sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
051f028436b6e4f3848be8fd7dc759472d9c7d429739f4fec2cba0c65bad0a5c | Jupyter | 8,378 | 364 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
c037258ac22732949de16e36568eca50cafe1ce655dc9877e2e280022afe436b | Jupyter | 8,384 | 202 | # %%
import h5py
from keplergl import KeplerGl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# %%
file_paths = ['../data/lidar/bali_icesat-2/177244051/processed_ATL08_20181223162928_13140108_003_01.h5',
'../data/lidar/bali_icesat-2/177253330/processed_ATL08_20181022192540_0369010... |
cd36361e5c7b4b6cd434481ed4a2a66a2e14d84de353bc74585970086b2530a2 | Jupyter | 8,388 | 365 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import (
savefig,
clean_plot_labels,
setup_plotting_env,
clean_ticks,
)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import os
# %% [markdown]
# # Helper functions... |
dff36e45afc380e38735b7f33812a1eb26a4491cd227d5b58a3c3308036ef889 | Jupyter | 8,399 | 361 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
d8bf6486bce498feb530bd4bf56658a217992c35e7b8e26b719aea2666c927e1 | Jupyter | 8,411 | 306 | # %%
# %% [markdown]
# %% [markdown]
# ```
# from dPCA import dPCA
# По обученной сети создается тензор
# actR = размером(num_trial, hidden_size, S, T)
# num_trial - число испытаний для каждого значения стимула
# hidden_size - число нейронов сети
# S - число стимулов (берем 10)
# T - число временных отсчетов в одно... |
0ecdb9acde5fc82dc1b7250fbca3ecf8e6ecc2ffe071426c03151374c3995b2b | Jupyter | 8,425 | 312 | # %%
# %% [markdown]
# %% [markdown]
# ```
# from dPCA import dPCA
# По обученной сети создается тензор
# actR = размером(num_trial, hidden_size, S, T)
# num_trial - число испытаний для каждого значения стимула
# hidden_size - число нейронов сети
# S - число стимулов (берем 10)
# T - число временных отсчетов в одно... |
5bb7d973ae48e42dabc9c0637dc725e888ab5a24a79b1836fdd0252f77fd36ed | Jupyter | 8,425 | 312 | # %%
# %% [markdown]
# %% [markdown]
# ```
# from dPCA import dPCA
# По обученной сети создается тензор
# actR = размером(num_trial, hidden_size, S, T)
# num_trial - число испытаний для каждого значения стимула
# hidden_size - число нейронов сети
# S - число стимулов (берем 10)
# T - число временных отсчетов в одно... |
76a785bc5ebfc8061106cc61ed4cdfee619294c5827beb754d18739041c45dd3 | Jupyter | 8,425 | 336 | # %%
%matplotlib
# %%
from common import colors, sbarlw, insetlw, savefig
from gating_util import (
mark_time_interval,
save_gating_params,
load_gating_params,
get_plot_coordinates_with_error,
)
# %%
import os; os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
from functools import partial
... |
d2b82c2539301e8886954591369ffb65114daae9d4df85dcafcdc1fe73cfa189 | Jupyter | 8,425 | 312 | # %%
# %% [markdown]
# %% [markdown]
# ```
# from dPCA import dPCA
# По обученной сети создается тензор
# actR = размером(num_trial, hidden_size, S, T)
# num_trial - число испытаний для каждого значения стимула
# hidden_size - число нейронов сети
# S - число стимулов (берем 10)
# T - число временных отсчетов в одно... |
8f0e3813910868ebfe198b2455846e745fd30fce36a389b938f23f03c6205edf | Jupyter | 8,436 | 283 | # %% [markdown]
# # Temperature-dependence of derivative encoding by 5-HT neuron populations
#
# Shown in fig. 7S1.
# %%
from common import colors, savefig
# %%
from __future__ import division
import os; os.chdir(os.path.join('..', '..'))
print os.getcwd()
# %%
import warnings
import re
import copy
import pickle
... |
190303e4a2841504b9158d661dbd402da615ed8c069a39c6bef1b712de8c57b5 | Jupyter | 8,444 | 277 | # %% [markdown]
# # Tutorial for resVAE ensemble
#
# Here, we will show a brief tutorial or demo for using resVAE ensemble.
#
# We will cover only resVAE ensemble here, so please refer to the documentations of scanpy or anndata as needed:
# * scanpy: https://scanpy.readthedocs.io/en/stable/
# * anndata: https://annda... |
1d94bc885afe2be22a6a0282a3a8abc1e8d6712eee6e7684b709e7470763e123 | Jupyter | 8,460 | 279 | # %%
from pathlib import Path
import os
%autoreload 2
import src.utils as utils
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
os.getcwd()
# %% [markdown]
# ### Choose experiment, print out configurations
# %%
experiment = "drosophila-3-rdpg-sbm"
run = 6
exp_path = Path(f"../models/runs/{ex... |
d526952827b7a845b321334f92d5d2645c1d0c018b6cadc9abd6cec77a3718e9 | Jupyter | 8,469 | 189 | # %% [markdown]
# # Session selection helper for converting ADNI/PPMI dicoms to BIDS nifti.
#
# This is how we select the most recent sessions of the subjects from PPMI and ADNI to get an elder population so that we have better matched control cohort for the local ET cohort.
