sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
788f80b29aa0180f020a7f048329c58ac376bdfdb383b285fa120401b87382e6 | Jupyter | 50,477 | 1,339 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from scipy.stats import pearsonr
from torch.utils.data import DataLoader
import tensorflow as tf
from tensorflow.keras.losses import Cate... |
d3990c7419975bdb8055790201561a4079f532fc0a825cd8b00c7b7f94eef896 | Jupyter | 52,271 | 1,178 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Rectangle
from scipy.stats import sem, pearsonr
from my_utils import *
from sklearn.lin... |
6bc1a297ccf697426ac161127e5bec16d6729c7624bc727d579362b1bf7efc0f | Jupyter | 52,653 | 623 | # %%
import pandas as pd
import numpy as np
import os
import re
from icecream import ic
import toml
# %%
import warnings
warnings.filterwarnings("ignore")
# %% [markdown]
# ## Preprocess NACC unique dataset
# %%
def features_conversion(df):
# df = df.replace({-4: np.NaN, '-4':np.NaN})
# MRI
df['mri_zip']... |
16b37a945aa97c2a080b83c2e97632a025f2b4f07317b8cc5fa03bdcb52195a3 | Jupyter | 53,052 | 1,721 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
410820c744de63a1b2ceda801bb52fdb08bbed42817d60ff9f3442e870e24eab | Jupyter | 53,190 | 1,082 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from scipy.interpolate import UnivariateSpline
from scipy.stats import gaussian_kde, sem
from matplotlib.patches impor... |
f706095a8064fe961716236b2eecbc7734e4e950a3a18b3fe0053367e503d9c4 | Jupyter | 53,879 | 1,552 | # %% [markdown]
# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"><li><span><a href="#配置环境" data-toc-modified-id="配置环境-1"><span class="toc-item-num">1 </span>配置环境</a></span></li><li><span><a href="#全局参数设置" data-toc-modified-id="全局参数设置-2"><span class="toc-item-n... |
6ff9b4e6b71da6da14cefc55418c90add7d768a8b3df0f051b88ae91119f930f | Jupyter | 55,466 | 1,853 | # %%
import os
import csv
import cv2
import torch
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imread
from gimpformats.gimpXcfDocument import GimpDocument
from skimage.metrics import structural_similarity as ssim
from sklearn.metrics import mean_squared_error as mse
from PIL import Im... |
41ec05b433f31bbafc7cd3a6cb5a7f0860cca2bf48fed5f63089277cf57d2dab | Jupyter | 55,522 | 1,257 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Rectangle
from scipy.stats import sem, pearsonr, mannwhitneyu
from my_utils import *
fr... |
bd602a025ddc0c6e60dc3ae296788201bc60c2cf3029a60f03645d2d6b49e6cf | Jupyter | 56,427 | 1,360 | # %%
# Matrix Manipulation/Management Libraries
import pandas as pd
import numpy as np
# Data Visualization Libaries
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
from scipy.stats import fisher_exact
# Bioinformatics Libraries
import pybedtools
# Statistical Tests, Machine Learning, ... |
5f986e9152bdd184b622694195c205aad073b3d1e1977e44bc1eebdecde6b3e8 | Jupyter | 57,191 | 1,756 | # %%
%load_ext autoreload
%autoreload 2
# %%
from argparse import ArgumentParser
from typing import List, Tuple
import os
import os.path
import sys
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
... |
c5d25a30437edac7a978c9606c64ab81caaf48ba7843e48dc82c3413f281cced | Jupyter | 57,564 | 1,717 | # %%
import numpy as np
import pandas as pd
import os
import torch
from torch.autograd import Variable
from matplotlib import pyplot as plt
%matplotlib inline
# %%
import sys
sys.path.append("../Network-SDE-Inference/utils")
import NeuGNN_model
import Self_func
import Interaction_func
from NeuGNN_model import *
from S... |
32de2b39cef2fc89b0bcff1ea269525dbe8e8790484e22d576733901a635b7a1 | Jupyter | 59,213 | 1,396 | # %% [markdown]
# ## Imports
# %%
import pandas as pd
from glob import glob
from pathlib import Path
from os import path
import numpy as np
from pprint import pprint
import pickle
import copy
from math import ceil, floor
%matplotlib inline
# pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns'... |
f9775abbc7071899b187090f063a36c6164ebdf42c8b9151ccd4f5bc4b2639f2 | Jupyter | 59,908 | 1,811 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
