sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
b2adce156196dd5791e0e6ee6cb42d237613cce16e87123ae631e704f7ea89e7 | Jupyter | 26,410 | 419 | # %% [markdown]
# # Toolkit Showcase: Prepare and run a protein-ligand simulation
#
# Welcome to the Open Force Field Toolkit! This introductory example will demonstrate how to prepare a system for Molecular Dynamics that combines solvent, a ligand using the Sage open force field, and a protein using a standard Amber ... |
c2f78d73909cddfef0fca1252cea47ba9756e5a9375d3250cc18b8b53543cef8 | Jupyter | 26,440 | 689 | # %% [markdown]
# # Load libraries
# %%
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
from joblib import Parallel, delayed
from numpy.random import default_rng
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import ... |
af8e362286b582cdeee8f0b14eb975eb0584a52db9fa0dc41a798dd0e3eaf732 | Jupyter | 26,497 | 591 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
00acac106d8bf0548109969d84613f8432c6245c4ac7d807218bef087f81a123 | Jupyter | 26,498 | 584 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
3983af360294ea0653509ecd17026ee46fcc347558c6275e630b80652bc192d1 | Jupyter | 26,522 | 587 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
10c6eca6d193fd544c415ecf016f8b1b920f8b987d5c6800c414e33a0f004d74 | Jupyter | 26,542 | 589 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
78b2c7aebc4d07a36aab048f3c195e15899ef61ff036ab0b28433784c2f3317c | Jupyter | 26,551 | 764 | # %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
from steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, Optimizer, EquilibriumSolver, TrapMetric
import networkx as nx
impor... |
1452b2544cce3bcd21ad3a42e44f1acaaba9d87f05108b90cc7bba24791ffefb | Jupyter | 26,561 | 591 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
7050c8a55ef4438c57f1fcbd76bd5ced7626d27b04d08b31f4c7314765f405a9 | Jupyter | 26,579 | 593 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
fafda1ec68f752cfc1960893615596953c4995e2ce423c022628569c5bbee928 | Jupyter | 26,580 | 586 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
14577a1fd47147a96a22cacc6cec8c8531c2d0bb53259d4873dcd19ad34b58eb | Jupyter | 26,604 | 589 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
f8ff1cf1215453b3629581b2b5aade607fc0ffaa768297c6f17e20b13e8873c7 | Jupyter | 26,620 | 591 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
8ed6887cccf40b8101ed64466d01969eaeaab816245eeb7f44aa16a2060c6eeb | Jupyter | 26,624 | 591 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
aa1ea276c7d079bfe9653c8b2f86523837e7c0efb0c57fc685df473b7481e66a | Jupyter | 26,626 | 588 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
9a528f28a3f72b493ddf58c0f8aa0b209cb1a30f68413b9549ac55a0f935c3bd | Jupyter | 26,628 | 589 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
dc1f6af570476634ace0e5df5e3859222b6bd4fc39576f743e6cacaf576d688b | Jupyter | 26,638 | 588 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
164ee0e074a98506f90f933f43d4dc8a160bd2281f9145ffb03eff0085471b2b | Jupyter | 26,643 | 593 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
0bbd20a790691fbbd123d8c7735dbf70058f87d6067f8f0a5d04b837f8c9fa04 | Jupyter | 26,651 | 593 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
cdd6b47623eb9faf3e1ea97895eb0cfffeaf88cfb702e087669275173a723b3d | Jupyter | 26,708 | 590 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
23cc1796ffa499c9d115b64bf70bf11bba6b4b540e53127f7769d3e248069434 | Jupyter | 26,710 | 591 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
86e2a7090ceaad7e26cbfc02dac427b3ea2ef4a413935c17de3cc52fb13eb7ca | Jupyter | 26,720 | 590 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
c63db5a10507bb689054625603f2b0b60e5dc21253a3515f7bd678d6abfd89c3 | Jupyter | 26,733 | 595 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