# %%
from pathlib import Path
import nipyp... |
d0ed340c6a4f146678741f19d9583d65d886962f7c9163e80514261ea600ce96 | Jupyter | 8,471 | 332 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
savefig,
clean_plot_labels
)
from rl_analysis.io.df import dlight_exclude
from scipy.stats import norm
from joblib import Parallel, delayed
imp... |
257641846e2cf88279ad459fa25edb3cbdba99ed23fd5379118a058f36d6a390 | Jupyter | 8,482 | 234 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from cgtasknet.instruments.instrument_subgroups import SubgroupFinder
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.l... |
c46081023ba00e73d818510d55ddadc2b0cffa1e0c8ce466e2021d8a2c8e751b | Jupyter | 8,487 | 332 | # %% [markdown]
# Derivate from https://colab.research.google.com/drive/13CAM8mL1u7ZsqNhfZLv7bNb1rdhMI64d
# %% [markdown]
# # Upload Radiographs to figshare
# %% [markdown]
# # Load Libraries
# %%
import hashlib
import json
import os
import requests
from requests.exceptions import HTTPError
import pandas as pd
# %... |
a93e23bc5ebda4966076e79e58350a39c40e4c4e777c60dde8226f577d2ad821 | Jupyter | 8,495 | 338 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env, clean_ticks, savefig
from rl_analysis.io.df import dlight_exclude_toml
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# ## ... |
fb19c5b02a40770ff03d8c0e4bc623b5750d4286c124cc09aa3a2b62c6044315 | Jupyter | 8,495 | 238 | # %% [markdown]
# <H1> Introduction to scPrisma- De novo reconstruction </h1>
# <h1>Analysis of the cell cycle in HeLa S3 cells</h1>
#
# This tutorial aims to provide a comprehensive overview of the scPrisma workflow, including the following steps:
#
# 1. Reconstruction of a target signal from scratch, utilizing only... |
3d6f0de082c5175edec10680978a9d1fe9682f2e19b64f4c71e9ac805df683a1 | Jupyter | 8,500 | 367 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
9a54e0f24df1cc635c055709aea9c93d9d44ff93eaecf4fa49377649d5d37b14 | Jupyter | 8,532 | 282 | # %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense
from matplotlib import... |
dbb7a28995cbfe980e7ca80bbf2dbc2f09a1ca9eeb98fdd148d556b921bf4510 | Jupyter | 8,538 | 228 | # %% [markdown]
# <H1> Introduction to scPrisma- De novo reconstruction </h1>
# <h1>Analysis of the cell cycle in HeLa S3 cells</h1>
#
# This tutorial aims to provide a comprehensive overview of the scPrisma workflow, including the following steps:
#
# 1. Reconstruction of a target signal from scratch, utilizing only... |
5cfaed2116a6e9e491a56a471ecb9c2067505848bad90c52bf98687f054759bc | Jupyter | 8,562 | 214 | # %% [markdown]
# # Preprocessing of QC measures
#
# ## Cohorts
# - MNI ET/NC, PPMI NC, ADNI2 NC
#
# ## Criterias
# - All QC scores (freesurfer, suit, maget) >=2
#
# ## Input
# MNI QC results: ../tab_data/qc/mni_qc.csv
#
# PPMI QC results: ../tab_data/qc/ppmi_qc.csv
#
# ADNI QC results: ../ta... |
f3645a9cb3629630fcc876364aa5bb06ce7a53d4676c396851578cd5e2408664 | Jupyter | 8,585 | 294 | # %%
from rdkit import Chem
from rdkit.DataStructs.cDataStructs import TanimotoSimilarity
from rdkit.Chem.MolStandardize import rdMolStandardize
import pubchempy as pcp
import pandas as pd
import matplotlib.pyplot as plt
# %%
initial_smi = pd.read_excel(r'../prediction_and_selection/Prediction_initializers_ROR_activ.x... |
422e2a33a59490fb5d16bf92d09f6bbc5b47db61ba5a4a2d485c577b5ddd6736 | Jupyter | 8,595 | 248 | # %% [markdown]
# # Code for analyzing volume output
# - Uses volumetric data CSVs (../results/) generated by "collect_volumes.sh" script part of [MAGeTBrain pipeline](https://github.com/CobraLab/documentation/wiki/MAGeTBrain#collecting-volumes)
# - Uses participants data (../metadata/) for inclusion/exclusion criteria... |
6b545cb19fb2d24726ec48407c0e760262775fdc80ae465a2ea567936a111b50 | Jupyter | 8,604 | 321 | # %%
"""
jupyter-nbconvert report_plots.ipynb --to python; python -u report_plots.py
"""
# %%
from __future__ import division
import os
import glob
import pickle
from natsort import natsorted
import argparse
import os
import sys
import numpy as np
import tqdm
import pandas as pd
import scipy as sp
import numpy as np... |
033da8d67b183e045d3752a227d49ee2efed733de29449b99cbd614a22bc640f | Jupyter | 8,622 | 365 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
dabe598e76bbcbaa95b90017095611aa16108f160c5952979010a9ec7ef9ddf5 | Jupyter | 8,640 | 220 | # %% [markdown]
# # 2-Cell and 3-Cell Motifs in PyC Subgraph (Th=100, with Proximity Constraints)
# %%
import os
import random
from time import time
import pandas as pd
import numpy as np
import networkx as nx
from utils.utils import *
from utils.proximity import *
from utils.graph_creation import *
from utils.motif_... |
9f54ce9a52fc71ec0f9ee6cefa7f99b26e99bca2a70be9163e7ebe816347cfc8 | Jupyter | 8,651 | 231 | # %% [markdown]
