sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
9e89231e8af9114979ab2718185e55a5d75cda04e5989b816f03cf5d36762761 | Jupyter | 28,173 | 682 | # %% [markdown]
# # Imports
# %%
import numpy as np
import pickle
import os.path
from os.path import isfile, join
from os import path,listdir
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
# %%
from matplotlib.patche... |
bfa261ee30be2e8129ed44776e4da3bdd258e727fdf03798b561c48ca49f919a | Jupyter | 28,249 | 1,158 | # %%
import sys
import os
import importlib
import gc
import glob
import random
from itertools import combinations
import numpy as np
import sklearn
from sklearn import metrics
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
import pyemma
import msmtools
im... |
e2c8062b5a1265cdc39e74b8cb67d8d0e8ff04eb68b147390e2d4978f617eab6 | Jupyter | 28,415 | 721 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import yaml
import sys
sys.path.append('./../..')
import os
BASE_PATH = './../dynaphos-core/'
sys.path.append(BASE_PATH)
from dynaphos import utils
import pandas as pd
import seaborn as sns
from scipy.optimize import curve_fit
from sc... |
663cef53ed620ec1e958c692f8505ba3d3a45bee2e0993029049fd05cb212e16 | Jupyter | 28,433 | 780 | # %%
import os
import nibabel as nib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import pickle
import random
from scipy import ndimage
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage import transform
from skimage.transform ... |
1784406f026fbfb7b39c0016dc1371ee8bd655304a172b2294d7ba3b981eef30 | Jupyter | 28,565 | 748 | # %%
# loop measurements and firing rate
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import networkx as nx
import copy
import seaborn as sns
from plotFunc import plotPaperTag, getConnHist, getNnodeLoop, plotXYcompr,... |
3eb6eeb8b38284ece69a9abbe211bc4a1bc36b85fd324ebfe85c4774b738f577 | Jupyter | 28,685 | 792 | # %%
import os
import nibabel as nib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import pickle
import random
from scipy import ndimage
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage import transform
from skimage.transform ... |
9e8535335fdcfbafe5e376dfd441cc05bfeb050f017dc6a3a830dbb4f667a9e8 | Jupyter | 29,213 | 432 | # %% [markdown]
# $\textbf{Lead Author: Adele Myers}$
#
# Inspired by: Guigui, Miolane, Pennec, 2022. Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats.
# %% [markdown]
# In this section, we will:
#
# 1. Define the Connection
# 2. Discuss parallel vector... |
fcc0f7331eec98c1cf676f59a7f7717535184cc45e06b764a586ea602edca5e9 | Jupyter | 29,880 | 812 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import yaml
import sys
sys.path.append('./../..')
from dynaphos import utils
import pandas as pd
import seaborn as sns
from scipy.optimize import curve_fit
from scipy.stats import linregress, norm
# import tqdm
# from tqdm.notebook i... |
ce971d10801d5912e3a5fbea80e57399a14a4d372674cdfd52990bb109822b8a | Jupyter | 30,195 | 895 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
from scipy.stats import sem, t, ttest_1samp
from sklearn import svm
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.preprocessing import Or... |
f8b62ba3708fc99422611957bd5437e13640af3f2fd9fcb0bd3bcb946e7fb23d | Jupyter | 30,231 | 939 | # %%
%load_ext rpy2.ipython
# %%
# %%R
# install.packages('MuMIn', repos='http://cran.us.r-project.org')
# %%
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
im... |
42f70c86d7d99bb60a4e695f43bb41840c5c16db084b5ae6afeaed01ce4aa7b9 | Jupyter | 30,368 | 871 | # %%
import random
import numpy as np
from scipy.spatial import Delaunay
from scipy.spatial.distance import pdist
from scipy.spatial.distance import cdist
from scipy.spatial.distance import squareform
from scipy.stats import gaussian_kde
from scipy.stats import gamma
from scipy.stats import truncnorm
from scipy.stats i... |
ebdea741b86455eaea3529752408fdec91d2975d482ce0553f04b2a549090497 | Jupyter | 30,384 | 998 | # %% [markdown]
