sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a3b19d5b8ecf4d740fcc4b487685e90d04e7be4367e7d889f55f65da73e1fb4b | Jupyter | 3,747 | 102 | # %%
## Following lines such that modules are automatically updated:
%load_ext autoreload
%autoreload 2
import matplotlib.pyplot as plt
import numpy as np
import os, sys
import popoff # custom package with all code routines
import pop_off_plotting as pop # module with plotting functions
import pop_off_functions as p... |
ff6f83874f49b36d0be3914bed120bf7cae08a7f0148ad3b717466a9a8776afc | Jupyter | 3,762 | 146 | # %%
using Championetal2023, Plots
# %% [markdown]
# # Figure 3B
# %%
heights = [0.2, -0.06]
starts = [62.83185307179586, 1430]
durations = [100.0, 10.0]
thisdict = Dict([
("g_Na", 35.0), #mS/cm2
("g_K", 9.0), #mS/cm2
("g_L", 0.5), #mS/cm2
("E_Na", 55.0), #mV
("phi", 15.0),
... |
25d204df89c3236763f69c2e5c1ef7551f529de735a7c1d9d7bfb99d34ff68e9 | Jupyter | 3,867 | 99 | # %%
%matplotlib inline
import sys
sys.path.append("/home/diego/Dropbox/hold_noise/iclamp-glm/")
%load_ext autoreload
%autoreload 2
from cycler import cycler
import seaborn as sns
size = 24
sns.set(style="ticks", rc={'figure.figsize': (3.5, 3.), 'axes.labelsize': size, 'lines.linewidth': 1.4, 'lines.markersize': 10,
... |
e763404fc19f991efa704cde9d199f61e2be84c843099e55c0c9d27767e17dad | Jupyter | 3,975 | 115 | # %% [markdown]
# # Computing the predictions
#
# It's time to see how your net performs on unseen `test` data. We will divide the tutorial in 3 parts depending on whether you want to:
# * Predict filepaths contained in a [dataset split .txt file](#Predicting-a-datasplit-txt-file)
# * Predict [local images](#Predictin... |
e1b8171625748d529af08ed4fdf75d47f31944823b0447e378267aee5fd4ccfe | Jupyter | 3,986 | 106 | # %% [markdown]
# # Test HCP data relevant scripts with ciftify
# %%
import ciftify
from ciftify.utils import run
import os
import datetime
import logging
from glob import glob
# %%
#working_dir = '/home/edickie/Documents/ciftify_tests/'
working_dir = '/scratch/edickie/ciftify_intergration_tests/'
src_data_dir= os.pa... |
77ff103e400309faa04b76bd33998264d25fcb1d5ca51f9ca204f7f13fcb4cdc | Jupyter | 3,996 | 174 | # %%
import random
import pickle
from collections import Counter
import scanpy as sc
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import scover
# SEACells
import SEACells
mpl.rcParams['figure.figsize'] = (6, 4)
# %%
random... |
46bc4cda55b1c58d8bda702c52214eb703c33eb18a282fe7c7e94a996f42e2a0 | Jupyter | 3,997 | 153 | # %% [markdown]
# # Example GSE123139 differential expression
#
# ### Dataset
# This is an demonstration of differential expression analysis with Normalisr between dysfunctional and naive T cells in human melanoma scRNA-seq. Only a subset of cells were extracted from GSE123139 dataset for this demonstration. You can c... |
8b39f7be4ef2e3bc5608aa8454157cec37917e3da5e9be8f8bf365e261575d54 | Jupyter | 4,002 | 112 | # %%
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
from matplotlib_venn import venn2, venn3
# %% [markdown]
# # Import GO enrichment analysis results
# %%
data_path = os.getcwd() + "/GO_enrichment/"
# %%
goea_csf_BP = pd.read_csv... |
55aa8663b78457f6abe914f7af043b9a0dc434827e6b718a35e715d97b4d77f3 | Jupyter | 4,008 | 99 | # %%
import tensorflow as tf
import utils
import tensorflow_addons as tfa
from data_parameters import data_param
import models.identification_models as models
print(tf.config.experimental.get_visible_devices())
per_sys = data_param['per_sys']
N_elem = len(per_sys)
spectrum_length = data_param['spectrum_length']
max_b... |
2550c47c7ac627f6b874105c56e3cabf023295a22892216c97e61452632c6179 | Jupyter | 4,010 | 103 | # %%
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import utils
from utils import Visuals
import evaluators
import tensorflow_addons as tfa
from data_parameters import data_param
print(tf.config.experimental.get_visible_devices())
per_sys = data_param['per_sys']
N_elem = len(per_sys)
spec... |
c745d147aa9845ba813d45043e4275bedf417b256ee2b336c48245ec6e211475 | Jupyter | 4,038 | 97 | # %%
import pandas as pd
import numpy as np
import glob
import sys
from sklearn.decomposition import PCA
from pyscf import scf,gto,lo
from qml.math import cho_solve
from qml.kernels import *
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from natsort import natsorted
from skle... |
fc00d059f771df003f729a8516e0100e8bae2239ce6161a7a9d812561ab7f96b | Jupyter | 4,109 | 146 | # %%
# symetric lower triangle and upper triangle
import numpy as np
import importlib
import h5py
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style("ticks")
# %%
areas = ['V1', 'LM', 'RL', 'AL', 'PM', 'AM']
# %% [markdown]
# # load anatomical data
# %%
# an... |
14e88c84ebb2690133e5b14f00d9edc7985d69bd6af32fcb8ec717c2d31334b6 | Jupyter | 4,146 | 125 | # %%
from nilearn import datasets
# %%
development_dataset = datasets.fetch_development_fmri(n_subjects=30,
data_dir = '/external/rprshnas01/tigrlab/scratch/edickie/')
# %%
msdl_data = datasets.fetch_atlas_msdl()
msdl_coords = msdl_data.region_coords
n_regions = ... |
1c7f4183604a87bfb5a68026a7499f639f2e35cb3678aa2c12faf00d3ff2f673 | Jupyter | 4,183 | 163 | # %%
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
np.random.seed(0)
matrix = np.zeros((12, 12))
for i in range(matrix.shape[0]):
for j in range(matrix.shape[1]):
matrix[i, j] = np.random.rand() * float(np.random.rand()>0.3)
if (i,j) in [(7,0),(7,1),(7,2),(7,3),(7,8),(7,9),(7... |
d05aa7de6ae85621622a27bca2ba856d1940c36bb2e774c5692ddd9c8f87595b | Jupyter | 4,338 | 126 | # %% [markdown]
# # Interactive visualization of topographic maps
#
# This interactive notebook allows for interactive visualization of polar angle, polar angle gradients, and visual field sign maps as a function of the participant’s assignment to the reported clusters.
