sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
0e119e19df261badb7c60076f5a8ba8650969ac5ea66f97c50dde8dc93f641f3 | Jupyter | 6,091 | 214 | # %% [markdown]
# # Convert ERA5 hourly data to daily data
# %%
proj_dir='/path/to/main_project_folder/' # edit this line
import pandas as pd
import numpy as np
import xarray as xr
import netCDF4 as nc4
import re
import sys
import os
import time
import multiprocessing as mp
years = range(1979, 2024)
yr_mon_args = []... |
0e5c466cdc2d731a8e086c0ef3f3e68ef7d04d630f16b3c9592d665502b6fb67 | Jupyter | 6,092 | 164 | # %% [markdown]
# # Import the required packages
# %%
import os
from typing import Any
from torch.nn import (
BatchNorm2d, Conv2d, Flatten, Linear, LogSoftmax, MaxPool2d, Module,
ReLU, Tanh, NLLLoss
)
from torchvision.datasets import FashionMNIST
from torch import device as torch_device
from torch.nn import ... |
7daee3d9336d14ca32d04bd87ebea67b576dbbeb8b0c844cf49d71a06cf25f68 | Jupyter | 6,102 | 149 | # %%
%pip install davos
import davos
davos.config.suppress_stdout = True
# %%
smuggle numpy as np # pip: numpy==1.24.2
smuggle matplotlib.pyplot as plt # pip: matplotlib==3.7.0
smuggle scipy as sp # pip: scipy==... |
0c57d502874c7365397f94985828731b2ae779785c2161fc156bd2a532a9a2e2 | Jupyter | 6,156 | 209 | # %% [markdown]
# # Figure 3 (Weakly coupled network)
# #### This code runs the simulations and reproduces Fig 3B, 3D, and 3E
# %%
from KeyFunctions.TheoryFunctions import *
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
import matplotlib as mp
# %% [markdown]
# ### Default p... |
07e4c7846c8a5c565ce2a953d6804b0edd310e179730a53ce543a6ebaced241a | Jupyter | 6,165 | 191 | # %% [markdown]
# # Import a model from NIR and deploy it to SPECK
#
# In this tutorial we will show how a NIR model can be deployed onto SynSense's Speck processor.
#
# We start by importing the model into sinabs and from there deploy it onto Speck. We will test the deployed model by streaming events onto the chip ... |
463376b8fe9e5ecaee10740f6769b87b1489e446ac315ac7b979eb8986e458c9 | Jupyter | 6,166 | 156 | # %%
# Copyright 2022 NVIDIA Corporation. All Rights Reserved.
#
# 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 applic... |
f0de2c2d46ff18c632b446cec94347ccf3eb1d3218d62fe26d8f82bd91a65de4 | Jupyter | 6,169 | 232 | # %%
!pip install zarr
!pip install scipy
# %%
import os
import dask
import dask.array as da
import dask.dataframe as dd
import pandas as pd
import numpy as np
import pyarrow as pa
from os.path import join
import shutil
# %%
from dask.distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=5) # ... |
39ab8d62f492a77387dd56db1dcea7f9291e30b41d8eb0744826262394ed38c4 | Jupyter | 6,179 | 186 | # %% [markdown]
# <!-- (evaluation)= -->
# # WeatherBench 2 Evaluation Quickstart
#
# <a target="_blank" href="https://colab.research.google.com/github/google-research/weatherbench2/blob/main/docs/source/evaluation.ipynb">
# <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
# ... |
139e17670aa11dc60299907ff2c09bf66a81c90797d15bf98179558075113543 | Jupyter | 6,196 | 194 | # %% [markdown]
# # How to extract features from fMOST data with [neuron_morphology](https://github.com/AllenInstitute/neuron_morphology)
#
# To obtain the package, please see [https://neuron-morphology.readthedocs.io/en/readthedocs/](https://neuron-morphology.readthedocs.io/en/readthedocs/).
#
# This step-by-step gu... |
8e845e969e7845c75b9e5b3d3d0f23c55cc0ed6c1a74a3e05f38cd7777e05c7e | Jupyter | 6,206 | 173 | # %% [markdown]
# # Import backage
# %%
import sys
sys.path.append("../..")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import random
%matplotlib inline
# %%
import torch
from torch import nn
from torch import distributions
from torch.nn.parameter import Parameter
from exp.sir.train i... |
18179802843cc2acf5cd5d5f81e2ac0c4b6ee100b701f8d1155bb71632a8e221 | Jupyter | 6,209 | 209 | # %%
from NER_processing import nlp, disease_service, chemical_service, genetic_service
import pysolr
# %% [markdown]
# # Replicate paragraphs in local
# %%
solr = pysolr.Solr('http://librairy.linkeddata.es/data/covid-paragraphs', timeout=10)
# %%
solr_local = pysolr.Solr('http://localhost:8984/solr/covid_paragraphs... |
a000506ed56b910727a46eaa096bbef43ee9e256b19aad25c47d5bdbcc6b4faf | Jupyter | 6,221 | 171 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib import cm
from scipy.in... |
5f705c88031595393d06d6a73946900d5a6df238e12df9eefbc78fc4bc146731 | Jupyter | 6,223 | 162 | # %%
# import the experiment utilities package
import exputils as eu
import numpy as np
# only collect data to plot every n'th step, otherwise the plots are too large
plot_every_nth_step = 500
def get_every_nth_step_inds(data):
# create indicies
inds = np.full(len(data), False)
step = 0
while ... |
a2570f9ade869d66a77c8e8a2ba35f2d15b03cdf4cc1d9482624a112817ea4eb | Jupyter | 6,238 | 152 | # %% [markdown]
# ## SHAP-based feature importance plots
# %%
import autoreload
%load_ext autoreload
%autoreload 1
%matplotlib inline
# %%
import numpy as np
import pandas as pd
import os
from plotnine import *
import glob
import yaml
import pickle
import utils
# %%
annotation_names = utils.ANNOTATION_NAMES
binary_a... |
71460cc513f4ce114131bfbdcd06ff6146a2548ccb2dbb5738a0dcb7012b1a2e | Jupyter | 6,269 | 176 | # %%
import sys
sys.path.append("../..")
