, find_consecutive_runs, add_column_by_key
from ethopy_analysis.data.loaders import (
get_ses... |
ef5a46825455441ebd44a47774c9a4ce91dd3c503bdd5dd2163fabd95ef34b6a | Jupyter | 13,720 | 352 | # %% [markdown]
# ### *This file allows to reproduce FigS2*
# %% [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... |
be1f529c0c939aa6e9e76c617c9954b291ada6d43f359195babe3eca01306d7c | Jupyter | 13,753 | 433 | # %% [markdown]
# # Watershed Distance Transform for 2D Data
# ---
# Implementation of papers:
#
# [Deep Watershed Transform for Instance Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bai_Deep_Watershed_Transform_CVPR_2017_paper.pdf)
#
# [Learn to segment single cells with deep distance estimato... |
c0202e0f089329c0e8747fa07590a1bf9d0ef7e6d2d79c959ac968f3edab5348 | Jupyter | 13,772 | 467 | # %% [markdown]
# ## Extended Data Figure 4
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from pathlib import Path
from itertools import combinations
logging.getLogger("matplotlib.font_manager").disabled = True
import scipy
import numpy as np
import pandas as ... |
6c589c0ce98b61ebfa823aa80ce2d6fef82aabdbbb0bb4c5a5cbef07039fd402 | Jupyter | 13,895 | 333 | # %% [markdown]
# # <font color=#B2D732> <span style="background-color: #4424D6"> Brain & Spinal Cord fMRI Quality check </font>
# <hr style="border:1px solid black">
#
# *Project: 2024_brsc_aging_project*
# *Paper: *
# **@ author:**
# > Caroline Landelle, caroline.landelle@mcgill.ca // landelle.caroline@gmail.... |
03389b10ccfa1f957d4db83bb052046b590e48241dba02ab415f4a6d3007fcd0 | Jupyter | 13,907 | 467 | # %% [markdown]
# ## Extended Data Figure 7
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from pathlib import Path
import yaml
import numpy as np
import pandas as pd
import seaborn as sns
import networkx as nx
import matplotlib_venn
import matplotlib.pyplot as p... |
4eef90d06110f18bfff927cdb5e43887544225a46add7a6aeb9f892a66b8e308 | Jupyter | 13,936 | 402 | # %%
%reset -f
%matplotlib inline
import numpy as np
import lib.io.stan
import lib.plots.stan
import matplotlib.pyplot as plt
import os
from matplotlib.lines import Line2D
# %%
data_dir = 'datasets/id002_cj'
results_dir = 'results/exp10/exp10.18'
fit_data_dir = f'{results_dir}/Rfiles'
os.makedirs(results_dir,exist_ok... |
2c29700b26a52b2e0f5341910b1d064e89e950f50e4ea95055bcde649adf16c3 | Jupyter | 13,973 | 374 | # %% [markdown]
# # Distributed training with VertexAI
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/distributed_training_vertex_ai.ipynb)
#
# ## Setup
# %%
pip i... |
40bbea1faa569c0c2632302ebdbf985b8df71e0db6f4790beb6cb888274e2afd | Jupyter | 14,006 | 471 | # %% [markdown]
# # **Experiments - Dataset FASHION_MNIST**
# %% [markdown]
# ## **Libraries**
# %%
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from torchmetrics.clas... |
1beb7b5de7384518f495cf61b34ef1362b1a99393f534e9284c0c566bd91a505 | Jupyter | 14,033 | 415 | # %% [markdown]
# # Decoding Responsivity (old version)
# **Can we predict whether a stimulus will be detected or not based on neuron's responsivity ?**
#
# Célien Vandromme
# 18/04/2024
#
# ---
# %% [markdown]
# ## Modules & data import
#
# ---
# %%
from unittest import result
import numpy as np
import pandas as... |
154135da0da330d388521b4dfd8caa11b4dabe1a355bf9b5b0b801d4aaced771 | Jupyter | 14,043 | 252 | # %% [markdown]
# # Protocol 2: Assessing cell type replicability against a pre-trained reference taxonomy
#
# Protocol 2 demonstrates how to assess cell types of a newly annotated dataset against a reference cell type taxonomy. Here we consider the cell type taxonomy established by the Brain Initiative Cell Census Ne... |
5cc2f5af8e3cea41b33d1a761c4aebc56fcac4c01213d5270b65fe64745af1ae | Jupyter | 14,152 | 381 | # %% [markdown]
# # Data Loading Functions Example
#
# This notebook demonstrates all the data loading functions available in the ethopy_analysis package. Each function is explained with its purpose, parameters, and example usage.