# %% [markdown]
# ## General settings
# %%
im... |
67366ceafd6e981ffcd800d8650625725c573476043c00025b3a31c5aa279f48 | Jupyter | 4,386 | 159 | # %%
import random
import pickle
from collections import Counter
import scanpy as sc
import numpy as np
import pandas as pd
from fbpca import pca
from geosketch import gs
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import scover
mpl.rcParams['figure.figsize'] = (6, 4)
# %%
random.s... |
37a10a96c0af08a936a1fdbd68075bdd100b33af98c6ef771bde6c5c17a09f0d | Jupyter | 4,431 | 150 | # %% [markdown]
# # Import libraries and data
# %%
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import pickle
import seaborn as sns
from sklearn.metrics import roc_auc_score, confusion_matrix
from utils import cont_variables
# %%
data_path = os.getcwd() + "/Dat... |
e3c197e58666383e2f44da68b6818cf512d189a6633dcee8e2a4ba7a6edb0a08 | Jupyter | 4,432 | 159 | # %%
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
%matplotlib inline
import scvelo as scv
import scrublet as scr
import scanpy.external as sce
import numpy as np
import pandas as pd
import scanpy as sc
import cellrank as cr
import phate
import bbkn... |
797c0370924a1fa6c07887aea403cff6a466de7157c5c96ebb987c5588994d45 | Jupyter | 4,492 | 134 | # %%
%matplotlib inline
import sys
sys.path.append("/home/diego/Dropbox/hold_noise/iclamp-glm/")
import matplotlib.pyplot as plt
import numpy as np
from icglm.models.lssrm import LSSRM
from icglm.kernels import KernelRect
from icglm.processes import OUProcess, GeneralCov
%load_ext autoreload
%autoreload 2
# %% [mar... |
0faa5ceea7d693ce6e82460b7fbf068870909417079647c7e339e6bb660e2a8e | Jupyter | 4,563 | 141 | # %%
import sentencepiece as spm
import numpy as np
import tensorflow as tf
import pandas as pd
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
#from tqdm import tqdm
from glob import glob
# %%
from glob import glob
physical_devices = tf.config.list_physical_devices('GPU... |
88288e6d95568d6858847778ebffbae924b7f432b3a23255dd578cad0f415c98 | Jupyter | 4,565 | 117 | # %%
import scanpy as sc
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import warnings
import seaborn as sns
import glob
# %%
sc.set_figure_params(scanpy=True, dpi=150, dpi_save=300, frameon=True, vector_friendly=True, fontsize=10, figsize=(8, 8)... |
d7c37855766d755d1030649af594c9382ff819512ead1266a3ca0764894fede6 | Jupyter | 4,593 | 167 | # %%
# Import all the necessary packages
%matplotlib inline
# %matplotlib widget
import numpy as np
import nibabel as nib # Read / write access to some common neuroimaging file formats
import itk # Jupyte... |
f38c11777b57f69b79124d2fee6c8eaff37472bb0e860b1b9381c6eca998e28b | Jupyter | 4,676 | 134 | # %% [markdown]
