sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
6bf618366056f450001c806b496eb5c7a68df392ffadd36c97cd912b1f429e8f | Jupyter | 41,135 | 1,191 | # %%
from utils_new import *
# %% [markdown]
# # Guanella Square
# %%
data_dir = '/Users/erihe/OneDrive - NTNU/Prosjekt/Main'
# if >0, plots the sheet of activity during the simulation on every livePlot'th step
livePlot = 100
# if =0, just give constant velocity. if =1, load trajectory from disk
useRealTrajectory = ... |
f2c1aec158c6b071fd22c0d503714306f976ee7bdae38e5e49cfe06240fa590c | Jupyter | 41,141 | 1,088 | # %% [markdown]
# Define functions
# %%
from utils_new import *
# %% [markdown]
# Get data from Waaga et al (2022) and load
# %%
data_dir = '/Users/erihe/OneDrive - NTNU/Prosjekt/Main/waaga'
fnames = glob.glob(data_dir + '/*')
for ff in fnames:
if ff.find('readme')>-1:
continue
f = np.load(ff, a... |
10f1950f6bd2dd0eedd02041e931d23889104817cf099ea5a27939e80b77ac5e | Jupyter | 42,719 | 1,140 | # %% [markdown]
# #### GBM image post-processing
# ##### using a copy of UO_edit_fov_visualization_cluster_spatial_singlecell.ipynb
# ##### 1st May 2023
# ##### 2nd May ; plots as subplots (red dots), create typing class,
# ##### 3rd May : updated both the typing classes, generated one set of figures for 1st image
# #... |
7909a26d2aca7403904d1327dee39b71a8974e9b31839c1e1a65baa89bce2ced | Jupyter | 43,879 | 1,171 | # %%
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole, ReactionToImage, MolToImage, MolsToGridImage
import rdkit.Chem.AllChem as AllChem
from IPython.display import SVG, display, clear_output
from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions
IPythonConsole.ipython_useSVG = True
IPythonC... |
e7f32670b48d9b5af9f38fd86260a379c32c9803b5c1eeba8cb5503bb6273558 | Jupyter | 44,163 | 1,536 | # %%
import logging
import os
import pickle
import pandas
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Fl... |
261211c9708ae5890e6e7179772269be87b1de3d04c6ce4446d2ee7d8af251d0 | Jupyter | 44,470 | 1,473 | # %% [markdown]
# # load data
# %%
import glob
import os
import json
import numpy as np
import scipy.linalg as linalg
import pandas as pd
import mne
import matplotlib.pyplot as plt
import seaborn as sb
from shutil import copyfile
from tqdm import tqdm
from data_util import load_subjects, load_subject_data, load_data, ... |
bcafbb137ccca08ac0dcc789f22a6c8b0356098471355555175cb7b3f9ae67bf | Jupyter | 44,513 | 1,543 | # %%
import logging
import os
import pickle
import pandas
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Fl... |
1f6082af5abbb756fb326dd7828f0c263b3cf2f3e46dede4c46547c9ff3ac276 | Jupyter | 44,560 | 1,254 | # %% [markdown]
# # import
# %%
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import random
import math
import copy
import matplotlib.pyplot as plt
import tdc
from torch.utils.data import DataLoader, Dataset, ConcatDataset
from torch.utils.... |
2db51ec19154635c19a0a98d014ed0652b84697b4d6307dd9d151b3782790f70 | Jupyter | 45,575 | 1,274 | # %% [markdown]
