sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
58bca5387122164d088ba8c1de2e8722580d58f5f9d6b0e463d994d6c5f81ce9 | Jupyter | 7,145 | 199 | # %%
%reset -f
%matplotlib inline
import numpy as np
import lib.io.stan
import matplotlib.pyplot as plt
import os
# %%
data_dir = 'datasets/id001_ac'
fit_data_dir = 'jureca/data'
results_dir = 'results/exp10/exp10.7/exp10.7.2'
os.makedirs(results_dir,exist_ok=True)
os.makedirs(f'{results_dir}/logs',exist_ok=True)
os.m... |
897a5e5b8a648f034bb5c1f67ab1e4238a26146a37036050ba324673ece9fdf7 | Jupyter | 7,181 | 305 | # %% [markdown]
# # scRNA-seq Example
# Examples to use ``GSEApy`` for scRNA-seq data
# %%
%load_ext autoreload
%autoreload 2
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %%
import gseapy as gp
import scanpy as sc
# %%
gp.__version__
# %% [markdown]
# ### Read Demo Data
#
# C... |
c7b8f94cf804731b6ef996697d310bd29d83d48e444c937a0ae5b6920d8233c2 | Jupyter | 7,237 | 231 | # %% [markdown]
# # E/I Balance (EIB) Experiment
#
# This notebook runs the E/I imbalance experiment.
#
# **Manipulation:** ReLU slope parameter (alpha) — controls the excitatory/inhibitory gain ratio.
#
# **Pipeline:** Load images -> Build CNN with custom activation -> Train across slope conditions -> Compute corre... |
f71718b853688eaa5110e088baeb8d205b678ed535595dc0ba650c5acfd13703 | Jupyter | 7,263 | 243 | # %% [markdown]
# # GWAS Locus Browser Generate Phenotype Variant File
# - **Author** - Frank Grenn
# - **Date Started** - April 2020
# - **Quick Description:** code to generate list of phenotype variants from other gwases. relies on files made in the coding variants scripts
# - **Data:** [GWAS Catalog](https://www.ebi... |
09a7ca1a03888900a0b5188abb21d0e9c460376817d1fe7dd3e1ffb7f8ce1117 | Jupyter | 7,268 | 181 | # %% [markdown]
# # 🧭 Getting started
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/getting_started.ipynb)
# %% [markdown]
# Decision Forests (DFs) are a family of m... |
61fb01b1b96907036fee8aea47cf6e72a504e7c865b9e77e1ae563425147ee19 | Jupyter | 7,391 | 234 | # %% [markdown]
# # Parcellation results
# %%
import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
import os
import TaskRest.paths as paths
from scipy.stats import ttest_rel
from TaskRest.plotting import covariance_order,covariance_palette2, covariance_order_replication
# %%
# Set paths
base_dir... |
60c9a3fc79abba2676c588df10c8a388facb28db403d86d75c78dccc7bec66b0 | Jupyter | 7,462 | 162 | # %%
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
from utils import decode_fnc, plot_fnc, convert_pvalue_to_asterisks, plot_trajectory
os.chdir("/data/users4/xli/interpolation")
from models.vae import VAE
os.chdir("/... |
adaed5deb60e08956b1565891bb464b3d3d54573211272ab172155316108f9da | Jupyter | 7,465 | 173 | # %% [markdown]
# # R and rpy2 installation guide
#
# This notebook will guide users to installing R and the required R packages in preparation to install and run the Tutorials using the fast-fmm-rpy2 Python package. This notebook assumes the user is familiar with Python and Jupyter Notebooks.
#
# The goal of this no... |
ead78304147727b6a8cd76a375155c5e9b58a6c147f40e6dafeed5db945564c9 | Jupyter | 7,512 | 240 | # %%
%reset -f
%matplotlib inline
import matplotlib.pyplot as plt
import scipy.signal as signal
import lib.io.stan
import numpy as np
from matplotlib.lines import Line2D
import os
# %% [markdown]
# ### 2D epileptor simulation
# %%
np.random.seed(0)
ntwrk = np.load('datasets/id002_cj/CJ_network.npz')
SC = ntwrk['SC']... |
1bef1bc4c6606a2e27f71991a32c0674d6f2a2c5ba6bb07db0d91e0df8a1791a | Jupyter | 7,531 | 202 | # %% [markdown]
# # In Java
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/java_standalone.ipynb)
# %% [markdown]
# ## Setup
# %%
pip install ydf -U
# %% [markdow... |
50806654c55c3f86adb3371f8b7b6bb4290982c8c583d1f160a77b92cd681590 | Jupyter | 7,539 | 223 | # %% [markdown]
# # This notebook generates figures and tables for the results
#
# **NOTE:** The variable characteristic tables are generated in `code/make_variable_tables.R`
# %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
BASE_DIR = Path(os.path.abspath... |
6be33ec482d476d038e8180f070e42b94969189e5d93ad09d3acd6c6c196769e | Jupyter | 7,618 | 243 | # %% [markdown]
# # Collect Variant Population Frequencies Using Annovar
# - **Author** - Frank Grenn
# - **Date Started** - April 2020
# - **Quick Description:** use annovar to get population frequencies for all gwas risk variants for the app
# %%
import pandas as pd
# %%
DATADIR= "$PATH/AppDataProcessing"
WRKDIR = ... |
f3d29512f989d41e437ec3e5adf85ab6bf74ab9ec9b23f011ee315af7170d4e6 | Jupyter | 7,658 | 222 | # %% [markdown]