3c6b831496a4204df50424a24b5fc0571c1513443ec04e9551d933b57838f8e7 | Jupyter | 26,742 | 751 | # %% [markdown]
# # GraphRAG Implementation with LlamaIndex - V2
#
# [GraphRAG (Graphs + Retrieval Augmented Generation)](https://www.microsoft.com/en-us/research/project/graphrag/) combines the strengths of Retrieval Augmented Generation (RAG) and Query-Focused Summarization (QFS) to effectively handle complex querie... |
5ea432b47046efb6e1f42d7a555366f0dd8661f96e2a8cf5434a0812a32fbbd8 | Jupyter | 26,815 | 589 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
be93f867342ca66e5a0d65c8c2ec1f2eb21d88e5260b2486daa790bfba1ebc67 | Jupyter | 26,816 | 589 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
ad3c84e0f5a5ff5a0fdabe71a51c064e5952c54e6b8e1a7a94a011dff75e139d | Jupyter | 26,876 | 595 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
a38f0c55c0368f2ba2135d5a182bba82ef950a2843da19399e8ee4b00d31dba9 | Jupyter | 26,924 | 726 | # %%
import os
import sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from IPython import get_ipython
colab_env = 'google.colab' in str(get_ipython()) if hasattr(__builtins__,'__IPYTHON__') else False
colab_initialized = True if os.path.exists("MotorNet") else False
if colab_env and no... |
4c5a4d81b3a6e0dbcf4569a985dde5d429a71a4538fe31eda29977a78b781169 | Jupyter | 26,937 | 848 | # %% [markdown]
# # Optimization of Titration rates of sub units to escape kinetic traps #
#
# ### Modes : ###
# ##### There are two types of optimization routines for this protocol - ##
# a) Single Rate - Where all the subunits are titrated at a single rate
# b) Multi Rate - In this case, two subunits are in bulk and... |
67b763b53f4d986dabfdc38bf7fc7063725d416d419d74979dd344ed7f9f0ece | Jupyter | 26,941 | 714 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/GraphRAG_v1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # GraphRAG Implementation with LlamaIndex
#
# ... |
8248454175565ef5539e1d777955088ec720853ed392460a38cb373b48432904 | Jupyter | 26,958 | 597 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
5525d119ccc2685e19d10d023b8c1ec00df3654f93e16b8b025042fa9be0c3fa | Jupyter | 27,409 | 728 | # %%
import os
import sys
import numpy as np
import torch as th
import matplotlib.pyplot as plt
from IPython import get_ipython
motornet_in_cwd = os.path.exists("MotorNet") or os.path.exists("motornet")
colab_env = 'google.colab' in str(get_ipython()) if hasattr(__builtins__,'__IPYTHON__') else False
colab_initialize... |
5c365a6acd8614bbd6517fa5ef314f0cb2d0cf4590f96b48759ca8e635e1657d | Jupyter | 27,452 | 938 | # %%
import numpy as np
import xarray as xr
import pandas as pd
from collections import OrderedDict
from pathta import Study
from frites import set_mpl_style
from frites.conn import conn_links
from myfrites.plot import plot_dist
from pingouin import ttest
from mne.stats import fdr_correction
from frites.stats import... |
4fcc1160219d25b89ac41fc4123bb55a8e9d1cb91625ee425072d893bb7eaf43 | Jupyter | 27,583 | 796 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
d04e3475a589c65e33696844d2b13346fa126350f3ff2d13887002bc16e629d3 | Jupyter | 27,810 | 1,079 | # %%
import torch
import torch.nn as nn
# 定义 MLP 模型
class MLP(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc2 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = torch.... |
ff97d20256572e5fe12af517f85b039978ffc08da626a7fdc175a34de69dbafc | Jupyter | 28,080 | 500 | # %% [markdown]
# ## notebook 03a: linear regression modelling of accuracy
#
# In this notebook we read in the experiment result dataframe and set up a linear regression model that predicts the position error from the condition (number of objects) as a fixed effect and the participant ID as a random effect.