# #### GBM image post-processing
# ##### 1st May 2023
# ##### 2nd May ; plots as subplots (red dots), create typing class,
# ##### 3rd May : updated both the typing classes, generated one set of figures for 1st image
# ##### 8th May : cleaned the plots
# ##### 19th May: Re-arraged cells, re-coded amp_c... |
58dff171b4a62c2b4c62c233f9247bc1545ed893d73f2b2d40c9f7deda74cfcf | Jupyter | 47,418 | 1,225 | # %% [markdown]
# ## Notebook for Figure 1 and related content: Cell types, QC, Experimental Distributions
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
import pickle
import numpy as np
%mat... |
9ac44490d526e4ae101067edcaa3fbf6e069a7313e72818ab6fb652dfad5df32 | Jupyter | 48,360 | 1,179 | # %% [markdown]
# # Prepare
# %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from... |
90d970f7fe6e2400fcf9415455c9a816ab4114de8eb2f6c2f2064658be25d63e | Jupyter | 48,437 | 1,669 | # %% [markdown]
# # Benchmarking Annotation Storage
#
# Click to open in: \[[GitHub](https://github.com/TissueImageAnalytics/tiatoolbox/tree/develop/benchmarks/annotation_store.ipynb)\]\[[Colab](https://colab.research.google.com/github/TissueImageAnalytics/tiatoolbox/blob/develop/benchmarks/annotation_store.ipynb)\]\[... |
d1442a2ac265ffdf9156857f1701f6d2f2bc6b0bc4451ffab693fb0f0aa59c05 | Jupyter | 49,074 | 1,259 | # %% [markdown]
# <img align="left" width="80" height="80" img src="app_screenshots/morgana_icon.png" alt="morgana">
#
#
# # MOrgAna workflow for advance python users
# <br />
#
# This workflow is intended for users with programming background to analyse multiple image folders at once. Users can also use this n... |
277453d5aa22119fada8c9fcdcdceadea30e98658cbca1d3f4eb379dc7a570a6 | Jupyter | 49,488 | 1,313 | # %% [markdown]
# # Quality Control
#
#
# ## - Total number of reads (All, Pass, Pass + Adapters, Pass + Adapters + Mapped) - DONE
#
# ## - Mean, median, and N50 read length for reads that aligned to the human genome - DONE
#
# ## - Percent of unique full length and full length reads per sample - DONE
#
# ## - XY... |
a772f27d22636ad61338cef5c5c0636df6b541d2af7e92bab87f0b809a25cf32 | Jupyter | 50,801 | 1,129 | # %%
import os
import math
import glob
import pathlib
import pandas as pd
import numpy as np
from PIL import Image
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
# %%
def get_clusters_type(filename):
types_dict = {
'noise': 0,
'mua... |
f41ae51ff5bb4dcfe05113164d9fab1b389c6228adeb0e0d17759064100249a2 | Jupyter | 50,864 | 1,263 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
e6259e007e99e7a9b0b00c3bf19518aae621f56054ee63e0b17edc541c86d19a | Jupyter | 50,910 | 1,335 | # %%
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
import nilearn.plotting as niplt
%matplotlib inline
import os
import seaborn as sns
from scipy.special import softmax
from scipy.stats import ttest_rel
import pandas as pd
# %%
story='black'
roi_mapping=np.load('/jukebox/griffiths/be... |
c16959349003c51597e4ad3b934f1f2e0156c4d39872d4d45eaa4e7b0366bd65 | Jupyter | 51,898 | 1,247 | # %% [markdown]