e77388d1973bf1a3c00aa184b5bf06498a3c6e02b4e420a4a75bac67c6c407b7 | Jupyter | 61,178 | 1,556 | # %%
import pandas as pd
import numpy as np
import sys
from pathlib import Path
from glob import glob
import dateutil.parser
import collections
import re
from datetime import datetime
from dateutil.relativedelta import relativedelta
import requests
import json
# %% [markdown]
# #### Read Redcap data into Pandas dataf... |
70f50dfb6e8f1f23590b80f17858ce44ac511cfe31058c97cbe17ad10ca53b0d | Jupyter | 63,755 | 1,356 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from scipy.interpolate import UnivariateSpline
from scipy.stats import gaussian_kde, sem
from matplotlib.patches impor... |
a89b4e23474b6dea48d0cb4fb950b44bd627504aff01176717f3d3791b49cd94 | Jupyter | 71,233 | 1,642 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Rectangle
from scipy.stats import sem, norm, mannwhitneyu, bootstrap
from my_utils impo... |
d6f8b16bc18e656f8fa17f9d63ab5bece5016b2cd4320c0a33b66e1cdfd2b228 | Jupyter | 74,145 | 2,196 | # %% [markdown]
# # Data Accquisition
# %% [markdown]
# **Drive mount**
# %%
from google.colab import drive
drive.mount('/content/drive')
# %% [markdown]
# **Path zip file**
# %%
import zipfile
import os
# Path to your ZIP file
dataset_zip_path = '/content/drive/MyDrive/ev_images.zip'
# Directory where you wan... |
a743a94d9db50376244664ff727cd8a30147d80fbe120b582def64ef91370d88 | Jupyter | 76,010 | 1,735 | # %% [markdown]
# # This R script takes results from elastic net Cox regression and xgboost models from the accompanying Python script
#
# ## It is used to benchmark model performance, highlight the most important features, and build figures
# %% [markdown]
# # Load packages
# %%
library(data.table)
library(surviva... |
5d844eae0e6852a314e67456013f3b7dffbe929b44a83039e8e6315315a5b5ad | Jupyter | 76,150 | 2,113 | # %% [markdown]
# © Laurent Jutras-Dubé
#
#
# # Nonreciprocal synchronization in embryonic oscillator ensembles
#
# <br>
#
# This Jupyter notebook accompanies (Ho, C., Jutras-Dubé, L. et al. (2024) "Nonreciprocal synchronization in embryonic oscillator ensembles") and reproduces most of its figures. Throughout this... |
e5d084d63e6e3ae4756bbb813355b89b26db46f4deb950f0f4cd06bf2a4f48f4 | Jupyter | 77,617 | 1,485 | # %%
#import packages
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.colors as colors
import pingouin as pg
import pandas as pd
import numpy as np
import os
from operator import itemgetter
import seaborn as sns; sns.set(style="ticks", context = "paper", font = "Arial", font_sc... |
2a5ebcab808b1177e57e813d951acef1d0537301342287c2ea059b0f9c96ef63 | Jupyter | 77,623 | 2,207 | # %% [markdown]
# # UMA Catalysis Tutorial
#
# Author: Zack Ulissi (Meta, CMU), with help from AI coding agents / LLMs
#
# Original paper: Bjarne Kreitz et al. JPCC (2021)
#
# ## Overview
#
# This tutorial demonstrates how to use the Universal Model for Atoms (UMA) machine learning potential to perform comprehensiv... |
e00db51801c687b4d1a5e5378251cd1594e13096b6db4aa57f386ffa20390e4a | Jupyter | 78,059 | 1,803 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from scipy.interpolate import UnivariateSpline
from scipy.stats import sem
from matplotlib.patches import Circle
from ... |
664a5c4a48950b8da0b09fe6929aca0ba7dfc70f10fd661ca13775de8442056d | Jupyter | 78,933 | 1,739 | # %%
import pandas as pd
import numpy as np
import networkx as nx
import itertools as it
import json
from collections import Counter
import math
import time
import random
import platform
import copy
# %%
# path to folder where all the files are stored
folder_path = '/Asymmetry_elegans/Final'
# %%
''' small functions ... |
dba2d1fc99928c9fc61cda7a0b0d24fda8557bcaab8ba767f5b6c0166d74e77f | Jupyter | 78,983 | 2,412 | # %%
import os
import time
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold
... |
3dcb33c00be9d5215bd17c2d5c66a82b1e3f1a35866c4c2598991f0666f798c8 | Jupyter | 79,400 | 2,435 | # %%
import os
import time
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold
... |
43eed719624ec6dfc222504f07995fe93c6bbd8894c3f95adebe2be26540146f | Jupyter | 81,423 | 1,908 | # %% [markdown]