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import random
%matplotlib inline
# %%
import torch
from torch import nn
from torch import distributions
from torch.nn.parameter import Parameter
from exp.sir.train import train
from models_new import R... |
e1a65c8d88ef573bbe0870e26a134abe219db121eea53b717eb62a0640a910e1 | Jupyter | 6,308 | 201 | # %% [markdown]
# # Figure 5 (Segregated I populations)
# ### This code runs the simulations and reproduces Fig 5B and 5C
# %%
from KeyFunctions.TheoryFunctions import *
from KeyFunctions.SimulationFunctions import *
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
import matplo... |
2b30e9b929403f32bf63303dc7e92ae365ff92fe2c3be369be310957aebefbc9 | Jupyter | 6,315 | 244 | # %%
%load_ext autoreload
%autoreload 2
# %%
import sys
import time
import logging
import argparse
import itertools
import matplotlib
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import numpy as np
sys.path.insert(0, '../network')
from measures import DataStore
# %%
directory = "4-state-transitions/da... |
f52e46654577a9c2a8aae4d00dddf358bfb44489e9c1ed2aee19fca6ca33ff5e | Jupyter | 6,322 | 182 | # %% [markdown]
# # 3rd - investigating the specificity and confounder effect on the RCPL model.
# %%
import pandas as pd
import os
import seaborn as sns
from scipy.stats import pearsonr
import pickle
from mlconfound.stats import partial_confound_test
from mlconfound.plot import plot_graph
os.environ["PATH"] += os.pa... |
2e1c522c0866b887eeace91111545c186709327b0cafb1fe230da65a11b47246 | Jupyter | 6,347 | 151 | # %% [markdown]
# # Running Tune experiments with AxSearch
#
# <a id="try-anyscale-quickstart-ray-tune-ax_example" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-ax_example">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anysca... |
c49638f04164153a4b5785afc5b347cd49c6d0dc58c485e62fc5d420d375c419 | Jupyter | 6,360 | 146 | # %% [markdown]
# (nirtorch_tracing)=
# # To NIR: Tracing PyTorch
#
# When creating NIR nodes from PyTorch, we go through the PyTorch modules to create a graph structure that we can populate with NIR nodes.
# This "going through" is what we refer to as "tracing" because we have to track the path of signals through pot... |
e18abd4ac2957cb087f4f7be0d43b25aa2693ad06154fce1c131bfbabd834369 | Jupyter | 6,403 | 126 | # %% [markdown]
# # Interactive Quality Control (QC) of an ECG and MRI Prediction Model
#
# In this notebook, we review phenotypes and model results to identify where our model has room for improvement. This is a quality control (QC) step in the development of a model.
#
# <div class="alert alert-block alert-info">
#... |
2e46d9a59f93e3d4740c0c760a0c21a452a6d9ab4dc19a90d6f4280a992c1e5d | Jupyter | 6,422 | 223 | # %% [markdown]
# # Process TUAB
# > Preprocessing and feature computation for the TUAB dataset
# %%
import pandas as pd
from pathlib import Path
from functools import partial
from dask.distributed import Client, LocalCluster, fire_and_forget
from dask_jobqueue import LSFCluster
from fastcore.transform import Pipelin... |
8c23ff46e9cb2096c8c94494f200616be9c9d7c4273b851ece7afbcb6f5924b6 | Jupyter | 6,439 | 151 | # %%
import numpy as np
import pickle
import pandas as pd
from Bio.PDB.Polypeptide import PPBuilder
import Bio.PDB
from Bio.PDB.PDBIO import PDBIO
import glob
# %%
def vtoa(v):
return np.array([float(x) for x in v])
def unit(v):
return v / (np.dot(v,v)**0.5)
def norm(v):
return np.dot(v,v) ** 0.5
#####... |
855631d2705f58c5270264bd1719d1515fbc7e5363f66c29be9385cf7b0a1178 | Jupyter | 6,469 | 142 | # %%
import lime
import sklearn
import sklearn.ensemble
import sklearn.metrics
# %% [markdown]
# ## Fetching data, training a classifier
# %% [markdown]
# In the [previous tutorial](http://marcotcr.github.io/lime-ml/tutorials/Lime%20-%20basic%20usage%2C%20two%20class%20case.html), we looked at lime in the two class c... |
fc48ed8f7b40d74b9a7e891481efd98c9abed1527848796a39b31466c286f4f3 | Jupyter | 6,500 | 232 | # %%
!pip install zarr
!pip install scipy
# %%
import os
import dask
import dask.array as da
import dask.dataframe as dd
import pandas as pd
import numpy as np
import pyarrow as pa
from os.path import join
import shutil
# %%
from dask.distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=5) # ... |