# MultiRAT analysis code
# ================
# Joanes Grandjean
#
# 
#
# # Foreword
#
# This is a jupyter notebook which contains all the code for reproducing my
# analysis. The code is meant to be followed step-wise.
#
# If re-using some of the scripts, please f... |
e1cafd506ac57ff15c16db921776627be2d799c75c26e7839cbcf9ff0a440a47 | Jupyter | 51,903 | 1,267 | # %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphore(max_connections) # 使用Semaphore方法
pool_sema.acquire() # 加锁，限制线程数
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
# torch.cuda.set_de... |
2452e361b4b9727f9ae1768ab8328343b820f4ef1484d12022dcfb4d8a031d83 | Jupyter | 51,922 | 1,269 | # %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphore(max_connections) # 使用Semaphore方法
pool_sema.acquire() # 加锁，限制线程数
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
# torch.cuda.set_de... |
351d5b777a4b7dca6fe7a137a444dd9d81948267373ba82010fbbe5e3949313c | Jupyter | 51,991 | 1,209 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Data pre-processing (Our Data)
#
# ### - Data pre-processing (GTEX Data)
#
# ### - Data pre-processing (Merge Data)
#
# ### - Make figures RegPlot (Relative Abundance)
#
# ### - Make figures RegPlot (Me... |
02401635b482ce306bec2ce9a9c7c80ef15bf70a0e3074b14d4492a8e78b730a | Jupyter | 52,198 | 1,750 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/BarCharts.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
import numpy as np
import matplotlib.pyplot as plt
AFPMnaive10 = [[... |
f72813ae3ce518c46a03dc2afc071faf92569401324e0c589a7a3b3311505bef | Jupyter | 52,812 | 1,292 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
cc0cc7e45dea5d6d4206961bfba2c6856383aa9a7e8ac29bb7343b7ab4ef997a | Jupyter | 52,822 | 1,295 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
c61856e73cb0de926bfa5621a3e4c1628d05bf0ab8127e04a34e42d5b5d03786 | Jupyter | 52,856 | 1,294 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
13e31543af3a56593f8080020fd0875e874a7151db279cc1e281d9f01a77d3d3 | Jupyter | 52,968 | 1,331 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
!python -m pip install git+https://github.com/AllenInstitute/aisynphys
!python -m pip install git+https://github.com/campagnola/neuroanalysis
from aisynphys.database import SynphysDatabase
exp_psp_rest = np.array(((0.11, -0.16,... |
a921529c776ce479f64a7e5e476214de249c7e3c08978783bff216eeddec6cb4 | Jupyter | 53,520 | 1,215 | # %% [markdown]
# # Import modules and give access to storage location
# %% [markdown]
# For questions email: vv86@sussex.ac.uk
# %%
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
import numpy as np
import csv
import os
import statsmodels.api as sm
# %%
from google.colab import drive
driv... |
550cf0bec60372f057deaeda1380bac9e6062534c47e4866466cbdb077222735 | Jupyter | 53,553 | 1,307 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
546443c5d24da53c843be6b74a7cf939a74fb846a82190f6a05aab0bb14ef36d | Jupyter | 53,671 | 1,280 | # %% [markdown]
# MultiRAT dataset description code
# ================
# Joanes Grandjean
#
# 
#
# This page shows the code to reproduce the figures in the manuscript in preparation. Results/figure selection is based on the analysis in the other scripts.
# %%
import os
import g... |
5cf724175dc95081cbb1e4a6fc68399773f14ecfe833689695bdfca54b58f14c | Jupyter | 54,165 | 1,317 | # %% [markdown]
# # 实验设计
#
# 不同unknown含量的datasets：40%，20%，10%，5%
#
# - 画图：h-score和total acc的变化图
# - 表格：known acc和unknown acc
# %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphor... |
0b2bdba864d4bbbca432d59318e859d1a4e56c96dd57333e8fb2f5a4fbec5522 | Jupyter | 54,352 | 1,560 | # %% [markdown]
# # Mount drive and import libraries
# %% [markdown]
# For questions email: vv86@sussex.ac.uk
# %% [markdown]
# ## Mount drive
# %%
from google.colab import drive
drive.mount('/content/drive')
# %% [markdown]
# ## Import libraries
# %%
import numpy as np
import matplotlib.pyplot as plt
import matpl... |
21f533ff98bcecb077f56e514c39a6992370f8036e61a35ec3cf9343b179d97c | Jupyter | 54,576 | 1,294 | # %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import ... |