# We motiv... |
75779adf8d84f1b4c04335d8809f164c6700d6fe8766686fbd7d375f5918b932 | Jupyter | 28,100 | 501 | # %% [markdown]
# ## notebook 03p: linear regression modelling of precision
#
# In this notebook we read in the experiment result dataframe and set up a linear regression model that predicts the position error standard deviation from the condition (number of objects) as a fixed effect and the participant ID as a rando... |
a16654bf7d8517d247b2b6d92732f239e47fbd5eaaaf8a558ad906c414a59ecf | Jupyter | 28,160 | 723 | # %% [markdown]
# #Thresholding
#
# This notebook aims to demonstrate how to obtain better thresholded images of osteocyte lacuno canalicular network. Using [Difference of Gaussians](https://en.wikipedia.org/wiki/Difference_of_Gaussians) works very well to obtain the canalicular network, as the canaliculi all have a ... |
e3458e9e66d034ef4e017ffcb37f2ad7e978a5c3bc62f857b93317b461a6ca43 | Jupyter | 28,260 | 726 | # %% [markdown]
# #Thresholding
#
# This notebook aims to demonstrate how to obtain better thresholded images of osteocyte lacuno canalicular network. Using [Difference of Gaussians](https://en.wikipedia.org/wiki/Difference_of_Gaussians) works very well to obtain the canalicular network, as the canaliculi all have a ... |
4211d9e25e9f78a12356440bc611922718ae146aed53fbba54b7caa91ba7b2df | Jupyter | 28,393 | 898 | # %%
import logging
import os
import pickle
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, Flatten, Input, L... |
ad0000dc1b09e55a20635efa6e75083d6876d340967bad6bb0e96b186abca88c | Jupyter | 28,823 | 1,071 | # %% [markdown]
# ### load data in specific shape (64*64)
# %%
import nibabel as nib
import os
import numpy as np
import random
from nibabel.affines import apply_affine
import time
import voxelmorph as vxm
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
# %%
def load_m (file_path):
... |
b52f0fe2c80249213278416428bf96af4dd787883c4758bad25092cf7a923763 | Jupyter | 28,870 | 906 | # %%
import logging
import os
import pickle
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, Flatten, Input, L... |
e8ad0b812e374d8d0fc6d5f85a12816f33b29c4280e0a7b21e2ea3579abcec47 | Jupyter | 28,965 | 790 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option)... |
9b5cceb073a77c53298fdb8f190298387b33269cfefe5d57be8de25bd43f8f4c | Jupyter | 29,305 | 764 | # %% [markdown]
# #Thresholding
#
# This notebook aims to demonstrate how to obtain better thresholded images of osteocyte lacuno canalicular network. Using [Difference of Gaussians](https://en.wikipedia.org/wiki/Difference_of_Gaussians) works very well to obtain the canalicular network, as the canaliculi all have a ... |
ee3a0993002d207bf3e7a1bfccc19d7a7aba1a2c0a46072e505f65e8518439d0 | Jupyter | 29,326 | 953 | # %% [markdown]
# # Alarm
# %%
from IPython.display import Audio
# %% [markdown]
# # progressbar
# %%
import IPython
def progress(value, max=100):
return IPython.display.HTML("""
<progress
value='{value}'
max='{max}',
style='width: 100%'
>
{value}
... |
31d6a136005aa4261ad1483ccffa3602551c59ba26670b06337a0292f85d46e7 | Jupyter | 29,328 | 871 | # %% [markdown]
# # Import modules and give access
# %% [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 scipy.stats as stats
# %%
from google.colab import drive
drive.mount('/content/dri... |
0ebebddb001fbca6d91a017fcf55756a59a5d9bff230e20976ba1bbea9e976d6 | Jupyter | 29,363 | 865 | # %%
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm, gridspec, ticker
import matplotlib.colors
from matplotlib.colors import LogNorm, Normalize
from matplotlib.ticker import EngFormatter
import matplotlib.patheffects as pe
from matplotlib.lines import Line2D
from mpl_toolkits.axes_gr... |
2562f3932014d7175c996b43e61568e433ea02b7d11c27b48ce36cba8ed117cb | Jupyter | 29,523 | 869 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/finetuning/cross_encoder_finetuning/cross_encoder_finetuning.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # How to... |
42658782b34b4bacbe28032283d8ba8a9be4ac4a3abd2678fc306acb0689813c | Jupyter | 29,740 | 668 | # %% [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... |
4aa3b045c22bea8c82e32bc6e84dc12a69d5cc59bdfc4dae2f93eb41d45027a8 | Jupyter | 29,803 | 669 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Evaluating Multi-... |