797ac86add4f4a62a40fc5b72f8ce8d2022ad31d16387bd7e4da2beed1835ebd | Jupyter | 6,501 | 195 | # %% [markdown]
# <!-- (evaluation)= -->
# # WeatherBench 2 Evaluation Quickstart
#
# <a target="_blank" href="https://colab.research.google.com/github/google-research/weatherbench2/blob/main/docs/source/evaluation.ipynb">
# <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
# ... |
483538912eb3a1e9da4a9717e9c890b3f984b23c2a4aee2f5616a5c9d3c200bf | Jupyter | 6,514 | 166 | # %%
import SimpleITK as sitk
import numpy as np
import os
import glob
import matplotlib.pyplot as plt
from rabies.visualization import otsu_scaling, plot_3d
import nilearn.plotting
from rabies.utils import recover_3D
from rabies.analysis_pkg.diagnosis_pkg.analysis_QC import masked_plot, percent_threshold
import pandas... |
61d8be07e42e1393da0c2fade3be3a124c6892cfd9341b41ee47305fd4a56171 | Jupyter | 6,541 | 227 | # %%
import omicverse as ov
#import scvelo as scv
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
ov.ov_plot_set()
# %%
import omicverse as ov
ov.utils.ov_plot_set()
from matplotlib import rcParams
# 设置全局字体为Arial
rcParams['font.family'] = 'Arial'
# %%
import os
metric_l... |
fcbdd553de45b5e3cf1705e8e6a79c03d953ae944dbaa214c711e5fdee2b5b4e | Jupyter | 6,559 | 132 | # %%
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
import pickle
from nilearn import plotting
import os
import nibabel as nib
from scipy import stats
from scipy.spatial.distance import squareform
from tqdm import tqdm
from statsmodels.stats.multitest import fdrcorrection
# %% [markdown]
# ##... |
ce160d911c3205d2072376ea5d54f60ca0fc957cc13dcfe25106c5da7cd47d9b | Jupyter | 6,560 | 195 | # %% [markdown]
# # Goal : Scan the 3D predcitions with the PD fold database (12/09/2023).
#
# ### A. Make the DB
# ### B. Run Foldseek
# ### C. Get the hits with good proba
# ### D. Report the folds
# ***
# %% [markdown]
# # A
# %%
# Generates the database :
# mkdir /media/concha-eloko/Linux/PhageDEPOdetection/PD_... |
01cb92e7ef5e47e3b748b6a3750fbc22f8a21faf619175ea988c7ce806f2e0d6 | Jupyter | 6,564 | 223 | # %% [markdown]
# # Load Wave files for ML
# %% [markdown]
# Loading modules
# %%
import os
import IPython
import IPython.display
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import time
#import math
#from tensorflow import keras
#import seaborn as sns
import random
#import tensorflow ... |
b320cdb062d6b4a0b2b96ee373844e596603c8fcee662570a200b1d9b6fe7f88 | Jupyter | 6,580 | 198 | # %% [markdown]
# # Goal : Scan the 3D predcitions with the PD fold database (12/09/2023).
#
# ### A. Make the DB
# ### B. Run Foldseek
# ### C. Get the hits with good proba
# ### D. Report the folds
# ***
# %% [markdown]
# # A
# %%
# Generates the database :
# mkdir /media/concha-eloko/Linux/PhageDEPOdetection/PD_... |
18ca6d799357336466c733f82385bd7de26b3f5d082e0d86308484c9f172ae97 | Jupyter | 6,581 | 219 | # %%
%load_ext autoreload
%autoreload 2
# %%
from multicamera_acquisition.interfaces import get_camera
# %%
# use spinview to get serial numbers for each camera
# %%
camera_list = [
{'name': 'top', 'serial': 22181547},
{'name': 'side1', 'serial': 22181612},
]
# %%
import csv
from multicamera_acquisition.pat... |
21c60a637d12ae6fb5a89999c74032c246a47a6028fd3301236e6b5bf1ecd77b | Jupyter | 6,583 | 171 | # %%
import scanpy as sc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from os.path import join
from IPython.display import display, Markdown, Latex
import sys
sys.path.append("/n/home12/khou/holystore/")
import paper_utils
from scipy.stats import ttest_ind
from scipy.stats import pearsonr, spe... |
93a4cc3de6251fdeab839f25974810e2003a1c6131aa0a857d2087fd3bcd704f | Jupyter | 6,594 | 215 | # %%
# import libaries
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import pandas as pd
from operator import itemgetter
import seaborn as sns
from tqdm import tqdm
import math
import pickle
from scipy import stats
import ... |
489d6c61764d43201e6c6afc3a89be6e4d33d53c3633d49b5d8f5f47f390ba3d | Jupyter | 6,595 | 238 | # %%
!pip install zarr
!pip install scipy
# %%
import os
import dask
import dask.array as da
import dask.dataframe as dd
import pandas as pd
import numpy as np
import pyarrow as pa
from os.path import join
import shutil
# %%
from dask.distributed import Client, LocalCluster
cluster = LocalCluster(n_workers=5) # ... |