4fea3103063d4f5a8175d8a8c0d78b2d24c53cd3ec08f163ccd345811c1f962c | Jupyter | 54,934 | 1,510 | # %% [markdown]
# ## Rat R
# %% [markdown]
# Import functions from library
# %%
from utils_new import *
# %% [markdown]
# Load classification of conjunctive cells - dataset found in Gardner et al (2022), see https://github.com/erikher/GridCellTorus
# %%
f = np.load('is_conjunctive_all.npz', allow_pickle = True)
isa... |
01f2a78308fc0925e4f4e1791dc7eb84a518ae6fa79be182f954b9adf21b9708 | Jupyter | 57,413 | 1,332 | # %%
import threading
import torch
import random
import os
import scanpy as sc
import pandas as pd
import numpy as np
import scipy
max_connections = 5 # 定义最大线程数
pool_sema = threading.Semaphore(max_connections) # 使用Semaphore方法
pool_sema.acquire() # 加锁，限制线程数
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
# torch.cuda.set_de... |
e0c911b180fb0d772ccdb8f1d21128ec5065cdb519892ce7dc9fddfa0f9ff68a | Jupyter | 61,379 | 1,430 | # %% [markdown]
# Copyright (c) MONAI Consortium
# 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... |
25294caaa01a1461322ce5bde29bc5aacc860bb298a555c92e608866e0d10e33 | Jupyter | 61,498 | 750 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("ggplot2")
library("pheatmap")
library("circlize")
# %%
snv_info <- fread("Data/snv_loci_v2.csv")
snvIDs <- snv_info[, SNVID]
mito_barcodes <- fread("Data/mito_barcodes.csv")
mitoIDs <- mito_barcodes[, ID]
chrmbases_properties <- fread(... |
7b297e2d5486f3b284cc1c993251d708f6dcb04c4ff312905e1451f7dcb9cf3b | Jupyter | 62,058 | 1,649 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/EOS_K_p1_I_p2_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Entropi... |
b4bb8fea2c090c5a97063dcc9670e6b5e143821389995a559734dc604fbe5977 | Jupyter | 63,439 | 1,670 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/EOS_K_p1_I_p2_p3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Entr... |
6ffe705dedbbfe973b64e5dde510c21e2fb20d4759cbda7a053c76e5da93fe72 | Jupyter | 64,676 | 1,570 | # %%
from utils_new import *
# %% [markdown]
# Get and load data from Obenhaus et al. (2022)
# %%
pd.set_option('display.max_rows', 10)
data_dir = '/Users/erihe/OneDrive - NTNU/TDA_review/functional'
AngularRate = pd.read_pickle(data_dir + '/AngularRate')
AngularRate_Stats = pd.read_pickle(data_dir + '/AngularRate_... |
7b723fca13a7acbf0366df94467a8692780ff04e64e1a50be69d821907b44d7f | Jupyter | 72,748 | 1,992 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Naive_K_p_1_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Naive thresholding, $\mathcal{K} = p_1$, $\m... |
ccaaf2a5c0c13312206cdb7f49d23486c293247c950ec644ce5331e373bf590d | Jupyter | 78,438 | 1,706 | # %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np
import h5py
import cv2
from scipy.stat... |
3a312689a110923844bf431ef3222a76d5774a28ab1287097d41c4dce29c2a21 | Jupyter | 80,494 | 2,286 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Naive_K_p_1_p_2_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Naive thresholding, $\mathcal{K} = p_1+p... |
30dc071a9ec22f4602e1c711d586db9a8088b5cd03ca1d9cad5c722aea911698 | Jupyter | 81,857 | 1,870 | # %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np
import h5py
import cv2
from scipy.stat... |
1a13b5c40325594713dd17edffb6ebb9396654d5ace862a049c8124582e889ce | Jupyter | 86,016 | 1,931 | # %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import ... |
e73c3e95801e9a91449a59b16e933809f141a6ad3dcfee2e1a86a54614ec1843 | Jupyter | 86,834 | 2,426 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Obj_K_p1_I_p3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Objecto... |
393c67b5b76272788dc5bd1c5e8675d21ce6868346db4f705c90d900771baefa | Jupyter | 87,558 | 2,450 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Naive_K_p_1_p_3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Naive thresholding, $\mathcal{K} = p_1+p... |
161977b3006e51f5d5a1524d39699a50efb2898a3a3a87d87d3bd3277249f444 | Jupyter | 88,254 | 2,446 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/EOS_K_p1_I_p3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Entropi... |