# %%
# Import all necessary modules
from ethopy_analysis.data.loaders import (
get_... |
0905cfde3c937ae42c804748e2f1f3ae7dfb3b464f36b32257cd0be71cd6a4bc | Jupyter | 14,164 | 443 | # %% [markdown]
# # Watershed Distance Transform for 3D Data
# ---
# Implementation of papers:
#
# [Deep Watershed Transform for Instance Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bai_Deep_Watershed_Transform_CVPR_2017_paper.pdf)
#
# [Learn to segment single cells with deep distance estimato... |
e31d1f57cc175da5cbcc95676443395ecea45bf6c06d85911f0dcc3a868e3cbf | Jupyter | 14,253 | 460 | # %% [markdown]
# # Watershed Distance Transform for 3D Data
# ---
# Implementation of papers:
#
# [Deep Watershed Transform for Instance Segmentation](http://openaccess.thecvf.com/content_cvpr_2017/papers/Bai_Deep_Watershed_Transform_CVPR_2017_paper.pdf)
#
# [Learn to segment single cells with deep distance estimato... |
bad63341cf5001b2bfabd5acbbfd370db4bcf7402f6c31f52284caeb9c93ae85 | Jupyter | 14,269 | 408 | # %% [markdown]
# ## This notebook will walk you through cleaning up noisy V4 Miniscope data
#
# Post questions or issues to the Miniscope Google Group: https://groups.google.com/g/miniscope
#
# You will need the following packages installed (you likely should create a virtual python environment using conda or simila... |
d13c842f4eb7475dcc367a2a5b2dcd762c56de5ebb3ff838656f7f33d1476372 | Jupyter | 14,478 | 465 | # %%
#figures 1i and 1j. multimodal macroscale validations of expression maps
# %%
import scripts.neurosynth_tools as nt
from scripts.mapping_helpers import get_indices
import pandas as pd
import numpy as np
import os
import nibabel as nb
import matplotlib.pyplot as plt
import matplotlib_surface_plotting as msp
import... |
d276bf164ce74c235913df46f10105b8bd4b46cb25a604c96f41592e4ffe91e0 | Jupyter | 14,537 | 300 | # %%
%load_ext autoreload
%autoreload 2
base = '/home3/ebrahim2/beyond-brainscore/'
# %%
import numpy as np
from matplotlib import pyplot as plt
import os
from sklearn.metrics import mean_squared_error
import sys
sys.path.append(base)
from plotting_functions import plot_across_subjects, plot_test_perf_across_layers... |
b5c4f76f3e7dbe08652577b2085a37a839a661c3cf8e67e270f95a2509873d9f | Jupyter | 14,548 | 411 | # %%
import warnings
warnings.filterwarnings('ignore')
import os
# Ensure CWD is the repo root (notebook kernels start in the notebook's directory)
if os.path.basename(os.getcwd()) == 'tutorial':
os.chdir('..')