# [](https://colab.research.google.com/github/TomGeorge1234/RatInABox/blob/dev/demos/paper_figures.ipynb)
# %% [markdown]
# # RatInABox: Code for replicating figures in the paper
# %% [markdown]
# Figures relating to the two ca... |
50c46f0cab1b9c2e0831ae4fcaab827573fde4791b199dc337a4d63b435a9c28 | Jupyter | 30,596 | 594 | # %%
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# %% [markdown]
# # PyRates - A Python framework for dynamical systems modeling
#
# PyRates is a software that allows to construct complex, dynamical systems in a common language and translate theese systems into various backend implementatio... |
98f55a8b37b8ddf602165664eaa099eb0b454da89dfa2f2ef7677a92430991b0 | Jupyter | 31,058 | 835 | # %% [markdown]
# # 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all future ... |
c16029250d9802a377c8552bc651ccae7c1c3db7b07a3df325c461d03fb23f88 | Jupyter | 31,090 | 881 | # %%
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils
import torch.utils.data
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
from math import pi
import pickle
import time
from scipy.optimize import fsolve
import math
opt_type = "LBFGS"
# ──... |
6028dd3298b3aa378835a96ee9a557c0a61416bd3a5b7020bf82c96f6977931f | Jupyter | 31,445 | 1,085 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.ticker import MaxNLocator
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
# %%
from scipy import stats
from scipy import sparse
import scipy.io as io
# %... |
d690c23cef6c420250f91f02c2c81077f0d6547b8fe3fe2f3a04aa08c1359f07 | Jupyter | 31,451 | 1,085 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.ticker import MaxNLocator
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
# %%
from scipy import stats
from scipy import sparse
import scipy.io as io
# %... |
7896b82db701ec3def670a02f2baa4cca28e1e40e8a8baf4c88915f019330b3b | Jupyter | 32,612 | 833 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import... |
43cdd5d4083cb46a77e3b6eefe138a6951c9e029bdfbb6a970d4dfa260a34421 | Jupyter | 32,845 | 938 | # %%
import os
import nibabel as nib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import pickle
import random
from scipy import ndimage
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage import transform
from skimage.transform ... |
ec21d6f1a7e64de31e1d08731669f7d5052370851080e4874a27d5a4292942e1 | Jupyter | 33,135 | 744 | # %%
#*----------------------------------------------------------------------------*
#* Copyright (C) 2024 ETH Zurich, Switzerland *
#* SPDX-License-Identifier: Apache-2.0 *
#* ... |
685868676a5e950a963641aace0e68aa5f0ef205d356dc528629767d74e167a2 | Jupyter | 33,348 | 1,038 | # %% [markdown]
# # Import all the packages used
# %%
import numpy as np
import mdtraj as md
import matplotlib.pyplot as plt
%matplotlib inline
import vampnet
from vampnet import data_generator as vamp_data_loader
from tensorflow.keras.models import Model
from tensorflow.keras.layers import (
Dense,
Activatio... |
dfc9081c6d8cde9f2f290d1739d2aad8f5b9eda36fdd214c191ba92f557f3ead | Jupyter | 33,691 | 620 | # %% [markdown]
# # score antitoxin variants and generate antitoxin sequences at various temperature using ProteinMPNN
# - code adapted from https://github.com/dauparas/ProteinMPNN
#
# ### inputs:
# - pdb file
#
# ### outputs:
# - scored antitoxin variants from proteinMPNN
# - generated sampled sequences
# %%
import... |
589a421c6cc84b660ccd7e33fa760ebf533721cf23c9175a829fee4533e76397 | Jupyter | 33,756 | 996 | # %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import copy
# %%
from scipy import stats
from scipy import sparse
# %%
from functools import partial
# %%
import mne
from mne.stats ... |
7f61d72b30a4f82d25b47c6c5eb0638bf41f0205c4e6cc2b996ccd23f2ca126b | Jupyter | 34,059 | 942 | # %%
%load_ext rpy2.ipython
# %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
from datetime import datetime, timedelta
import pickle
import glob
from tabulate import tabulate
from tqdm... |
09e1844662f9e3cc24cc173b4fa4508209237b3d5401fd622bbb7eba296d661d | Jupyter | 34,286 | 992 | # %%
# preprocess multiSim and. analyze results
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
from collections import defaultdict
import pickle
import json
import copy
from matplotlib import colors
import itertools