4ff4c336e1773b1716c578dc6b0a02e612c0243546120d54ec1180bc24d9b2b9 | Jupyter | 94,617 | 2,632 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/EOS_K_p1_p2_I_p3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Entr... |
1492a5c22df6ec93537ccda21872ff815ed8901d5257a6a56501715811fe21e3 | Jupyter | 97,339 | 2,805 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Naive_K_p_1_p_2_p_3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Naive thresholding, $\mathcal{K} = p... |
f2c926709070ad9c5a44312eba2d42763c5efee0b0eb042f1d2717d71a5db180 | Jupyter | 99,500 | 2,649 | # %%
from utils_new import *
# %% [markdown]
# # Compute and Plot
# %% [markdown]
# Download data from Campbell et al. 2021
# %%
data_dir = '/Users/erihe/OneDrive - NTNU/Giocomo/giocomo_data'
# %% [markdown]
# The identified modules and sessions
# %%
ind_fis = {}
ind_fis['npI1_0417'] = ((1,(0,1,2)),
... |
172e24377561ce48ffd451e92c53619bc8f5523e999148a19e22efa155b03942 | Jupyter | 100,448 | 2,857 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Obj_K_p1_p2_I_p3_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in the ***open*** world, Obje... |
7ebe3587b9f7358632aa92750ae39bbbc037f73c2d9ebe8baec190449d5e4911 | Jupyter | 102,324 | 1,395 | # %% [markdown]
# ## Neural mass computational model to test effect of task on functional connectivity estimates
# %% [markdown]
# Code for running the neural mass simulation from: Cole MW, Ito T, Schultz D, Mill R, Chen R, Cocuzza C (2019). "Task activations produce spurious but systematic inflation of task functiona... |
b45f55f82c5ce0fb0af9512209f8bd32ed723c61d50e8c408203462f96d2ba0e | Jupyter | 102,528 | 283 | # %% [markdown]
# # Sub Question Query Engine powered by NVIDIA NIMs
#
# A Sub Question Query Engine takes a single, complex question and breaks it into multiple sub-questions, each of which can be answered by a different tool. We'll use NVIDIA NIMs to power our sub-question generation and answer retrieval.
# %% [mar... |
69b37b9fa5ad4d5d5bf413b0914bd6f681122fd7309187683ce6854dbe0d150f | Jupyter | 106,495 | 3,271 | # %%
def convert_to_molar(value, unit):
if unit == "mM":
return value * 1e3
elif unit == "uM":
return value * 1
elif unit == "nM":
return value * 1e-3
else:
return None
# %%
ki_values = []
kd_values = []
IC50_values = []
for line in lines:
cols = line.split("\t")
... |
4f9252612d3ecd2e3b5dd4b4a158009f812f8662585c3562c7250a6a3a4e54c2 | Jupyter | 108,494 | 3,374 | # %% [markdown]
# # install libs
# %%
!nvidia-smi
# %%
!pip3 install numpy scipy pandas timm pytorch_lightning pytorch_metric_learning mne moabb pyts --upgrade
!pip3 install https://api.github.com/repos/braindecode/braindecode/zipball/master --upgrade
!pip3 install git+https://github.com/pyRiemann/pyRiemann --upgrade... |
644edf96a9b50a2e14de0a91aec5aaaadfb08f953b6c11a4aad7ee16518174e1 | Jupyter | 108,600 | 3,408 | # %% [markdown]
# ## install dependencies
# %%
!nvidia-smi
# %%
!pip3 install numpy scipy pandas pytorch_lightning pytorch_metric_learning mne moabb pyts --upgrade
!pip3 install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
!pip3 install... |
4f8067c5bf4bfe84364eecbdb24a6d88d1c1c30010d5b3a56fdebcb836bb3099 | Jupyter | 109,016 | 3,387 | # %% [markdown]
# # install libs
# %%
!nvidia-smi
# %%
!pip3 install numpy scipy pandas timm pytorch_lightning pytorch_metric_learning mne moabb pyts --upgrade
!pip3 install https://api.github.com/repos/braindecode/braindecode/zipball/master --upgrade
!pip3 install git+https://github.com/pyRiemann/pyRiemann --upgrade... |
2a9676885323469b9dc0ef77b67445f10af4a91367a0e1f3dbb80bbdcc5acb01 | Jupyter | 109,295 | 3,390 | # %%
hdf5_file_path = "/data/hongyiling/XAI4e3nn/dataset/scope/training/d1aoga3.hdf5"
h5File = h5py.File(hdf5_file_path, "r")
structure = create_structure_from_hdf5(hdf5_file_path)
# %%
h5File.keys()
# %%
h5File['amino_types'][:]
# %%
len( h5File['amino_types'][:] )
# %%
max(h5File["atom_residue_id"][:])
# %%
res... |
2dace7e78dba9b54c169241fc9a1e143953ecd0695183d5330030f74d412ccfa | Jupyter | 116,606 | 3,058 | # %% [markdown]