# # DrugMechDB — Top Metapaths Sankey
#
# Rebuilds the Figure-4 mechanistic-path Sankey with manuscript-style styling:
# concept-type colors, abbreviated labels, fixed left-to-right columns, and
# source-colored translucent links.
#
# The set of paths shown is **derived** (top-N most frequent metapath... |
9fa79409b755adb7584c1bef7e9d6b58ae9a53f524181277ac654e156382d3f5 | Jupyter | 7,678 | 200 | # %%
#reproduce fig 3c wgcna analysis table
# %%
import numpy as np
import nibabel as nb
import os
import matplotlib.pyplot as plt
from wgcna_module_enrichments import WGCNApostprocessing
import pandas as pd
fetal=False
adult=False
combi=True
proc = WGCNApostprocessing()
print('eigengenes')
concat= np.load('/data1/a... |
b09c42a78e0875e01c9ea2dcf91436c89356af4e923609c948502095d8f3267d | Jupyter | 7,679 | 168 | # %%
from trained_untrained_results_funcs import load_perf
import seaborn as sns
import numpy as np
import pandas as pd
import pickle
from matplotlib import pyplot as plt
# %%
exp = ['384', '243']
br_labels_dict = {}
num_vox_dict = {}
subjects_dict = {}
data_processed_folder_pereira = f'/data/LLMs/data_processed/pere... |
6d95158528bbcb96a288fe13249cbb7543af0d2f94bd51b4ebcfaeba64e411e7 | Jupyter | 7,710 | 248 | # %% [markdown]
# # Demonstration of preprocessing data and training GANs in REKINDLE
#
# In this notebook we show you how to preprecess the training data (kinetic parameter sets generated by ORACLE) and then train the GANs for efficient generation. For this demonstration we provide a toy dataset consisting of 1000 ki... |
764b92a0c962ec8927cf0893d82aa11279262d1e1e7d23ad1170b40677eca6d7 | Jupyter | 7,712 | 259 | # %% [markdown]
# ## Extended Data Figure 13
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from tqdm import tqdm
from pathlib import Path
sys.path.insert(0, "./prepare_data/")
import yaml
import numpy as np
import pandas as pd
import seaborn as sns
import matp... |
ac1fa79cbd45f18032ad327125e170afc705211f9303d53fd50e62119700519a | Jupyter | 7,720 | 199 | # %%
%matplotlib inline
import numpy as np
import lib.io.stan
import lib.plots.stan
import lib.utils.stan
import subprocess
import matplotlib.pyplot as plt
import os
from matplotlib.lines import Line2D
import importlib
# %%
data_dir = 'datasets/id002_cj'
results_dir = 'results/exp10/exp10.57.14'
os.makedirs(results_d... |
aa11de56c68d6ccc5023b4685a39caace55799832f2c21c6617c89bc7fd5b565 | Jupyter | 7,856 | 232 | # %%
import numpy as np
import pandas as pd
import glob, os, subprocess, vcf, shutil, sparse, yaml, sys, pickle, itertools
from Bio import Entrez, Seq, SeqIO, SeqUtils
import scipy.stats as st
# load all utils functions
os.chdir("../")
sys.path.append("utils")
from saliency_utils import *
from inSilicoMut_utils import... |
b62468e8406b94e527107031568e5c2bec34592b6ca5643c9565ce82d8f7ea0d | Jupyter | 7,929 | 183 | # %% [markdown]
# # <font color=black> Figure 1 Spinal cord morphometry </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import sys,json
import glob, os
import pandas as pd
import numpy as np
from palettable.colorbrewer.sequential import GnBu_9
import numpy as np
from matplotlib.colors... |
5871472ba37acee998f57560bbad264d05ade553887b6f5a8c5063b497b68150 | Jupyter | 8,013 | 237 | # %% [markdown]
# # In C++ [Standalone]
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/cpp_standalone.ipynb)
# %% [markdown]
# ## Setup
# %%
pip install ydf -U
# ... |
2c5d16019eba238c0068f15cb97932afa3de9ccfe4f1409bc8fb50260515df69 | Jupyter | 8,057 | 274 | # %% [markdown]
# # Installation
#
# conda create -n liana -y python=3.8 ipykernel # liana requires ipykernel
#
# conda activate liana
#
# -
#
# conda install -c anaconda pytables
#
# pip install torch
#
# pip3 install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
#
# -
#
# pip insta... |
abd7c827842840de846b9adcc8783234334303705ad7766ea3e19971adc4008e | Jupyter | 8,147 | 236 | # %%
import warnings
import matplotlib
warnings.filterwarnings("ignore", category=matplotlib.MatplotlibDeprecationWarning)
# %%
# %matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
# import pandas as pd
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
from src... |
2783894c364bd6ca82f1180259edf0073af5dba8a574b69ff11d5b31b609a9bb | Jupyter | 8,245 | 195 | # %% [markdown]
# # <font color=black> Figure 3 - Brain and spinal cord morphometry </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import sys,json, os
import pandas as pd
import numpy as np
main_dir="/cerebro/cerebro1/dataset/bmpd/derivatives/Aging_project/"
sys.path.append(main_di... |
f115a0df6f8dc1db4dcdfe9359c319baf8f6d879af47cee86e70d7feac8eed7b | Jupyter | 8,284 | 276 | # %% [markdown]
# # Evaluate the similarity between covariances
# %%
import numpy as np
import TaskRest.paths as trest_paths
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import TaskRest.plotting as plotting
from scipy.stats import ttest_rel
# Set trest_paths
base_dir = ... |
9a14af9a828c6b9e7810744ba8731bce10d37214b5c80b499083a76444db7caa | Jupyter | 8,306 | 285 | # %% [markdown]
# # Installation
#
# conda create -n liana -y python=3.8 ipykernel # liana requires ipykernel
#
# conda activate liana
#
# -
#
# conda install -c anaconda pytables
#
# pip install torch
#
# pip3 install torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
#
# -
#
# pip insta... |
35140caf47fae14b7e5476601de51e67aebe7c82d43d5bc421cc02724cbb7aa0 | Jupyter | 8,370 | 236 | # %% [markdown]