from pathlib i... |
31e4e01e194df8e47ffe01f27e8f8de74f945586e3ab573cf0641a67ed3b347b | Jupyter | 34,373 | 565 | # %% [markdown]
# # Case study: Reinforcement Learning (via Actor-Critic) [](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/actor_critic_example.ipynb)
# ### We use RatInABox to train an Agent to find a navigate ... |
e11f7d05e16bb69456b69792c491161fa33502e5b68b0ca47bdbb31ebf05dcaa | Jupyter | 35,708 | 941 | # %%
%matplotlib inline
import sys
import warnings
import traceback
from pathlib import Path
import pickle
from types import SimpleNamespace
from joblib import delayed, Parallel
from importlib import reload
import numpy as np
import pandas as pd
import scipy.signal as signal
import scipy.stats as stats
from sklearn... |
97d608b09e1e62632263855626263c8baf75300ed6b3827940818448a6e0aef6 | Jupyter | 35,830 | 1,183 | # %% [markdown]
# ## updated of figures notebook coded from plot functions in the library
# %%
%matplotlib inline
from pathlib import Path
from types import SimpleNamespace
from joblib import delayed, Parallel
import time
import traceback
import numpy as np
import pandas as pd
import scipy.stats as stats
pd.set_opti... |
0e8de0531c620fae9985a54cec1ecb6cf75e7d88bf563ae28f07554db9588cc7 | Jupyter | 36,118 | 1,089 | # %% [markdown]
# # Initialization
#
# Equation definitions, making the network, probabilities, etc.
# %%
from brian2 import *
from scipy.signal import welch
from scipy.integrate import simpson
from matplotlib import ticker
from ModelFiles.equations import *
from ModelFiles.functions import *
from ModelFiles.global_... |
73fd437a648284e1579e7d757c8e60448ea534b6aad2a0cbbcd4449572bb6357 | Jupyter | 37,327 | 1,108 | # %% [markdown]
# Load data from drive
# %%
from google.colab import drive
drive.mount('/content/drive')
# %%
import pandas as pd
import numpy as np
from google.colab import drive
drive.mount('/content/drive')
import copy
from scipy import stats
from sklearn.impute import KNNImputer
#import visualization libraries
i... |
7110851a30df265459faf520d81671117c9f6a77a27a046473d3a194a1e8ee06 | Jupyter | 37,885 | 779 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
8fab77b6c525d3d72b19ab0da1137a79c5e8506f7ddd4b05b17971c8edc05684 | Jupyter | 38,116 | 1,123 | # %% [markdown]
# # Image classification with Swin Transformers
# %% [markdown]
# This example implements [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
# by Liu et al. for image classification, and demonstrates it on the
# [CIFAR-100 dataset](https://www.cs... |
6995abd95c15a9bb1b80c10da78a4a48ecf88949f869436c4d85786143b3f6fa | Jupyter | 38,206 | 1,119 | # %% [markdown]
# ## Music-selectivity in DNN
# %%
# Common
import numpy as np
import matplotlib.pyplot as plt
import os; cwd = os.getcwd()
import os.path as osp
from argparse import Namespace
from tqdm.notebook import tqdm
import random
import seaborn as sns
import numpy.matlib
import matplotlib.pyplot as plt
import ... |
991ab4d3422c4419401c5741524dab4100b75a750583adea1085b479a4e4f626 | Jupyter | 39,142 | 762 | # %%
import numpy as np
from jPCA.util import plot_projections, preprocess
import jPCA
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from utils.utils import (generate_response, compute_lambdas)
from utils.vis import peth_normalize
from utils.color_palettes import blue_yel... |
99d905df319605104e26ab9201878d75e3d0fb14030294a39579c447369d3720 | Jupyter | 40,022 | 1,311 | # %% [markdown]
# # notebook to develop open field analysis scripts.
# %%
import numpy as np
import pandas as pd
from scipy import signal, ndimage, interpolate, stats, spatial, fftpack
import sys, os, time, json
from pathlib import Path
import pickle as pkl
sys.path.append('../PreProcessing/')
sys.path.append('../Lib... |
d522df8822947f7d9c94194508dba806eca4cfb7ea9be2fd218d14d2d1a17fa8 | Jupyter | 40,250 | 1,049 | # %% [markdown]
# # Shape Analysis of Cancer Cells
# %% [markdown]
# Lead author: Nina Miolane.
#
# This notebook studies *Osteosarcoma* (bone cancer) cells and the impact of drug treatment on their *morphological shapes*, by analyzing cell images obtained from fluorescence microscopy.