# # Generate data for training MLPs for XAI and uncertainty analysis.
# %% [markdown]
# ### First load some dependencies and required files for data generation
# %%
## Load dependencies and set defaults
# Import dependencies
import numpy as np
import os
import nmrglue as ng
import random
# %%
os.get... |
98332d5bb220706369ffe3c4bf4b1b2fe5776a7fe5bb8c6a511c80b2e21f2045 | Jupyter | 123,967 | 181 | # %% [markdown]
# <a href="https://colab.research.google.com/drive/1EpokQ8Y_5jIJ7HdixZms81Oqgh2sp7-E?usp=sharing" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # LlamaIndex Cognee GraphRAG Integration
#
# Connecting external knowledge ... |
4220644bff84b7ac150f24e38ca770e32eac08bf8a7df49c41fede2c8f41c8fa | Jupyter | 124,693 | 3,021 | # %%
import os
os.chdir('..')
# %% [markdown]
# # IMPORTS
# %%
import matplotlib
from matplotlib import rcParams, cycler
# matplotlib.use('Agg')
CUDA_VISIBLE_DEVICES = ""
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import pandas as pd
import numpy as np
import random
import json
import copy
i... |
f5e9fdd6b27f57f6517790f5aee9ed40731a5450af3b6c8e52c144907d8967dc | Jupyter | 125,228 | 4,819 | # %% [markdown]
# # Initialisation
# %%
from numpy import loadtxt
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
import random
from numpy import linalg as LA
import time
import pickle
from matplotlib import rc, font_manager
from scipy import stats
import statistics
import itertoo... |
21a5cbaef5163eb8b32efbb23113775d101a3b584c41ecc1b6a8b6f1b874a802 | Jupyter | 125,342 | 2,827 | # %%
import os
os.chdir('..')
# %% [markdown]
# # IMPORTS
# %%
import matplotlib
from matplotlib import rcParams, cycler
from src.utils.batch_effect_removal import get_berm # , remove_batch_effect
# matplotlib.use('Agg')
CUDA_VISIBLE_DEVICES = ""
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
i... |
ed2ca568940bafa343ec2c65558c9337ea745ea5e8a1eaf706d96856fa7e90b7 | Jupyter | 140,149 | 3,268 | # %%
import os
os.chdir('..')
# %% [markdown]
# # IMPORTS
# %%
import matplotlib
from matplotlib import rcParams, cycler
# matplotlib.use('Agg')
CUDA_VISIBLE_DEVICES = ""
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import pandas as pd
import numpy as np
import random
import json
import copy
i... |
9e6af517c213a40c11d67984713a9459cb2a172c059bb03aabc90b81a9329e34 | Jupyter | 143,150 | 3,387 | # %% [markdown]
# # Prepare
# %%
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
from torch.nn import functional as F
from torch.autograd import Variable
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from... |
584d095950bdfa2dbf398375e62f54ec6b2511b793bf508272e0942946ee782a | Jupyter | 152,828 | 1,453 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimization Framework using auto-diff to optimize binding rates\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import necessary modules\n",
"\n",
"Every Jupyter Not... |
c310469642f7dd3baf4f69ab55d9b16fd473da665eeedfb0d47bfd253c7328e2 | Jupyter | 164,599 | 4,960 | # %% [markdown]
# ## install dependencies
# %%
!nvidia-smi
# %%
!pip3 install numpy scipy pandas pytorch_lightning pytorch_metric_learning mne moabb pyts --upgrade
!pip3 install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu113.html
!pip3 install... |
f475a680933be31b201c207907c0e1a87029b4595f3dac64534978213f445433 | Jupyter | 168,186 | 317 | # %% [markdown]
# # Oracle Cloud Infrastructure Generative AI
#
# Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.