# %% [markdown]
# # ProtoCloud Tutorial
#
# ProtoCloud is a prototype-based Variational Autoencoder (... |
bc39d03fedeb500bea338511056d0a7f4a8188b9d6534bebf16edba97451f060 | Jupyter | 14,621 | 421 | # %%
%reset -f
%matplotlib inline
import numpy as np
import lib.io.stan
import lib.plots.stan
import matplotlib.pyplot as plt
import os
from matplotlib.lines import Line2D
import lib.preprocess.envelope
import matplotlib.colors
# %%
data_dir = 'datasets/RetrospectivePatients/id001_bt'
results_dir = 'results/exp10/exp1... |
4b016e490843162ec9631d6dbaceebddb9281b02a5678af6d58ac800b71772e5 | Jupyter | 14,624 | 345 | # %%
import numpy as np
import argparse
import imageio
import os
import sys
import time
import torch
from datasets import load_dataset
from halluc_vae import HallucVAE
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
from torch.utils.data.distributed import Distr... |
3afa88dd9f5153ebc064dcb70e34ba23c78c9c880dd1695069a17e5a527a9b85 | Jupyter | 14,637 | 498 | # %% [markdown]
# # Diversity Metrics for Synthetic EEG
#
# This notebook illustrates three complementary **diversity domains** for evaluating synthetic EEG:
#
# 1. **Coverage diversity (manifold coverage)**
# - Do synthetic samples cover the same regions as real data and avoid unrealistic outliers?
#
# 2. **G... |
a21e37d9e9e5a8325ea7a5fb91289757c46728840c683c19c6e72dbb3db8aa99 | Jupyter | 14,665 | 392 | # %% [markdown]
# # Data Loading Functions Example
#
# This notebook demonstrates all the data loading functions available in the ethopy_analysis package. Each function is explained with its purpose, parameters, and example usage.
# %%
# Import all necessary modules
from ethopy_analysis.data.loaders import (
get_... |
a8bca5c3788a1ec7312bad203726ecec70f806ca2833973ced0b556901a8c2d2 | Jupyter | 14,837 | 349 | # %% [markdown]
# # <font color=black> Time-series features: BrainDyn and SpiDyn </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ## <font color=#B14263> Imports
# %%
import sys,json,glob, re, os
#os,
import pandas as pd
import numpy as np
main_dir='/cerebro/cerebro1/dataset/bmpd/derivatives/Aging_p... |
31374c4ffddf248a1954e2b558af468a2085928701ab1642c59f6e0fdb6df17f | Jupyter | 14,872 | 459 | # %% [markdown]
# # 3D 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 datetime
import numpy as np
import deepcell
# %% [markdown]
# ... |
2421ccbf058f3d93d5daaff6ad572c98046a9b9b34db2615dfafb72747b80d1c | Jupyter | 15,001 | 344 | # %%
import TaskRest.paths as indiv_paths
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
import numpy as np
import TaskRest.plotting as plotting
from scipy.stats import ttest_rel, ttest_1samp
# Set paths
base_dir = indiv_paths.set_base_dir()
atlas_dir = indiv_paths.set_atlas_dir(base_dir)
fig... |
74a82c93690bb0be96c4795d99332568c84198fa910f19fba9a12784ae9c984a | Jupyter | 15,033 | 312 | # %% [markdown]
# # <font color=black> Time-series features </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ## <font color=#B14263> Imports
# %%
import sys,json, os, glob
import pandas as pd
import numpy as np
import nibabel as nib
from scipy.stats import spearmanr
main_dir='/cerebro/cerebro1/dataset... |
cf4bf3c6f4c96ce69df358bd5873e1f7ba1b941c45a6e8cdd9d78dd0e94dc519 | Jupyter | 15,052 | 451 | # %% [markdown]
# # Outline code test for IGRF candidate evaluation
#
# # Example for the DGRF2015 candidates submitted in October 2019
#
# Load available candidates
# Use the alphebetical ordering and labels from each candidate.
#
# There is a set naming convention:
# - first three lines start with #
# - third li... |
f59e1bdf9054b8e848f92c2cc3a4196cbf8cab2afff45710958a695117c88495 | Jupyter | 15,052 | 451 | # %% [markdown]