#
# This analysis relies on t... |
2eaf3a795924469b4cd9cd3ca9ef6535e30c8cfbc3eee115a316ea9cc398c53e | Jupyter | 40,420 | 1,051 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import... |
42deb64279891e2b7220806d9c42584f51f7a4569360d557f42a9c826795d87b | Jupyter | 40,615 | 1,059 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import... |
e3e26ffdb537961bfe6e2e79dec5eb6ef3df96ca2e0c460601fda1002072fdad | Jupyter | 41,150 | 1,054 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
from cluster_helper.cluster import cluster_view
# Scientific
import numpy as... |
8e5e1815d5c689199c14517e12a654f3c0b07bbd20c9fb372ca7a3a44c5c81e1 | Jupyter | 42,984 | 1,030 | # %%
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import set_trace as st
import copy
import... |
bf20e0f9e47105d64ebb91797514e21cff94e9e320c82b932b233277f0e003e1 | Jupyter | 43,737 | 1,029 | # %% [markdown]
# # Federated Runtime: CrowdGuard
# %% [markdown]
# This work is based on the [CrowdGuard demo code](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/workflow/CrowdGuard). It has been adapted to demonstrate CrowdGuard in the `FederatedRuntime`.
# %% [markdown]
# #... |
a1009fc231a083b03b36935c755a2bd61e15175d53a22f60096d22f4ae23bb5a | Jupyter | 43,969 | 996 | # %%
# mainSim code
# thalamic model
# AUG 9 2021
# deprecatd. this is a test of the thalamic model with the CX inhibiting the thalamus.
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import copy
import seaborn as... |
d9729a03096bd5be7bf67c344bb372434cedccd22398db717685218e7a9f0b0e | Jupyter | 43,993 | 1,355 | # %%
%load_ext rpy2.ipython
# %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.ticker import MaxNLocator
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
# %%
from datetime import dat... |
bf397ce61ef0f03718ab810b47e6da1d4d5f5e3f65c62d17f7a506458dc86d3c | Jupyter | 44,724 | 1,055 | # %% [markdown]
# # 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all future ... |
192c217736c1e50e8d41f44dc710a1bee3e9846a16175020e6d2ea67a0400148 | Jupyter | 44,846 | 1,419 | # %% [markdown]
# # DeepONet for Acoustic Wave Propagation
#
# **PhD Autumn School - Scientific Machine Learning**
#
# ## Learning Objectives
#
# In this 1-hour hands-on exercise, you will:
# 1. Understand the DeepONet architecture for operator learning
# 1. Understand the data.
# 2. Compare different activation fun... |
b05c4e0b099cc2009580b4eb3be67a224dce35440e904d8c564e45e31290c98e | Jupyter | 45,418 | 1,421 | # %% [markdown]
#
# # Source Code
#
# accompanying the paper:
#
# **Limited Capability of MRI Radiomics to Predict Primary Tumor Histology of Brain Metastases in External Validation**
#
# Neuro-Oncology Advances 2024
#
# DOI: 10.1093/noajnl/vdae060
# %% [markdown]
# ## Prerequisites
#
# ### Naming Conven... |
8c34c21b5963b34de434953225ef26389855f9998a765b4b21ca47f6efecb0e5 | Jupyter | 45,787 | 1,072 | # %% [markdown]