# Using the ... |
8f20d3b4bdc277741191b901d84a8412a2d0b3fb45eba476529966feb5255e5f | Jupyter | 200,000 | 25 | # %% [markdown]
# # Document Research Assistant for Blog Creation
#
# [](https://console.brev.dev/launchable/deploy?launchableID=env-2qRrBuBdUGzzauhql87XCd7U1wR)
#
# Deploy with Launchables. Launchables are pre-configured, fully o... |
c72ed7c2ea90819fcb2ae67cb5fb60bc3e4368ebf0d3335b29357fd04cbdde41 | Jupyter | 200,000 | 4,589 | # %%
import numpy as np
import matplotlib.pyplot as plt
import scipy
import copy
import sys
sys.path.append('/content/drive/MyDrive/neuro_research/familiarity_detection/')
import networks as nets
import exc_vip_sst_net as nets_exc
import net_helpers as net_helpers
import stim_change_task as task
import analysis as a... |
c8fb6ed44820ec2046408e3d2f13a7c1e74f13f9095fe1cfbf42ccfaa1a84c3f | Jupyter | 200,000 | 3,942 | # %% [markdown]
# # Summary Stats for New Genes and Transcripts
#
# ### 1) Number of exons for novel genes and transcripts across stringency thresholds.
#
# ### 2) Exonic length for transcripts coming from novel RNA isoforms in known genes and novel RNA isoforms in new genes.
#
# ### 3) Number of new transcripts and... |
c22398e15c7ca270875eb417f0cf155be2c5c1dec0ecbd78aae131ff3f234693 | Jupyter | 200,002 | 1,248 | {
"cells": [
{
"cell_type": "markdown",
"id": "pressed-interim",
"metadata": {},
"source": [
"## Import necessary modules\n",
"\n",
"Every Jupyter Notebook requires the path to the KineticAssembly_AD modules (.py files in the root directory) to be mentioned. This can be done by adding the pat... |
c70343fc7fea0e3d8e453a2f9c782fc1d60c188024dad8ffb0817ef6931c0b79 | Jupyter | 200,007 | 274 | # %% [markdown]
# # 🏔️ Step-back prompting with workflows for RAG with Argilla
# %% [markdown]
# This tutorial will show how to use step-back prompting with LlamaIndex workflows for RAG integrated with Argilla.
#
# This prompting approach is based on "[Take a Step Back: Evoking Reasoning via Abstraction in Large Lan... |
ca666b019cae34a69ee2a8bb4cccd95746624a5b0a4aaa17d1c85f85aa9a3d74 | Jupyter | 200,027 | 425 | # %% [markdown]
# # Tool Selection Benchmark with 2 LLMs (Gemini Flash and Pro)
#
# This notebook demonstrates how to benchmark the tool-calling capabilities of Google's Gemini models (gemini-2.5-flash and gemini-2.5-pro) using the Arize Phoenix evaluation framework and LlamaIndex Evaluator. Reliable tool selection is... |
64438e422094df96c7e274c736f1b485194f571453637544d80dbff96ee13e0e | Jupyter | 200,028 | 623 | # %% [markdown]
# # Patch Prediction Models
#
# Click to open in: \[[GitHub](https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/05-patch-prediction.ipynb)\]\[[Colab](https://colab.research.google.com/github/TissueImageAnalytics/tiatoolbox/blob/develop/examples/05-patch-prediction.ipynb)\]
#
# %... |
3b98fd38f0b172b8eabe5da468aaeecf1553433a74373249f9c8ed8c9583cc7e | License | 267 | 1 | This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. |
6898f2087c08a0440a1e4115e845e53cdf9ced16d8bd23d582cdea51dfabe59e | License | 382 | 5 | The six dataset in the directories data by University of Bologna, Bologna, Italy, is licensed under a Creative Commons Attribution 4.0 International License.
All code in the directories code, code/matlab are each published under the BSD 3 clause licenses.
Please see the LICENSE.txt or LICENSE files in individual subd... |
8d1ef2edf184a3f5319ebe9681ab582024dad3807e84095bab1aa8cb78b102ec | License | 577 | 13 | Copyright 2022 Joanes Grandjean (joanes85@gmail.com)
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... |
8435cc2e7d646b505c19030ab9b6211d0427e970d81daa9583e38fe7f3160345 | License | 694 | 15 | Copyright, 2024, University of Oxford. All rights reserved.