# # We are assembling all elements of figure 2 of the TwinC paper in this notebook.
# %%
import sys
sys.path.append("../../twinc")
import os
import gzip
import torch
import cooler
import pyBigWig
import hicstraw
import argparse
import matplotlib
import numpy as np
import seaborn as sns
import _pickle... |
f68a45f87aa18688afc8a0f4d8970585e35b688c4f00985a81b20135cb5564b3 | Jupyter | 8,463 | 200 | # %%
import nibabel as nib
import os
import h5py
from sklearn import linear_model
from scipy import stats
import numpy as np
import json
import glob
#path to data (change to your path)
fmripreppath = '/data/MoL_clean/fmriprep/'
#path to output (change to your path)
prepath = '/data/MoL_clean/preprocessed/'
#list of s... |
2c33162ab35128084d360bb8ff1bc53c07c4cf7f46dc2c51aed6ff109088ef40 | Jupyter | 8,501 | 191 | # %% [markdown]
# # <font color=#5b797e> Figure 3 | Spinal cord func / morphometry coupling </font>
# <hr style="border:1px solid black">
#
# to read: https://elifesciences.org/articles/62116
# %% [markdown]
# ### Imports
# %%
import sys,json
import glob, os
import pandas as pd
import numpy as np
from palettable.col... |
4f22f45140297699fc331e92b3b6ff2988f31768de9f8eed5dbc6795f533b9c3 | Jupyter | 8,578 | 241 | # %% [markdown]
# # In this notebook, we assemble Figure 5 G-Quadruplex boxplot for the TwinC paper.
# %%
import os
import mne
import scipy
import matplotlib
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
from sklearn import metrics
import matplotlib.pyplot as plt
from scipy.stat... |
b7f7f3969e2b6453f58d63e7e8b8ee1015eca4fd3eda51d549c0679b2c06007d | Jupyter | 8,608 | 228 | # %% [markdown]
# # LogBook
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/log_book.ipynb)
#
# The **Log Book** is a utility for tracking and analyzing ML experimen... |
5d004c24be4029e681d4869dcb4460c3c9dd5dea1f1dc213a924821b6005896d | Jupyter | 8,621 | 209 | # %% [markdown]
# # Adding a Custom Estimator
#
# ``cinnabar`` uses estimator objects to turn relative free energy measurements into per-ligand absolute free energy estimates. The built-in default is the maximum likelihood estimator (MLE), but you can provide your own estimator by subclassing ``Estimator`` and passing... |
1f1cec002891e8bbe32e57545779436af12ef3b315dc47e524ce65753a336b5e | Jupyter | 8,635 | 192 | # %%
import numpy as np
from scipy.stats import wilcoxon
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
# %%
model_names = ['Llama', 'rwkv', 'roberta-large', 'gpt2xl']
# %% [markdown]
# ## Wilcoxon across voxels/electrodes/fROIs
# %%
store_pvalues = {}
for dataset in ['pereira', 'blan... |
96f40f01e8ad26a4927fc0a5a10fbf9c6d692fafe4e97e2ab27f68c864bf9c82 | Jupyter | 8,771 | 196 | # %% [markdown]
# # Time sequences
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/time_sequences.ipynb)
#
# ## Setup
# %%
# pip install ydf temporian -U
# %%
impo... |
6a7a8d995e4313bd72b4d4529dba3d723334135782824c7c52526a56885741f1 | Jupyter | 8,826 | 217 | # %% [markdown]
# # Counterfactual
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/counterfactual.ipynb)
#
# ## Setup
# %%
pip install ydf scikit-learn umap-learn p... |
3c1f53c8a8b683bed3fe941142287af7eb579afb982cf8b4c98148000882433b | Jupyter | 8,894 | 249 | # %%
%matplotlib inline
from tvb.simulator.lab import *
import os.path
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib import colors, cm
import time
import scipy.signal as sig
import scipy.spatial.distance as dists
import numpy as np
import time
from scipy.optimize import fsolve
i... |
420f5970ee45368031b0a5b10b1ff29a10642df93210a7b0934011c43282a554 | Jupyter | 8,912 | 241 | # %%
%matplotlib inline
import numpy as np
import lib.io.stan
import lib.plots.stan
import lib.plots.tvb
import lib.io.tvb
import subprocess
import matplotlib.pyplot as plt
import os
from matplotlib.lines import Line2D
import importlib
import scipy.spatial.distance
import lib.preprocess.envelope
import lib.utils.stan
... |
252b9668c6f7cc5deb48ffc3f164e4829df3760ee5ffad18060ef4813b6abbc2 | Jupyter | 8,930 | 193 | # %% [markdown]
# # Anomaly detection
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/anomaly_detection.ipynb)
# %% [markdown]
# ## Setup
# %%
pip install ydf ucimlrep... |
c4484529713d55324201ce911533c45ccc629b5c1e0a77fb8d8242c43c0dddc8 | Jupyter | 8,965 | 366 | # %% [markdown]
# # GWAS Locus Browser QTL and GWAS Gene Data for (1) all Genes or (2) one Gene
# - **Author(s)** - Frank Grenn and Hirotaka Iwaki
# - **Date Started** - October 2019
# - **Quick Description:** collect eQTL and GWAS data for genes
# - **Data:**
# %%
library(data.table)
library(dplyr)
# %%
WRKDIR = '$... |
d403cbaa7790d257c628b29b29116e9e44fba37b6b47bdab87e5b40c4e897733 | Jupyter | 8,981 | 317 | # %% [markdown]
# ## Figure 5
#
# 
# %%
%load_ext autoreload
%autoreload 2
import sys