# Computing the global statistics of the predictions when we train different models from a cross-validation approach.
# %%
# https://towardsdatascience.com/metrics-and-python-ii-2e49597964ff
# https://en.wikipedia.org/wiki/Sensitivity_and_specificity
# en el apartado "a worked example" de wikipedia vie... |
85f03e7ef235519d1b3ed8ce8ed8ddda9269a05d14b077f5dde4cc2dd24ee649 | Jupyter | 4,682 | 110 | # %%
from torch.utils.data import Dataset, DataLoader, SequentialSampler
from HiSiNet.HiCDatasetClass import HiCDataset,GroupedHiCDataset
from HiSiNet.reference_dictionaries import reference_genomes
from HiSiNet import models
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
i... |
9b408da58ef8e5346f1a553c7af4729d1cbe1e5d473e171083e115cd1d7aef34 | Jupyter | 4,708 | 164 | # %%
from train_network_two_qubit_channel import *
from multiprocessing import Pool
import multiprocessing as mp
import scipy.io as scio
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# %%
# initialization
counts_X = 50 ;
X_list = [4 * pi * np.random.random((2,8)) for i in range(counts_X)] ;
# ... |
67b7eccb8732092864944509cdfade4ab509e2030c09c93a89fc3cbe30fd1685 | Jupyter | 4,738 | 164 | # %%
from train_network_two_qubit_channel import *
from multiprocessing import Pool
import multiprocessing as mp
import scipy.io as scio
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# %%
# initialization
counts_X = 50 ;
X_list = [4 * pi * np.random.random((2,8)) for i in range(counts_X)] ;
# ... |
586426bd6895600026caa0d2ee134fd8bbb60c9f64c0e78471d9ffdf6441b136 | Jupyter | 4,767 | 146 | # %%
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import utils
from data_parameters import data_param
import json
# %% [markdown]
# Start by getting the SI preprocessed (normalized, zero padded, alligned, ...) and save it to a TFRecord so that we dont have to repeat the operations every t... |
781147e96e2f333017efd1ff9ebecc496ca96ccff05593f9718d8f0ebca8829b | Jupyter | 4,821 | 184 | # %% [markdown]
# # Training of segmentation algorithm GCN
# %%
1+1
# %%
import os.path as osp
from m2nist_superpixels import M2NISTSuperpixels
from torch_geometric.data import DataLoader
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.datasets import MNISTSuperpixels
import t... |
2d391ecbbd74c0e74bcec7ddfa3c34ad44895ac053f4f5c8231e2b4149e5c775 | Jupyter | 4,828 | 120 | # %% [markdown]
# # Visualizing the saliency maps
#
# Once your classifier is trained you can visualize which pixels where the most relevant to classify an image as a certain label. This is what we call the *saliency maps*.
#
# We will use the implementations of different saliency functions from the [deep-viz reposit... |
37d2e95867cab9c2cd13df68178aa57ef014694bcbb3e3bcc7d6a0796acaa982 | Jupyter | 4,834 | 120 | # %% [markdown]
# # Visualizing the saliency maps
#
# Once your classifier is trained you can visualize which pixels where the most relevant to classify an image as a certain label. This is what we call the *saliency maps*.
#
# We will use the implementations of different saliency functions from the [deep-viz reposit... |
607e5dd9f1c61329f7aa235d365ba47dee60a2b534fcd8c42fe1331a0be8edb0 | Jupyter | 4,844 | 120 | # %% [markdown]
# # Visualizing the saliency maps
#
# Once your classifier is trained you can visualize which pixels where the most relevant to classify an image as a certain label. This is what we call the *saliency maps*.
#
# We will use the implementations of different saliency functions from the [deep-viz reposit... |
5090db78f780d76dbcab41003b37cb774642566df2bf62b598b3b2c7f63bea36 | Jupyter | 4,850 | 120 | # %% [markdown]
# # Visualizing the saliency maps
#
# Once your classifier is trained you can visualize which pixels where the most relevant to classify an image as a certain label. This is what we call the *saliency maps*.
#
# We will use the implementations of different saliency functions from the [deep-viz reposit... |
14f9c23795e2d8959034abeaab4fabb604d5293cfd56e7d3a12fbbd37055990d | Jupyter | 4,866 | 106 | # %% [markdown]
# <span style="color:black"><span style = "font-size:30px"> Critical subregion identification</span>
#
#
#
# In this process, we identified the critical subregion for promoter strength by varying promoter sequences and assessing its impacts. The CNN model (CNN training.ipynb)... |
fb0357daa2c0253e1a1c2ca9d68e0d65542ec4864ce0aa1cff7db3d0d5a5bf44 | Jupyter | 4,903 | 119 | # %% [markdown]
# <span style="color:black"><span style = "font-size:30px"> CNN model training</span>
#
#
#
# In this process, we built a convolutional neural network model (CNN) to predict promoter strength. We used the training data set from the previous step (see the section of ‘Acquisiti... |
fee62c1faa8c0c59c903e7576a320c37c97de165f57f45cfbdafc8eda41422a0 | Jupyter | 4,950 | 161 | # %%
import json
import nltk
from textblob import TextBlob
import glob
import textstat
import re
import math
import demoji
from nltk.sentiment import SentimentIntensityAnalyzer
import collections
from collections import Counter, defaultdict
from nltk.corpus import stopwords
# %%
def compute_entropy(data, unit='natural... |
92c01e416e61e8a21346fb4c788ec1f13d19d8f5731d7bba2fcc87af244e38f3 | Jupyter | 5,000 | 164 | # %% [markdown]
# ### Alignment
#
# An example of how we aligned samples for mCREATE. This aligns FASTQ files against an expected variant template, and then exports the counts and enrichment of one sample over another.