# # Render 3D Scale Bar
# This demonstrates two techinques to create 3D scale bars on 3D visualization plots.
# <span style="background-color: #aaaaaa">**NOTE:**</span> This example requires access to an X windows system to view the results.
# %%
# lets import some stuff..
from meshparty import trime... |
b80588de3b50c077c40fbefbcb866369c57bbd87f27a4659684880cb303c2c77 | Jupyter | 8,652 | 201 | # %%
import sys
sys.path.append('/home/thijs/repos/dnp-code/PGM3_correct/source/')
sys.path.append('/home/thijs/repos/dnp-code/PGM3_correct/utilities/')
sys.path.append('PGM3_correct/source/') # the path where the folder PGM is.
sys.path.append('PGM3_correct/utilities/') # the path where the folder PGM is.
# %%
import... |
a6bac188c577a2d81f4adcc8fbbb8c02699f4bba38f6504cbd929989af9c2eb2 | Jupyter | 8,656 | 284 | # %% [markdown]
# # Import Packages
# %%
# Default packages
import os
from os.path import join
from datetime import date, datetime, timedelta
from joblib import load, dump
# Set current directory to "src"
os.chdir(join(os.getcwd(), os.pardir, "src"))
print(f"Current working directory: {os.getcwd()}")
# Installed pac... |
e7d57816d4a50e4973c15cdfecf61e017504228700e1b624e80430171ebfc8d8 | Jupyter | 8,699 | 296 | # %% [markdown]
# # Here we are computing the "binned" correlation between dLight and scalar variables shown in Figure 1
# %% [markdown]
# Specifically, in this notebook we are:
#
# 1. Z-scoring the dLight data per session
# 1. Binning dLight and scalars
# 1. For dLight computing the average per bin and the peak rate... |
6903a11acf18d4d71fe9a7083be7f73775460f954672b6d077d6130fb000b1cc | Jupyter | 8,711 | 312 | # %% [markdown]
# # Responses to current steps recorded in DRN slices
#
# Shown in fig. 1.
# %%
from common import colors, sbarlw
# %%
import os; os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import pandas as pd
import ... |
4881f1b31b9783ac3c12dc00a30127e0c57b67dbffca3e993de906df412f4732 | Jupyter | 8,735 | 228 | # %% [markdown]
# # Command line OAK usage for ontology editors (Alpha)
#
# ## Background (read this carefully)
#
# This guide is for usage of OAK on the command line with the *-edit* version of ontologies.
#
# In general, most users should **never** use the edit version. The exception is if you are part of the
# de... |
34e44ad848b0140bff6f497aea9a9d735f18c3c2c22aef2eccafb96f8d0473b3 | Jupyter | 8,802 | 237 | # %% [markdown]
# ## Why do we need this?
#
#
# We are trying to solve several problems by proposing a new file format. Specifically:
#
# * Saving each conformation as individual file is producing too many files
# * Using pickle-based approaches (joblib) makes format python-specific and not backwards compatible; t... |
9eb48edad2ef08b021b3cfb316bc0aad578c55c5b4bfd5526a55862693553425 | Jupyter | 8,826 | 205 | # %% [markdown]
# <a href="https://colab.research.google.com/github/vekteo/ASRT_ultra_fast_consolidation/blob/main/Offline_online_changes_analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # **Code for calculating offline vs.... |
23ffd475232bd4a07eca79bbf71d2190a0015c135fcd0f6dca4e1b87233e995b | Jupyter | 8,855 | 297 | # %%
import os
os.chdir(os.path.join('..', '..'))