803f1c959fc70f199c77677063e133f234dcd0fb2c3bfb92ca93c26974236929 | Jupyter | 6,607 | 217 | # %% [markdown]
# # Using Weights & Biases with Tune
#
# <a id="try-anyscale-quickstart-tune-wandb" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=tune-wandb">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anyscale-quickstart">
# </a>
#... |
e4c8dce0d4db3a893dc1ed6a2538ed9ec35df2c1cdd5a7426a00b1ed43bcf6ac | Jupyter | 6,611 | 198 | # %% [markdown]
# # Training by backpropagation through time (BPTT)
#
# BPTT is normally a procedure used while training recurrent neural networks. In the case of spiking networks, even if the network is not recurrent, it has a memory of its previous processing steps through the persistence of membrane potentials. Unl... |
63985240764941be4f9a7d64664e2c9dc3aacb34ff7340484511890db16ddc2c | Jupyter | 6,634 | 189 | # %%
# Libraries
# needs to be 3.5
import os
import sys
import nibabel as nib
import subprocess
import glob
print('done imports')
# %%
if 'ipykernel' in os.path.split(sys.argv[0])[1]:
sub = '100206'
structure = 'cortex'
density = '2mm'
else:
sub = sys.argv[1]
structure = sys.argv[2]
density = s... |
02eabb14d544cb5a83713ee27b3c9b84bf94d062cda9b6f7f5d006058bc36949 | Jupyter | 6,637 | 183 | # %%
rsync -avzhe ssh \
conchae@garnatxa.srv.cpd:/home/conchae/PhageDepo_pdb/Dpo_domains.phagedepo.0805.final.tsv \
/media/concha-eloko/Linux/depolymerase_building
# %% [markdown]
# ***
# # Local work :
# %%
import subprocess
import os
path_project = "/media/concha-eloko/Linux/depolymerase_building"
path_pdb = f"{p... |
5fa788c23c0ace4f03c9c5a3aeaca1a9c1afab6bfbf93d8a8f2b25bb4037df9a | Jupyter | 6,641 | 258 | # %%
%load_ext autoreload
%autoreload 2
# %%
#!/home/dattalab/code/ArenaSDK_v0.1.68_Linux_x64/ArenaSDK_Linux_x64/Utilities/IpConfigUtility /list
# %%
from multicamera_acquisition.interfaces.arduino import (
packIntAsLong,
wait_for_serial_confirmation,
)
import struct
import time
import numpy as np
from mult... |
4c2ff8ef0d1d9769ec53a7d479e49997559a3ec85b18e671170b49584a6fc21b | Jupyter | 6,643 | 251 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
632ece972b354c12655f484b6e997b200fe607f3e8f2491e979270b1f6b74d01 | Jupyter | 6,659 | 289 | # %%
# required packages: countdown-timer pyserial numpy av
# %%
#from llpyspin import secondary, primary
import numpy as np
import matplotlib.pyplot as plt
import os, time, serial, glob, struct
def packIntAsLong(value):
"""Packs a python 4 byte integer to an arduino long"""
return struct.pack('i', value)
... |
24128a32cafea99be13cd61bf6e15c2f332b54b6dc39840f24ebe92b45e2b3d5 | Jupyter | 6,682 | 290 | # %%
# required packages: countdown-timer pyserial numpy av
# %%
#from llpyspin import secondary, primary
import numpy as np
import matplotlib.pyplot as plt
import os, time, serial, glob, struct
def packIntAsLong(value):
"""Packs a python 4 byte integer to an arduino long"""
return struct.pack('i', value)
... |
4bd4f60ee570f0b551b11ef1bed45bca4c2278be5e5dfdaf579f08a38bbf02c5 | Jupyter | 6,696 | 223 | # %%
#import
import glob
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
# %%
projections='PrL-ACC' # Fig.1 mn
#projections='ACC-PrL' #Supplementary Fig. 3 mn
if projections=='PrL-ACC':
samples = [11215, 11217,11260] # Fig.1 mn
else:
s... |
1826435b0f090da837dd82f5a0edb8ff84a9452811ec4f5c9f54e08c9f945461 | Jupyter | 6,723 | 185 | # %%
import scanpy as sc
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import omicverse as ov
import anndata
import os
from cellphonedb.src.core.methods import cpdb_statistical_analysis_method
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.... |
635349fe75bbda0afa928d5744ebfa887f7f1aa6a3df5472f145163d785b8230 | Jupyter | 6,738 | 193 | # %% [markdown]
# # Test spatial patterns for `scDRS` scores
# %%
%load_ext lab_black
import numpy as np
import pandas as pd
import scanpy as sc
from os.path import join
from scipy import stats
import statsmodels.api as sm
import os
# %%
for dataset in [
"zeisel_2015",
"zeisel_2018",
"ayhan_2021",
"y... |
30421316c56282e341b897b5daefc431daae28e3360aa4e798ba2e3591d58ba9 | Jupyter | 6,759 | 152 | # %% [markdown]
# # Visualize DVS Input
#
# Speck integrates a Dynamic Vision Sensor(DVS) on the chip itself. Users can read, visualize, and save events generated by the embedded DVS after modifying the hardware configuration.