e611bef162c3d43f2e6ecb0d0eb95902f41c3bdd7b3272c6dc1359c60d7096a4 | Jupyter | 29,840 | 765 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/prometheus_evaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Evaluation using [Prometheus](http... |
45a4c0b2ef447b0e9e27aa8cbbd3ff204a076007fe42a745cc9ca68446615982 | Jupyter | 30,045 | 741 | # %% [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... |
656ef44951e80b5d09230cc2d04c4cb0eb6ed5f3d84caf6e52513cc104940b00 | Jupyter | 30,278 | 795 | # %% [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... |
e6b4268e229dcbc1ac542bcdce03c7d98f452e2b3760803db8ea9bb00552e800 | Jupyter | 30,288 | 796 | # %% [markdown]
# ## Notebook for Figure 5 and related content: Gene expression changes in beta-cells from T2D donors and obesity
# ### 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
... |
9719b34db2b0b8fdae6a3b08e46d15704a7c312ee9970d2f2632871eeecab948 | Jupyter | 30,413 | 757 | # %% [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... |
27663a84999f30cb18fc869cfc26099742e2948f3137a3b8b61771e5aea30ed9 | Jupyter | 30,479 | 990 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
# %%
from steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, O... |
c5289f51324f94bcd0fb0fc9a00ca09344c910134c48a3b8fbb13abd95a17f09 | Jupyter | 30,665 | 930 | # %%
import numpy
import matplotlib
from matplotlib import pyplot
import pandas
import scipy
import scipy.stats
import scipy.integrate
import seaborn
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 120
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
# %%
# Colors based on design method
seq_colors_dic... |
62e7e62250e84b2854b21e5c3fd7023c7252401acb2760cb0509608e962d80da | Jupyter | 30,696 | 983 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
# %%
from steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, O... |
9aaba267152a81435d6a179adf528c12bac4b6525c8d0d1ae887dbbc6d3e7c49 | Jupyter | 30,809 | 991 | # %% [markdown]
# ## notebook 07: MLE bootstrap
#
# In this notebook we generate the bootstrapped fitted MLE modelling parameters that are plotted in Figure 9 in the publication. To execute this script first run all cells in notebook 00: process_raw_data.
# %%
# importing python packages
import numpy as np
import mat... |
44ecdd1e4af7979d6ac32cbfe11bd53bfc51cafdc8a45ec81c9e577bd6565bfc | Jupyter | 31,014 | 918 | # %%
from IPython.display import Audio
import IPython
def progress(value, max=100):
return IPython.display.HTML("""
<progress
value='{value}'
max='{max}',
style='width: 100%'
>
{value}
</progress>
""".format(value=value, max=max))
# %%
imp... |
bbc662ab6661ce8d0c999a896b2d1339031583aedd226ebb82943ebfa85e1f48 | Jupyter | 31,070 | 707 | # %%
import datetime
def printbar():
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("\n"+"="*8 + "%s"%nowtime)
printbar()
# %%
import numpy as np
noesypr1d_20 = np.load(r'MTBLS242_463serum_noesypr1d.npy')
cpmgpr1d_DL = np.load(r'MTBLS242_463serum_small.npy')
ledbp_DL = np.load(r'MTBLS2... |
f2cd84e958541c79aff09287a3f85c428d787f0875d682550e321cea38d60246 | Jupyter | 31,134 | 797 | # %%
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.core.display import display, SVG
import rdkit.Chem as Chem
from rdkit.Chem import PandasTools, Descriptors
from rdkit.Chem.Draw import rdMolDraw2D
from rdkit.Chem.AtomPairs import Pairs
import numpy as np
# %%
''' # t... |
8077d87b4de8298c0190f5667d6aaef7cef4fbdeb8030b1d2fc8944ea1a275d0 | Jupyter | 31,164 | 750 | # %% [markdown]
# # Moves in chemical space
#
# ChemicalEnvironments were designed for the purpose of making moves/changes to the chemical perception of a SMIRNOFF force field. In this notebook we show how to use the environments to make these types of changes.
#
# **Authors**
# * Christopher I. Bayly (OpenEye Scien... |
dc7e08c9fa947d55163d8186a1f1797a4a5ff165727ce35d004b1468fd1d571f | Jupyter | 31,199 | 601 | # %% [markdown]
# ## notebook 03: linear regression modelling
#
# In this notebook we read in the experiment result dataframe and set up a linear regression model that predicts the position error from the condition (number of objects) as a fixed effect and the participant ID as a random effect.