print(os.getcwd())
# %%
import pickle
import warnings
import re
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import seaborn as sns
from grr.pltools import hide_border
# %%
plt.style.use(os.path.join('figs', 'scripts', 'writeup... |
7d1eaaa5a9678070c3f2a1b5aa38fdd83aed092a3124953e3d1b965051ab4b38 | Jupyter | 8,937 | 218 | # %% [markdown]
# ### 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all futur... |
958fb9a00885181a45f3d4d48465b0f0638c860135e704b03ee7dabbeacfbb2b | Jupyter | 8,969 | 396 | # %%
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env, geno_palette
from rl_analysis.maya.process import (
TARGETS,
add_area,
add_trial_count,
add_onset_and_duration,
)
from rl_analysis.io.df import exclude_uuids
import pandas as pd
import matplotlib.pyplot as plt
import nu... |
117a674389dc7c7c46d1a5db84f9faff1065773f939b801e4a31d379ca6a333c | Jupyter | 8,973 | 324 | # %% [markdown]
# <style>
# .extra-info p {
# color: firebrick;
# font-size: 13px;
# font-weight: bold;
# }
# </style>
#
# # Dendrify basics
#
# In this first part of the tutorial series, we’ll focus on creating single compartments and updating their equations. In the second part, we’ll dive into how to... |
95aeb1fb108b76f5ae48dc3fa6ec6157595d503b6daae6418a476265df3bdc60 | Jupyter | 8,974 | 265 | # %% [markdown]
# # Multi-task scorers
# The scorers in the reinforcement learning framework described in the previous notebook are designed to work with a single task.
# Multiple scorers can be added to the environment to handle multiple tasks.
# However, we can also design a single scorer that can handle multiple tas... |
816a07afee90c268d5f4b28e67b148da90c668bbd7b0df8db2f1a8339f896b4f | Jupyter | 9,000 | 205 | # %%
%cd ..
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
from tqdm.auto import tqdm
import torch
import torch.nn as nn
import torchvision
from dataloading.nvidia import NvidiaDataset, NvidiaTrainDataset, NvidiaValidationDataset, Normalize
from pilotnet import P... |
7e676045a186204a6c3c3f6ff3946cb8253decb39d3c881a8d633ead0a6eec1b | Jupyter | 9,007 | 261 | # %%
import pickle
import os
import time
import numpy as np
import polychrom
from polychrom import polymerutils
from polychrom import forces
from polychrom import forcekits
from polychrom.simulation import Simulation
from polychrom.starting_conformations import grow_cubic
from polychrom.hdf5_format import HDF5Reporter... |
dd2cf64cdb9dd9b0107a074d0b35eb25807cb6a4a6c46343b1b3c1e3b183ff14 | Jupyter | 9,027 | 294 | # %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense
from matplotlib import... |
60b513b472611079ba97e885ec194459096f6adad8e1ba049ffa9141dba0c496 | Jupyter | 9,079 | 280 | # %% [markdown]
# # Run Temporal Patch Classifier
# Currently set up to run a temporal patch classifier that takes inputs of shape `(batch_size, h, w, 24)`
# %%
%load_ext autoreload
%autoreload 2
# %%
import json
from keplergl import KeplerGl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas ... |
15f184500f9787182fa8bd4856b08bdd8818d42941e1af8b0b399f3f3733f8fb | Jupyter | 9,083 | 373 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
syllable_aliases,
clean_ticks,
savefig
)
from rl_analysis.util import rle
import pandas as pd
import os
import seaborn as sns
import numpy as np
import matp... |
788b96455573ae3c7c39d98d1705e77520fd9c703f89e2a0ab1a988ef465edf6 | Jupyter | 9,084 | 222 | # %% [markdown]
# # 3T3 Cell Line
# %% [markdown]
# # Imports data structuring
# %%
#organização dos arquivos
import os
#salvar/carregar arquivos em diferentes formatos
import joblib
#organizando os arquivos de forma aleatória
import random
#gerar gráfico
import matplotlib
#estruturação dos dados
import numpy as np
#... |
0e42dfd7bbc2f3ac97d7000c6aca4c6ccde0947695ccdbdd3b8ff6af8e25483b | Jupyter | 9,090 | 291 | # %% [markdown]
# # Check GPU
# %%
#Check for GPU
import tensorflow as tf
# if tf.test.gpu_device_name():
# print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
# else:
# print('Please install GPU version of TF')
tf.test.is_gpu_available()
# %%
#check keras and tf versions
import keras
print(kera... |
ebdec8f85ba2051af479b58115dc0654a1803ced3e589366ef66a9cf71a00f8c | Jupyter | 9,105 | 263 | # %% [markdown]
# # Import Packages
# %%
# Default packages
import os
from os.path import join
from datetime import date, datetime, timedelta
# Set current directory to "src"
os.chdir(join(os.getcwd(), os.pardir, "src"))
print(f"Current working directory: {os.getcwd()}")
# Installed packages
import numpy as np
impor... |
97ff830efe6720415ec348b3f8f12d2bf1438c18e2d78d3b1118f6247e3f7c24 | Jupyter | 9,107 | 236 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
import seaborn as sns
import os
from pathlib import Path
import itertools
import sys
import pickle
sys.path.append(os.path.join(Path(os.getcwd()).parents[0], "functions"))
import... |
7faab723d24e08fde375b5e2dc9cfb6415ac77fc724361048872ccc10b550b7d | Jupyter | 9,112 | 264 | # %%
from caveclient import CAVEclient
import nglui
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %%
# this is the datastack name of the public release
# passing it will auto-configure many of the services
client = CAVEclient('minnie65_public_v117')
# %%
client.materialize.get_tables()
# ... |
fdf72e16ec96fd7075fd3a19b59bd42dca4a3389c9030aa52a3eb193fe7033f2 | Jupyter | 9,116 | 260 | # %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from sklearn.metrics import roc_curve
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import auc
from sklearn.metrics import classification_report
from sklearn.metri... |
a28874d974a7daf732d7739cc7218a2c625ae48d9b46cb3598d4746ed0407386 | Jupyter | 9,130 | 345 | # %% [markdown]