# # Outline code test for IGRF candidate evaluation
#
# # Example for the IGRF2020 candidates submitted in October 2019
#
# Load available candidates
# Use the alphebetical ordering and labels from each candidate.
#
# There is a set naming convention:
# - first three lines start with #
# - third li... |
8d3faa295aaee63be4c553b8fe79f61dcde1ed0fde8b52212111ac805b07d85d | Jupyter | 15,060 | 464 | # %% [markdown]
# # Installation
# %% [markdown]
# In order to run Scenic, we had to specify several package versions. This combination made it work for us:
#
# conda create -n pyscenic python==3.10
# conda activate pyscenic
# pip install pyscenic==0.12.1 numpy==1.23.4 distributed==2024.2.1 dask-expr==0.5.3 ipykernel... |
c462849c14a4859332767ae79141a137d4586579bd3b5658a2acd53fc0ecd009 | Jupyter | 15,071 | 450 | # %% [markdown]
# # Outline code test for IGRF candidate evaluation
#
# # Example for the IGRF2020-SV candidates submitted in October 2019
#
# Load available candidates
# Use the alphebetical ordering and labels from each candidate.
#
# There is a set naming convention:
# - first three lines start with #
# - third... |
3c49b753ee58fdc2b13e620ca7b5a30d143dbe1eca4fe93bb1fd4d2af453efea | Jupyter | 15,273 | 436 | # %% [markdown]
# ### *This file allows to reproduce FigS4D-F*
# %% [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
... |
1ba2c1037de064f3f742aae65d1d9ef8f5f344670c22b581d1edd53988faea69 | Jupyter | 15,453 | 312 | # %% [markdown]
# ### *This file allows to reproduce results from the original paper of the CPG*
# %% [markdown]
# # **Useful packages and functions**
# %%
using Plots, LaTeXStrings, Random, Dierckx, DelimitedFiles, ProgressMeter
include("network_STG_kinetics.jl") # Loading of STG kinetics of gating variables
include... |
58a03aaacb7d3078d7c6c63a4f9dbff37c5a35fcf12323c4f00d7a9b435ba5fb | Jupyter | 15,463 | 491 | # %%
library(enrichplot)
library(DOSE)
library(tidyr)
library(clusterProfiler)
library(ggplot2)
library(patchwork)
source('/home/hsarkar/Workplace/slide-seq-de/R/spatial_util.R')
source('/home/hsarkar/Workplace/slide-seq-de/R/function.GO.R')
source('/home/hsarkar/Workplace/slide-seq-de/R/linear_model.R')
source('/home/... |
b482f7047d2c05284ef27b7ab92cfb4e79bd44272ab11c5eb5c850b63fb192f8 | Jupyter | 15,469 | 550 | # %% [markdown]
# # **Libraries**
# %%
import sys
sys.path.append('../../Utils')
# %%
import numpy as np
import pandas as pd
from trainer import Trainer
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from sklearn.model_selection import train_test_split
from torchmetrics.cl... |
f42d65c7f67f02261e440f7aff0c78d3d1b20f95ed9f4c691ab7b60dde5335d9 | Jupyter | 15,549 | 671 | # %% [markdown]
# ### DrugMechDB: A Comprehensive Curated Database of Drug Mechanisms
# %% [markdown]
# Notebook to recreate figures of DrugMechDB manuscript
# %%
#import libraries
import pandas as pd
import networkx as nx
import re
import yaml
from itertools import chain
from pathlib import Path
from operator impo... |
b20d2dafbce7760fb5ec70d82be731a75229bda1818ef0031b8a3ec67e19c910 | Jupyter | 15,702 | 391 | # %%
import numpy as np
import sys
sys.path.append('/home3/ebrahim2/beyond-brainscore/run_reg_scripts/')
from helper_funcs import combine_MSE_across_folds
from matplotlib import pyplot as plt
# %%
import numpy as np
def _mean_across_participants(values_per_unit, participant_info):
"""
values_per_unit: 1D arra... |
c1881ac5eeb0e683a985fd10427b00ab89ee845b581c97e35cb36b9d61bdd0d9 | Jupyter | 15,763 | 645 | # %% [markdown]