# # Quality control prior to EasyQC
#
# This is script to perform minimal quality control of the raw summary statistics which are then fed into EasyQC.
#
# It uses 1000 Genome phase 3 as reference.
#
# Missing values will be indicated with a period.
#
# The minimal QC includes following:
# - Changin... |
1363055b1d46d5a8c5e8c1a67111d0ab0d1292076f545d764a03a518758d3f37 | Jupyter | 81,795 | 2,066 | # %% [markdown]
# # B03 Revealing the Dependency of Default Network Perturbation on Pain Experience and the Influence of Coping
# %% [markdown]
# This Jupyter Notebook is the basis of the Manuscript by *Nils Jannik Heukamp et al.*. All code is written by *Nils Jannik Heukamp*.
#
# If you refer to the analysis or cod... |
2d58df8e5ee92c8a1b33afc11800525ddaa3971783e07d3ca32dd76b1ed59038 | Jupyter | 82,996 | 2,555 | # %% [markdown]
# ## Preamble
# %%
import numpy as np
import scipy as sp
import scipy.stats as stats
from scipy.stats import linregress,t
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
from matplotlib import rc
plt.rc('text', usetex=True)
font = {'... |
c24e10ce743204e3afc368b10688b26115a3613421f528c47467ee360a6f5bee | Jupyter | 84,170 | 1,695 | # %%
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
# import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout,Input,Reshape,BatchNormalization
from tensorflow.keras.losses import CategoricalCrosse... |
94eb6af6c297233d8bded85d077690be6cce7c01b818ec307079a302e50fad10 | Jupyter | 84,857 | 1,907 | # %% [markdown]
# # Overview
# %% [markdown]
# The purpose of this notebook is to perform preliminary sample quality control (QC) in order to identify and/or remove outlier samples in preparation for the batched steps of the GATK-SV pipeline.
#
# This notebook allows the user to analyze sample QC metrics produced by ... |
2d5a42c8d1ae0a6f30ae744399a2db59a2d08db30ed02d10bc59634eb8b6626d | Jupyter | 85,919 | 2,013 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb is looking at what value should R attain and what it does to the correlation between two measures
#
# The goal here is to take two measures that have high reliability and change the number of trials (or subjects) to reduce R and check what effect it has on thei... |
021863a9d3593ca2c38d1334fc1345f273a80d562861db697c672677630f00c6 | Jupyter | 86,800 | 2,962 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
1a86a245d884129ba22202263aa619732b6d1da159ab4e024ac58e5945e75245 | Jupyter | 89,234 | 205 | # %% [markdown]
# ## The goal here is to scan the (filtered) MSA with the depolymerase DB
#
# ## 1. hmmcan the MSA
# ## 2. Scan the results
# ***
# %% [markdown]
# > <b>1. hmmscan command
# %%
import os
import random
import subprocess
from multiprocessing import Pool
import time
import pandas as pd
# Generate a lis... |
26bb486707a0eef6ad3cbaf762f2a51bcdbdf2e09b58a20191b3225d4c949ee9 | Jupyter | 89,256 | 206 | # %% [markdown]
# # **III. Proteins**
# ## The goal here is to scan the (filtered) MSA with the depolymerase DB
#
# ## 1. hmmcan the MSA
# ## 2. Scan the results
# ***
# %% [markdown]
# > <b>1. hmmscan command
# %%
import os
import random
import subprocess
from multiprocessing import Pool
import time
import pandas ... |
3864db492c1c83cff5a45e63d6d1e07058380b52654a368af11dca1a8c67ec86 | Jupyter | 89,847 | 2,282 | # %% [markdown]
# ## init Keras
# %%
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# %%
from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from ke... |
89486e693f7649184e620686f80bceb6c61a70f673e120e0225eb71051dbe4de | Jupyter | 91,519 | 2,615 | # %% [markdown]