# # Structure-Function Analysis
# - Related to Figure 7.
# - Visual response is correlated with connectivity.
# %%
import numpy as np
import pandas as pd
import pickle
import datajoint as dj
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
import statsmode... |
ced5ff226873a09a4160ad075902fb2e56745923b6a5fd17863850a9805c771f | Jupyter | 9,146 | 255 | # %% [markdown]
# # Code for analyzing volume output
# - Uses cortical thickness data CSVs (../results/FreeSurfer) generated by "../scripts/freesurfer_surface_measures.sh" script using aparcstats2table utility from [FreeSurfer pipeline](http://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferAnalysisPipelineOverview)
# - Us... |
cac2ca2574e4a147f7db00afc6a6d5e69014aabef04e38486930f8683f212d9b | Jupyter | 9,150 | 253 | # %% [markdown]
# # Replay Model
# %%
from network import Network
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from functools import partial
from multiprocessing import Pool
# %% [markdown]
# ## Set Hyperparameters
# %%
# Random seeds
seeds_n = 20
mds_seed = 1
# Number of ... |
82a2e78eb0ba5ad59e910f24db0fc2174af0e4230c331973b005e3fa2e3abb1e | Jupyter | 9,167 | 241 | # %% [markdown]
# # GCN for predicting binding energy of transaminase mutants
#
# Use IGT or Spektral or any other form of GCN to predict the binding energy of a given transaminase mutant.
#
# The aim is to use binding energy data from 100,000 VfTA mutants (generated by Rosetta).
#
# Dataset is a set of ... |
77089d225ca8cebab3e8f8cbc38562ee2c66ceaace32ed74a2eb746b0041fd3d | Jupyter | 9,174 | 345 | # %%
import toml
import joblib
import random
import numpy as np
import pandas as pd
import colorcet as cc
import seaborn as sns
import matplotlib.pyplot as plt
from toolz import get
from scipy import signal
from os.path import join
from tqdm.auto import tqdm
from rl_analysis.behavior.moseq import syll_onset
from rl_an... |
c9b7e4e43e12ef9595b74591e42332ef0332a1265199db6025a1fc9280d4f8e0 | Jupyter | 9,195 | 191 | # %% [markdown]
# # THINGS-fMRI usage notes
# %% [markdown]
# For a detailed description of the data and the procedures that generated it, see [the THINGS-data preprint](https://doi.org/10.1101/2022.07.22.501123).
# %%
from os.path import join as pjoin
import glob
import numpy as np
import pandas as pd
from nilearn.m... |
3c39d8aa066a81e0c05f598ef427bc2233cda50c32a3f92cdc7e3368f08c0204 | Jupyter | 9,205 | 123 | # %%
import pandas as pd
import numpy as np
import json
# %%
#in here are automatic intervention counts that may be wrong and do not exclude intersections and traffic
results = """
Camera v1 (May)
{'mae': 0.23088668332126538, 'rmse': 0.30444885103275254, 'max': 1.3838765496927377, 'failure_rate': 0.961781827385472,'di... |
e929d0eead55a1d56dca58beedac3f4a2aa6f9cd5b083d4c2a00e88612ad7512 | Jupyter | 9,226 | 266 | # %%
import sys
import os
import itertools
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import scipy.optimize as spopt
import scipy.integrate as spi
import scipy.stats as stats
import tensorflow as tf
import pandas as pd
sys.path.append("../../ndsvae/... |
56df79080cecdb49cf96082fc6dc42afaf07ec5adb057b5710c1c85038904c27 | Jupyter | 9,253 | 206 | # %% [markdown]
# # 1. Get doctor wise accuracy
# %%
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from base_code import *
valid_data = pd.read_csv('../neurosurgeon35_data/neurosurgeon35.csv', index_col='Respondent ID')
xai25 = pd.read_csv('../computational_data/xai25.csv')
dr_wi... |
493185cab8f238930776a4b6656154cb93649eddb1451410b3653464049cce2f | Jupyter | 9,257 | 277 | # %%
import geojson
import matplotlib.pyplot as plt
import json
import numpy as np
import pandas as pd
from PIL import Image
import rasterio as rs
from rasterio.plot import show
from rasterio import warp
from skimage import morphology
from skimage.feature import blob_doh
# %%
def pixels_to_coords(x, y, src_img):
l... |
774ca0a1649c170079a813aa343cfa357828adb64ee3092e54ca3149bac8e715 | Jupyter | 9,269 | 285 | # %% [markdown]