# %%
import os
from pepars.fileio import fileio
from protfarm.workspace import Workspace as ws
fr... |
2a504a7fcca72675d910a93d4e4a539b794ed0719015b7049b8c4df5260acfe1 | Jupyter | 5,051 | 175 | # %% [markdown]
# # SVAE-based variant generation using pulldown assay data: LY6C1-Fc
# %%
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
import pandas as pd
import pickle
import random
import tensorflow as tf
import os
import sys
from pathlib import Path
current_dir = Path(os.getcwd())
top_dir = current... |
cb8ad365355be8c20c65161c956d835d97c207d58674b498bd6283814206a2b6 | Jupyter | 5,091 | 148 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from plots_import import *
def MAD(x):
return np.median(np.abs(x - np.median(x)))
# %%
data = '../../data/Barnes/all.csv'
data1 = '../../data/Barnes/broad-PN.csv'
data2 = '../../data/Barnes/ORN-PN.csv'
df = pd.read_csv(data)
df1 = pd.read_... |
612db6ddc90f23b2795df99f873da33bb9e805b1f3a4b8e500e50683f6f00ada | Jupyter | 5,126 | 176 | # %%
# from create_dataset_shorter import *
from create_dataset_more_conectivity import *
import matplotlib.cm as cm
from skimage.segmentation import *
%matplotlib inline
# %%
# def create_one_sample(i):
# x = []
# y = []
# pos = []
# edge_index = []
# edge_slice = [0]
# print('Generating patc... |
22dc4769c49c2e1970269908d435bddc328e3ea867efab61355244c7156719e4 | Jupyter | 5,134 | 160 | # %% [markdown]
# # 0.0 Divide your data into distributed train, val and test sets
#
# Our file todo.txt contains all the images and its corresponding classes. Now we want to split our images into train, val and test files, so that the classes are well distributed (e.g. if the dataset is highly imbalanced the images a... |
4092b7d1fe3d721dc15a12c9e498ba7b31275315450a8632bcb584d3a9397478 | Jupyter | 5,135 | 147 | # %%
from tqdm import tqdm
import multiprocessing
from multiprocessing import Pool
import numpy as np
import pandas as pd
# %%
'''Phenotype extraction code based on UKB_coding_mapping_table and self_report_ICD10_mapping_treeRespect.tsv code files from https://github.com/rivas-lab/public-resources/tree/master/uk_bioban... |
f2e93fd89765938cf8d22f75a94a4e055327c5565d21eff835cec498bbe007d7 | Jupyter | 5,144 | 164 | # %% [markdown]
# # Pulldown assay data processing: LY6A-Fc, LY6C1-Fc
#
# `sequence`: variant DNA sequence
# `AA_sequence`: variant 7mer amino acid sequence
#
# **Data samples and replicates**:
# - starter-virus (starter, 3 replicates)
# - `starter_virus_1_counts`
# - `starter_virus_2_counts`
# - `star... |
c488a53dce2b0eb6153237f73f4739f7d2bffbbd05cd63c00351813c68cf1934 | Jupyter | 5,156 | 118 | # %% [markdown]
# ## Before you start:
#
# First, make sure you are using the conda environment `pope` that is included in the `popping-off` repo.
#
# Second, the cell block below loads the data, using the data path that is given in `popping-off/data_paths.json`.
# You need to customise the following:
#
# - `base_p... |
6d2f5e11ca134c1a9e3505981f060a852c0c976ddfdbf0baeb4e0ffb9fe77e4a | Jupyter | 5,170 | 142 | # %% [markdown]
# <span style="color:black"><span style = "font-size:30px"> Data refinement and new CNN model training</span>
#
#
#
# We built a new CNN model with a TANNNT-containing promoter dataset during this process. As a result, we achieved higher prediction accuracy than the previous ... |
d756a9db0f5f8cd8343c6afc5d3b1834c4e609ce1dc0224fc0219dc192b109cc | Jupyter | 5,187 | 160 | # %% [markdown]
# # Periodic (Welch's method) and aperiodoc (FOOOF) activity estimations
# ## Importing needed packages
# %%
import csv
import matplotlib
import pathlib
import os
import numpy as np
import pandas as pd
# no need to use np. anymore
from pylab import *
# MNE-Python tools
import mne
from mne import read_s... |
7423e8687d195ea28d3122a3797b303544f1094f748f180e100f3a0171b611b5 | Jupyter | 5,209 | 176 | # %%
# from create_dataset_shorter import *
from create_dataset_more_conectivity import *
import matplotlib.cm as cm
from skimage.segmentation import *
%matplotlib inline
# %%
# def create_one_sample(i):
# x = []
# y = []
# pos = []
# edge_index = []
# edge_slice = [0]
# print('Generating patc... |
7d2b5bb7a2f874d6d7f3a5e89d2a38a2fc3237485914916b15406feb39fb9c42 | Jupyter | 5,222 | 171 | # %%
from train_network_one_qubit_channel import *
from multiprocessing import Pool
import multiprocessing as mp
import scipy.io as scio
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# %%
eps = 0.15 ; iters = 60;
# %%
# initialization
counts_X = 50 ;
X_list = [4 * pi * np.random.random((2,5)) ... |
44349859c5d48f635dbb0850a144734b04a1e862b5b5a5d71f7d2d3c0674fba1 | Jupyter | 5,232 | 158 | # %%
# Author: Ran Liu <rliu20@stanford.edu>
""" Demo of phase amplitude coupling (PAC) analysis """
import numpy as np
from matplotlib import pyplot as plt
from numpy import pi as pi
from somata.oscillator_search import IterativeOscillatorModel
from somata.pac.pac_model import fit_pac_regression, kmod, phimod, optimi... |
2f632b575ebc29baad92bf6447dba6c40170633d08e98b9184952d70052838f1 | Jupyter | 5,236 | 185 | # %%
import sys, os
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib inline
# %%
from skimage import segmentation as segment, draw
from skimage.measure import regionprops
from skimage.future import graph
def _scale(image, scale=8):
if scale == 1:
return image
else:
... |
e2b8d80bcfc8e5247053709a01f22c9df0582dd4cd4abda1781f89e106e87c93 | Jupyter | 5,239 | 144 | # %%
import numpy as np
import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors
import matplotlib as mpl
from matplotlib.patches import Rectangle
import seaborn as sns
import anndata as ad
from sklearn.metrics.pairwise import pairwise_distances
import scipy.stats as ss
f... |
325fa743b8e784278514a9896d5c1e202638465884af2e13bb78e841b4773ce2 | Jupyter | 5,244 | 171 | # %%
from train_network_one_qubit_channel import *
from multiprocessing import Pool
import multiprocessing as mp
import scipy.io as scio
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# %%
eps = 0.15 ; iters = 60;
# %%
# initialization
counts_X = 50 ;
X_list = [4 * pi * np.random.random((2,5)) ... |
86c668c773206971bc6dbefe752d3439c82c6b32ce948505ee7981ae1f1266d6 | Jupyter | 5,262 | 185 | # %%
import sys, os
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter('ignore')
%matplotlib inline
# %%
from skimage import segmentation as segment, draw
from skimage.measure import regionprops
from skimage.future import graph
def _scale(image, scale=8):
i... |
27b432ddd7efe213cce21fb7e16af5889685d7c22d1255bbf394020967d77339 | Jupyter | 5,279 | 138 | # %%
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
%matplotlib inline
import scvelo as scv
import scrublet as scr
import scanpy.external as sce
import numpy as np
import pandas as pd
import scanpy as sc
import phate
import scvelo as scv
import stati... |
d99ba959e158b0bdc07b8c1b9703d5c7cea0516b789962f252d42774d5f36c70 | Jupyter | 5,282 | 186 | # %%
using Plots
# %%
#some utilities for inputs
mutable struct InputParams
pulsestarts::Array{Float64,1}
pulsestops::Array{Float64,1}
pulseheights::Array{Float64,1}
pulsecounter::Int64
pulsewindow::Int64
input::Float64
end
function updateinput!(inputstructure::InputParams, currenttime)
#... |
1e9f0a970f22b8a5b12c82af2d88f3e7d8b04e5bcd05353cbd90a492c2263cc1 | Jupyter | 5,306 | 176 | # %%
import numpy as np
import pandas as pd
from sklearn import preprocessing
import matplotlib as plt
from matplotlib import pyplot
import seaborn as sns
from alignment import TrivialAlignment, CCA, ManifoldLinear, manifold_nonlinear
from correspondence import Correspondence
from distance import SquaredL2
from neighb... |
c50fa548f622d6cc8da37f34005e1a4a241f18f0491fe37fb18d1cc99b55a4db | Jupyter | 5,386 | 145 | # %%
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
from matplotlib import patches
from matplotlib_venn import venn2
from pathlib import Path
from scipy.stats import fisher_exact
from statsmodels.stats.multitest import multipletests
# ignore ... |
a16858e5b61cbeafa1bddc6dd21b1f084ce9dd27532aba692f0746c67da80194 | Jupyter | 5,441 | 132 | # %%
#Loads packages
import numpy as np
import tifffile
import napari
import pandas as pd
from skimage.morphology import ball, binary_dilation, dilation
from skimage.measure import label, regionprops
# %% [markdown]
# This notebook is used to quantify interactions between mossy fiber boutons and complex spines. The a... |
42136e147d33cb72a44e03fcaf662d991434373286f61a591e2f35c13179366a | Jupyter | 5,457 | 219 | # %%
!git clone https://github.com/aiporre/gcn_segmentation.git gcn_air
# %%
# %%
# import sys
# sys.path.append('./gcn_air')
%cd gcn_air
# %%
from google.colab import files
uploaded = files.upload()
# %%
!rm data.tar.xz
!wget https://transfer.sh/3rm5B/data2.tar.xz -O data.tar.xz
# %%
# !mv 'data.tar (1).xz' data... |
2a02f20e9371772fc7397d5fb0f86ceb3585060c159e5d95f6f1674563f799a2 | Jupyter | 5,656 | 179 | # %% [markdown]
# # ANOVA over all DBS configurations for a specific walking test
# %% [markdown]
# What we are roughly doing in this notebook:
# ```
# for each paradigm (fast, free,...):
# for each patient:
# for each gait parameter (stride legnt, gait speed, ...):
# if there are significant differe... |
8d052e91eeeb53aa03bd096ddfb37906da83208b7874cfdb6a92671f6141ed53 | Jupyter | 5,669 | 197 | # %% [markdown]
# # Figure 4 and 4S (supplemental) panels
# %%
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import Image
import re
from pathlib import Path
import utils.fig_utils as fig_utils
# %%
import importlib
%load_ext... |
453330752eb5c723516ba28c7c0c363305d18ffa53ba43c210fc07d24d385705 | Jupyter | 5,803 | 183 | # %%
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import pandas as pd
import lib.Metrics as Metrics
from lib.models.IRNN_Full_Bayes import IRNN_Full_Bayes
from lib.models.IRNN import IRNN
from lib.train_functions import fit
from lib.utils import *
from lib.regional_data_builder... |
56fac288bfd8adcc66a14b26a74e5539d57ec2d68b47e0b2fe5a83f2b93a7123 | Jupyter | 5,871 | 200 | # %% [markdown]
# # Example GSE123139 co-expression
#
# ### Dataset
# This is an demonstration of co-expression analysis with Normalisr within dysfunctional T cells in human melanoma scRNA-seq. This includes iterative removal of top GO pathways. Only a subset of cells were extracted from GSE123139 dataset for this dem... |
9a8ca07020dadd9aa67bb684016e371227e6f7ab4ff790324f6963b38ed487dd | Jupyter | 5,906 | 145 | # %%
import json
from sc3D import SpatialOmicArray
from IPython.display import display
corres_tissues = "data/corresptissues.json"
with open("data/tissuescolor.json") as f:
colors_paper = json.load(f)