# __Summary:__
#
# This code calculates the angular eye velocity as well as adaptive intervals to determine gazes and saccades.
# Importantly, the angular velocities are calculated using a correction for translational movement.
# For details see Nolte et al., submitted
#
# The code is based on ideas b... |
50bef69ff557090e89cfe7bea6bacdbf252c6ba47d0670ef71b5560bd4b4c38d | Jupyter | 91,803 | 2,323 | # %% [markdown]
# ## init Keras
# %%
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# %%
from keras.layers import Lambda, Input, Dense
from keras.models import Model
from keras.datasets import mnist
from keras.losses import mse, binary_crossentropy
from ke... |
f05dc273d41038cd392a36ce1167aacd3e262dc546137e590e5c6c2c0e7f4644 | Jupyter | 93,432 | 2,411 | # %%
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Outline
#
# This notebook allows to analyse results from Gym1. It assumes that the evaluation run was already performed using the `run_gym1.py` script.
# %%
import gym
import json
import os
import io
import gym_sted
import pfrl
import torch
import sys
impor... |
32b3f898f29d3531795aea7b30d9783c1ef651ada53d167fa699872158972f80 | Jupyter | 95,327 | 2,134 | # %%
import numpy as np
from numpy.random import default_rng
import torch
import torch.nn.functional as F
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.patches import Rectangle
from scipy.stats import sem, norm, bootstrap
from my_utils import *
from skle... |
94e990878eb70f687cd9922c7980270edf03024ba58dc4eca1f4e95a8760f446 | Jupyter | 97,026 | 2,961 | # %% [markdown]
# ## Preamble
# %%
import numpy as np
import scipy as sp
import scipy.stats as stats
from scipy.stats import linregress,t
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
from matplotlib import rc
plt.rc('text', usetex=True)
font = {'... |
fca68a7ba1ac61575e92ef7f7ab9db7aeb1ca4de686ccb086859fe2091da85ec | Jupyter | 101,917 | 3,079 | # %% [markdown]
# ## Preamble
# %%
import numpy as np
import scipy as sp
import scipy.stats as stats
from scipy.stats import linregress,t
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
from matplotlib import rc
plt.rc('text', usetex=True)
font = {'... |
42d806dd223ac9f4448656ff28d9aa14931fa4053b760c68fccc86628e2e483d | Jupyter | 104,526 | 2,627 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
75c83b10c4d38b364973465525ae4985eda34791219155abe0c0a356b0afaeec | Jupyter | 104,850 | 1,830 | # %% [markdown]
# # Imports
# %%
proj_dir = "/path/to/main_project_folder/" # edit this line
import numpy as np
import json
import random
import xarray as xr
import time
import glob as glob
import h5py
import pandas as pd
import matplotlib.pyplot as plt
from tensorflow.random import set_seed as tf_set_seed
from tenso... |
2ac79dec6f2195c2ee3dbae37c4b4661f44ce160b67512df05a6c83fd5ffd819 | Jupyter | 106,102 | 2,999 | # %%
import os
import copy
import math
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from scipy import ndimage
import nibabel as nib
import torch
import monai
print(monai.__version__, monai.__file__)
import monai.transforms.spatial.old_array as old
import monai.transforms.s... |
a554d3a4a8a93c61427a31993f335ae6c257c7d69692810a9079bc4d5392b40b | Jupyter | 112,079 | 2,942 | # %% [markdown]
# # Plots created for the publication
# %%
%reset # only run when space is needed (variables will be deleted from memory)
# %%
import warnings # hide warnings
import pandas as pd # operate with dataframes
import matplotlib.pyplot as plt # mother of plots focr Python
import numpy as np # array/matr... |
5143b46057ac78e11535fec5bf850bbdc03e84c0050124600620ffb47a182393 | Jupyter | 114,907 | 2,917 | # %%
import numpy as np
import scipy.sparse as sp
import tensorflow as tf
# import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense, Dropout,Input,Reshape,BatchNormalization
from tensorflow.keras.losses import CategoricalCrosse... |
e88bc8ad20a6944a3a8e5b6dbe1728e2ae5da82e019d8be65178267e8094b3ec | Jupyter | 117,259 | 3,001 | # %%
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Analysis