# # Dissecting cell types for anatomical structures - Lung Epithelial Cells (advanced)
#
# This how-to walks through the process of extracting cell types for anatomical structures.
# It illustrates some of the challenges and nuances of ontology extraction in domains such as anatomy.
#
# In theory it s... |
a118801d3fab8e045ff0a16cde898d3b860e8fb160d6ca266041de1c613a1d1a | Jupyter | 9,290 | 309 | # %% [markdown]
# # Generating stability plots and frequency spectra heatmaps for the macroscopic model
# %%
# this path append is for binder only
import sys
sys.path.append("../../")
# spectrome modules
from spectrome.utils import functions, path
from spectrome.brain import Brain
from spectrome.forward import ntf_m... |
0293f732fd10836f67e7eb3f8fe02521a85fab1c4da64fc978f1034fb5feebc7 | Jupyter | 9,312 | 305 | # %%
import sys
import os
import itertools
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from mpl_toolkits import mplot3d
from sklearn.decomposition import PCA
import pandas as pd
sys.path.append("../../ndsvae/")
import ndsvae as ndsv
sys.path.append(... |
fb0d57e5e1ebf0e1513026905d1d816916ba1ddfb1bdff7e61cecc6e90a3e10d | Jupyter | 9,353 | 262 | # %%
import os
import pandas as pd
import numpy as np
import torch
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.utils import shuffle
from tqdm import tqdm
# %%
np.random.seed(42)
torch.manual_seed(42)
# %% [markdown]
# # Study population
# %%
data_dir = '../datafiles/chexpert/'
input_csv = 'che... |
dfe02b15a6b8aa8df2cc390f35266f02c202faf70f681d9c93871ea388b3f896 | Jupyter | 9,355 | 301 | # %% [markdown]
# # Check GPU
# %%
#Check for GPU
import tensorflow as tf
# if tf.test.gpu_device_name():
# print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
# else:
# print('Please install GPU version of TF')
tf.test.is_gpu_available()
# %%
#check keras and tf versions
import keras
print(kera... |
b513ab276213f822678a413d8e65f7a414a6907164e519b16a73e1004777b80a | Jupyter | 9,373 | 275 | # %%
import os
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
sns.set(context='notebook', style='white', font_scale=1.2)
# %%
try:
import crossmapy as cmp
except:
import sys
sys.path.append('../')
... |
7f5efb0179674d1832044d3c3b8bfbe9415a91614ebe3c2459f941bdf0af4e30 | Jupyter | 9,463 | 354 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
savefig
)
from rl_analysis.models_rl.util import zscore_missing_data, shuffle_rows_copy
from rl_analysis.util import count_transitions
from rl_analy... |
ec9354be593c1fe068d15b10f6e227a1d8667a0d6284114cce8a717db3215c7e | Jupyter | 9,474 | 373 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import setup_plotting_env, clean_ticks, savefig
import pandas as pd
import os
import seaborn as sns
import numpy as np
# %%
import toml
with open("../analysis_configuration.toml", "r") as f:
anal... |
0aa6507332224b437a633579f87c8f639e6e289600b7b140afff673d09303691 | Jupyter | 9,483 | 282 | # %%
import numpy as np
import os
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
hfont = {'fontname':'Myriad Pro'}
import matplotlib.ticker as mtick
from matplotlib import gridspec
import matplotlib.colors as colors
import sys
sys.path.append(os.path.jo... |
56c6f2b0870e7398dcf971630eb68fdb6b769ba801f31b5d941e1da11c4b4fed | Jupyter | 9,520 | 269 | # %%
%pylab inline
import os
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
# %%
def find_variants(pval_file, start, end):
x = []
header = True
for line in open(pval_file):
if header:
header = False
continue
elems =... |
70c998b2fa1bdaddfe839da66e8ad85da7ef43e09395631e8e2387328b49906d | Jupyter | 9,563 | 420 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
0bac88c632ab8b7bb8095574075c32ca7ef1074cee9cbb22c374b7a48bbb1594 | Jupyter | 9,612 | 304 | # %% [markdown]
# ## This is an example for doing 1D loop extrusion simulations
#
# The class is designed to be very flexible and easy to modify. It provides several small tools to make efficient simulations, and shows how to deal with LEF states, with two legs, etc.