colors_paper = {eval(k): v for k, v in colors_paper.items()}
%matplotlib inline
# %% [markdown]
# # Setting u... |
ae58be46326e6589f63b4cd14c48649591408469014d6991658764bf96f6a58a | Jupyter | 5,910 | 207 | # %%
from neuron import h
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
h.load_file("stdrun.hoc")
def det_impedance(pos, sec, v0, freqs, dur, extend_impedance: bool):
imps = []
for f in freqs:
h.finitialize(v0)
h.continuerun(dur)
imp = h.Im... |
722af6f36c704aee110579bd685221ab4946f3a5326a234ffa9c17983b4d3fab | Jupyter | 5,941 | 183 | # %%
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import pandas as pd
import lib.Metrics as Metrics
from lib.models.IRNN_Full_Bayes import IRNN_Full_Bayes
from lib.models.IRNN import IRNN
from lib.train_functions import fit
from lib.utils import *
from lib.regional_data_builder... |
8e66fa88638ce735b34c806fd1f3338a3b1b546e31a7140b39dea6ffdab41b13 | Jupyter | 5,992 | 141 | # %%
import pandas as pd
from pepars.analysis import heat
from pepars.utils import DNA
import numpy as np
import os
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = [12, 10]
# %%
# User parameters
#set your path, where your variant Excel file is located
WORKSPACE_DIRECTORY = "example_data... |
374f7260324839422cc2fc8f0ebd567ed37cc35adb7d7f726976c6a2cc563560 | Jupyter | 6,000 | 207 | # %% [markdown]
# # Machine Learning Algorithms: Logistic Regression
# %% [markdown]
# ## Multinomial Logistic Regression applied to Fashion MNIST (Image Classification)
# %% [markdown]
# ### Implementation with sklearn
# %%
# Import Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
... |
e6f687c9af0957fac86275ffe53c8f1dfaeea99eac523a38b8c1650af8d5f19d | Jupyter | 6,025 | 156 | # %%
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
from matplotlib import patches
from matplotlib_venn import venn2, venn3
from scipy.stats import fisher_exact
from statsmodels.stats.multitest import fdrcorrection
# %% [markdown]
#... |
bb00e35f432d4149aaddf7a26e398db3b28a2ce1ea13425559265a368c88d517 | Jupyter | 6,040 | 273 | # %% [markdown]
# # Figure 2 and 2S (supplemental) panels
# %%
import re
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from IPython.display import Image
import utils.fig_utils as fig_utils
# %%
import importlib
%load_ex... |
debe2645d0cc20076186834aac53186a1b0c18c0aa38f6890b543d3e0ed47c86 | Jupyter | 6,119 | 166 | # %% [markdown]
# # Running ciftify integration tests using the ABIDE NYU data
#
# Here we are splitting out the bit of the integration test that can be run on ABIDE NYU data in prep for running some tests in parallel
#
# The purpose of the tests run with ABIDE NYU Data are:
# + to test ciftify_recon_all on a freesur... |
76e4f52f768594002ea6974bd54c4e2c7bcc7f049a512577ca3f4dcfe58eb349 | Jupyter | 6,182 | 168 | # %%
#Loads packages
import numpy as np
import tifffile
import napari
import pandas as pd
from scipy import ndimage as ndi
from skimage.exposure import rescale_intensity
from skimage.morphology import ball, opening,binary_dilation, dilation
from skimage.filters import threshold_otsu
from skimage.measure import label, ... |
0aab84e3d15fb7bcb7755e0a11541399143ec7892643c966458d2e3c24c5b772 | Jupyter | 6,231 | 245 | # %%
import sentencepiece as spm
#Train the tokenizer if needed.
#spm.SentencePieceTrainer.train(input='all_classified_paragraphs_text.txt' , model_prefix='FIN_P2A_32k',split_digits=True, train_extremely_large_corpus=False, vocab_size=32000,input_sentence_size=5000000)
# %%
# %%
# %%
import sentencepiece as spm
... |
9699f77f50a7b881d06c80b823986ea8f33d11d68410441bee6a7037c21e7087 | Jupyter | 6,248 | 245 | # %%
import sentencepiece as spm
#Train the tokenizer if needed.
#spm.SentencePieceTrainer.train(input='all_classified_paragraphs_text.txt' , model_prefix='FIN_P2A_32k',split_digits=True, train_extremely_large_corpus=False, vocab_size=32000,input_sentence_size=5000000)
# %%
# %%
# %%
import sentencepiece as spm
... |
b247b440841a8715dc48dfcf63e743e17244ce65a0bd4e9468ff896cd3aca147 | Jupyter | 6,248 | 248 | # %%
import numpy as np
import pandas as pd
from sklearn import preprocessing
# from sklearn.manifold import TSNE
# import matplotlib as plt
from matplotlib import pyplot
import seaborn as sns
from alignment import TrivialAlignment, CCA, ManifoldLinear, manifold_nonlinear
from correspondence import Correspondence
from... |
e60f41d672f1ed5dde7c02899a715d2e12d7b87afbc323dc367209da54afa5ca | Jupyter | 6,282 | 158 | # %%
# Imports
from plotting import plot_heatmap_noise,plot_metrics,plot_violin,plot_histogram,plot_ou_tau,plot_timecourse
from statistical_analysis import normality,equal_variance,comparison
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import scipy as sp
from scipy.io i... |
a24e6b8a4c93e4cb479318f73220fc9532a14ef544ddaaa7358be2d4990d64cd | Jupyter | 6,287 | 194 | # %%
import nilearn.plotting
% pylab inline
# %%
VI_striatum = '../templates/Striatum_network_1.nii.gz'
SM_striatum = '../templates/Striatum_network_2.nii.gz'
DA_striatum = '../templates/Striatum_network_3.nii.gz'
VA_striatum = '../templates/Striatum_network_4.nii.gz'
FP_striatum = '../templates/Striatum_network_6.nii... |
52b463a62fc679f138bfb185eda8e77d6f08bc5c72933419ee05086019508db0 | Jupyter | 6,292 | 204 | # %%
import os.path as osp
import sys, os
sys.path.insert(0, '..')