#
# You can click on the following sections
#
# - <a href="#1parameters">1 parameter</a>
# - <a href="#2parameters">2 parameters</a>
# - <a href="#3parameters">3 parameters</a>
# - <a href="#Nparameters">N parameters</a>
# %%
import numpy
import... |
9c00ed5040caa05daf7a121669b025be0f7baa7046cf558e253fb35219657164 | Jupyter | 122,407 | 2,793 | # %% [markdown]
# ## Load in packages
# %%
library(data.table)
library(ggplot2)
library(corrplot)
library(patchwork)
library(gtools)
library(tableone)
library(RColorBrewer)
library(survival)
library(survminer)
library(pROC)
library(tableone)
library(splines)
library(forestplot)
# %% [markdown]
# ## Write custom funct... |
2c66635a6cb3821cc74c933cf02823214f5c20af988e66647d98c933ff7b9aba | Jupyter | 122,488 | 3,515 | # %% [markdown]
# This repository provides sample code to reproduce figures from Liu, Schilling, et al, Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma, Nat Med 2019 (PMID: 31792460 DOI: 10.1038/s41591-019-0654-5) with associated source data (supplem... |
50b5ae03b82dc6476129e3026b709bf7eceddb7beec6db23abcc14e358abc8fa | Jupyter | 131,555 | 3,346 | # %%
%load_ext autoreload
%autoreload 2
# %% [markdown]
# # Analysis
#
# You can click on the following sections
#
# - <a href="#1parameters">1 parameter</a>
# - <a href="#2parameters">2 parameters</a>
# - <a href="#3parameters">3 parameters</a>
# - <a href="#Nparameters">N parameters</a>
# %%
import numpy
import... |
922bb3393fdc3f8f5b21e42638cbf29c6e8aa75ae646bd6f78cba36c8949897c | Jupyter | 152,301 | 4,005 | # %% [markdown]
# ##### top
# %% [markdown]
# # Find and fit curves of reliability vs num trials in all tasks in our battery
#
#
# Tasks:
# * [CCMT - Cambridge car memory test](#CCMT)
#
# * [CFMT - Cambridge face memory test](#CFMT)
#
# * [Car matching](#Car-matching)
#
# * [Emotion matching](#Emotion-mat... |
7a632eafc32a7a2c30378f7216e9e8a5e07eed9839a9fc646dd11b762462e970 | Jupyter | 161,784 | 2,712 | # %% [markdown]
# # Imports
# %%
proj_dir = "/path/to/main_project_folder/" # edit this line
import matplotlib.pyplot as plt
import json
from matplotlib import dates as mdates
import matplotlib.font_manager
import importlib
import xarray as xr
import pandas as pd
import numpy as np
import statsmodels.api as sm
import... |
575cd2de70e7234ee72196e4e0984115c96600e6c6e4113cebc8e91d6ed81f6f | Jupyter | 184,713 | 4,902 | # %% [markdown]
# # Table of Contents
#
# 1. [Import stuff](#import_everything)
# 2. [Set whether these analyses are done for this analysis or not](#set_whether_these_analyses_are_done_for_this_analysis_or_not)
# 3. [Load data](#os.path.exists_checkpoint_fig_dir__load_data)
# 1. [Set values for indexing](#Set_some... |
3f848af12ae406fe374c359fd9fd09811946dd4387190d747be587468ef66ddb | Jupyter | 192,420 | 5,259 | # %% [markdown]
# # Table of Contents
#
# 1. [Import stuff](#import_everything)
# 2. [Set whether these analyses are done for this analysis or not](#set_whether_these_analyses_are_done_for_this_analysis_or_not)
# 3. [Load data](#os.path.exists_checkpoint_fig_dir_load_data)
# 1. [Set values for indexing](#Set_some_... |
cf9455ae0f9bc67bffffc93bf3003da13d0f6d46e4a6f47b018f3b3e5ae00518 | Jupyter | 193,099 | 5,271 | # %% [markdown]
# # Table of Contents
#
# 1. [Import stuff](#import_everything)
# 2. [Set whether these analyses are done for this analysis or not](#set_whether_these_analyses_are_done_for_this_analysis_or_not)
# 3. [Load data](#os.path.exists_checkpoint_fig_dir_load_data)
# 1. [Set values for indexing](#Set_some_... |
484cb6c7807ed049af7f51628e031fc9e9ccc6311c44022e51ef1b9c8af58c3d | Jupyter | 200,000 | 4,011 | # %%
#@title 1. INIT - Imports and clone data from GitHub repo
import gc
import math as mt
import scipy as sp
import random
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from collections import namedtuple, deque
import networkx as nx
import pickle as pkl
from skl... |
6d42b0730f5a72be304fd086c8e11ca15f75dbcd3e798113ac2eab6e6e99bd17 | Jupyter | 200,000 | 5,277 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb estimates reliability of each task by spliting it in halves and looking at correlations in performance