# # Regression example using PT-MELT
# This is a basic regression example using the models in PT-MELT.
#
# Requires the following additional packages:
# * ipykernel
# * scikit-learn
# * matplotlib
# * torchinfo
# %%
import sklearn.datasets as sdt
import matplotlib.pyplot as plt
# Create surogate data... |
d3000839f893efa6356e09ccca4e1dd7141e6972e739e2ff21931e532ba0ecc8 | Jupyter | 16,217 | 466 | # %% [markdown]
# ## Script to run inference on our RDM models
#
# Notes for reproducibility:
# - File paths in cell 2 are absolute, so they should be changed to wherever RDMs are kept
# - Partial correlation functions are messy, but unfortunately necessary due to no similar function in rsatoolbox. A better way would... |
1592b14bca92786000cbe0c249d4e6e1dea604d89cc95d3c009e517159f61a57 | Jupyter | 16,234 | 318 | # %% [markdown]
# # With TF Serving
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/tf_serving.ipynb)
#
# This tutorial demonstrates how to train a YDF model, export... |
3bdcc75ee657403c48f59a9556e37b14a53eb88359fb921d8223c6c9676a511a | Jupyter | 16,329 | 365 | # %%
import sys
from pathlib import Path
import time
from scipy.optimize import minimize
import pprint
from matplotlib import font_manager
import matplotlib.pyplot as plt
# Add the path to the downloaded fonts
font_dirs = ['/home/simoneponcioni/Documents/99_OTHERS/my_fonts/'] # Replace with the actual path to your f... |
4677917946ac0f886f4d07682fb6e8fbedcc4389b9659ca7356c2a6c12d0c143 | Jupyter | 16,364 | 272 | # %% [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... |
c922a71626a64ae830a110d34ccc3a083326bb8a6a8454edd283f5d3962af3a0 | Jupyter | 16,370 | 468 | # %% [markdown]
# ## Figure 7
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from itertools import combinations
from pathlib import Path
import numpy as np
import scipy
import matplotlib.pyplot as plt
from mycolorpy import colorlist as mcp
from scipy.stats import ... |
68b3a23aa88c1cb1dd872e95223cfab912a4048d743c1a5d32f91c8255ae9f9f | Jupyter | 16,450 | 145 | # %% [markdown]
# # Photometry FLMM Guide Part V: Interactions -- Probing Learning and Changes over Time
# ## Authors: Gabriel Loewinger, Erjia Cui
# ### 2024-09-07
# ### rpy2 implementation: Josh Lawrimore
# %% [markdown]
# # Part V: Probing Learning and Changes over Time
#
# Neuroscience studies often use rich long... |
6300cbd02627d9301194800ad78c3de4080eda2cb4cb759c2037a97a2c60b263 | Jupyter | 16,537 | 194 | # %% [markdown]
# # Demo for generating substrates and running a single simulation
# This notebook is intended as a demo that reproduces a single simulation with generic parameters for the substrate geometry and the sequence applied. Readers are free to alter the parameters and use this as a base example for their own ... |
bce81cdbecfe7bbe1aed552e208be68c74cbab5597f0184d10d926c65ef86c43 | Jupyter | 16,564 | 481 | # %% [markdown]
# # Training a Graph Neural Network with NAGL with multiple objectives
# %% [markdown]
# This notebook will go through the process of training a new Graph Neural Network (GNN) on a small dataset of alkanes with multiple objectives. Please see the `train-gnn-notebook` tutorial for more on what's happeni... |
fa82991977abc8f0e3dd1bac00d68dfa68e56a06db5a6b6a0276699b41f92ab3 | Jupyter | 16,577 | 585 | # %%
# %%
source('/home/meisl/bin/bin/bin/source.R')
# %%
scon = readRDS('../F1.conos.rds')
# %%
load('/home/meisl/Workplace/Prostate/healty.data/conos/Figures.v2/F7.slide.seq/RL.RData')
# %%
# %%
annot.palf2 <- function(n) return(scon$misc$annot.pal2[1:n])