# # Notebook for running analyses for TreeMazeAnalyses2.
# %% [markdown]
# ### Imports
# %%
%matplotlib inline
from pathlib import Path
from types import SimpleNamespace
from joblib import delayed, Parallel
import time
import sys
import traceback
import numpy as np
import pandas as pd
pd.set_option(... |
0e477499597fa41e06a13c71e8648d32da8a74d1ba75be4a330acce29333d414 | Jupyter | 46,174 | 1,113 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
from cluster_helper.cluster import cluster_view
# Scientific
import numpy as... |
54d7ed8e4038e2a8a1f8fa14f2c2ed5d0f0cdbae9a0057efec43760f267d33b9 | Jupyter | 48,122 | 1,462 | # %%
# load data and processing
# %%
import numpy as np
import scipy as sp
from scipy.linalg import norm
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy.matlib
import pandas as pd
from allensdk.api.queries.ontologies_api import OntologiesApi
from allensdk.core.mouse_connectivity_cache import Mo... |
5a33fcbdbbc3114529e75d4d85f638d38084453158b936e1754dc955e5c210b5 | Jupyter | 48,641 | 1,066 | # %%
# mainSim code
# thalamic model
# AUG 9 2021
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import copy
import seaborn as sns
# %%
# load helper functions
from helper import generate_pref, plot_pref
import mod... |
6f26282c9342f4fdfb7a1dfbc81c5a795b9513ce5fda5583502cfd318a9b9eb9 | Jupyter | 48,987 | 1,497 | # %% [markdown]
# # Grid cell score encoding model development notebook
# %% [markdown]
# Imports
# %%
%matplotlib inline
import sys
import os
import shutil
import time
import traceback
from pathlib import Path
import h5py
import json
import pickle
from types import SimpleNamespace
from joblib import delayed, Parall... |
c326069e1b93f3a3d1de38c4cfe588638f2da58953438f4909fbd5a73fd05be8 | Jupyter | 50,228 | 1,272 | # %%
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import set_trace as st
import copy
import... |
bbc19994c25b9363f8b39affab032a953e905a43e1e802563f7a7988f8f02463 | Jupyter | 50,517 | 1,314 | # %%
import sys
import numpy as np
import pandas as pd
import matplotlib
import scipy
import sklearn
import networkx
import seaborn
import brian2
import allensdk
import mcmodels
library_versions = {
'Python': sys.version,
'NumPy': np.__version__,
'Pandas': pd.__version__,
'Matplotlib': matplotlib.__ve... |
8ed6953cbb931acce0526d1a640854944dfdc941491ef7eb2891e76e3b738baf | Jupyter | 50,649 | 1,074 | # %% [markdown]
# # Imports
# %%
import numpy as np
import pickle
import os.path
from os.path import isfile, join
from os import path,listdir
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import itertools
# %%
def add_data(df, date):
Stimuli_Names = ['Left','Ri... |
b2f17f0cdb4be000806f8b166c61d2ea69f8e7947dc59de9aadac9d338c72eaa | Jupyter | 52,617 | 1,116 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
4ef1ec091d7a18225e35fb1711c4828473cc234ec1137bfe268a5afc85a889b2 | Jupyter | 53,036 | 1,256 | # %%
import numpy as np
import os
import matplotlib
from pathlib import Path
import pylab as pl
from tifffile import imsave, imread
import gzip
import pickle
from scipy.stats import ttest_ind, ttest_1samp
from scipy.stats import linregress
from scipy import stats
from sklearn.cluster import KMeans
import seaborn as sns... |
2b63413c6409d551be3e2ce8bcb78cd0e3a128bf3be2db73d0f1461ccf686ae3 | Jupyter | 53,516 | 1,005 | # %% [markdown]
# # Empirical data: evaluate analyses
# %% [markdown]
# We have previously analyzed empirical datasets with CCA and PLS. Here, we illustrate the outcomes.
#
# *Note:* These empirical datasets are not included in this release.
# %% [markdown]
# ## Setup
# %%
import os
import numpy as np
import xarra... |
0c831956ac65b51733d2b8cd6c5710a8b649cb9e4efb44cd7660d28d74b6edd9 | Jupyter | 53,633 | 1,450 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import json
import sys
import operator
from scipy import ndimage
from pdb import... |
c29467f78fe3d6c86f3a51585beae89b35df10d58a704e445af06f6c24c49fe0 | Jupyter | 54,679 | 1,334 | # %%