import logging
from tqdm import tqdm
from pathlib import Path
sys.path.insert(0, "./prepare_data/")
import yaml
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
impo... |
26f2c1eb5e40855d313c949e3dbdf862dc5709e1ee908e55015c6cd9aa51c367 | Jupyter | 9,065 | 274 | # %%
# SET ARGUMENTS
# input_path <- snakemake@input[["counts_scaled"]]
input_path <- "/scratch/tweber/DATA/MC_DATA/STOCKS/2023-07-28-HL25JAFX5/KM1116LENTIx01/counts/multistep_normalisation/KM1116LENTIx01.txt.scaled.gz"
# gc_path <- snakemake@params[["gc_matrix"]]
gc_path = "/g/korbel2/weber/workspace/ashleys-qc-pipeli... |
600ba49f7942aef4fbcaca75bcef61da3d3f593601b68792f7342a7ba61b7b3f | Jupyter | 9,142 | 260 | # %% [markdown]
# # **Libraries**
# %%
import sys
sys.path.append('../../Utils')
# %%
# Utils libraries
import numpy as np
from shap.plots.colors import red_white_blue
from matplotlib.colors import ListedColormap
# Neural libraries
import torch
import torch.nn as nn
from torch import optim
from sklearn.metrics impor... |
148782914f7039e7ef5b3424998e71b3d2a781cde3f1ba81f1f96037fff98bad | Jupyter | 9,154 | 299 | # %%
import os, sys
import time
import numpy as np
import pandas as pd
import yaml
import h5py
import pickle
import configparser
import argparse
import matplotlib.pyplot as plt
# %% [markdown]
# ## Data preprocessing - logNorm transformation
# %%
if __name__ == "__main__":
start = time.time()
print('\nSTART ... |
00928e3f727a1efdcbd0696123a6982d4bf8b21e652646cd22589deb670798a9 | Jupyter | 9,285 | 269 | # %% [markdown]
# # We are assembling all elements of figure 3 of the TwinC paper in this notebook.
# %%
import os
import scipy
import numpy as np
import configparser
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ranksums
if not os.path.exists(f"../../figures"):
os.system(f"mkdir ... |
2f0e70e25ae3c69c33594bb43f59e76548749bf40253b09ffbac2ebd441c4f4a | Jupyter | 9,316 | 343 | # %% [markdown]
# # LOAD DATA
# %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 8 16:01:23 2022
@author: alex
"""
from copy import deepcopy
import os
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
sns.set_theme(style="ticks", palette="pastel")
def add_chs... |
3db6cf142f60b1059aac7c0f2819ad080c915b5b6db90f631f552f1ad404ca53 | Jupyter | 9,319 | 256 | # %% [markdown]
# ### Import necessary libraries
# %%
import numpy as np
import pandas as pd
import seaborn as sns
from statannotations.Annotator import Annotator
import cortico_cereb_connectivity.run_model as rm
import matplotlib.pyplot as plt
from plotting import covariance_palette2, covariance_order
import TaskRest... |
010e979b90af93cf7343fb61f0c13762a4f0f7e07e0c1602b9425fc18c59af05 | Jupyter | 9,366 | 270 | # %%
## Imports
import os, sys
import time
import yaml
import h5py
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
#from cGANtools.GAN import CGAN
#from keras.models import load_model
parent_dir = os.path.abspath(os.path.join(os.getcwd(), os.pardir))
sys.path... |
881fae5c3b430d6eb1cc47c89d8390190aabfc8deb89b90a97c86a3b58acc187 | Jupyter | 9,453 | 312 | # %% [markdown]
# # Probabilistic Temporal Transformer Forecasting
#
# This notebook demonstrates how to use the temporal transformer with a mixture density output head for probabilistic forecasting. It compares a deterministic transformer against an MDN transformer on the same heteroscedastic synthetic forecasting ta... |
aece6447dd029ca4a1cdc75ececc2ca34e1b22a2cbf9d648fd7475ec3adae67a | Jupyter | 9,455 | 254 | # %% [markdown]
# # Vector Sequence
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/numerical_vector_sequence.ipynb)
#
# ## Setup
# %%
pip install ydf transformers ... |
af4b308c218c099e0fc4fa27c3e1daf87689a6481e0ba3ad756863499873242f | Jupyter | 9,541 | 396 | # %%
import sklearn.datasets as sdt
import matplotlib.pyplot as plt
# Create surogate data
n_samples = 1000
n_features = 10
n_info = 5
n_targets = 3
noise = 10
x, y = sdt.make_regression(
n_samples=n_samples,
n_features=n_features,
n_informative=n_info,
n_targets=n_targets,
noise=noise,
random... |
3616d2395b3d7be3a312e44c331a43fbde1aeeb33e2d59e567b1554fd8021ed4 | Jupyter | 9,648 | 243 | # %% [markdown]
# # In this notebook, we assemble Figure 5 transcription factor gene expression boxplot for the TwinC paper.
# %%
import os
import mne
import scipy
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import stats
from pyjaspar import jaspardb
import matplotlib.pyplot as plt
from ma... |
32bf61f392a9264a60180c47e640017dffd51b51f9b449a69020ee0d03c625af | Jupyter | 9,670 | 293 | # %% [markdown]
# # Using DeepMReye, look at the median of 2 and 3 and comparing these within the cluster window (and other correlations)
# %% [markdown]
# As we have a lower sampling late, need to accommodate for misaligned timepoints. Cluster window buffered to 1s to account for this, no rounding.