annot.pal2 = readRDS('../annot.pal2.rds')
annot.palf2... |
91c0a9cd3ca94ff56c421587c386d360e4741d207c2358c3822b085c11258ea8 | Jupyter | 16,672 | 303 | # %% [markdown]
# # <font color=#B2D732> <span style="background-color: #4424D6"> Spinal cord microstructural preprocessings </font>
# <hr style="border:1px solid black">
#
# *Project: 2024_brsc_aging_project*
# *Paper: in prep*
# **@ author:**
# > Caroline Landelle, caroline.landelle@mcgill.ca // landelle.caro... |
9d621e84896603ecb9f8978f252e8aadb144a83679868f2c473b33982dc765b8 | Jupyter | 16,872 | 462 | # %%
import numpy as np
import pandas as pd
import glob, os, sys, subprocess
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import scipy.stats as st
import statsmodels.stats.api as sm
import Bio.PDB
from Bio import Seq, SeqIO
from Bio.PDB.MMCIFParser import MMCIFParser
from Bio.PDB.DSSP import make... |
135bc7cc4f6887b09df0d8dd1ba9a2b73fc05e7a193edc9b616bad9f73c4682c | Jupyter | 17,026 | 494 | # %%
from msi_visual.normalization import total_ion_count, spatial_total_ion_count
from msi_atlas.annotations import get_dataset
import numpy as np
from argparse import Namespace
from pathlib import Path
import joblib
array = np.array
import os
path = r"/home/jacob/Desktop/atlas_verification"
extraction_args = eval(... |
a4641a2658ce4c35e24f8d6b1b77ad75716d1be207fa68613ac43a364f74ab0b | Jupyter | 17,158 | 404 | # %%
import os
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from scipy import stats
from scipy.stats import wilcoxon
from sklearn.cluster import KMeans
from scipy.stats import mannwhitneyu
from utils import plot_fnc, convert_pvalue_to_asterisks
os... |
8d22332d1be4ea522c83c8ca50f51bad1de592583b5210da68778f89c0c1d7a5 | Jupyter | 17,172 | 337 | # %% [markdown]
# # Training a Graph Neural Network with NAGL
# %% [markdown]
# This notebook will go through the process of training a new Graph Neural Network (GNN) on a small dataset of alkanes, and demonstrate inference with the resulting model. On the way, we'll put together a tiny test dataset, and talk a bit ab... |
78341ba55fd295d4c24c4fc342dcb6ac3675d8bade8050d4344b415b6bbf34b7 | Jupyter | 17,272 | 430 | # %% [markdown]
# # Descriptive Statistics & Table 1
# **Study:** APOE ε4 × Hippocampal Volume × Cognitive Decline
#
# Requires: `reports/ADNI_Baseline_Analysis.csv` and `reports/ADNI_Complete_Cases.csv` from `01_data_pipeline.ipynb`.
#
# **Produces:**
# - Table 1 by APOE ε4 dose
# - APOE4 frequency bar chart
# - Cog... |
43891a5ef7a96b429384aaad8c1f55009ca7404be0679553fc93d4b4415d524d | Jupyter | 17,286 | 466 | # %% [markdown]
# Script to identify any skew in horizontal and vertical eye movements as a function of different conditions
# %%
import numpy as np
import pandas as pd
import copy
df_1d = pd.read_csv('/Users/alex/Documents/action_hippo/action_hippo/eyes/20250516_timecourse_df_allsubs_lowerexclusion.csv')
# change n... |
b144e51741d0c18833ba39961d055d77eeb1cf32d3b897346521b5a6a7edd3e2 | Jupyter | 17,297 | 321 | # %%
import numpy as np
import sys
sys.path.append("/home3/ebrahim2/beyond-brainscore/analyze_results/figures_code")
from trained_untrained_results_funcs import find_best_layer, elementwise_max, custom_add_2d, load_perf, loop_through_datasets
from untrained_results_funcs import load_untrained_data
from plotting_functio... |
7168f38fb13de1f63d0b6cf4e4273dc7363c994718b23f41ea6211fef4e504aa | Jupyter | 17,680 | 516 | # %% [markdown]
# ### *This file allows to reproduce FigS4A-C*
# %% [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
... |