# process the inhibition study
# run simulations with same parameters.
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import networkx as nx
import copy
import seaborn as sns
from plotFunc import plotPaperTag, get... |
2267b85c1754dfb7a3027a67bfcc1d0f6e0bd555723188b2b7662d2566efda3f | Jupyter | 55,609 | 1,738 | # %%
import time
import timeit
# %%
print('Script started at: ' + time.strftime('%X %x %Z'))
# %%
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
import pickle... |
3e0c2efc67900cf302cc97fc30a9ddff7581a980dde772d87f3e23701d2d3111 | Jupyter | 56,774 | 1,322 | # %%
# mainSim code
# author: Xingyu Ding
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import json
import pickle
import copy
# import plotly.express as px
# %%
# load helper functions
from helper import generate_pref, plot_pref
# impo... |
8d837bfb0659d7d174160472be40c709dbf7fcf2777eb34fa1615ce3395edbf5 | Jupyter | 56,854 | 1,912 | # %% [markdown]
# # PV-IN KCNC2 Kv3.2 A new genetic cause of childhood epilepsy
# %% [markdown]
# ## imports and config
# %%
%load_ext autoreload
%autoreload 2
import logging
import platform
from functools import lru_cache
from itertools import product
from neuron import h
from tqdm import tqdm
from typing import Op... |
30b6d8d5eb8ad30900e6e7437eb45b0138ab6ae71fe4463812686ee01fea3b18 | Jupyter | 58,149 | 1,566 | # %% [markdown]
# # Grid Encoding Model 2:
# This model uses the fields themselves as covariates to predict point wise firing rate or spikes
# dev. 10/29/2020
# %% [markdown]
# Imports
# %%
%matplotlib inline
import sys
import warnings
import traceback
from pathlib import Path
import pickle
from types import Simple... |
ee863b8e4d27dac172c26f410a324d2dd8026a1bc20828a573d3e490ffbe70dd | Jupyter | 59,215 | 1,251 | # %%
import sys
sys.path.append('../')
import os
from functools import partial
from scipy import stats
import pandas as pd
import numpy as np
# import xarray as xr
from sklearn.linear_model import LinearRegression
import seaborn as sns; sns.set(style='ticks', context='paper',
rc={'font.s... |
cccf8c58cf99c587115bb92f556997f83a99b39bd4d62bad948ebb80f45707c0 | Jupyter | 59,570 | 1,555 | # %%
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils
import torch.utils.data
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
from math import pi
import pickle
import time
from scipy.optimize import fsolve
import math
opt_type = "LBFGS"
# ───... |
86d8e8cd04d9ddc774a23c421e5a002b517c95783cd9b4cebecf718cbd4c09a0 | Jupyter | 60,311 | 1,431 | # %% [markdown]
# <a href="https://colab.research.google.com/github/mattrosenblatt7/leakage_neuroimaging/blob/main/make_plots.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
import pandas as pd
import numpy as np
import scipy.io as sio
import o... |
c664f5ec4efd4f50c0d77fdfa5f605c8b98cd6287c9c434f6ca50e95c16d1de3 | Jupyter | 60,647 | 1,392 | # %%
# General
import sys
import os
import os.path as op
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import warnings
# Scientific
import numpy as np
import pandas as pd
import xarray
pd.options.display.max_rows = 200
pd.options.display.max_columns = 999
# Personal
sys.... |
07fcd84c5bb2922e1242f5f5187946d8119ef76acee023a9a026cff3645e83ee | Jupyter | 61,656 | 1,401 | # %%
### 0.0 loading package
import numpy as np
import pandas as pd
#import modin.pandas as modin_pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy ... |
382f62595b7f6fbd2b1482f9c7048992dcba22c189c0413f11ee0f57316f6d3d | Jupyter | 61,798 | 1,959 | # %%
%load_ext rpy2.ipython
# %%
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
import pandas as pd
from datetime import datetime, date, time, timezone, timedelta
import seaborn as sns
import pingouin as pg
import scipy.stats as stats
from matplotlib.lines imp... |
5e59cd943b93818355a9bf288f4317528e3029e955cff96f2453a72eaa96c225 | Jupyter | 62,465 | 1,981 | # %%
%load_ext rpy2.ipython
# %%
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
import pandas as pd
from datetime import datetime, date, time, timezone, timedelta
import seaborn as sns
import pingouin as pg
import scipy.stats as stats
from matplotlib.lines imp... |
76ecb07c34d8924c18774a49b81d8e8d2df22a1b010cf829d6f5df45b2e60955 | Jupyter | 62,928 | 1,564 | # %%
import numpy as np
import os
import matplotlib
from pathlib import Path
import pylab as pl
from tifffile import imsave, imread
import gzip
import pickle