#
# Tasks:
# * [CCMT - Cambridge car memory test](#CCMT)
# * [Run split halves CCMT](#Run-split-halves-CCMT)
# * [Run overlap CCMT](#Run-overlap-C... |
a2ae4f1935efd2e1af2afc72d64a834dfe0a9c332e3692eb8452a01b01c3d393 | Jupyter | 200,000 | 5,268 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
import matplotlib.font_manager
from matplotlib import rcParams, cm
import scipy.stats as ss
import... |
d999fdbbad48152b1f2aabff4e13e58e2f29311f8bd95385a0f45fb30a4c981b | Jupyter | 200,000 | 4,960 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.gridspec as gridspec
from matplotlib.colors import LinearSegmentedColormap, ListedColormap
import matplotlib.font_manager
from matplotlib import rcParams, cm
import scipy.stats as ss
import... |
89795552338c181d33625df28829ca1ea5aa7d9956b97e65896d9f18de68d28e | Jupyter | 200,002 | 4,558 | # %% [markdown]
# ##### top
# %% [markdown]
# # This ntb processes and cleans all data for all tasks
# This is part of the original *Reliability_split_halves.ipynb* but only the preprocessing steps.
#
# Tasks:
# * [CCMT - Cambridge car memory test](#CCMT)
# * [Prepare and process CCMT](#Prepare-and-process-CCMT)
... |
0ff8ba8689e20b42fed50eb6becc65be76e53d0db7d7e41e1d1a09ff9610bbf3 | Jupyter | 200,016 | 4,584 | # %%
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['font.sans-serif'] = 'Helvetica'
import seaborn as sns
sns.set_context("paper")
import os
import glob
import statsmodels.api as sm
from pathlib i... |
b9b524a2069e03263a8468b2d4bd273125984ee05fd0228e4eb927c496ea3a13 | Jupyter | 200,165 | 5,729 | # %% [markdown]
# # Prephase
# This notebook is for conducting all analysis of mice neurons Hi-C and ATAC-seq data.
# %% [markdown]
# # Imports
# %%
# If you are a developer, you may want to reload the packages on a fly.
# Jupyter has a magic for this particular purpose:
%load_ext autoreload
%autoreload 2
#data an... |
84a29ce8d7a5613a8aae1778ba84d10094739243dd0662547d808a9ed3e0d6a8 | License | 281 | 1 | This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. |
1d6ff8264b029cf1fe5b7f9590c8d1a044f647f807fe3f9a60a707ca79bd2ce0 | License | 308 | 1 | This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, 444 Castro Street, Suite 900, Mountain View, California, 94041, USA.
|
c7b6730919d0a35ab164d562fd52105e9ce58b7ff089b1f9a8759479232804d5 | License | 655 | 20 |
Facet version 4.0.0
==================
Copyright 2017-2021 Kevin Tedore & Lund University
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses... |
b52ccba2f99920ac22225245667ffe7a3d64c3db4d4466507cb2c5975090b31c | License | 1,053 | 7 | Copyright 2021 Jack Goffinet
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicen... |
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Copyright (c) 2017 QData
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
a8534e98ab16fe5764b98b5e5ef01d829368b2bdbef748b433db465037a326c7 | License | 1,062 | 21 | MIT License
Copyright (c) 2024 arwin
Permission is hereby granted, free of charge, to any person obtaining a copy
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115b18041901e21c7683901e38d29a86b90817b0dbaf8e50e3420a42772b2449 | License | 1,063 | 17 | MIT License
Copyright (c) 2020 Allen Lynch
Permission is hereby granted, free of charge, to any person obtaining a copy
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157d4665984c66b2e6209397f5e3ae795dbabb9be5372e859988f7395a779bfb | License | 1,063 | 21 | MIT License
Copyright (c) 2018 LucaSC
Permission is hereby granted, free of charge, to any person obtaining a copy
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227d0f877aa4f45eaf181fd46cf5a1ac3cc51b32d1d900ef975cc403b67425c1 | License | 1,063 | 21 | MIT License
Copyright (c) 2019 Tim VW
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Copyright (c) 2024, DCISIv
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a26d3c2f8bd909a008bc7c7bb2427f16a18f2ae2ce9762228b0b80240c9e9c27 | License | 1,063 | 7 | Copyright (c) 2024 Jan Antolik
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Copyright (c) 2023 Gloria