# %%
import os
im... |
4258915ef632ee4808ed385f6d4b55362c1ff0abd0ed45df0a295219d3df71c8 | Jupyter | 9,714 | 224 | # %%
GROUPSTATS_DATE = '2025_07_26'
# %%
"""Plot Simpson's paradox by brain network."""
import sys
import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
import scipy
import matplotlib as mpl
import matplotlib.ticker as ticker
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d ... |
f432cb3cabe0379fb0a9243ebacc8697cc93530930bb15541c377672df78f4e9 | Jupyter | 9,736 | 176 | # %%
from scipy.io import mmread
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
import pymn
# %%
%matplotlib inline
# %%
#These save characters as text in PDFs
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'... |
55b577728d5b61b23b4e57eabb3a3cdb4460c5b13de994c4034f99384b4a9c8c | Jupyter | 9,788 | 285 | # %%
%reset -f
%matplotlib inline
import numpy as np
import lib.io.stan
import lib.plots.stan
import lib.utils.stan
import subprocess
import matplotlib.pyplot as plt
import os
from matplotlib.lines import Line2D
# %%
data_dir = 'datasets/id002_cj'
results_dir = 'results/exp10/exp10.38.1'
os.makedirs(results_dir,exist... |
7bb49c9bb20b38f3decf45487dc2647017c8073844a90cecc4f5a2773babdc71 | Jupyter | 9,809 | 303 | # %%
import numpy as np
import pandas as pd
import glob, os, subprocess, vcf, pysam, shutil, sparse, yaml, sys, pickle, itertools
from Bio import Entrez, Seq, SeqIO, SeqUtils
import scipy.stats as st
# utils files are in a separate folder
os.chdir("../")
sys.path.append("utils")
from saliency_utils import *
from inSil... |
d62e358267a9372fb91b6008cbc8bfef8801e703cd0d46ee29774bc24e7d2d3e | Jupyter | 9,809 | 238 |
# First import the relevant packages and functions
from local_optim_fit import forge_axcaliber, fit_params
from dmipy.signal_models import gaussian_models, cylinder_models
from dmipy.core.acquisition_scheme import acquisition_scheme_from_qvalues
import numpy as np
import matplotlib.pyplot as plt
from matplotlib impo... |
880b70c1ad7a718f4326b436cade9931fb88f61c6d78011c97090cd1eb453ab1 | Jupyter | 9,824 | 210 | # %%
import sys
sys.path.append('/home3/ebrahim2/beyond-brainscore/analyze_results/figures_code/')
from trained_untrained_results_funcs import find_best_layer,load_mean_sem_perf, loop_through_datasets
import numpy as np
from matplotlib import pyplot as plt
# %%
exp = ['243', '384']
br_labels_dict = {}
num_vox_dict =... |
0695193f90d47ea1e793e2fa7f412e63431a7d705792baf876227a1599183dc0 | Jupyter | 9,881 | 219 | # %%
from trained_untrained_results_funcs import loop_through_datasets, load_mean_sem_perf, custom_add_2d
from matplotlib import pyplot as plt
import numpy as np
from scipy.stats import false_discovery_control
# %%
def compute_voxel_pvalues(voxel_performance, null_distribution):
"""
Compute one-sided p-values ... |
c91adc54c5f4a5e38481577885919f20aa3a25fee06ee37e668e049ef6639058 | Jupyter | 9,964 | 359 | # %% [markdown]
# # **Experiments - Dataset FASHION_MNIST**
# %% [markdown]
# ## **Libraries**
# %%
import sys
sys.path.append('../../Utils')
# %%
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchinfo
import numpy as ... |
def5891b04f298f5608b4e796000b699f44fcfc5f28ed7c5354ccaeaf77a3000 | Jupyter | 9,975 | 353 | # %%
source('../../ProatsteCancerAanalysis/lib.r')
# %%
# load conos object
scon = readRDS('../../F1.conos.rds')
# %%
# read color palates
annot.pal = readRDS('../../annot.pal2.rds')
load('../../color.RData')
# %%
annot.palf2 <- function(n) return(annot.pal[1:n])
a1 <- scon$plotGraph(alpha=0.05,font.size = c(4.7,6)... |
f5a98242995df4eabb1bb1cfd2a055885903c0a11b6effd90ea1361bd3585b9e | Jupyter | 10,003 | 240 | # %%
GROUPSTATS_DATE = '2025_07_26'
# %%
"""Produces polar bar plots displaying within-subjects results in model-based
subgroups."""