#
# This notebook demonstrates how to visualize the DVS Sensor events on the speck2e/speck2... |
02ac34a9532bb998c41f9eb319d54bf53b10707596c2eeaf0c9b8a41850eeb3c | Jupyter | 6,769 | 179 | # %% [markdown]
# # Quickstart Sinabs
# If you're familiar with how SNNs work, you might find this quick overview about *Sinabs* useful.
#
# ## Sinabs is based on PyTorch
# All of Sinabs' layers inherit from `torch.nn.Module`. Thus you will be able to access your parameters, wrap layers in a `nn.Sequential` module and... |
f64bc8081e7a82ea44e55f6fa572211a938cf99311e84095391a062fd1878766 | Jupyter | 6,787 | 133 | # %% [markdown]
# # scoreCT: Automated cell type annotation in scRNA-seq data
# ScoreCT is a script allowing automated annotation of scRNA-seq data analyzed with Scanpy. Providing your own reference or using the reference regularly updated and provided with the package, you can automatically assign clusters from your S... |
6bce8f164ca8f533582bde1b3b2c2697359074087a5fcbc6a7ce866208972f99 | Jupyter | 6,822 | 208 | # %% [markdown]
# # Goal : Scan the 3D predcitions with the Dpo fold database
#
# ### A. Run Foldseek
# ### B. Get the hits with good proba
# ### C. Report the folds
# ***
# %%
rsync -avzhe ssh \
conchae@garnatxa.srv.cpd:/home/conchae/PhageDepo_pdb/Dpo_domains.phagedepo.0805.final.tsv \
/media/concha-eloko/Linux/depo... |
4af0934569511bd9446c7f4f1133e6b8c0f4eb49c3d84d6be2839c995af805c4 | Jupyter | 6,853 | 160 | # %% [markdown]
# ## This demo notebook can be used to (optionally) decompress ephys data and create two average waveforms per session needed for Unit Match.
# %%
%load_ext autoreload
%autoreload
import sys
from pathlib import Path
import UnitMatchPy.Extract_raw_data as erd
import numpy as np
from pathlib import ... |
0ef1b83a66226891da11d0c0f8a8cabbd36a106162771c64c5e0a23af4548d5b | Jupyter | 6,857 | 294 | # %%
# required packages: countdown-timer pyserial numpy av
# %%
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
import subprocess
import datetime
import multiprocessing as mp
import sys, time
#sys.path.append('simple_pyspin/')
from multicamera_acquisition.simple_pyspin import Camera
import PySpin
from cou... |
25f232645f2bdecdd186fc7d02d84d5553208a1dea17d44e3308c87cca057dcd | Jupyter | 6,865 | 206 | # %%
# compare SOTA algorithms
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClas... |
19b7cdbe62aec73db2b112b3092ff1686d9f6f314e8b841c3e5da1a6a8e153af | Jupyter | 6,917 | 240 | # %% [markdown]
# <a href="https://colab.research.google.com/github/cmartinezUCSC/Lyrics_Gender_Violence/blob/main/BETO_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
!pip install transformers
# %%
from transformers import BertTokenizer... |
c9c6e32b0dc9f2c1386fe6ff5c20b207466f0544c2d79b0bcf0d9e77ec11af5e | Jupyter | 6,932 | 207 | # %% [markdown]
# # The Depo Detection tool
# >We finetuned the ESM2 model successfully (92% accuracy)<br>
# >The goal now is to stack a RNN layer for a binary classification into Dpo or Not Dpo categories
# ***
# ## I. Load prebuilt model
# ## II. Stack RNN layer
# ## III. Train Eval
# ## IV. Metrics
# ***
# %% [mar... |
d5ac4618b91074abb01456347f620a3a8d6956319557003fe2f7965df3a759a8 | Jupyter | 6,946 | 257 | # %% [markdown]
# # MNIST with SciKit-Learn and skorch
#
# This notebooks shows how to define and train a simple Neural-Network with PyTorch and use it via skorch with SciKit-Learn.
#
# <table align="left"><td>
# <a target="_blank" href="https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks... |
0584c1640c0eeb1579dcf89803ad822677c6c6044fad374d698033737b1eef22 | Jupyter | 6,947 | 280 | # %%
import numpy as np
np.random.seed(2)
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from scipy.optimize import fsolve
# %%
x1, x2 = np.array([1., 0.]), np.array([0., 1.])
def sigma(y):
if y<0.05:
return 0.05
else:
return y#1.5*np.tanh... |
248df37c542287cf713515aceb743ee29f645cf9ea954bed989c9b774a70fceb | Jupyter | 6,973 | 256 | # %%
%load_ext autoreload
%autoreload 2
# %%
import os
import os.path
import skimage
import skimage.segmentation
import sklearn.preprocessing
import sklearn.model_selection
import math
import shutil
import pathlib
import glob
import shutil
import uuid
import random
import platform
import torch
import torchvision
impor... |
f96d0dd5c055ae38033235b682872b96c7298fe90d8e5e424a279c400f610bab | Jupyter | 6,985 | 201 | # %%
#@markdown Check type of GPU and VRAM available.
!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader
# %% [markdown]
# https://github.com/ShivamShrirao/diffusers/tree/main/examples/imagic
# %% [markdown]
# ## Install Requirements
# %%
!wget -q https://github.com/ShivamShrirao/diffusers/r... |
a930dab1a3f85b95e52a13b0d80c171415b9b1289f658f1b9dd4a06bc200e901 | Jupyter | 6,989 | 251 | # %% [markdown]
# # Evaluate CCSBase train and test
# %%
from mol2ccs.utils import performance_evaluation
import pandas as pd
import seaborn as sns
import matplotlib.gridspec as gridspec
from matplotlib import pyplot as plt
from matplotlib_venn import venn3
from utils import (
compare_models_by_adduct,
compar... |
02246857b4688dc54621a110ccaf01fbd5300d78d6c759f1b24597d13f59da44 | Jupyter | 6,994 | 234 | # %%
%load_ext autoreload
%autoreload 2
# %%
import sys
import time
import logging
import argparse
import itertools
import matplotlib
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import numpy as np
from measures import DataStore
# %%
directory = "5-bilinear-continuum/data/"
keys=['I_ext_1','I_ext_2']
n... |
1a7c0ed19d73b8d54e639c1451bb98768081be8ffdc25c0153e97e1687df641a | Jupyter | 6,994 | 268 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
!pip install -U kaleido
# %%
from os.path import join
import anndata
import scanpy as sc
import dask.dataframe as dd
import dask.array as da
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# %%
PATH... |
257f9b34cb8022ab5fde5d1a77fd05f45888146eaa41efabe24b9ce362e3ae21 | Jupyter | 6,994 | 197 | # %% [markdown]
# ### 3 Visualization - Prediction Confidence Heatmaps
#
# A sliding window approach was applied on the whole slide images (WSIs) with the trained CNN model to generate confidence heatmaps.