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18c4eb20924a874cd796fd1d81c2459a034a54652f68657303f24d09eab2b312 | License | 1,064 | 21 | MIT License
Copyright (c) 2023 jrmtnez
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233cba3fd2137dc8ae788fcea2e986c3417027bca9f6c99aa0ba05bc187ab8d7 | License | 1,064 | 21 | MIT License
Copyright (c) 2023 Lorenzo
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25afabc48c3f515e8ff9ddd4e13a3e63b78cae115b5bb8efb9b06ef089ca7d6d | License | 1,064 | 21 | MIT License
Copyright (c) 2024 Jinmok2
Permission is hereby granted, free of charge, to any person obtaining a copy
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38e114383f9c6e7790e97d54beb314acb56c10089469e79a04df741619f42511 | License | 1,064 | 21 | MIT License
Copyright (c) 2018 Ye Yuan
Permission is hereby granted, free of charge, to any person obtaining a copy
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40ad5f59491fcc1907ebbd3ca504153f68ff7ac5fde4bee0cb4f180b19b947d7 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 jrmtnez
Permission is hereby granted, free of charge, to any person obtaining a copy
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4ca785cfdf0c556d44e797164fd5f01c878a19fcf98e729a5ed63469bcb3f46c | License | 1,064 | 21 | MIT License
Copyright (c) 2020 lulizou
Permission is hereby granted, free of charge, to any person obtaining a copy
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4ec3b8cc746a6720ca946ad68872dff941e728f1860c397258136642650ba6bb | License | 1,064 | 21 | MIT License
Copyright (c) 2018 tpesout
Permission is hereby granted, free of charge, to any person obtaining a copy
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57a18adb9bfc5226f9d55d499e3576be5622172f7d3910bf051a71282aa71530 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 pwenner
Permission is hereby granted, free of charge, to any person obtaining a copy
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8f8119f7acf9be0335afc70a22d83412e5a53058b05cdd59bbcf8bb4ce5b17d5 | License | 1,064 | 21 | MIT License
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Copyright (c) 2024 SNU-NCL
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9f491ae956d4a0b39071531f3273b21954cf9f4eb3972aa4a4d1a56cc5683e3c | License | 1,064 | 21 | MIT License
Copyright (c) 2024 Chen Ao
Permission is hereby granted, free of charge, to any person obtaining a copy
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ad282b787fd7d929ca26080d5059567f18b7548f9242a8823ae1a5f47287b312 | License | 1,064 | 21 | MIT License
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bda045d3890445bbf85f2bc586645f724936ee797bd0023f1e3ff822cd3f2cce | License | 1,064 | 21 | MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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d156672463cb0f5ee245787403b60c661a1e606d45fe604bf47da6de71a6dc5f | License | 1,064 | 21 | MIT License
Copyright (c) 2022 Rick Li
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051a0a95a91ea6595a49ee186ee7d8f1422b9ef447442983bdc32da60587a748 | License | 1,065 | 21 | MIT License
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05a79b6482fb634e013b3f43f6f4cb0376f9a7fbab5ee387dab682c7fc9c8283 | License | 1,065 | 21 | MIT License
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0906d5afe9c14069450a99855a2d0a6b887c59e296593afeb6a3b74ba0b5bb6f | License | 1,065 | 21 | MIT License
Copyright (c) 2023 PlenzLab
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0b42f925289d4b11a9194c490083f6160219c48d8f8a283872dd6f43d23818b4 | License | 1,065 | 21 | MIT License
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10deb07915f875fe048d73ceb8b25684c3f4ac3e2e446f0cd286be886b850437 | License | 1,065 | 21 | MIT License
Copyright (c) 2018 Fei Wang
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of this software and associated documentation files (the "Software"), to deal
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1408978ef9b3ec132b79841750ad71246792cd9ad3bf5240e0227b30695cd44a | License | 1,065 | 21 | MIT License
Copyright (c) 2023 OmnesRes
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