import time
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.datasets import MNISTSuperpixels
import torch_geometric.transforms as T
from torch_geometric.data im... |
71bb48cfdb0337166261aa90b729a86d713a55047adb38f0b11390d5c49d9fe0 | Jupyter | 6,334 | 154 | # %% [markdown]
# # Import libraries and data
# %%
# import libraries
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import re
import seaborn as sns
from Bio.SeqUtils.ProtParam import ProteinAnalysis
from matplotlib import patches
from matplotlib_venn import venn2, venn3
from scipy.... |
dc2f44349b460212ef6f7f6fec6455a74f8a5c25afb0a9fe8c18e4462fdebba7 | Jupyter | 6,353 | 187 | # %% [markdown]
# # Exploration phase (**optional**)
#
# In this exploration phase we will visualize some features of your dataset to help you make more informed decisitions in case you want to **adapt the default parameters** to your dataset.
#
# You can always skip this part and go with the default parameters but i... |
32b0e51469463db744705239b1e1cb76e1e234e046aa48fb833e8459da371ac0 | Jupyter | 6,398 | 150 | # %% [markdown]
# # Other analyses
#
# This interactive notebook allows for the generation of Supplementary Table 2 (the mean deviation in gaze position along the X and Y axis across runs of retinotopic mapping stimuli presentation and individuals from each cluster) and the aggregation of demographic information and f... |
a98049c1de7ff1c4b210e53376b15aec0765fa9b8571e6869d280b88b2aa10aa | Jupyter | 6,474 | 197 | # %%
import numpy as np
import pandas as pd
import tensorflow as tf
import seaborn as sns
from functools import partial
import random as rand
# %% [markdown]
# ## MMD Functions
# %%
def maximum_mean_discrepancy(source_samples, target_samples, minimum=0., unbiased=False):
""" This Maximum Mean Discrepancy (MMD) lo... |
f06f51b2bfab2396a84914cfeb679c7235257ed24b96ea26bd4e347eb71707da | Jupyter | 6,498 | 199 | # %%
import sys
sys.path.append("../")
# Get rid of annoying tf warning
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import bayesflow as beef
import pandas as pd
from scipy.stats import median_abs_deviation
from ex... |
8c60530e0496903bf8316b9e263a185ff3ea8247af2df1f0bcc970a14de22394 | Jupyter | 6,519 | 139 | # %%
from pyscf import scf,gto,lo
import glob
import numpy as np
import pandas as pd
from natsort import natsorted
from sklearn.decomposition import PCA
# %%
def Full_MAOC(path=None, basis_set='pcseg-0',charge=0,spin=0):
atom=['H','He','Li','Be','B','C','N','O','F','Ne','Na','Mg','Al','Si','P','S','Cl','Ar','K','C... |
905b935c7d4c617266c6226605698a848f6c6eca53c61714b8c0b45bc221dd18 | Jupyter | 6,589 | 159 | # %%
import os.path
import sys
from bids.grabbids import BIDSLayout
import ciftify.utils
from ciftify.utils import run, get_number_cpus, section_header
import logging
from docopt import docopt
import pandas as pd
import ciftify.bidsapp.run as run_script
# %%
def freesurfer_is_complete(settings, participant_label):
... |
7fc2efbbed350b46c5579a849a27f92989471d2f8316abfbdce8d34280c80298 | Jupyter | 6,611 | 164 | # %%
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.image as mpimg
from scipy import stats
from itertools import cycle
from activation_utils import *
from plotting import *
matplotlib.rcParams["figur... |
7b66b1fd5f93f8c955e3ad2013133f79f1db1b42c9dcfbcd2e1d6810d47d5150 | Jupyter | 6,668 | 164 | # %%
import os
import json
import numpy as np
from imgclas.data_utils import load_image, load_class_names
from imgclas import paths, plot_utils
from imgclas import test_utils
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import confusion_matrix
import warnin... |
91834dd167af7e3aa961492f56375a7ef3253e0077c1f1736ebeec73f14531e2 | Jupyter | 6,706 | 196 | # %% [markdown]
# # Example GSE120861
#
# ### Dataset
# This is an demonstration of analyzing CRISPRi scRNA-seq screen with Normalisr. Only a subset of 30 gRNAs and ~7000 cells are extracted from GSE120861 highmoi dataset for this demonstration. You can change it to the full highmoi dataset or other datasets, by follo... |
f4930bf48bcbb66d7ed639b22923b8fc337f191b9895c54a991354ed8e5f7bb1 | Jupyter | 6,740 | 153 | # %% [markdown]