#
# The WSIs were tiled into 1536 x 1536 images patches in the prepocessing steps. A sliding window approached ... |
1a996dbeaf51f54d02747093a56bda55577c51a6e7ef69e5e5e903e1d34bbe34 | Jupyter | 7,002 | 207 | # %% [markdown]
# # Using MLflow with Tune
#
# <a id="try-anyscale-quickstart-tune-mlflow" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=tune-mlflow">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anyscale-quickstart">
# </a>
# <br></b... |
077eaa858d73e3206b102e34ae01009eb0387cee45bb47ad8b28f1ce52ded445 | Jupyter | 7,021 | 254 | # %%
%load_ext lab_black
import os, sys
%load_ext autoreload
%autoreload 2
import matplotlib
matplotlib.rcParams["pdf.fonttype"] = 42
matplotlib.rcParams["ps.fonttype"] = 42
import scdrs.util as util
import scdrs.data_loader as dl
import scdrs.method as md
import submitit
import os
import yaml
import os, sys
import... |
2308b3eb994f1e541e635f0ba5d523ed5b1fec8a1161d19f59077f4461617e9c | Jupyter | 7,093 | 147 | # %% [markdown]
# # STARDUST (cell-based version)
# %% [markdown]
# This Jupyter Notebook provides an interactive version of STARDUST for analysis using cell-based fluorescence data (average fluorescence per cell). The required input for this analysis one mask/map and the time series generated from the map. Functions ... |
fbb732e74dfb8cadfe3b346773cf96ffe09309f6c4c1a651fe2bad139f14c11e | Jupyter | 7,101 | 198 | # %% [markdown]
# # Generate a MSA for each candidate protein
# ***
# ## A. Generate a fasta database with all the candidate proteins
# ## B. Run mmseqs on the fasta database vs Uniref90
# ## C. Generate an MSA for each proteins
# ***
# %% [markdown]
# A. Generate the query database
# %%
import os
from tqdm import... |
3d22841f404044130dc38745bd124b8feee27112734c1818275ea42ece96568f | Jupyter | 7,106 | 179 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
9ee9988bee232e98a1fce59915b2c970a1c85b76c7d588a37091ebf60342a144 | Jupyter | 7,106 | 192 | # %% [markdown]
# # Demonstration of using Hugging Face vision transformer with skorch
# %% [markdown]
# Based on this [blog post](https://huggingface.co/blog/fine-tune-vit), we show how with only a few lines of custom code, we can fine-tune a Vision Transformer model for a classification task.
# %% [markdown]
# In a... |
18f83055a43d61ac56317e348181a71a6406d70dc0996f3f75c93795e63dfb1d | Jupyter | 7,115 | 202 | # %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.pylab as pl
import matplotlib.gridspec as gridspec
import scipy.ndimage as ndimage
from matplotlib.ticker import AutoMinorLocator, FormatStrFormatter
import matplotlib.mlab as mlab
from matplotlib i... |
2205ac621a21c733ce44f4233f86f1a1cfddab377068f1c6a8023f110e5e0684 | Jupyter | 7,133 | 218 | # %% [markdown]
# # Create one DataFrame out of the split participant 8
# %% [markdown]
# __Data Saved: (same as in 1v but only for participant 8)__
# %% [markdown]
# uid == "19a33ac1-149b-407c-a506-a2c7f4f3fea1"
# - __Timestamps_try_uid.json__ --> json with substracted start from most behavioral timestamps
# - __Tim... |
2653ecda53fb5996fcf31a8a76035232913b34cc145793df09edaa8f218ddb91 | Jupyter | 7,141 | 168 | # %% [markdown]
# # Imports
# ---
# %%
from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
import numpy as np
import time
import math
# %% [markdown]
# Defining an "Epoch Dataloader with Shuffle"
# ---
# This i... |
27e28f15ec2cd52385695eba302ff97790dbee89b87dc07e29fed8f294439357 | Jupyter | 7,170 | 176 | # %% [markdown]
# ### Import modules
# %%
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
from rdkit import Chem
import os
from typing import Optional
from rdkit import Chem
# %% [markdown]
# ### Generate docking grid file
# %%
def calculated_docking_grid_sdf(work_path, json_path, pdbid, lig... |
e4cbbcb5f7cf3306b6d30cb2a9c37bb0d58094cf272d3049c5baeaf862525405 | Jupyter | 7,199 | 187 | # %% [markdown]
# # Running Tune experiments with HyperOpt
#
# <a id="try-anyscale-quickstart-ray-tune-hyperopt_example" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-hyperopt_example">
# <img src="../../_static/img/run-on-anyscale.svg" alt... |
f0555ca17bc0f55b26d992047fc9d36bcf1e4758025f9459f6bce35d4f3be71c | Jupyter | 7,199 | 222 | # %% [markdown]
# # scTools
#
# > Single-cell large-scale analysis tools
# %%
#| default_exp tools
# %%
#| hide
from nbdev.showdoc import *
# %%
#| hide
import nbdev; nbdev.nbdev_export()
# %% [markdown]
# # import
# %%
#| export
import numpy as np
import scanpy as sc
import math
import matplotlib.pyplot as plt
... |
ceea42928a042e6e614579fbe538b5d311ba6deeaec564e1cbfd3917d7a5615e | Jupyter | 7,205 | 221 | # %% [markdown]
# # Running Tune experiments with BOHB
#
# <a id="try-anyscale-quickstart-ray-tune-bohb_example" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=ray-tune-bohb_example">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anysca... |
2559726f58437d74b55557238433f36f9be00fad9bbbde4ceb9ba0f63cc0a9e8 | Jupyter | 7,210 | 205 | # %% [markdown]
# ### 4.1 Saliency Mapping - Feature Occlusion
#
# Feature occlusion studies were performed to show the influence of occluding regions of input image to the confidence score predicted by the CNN model.