# We motivate splitting... |
d0cd006f0b72ef4442a7b41483d5bcefb93b7f6144c70dd3aa639187191868f3 | Jupyter | 31,558 | 908 | # %%
import random as rnd
import math
import time
import numpy as np
# %%
class Problem:
subnetworks = 20
type = 2
costs = [100, 150, 100, 150, 100, 150, 100, 100, 150, 100, 150, 100, 100, 100, 100, 150, 100, 100, 100, 100
]
directs = [ 10, 10,20, 20, 15, 20, 10, 10, 10, 20, 20, 10, 5, 1, 5, 10... |
220f46a735676c25b76be58cdfbda48f015d67e8a8bbdf920c0d40fbb48e34b4 | Jupyter | 31,765 | 931 | # %%
import numpy as np
from scipy.stats import zscore
import matplotlib.pyplot as plt
from matplotlib import colors
import seaborn as sns
from sklearn.decomposition import PCA
from scipy.spatial.distance import cdist
import nibabel as nib
from nilearn.plotting import plot_glass_brain
# %%
# Load in Schaefer atlas
sch... |
ed6442bc16130ae37ba8e6cd2c83470c7b9e85f68f647f0ba1f8bd0886d609c3 | Jupyter | 32,050 | 1,210 | # %% [markdown]
# [](https://colab.research.google.com/github/mahdimplus/DeepRetroMoco/blob/main/functions.ipynb)
# %%
pip install voxelmorph
# %% [markdown]
# ### Requirement libraries.
# %%
import nibabel as nib
import os
import numpy as np
... |
3a3799b7fe36587cfe5d1e799ab8e7cbde10405eda27755fba0879981da2567c | Jupyter | 32,077 | 755 | # %% [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... |
1beffc61713b0a7c5525e8bb7cfdc82c89977ae36a81106338618726d5f6ae9a | Jupyter | 32,498 | 812 | # %% [markdown]
# #Thresholding
#
# This notebook aims to demonstrate how to obtain better thresholded images of osteocyte lacuno canalicular network. Using [Difference of Gaussians](https://en.wikipedia.org/wiki/Difference_of_Gaussians) works very well to obtain the canalicular network, as the canaliculi all have a ... |
b232f9f0e8f0c79d9cc1c0a59541dfa8837f55a49e788bd585a44a6cbffd4e1d | Jupyter | 32,934 | 901 | # %%
import numpy as np
import math
import peakutils
import numpy.fft as fft
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import cm
import scipy.integrate
import scipy.signal as signal
import pandas as pd
import pickle
import random as rand
from ... |
5d0992fecc3c632916cb71d93b75864b554f3b1733976337ce2e0a0fd4e61542 | Jupyter | 32,937 | 901 | # %%
import numpy as np
import math
import peakutils
import numpy.fft as fft
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import cm
import scipy.integrate
import scipy.signal as signal
import pandas as pd
import pickle
import random as rand
from ... |
d05e583764970bec36e964c2dc05f3a30c3dd6b01c4909ef434d340d9ec815a1 | Jupyter | 32,957 | 901 | # %%
import numpy as np
import math
import peakutils
import numpy.fft as fft
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import cm
import scipy.integrate
import scipy.signal as signal
import pandas as pd
import pickle
import random as rand
from ... |
2ca2b241f2c2c0a3ad341694abd16c538eeee6400fa242c82b4e1a889cf484e0 | Jupyter | 33,388 | 650 | # %% [markdown]
# ## notebook 03: linear regression modelling
#
# In this notebook we read in the experiment result dataframe and set up a linear regression model that predicts the position error from the condition (number of objects) as a fixed effect and the participant ID as a random effect.
# We motivate splitting... |
777618cd73309dd12b880c8bd2a234b3666871adc06597eea3c74103c116e472 | Jupyter | 34,399 | 953 | # %%
import numpy as np
import math
import peakutils
import numpy.fft as fft
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib import cm
import scipy.integrate
import scipy.signal as signal
import pandas as pd
import pickle
import random as rand
from ... |
c6a2a4be4836bdcc4e44738b70202a3533ad2a08885b4ffe8545d90540a20b5c | Jupyter | 34,559 | 657 | # %% [markdown]
# ## notebook 03: linear regression modelling
#
# In this notebook we read in the experiment result dataframe and set up a linear regression model that predicts the position error from the condition (number of objects) as a fixed effect and the participant ID as a random effect.