#
# %%
import numpy as np
import pandas as pd
i... |
5ee6ba3c52efed5c79546313ecf6b9eb7b96c08f683a606735e059cb1f56e26b | Jupyter | 9,638 | 355 | # %% [markdown]
# # Libraries import
# %%
import matplotlib.pyplot as plt
import pandas as pd
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
import rdkit
from rdkit.Chem import QED
import warnings
warnings.filterwarnings('ignore')
# %% [markdown]
# # Data loading
# %%
ROR_mol = pd.re... |
4b048ab25c898379e663ef1d461c517b67853496a1063e68f645b3ce20408b8d | Jupyter | 9,650 | 155 | # %% [markdown]
# # Data Sets
#
# You have to preprocess the data depending on the model you want to build. You can refer to sections relevant for your model of interest:
#
# 1. [Graph-Based Transformer](#Preparing-Data-for-the-Graph-Based-Transformer)
# 2. [Smiles-Based Transformer](#Preparing-Data-for-the-Smiles-Ba... |
4543af0bbbd9620ef3ecafdf47114ae086a4f5f55aed5889b70250f3321cbfe2 | Jupyter | 9,658 | 421 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
2125389f82c3ac71805da1545705f2c4f1093163cfdc553dcdfad46e2b47618c | Jupyter | 9,670 | 349 | # %%
import sys
import pickle
import pandas as pd
import glob
import re
# %%
# TODO define the folder of etabs you want to study here
STUDY_PATH = "../../frappier_termless/tic0/*.etab"
# %%
myAmino = ["R","H","K","D","E","S","T","N","Q","C","G","P","A","V","I","L","M","F","Y","W"]
FullAmino = ["ARG","HIS","LYS","ASP... |
e21537751cd123abd1b86e849d18115deff498d08ae66dfb5386ec444c574fbd | Jupyter | 9,673 | 357 | # %% [markdown]
# # Libraries import
# %%
import matplotlib.pyplot as plt
import pandas as pd
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
import rdkit
from rdkit.Chem import QED
import warnings
warnings.filterwarnings('ignore')
# %% [markdown]
# # Data loading
# %%
ROR_mol = pd.re... |
8f0ef2ab0531f475c7abada210aac4a9fb007d6ad2ecb51fc5ce3de56bb8cb00 | Jupyter | 9,682 | 309 | # %%
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import os
import pickle
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from tensorflow import keras
from tensorflow.keras import layers
from tqdm import tqdm
np.random.seed(1)
# %%
def n... |
01143b174dd666ccc36eab5818ade840b3656d91191de23907e21593462d8c20 | Jupyter | 9,705 | 303 | # %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
# %% [markdown]
# # Study population
# %%
data_dir = '../datafiles/chexpert/'
df_demo = pd.DataFrame(pd.read_excel(data_dir + 'CHEXPERT DEMO.xlsx', engine='openpyxl'))
df_demo = df_demo.r... |
25359d6ecced14ee1e2fcfe28a2eae56bbd1a88767b6981b802a6518291a6d6c | Jupyter | 9,716 | 193 | # %% [markdown]
# ## Figures for The Paper
# %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
#This how we load txt files from each forsal model of 10
#Train
fused1_t_avg_cos = np.loadtxt('Fused_Train_AvgCosSim_1.txt', dtype="float",delimiter=",")
fused2_t_avg_cos = np.loadtxt('Fused_... |
711b3eebd21ce776bf6c1ca6637ce587fbd1d331943fe44604e512f47f9ea752 | Jupyter | 9,732 | 409 | # %%
import numpy as np
import torch.nn.functional as fn
import torch
#load the entity list
E = {}
with open('entity2id.txt','r') as flr:
line=flr.readline()
line=flr.readline()
while(len(line)>0):
(name, idd)=line.strip().split('\t')
E[int(idd)]=name
line=flr.readline()
#load the r... |
b104568c091d0ef05511555a1d7e8a325093bfe64d55303519cfeedeccd3220c | Jupyter | 9,768 | 157 | # %% [markdown]
# # Analyzing Mutations with Trained Models
#
# In this tutorial, we will explore some of Selene's tools for exploring and interpreting sequence predictions.
# We are generally interested in models that predict various output labels for an input biological sequence.
# For instance, we might want to pre... |
28a81329884f5b9daaef6f84f7bf93589cddd669a43ef9493a458140136a69ab | Jupyter | 9,773 | 299 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
import seaborn as sns
sns.set_style("ticks")
import os
from pathlib import Path
import itertools
import sys
import pickle
import sys
sys.path.append(os.path.join(Path(os.getcwd()... |
70db40658d3f1ccfae291552a77edf559502b3e8195e90e32dcadcda28886613 | Jupyter | 9,773 | 423 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
cb72bd5b40f293dc95df15215139661a47c4fecd44f9b3aa31afabfe23c35333 | Jupyter | 9,783 | 265 | # %% [markdown]
# <H1> Introduction to scPrisma </h1>
# <h2> Reconstruction with partial prior knowledge</h2>
#
# This tutorial aims to provide a comprehensive overview of the scPrisma workflow, including the following steps:
#
# 1. Reconstruction of a target signal from scratch, utilizing prior knowledge of cell cyc... |
169e9ee99f7167f07f0ffe612346ff0f2c3e2787fd0e514d866050751d9915e3 | Jupyter | 9,819 | 266 | # %% [markdown]
# <H1> Introduction to scPrisma </h1>
# <h2> Reconstruction with partial prior knowledge</h2>
#
# This tutorial aims to provide a comprehensive overview of the scPrisma workflow, including the following steps:
#
# 1. Reconstruction of a target signal from scratch, utilizing prior knowledge of cell cyc... |
3b906806ca0d046957e1687cd26f8f1180be33f8c1355b301ebe8619d24fd150 | Jupyter | 9,937 | 270 | # %%