#
# The occlusion map was computed by replacing a region of the image with a pure white patch and g... |
09a7aff27d8a20e75dc3da48580f3c0e847023b11eaf6633595ac3619fc4c7b5 | Jupyter | 7,236 | 162 | # %% [markdown]
# # Data preparation
# %% [markdown]
# NeuralGCM models take and produce data on defined on [37 pressure levels](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels), including the following variables, provided in SI units and on the NeuralGCM model's native grid:
#
# - ... |
8305493d9c00f8be1cb5e1d6184feb8f0f5317f830fb1f738075fe4724649a4a | Jupyter | 7,288 | 192 | # %% [markdown]
# # Perform Experiments with DeepFace on LFW dataset
# %%
# built-in dependencies
import os
# 3rd party dependencies
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.datasets import fetch_lfw_pairs
from... |
0356bcac55ca2e4bc4d229fe25188e33873b08598f0397c27628dc1ba929015f | Jupyter | 7,289 | 179 | # %% [markdown]
# # Machine learning and deep learning methods applied to predicting customer status
#
# Purpose: Predict active and not active clients based on the proposed data structure and naive data structure. Here, we implemented Support Vector Machines (SVM), Random Forest (RF), K-nearest Neighbours (KNN) and L... |
89383738855c2b68ebf5f1a2052224644794cf476f8d221249a0dd5bb9a281a2 | Jupyter | 7,345 | 196 | # %%
import os
import json
import glob
import numpy as np
from collections import defaultdict
import matplotlib.pyplot as plt
import csv
# %%
# Set path to MRIQC outputs
mriqc_path = '../../derivatives/mriqc/sub-*/ses-*/*/'
def get_mriqc_summary(mriqc_path, image_type, metrics_to_plot):
json_files = glob.glob(os... |
0c740a96780fec42ce0baa38fd9a42c9573e126d3c66c679c3ce0c797a229223 | Jupyter | 7,359 | 244 | # %% [markdown]
# # Simple HRNet
# This is a light Google Colab notebook showing how to use the [simple-HRNet](https://github.com/stefanopini/simple-HRNet) repository.
#
# It includes the conversion to TensorRT and a test of the converted model.
# Please skip the section "TensorRT" if not interested.
#
# Initial idea... |
e8a59b644e39f1830bd5c52978682071b4b86512d3d044fbbd798ff9644c48e3 | Jupyter | 7,399 | 230 | # %% [markdown]
# ### Models Genesis in Diffusion MRI
# %% [markdown]
# This is an example on how to implement the transforms proposed in Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysi (Zhou et al., https://arxiv.org/abs/1908.06912).
# The transforms here follow the propositions of Models Ge... |
4a668823ae00a3d41f92f221ecdf7d65ccb6e6529fa59c1a4ab084b3983ba9d9 | Jupyter | 7,400 | 305 | # %%
!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps
# %%
!pip install -q SPARQLWrapper
# %%
# !pip install -q cellxgene-census
!pip install -q obonet
# %%
%load_ext autoreload
# %%
import pickle
import os
from os.path import join
import numpy as np
import pandas as pd
import seaborn as sns
import ... |
4cf875f3ec3948e51ca6e78f31c0941b86f69436219124f80635c18d619e297e | Jupyter | 7,414 | 231 | # %% [markdown]
# # Create Deep Neural Network for SF Analysis by Functional Term
# %%
#!conda install pytorch torchvision torchaudio cpuonly -c pytorch -y
# %%
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
from sklearn.metrics import r2_score
import torch
import torch.nn as nn
impo... |
500c64cc4651997d06c84c58b43951cffaaf012973e4fce142850b5c8ade146b | Jupyter | 7,421 | 179 | # %%
# Copyright 2022 NVIDIA Corporation. All Rights Reserved.
#
# 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 applic... |
ad231868bb26516f55bf8ad8238db9347e5a1a31405506fe3c792c431fdf0ef0 | Jupyter | 7,428 | 235 | # %% [markdown]