# We motivate splitting... |
dad2ba5e9d16fa691e3860a290b4e5e918c8b9b123d29a3f0531a0299c662f7b | Jupyter | 34,636 | 972 | # %%
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"] = '2'
torch.cuda.set_dev... |
15b668ad50ca721f189e9b67c1b836b50a62b2686a11b079c65b7294742f1a71 | Jupyter | 35,035 | 632 | # %%
import pandas as pd
import numpy as np
import os
from ComputeInputOutputDeltaSNRs import ComputeInputOutputDeltaSNRs
fs = 250
folder_name = '/../../../../ResultsComparison120s/'
csv_name = 'CleanSignals'
repetition = 1
RawDataDF = pd.read_csv(os.getcwd() + folder_name + csv_name + str(repetition) + '.csv',
... |
eb38db2a206e1db8c333be19ed051fabe7436c6990a7eb55feaee6a7f949eb31 | Jupyter | 35,047 | 632 | # %%
import pandas as pd
import numpy as np
import os
from ComputeInputOutputDeltaSNRs import ComputeInputOutputDeltaSNRs
fs = 250
folder_name = '/../../../../ResultsComparison120s/'
csv_name = 'CleanSignals'
repetition = 1
RawDataDF = pd.read_csv(os.getcwd() + folder_name + csv_name + str(repetition) + '.csv',
... |
0363345663bb6937ba2804a391bd096f17ad9b22b6277e5dbd6b0a479c529c56 | Jupyter | 35,058 | 658 | # %% [markdown]
# # Learned adaptive illumination multiphoton microscopy
# In this notebook, we demonstrate how to implement [Learned Adaptive Multiphoton Illumination microscopy (LAMI)](https://doi.org/10.1038/s41467-021-22246-5), using [Pycro-Manager](https://pycro-manager.readthedocs.io/en/latest/) and [Micro-Magell... |
6322f788533bba00dacd32acb7fcbff465222084b579cccf27d4e36890540b41 | Jupyter | 35,268 | 822 | # %% [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... |
8e95ed9207a31aef1e554de0395014fd11c4f24547e539811b7a611316e9819b | Jupyter | 35,407 | 832 | # %% [markdown]
# ###### Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Figure 2
#
# ### - Figure 3
#
# ### - Figure 4
#
# ### - Figure 5
#
# ### - Output novel high-confidence only annotation
# %% [markdown]
# # Import libraries and define functions + Initial setup
... |
0cb99adba52a691085a8dc315ec4cb95b730266cff6ef96d423f950486ede52f | Jupyter | 35,516 | 1,121 | # %%
from IPython.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
# %%
import os
data_dir = os.getcwd() + '/../../../../QTDataBase/' # data (records) directory
data_files = os.listdir(data_dir) # get a listing of the directory, i.e. the rec... |
c135f674ab04421f502d0e2f6e19ece1ffff6c28a48f38a12bd43c12ef603ae5 | Jupyter | 35,552 | 615 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("ggplot2")
library("ggsignif")
# %%
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("Report/artifact/ch... |
6140184840d2a73647ec93a274559462195c5e8e869762b9f3d1b0474bfec5a4 | Jupyter | 35,763 | 971 | # %%
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"] = '2'
torch.cuda.set_dev... |
523a90b2039a921f83c7c9381218fe2bbf505993649aabc84b351c53049bc412 | Jupyter | 35,825 | 944 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
41ee1ca4a71e517a9093115ff9e1d5e9386eb634f8e2a1ff36e90ee3b7bd06c5 | Jupyter | 36,133 | 1,184 | # %% [markdown]
# <a href="https://colab.research.google.com/github/aglucaci/AlignmentProfiles/blob/main/AlignmentProfiler_Public.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# **Description:**
#
# A useful notebook to assess a c... |
a5619271d9273042030fd35ae1177ea9c946eb2f8a0db56fc90e22b6de2d3c49 | Jupyter | 36,236 | 957 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
2c5c892bec43be4d783b200f5f238602485934988f1950f24b873105a37e728d | Jupyter | 36,915 | 779 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold3_of3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # AlphaFold 3 (OpenFold3)
#
# Predict protein, RNA, DNA, and small-mo... |
c2baa62ccb84495c5ad1272d490cc600174495625594c19c8515214019c392c3 | Jupyter | 37,098 | 867 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pandas as pd
import seaborn as sns
import glob