# # Reading EEG Data from events
#
# Each events array has an `eegfile` field, which points to the directory that contains the associated
# EEG files. To read this data, you use the EEGReader class.
#
# Two other important classes for loading EEG data are the TalReader class, which loads information... |
22991b8ac84d4935f35f933e136adc28fd0e2459af9cea71754087322d4cf955 | Jupyter | 6,755 | 196 | # %% [markdown]
# # WARNING: this is work in progress, results may change
#
# To do:
# - clean up
# - test for normality before using posthoc ttest
# %%
import scikit_posthocs as sp
import pandas as pd
import pickle
%load_ext nb_black
# %%
with open("../../results/anova_per_patient.pickle", "rb") as handle:
an... |
b5939762f71f59d5f7436ba8be7c56433d28e9346cc3d53ee608e9b2769157ab | Jupyter | 6,817 | 177 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import MDAnalysis as mda
# %%
# Preprocess the input structures in VMD
# Select waters in the cylindrical volume defined by the min/max cartesian coorinates of the protein
# Do this for each frame, saving the coordinates of the protein and sel... |
8371dfa0f4459c214206b1b54931fa72c21cffc653df4aca98415bbb5da5cfd7 | Jupyter | 6,912 | 187 | # %%
import os
import matplotlib.pyplot as plt
import tensorflow as tf
from bayes_opt import BayesianOptimization
import numpy as np
import pandas as pd
import lib.Metrics as Metrics
from lib.models.IRNN_Full_Bayes import IRNN_Full_Bayes
from lib.models.IRNN import IRNN
from lib.train_functions import fit
from lib.ut... |
d6de7a7bf5c86d70b55b3920491d271f0540ef289f320f441142575beb8e03c7 | Jupyter | 6,988 | 217 | # %% [markdown]
# # Performance of SigmaCCS on the test set with different coordinates generated by ETKDG and MMFF94
# %%
import sys
sys.path.append("..")
from sigma.GraphData import *
from sigma.model import *
import pandas as pd
import random
from pandas import Series,DataFrame
import numpy as np
from tqdm import tq... |
310fbeef664eb0ab659554d8e13c6d59d3f672dc1f954e2a0c4c19e693ee5baf | Jupyter | 7,032 | 221 | # %% [markdown]
# # Load libraries
# %%
%load_ext autoreload
%autoreload 2
import sys
sys.path.insert(0,'/home/beangoben/projects/gpmol')
# %%
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina'
gpm.plot_settings()
plt.rcParams["font.family"] = "Centu... |
aa8610ddfc0dfa8675fc083e3000d3b3402d65e98ec147280667ff1863e7ae5c | Jupyter | 7,043 | 316 | # %%
import pandas as pd
import plotly.graph_objects as go
import os, sys
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import itertools
import seaborn as sns
import os, sys
import math
import scipy
from os import listdir
from os.path import isfile, join
from sklearn.metr... |
6f807bf735e495fab4eacc84483a94f4db2c831bc7459c2f46dcf7d431d2418a | Jupyter | 7,130 | 218 | # %%
import sys
sys.path.append("../")
sys.path.append("../../../assets")
# Get rid of annoying tf warning
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import bayesflow as beef
import pandas as pd
from experiments... |
e1d510283deb0e729fdc24938531c6fad7bf649326224960be71537048bbfe09 | Jupyter | 7,176 | 218 | # %%
#Loads packages
import numpy as np
import pandas as pd
import tifffile
import napari
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage.filters import threshold_multiotsu, threshold_otsu
from skimage.morphology import ball, binary_dilation,erosion
from skimage.exposure import rescale_int... |
379d57bc892eba60323457670f581147261b90e088cd3fa6b22d8d9a3b54fd00 | Jupyter | 7,207 | 222 | # %%
import scanpy as sc
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import re
import scanpy.external as sce
import sys
import warnings
import glob
# %%
def is_outlier(adata, metric: str, nmads: int):
M = adata.obs[metric]
outlier = (M <... |
50f56dffd588c1dc8cde093a196282ae577235572c8bda20cb1e0d8a081d2d1e | Jupyter | 7,219 | 150 | # %% [markdown]
# # Variability of visual field maps in human early extrastriate cortex challenges the canonical model of organization of V2 and V3
# %% [markdown]
# This interactive notebook shows how to run the main analyses reported in our manuscript. Before doing so, download the data following the steps documente... |
27defb472f9875bc94d22f3eac0fc87e25ea0a4410d07c5bb15081a11bd4e004 | Jupyter | 7,240 | 229 | # %%
import nilearn.plotting
% pylab inline
# %%
VI_striatum = '../templates/Striatum_network_1.nii.gz'
SM_striatum = '../templates/Striatum_network_2.nii.gz'
DA_striatum = '../templates/Striatum_network_3.nii.gz'
VA_striatum = '../templates/Striatum_network_4.nii.gz'
FP_striatum = '../templates/Striatum_network_6.nii... |
d1b43bcf7b8c5f8e7bf00a05d9dafd1e62999791220456140c16cd5ccb3c93a8 | Jupyter | 7,265 | 153 | # %%
import nilearn.plotting
import matplotlib.pyplot as plt
%matplotlib inline
# %%
VI_cerebellum_L = '/projects/dmiranda/subcortical/volume_labels/L_cerebellum_1.nii.gz'
VI_cerebellum_R = '/projects/dmiranda/subcortical/volume_labels/R_cerebellum_1.nii.gz'
SM_cerebellum_L = '/projects/dmiranda/subcortical/volume_l... |
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