# # Summarize results at cell-type level for the purpose of contrasting results across species
# 1. Proportion of FDR < 10% at various parameter for scDRS.
# 2. Cell type level p-value for scDRS
# 3. Geary's C statistics for 10kb, 1000 genes default settings.
# 4. LDSC-SEG p-value
# %%
%load_ext lab_b... |
822087302695604df851cb3b5fdd5321407446d71d21fed6fe5a71d58d51f25b | Jupyter | 7,445 | 136 | # %% [markdown]
# (nirtorch_interpreting)=
# # To PyTorch: Interpreting NIR
#
# We rely on [`torch.fx`](https://pytorch.org/docs/stable/fx.html) to interpret NIR graphs.
# We first translate all the NIR nodes into PyTorch nodes, by going through the nodes in the NIR graph one by one.
# This mechanism relies on a dicti... |
fb8b58d2c8eda00a8333845a0651a0ee38189b6bb2d3677b5c425155b08f2174 | Jupyter | 7,447 | 198 | # %% [markdown]
# # GOAL : generate ESMfold prediction for the selected proteins after V3
# ### I. Generate the sequence file
# ### II. The prediction script
# ***
# %% [markdown]
# I.
# %%
# Build the sequence idex file :
import os
from Bio import SeqIO
import pandas as pd
path_millard = "/home/conchae/ML_depolym... |
d8602430c2522706b58753261b195a6c74a2bfefbd8f23897a10d019a77cf47a | Jupyter | 7,448 | 200 | # %% [markdown]
# # GOAL : generate ESMfold prediction for the selected proteins after V3
# ### I. Generate the sequence file
# ### II. The embedding script
# ***
# %% [markdown]
# I.
# %%
# Build the sequence idex file :
import os
from Bio import SeqIO
import pandas as pd
path_millard = "/home/conchae/ML_depolyme... |
369c24bfc514a091f1b0ed0e43eb65b727475c86918284ef2d699422752ad747 | Jupyter | 7,450 | 278 | # %%
import Pyomic
%matplotlib inline
# %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from scipy.stats import norm
from scipy import stats
import networkx as nx
import datetime
import seaborn as sns
import pandas as pd
from scipy.cluster import hierarchy
f... |
912059e3a9912d31467a96324215108607007120b2a89159aece43e1d7950874 | Jupyter | 7,453 | 206 | # %%
import numpy as np
import matplotlib.pyplot as plt
from lgca import get_lgca
from itertools import product
import pickle as pkl
import string
from itertools import cycle
PATH = '.\\data\\gog\\nonlocaldensity_10reps\\'
parameters = np.load(PATH+'params.npz', allow_pickle=True)
constparams = parameters['constparams... |
e6849c8101afbcca6164efa693a179c89bfd0d497574b44e65d20b7a8ae81302 | Jupyter | 7,479 | 204 | # %%
from sklearn.metrics import multilabel_confusion_matrix, classification_report, roc_curve, auc, confusion_matrix, \
RocCurveDisplay, precision_score, recall_score, average_precision_score, PrecisionRecallDisplay, precision_recall_curve
import numpy as np
import seaborn as sns
import pandas as pd
import numpy ... |
5a6ba9dbd5b8ae5eb2bfdaa54eb405c315a4096dd30c39f233990e175cb95954 | Jupyter | 7,501 | 166 | # %% [markdown]
# # Lava-dl hdf5 export
#
# In order to test the export process, we used the [Oxford example](https://lava-nc.org/lava-lib-dl/slayer/notebooks/oxford/train.html) available in the LAVA-dl repository. Since this page is focused on NIR implementations, we are not covering the training aspects, but readers... |
3ecf15500eb7472d266299ec574d8dbf2693ea75db71b53d3f12ebdd1dc88861 | Jupyter | 7,504 | 194 | # %%
import math
from sklearn import metrics
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import time
import datetime
import random
random.seed(1234)
from scipy import interp
import warnings
warnings.filterwarnings("ignore")
from collections import... |
c47cd0d07da4f485c902bb7b97c94ab00f61f74b9acd5d432f412c4039e4c735 | Jupyter | 7,556 | 206 | # %% [markdown]
# # Change activations in spiking layers
# Sometimes it's helpful to change spiking, membrane reset or backpropagation behaviour. Let's look at a few examples based on the Integrate and Fire (IAF) layer.
# %%
import torch
import torch.nn as nn
import sinabs
import sinabs.layers as sl
import sinabs.acti... |
ae2a17979430678b9244fffb8ee1bae42dd25e4ece5a5b864d9831ff2a58d37c | Jupyter | 7,557 | 229 | # %%
import scanpy as sc
import seaborn as sns
import scvi
import matplotlib.pyplot as plt
# %% [markdown]
# ## Load data
# %%
adata=sc.read_loom('merged_IN.loom')
plt.rcParams['figure.figsize']=(8,8) #rescale figures
plt.rcParams.update({'font.size': 18})
# %%
adata.obs_names=adata.obs.obs_names
adata.var_names=ada... |
ad04e6814f92218dadaef8d3e3dd54f139b135d0dc56bf88563183600f1039aa | Jupyter | 7,560 | 236 | # %% [markdown]
# # Simple Parallel Model Selection
#
# <a id="try-anyscale-quickstart-plot_hyperparameter" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=plot_hyperparameter">
# <img src="../../_static/img/run-on-anyscale.svg" alt="try-anyscale-quic... |
a74e09873c478bf19d32b8dc27351b1560a9e8b9ffb0bf2b3ca530b6098ff527 | Jupyter | 7,568 | 221 | # %%
import numpy as np
import os
from pynwb import NWBFile, NWBHDF5IO, image
from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, ImagingPlane
from pynwb.file import Subject
from pynwb.device import Device
import math
import statistics
import pandas as pd
import h5py #Provides methods to supple... |
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