import sys
import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.gridspec import GridSpec
import matpl... |
5df01af43a583142584fe87f3ece5046ae3c21889dd8af8a8ef69e53ce60ab75 | Jupyter | 10,047 | 313 | # %% [markdown]
# # We are assembling all elements of figure 4 of the TwinC paper in this notebook.
# %%
import os
import configparser
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ranksums, ttest_ind
# %%
SMALL_SIZE = 48
MEDIUM_SIZE = 64
BIGGER_SIZE = 80
plt.rcPar... |
265d62a2e13e91179a240be66b70df83fb6a2cff043780fcc29fea236dfe360b | Jupyter | 10,054 | 450 | # %% [markdown]
# # Generate GWAS Locus Browser Psychencode QTL and GWAS Gene Data
# - **Author(s)** - Frank Grenn
# - **Date Started** - March 2020
# - **Quick Description:** collect Psychencode QTL and GWAS data for genes
# - **Data:**
# %%
library(data.table)
library(dplyr)
library("EnsDb.Hsapiens.v86")
# %%
WRKD... |
f1eb858a228da8f44638ad4ba60796baaa0c9927c5a0996bb8d9937652a51fbd | Jupyter | 10,079 | 259 | # %% [markdown]
# # DrugMechDB association types: bootstrap evaluation against MechRepoNet
#
# For every DrugMechDB (DMDB) *concept-type association* (e.g. `Protein_BiologicalProcess`),
# this notebook measures what % of those DMDB edges are also present in MechRepoNet (MRN), then
# compares that observed overlap agai... |
9adef978fd5288772d1efde3098b3e524789b89424282b087439291371fe7d4a | Jupyter | 10,106 | 372 | # %% [markdown]
# This notebook is part of the `deepcell-tf` documentation: https://deepcell.readthedocs.io/.
# %% [markdown]
# # Training a segmentation model
#
# `deepcell-tf` leverages [Jupyter Notebooks](https://jupyter.org) in order to train models. Example notebooks are available for most model architectures in... |
bad83a0ed3514846ffc3ba05f074d18d8d3a5389a998b8933ff9ed83fcdcd3f0 | Jupyter | 10,211 | 257 | # %%
GROUPSTATS_DATE = '2025_07_26'
# %%
"""Plots results of representational similarity analysis."""
import sys
import subprocess
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib as mpl
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.gridspec import GridS... |
fcfdd3e8e1b8a4f1b546b132d2b1ef415d929c15fe97a7c5776562a24b798412 | Jupyter | 10,287 | 225 | # %%
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
from scipy.stats import wilcoxon
from utils import decode_fnc, plot_fnc, mix_colors, convert_pvalue_to_asterisks
os.chdir('/data/users4/xli/interpolation')
from models.vae import VAE
os.chdir('/data/users... |
c283573ef46d58ecb44e6f127c6ec6966d18754de6bee841cc74ef9d78f27afe | Jupyter | 10,325 | 304 | # %%
import numpy as np
import sys
sys.path.append('/data/LLMs/LMMS/')
from transformers_encoder import TransformersEncoder
from transformers import RobertaModel, RobertaTokenizer
from vectorspace import SensesVSM
import spacy
en_nlp = spacy.load('en_core_web_trf') # required for lemmatization and POS-tagging
en_nlp_... |
b724b3039c24f9678eef35c60c5c839cc9c7822d8f0615756ff886b45c149306 | Jupyter | 10,441 | 198 | # %%
import pandas as pd
import glob
import matplotlib.pyplot as plt
import os
folder_path = 'output'
"""
Generates two master wide-format CSV tables:
1. group_emd_wide_with_counts.csv: EMD per subject and movement type (Forward only).
2. group_performance_wide_with_counts.csv: Metrics per subject, movement type, an... |
8a2d7280ded70a353f7afd59c35466a4ed3f5dcaaaaf3d031cd7f5cb18e18981 | Jupyter | 10,466 | 286 | # %% [markdown]
# ### *This file allows to reproduce Fig6*
# %% [markdown]
# # **Useful packages and functions**
# %%
using Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles, ProgressMeter
include("network_STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("network_STG_mode... |
1a5b0bc77decf2939688f9057a54067e419f9ef7c0872f819ded593a89a736c9 | Jupyter | 10,535 | 348 | # %% [markdown]
# # Train a GNN directly to an electric field
#
# To execute this example fully, the following packages are required.
#
# * openff-nagl
# * openff-recharge
# * openff-qcsubmit
# * psi4
#
# However, if you wish to just follow along the training part without first creating the training datasets yoursel... |
e700709794a1839479d9268843c7cb8864ce2526bc34ee730d812e47760e051c | Jupyter | 10,635 | 291 | # %% [markdown]
# ## Figure 1 - Cheese3D accuracy
# %% [markdown]
# This notebook includes all the code necessary to reproduce Figure 1a and i.
# To run this code, you need the following data:
# - Anipose projects: `20231102-3D-structure-rig2`
# - 3D scanner data
# %%
%load_ext autoreload
%autoreload 2
# Update path... |
15fee2a7f69384c75bf6396ab3d7c73571c4d85c81a4ad67e5cf2e2433191b2a | Jupyter | 10,677 | 304 | # %%
%reset -f
%matplotlib inline
import matplotlib.pyplot as plt
import scipy.signal as signal
import lib.io.stan
import numpy as np
from matplotlib.lines import Line2D
import os
# %%
np.random.seed(0)
ntwrk = np.load('datasets/id002_cj/CJ_network.npz')
SC = ntwrk['SC']
K = np.max(SC)
SC = SC / K
SC[np.diag_indices(S... |
ec6d3f9ae54e60f90f72879782c1342deae2c3670306743b07cbaac6fef563be | Jupyter | 10,686 | 308 | # %% [markdown]
# # Early Stopping Example
# In this notebook, we will train an Multi-Layer Perceptron (MLP) to classify images from the [MNIST database](http://yann.lecun.com/exdb/mnist/) hand-written digit database, and use early stopping to stop the training when the model starts to overfit to the training data.