_Code_ is 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 l... |
48b217a2afcb7e1e00685071e54316cf8c9d8a2dccec335587cc12f399927a61 | License | 1,025 | 22 | #################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for details.... |
81c7284a495edd3db0e465674160f6ddce9b56addbbd4a1cb23d37d3d4524cb9 | License | 1,041 | 22 | #################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for deta... |
204983bcaf7dc8a708d9e2e504fb57d808edcac40c38ef9471f7665d62620b00 | License | 1,051 | 19 | Copyright (c) 2021 scpeiyao
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, sublicens... |
2933865bf97a51e81ed4c239ad6b1362a3dc6e82a6f2a2fc706452ffdc413219 | License | 1,058 | 20 | Copyright (c) 2023-2024 Elena Bolt
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, su... |
b5d4eceb86560557f116a3a165302a7b30a4d92db60c3d941397db409f8c3fe7 | License | 1,060 | 21 | MIT License
Copyright (c) 2024 IMO
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, s... |
674a74ae6b674df5f3f8c81d94ca3fb66a971192f9e2fbde5cb1543c53f47dbf | License | 1,061 | 21 | MIT License
Copyright (c) 2023 3dem
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, ... |
74988b96b421e92c74d9c0872fdfe648d63e581eaf5095bbd751ffd5b14be2f6 | License | 1,061 | 21 | MIT License
Copyright (c) 2017 LVPP
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, ... |
e87db35978e80247634bddd15029c7b69ec0ac9f492a6954133dbe08c71e1372 | License | 1,061 | 21 | MIT License
Copyright (c) 2019 benny
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,... |
1c4f09ca4917f18c221ca865a3b5339cec3f63850b1a5c2296d2a807a3371948 | License | 1,062 | 21 | MIT License
Copyright (c) 2023 lyshc
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,... |
b73907145aecb4837f2fa81740bf966f81a8fe5c28fe31fea2a16c759915d065 | License | 1,062 | 21 | MIT License
Copyright (c) 2020 xdluo
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,... |
efaa6f60ee9ada6c578dbe44ab5dd9edfa0b694c6910d243c0975e434f21a9f4 | License | 1,062 | 21 | MIT License
Copyright (c) 2020 Rohan
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,... |
1e7c70c63ad36ea4373f35e2ba877335a2286593f9bfd1628b443e0995878d66 | License | 1,063 | 21 | MIT License
Copyright (c) 2024 nwu-zn
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... |
2534367fa3635f639f260afcdbe040b56b2511967b6fb05557d05954daeb0321 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 李彬
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, su... |
2c6c7be79262db66236c2e1da36c11f5f2713566af303b41f5da8666382269a3 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 Yue Wu
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... |
4899cf07591bb7bcc12ef8eeb12505323a45d930f8d27c8c006f9ca9ce9a26cf | License | 1,063 | 21 | MIT License
Copyright (c) 2024 ml4bio
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... |
62970ef2ebbcfcda6f6ea6c46e8b0adcf4a6e1400f02b187d006224223d1a878 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 yocra3
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... |
c25c0b92c6e05abe73195cbe2a35143ecd7f2fb5fbc01fe4ec31ea5281028876 | License | 1,063 | 21 | MIT License
Copyright (c) 2022 Farzin
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... |
d580a96405fcc677f32367ada51d3245895ace34d1fdb341696aeeb70298b1e0 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 FengZJ
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... |
ebc315f062c8cb9a8ceac7a57a6c2ef59bcdb7d6a2b61b6719218d4d51e38410 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 Vandad
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... |
00fcc36957170dcd89b2d9e50247f20bb628caa39bb2d3f50a09c67846387555 | License | 1,064 | 21 | MIT License
Copyright (c) 2019 mtstamp
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, distribut... |
4c8f51ab1cbb18a07fdb2c50acb990df6f138369c57de2d83ba4f6d240519fd4 | License | 1,064 | 21 | MIT License
Copyright (c) 2021 mkara44
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, distribut... |
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