from scipy.stats import ttest_ind, ttest_1samp
from scipy.stats import linregress
from scipy import stats
from sklearn.cluster import KMeans
import seaborn as sns... |
7e29c7a06859d1b2c6b18619f64e737e9093003fc9644981fb364c59e6c95380 | Jupyter | 63,686 | 1,862 | # %% [markdown]
# # --- FlexiTerm: multi-word term recognition
# %%
# --- dependencies ---
import csv
import jellyfish
import json
import math
import numpy as np
import os
import pprint
import random
import re
import spacy
import sqlite3
import sys
import time
from nltk.stem.porter import PorterStemmer
from pathlib i... |
f21429c9960b5b591f46b32c03bd64f54c62ad4105f69334b540a456b09a9a1c | Jupyter | 63,689 | 1,673 | # %%
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils
import torch.utils.data
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
from math import pi
import pickle
import time
from scipy.optimize import fsolve
import math
def exact_solution_alpha(a... |
5e2ac6b7ee58797cf2600c0408fa4d0d70497d9ca7d95a3ccec27369b8a68465 | Jupyter | 64,844 | 1,514 | # %%
# General
import sys
import os
import os.path as op
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import warnings
# Scientific
import numpy as np
import pandas as pd
import xarray
pd.options.display.max_rows = 200
pd.options.display.max_columns = 999
# Personal
sys.... |
ba7ef2e67875045601596b731a190074c8eb767d3f0980d70b1f44c4d73c9a56 | Jupyter | 64,857 | 1,514 | # %%
# General
import sys
import os
import os.path as op
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import warnings
# Scientific
import numpy as np
import pandas as pd
import xarray
pd.options.display.max_rows = 200
pd.options.display.max_columns = 999
# Personal
sys.... |
5801ba6f87911694e3efaa71e3670e7fc650eec3eaeb4421730c9610c4862bf4 | Jupyter | 71,985 | 1,435 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
e1a827b58a030da20d2ee7d215fd452bd5d924e5e1b9afcbec55e89fca057e90 | Jupyter | 72,971 | 2,018 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
from cluster_helper.cluster import cluster_view
# Scientific
import numpy as... |
2781830ab5adc318c32c4d447d34561e261338d2099e6cd897010ecdaec7caa6 | Jupyter | 76,406 | 1,993 | # %% [markdown]
# # Notebook to create OF figures for manuscrip.
# %% [markdown]
# Imports
# %%
%matplotlib inline
from pathlib import Path
from types import SimpleNamespace
from joblib import delayed, Parallel
import time
import traceback
import numpy as np
import pandas as pd
import scipy.stats as stats
pd.set_op... |
364eb1804f903d571600199c5bd21aaac7e3fbb245fa59695f8bda33d4ded196 | Jupyter | 80,049 | 2,029 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
from cluster_helper.cluster import cluster_view
# Scientific
import numpy as... |
ee65451663763faf5ecc5a49edbb4a7bf6b4e498040d5a4322b94f2d599944d0 | Jupyter | 94,464 | 2,461 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
# from cluster_helper.cluster import cluster_view
# Scientific
import numpy ... |
b9848474eee3d3eadf7f8eb9875d13ed09b9dd0ce76b79df467fd6496014cfc2 | Jupyter | 102,384 | 2,688 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
from covesTools import read_sampled_mut_key
from scipy.sparse.linalg import lsmr
from copy import deepcopy
import sys
sys.path.append('../')
import importlib
import myTools
importlib.reload(myTools)
from myTools import... |
b24f0527f75759c6b4af17f739fa944164d8cdbaececcf6209bea51512b02105 | Jupyter | 103,090 | 1,424 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
c657be2f8fb80d0427b076a0bcb208bd5d30a04d992ba8f3fcd745996e54e905 | Jupyter | 105,045 | 1,983 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
cf7d32dfc55b952f8029bce0c8df3c7e99f4315e18e47de88f27c65a363a81cc | Jupyter | 110,615 | 1,770 | # %% [markdown]
# `Cell 01`
#
# # Round 3 revision — one-pass cross-validation rerun, unified evaluation, statistics and figures
#
# **Run on an A100 (Colab: Runtime ▸ Change runtime type ▸ A100 GPU, High-RAM), then *Runtime ▸ Run all*.**
#
# > **Version 14 (27 Sep 2026) — re-run only to add the per-variety Phase 1 ... |
ecccb03b8e30be1c3ea00767ac588badbbd12732abcab1f07cdc634ee356d54a | Jupyter | 113,945 | 3,054 | # %% [markdown]
# ## Set up general libraries for Terra
# %%
# Use the os package to interact with the environment
import os
# Use StringIO for working with file contents
from io import StringIO
# Enable IPython to display matplotlib graphs
import matplotlib.pyplot as plt
%matplotlib inline