# ... |
adc9c2f02ac82bc6eadfb3893f6054e5a08a4dad802844fb73a41cfde23521d4 | Jupyter | 10,849 | 219 | # %%
"""
05 MARCH 2024
Adrien Corniere
Making boxplot from different mlr models
"""
import os
from percephone.core.recording import RecordingAmplDet
import matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random as rnd
import percephone.core.recording as pc
import percephone.an... |
8f252c8d6ed3450592f37d16ffc81490b9fad3dd13b62de9b0341699298a38a0 | Jupyter | 10,877 | 375 | # %% [markdown]
# # Generating Coding Variants With IPDGC Data
# - **Author** - Frank Grenn
# - **Date Started** - April 2020
# - **Quick Description:** use plink to get variants in LD with risk variants in the app. Then use plink to get the r2 and D' values and annovar to get the frequencies and CADD scores.
# %%
im... |
3b9cb2be110b9b858fe27fee10783550e57cbe45e37f1c8898a9a9fdaaf95803 | Jupyter | 10,934 | 229 | # %%
import numpy as np
import pandas as pd
import percephone.core.recording as pc
import os
import percephone.plts.behavior as pbh
import percephone.plts.stats as st
import matplotlib
import percephone.plts.stats as ppt
import matplotlib.pyplot as plt
import percephone.analysis.mlr_models as mlr_m
from percephone.anal... |
64fa2ee4a8bcdadf29d3dfe804a5c61459bd686a691d06a24802250bb01fb667 | Jupyter | 10,989 | 278 | # %%
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from utils import RESULTS_DIR, SUBJECTS
from data import MODALITY_AGNOSTIC
from eval import ACC_IMAGERY, ACC_IMAGERY_WHOLE_TEST
from notebook_utils import create_result_graph, load_results_data, ACC_MEAN, get_data_default_feats, F... |
017ec3945f5ca234ac2f56eb24ec8702da7e305025b45a3987a68fc4d4b70e0a | Jupyter | 11,042 | 364 | # %%
import warnings
import matplotlib
warnings.filterwarnings("ignore", category=matplotlib.MatplotlibDeprecationWarning)
# %%
# %matplotlib widget
import numpy as np
import matplotlib.pyplot as plt
# import pandas as pd
import matplotlib.gridspec as gridspec
from matplotlib.ticker import FormatStrFormatter
from src... |
0594d8a88525fa8c24f602abee87561ce50e7b17758f10b2384c9b377dd9e973 | Jupyter | 11,122 | 388 | # %% [markdown]
# This notebook is part of the deepcell-tf documentation: https://deepcell.readthedocs.io/.
#
# # Training a cell tracking model
# %%
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.callbacks import CSVLogger
from tensorflow_addons.optimizers import RectifiedAdam
import yam... |
0013f00d7c64ae486bd11f0d7896c5d268da2177e035219f475d69bb1b85bf46 | Jupyter | 11,224 | 150 | # %% [markdown]
# # Photometry FLMM Guide Part IV: Testing effects of factor variables -- akin to ANOVA
# ## Authors: Gabriel Loewinger, Erjia Cui
# ### 2024-09-07
# ### rpy2 implementation: Josh Lawrimore
#
# Please install the R packages lmerTests and emmeans in R prior to running this notebook.
#
# ```R
# install.... |
628d4a59e44d4fd20ac5ebce756c51ca23ef7547230c832c9f6aeb2dccc5b9b8 | Jupyter | 11,233 | 292 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tqdm import tqdm
from glob import glob
import pickle
from PIL import ImageColor
import matplotlib.colors
from utils import NN_FEATURES_DIR, RESULTS_DIR, SUBJECTS, NUM_TEST_STIMULI
from analyses.ridge_regre... |
c6090467af5b47591f040fc8d440a17600cec7b9f1e6002472a41b8d582b9e5f | Jupyter | 11,375 | 318 | # %% [markdown]
# ### *This file allows to reproduce Fig2A-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... |
6d35d1342ef152891b47a574423ab3d64e7e5d4b6042b221e6412c97ceee8018 | Jupyter | 11,412 | 259 | # %%
import numpy as np
from trained_untrained_results_funcs import loop_through_datasets, load_perf, load_mean_sem_perf
import pandas as pd
from scipy.stats import pearsonr
# %%
def load_mean_sem_perf(model_name, dataset, feature_extraction, layer_num,
resultsPath='/data/LLMs/brainscore/', see... |
365fa661f8506cc81c3eae209e9ad214ba423c3377c8678707fdccd31ba4ce8e | Jupyter | 11,413 | 206 | # %%
base = '/home3/ebrahim/what-is-brainscore/'
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
base = '/home3/ebrahim2/beyond-brainscore/'
from matplotlib import pyplot as plt
import os
from sklearn.metrics import mean_squared_error
import sys
sys.path.append('/home3/ebrahim2/beyond-brainscore/')
from hel... |
da8b88dbe566a56531c9087836ef5cdf1b91b29ed1ee105be6b0519eb0ac2b1a | Jupyter | 11,434 | 244 | # %% [markdown]
# # Permeability-related figures
# This notebook reproduces result figures in the paper that came from the fixed diameter cases, with either no MT or permeability at all (Figure 4) or permeability only (Figure 5)
# %%
# First import the relevant packages and functions
from local_optim_fit import forge_... |
25b8b2603d9c3ad427f436a98f82d725a05f8d0114dc3b381dcf877fcf06ee71 | Jupyter | 11,465 | 298 | # %%
import spacy
import numpy as np
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from transformers import RobertaTokenizer, RobertaModel