import numpy as np
import os
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import matplotlib.lines as mlines
import matplotlib.ticker as mtick
from matplotlib import gridspec
import sys
sys.path.append(os.path.join(Path(os.getcwd()).parents[0], "fu... |
1c86a28847c00c0709c9d4c5e032ac4571a13e192cc3f76e226f884ece54ec99 | Jupyter | 9,957 | 193 | # %% [markdown]
# ## Figures for The Paper
# %%
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
#This how we load txt files from each forsal model of 10
#Train
dorsal1_t_avg_cos = np.loadtxt('Dorsal_Train_AvgCosSim_1.txt', dtype="float",delimiter=",")
dorsal2_t_avg_cos = np.loadtxt('Dor... |
57eb6d0e4b16907f8d1876c4f384416cb96f4504eaecf5d933e3e04eaab48340 | Jupyter | 9,964 | 308 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
import pickle
mpl.rcParams.update({'font.size': 14, 'axes.labelsize': 'medium', 'axes.titlesize': 'medium', 'legend.fontsize': 12})
# %%
def fold_analysis(run_name):
dfs_fold = pd.read_csv(f'... |
07a86889ef16c9f328d5510a93f31896968e0c2ea4d4e275885a8e359f64c300 | Jupyter | 9,972 | 264 | # %% [markdown]
# # 2-Cell and 3-Cell Motifs in PyC Subgraph (Th=100)
# %%
import os
import random
from time import time
import pandas as pd
import numpy as np
import networkx as nx
from utils.utils import *
from utils.proximity import *
from utils.graph_creation import *
from utils.motif_counts import *
from utils.v... |
f7f1a9aef54df6a77b5b6772e9b18c270af2448f6c84640ba303e6c6a13d1f8b | Jupyter | 9,987 | 352 | # %% [markdown]
# ## Single-task DNN keras model training on Tox21
# %% [markdown]
# This notebook creates a single-task DNN classification model to predict toxicity (toxic / nontoxic) on the RTECS dataset, using
# - same splits as pytorch MTDNN and STDNN
# - same architecture as the pytorch STDNN on RTECS
# - FP... |
4a7f23d7881e4faddc733beb44bb6310ab5361646c1e97c121fc5a2f9b565279 | Jupyter | 10,014 | 272 | # %% [markdown]
# # ROI detection with Deep Learning
# Notebook used to test training of deep neural network with PyTorch for detecting ROIs.
# %%
%matplotlib inline
import os, sys, time, shutil
import random
import ipywidgets as widgets
from ipywidgets import interact
import numpy as np
import matplotlib
import mat... |
d82da57d96d134ca1ec81fef6f48754c6cb57706b91e676ce5cccd1579eecd27 | Jupyter | 10,015 | 372 | # %%
import numpy as np
import pandas as pd
from scipy.io import loadmat
import glob
import os
import sys
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="white")
from scipy import stats
from mne.viz import circular_layout, plot_connectivity_circle
import statsmodels.api as sm
from statsmodels.form... |
2b44a0f1c882ee0471513b8e29a25ded27949e461d89f03cd461f05d4bf9535f | Jupyter | 10,024 | 402 | # %%
# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), '../..'))
print(os.getcwd())
except:
pass
# %% [markdown]
# # Signal f... |
66b2588d0acfd26525902d461706ee2db5673cc61075060bb4047a36b7ce4ea5 | Jupyter | 10,035 | 308 | # %%
import os
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import metrics
sns.set(context='notebook', style='white', font_scale=1.2)
# %%
try:
import crossmapy as cmp
except:
import sys
sys.path.append('../')
... |
a1c9119269302aebda2e470270f2a7682268244388348a2e6d906559e7995bf2 | Jupyter | 10,038 | 294 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
import seaborn as sns
sns.set_style("ticks")
import os
from pathlib import Path
import itertools
import sys
import pickle
import sys
sys.path.append(os.path.join(Path(os.getcwd()... |
dae752f81d818b9bfe3101705e227f4086e46c8352e5e139af7ada3336072f5b | Jupyter | 10,074 | 355 | # %% [markdown]
# # Compute syllable sequences and entropy in an expanding window after syllable onset (for display examples)
#
# 1. Note that you can use a dask scheduler by setting `dask_address` to the address of the scheduler (see `../analysis_configuration.toml`)
# 1. Note that you must compute `syllable_stats_of... |
318d16cc1e814a62c538e1b305d400e7306e5aecdd236dbafaad5b5dbd9410b7 | Jupyter | 10,092 | 368 | # %% [markdown]
# # Libraries import
# %%
import matplotlib.pyplot as plt
import pandas as pd
from rdkit import Chem, DataStructs
from rdkit.Chem import AllChem, Descriptors
import rdkit
from rdkit.Chem import QED
import warnings
warnings.filterwarnings('ignore')
from chembl_structure_pipeline import standardize_mol, ... |
6139c18371710ff6d72604a95b6c1cefc2bf7d249a78ea8f9b56ccee594c7cf1 | Jupyter | 10,142 | 441 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from norse.torch.functional.lif_adex import LIFAdExParameters
from norse.torch.functional.lif_adex_refrac import LIFAdExRefracParameters
from tqdm import tqdm
from cgtasknet.net.lifadexrefrac import SNNlifadexrefrac
from cgtasknet.instruments.instru... |
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