# Import seaborn for plo... |
e4a1bf7652b8fc48ec0df8bddafb71f52490024a88f5537dc1e3ce4b131180d6 | Jupyter | 117,889 | 3,093 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
# from cluster_helper.cluster import cluster_view
# Scientific
import numpy ... |
06d22a791b2ce2b286b55776da7e98c81e4da526a4accbbb60a80a1900b66a90 | Jupyter | 200,000 | 4 | # %% [markdown]
# <div align="center">
# <img
# src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACOUAAARWCAYAAAB+G5SbAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEbAAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAACWAAAAAQAAAJYAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAACOWgAwAEAAAAAQAABFYAA... |
a38f629d529903852c4e4c7de444dba3007070f447f1924b963f5885bdcda152 | Jupyter | 200,000 | 3,139 | # %% [markdown]
# # Network analysis `Yim-Choe-Bae-et_al-2024`
# > Contact; 'ggtaeyun@gmail.com' (Choe)
# %%
import numpy as np
import pandas as pd
import networkx as nx
import os
import random
import matplotlib.pyplot as plt
from tqdm import tqdm
from scipy import stats
import function_compilation as fc
# %% [markdo... |
18d65b1f1201672c78cd3ffe176f22d6d434ae9bc5b7629d63a558840828e1b3 | Jupyter | 200,012 | 5,347 | # %%
# General
import sys
import os
import os.path as op
import glob
from copy import deepcopy
from time import time
from collections import OrderedDict as od
from importlib import reload
from glob import glob
import itertools
import warnings
from cluster_helper.cluster import cluster_view
# Scientific
import numpy as... |
ca38eeb413d77eb0348618fe12e2d0d2cc3db25fce7c121beb7f58c646ec9fcd | Jupyter | 200,044 | 2,521 | {
"cells": [
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 1,
=======
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# todo gif for readme"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# nee... |
56ef22c487ed6a2f5a649aa93c1d9925dba60256932b47ebf208882c24bb4071 | License | 327 | 3 | This work is licensed under the Creative Commons CC0 1.0 Universal license.
To view a copy of this license, visit https://creativecommons.org/publicdomain/zero/1.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Additionally, we request that groups utilizing these data reference this ... |
b9ae62cc6d8e42d6ec96c21b2635ea40363c6a7f7e3f0e03cd6a8282ee0cfd68 | License | 591 | 14 | Copyright (c) 2015 by Contributors
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/LICENSE-2.0
Unless required by applicable law or agreed to i... |
42137790f854ae2b9d29a0a72a4da3f6fb9a21d820b3f276f23b0575af72e86e | License | 607 | 13 | MIT License
Copyright (c) 2022 ACEsuit/mace
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, dist... |
1d2831614f6ba4a0329e2425bd7468fbd00e817d4b6a177980f3610047f6c1ab | License | 737 | 8 | # Licence
This repository contains the LaTeX source, figures and figure data/code for the Zapit manuscript.
- **Manuscript text and figures** (`*.tex`, `references.bib`, and the figure images under `Figures/`) are licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/... |
1bacc9ac00cbbe33a6a7b0735565f490a3f5c40c83f6d44538ab1025fc01eed0 | License | 813 | 11 | Implementations of the LF-LDA and LF-DMM latent feature topic models
Copyright (C) 2015-2016 by Dat Quoc Nguyen
dat.nguyen@students.mq.edu.au
Department of Computing, Macquarie University, Australia
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public Lic... |
7cb750713252efd1d578837ba8785b61319109906f0c10b87adab7cf4badfc42 | License | 947 | 17 | MIT No Attribution
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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 ... |
5dd580ef7833dc7554cc6b635b39bc9766fc9eeb6a3528ddf62159abb290b855 | License | 1,035 | 20 | MIT License
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, sublicense, and/or sell co... |
0068882459172e015213e2f3d07403e71c17c8ed24dd37b7fe16f186dd61ddd9 | License | 1,053 | 7 | Copyright 2022 Georgios Rizos
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, sublice... |
1f9978a442976337a86ea9ea5a0c97ae6b1bdab56d9661d05938f163b8e55be3 | License | 1,055 | 21 | MIT License
Copyright (c) 2017
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, subli... |
06738824e7714c8c666cd3dffd99825ea7a95c34d159a7759d07fd79893e7928 | License | 1,056 | 21 | MIT License
Copyright (c) 2016
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, subli... |
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