import torch
import re
from spacy.tokenizer import Tokenizer
from spacy.training import Alignment
device_number = 2
device = torch.device(f"cuda:{device_number... |
b361d974ae4f687ce91ef4656bc5191ef55896f0624085b6ce072e3c3f12c5ed | Jupyter | 11,589 | 202 | # %%
base = '/home3/ebrahim2/beyond-brainscore/'
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
from matplotlib import pyplot as plt
import os
from sklearn.metrics import mean_squared_error
import sys
from plotting_functions import plot_test_perf_across_layers, plot_across_subjects, save_fMRI_simple, singl... |
42e3dc54e21e8743db8e7d7ca79d9cb38ba61e6dc3e0ed196eb011961f7c8476 | Jupyter | 11,616 | 326 | # %% [markdown]
# ### *This file allows to reproduce Fig6*
# %% [markdown]
# # **Useful packages and functions**
# %%
using Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles, ProgressMeter
include("network_STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("network_STG_mode... |
f1190a99852a2ac549df72d690732ea2a14dadaa9b00b5c81758776ab06d5de7 | Jupyter | 11,626 | 419 | # %% [markdown]
# ## Extended Data Figure 9
#
# 
# %%
%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... |
dac5c6f09ae38c049e33658877e6e8f2dbbe9a2212aef378982621fe05395738 | Jupyter | 11,713 | 248 | # %% [markdown]
# ### *This file allows to compute what is in Fig5*
# %% [markdown]
# # **Useful packages and functions**
# %%
using DifferentialEquations, Plots, Plots.PlotMeasures, LaTeXStrings, Random, Dierckx, DelimitedFiles
include("STG_kinetics.jl") # Loading of STG kinetics of gating variables
include("STG_mod... |
73349428cf034502ff710be0b713be7a2a5394ead17ace4ab6d55d7024b74a91 | Jupyter | 11,726 | 354 | # %% [markdown]
# # Demo for plotting subgraphs
# %%
import numpy as np
import pandas as pd
from pathlib import Path
# %% [markdown]
# ### Load in information about the full network so we can demonstrate advanced features
# %%
load_dir = Path('../../metapaths/2_pipeline').resolve()
nw_dir = load_dir.joinpath('10_Se... |
5cb4b304de0e96ecbefcccd266f0821f76a12e79ba2d1571d358127183fc4aed | Jupyter | 11,744 | 267 | # %% [markdown]
# # <font color=black> Spinal cord networks: Functional connectivity </font>
# <hr style="border:1px solid black">
# %% [markdown]
# ### Imports
# %%
import sys,json
import glob
import pandas as pd
import numpy as np
import nibabel as nib
import seaborn as sns
import os
import statsmodels.api as sm
fr... |
62201666538ba4dbdf9a51ce2c8219a669d87fccb39db811ed84e2125472190c | Jupyter | 11,803 | 296 | # %%
import pandas as pd
import numpy as np
import os
import h5py
from sklearn import linear_model
import matplotlib.pyplot as plt
import plotly.graph_objects as go
#import deepdish as dd
import math
import scipy
import scipy.stats as stats
#import seaborn as sns
import glob
from matplotlib.animation import FuncAnimati... |
3a3d5115d33a9983fcb09f188c0f690da4655ba26d0f8d5ac464171e084c3f67 | Jupyter | 11,877 | 207 | # %% [markdown]
# # Cycle Closure Error Analysis
#
# Cycle closure analysis is a useful internal consistency check for relative binding free energy networks. For a closed loop of transformations, the signed sum of the calculated ΔΔG values should be zero (assuming perfect sampling). A large non-zero value indicates th... |
a86d5e1b9a430922444ac42ff7b4b818cc8d9a5cee51fe851b09e5c93f1fa7b1 | Jupyter | 11,909 | 418 | # %% [markdown]
# # Finemapping Processing
# - **Author** - Frank Grenn
# - **Date Started** - April 2020
# - **Quick Description:** filter the finemapping data for the GWAS browser. This code filters the data by prob > 0.01, assigns variants to the browser locus numbers and looks for coding variants for each variant. ... |
a2c8b5f826cb2ac7c81ccf6e81ac45970069f54cde94422661b3376ecb6638de | Jupyter | 11,929 | 418 | # %% [markdown]
# # Finemapping Processing
# - **Author** - Frank Grenn
# - **Date Started** - April 2020
# - **Quick Description:** filter the finemapping data for the GWAS browser. This code filters the data by prob > 0.01, assigns variants to the browser locus numbers and looks for coding variants for each variant. ... |
9943caea68a4fa760e4ea2f4b3d871c90af79e4f8cf4810c10d3d73913b416a5 | Jupyter | 11,938 | 285 | # %% [markdown]
# # Calculate and save the matrices in RAS space
# %%
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from numpy.linalg import svd
import numpy as np
import math
from scipy import interpolate
import random
from numpy.linalg import inv
import scipy.io
import pydicom
import cmath
... |
665f2ebe395594c3af40348ae2bcba5bb3fdefa202e24cda503fe4c62eef8378 | Jupyter | 11,970 | 293 | # %% [markdown]
# # Custom Loss
#
# [](https://colab.research.google.com/github/google/yggdrasil-decision-forests/blob/main/documentation/public/docs/tutorial/custom_loss.ipynb)
#
# ## Setup
# %%
pip install ydf
# %% [markdown]
# ## What is a... |
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