dd9f5a193d32de85a977c2c255514394aaa2f3a83572a0d001ad4c9c5042863a | Jupyter | 17,730 | 511 | # %%
import numpy as np
import TaskRest.paths as trest_paths
import numpy as np
import covariance as cov
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import TaskRest.plotting as plotting
import PcmPy as pcm
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from scipy.spatial.transform ... |
b3fd3ff24c4e8c0d934997cdd708ff427608cf529d1016700aec10fe9be16c45 | Jupyter | 17,859 | 343 | # %%
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
# import GLM_helper as gh
import scipy.stats as stats
from joblib import Para... |
8236d30382bd83e58dd287d1a2a31dd6722cf14bdda0c97f37fc9bee6fdb3b7c | Jupyter | 17,905 | 555 | # %% [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_Models'
os.makedirs(model_save_dir, exist_ok=True)
print(f"Model weights will be saved in: {model_save_dir}")
# %%
im... |
cc19ce4a13936c331a41a2c801e47f75db453cd943ff2733710b0515facb0f15 | Jupyter | 17,928 | 573 | # %% [markdown]
# ## Environment Setup
# %%
! pip install kagglehub
# %%
from google.colab import drive
import os
drive.mount('/content/drive')
model_save_dir = '/content/drive/My Drive/BRISC2025_Models'
os.makedirs(model_save_dir, exist_ok=True)
print(f"Model weights will be saved in: {model_save_dir}")
# %%
impo... |
2a56dcfb3167886075c1a27ceadb0820f7a2b1f0e71397c06e78694f8de452c8 | Jupyter | 17,957 | 544 | # %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap,Normalize
from scipy.stats import mode, entropy
from scipy import interpolate
from scipy.spatial.distance import jensenshannon
import helper as hp
import warnings
wa... |
fa3669417daba0f5abadf230f69d43519109da857079bfe8be5bdd41b51f59ca | Jupyter | 17,989 | 760 | # %% [markdown]
# # Generate QTL Plot Info From Data
# - **Author(s)** - Frank Grenn
# - **Date Started** - May 2020
# - **Quick Description:** Identify which genes we can create different qtl Locus Compare plots for. See if there is enough data to create a plot and if the plot has the risk variant in its data. Output ... |
4b78dab51a7e912a7343399b28590ca8a10313920fc3abc86cabb3faf23e9924 | Jupyter | 18,016 | 511 | # %%
import numpy as np
import pandas as pd
import glob, os, warnings
warnings.filterwarnings('ignore')
# cc_df = pd.read_csv("/n/data1/hms/dbmi/farhat/Sanjana/MIC_data/criticalConcentrations_updated.csv")
cc_df = pd.read_csv("drug_CC.csv")
cc_df_who = pd.read_csv("/home/sak0914/who-analysis/data/drug_CC.csv")
isolat... |
bdf14280b55c35a4aab50a4681a9b57a1b8be39002008c01fd56c25f02ee7fab | Jupyter | 18,044 | 474 | # %%
## Imports
import json
import os, sys
import time
import yaml
import h5py
import pickle
import numpy as np
import collections
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
#from cGANtools.GAN import CGAN
#from keras.models import load_model
from iter... |
15c5e75135797b5d04f09e66420ceb785240ba845879ceee9f3b8938808a65d2 | Jupyter | 18,059 | 571 | # %% [markdown]
# ### Extensive vs. Intensive variables - a direct comparison
#
# Author: Simone Poncioni
#
# Date: 19.09.2024
# %%
import sys
from pathlib import Path
from matplotlib import font_manager
import matplotlib.pyplot as plt
# Add the path to the downloaded fonts
font_dirs = ["/home/simoneponcioni/Docum... |
b29cc2af5e2faa23d0df4a1421a7988cffaf743513639eacafff659359770c66 | Jupyter | 18,059 | 549 | # %% [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_Orvile_Models'
os.makedirs(model_save_dir, exist_ok=True)
print(f"Model weights will be saved in: {model_save_dir}")
... |
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