import statistics
import math
from tqdm import tqdm
from os import listdir
import os
from os.path import isfile, join
from coolpuppy import coolpu... |
c7fb23a0c93a4d17631acca13f799a6539a9e3b6bcb338f1fae41adb3050cb36 | Jupyter | 37,195 | 1,233 | # %% [markdown]
# First import libraries and look for data
# %%
#seaborn.set_palette(matplotlib.cm.jet,10)
#sns.palplot(sns.color_palette())
%run rcp
# %%
%matplotlib inline
import matplotlib
import os
import pandas as pd
import numpy as np
import scipy.stats as stats
pd.set_option('display.mpl_style', 'default')
pd.... |
8e9aefc5cc593721fae15afd5bee2163a7bb99415260b59b41a47f65a6415a79 | Jupyter | 37,224 | 879 | # %% [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... |
f778f9c50b91a7a55e1357509cbcd21caef93d8ae1ec3e55c3f7d6210620d225 | Jupyter | 37,709 | 876 | # %% [markdown]
# <a href="https://colab.research.google.com/github/BalytskyiJaroslaw/PathogensRamanOpenSet/blob/main/Closed_World_all_30_classes_K_p1_p2_p3_p4_submit.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Operating in t... |
f88457a6d778e63b731dcc9e729be17a7aad65d8715cf2066305437082414578 | Jupyter | 38,886 | 1,014 | # %% [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... |
372846b5d7bc752201ab33289ac892b393b9dd1ad09c3a22a9b1b5bf28a4aa0b | Jupyter | 38,933 | 1,010 | # %% [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... |
78855298c815f7b8df57c8b70b71ab256676664101fe8e88324127d2badfad4b | Jupyter | 39,140 | 826 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/ColabFold2_preview.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # ColabFold2 preview
#
# Predict protein, RNA, DNA and small-mole... |
61a5078b9fd40670c8086ed6b2d19fc099601dbc6aa41225e889e9dc18b4bbe2 | Jupyter | 39,466 | 1,114 | # %%
import os
import numpy
import matplotlib
from matplotlib import pyplot
from matplotlib.gridspec import GridSpec
import pickle
import pandas
import scipy
import seaborn
import tensorflow
import tensorflow.keras
from tensorflow.keras.layers import Layer
from Bio import SeqIO
# %%
%matplotlib inline
matplotlib.rcP... |
99bd655170d3ae9e60855f6a22c3ca2ffa358f19b4f225dea0c1b0388b6faad2 | Jupyter | 40,447 | 1,161 | # %%
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import os
import numpy as np
import pandas as pd
from src.PID import PID_utils
from scipy import stats
import copy
# %%
from matplotlib.lines import Line2D
import tikzplotlib
from sklearn.feature_selection import SelectFdr
import statsmodels... |
9e06ff5ce0d15262341f7a9b36e62166d4b2d0c6349240701b85c138b1968354 | Jupyter | 40,809 | 921 | # %% [markdown]
# # Table of Contents
#
# ## - Import libraries and define functions + Initial setup
#
# ## - Pre-process files
#
# ## - Make graphs
#
# ## - Make output GTFs
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
f... |
d09b8d9fbfa387d0a57055f2780ae6e99ed5d978c1c2503179f3ab384cd27926 | Jupyter | 40,903 | 925 | # %% [markdown]
# # Table of Contents
#
# ## - Import libraries and define functions + Initial setup
#
# ## - Pre-process files
#
# ## - Make graphs
#
# ## - Make output GTFs
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
f... |
a27de214d66207b9cc5a4815bd58516026be0e814ddcf71e771781b395f40c9a | Jupyter | 40,914 | 1,048 | # %% [markdown]
# # SVM Population decoding (Fig 7)
# Population decoding of the object identity and modality condition based on support vector machine.
#
# 1) Modality-specific decoding (Fig 7A-C)
# 2) Modality-invariant decoding (Fig 7D-F)
# %%
import os
from pathlib import Path
import numpy as np
import pandas as ... |
59b3f2c1a9ae0be9217264653140067da3917b7cbb9451ca0b66513d4b53cbdd | Jupyter | 40,970 | 395 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("ggplot2")
library("pheatmap")
library("viridisLite")
# %%
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 <- fre... |
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